From 7fd72d9ee1e8847717195859bf6d608268a94e2f Mon Sep 17 00:00:00 2001 From: martinb-bb <105685594+martinb-bb@users.noreply.github.com> Date: Sat, 8 Oct 2022 08:54:43 -0400 Subject: [PATCH] Forecasting Menu [Work in Progress] (#1933) * Gave forecasting memory * Fixed scripts, refactored * FIxed poetry lock * edge case check for forecast target * Improved combine and load functionality * Cleaned up translations * Fixed issue with covariates * Fixed issue checking covariates * Another covariates check fix * Ignored regr and linregr warnings * Fixed covariate issues * switched from forecasting to forecast * Finished transition to forecast * Can add entire dataset with one command * Improved combine description * Removed naming covariates * Created new installation * typo * Make plot show dates if available * Added better handling or users without the menu * Removed unused file * Fix * Better handling for nontraditional datasets * Fixed black and pylint * Fixed tests * Added darts install to main tests * Working on darts with CI * Added back test file * Made large tables print better * naive baseline * typo * Finished naive * no dollar on prediction * fixed positive MAPE bug * quick refactoring * Fixed two different args for same thing * added extra patience * linreg mape fix * info fix * Refactored API, bumped to Darts 0.21.0 * Added fixes * Increased verbosity for wrong column * Updated dependencies * Hid warnings * Fixed importing * Fixed tests * Fixed ugly seasonal plotting * Fixed forecast line color * Switched chart output to blue * Simplified lambda_price_prediction_color * fixed residuals * Chnage * Removed darts from CI per Chavi * Added fixes to tests * Added knnfix * Fixed issue where n!= o * Added changes * Added changes * Imrpoved forecast dash * Added Theo notebook * Added enhancements to dash * Added notebook * Added fix for jupyter lab * Added debug stuff * Change * Updated docs * Fixed formatting * Fixed formatting * Removed prints * Filtered some info * Added button to run model * Improved api * Added secret feautr (no peeking Martin) * Cleaned code * Fixed tests * Added test fixes * Added fixes * Fixes * FIxes for pres * Remove bad tests * Removed knn * Fixed issues with removing mc * doc for conda * Added forecast improvements * Added streamlit support * Fixed issues * fix expo with streamlit due to quantile() * fixed performance issues with streamlit for now.. * clean up historical forecast with new trainer * quick fix for regression trainer params * Added fixes * quick fix for other fix for regression trainer params * table formatting for timestamp * potential fix for inf in feature engineered datasets * Basic working in new format * dw * Trying * Fixed issues * Improved graphing * fixing trainer for LR and formatting * doge and linting * page break * automatic cleaning of datasets * automatic cleaning of datasets- fix * Fixed forecast dates * Made dashboard prettier * Added fixes * Added fixes * Added options * Fixed error * remove caching * adding in spinner * Added vairable n_predict in streamlit * Added mypy fix * renaming and range change * new index for n predict * check positive float for window size * Update _index.md * Update _index.md * Update _index.md * Update _index.md * Update _index.md * Update _index.md * Update _index.md * Update _index.md * Update _index.md * renaming * reorg files * Update _index.md * hidden which command for versions * Update _index.md * Update _index.md * which: ns parser * hugo for: which * hugo for: forecasting fix * formatting black * update stock controller test * Lay groundwork for better residual plotting * improved delete to allow for periods in title * improved automatic cleaning of inf * Added new API * Added new API * Added new API * formatting for black * Updated our testing CI * Reverted changes * Added forecast docs * Fixed mypy issues * Fixes tests * Did some refactoring, added a report * new api in streamlit * Added integrated tests * Update _index.md * improved loading in custom dataset * menu spacing * installer fixes * Added docs fixes * Adding comments to test if commit working * Fixed report * naming conventions * formatting * removing unused var * Made last report imporvements * Update README.md * Added fix * Switched to warning * Added fixes * Added fixes * Added fixes * Added fixes * Update economy av view test * Remove forgotten print statement * Update depencencies * Added verbosity to pytest * Added fixes * Fixed pylint * Fixed actions checkout * Added fixes Co-authored-by: colin99d Co-authored-by: Colin Delahunty <72827203+colin99d@users.noreply.github.com> Co-authored-by: James Simmons Co-authored-by: minhhoang1023 <40023817+minhhoang1023@users.noreply.github.com> Co-authored-by: Theodore Aptekarev --- .github/workflows/test.yml | 2 +- .gitignore | 7 + build/conda/conda-3-9-env-full.yaml | 16 + build/pyinstaller/build4mac.sh | 12 +- build/pyinstaller/hooks/hook-rich.py | 3 + build/pyinstaller/terminal.spec | 3 +- 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a/.github/workflows/test.yml b/.github/workflows/test.yml index 4066250c3e4..b617161d3b1 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -13,7 +13,7 @@ jobs: if: github.event_name == 'push' || github.event.pull_request.head.repo.full_name != github.repository steps: - name: Checkout Code - uses: actions/checkout@v2 + uses: actions/checkout@2541b1294d2704b0964813337f33b291d3f8596b # actions/checkout v3.0.2 - name: Setup Python 3.9 uses: actions/setup-python@v4 diff --git a/.gitignore b/.gitignore index f5f02d01efd..ec2ff54e88f 100644 --- a/.gitignore +++ b/.gitignore @@ -59,3 +59,10 @@ styles/user # the file containing the API keys for the web version openbb_terminal_web/setenv + +# dart models +darts_logs/ + +# User data +custom_imports/*.csv +custom_imports/*/*.csv diff --git a/build/conda/conda-3-9-env-full.yaml b/build/conda/conda-3-9-env-full.yaml new file mode 100644 index 00000000000..45d922dc630 --- /dev/null +++ b/build/conda/conda-3-9-env-full.yaml @@ -0,0 +1,16 @@ +channels: + - conda-forge + - defaults +dependencies: + - pytorch-lightning=1.6.5 + - u8darts[torch]=0.21.0 + - python=3.9.6 + - pip + - poetry=1.1.13 + - numpy=1.23.0 + - cvxpy=1.2.1 + - pytest + - black=22.6.0 + - sphinx=4.5.0 + - charset-normalizer=2.1.0 + - rich=10.16.2 diff --git a/build/pyinstaller/build4mac.sh b/build/pyinstaller/build4mac.sh index 8159341362b..54751f357be 100755 --- a/build/pyinstaller/build4mac.sh +++ b/build/pyinstaller/build4mac.sh @@ -11,7 +11,17 @@ rm -rf build/terminal && rm -rf dist && rm -rf DMG # Clean up local logging id rm -rf openbb_terminal/logs -pyinstaller build/pyinstaller/terminal.spec +# Removing Sigtool package which breaks build +echo "Removing Sigtool Package" +if conda remove --force sigtool -y &> /dev/null + then + echo "Succesfully Removed Sigtool Package" +else + echo "Sigtool Already Removed" +fi + +# Running build +pyinstaller build/pyinstaller/terminal.spec --clean # Assign icons to the built folder and launcher osascript build/pyinstaller/setup_icons.applescript diff --git a/build/pyinstaller/hooks/hook-rich.py b/build/pyinstaller/hooks/hook-rich.py new file mode 100644 index 00000000000..716282ff996 --- /dev/null +++ b/build/pyinstaller/hooks/hook-rich.py @@ -0,0 +1,3 @@ +from PyInstaller.utils.hooks import copy_metadata, collect_data_files + +datas = copy_metadata("rich") + collect_data_files("rich") diff --git a/build/pyinstaller/terminal.spec b/build/pyinstaller/terminal.spec index e8d74953a70..a73bcbb8547 100644 --- a/build/pyinstaller/terminal.spec +++ b/build/pyinstaller/terminal.spec @@ -91,6 +91,7 @@ hidden_imports = [ "feedparser", "pymongo", "bson", + "_sysconfigdata__darwin_darwin", ] @@ -100,7 +101,7 @@ analysis_kwargs = dict( binaries=[], datas=added_files, hiddenimports=hidden_imports, - hookspath=[], + hookspath=["build/pyinstaller/hooks"], hooksconfig={}, runtime_hooks=[], excludes=[], diff --git a/exports/cryptocurrency/discovery/.gitkeep b/custom_imports/forecasting/.gitkeep similarity index 100% rename from exports/cryptocurrency/discovery/.gitkeep rename to custom_imports/forecasting/.gitkeep diff --git a/exports/cryptocurrency/due_diligence/.gitkeep b/custom_imports/stocks/btc.csv/discovery/.gitkeep similarity index 100% rename from exports/cryptocurrency/due_diligence/.gitkeep rename to custom_imports/stocks/btc.csv/discovery/.gitkeep diff --git a/exports/cryptocurrency/nft/.gitkeep b/custom_imports/stocks/btc.csv/due_diligence/.gitkeep similarity index 100% rename from exports/cryptocurrency/nft/.gitkeep rename to custom_imports/stocks/btc.csv/due_diligence/.gitkeep diff --git a/exports/cryptocurrency/onchain/.gitkeep b/custom_imports/stocks/btc.csv/nft/.gitkeep similarity index 100% rename from exports/cryptocurrency/onchain/.gitkeep rename to custom_imports/stocks/btc.csv/nft/.gitkeep diff --git a/exports/cryptocurrency/overview/.gitkeep b/custom_imports/stocks/btc.csv/onchain/.gitkeep similarity index 100% rename from exports/cryptocurrency/overview/.gitkeep rename to custom_imports/stocks/btc.csv/onchain/.gitkeep diff --git a/exports/cryptocurrency/technical_analysis/.gitkeep b/custom_imports/stocks/btc.csv/overview/.gitkeep similarity index 100% rename from exports/cryptocurrency/technical_analysis/.gitkeep rename to custom_imports/stocks/btc.csv/overview/.gitkeep diff --git a/openbb_terminal/common/prediction_techniques/__init__.py b/custom_imports/stocks/btc.csv/technical_analysis/.gitkeep similarity index 100% rename from openbb_terminal/common/prediction_techniques/__init__.py rename to custom_imports/stocks/btc.csv/technical_analysis/.gitkeep diff --git a/i18n/en.yml b/i18n/en.yml index aa16438e6e2..d223ae4e44d 100644 --- a/i18n/en.yml +++ b/i18n/en.yml @@ -80,6 +80,7 @@ en: funds: mutual funds search, overview, holdings and sector weights alternative: alternative datasets, such as COVID and open source metrics econometrics: statistical and quantitative methods for relationships between datasets + forecast: timeseries forecasting with machine learning portfolio: perform portfolio optimization and look at portfolio performance and attribution dashboards: interactive dashboards using voila and jupyter notebooks reports: customizable research reports through jupyter notebooks @@ -124,7 +125,7 @@ en: stocks/bt: strategy backtester, simple ema, ema cross, rsi strategies stocks/ta: technical analysis, ema, macd, rsi, adx, bbands, obv stocks/qa: quantitative analysis, decompose, cusum, residuals analysis - stocks/pred: prediction techniques, regression, arima, rnn, lstm + stocks/forecast: forecast techniques, rnn, nbeats, transformer, block rnn stocks/options/unu: show unusual options activity stocks/options/calc: basic call/put PnL calculator stocks/options/screen: screens tickers based on preset @@ -475,20 +476,6 @@ en: stocks/qa/cusum: detects abrupt changes using cumulative sum algorithm of prices stocks/qa/capm: capital asset pricing model stocks/qa/beta: display beta w.r.t to any reference ticker - stocks/pred/load: load new ticker - stocks/pred/pick: pick target column for analysis - stocks/pred/_ticker: Ticker - stocks/pred/_target: Target Column - stocks/pred/_models_: Models - stocks/pred/ets: exponential smoothing (e.g. Holt-Winters) - stocks/pred/knn: k-Nearest Neighbors - stocks/pred/regression: polynomial regression - stocks/pred/arima: autoregressive integrated moving average - stocks/pred/mlp: MultiLayer Perceptron - stocks/pred/rnn: Recurrent Neural Network - stocks/pred/lstm: Long-Short Term Memory - stocks/pred/conv1d: 1D Convolutional Neural Network - stocks/pred/mc: Monte-Carlo simulations crypto/load: load a specific cryptocurrency for analysis crypto/find: find coins crypto/price: real-time price and interval of confidence @@ -507,7 +494,7 @@ en: crypto/nft: non-fungible tokens, today drops crypto/dd: due-diligence for loaded coin, coin information, social media, market stats crypto/ta: technical analysis for loaded coin, ema, macd, rsi, adx, bbands, obv - crypto/pred: prediction techniques, regression, arima, rnn, lstm, conv1d, monte carlo + crypto/forecast: forecast techniques, rnn, nbeats, transformer, block rnn crypto/qa: quantitative analysis decompose, cusum, residuals analysis crypto/disc/cgtop: top coins (with or without category) crypto/disc/cgtrending: trending coins @@ -672,20 +659,6 @@ en: crypto/ta/obv: on balance volume crypto/ta/_custom_: Custom crypto/ta/fib: fibonacci retracement - crypto/pred/load: load new ticker - crypto/pred/pick: pick target column for analysis - crypto/pred/_ticker: Coin loaded - crypto/pred/_target: Target column - crypto/pred/_models_: Models - crypto/pred/ets: exponential smoothing (e.g. Holt-Winters) - crypto/pred/knn: k-Nearest Neighbors - crypto/pred/regression: polynomial regression - crypto/pred/arima: autoregressive integrated moving average - crypto/pred/mlp: MultiLayer Perceptron - crypto/pred/rnn: Recurrent Neural Network - crypto/pred/lstm: Long-Short Term Memory - crypto/pred/conv1d: 1D Convolutional Neural Network - crypto/pred/mc: Monte-Carlo simulations crypto/qa/load: load new ticker crypto/qa/pick: pick target column for analysis crypto/qa/_ticker: Ticker @@ -727,7 +700,6 @@ en: etf/pir: create (multiple) passive investor excel report(s) etf/compare: compare multiple different ETFs etf/ta: technical analysis, ema, macd, rsi, adx, bbands, obv - etf/pred: prediction techniques, regression, arima, rnn, lstm etf/disc/gainers: top gainers etf/disc/decliners: top decliners etf/disc/active: most active @@ -764,20 +736,6 @@ en: etf/ta/obv: on balance volume etf/ta/_custom_: Custom etf/ta/fib: fibonacci retracement - etf/pred/load: load new ticker - etf/pred/pick: pick target column for analysis - etf/pred/_ticker: Ticker loaded - etf/pred/_target: Target column - etf/pred/_models_: Models - etf/pred/ets: exponential smoothing (e.g. Holt-Winters) - etf/pred/knn: k-Nearest Neighbors - etf/pred/regression: polynomial regression - etf/pred/arima: autoregressive integrated moving average - etf/pred/mlp: MultiLayer Perceptron - etf/pred/rnn: Recurrent Neural Network - etf/pred/lstm: Long-Short Term Memory - etf/pred/conv1d: 1D Convolutional Neural Network - etf/pred/mc: Monte-Carlo simulations economy/_database_: Databases economy/_stored: Stored datasets economy/overview: show a market overview of either indices, bonds or currencies @@ -796,7 +754,7 @@ en: economy/valuation: valuation of sectors, industry, country economy/performance: performance of sectors, industry, country economy/spectrum: spectrum of sectors, industry, country - economy/pred: Open the prediction menu to analyse stored data + economy/forecast: forecast techniques; rnn, nbeats, transformer, block rnn economy/qa: Open quantitative analysis menu with stored data economy/qa/pick: pick new series from stored economy data economy/qa/_series: Selected series @@ -821,18 +779,6 @@ en: economy/qa/raw: print raw data economy/qa/decompose: decomposition in cyclic-trend, season, and residuals of prices economy/qa/cusum: detects abrupt changes using cumulative sum algorithm of prices - economy/pred/pick: pick new series from stored economy data - economy/pred/_series: Selected series - economy/pred/_models_: Models - economy/pred/ets: exponential smoothing (e.g. Holt-Winters) - economy/pred/knn: k-Nearest Neighbors - economy/pred/regression: polynomial regression - economy/pred/arima: autoregressive integrated moving average - economy/pred/mlp: MultiLayer Perceptron - economy/pred/rnn: Recurrent Neural Network - economy/pred/lstm: Long-Short Term Memory - economy/pred/conv1d: 1D Convolutional Neural Network - economy/pred/mc: Monte-Carlo simulations forex/_ticker: Ticker forex/_from: From forex/_to: To @@ -843,7 +789,6 @@ en: forex/fwd: get forward rates for loaded pair forex/ta: technical analysis, ema, macd, rsi, adx, bbands, obv forex/qa: quantitative analysis, decompose, cusum, residuals analysis - forex/pred: prediction techniques regression, arima, rnn, lstm, conv1d, monte carlo forex/forex: Forex brokerages forex/oanda: Oanda menu forex/ta/_currency: Currency pair loaded @@ -891,19 +836,6 @@ en: forex/qa/raw: print raw data forex/qa/decompose: decomposition in cyclic-trend, season, and residuals of prices forex/qa/cusum: detects abrupt changes using cumulative sum algorithm of prices - forex/pred/pick: pick new target variable - forex/pred/_pair: Pair loaded - forex/pred/_target: Target column - forex/pred/_models_: Models - forex/pred/ets: exponential smoothing (e.g. Holt-Winters) - forex/pred/knn: k-Nearest Neighbors - forex/pred/regression: polynomial regression - forex/pred/arima: autoregressive integrated moving average - forex/pred/mlp: MultiLayer Perceptron - forex/pred/rnn: Recurrent Neural Network - forex/pred/lstm: Long-Short Term Memory - forex/pred/conv1d: 1D Convolutional Neural Network - forex/pred/mc: Monte-Carlo simulations forex/oanda/summary: shows account summary forex/oanda/calendar: show calendar forex/oanda/list: list order history @@ -1079,6 +1011,8 @@ en: portfolio/po/params/arg: set a different value for an argument portfolio/po/params/_parameters_: Parameters dashboards/stocks: historic stock information + dashboards/forecast: forecasting for timeseries datasets + dashboards/forecasting: forecasting for timeseries datasets dashboards/correlation: stock correlations dashboards/vsurf: options volatility surface dashboards/chains: options chain analysis @@ -1086,3 +1020,40 @@ en: dashboards/crypto: cryptocurrency exchange rates against USD dashboards/futures: historical futures performance reports/_reports_: Select one of the following report + forecast/_disclaimer_: DISCLAIMER + forecast/_data_loc: Looking for data in + forecast/load: load a dataset from csv + forecast/_loaded: Loaded files and data columns + forecast/_exploration_: Exploration + forecast/show: Show a portion of a loaded dataset + forecast/plot: Plot a specific column of a loaded dataset + forecast/clean: Clean a dataset by filling or dropping NaNs + forecast/desc: Show descriptive statistics of a dataset + forecast/corr: Plot the correlation coefficients for dataset features + forecast/season: Plot the seasonality for a dataset column + forecast/combine: Combine columns from different datasets + forecast/delete: Delete columns from dataset + forecast/rename: Rename columns from dataset + forecast/export: Export a processed dataset + forecast/_feateng_: Feature Engineering + forecast/ema: Add Exponentially Weighted Moving Average + forecast/sto: Add Stochastic Oscillator %K and %D + forecast/rsi: Add Relative Strength Index + forecast/roc: Add Rate of Change + forecast/mom: Add Momentum + forecast/delta: Add % Change + forecast/atr: Add Average True Range + forecast/signal: Add Price Signal (short vs. long term) + forecast/_tsforecasting_: TimeSeries Forecasting + forecast/arima: Arima (Non-darts) + forecast/expo: Probabilistic Exponential Smoothing + forecast/theta: Theta Method + forecast/linregr: Probabilistic Linear Regression + forecast/regr: Regression + forecast/rnn: Probabilistic Recurrent Neural Network (RNN, LSTM, GRU) + forecast/brnn: Block Recurrent Neural Network (RNN, LSTM, GRU) (feat. Past covariates) + forecast/nbeats: Neural Bayesian Estimation (feat. Past covariates) + forecast/tcn: Temporal Convolutional Neural Network (feat. Past covariates) + forecast/trans: Transformer model (feat. Past covariates) + forecast/tft: Temporal Fusion Transformer (feat. Past covariates) + forecast/_comingsoon_: Coming Soon diff --git a/openbb_terminal/README.md b/openbb_terminal/README.md index 538dec83927..9ea39700f0c 100644 --- a/openbb_terminal/README.md +++ b/openbb_terminal/README.md @@ -121,10 +121,23 @@ issues. or `diamondhands`, we recommend something simple and intuitive like `obb`. This is because this name will be used from now onwards. + Please note, the following setup has been confirmed to work for all OS (including M1) with the standard + miniconda distribution. + + `https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh` + + If you are using a different distribution, you will need to install it manually before proceeding. + ```bash conda env create -n obb --file build/conda/conda-3-9-env.yaml ``` + Or, to include machine learning type: + + ```bash + conda env create -n obb --file build/conda/conda-3-9-env-full.yaml + ``` + Note: Using python 3.10 can lead to undesirable functionality for certain commands. 6. Activate the virtual environment @@ -143,10 +156,10 @@ issues. poetry install ``` - To enable the `prediction` menu install additional dependencies after installing main dependencies: + For machine learning instead type: ```bash - conda install -c conda-forge tensorflow + poetry install -E prediction ``` If you are having trouble with Poetry (e.g. on a non-conda python), simply install requirements.txt with pip @@ -189,7 +202,7 @@ before you call `python terminal.py` again. ## Advanced User Install - Custom installation procedures By default we advice using `conda` and `poetry` for environment setup and dependency management. -Conda ships binaries for packages like `numpy` and `tensorflow` so these dependencies are +Conda ships binaries for packages like `numpy` so these dependencies are not built from source locally by `pip`. Poetry solves the dependency tree in a way that the dependencies of dependencies of dependencies use versions that are compatible with each other. diff --git a/openbb_terminal/api.py b/openbb_terminal/api.py index 11369c2fc84..8b969912c3b 100644 --- a/openbb_terminal/api.py +++ b/openbb_terminal/api.py @@ -1,14 +1,39 @@ """OpenBB Terminal API.""" # flake8: noqa -# pylint: disable=unused-import - -# pylint: disable=C0302,W0611,not-callable +# pylint: disable=unused-import,wrong-import-order +# pylint: disable=C0302,W0611,not-callable,ungrouped-imports from inspect import signature, Parameter +import warnings import types import functools import importlib from typing import Optional, Callable, List +from .stocks import stocks_api as stocks +from .alternative import alt_api as alt +from .cryptocurrency import crypto_api as crypto +from .economy import economy_api as economy +from .econometrics import econometrics_api as econometrics +from .etf import etf_api as etf +from .forex import forex_api as forex +from .mutual_funds import mutual_fund_api as funds +from .portfolio import portfolio_api as portfolio + +from openbb_terminal.rich_config import console + +try: + import darts + + forecasting = True +except ImportError: + forecasting = False + warnings.warn( + "Forecasting dependencies are not installed." + " This part of the SDK will noy be usable" + ) + +from .config_terminal import theme + from openbb_terminal.helper_classes import TerminalStyle # noqa: F401 from openbb_terminal import helper_funcs as helper # noqa: F401 from .reports import widget_helpers as widgets # noqa: F401 @@ -1918,22 +1943,95 @@ functions = { "keys.santiment": {"model": "openbb_terminal.keys_model.set_santiment_key"}, "keys.mykeys": {"model": "openbb_terminal.keys_model.get_keys"}, } +forecast_extras = { + "forecast.load": {"model": "openbb_terminal.forecast.forecast_model.load"}, + "forecast.show": { + "model": "openbb_terminal.forecast.forecast_view.show_df", + "view": "openbb_terminal.forecast.forecast_view.show_df", + }, + "forecast.plot": { + "model": "openbb_terminal.forecast.forecast_view.display_plot", + "view": "openbb_terminal.forecast.forecast_view.display_plot", + }, + "forecast.clean": {"model": "openbb_terminal.forecast.forecast_model.clean"}, + "forecast.combine": { + "model": "openbb_terminal.forecast.forecast_model.combine_dfs" + }, + "forecast.desc": { + "view": "openbb_terminal.forecast.forecast_view.describe_df", + "model": "openbb_terminal.forecast.forecast_model.describe_df", + }, + "forecast.corr": { + "view": "openbb_terminal.forecast.forecast_view.display_corr", + "model": "openbb_terminal.forecast.forecast_model.corr_df", + }, + "forecast.season": { + "model": "openbb_terminal.forecast.forecast_view.display_seasonality" + }, + "forecast.delete": { + "model": "openbb_terminal.forecast.forecast_model.delete_column" + }, + "forecast.rename": { + "model": "openbb_terminal.forecast.forecast_model.rename_column" + }, + "forecast.export": {"model": "openbb_terminal.forecast.forecast_view.export_df"}, + "forecast.signal": {"model": "openbb_terminal.forecast.forecast_model.add_signal"}, + "forecast.atr": {"model": "openbb_terminal.forecast.forecast_model.add_atr"}, + "forecast.ema": {"model": "openbb_terminal.forecast.forecast_model.add_ema"}, + "forecast.sto": {"model": "openbb_terminal.forecast.forecast_model.add_sto"}, + "forecast.rsi": {"model": "openbb_terminal.forecast.forecast_model.add_rsi"}, + "forecast.roc": {"model": "openbb_terminal.forecast.forecast_model.add_roc"}, + "forecast.mom": {"model": "openbb_terminal.forecast.forecast_model.add_momentum"}, + "forecast.delta": {"model": "openbb_terminal.forecast.forecast_model.add_delta"}, + "forecast.expo": { + "model": "openbb_terminal.forecast.expo_model.get_expo_data", + "view": "openbb_terminal.forecast.expo_view.display_expo_forecast", + }, + "forecast.theta": { + "model": "openbb_terminal.forecast.theta_model.get_theta_data", + "view": "openbb_terminal.forecast.theta_view.display_theta_forecast", + }, + "forecast.linregr": { + "model": "openbb_terminal.forecast.linregr_model.get_linear_regression_data", + "view": "openbb_terminal.forecast.linregr_view.display_linear_regression", + }, + "forecast.regr": { + "model": "openbb_terminal.forecast.regr_model.get_regression_data", + "view": "openbb_terminal.forecast.regr_view.display_regression", + }, + "forecast.rnn": { + "model": "openbb_terminal.forecast.rnn_model.get_rnn_data", + "view": "openbb_terminal.forecast.rnn_view.display_rnn_forecast", + }, + "forecast.brnn": { + "model": "openbb_terminal.forecast.brnn_model.get_brnn_data", + "view": "openbb_terminal.forecast.brnn_view.display_brnn_forecast", + }, + "forecast.nbeats": { + "model": "openbb_terminal.forecast.nbeats_model.get_NBEATS_data", + "view": "openbb_terminal.forecast.nbeats_view.display_nbeats_forecast", + }, + "forecast.tcn": { + "model": "openbb_terminal.forecast.tcn_model.get_tcn_data", + "view": "openbb_terminal.forecast.tcn_view.display_tcn_forecast", + }, + "forecast.trans": { + "model": "openbb_terminal.forecast.trans_model.get_trans_data", + "view": "openbb_terminal.forecast.trans_view.display_trans_forecast", + }, + "forecast.tft": { + "model": "openbb_terminal.forecast.tft_model.get_tft_data", + "view": "openbb_terminal.forecast.tft_view.display_tft_forecast", + }, +} - -""" -api = Loader(functions=functions) -api.stocks.get_news() -api.economy.bigmac(chart=True) -api.economy.bigmac(chart=False) - - -TO USE THE API DIRECTLY JUST IMPORT IT: -from openbb_terminal.api import openbb (or: from openbb_terminal.api import openbb as api) -""" +if forecasting: + functions = {**functions, **forecast_extras} def copy_func(f) -> Callable: - """Copies the contents and attributes of the entered function. Based on https://stackoverflow.com/a/13503277 + """Copies the contents and attributes of the entered function. Based on + https://stackoverflow.com/a/13503277 Parameters ---------- f: Callable @@ -2184,7 +2282,15 @@ class Loader: module_path, function_name = function_path.rsplit(sep=".", maxsplit=1) module = cls.__load_module(module_path=module_path) - return getattr(module, function_name) + try: + function = getattr(module, function_name) + except AttributeError: + function = None + console.print("[red]Could not find the item below:[red]") + console.print(f"function: {function_name}") + console.print(f"module path: {module_path}") + console.print(f"module: {module}") + return function @classmethod def build_function_map(cls, funcs: dict) -> dict: @@ -2236,4 +2342,6 @@ class Loader: return function_map +# TO USE THE API DIRECTLY JUST IMPORT IT: +# from openbb_terminal.api import openbb (or: from openbb_terminal.api import openbb as api) openbb = Loader(funcs=functions).openbb diff --git a/openbb_terminal/common/__init__.py b/openbb_terminal/common/__init__.py index c4d900d4470..dd2a17b6de9 100644 --- a/openbb_terminal/common/__init__.py +++ b/openbb_terminal/common/__init__.py @@ -1,7 +1,6 @@ # flake8: noqa from . import ( behavioural_analysis, - prediction_techniques, quantitative_analysis, technical_analysis, ) diff --git a/openbb_terminal/common/prediction_techniques/FUNE-TUNING-NN-models.md b/openbb_terminal/common/prediction_techniques/FUNE-TUNING-NN-models.md deleted file mode 100644 index fedd4f30acd..00000000000 --- a/openbb_terminal/common/prediction_techniques/FUNE-TUNING-NN-models.md +++ /dev/null @@ -1,186 +0,0 @@ -# Validate/Tweaking Neural Network Model - -Note: Modeling a NN this way is intended for learning purposes (or fun) only. A user guess or YOLOing on GME may -present better returns - No financial advisor tho. - -1. **Select the stock that you want to attempt to predict share price** - - ```text - load MSFT - ``` - -2. **Slice the stock price to only relevant data** - - Look at the overall share price and try to understand from when does it start behaving similarly. E.g. looking at - MSFT, for me, it starts behaving similarly after 2014. Before that, the stock price is rather flat, and not - representative of the current pattern. - - ```text - load MSFT -s 2014-01-01 - ``` - -3. **Out of this training data, select which part will be used for validation** - - Anyone reading this, note this is quite a discussion between the devs. Since this is a time series, there is an - inherent ordering of the data. However, since returns are random, it is hard to predict, and ordering data may not - have any meaning. - - To this end, we allow users to specify how much validation data to use and if it should be shuffled. By default, we - will split into 10% validation data with a random shuffle. This will allow the user to see how the model performs - at various times in your sequence. - - ```text - -v/--valid : validation split of data. Default 0.1 (10%) - --no_shuffle : Flag that will order the validation data so that the last (-v) percent of data is the validation. - ``` - - To try "backtesting" to a certain data, you can specify the end date. This will forecast and compare the true data - from that time. - - ```text - -e/--end : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None. - ``` - -4. **Pre-processing data** - - Decide whether you want to pre-process your share price data or not. I.e. do you want to use these values between 18 - and 245, or perhaps normalize these between 0 and 1? The later may improve performance. - - ```text - Preprocess = "minmax" - ``` - - This is defined in the config file. - -5. **Select input, prediction days and jumps in training data** - - The **input** corresponds to the number of days that our model will use for prediction. - - The more past days, the more information our model will have to make a sensible prediction. However, the more days - being used will lead to a model that takes longer to train, and some days in the past may have no affect in the - prediction. In order to facilitate this decision, think of it in terms of months, e.g. 40 days corresponds to - roughly 2 months of data. If you think that the share price today can impact the value that it will take in 2 - months, then 40 is a good value, otherwise pick a shorter value. - - ```text - -i/--input : number of days to use for prediction. Default 40. - ``` - - The **prediction days** is the number we want to predict for. - - Personally, I recommend to set this for a really short window. This is because the bigger the window, the less - reliable the prediction is. This is mainly due to the fact that by having a larger prediction horizon, more - unexpected events (news, insider trading activity, rumours, ...) can occur, which may shift share price. - - ```text - -d/--days : prediction days. Default 5. - ``` - -6. **Select NN architecture.** TL;DR: LSTM > RNN > MLP - - **MLP**: The neurons don't have any memory. - **RNN***: The neurons can only retain really recent memory. - **LSTM** (improved RNN): The neurons have a long-term memory, hence why this is used for text prediction, since the - beginning of a sentence is important to predict the ending. - - Have a look at: - -7. **Define NN Model** - - **Number of hidden layers**: Due to the complexity of these NN models (particularly LSTM), this value should be kept - low (2, 3 or 4). Otherwise, the model will not generalize well to unseen examples (i.e. it will overfit). - - **Number of units per layers**: This is one of the hardest to know how to set, I usually set this one by trial and - iteration, and understanding how the model performs. Note: Be carefully about this iterative process, to not overfit - your hyperparameters to work well in the validation data. Because then it may fail badly on the real prediction. - - **Activation function**: _tanh_ is recommended. It is proved to outperform other activation functions. - See - - **Dropout layer**: As a rule of thumb, an LSTM layer should be followed by a Dropout layer with a 20% rate. This is - in order to prevent overfitting, while keeping model accuracy. - - **Dense layer**: Dense layer to represent outputs. - - ```python - Long_Short_Term_Memory \ - = [ {'LSTM': - {'units':25, 'activation':'tanh', 'return_sequences':True} }, - {"Dropout": - {"rate": 0.2} }, - {'LSTM': - {'units':15, 'activation':'tanh', 'return_sequences':False} }, - {'Dense': - {'activation':'linear'} }] - ``` - - This model is set in [config_neural_network_models.py](/config_neural_network_models.py) - -8. **Model compilation with loss function and optimization technique** - - **Loss function**: Often chosen having the activation functions of the hidden layers in mind. - See , - and . - - ```python - Loss = "mae" - ``` - - This is defined in the config file. - - **Optimizer technique**:Adaptive Moment Estimation (_adam_) is usually the default choice for this type of problems. - See . - - The optimizer selection has been moved to the config_neural_network_models.py file and the new argument is the - learning rate. This tells your model how "fast" to train by adjusting the factor that gets multiplied to the loss - function for updating weights. - - ```text - --lr : learning rate. Default 0.01. - ``` - -9. **Training epochs** - - Select the number of epochs to train your model. The bigger number of epochs, the longer the training will take, and - also increase chances of overfitting. - - An early stopping has been implemented that will (if desired) stop training once the validation loss has plateaued. - The patience (number of epochs with no improvement) can be found in the config file. - - ```text - --epochs : number of training epochs. Default 200. - ``` - -10. **Model training loops** - - Select the number of loops to - using the defined model - train and predict. This allows to build confidence on the - model you are building, and understand what's the median results and also the lower and upper quantiles, of 10 and - 90%, respectively. - - ```text - --loops: number of loops to iterate and train models. Default 1. - ``` - -11. **Interpret accuracy metrics** - - Interpret the following metrics: MAPE, R2, MAE, MSE and RMSE. - -12. **Advanced User** - - ```text - --batch_size: batch size for model training, should not be used unless advanced user. Default None. - ``` - - This can be set to `floor(log2(input_layer_size))` for much faster training if a large amount of input samples - (assuming you have the RAM for it). It shouldn't have much effect on the results. - - ```text - --xla_cpu: if present, will enable XLA for CPU (overrides environment variables during run). - --xla_gpu: if present, will enable XLA for GPU (overrides environment variables during run). - --force_allow_gpu_growth: if true, will force TensorFlow to allow GPU memory usage to grow as needed. - Otherwise will allocate 100% of available GPU memory when CUDA is set up. Default true. - ``` - - See - - In the Roadmap: Cross-Validation (Forward Chaining, K-Fold, Group K-fold), NN to use sentiment analysis diff --git a/openbb_terminal/common/prediction_techniques/README-gpu-accel.md b/openbb_terminal/common/prediction_techniques/README-gpu-accel.md deleted file mode 100644 index 4557f74225d..00000000000 --- a/openbb_terminal/common/prediction_techniques/README-gpu-accel.md +++ /dev/null @@ -1,74 +0,0 @@ -# GPU Acceleration - -## GPU acceleration with CUDA - -Most up-to-date docs and recommendations: - -### CUDA 11.0.3, cuDNN 8.0.5 - Tested working - -1. Install [CUDA 11.0.3](https://developer.nvidia.com/cuda-toolkit-archive) - -2. Install [cuDNN 8.0.5 for CUDA 11.0](https://developer.nvidia.com/rdp/cudnn-archive). This requires signing up for -the nvidia developer program. To install, copy the bin/, lib/, include/ dirs into your CUDA install dirs. - - | Platform | CUDA path | - | -------- | ---------------------------------------------------------- | - | Linux | /usr/local/cuda-X.Y | - | Windows | C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v | - -3. Set the environment variables `CUDA_PATH` and `CUDA_PATH_V11_0` if needed. These are usually set by the CUDA installer. - -### CUDA 11.1, cuDNN 8.0.5 - not tested - -### CUDA 11.2.1, cuDNN 8.1 - may work, requires hack - -Getting this to run at all requires you to make a link for cusolver10. Even with this fix I ran into out-of-memory -issues, so your mileage may vary. See . - -Linux: - -```bash -cd $LD_LIBRARY_PATH -sudo ln libcusolver.so.11 libcusolver.so.10 -``` - -Windows: - -```cmd -cd "%CUDA_PATH_V11_2%\bin" -mklink /h cusolver64_10.dll cusolver64_11.dll -``` - -## Enable XLA for Neural Networks - -For neural network training, there are flags defined to enable XLA and GPU options. Note that XLA and especially GPU -acceleration may be slower than the defaults if you are not configuring a larger batch size. The default settings -produce matrices that are too small to effectively take advantage of these features. - -```bash -> load -t GME -> pred -> lstm -h # display the help menu which lists all args -> lstm --xla_gpu --force_allow_gpu_growth true # enable xla with gpu acceleration and flexible memory usage -``` - -## Globally enable XLA - -XLA is a domain specific compiler for linear algebra that can accelerate TensorFlow models if you can get it working. - - -| Env var name | Value | -| ------------------------- | ------------------------------------------------------------ | -| XLA_FLAGS | --xla_gpu_cuda_data_dir=CUDA_PATH_V11_0 | -| TF_XLA_FLAGS | --tf_xla_cpu_global_jit --tf_xla_enable_xla_devices | - To enable profiling (useful for debugging), add `--xla_hlo_profile` to `XLA_FLAGS` - -## Globally enable GPU + XLA - -By default, TensorFlow will allocate 100% of your available GPU memory before it starts doing work. Using -`TF_FORCE_GPU_ALLOW_GROWTH` below will override this behavior so that it only allocates memory as needed. - -| Env var name | Value | -| ------------------------- | ------------------------------------------------------------ | -| TF_XLA_FLAGS | --tf_xla_auto_jit=2 --tf_xla_enable_xla_devices | -| TF_FORCE_GPU_ALLOW_GROWTH | true | diff --git a/openbb_terminal/common/prediction_techniques/arima_model.py b/openbb_terminal/common/prediction_techniques/arima_model.py deleted file mode 100644 index 4ab3bf3ed5a..00000000000 --- a/openbb_terminal/common/prediction_techniques/arima_model.py +++ /dev/null @@ -1,76 +0,0 @@ -"""Arima Prediction Model""" -__docformat__ = "numpy" - -import logging -from typing import Any, List, Tuple, Union - -import pandas as pd -import pmdarima -from statsmodels.tsa.arima.model import ARIMA - -from openbb_terminal.decorators import log_start_end - -logger = logging.getLogger(__name__) - -ICS = ["aic", "aicc", "bic", "hqic", "oob"] - - -@log_start_end(log=logger) -def get_arima_model( - values: Union[pd.Series, pd.DataFrame], - arima_order: str, - n_predict: int, - seasonal: bool, - ic: str, -) -> Tuple[List[float], Any]: - """Get an ARIMA model for data - - Parameters - ---------- - values : Union[pd.Series, pd.DataFrame] - Data to fit - arima_order : str - String of ARIMA params in form "p,q,d" - n_predict : int - Days to predict - seasonal : bool - Flag to use seasonal model - ic : str - Information Criteria for model evaluation - - Returns - ------- - List[float] - List of predicted values - Any - Fit ARIMA model object. - """ - if arima_order: - model = ARIMA( - values, order=tuple(int(ord) for ord in arima_order.split(",")) - ).fit() - l_predictions = list( - model.predict( - start=len(values.values) + 1, - end=len(values.values) + n_predict, - ) - ) - else: - if seasonal: - model = pmdarima.auto_arima( - values.values, - error_action="ignore", - seasonal=True, - m=5, - information_criteria=ic, - ) - else: - model = pmdarima.auto_arima( - values.values, - error_action="ignore", - seasonal=False, - information_criteria=ic, - ) - l_predictions = list(model.predict(n_predict)) - - return l_predictions, model diff --git a/openbb_terminal/common/prediction_techniques/arima_view.py b/openbb_terminal/common/prediction_techniques/arima_view.py deleted file mode 100644 index 117b84da2a7..00000000000 --- a/openbb_terminal/common/prediction_techniques/arima_view.py +++ /dev/null @@ -1,316 +0,0 @@ -""" ARIMA Prediction View """ -__docformat__ = "numpy" - - -import datetime -import logging -import os -from typing import Union, Optional, List - -import matplotlib.pyplot as plt -import pandas as pd - -from openbb_terminal.common.prediction_techniques import arima_model -from openbb_terminal.common.prediction_techniques.pred_helper import ( - print_prediction_kpis, - print_pretty_prediction, -) -from openbb_terminal.config_terminal import theme -from openbb_terminal.config_plot import PLOT_DPI -from openbb_terminal.decorators import log_start_end -from openbb_terminal.helper_funcs import ( - export_data, - get_next_stock_market_days, - plot_autoscale, - print_rich_table, - is_valid_axes_count, -) -from openbb_terminal.rich_config import console -from openbb_terminal import rich_config - -logger = logging.getLogger(__name__) - -# pylint:disable=too-many-arguments,too-many-branches - -# TODO: HELP WANTED! The logic of this view is pretty convoluted and it overlaps with -# other views of this submenu. It would be great to refactor it in a way the -# `pylint:disable` flag above can be removed and the backtesting related code is -# reused by multiple views. - - -@log_start_end(log=logger) -def display_arima( - dataset: str, - values: Union[pd.DataFrame, pd.Series], - arima_order: str, - n_predict: int, - seasonal: bool, - ic: str, - results: bool, - end_date: str = "", - export: str = "", - time_res: str = "", - external_axes: Optional[List[plt.Axes]] = None, -): - """View fit ARIMA model - - Parameters - ---------- - dataset : str - String indicating dataset (for plot title) - values : Union[pd.DataFrame, pd.Series] - Data to fit - arima_order : str - String of ARIMA params in form "p,q,d" - n_predict : int - Days to predict - seasonal : bool - Flag to use seasonal model - ic : str - Information Criteria for model evaluation - results : bool - Flag to display model summary - end_date : str, optional - Specified end date for backtesting comparisons - export : str, optional - Format to export image - time_res : str - Resolution for data, allowing for predicting outside of standard market days - external_axes : Optional[List[plt.Axes]], optional - External axes (1 axis is expected in the list), by default None - """ - - if arima_order: - t_order = tuple(int(ord) for ord in arima_order.split(",")) - if end_date: - if not time_res: - future_index = get_next_stock_market_days( - last_stock_day=end_date, n_next_days=n_predict - ) - else: - future_index = pd.date_range( - end_date, periods=n_predict + 1, freq=time_res - )[1:] - - if future_index[-1] > datetime.datetime.now(): - console.print( - "Backtesting not allowed, since End Date + Prediction days is in the future\n" - ) - return - - df_future = values[future_index[0] : future_index[-1]] # noqa: E203 - values = values[:end_date] # type: ignore - - l_predictions, model = arima_model.get_arima_model( - values, arima_order, n_predict, seasonal, ic - ) - - # Prediction data - if not time_res: - l_pred_days = get_next_stock_market_days( - last_stock_day=values.index[-1], - n_next_days=n_predict, - ) - else: - l_pred_days = pd.date_range( - values.index[-1], periods=n_predict + 1, freq=time_res - )[1:] - - df_pred = pd.Series(l_predictions, index=l_pred_days, name="Price") - - if results: - console.print(model.summary()) - console.print("") - - # This plot has 1 axis - if external_axes is None: - _, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI) - elif end_date and not is_valid_axes_count( - external_axes, - 3, - prefix_text="If there is end_date", - suffix_text="when backtesting", - ): - return - elif not end_date and not is_valid_axes_count( - external_axes, - 1, - prefix_text="If there is no end_date", - suffix_text="when backtesting", - ): - return - else: - ax = external_axes[0] - - ax.plot(values.index, values) - - # pylint:disable=no-member - - if arima_order: - # BACKTESTING - if end_date: - ax.set_title( - f"BACKTESTING: ARIMA {str(t_order)} on {dataset} - {n_predict} step prediction" - ) - else: - ax.set_title( - f"ARIMA {str(t_order)} on {dataset} - {n_predict} step prediction" - ) - else: - # BACKTESTING - if end_date: - ax.set_title( - f"BACKTESTING: ARIMA {model.order} on {dataset} - {n_predict} step prediction" - ) - else: - plt.title(f"ARIMA {model.order} on {dataset} - {n_predict} step prediction") - ax.set_xlim(values.index[0], l_pred_days[-1]) - ax.set_ylabel("Value") - ax.plot( - [values.index[-1], df_pred.index[0]], - [values.values[-1], df_pred.values[0]], - color=theme.up_color, - linestyle="--", - ) - ax.plot(df_pred.index, df_pred, color=theme.up_color) - ax.axvspan(values.index[-1], df_pred.index[-1], alpha=0.2) - _, _, ymin, ymax = plt.axis() - ax.vlines(values.index[-1], ymin, ymax, linestyle="--") - - # BACKTESTING - if end_date: - ax.plot( - df_future.index, - df_future, - color=theme.up_color, - linestyle="--", - ) - ax.plot( - [values.index[-1], df_future.index[0]], - [ - values.values[-1], - df_future.values[0], - ], - color=theme.up_color, - linestyle="--", - ) - - theme.style_primary_axis(ax) - - if external_axes is None: - theme.visualize_output() - - # BACKTESTING - if end_date: - # This plot has 3 axes - if external_axes is None: - _, axes = plt.subplots( - 2, 1, sharex=True, figsize=plot_autoscale(), dpi=PLOT_DPI - ) - (ax2, ax3) = axes - elif is_valid_axes_count(external_axes, 3): - (_, ax2, ax3) = external_axes - else: - return - - ax2.plot( - df_future.index, - df_future, - color=theme.up_color, - linestyle="--", - ) - ax2.plot(df_pred.index, df_pred) - ax2.scatter( - df_future.index, - df_future, - color=theme.up_color, - ) - ax2.plot( - [values.index[-1], df_future.index[0]], - [ - values.values[-1], - df_future.values[0], - ], - color=theme.up_color, - linestyle="--", - ) - ax2.scatter(df_pred.index, df_pred) - ax2.plot( - [values.index[-1], df_pred.index[0]], - [values.values[-1], df_pred.values[0]], - linestyle="--", - ) - ax2.set_title("BACKTESTING: Values") - ax2.set_xlim( - values.index[-1], - df_pred.index[-1] + datetime.timedelta(days=1), - ) - ax2.set_ylabel("Value") - ax2.legend(["Real data", "Prediction data"]) - theme.style_primary_axis(ax2) - - ax3.axhline(y=0, linestyle="--") - ax3.plot( - df_future.index, - 100 * (df_pred.values - df_future.values) / df_future.values, - color=theme.down_color, - ) - ax3.scatter( - df_future.index, - 100 * (df_pred.values - df_future.values) / df_future.values, - color=theme.down_color, - ) - ax3.set_title("BACKTESTING: % Error") - ax3.plot( - [values.index[-1], df_future.index[0]], - [ - 0, - 100 * (df_pred.values[0] - df_future.values[0]) / df_future.values[0], - ], - ls="--", - color=theme.down_color, - ) - ax3.set_xlim( - values.index[-1], - df_pred.index[-1] + datetime.timedelta(days=1), - ) - ax3.set_ylabel("Prediction Error (%)") - theme.style_primary_axis(ax3) - - if external_axes is None: - theme.visualize_output() - - # Refactor prediction dataframe for backtesting print - df_pred.name = "Prediction" - df_pred = df_pred.to_frame() - df_pred["Real"] = df_future.values - - if rich_config.USE_COLOR: - df_pred["Real"] = df_pred["Real"].astype(float) - df_pred["Prediction"] = df_pred["Prediction"].astype(float) - df_pred["Dif"] = 100 * (df_pred.Prediction - df_pred.Real) / df_pred.Real - print_rich_table( - df_pred, - headers=["Predicted", "Actual", "% Difference"], - index_name="Date", - show_index=True, - title="ARIMA Model", - ) - else: - df_pred["Real"] = df_pred["Real"].astype(float) - df_pred["Prediction"] = df_pred["Predicted"].astype(float) - df_pred["Dif"] = 100 * (df_pred.Prediction - df_pred.Real) / df_pred.Real - print_rich_table( - df_pred, - headers=["Date", "Predicted", "Actual", "% Difference"], - show_index=True, - title="ARIMA Model", - ) - - print_prediction_kpis(df_pred["Real"].values, df_pred["Prediction"].values) - - else: - # Print prediction data - print_pretty_prediction(df_pred, values.values[-1]) - - export_data(export, os.path.dirname(os.path.abspath(__file__)), "arima") diff --git a/openbb_terminal/common/prediction_techniques/ets_model.py b/openbb_terminal/common/prediction_techniques/ets_model.py deleted file mode 100644 index 72f8dd69bdb..00000000000 --- a/openbb_terminal/common/prediction_techniques/ets_model.py +++ /dev/null @@ -1,176 +0,0 @@ -"""ETS Prediction Model""" -__docformat__ = "numpy" - -import logging -from typing import Union - -import numpy as np -import pandas as pd -from statsmodels.tsa.holtwinters import ExponentialSmoothing - -from openbb_terminal.decorators import log_start_end - -logger = logging.getLogger(__name__) - -TRENDS = ["N", "A", "Ad"] -SEASONS = ["N", "A", "M"] - - -@log_start_end(log=logger) -def get_exponential_smoothing_model( - data: Union[pd.Series, np.ndarray], - trend: str = "N", - seasonal: str = "N", - seasonal_periods: int = 5, - n_predict: int = 5, -): - """ - Perform exponential smoothing - Parameters - ---------- - data: Union[pd.Series, np.ndarray] - Series of closing values - trend: str - Trend component. One of [N, A, Ad] - seasonal: str - Seasonal component. One of [N, A, M] - seasonal_periods: int - Number of seasonal periods in a year - n_predict: int - Number of days to forecast - - Returns - ------- - model: - Exponential smoothing model - title: str - String describing selected model - forecast : List[float] - List of predictions - """ - if trend == "N": # None - if seasonal == "N": # None - title = "Trend='N', Seasonal='N': Simple Exponential Smoothing" - ETS = ExponentialSmoothing( - data, - trend=None, - damped_trend=False, - seasonal=None, - ) - model = ETS.fit( - smoothing_level=None, smoothing_trend=None, damping_trend=None - ) - - elif seasonal == "A": # Additive - title = "Trend='N', Seasonal='A': Exponential Smoothing" - ETS = ExponentialSmoothing( - data, - trend=None, - damped_trend=False, - seasonal="add", - seasonal_periods=seasonal_periods, - ) - model = ETS.fit(smoothing_level=None, smoothing_seasonal=None) - - elif seasonal == "M": # Multiplicative - title = "Trend='N', Seasonal='M': Exponential Smoothing" - ETS = ExponentialSmoothing( - data, - trend=None, - damped_trend=False, - seasonal="mul", - seasonal_periods=seasonal_periods, - ) - model = ETS.fit(smoothing_level=None, smoothing_seasonal=None) - - elif trend == "A": # Additive - if seasonal == "N": # None - title = "Trend='A', Seasonal='N': Holtโ€™s linear method" - ETS = ExponentialSmoothing( - data, - trend="add", - damped_trend=False, - seasonal=None, - ) - model = ETS.fit( - smoothing_level=None, smoothing_trend=None, damping_trend=None - ) - - elif seasonal == "A": # Additive - title = "Trend='A', Seasonal='A': Additive Holt-Wintersโ€™ method" - ETS = ExponentialSmoothing( - data, - trend="add", - damped_trend=False, - seasonal="add", - seasonal_periods=seasonal_periods, - ) - model = ETS.fit( - smoothing_level=None, smoothing_trend=None, smoothing_seasonal=None - ) - - elif seasonal == "M": # Multiplicative - title = "Trend='A', Seasonal='M': Multiplicative Holt-Wintersโ€™ method" - ETS = ExponentialSmoothing( - data, - trend="add", - damped_trend=False, - seasonal="mul", - seasonal_periods=seasonal_periods, - ) - model = ETS.fit( - smoothing_level=None, smoothing_trend=None, smoothing_seasonal=None - ) - - elif trend == "Ad": # Additive damped - if seasonal == "N": # None - title = "Trend='Ad', Seasonal='N': Additive damped trend method" - ETS = ExponentialSmoothing( - data, - trend="add", - damped_trend=True, - seasonal=None, - ) - model = ETS.fit( - smoothing_level=None, smoothing_trend=None, damping_trend=None - ) - - elif seasonal == "A": # Additive - title = "Trend='Ad', Seasonal='A': Exponential Smoothing" - ETS = ExponentialSmoothing( - data, - trend="add", - damped_trend=True, - seasonal="add", - seasonal_periods=seasonal_periods, - ) - model = ETS.fit( - smoothing_level=None, - smoothing_trend=None, - damping_trend=None, - smoothing_seasonal=None, - ) - - elif seasonal == "M": # Multiplicative - title = "Trend='Ad', Seasonal='M': Holt-Wintersโ€™ damped method" - ETS = ExponentialSmoothing( - data, - trend="add", - damped_trend=True, - seasonal="mul", - seasonal_periods=seasonal_periods, - ) - model = ETS.fit( - smoothing_level=None, - smoothing_trend=None, - damping_trend=None, - smoothing_seasonal=None, - ) - - if model.mle_retvals.success: - forecast = [max(i, 0) for i in model.forecast(n_predict)] - - else: - forecast = [] - - return model, title, forecast diff --git a/openbb_terminal/common/prediction_techniques/ets_view.py b/openbb_terminal/common/prediction_techniques/ets_view.py deleted file mode 100644 index 92021f42619..00000000000 --- a/openbb_terminal/common/prediction_techniques/ets_view.py +++ /dev/null @@ -1,317 +0,0 @@ -"""ETS Prediction View""" -__docformat__ = "numpy" - -import datetime -import logging -import os -import warnings -from typing import Union, Optional, List - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd - -from openbb_terminal.config_terminal import theme -from openbb_terminal.common.prediction_techniques import ets_model -from openbb_terminal.common.prediction_techniques.pred_helper import ( - lambda_price_prediction_backtesting_color, - print_prediction_kpis, - print_pretty_prediction, -) -from openbb_terminal.config_plot import PLOT_DPI -from openbb_terminal.decorators import log_start_end -from openbb_terminal.helper_funcs import ( - export_data, - get_next_stock_market_days, - patch_pandas_text_adjustment, - plot_autoscale, - is_valid_axes_count, -) -from openbb_terminal.rich_config import console -from openbb_terminal import rich_config - -logger = logging.getLogger(__name__) - -warnings.filterwarnings("ignore") -# pylint:disable=too-many-arguments - - -@log_start_end(log=logger) -def display_exponential_smoothing( - dataset: str, - data: Union[pd.DataFrame, pd.Series], - n_predict: int, - trend: str = "N", - seasonal: str = "N", - seasonal_periods: int = 7, - end_date: str = "", - export: str = "", - time_res: str = "", - external_axes: Optional[List[plt.Axes]] = None, -): - """Perform exponential smoothing - - Parameters - ---------- - dataset : str - Dataset being smoothed - data : Union[pd.DataFrame, pd.Series] - Raw data - n_predict : int - Days to predict - trend : str, optional - Trend variable, by default "N" - seasonal : str, optional - Seasonal variable, by default "N" - seasonal_periods : int, optional - Number of seasonal periods, by default 5 - end_date : str, optional - End date for backtesting, by default "" - export : str, optional - Format to export data, by default "" - time_res : str - Resolution for data, allowing for predicting outside of standard market days - external_axes : Optional[List[plt.Axes]], optional - External axes (1 axis is expected in the list), by default None - """ - if end_date: - if not time_res: - future_index = get_next_stock_market_days( - last_stock_day=end_date, n_next_days=n_predict - ) - else: - future_index = pd.date_range( - end_date, periods=n_predict + 1, freq=time_res - )[1:] - - if future_index[-1] > datetime.datetime.now(): - console.print( - "Backtesting not allowed," - + " since End Date + Prediction days is in the future\n" - ) - return - - df_future = data[future_index[0] : future_index[-1]] # noqa: E203 - data = data[:end_date] # type: ignore - - # Get ETS model - model, title, forecast = ets_model.get_exponential_smoothing_model( - data, trend, seasonal, seasonal_periods, n_predict - ) - - if not forecast: - console.print("No forecast made. Model did not converge.\n") - return - - if np.isnan(forecast).any(): - console.print("Model predicted NaN values. Runtime Error.\n") - return - - if not time_res: - l_pred_days = get_next_stock_market_days( - last_stock_day=data.index[-1], - n_next_days=n_predict, - ) - else: - l_pred_days = pd.date_range( - data.index[-1], periods=n_predict + 1, freq=time_res - )[1:] - - df_pred = pd.Series(forecast, index=l_pred_days, name="Price") - - console.print(f"\n{title}") - console.print("\nFit model parameters:") - for key, value in model.params.items(): - console.print(f"{key} {' '*(18-len(key))}: {value}") - - console.print("\nAssess fit model:") - console.print(f"AIC: {round(model.aic, 2)}") - console.print(f"BIC: {round(model.bic, 2)}") - console.print(f"SSE: {round(model.sse, 2)}\n") - - # Plotting - - # This plot has 1 axis - if external_axes is None: - _, ax1 = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI) - - elif end_date and not is_valid_axes_count( - external_axes, - 3, - prefix_text="If there is end_date", - suffix_text="when backtesting", - ): - return - elif not end_date and not is_valid_axes_count( - external_axes, - 1, - prefix_text="If there is no end_date", - suffix_text="when backtesting", - ): - return - else: - ax1 = external_axes[0] - - ax1.plot(data.index, data.values) - - # BACKTESTING - if end_date: - ax1.set_title(f"BACKTESTING: {title} on {dataset}", fontsize=12) - else: - ax1.set_title(f"{title} on {dataset}", fontsize=12) - - ax1.set_xlim( - data.index[0], - get_next_stock_market_days(df_pred.index[-1], 1)[-1], - ) - ax1.set_ylabel("Value") - ax1.plot( - [data.index[-1], df_pred.index[0]], - [data.values[-1], df_pred.values[0]], - color=theme.down_color, - linestyle="--", - ) - ax1.plot(df_pred.index, df_pred, color=theme.down_color) - ax1.axvspan( - data.index[-1], - df_pred.index[-1], - facecolor=theme.down_color, - alpha=0.2, - ) - _, _, ymin, ymax = plt.axis() - ax1.vlines( - data.index[-1], - ymin, - ymax, - linestyle="--", - color=theme.get_colors(reverse=True)[0], - ) - - # BACKTESTING - if end_date: - ax1.plot( - df_future.index, - df_future, - color=theme.up_color, - linestyle="--", - ) - ax1.plot( - [data.index[-1], df_future.index[0]], - [ - data.values[-1], - df_future.values[0], - ], - color=theme.up_color, - linestyle="--", - ) - - theme.style_primary_axis(ax1) - - if external_axes is None: - theme.visualize_output() - - # BACKTESTING - if end_date: - # This plot has 3 axes - if external_axes is None: - _, axes = plt.subplots( - 2, 1, sharex=True, figsize=plot_autoscale(), dpi=PLOT_DPI - ) - (ax2, ax3) = axes - elif is_valid_axes_count(external_axes, 3): - (_, ax2, ax3) = external_axes - else: - return - - ax2.plot( - df_future.index, - df_future, - color=theme.up_color, - linestyle="--", - ) - ax2.plot(df_pred.index, df_pred) - ax2.scatter( - df_future.index, - df_future, - color=theme.up_color, - ) - ax2.plot( - [data.index[-1], df_future.index[0]], - [ - data.values[-1], - df_future.values[0], - ], - color=theme.up_color, - linestyle="--", - ) - ax2.scatter(df_pred.index, df_pred) - ax2.plot( - [data.index[-1], df_pred.index[0]], - [data.values[-1], df_pred.values[0]], - linestyle="--", - ) - ax2.set_title("BACKTESTING: Values") - ax2.set_xlim( - data.index[-1], - df_pred.index[-1] + datetime.timedelta(days=1), - ) - ax2.set_ylabel("Value") - ax2.legend(["Real data", "Prediction data"]) - theme.style_primary_axis(ax2) - - ax3.axhline(y=0, linestyle="--") - ax3.plot( - df_future.index, - 100 * (df_pred.values - df_future.values) / df_future.values, - color=theme.down_color, - ) - ax3.scatter( - df_future.index, - 100 * (df_pred.values - df_future.values) / df_future.values, - color=theme.down_color, - ) - ax3.set_title("BACKTESTING: % Error") - ax3.plot( - [data.index[-1], df_future.index[0]], - [ - 0, - 100 * (df_pred.values[0] - df_future.values[0]) / df_future.values[0], - ], - ls="--", - color=theme.down_color, - ) - ax3.set_xlim( - data.index[-1], - df_pred.index[-1] + datetime.timedelta(days=1), - ) - ax3.set_ylabel("Prediction Error (%)") - theme.style_primary_axis(ax3) - - if external_axes is None: - theme.visualize_output() - - # Refactor prediction dataframe for backtesting print - df_pred.name = "Prediction" - df_pred = df_pred.to_frame() - df_pred["Real"] = df_future - - if rich_config.USE_COLOR: - - patch_pandas_text_adjustment() - - console.print("Time Real [$] x Prediction [$]") - console.print( - df_pred.apply( - lambda_price_prediction_backtesting_color, axis=1 - ).to_string() - ) - else: - console.print(df_pred[["Real", "Prediction"]].round(2).to_string()) - - console.print("") - print_prediction_kpis(df_pred["Real"].values, df_pred["Prediction"].values) - - else: - # Print prediction data - print_pretty_prediction(df_pred, data.values[-1]) - export_data(export, os.path.dirname(os.path.abspath(__file__)), "ets") diff --git a/openbb_terminal/common/prediction_techniques/knn_model.py b/openbb_terminal/common/prediction_techniques/knn_model.py deleted file mode 100644 index ce3e342e524..00000000000 --- a/openbb_terminal/common/prediction_techniques/knn_model.py +++ /dev/null @@ -1,101 +0,0 @@ -"""KNN Prediction Model""" -__docformat__ = "numpy" - -import logging -from typing import Any, Tuple, Union - -import numpy as np -import pandas as pd -from sklearn import neighbors - -from openbb_terminal.common.prediction_techniques.pred_helper import ( - prepare_scale_train_valid_test, -) -from openbb_terminal.decorators import log_start_end -from openbb_terminal.helper_funcs import get_next_stock_market_days -from openbb_terminal.rich_config import console - -logger = logging.getLogger(__name__) - - -@log_start_end(log=logger) -def get_knn_model_data( - data: Union[pd.Series, pd.DataFrame], - n_input_days: int, - n_predict_days: int, - n_neighbors: int, - test_size: float, - end_date: str, - no_shuffle: bool, -) -> Tuple[pd.DataFrame, np.ndarray, np.ndarray, np.ndarray, Any]: - """Perform knn model fitting and predicting on data - - Parameters - ---------- - data : Union[pd.Series, pd.DataFrame] - Data to fit - n_input_days : int - Length of input series - n_predict_days : int - Number of days to predict - n_neighbors : int - Number of neighbors for nn - test_size : float - Fraction of data for testing - end_date : str - End date for backtesting - no_shuffle : bool - Flag to not shuffle train/test data - - Returns - ------- - pd.DataFrame: - Dataframe of preditions - np.array: - Array of validation predictions - np.array: - Array of validation data - np.array: - Array of validation dates - Any: - Scaler for processing data - """ - ( - X_train, - X_valid, - y_train, - y_valid, - _, - _, - _, - y_dates_valid, - forecast_data_input, - dates_forecast_input, - scaler, - is_error, - ) = prepare_scale_train_valid_test( - data, n_input_days, n_predict_days, test_size, end_date, no_shuffle - ) - if is_error: - return pd.DataFrame(), np.array(0), np.array(0), np.array(0), None - - future_dates = get_next_stock_market_days( - dates_forecast_input[-1], n_next_days=n_predict_days - ) - console.print( - f"Training on {X_train.shape[0]} sequences of length {X_train.shape[1]}. Using {X_valid.shape[0]} sequences " - f" of length {X_valid.shape[1]} for validation" - ) - # Machine Learning model - knn = neighbors.KNeighborsRegressor(n_neighbors=n_neighbors) - knn.fit( - X_train.reshape(X_train.shape[0], X_train.shape[1]), - y_train.reshape(y_train.shape[0], y_train.shape[1]), - ) - - preds = knn.predict(X_valid.reshape(X_valid.shape[0], X_valid.shape[1])) - forecast_data = knn.predict(forecast_data_input.reshape(1, -1)) - forecast_data = scaler.inverse_transform(forecast_data.reshape(1, -1)) - forecast_data_df = pd.DataFrame(list(forecast_data.T), index=future_dates) - - return forecast_data_df, preds, y_valid, y_dates_valid, scaler diff --git a/openbb_terminal/common/prediction_techniques/knn_view.py b/openbb_terminal/common/prediction_techniques/knn_view.py deleted file mode 100644 index 064161af145..00000000000 --- a/openbb_terminal/common/prediction_techniques/knn_view.py +++ /dev/null @@ -1,92 +0,0 @@ -""" KNN Prediction View""" -__docformat__ = "numpy" - -import logging -from typing import Union, Optional, List - -import pandas as pd -from matplotlib import pyplot as plt - -from openbb_terminal.common.prediction_techniques import knn_model -from openbb_terminal.common.prediction_techniques.pred_helper import ( - plot_data_predictions, - print_pretty_prediction, -) -from openbb_terminal.decorators import log_start_end -from openbb_terminal.rich_config import console - -logger = logging.getLogger(__name__) - -# pylint:disable=too-many-arguments - - -@log_start_end(log=logger) -def display_k_nearest_neighbors( - dataset: str, - data: Union[pd.DataFrame, pd.Series], - n_neighbors: int, - n_input_days: int, - n_predict_days: int, - test_size: float, - end_date: str = "", - no_shuffle: bool = True, - time_res: str = "", - external_axes: Optional[List[plt.Axes]] = None, -): - """Display predictions using knn - - Parameters - ---------- - dataset : str - Dataset label - data : Union[pd.DataFrame, pd.Series] - Data to use for ML - n_neighbors : int - Number of neighbors for knn - n_input_days : int - Length of input sequences - n_predict_days : int - Number of days to predict - test_size : float - Fraction of data for testing - end_date : str, optional - End date for backtesting, by default "" - no_shuffle : bool, optional - Flag to shuffle data randomly, by default True - time_res : str - Resolution for data, allowing for predicting outside of standard market days - external_axes : Optional[List[plt.Axes]], optional - External axes (1 axis is expected in the list), by default None - """ - ( - forecast_data_df, - preds, - y_valid, - y_dates_valid, - scaler, - ) = knn_model.get_knn_model_data( - data, n_input_days, n_predict_days, n_neighbors, test_size, end_date, no_shuffle - ) - - if forecast_data_df.empty: - console.print("Issue performing data prep and prediction") - return - - if time_res: - forecast_data_df.index = pd.date_range( - data.index[-1], periods=n_predict_days + 1, freq=time_res - )[1:] - print_pretty_prediction(forecast_data_df[0], data.values[-1]) - plot_data_predictions( - data=data, - preds=preds, - y_valid=y_valid, - y_dates_valid=y_dates_valid, - scaler=scaler, - title=f"KNN Model with {n_neighbors} Neighbors on {dataset}", - forecast_data=forecast_data_df, - n_loops=1, - time_str=time_res, - external_axes=external_axes, - ) - console.print("") diff --git a/openbb_terminal/common/prediction_techniques/mc_model.py b/openbb_terminal/common/prediction_techniques/mc_model.py deleted file mode 100644 index db62ff3ad33..00000000000 --- a/openbb_terminal/common/prediction_techniques/mc_model.py +++ /dev/null @@ -1,59 +0,0 @@ -"""Monte Carlo Model""" -__docformat__ = "numpy" - -import logging -from typing import Union - -import numpy as np -import pandas as pd -from scipy.stats import norm - -from openbb_terminal.decorators import log_start_end - -logger = logging.getLogger(__name__) - -DISTRIBUTIONS = ["normal", "lognormal"] - - -@log_start_end(log=logger) -def get_mc_brownian( - data: Union[pd.Series, np.ndarray], n_future: int, n_sims: int, use_log=True -) -> np.ndarray: - """Performs monte carlo forecasting for brownian motion with drift - - Parameters - ---------- - data : Union[pd.Series, np.ndarray] - Input data. - n_future : int - Number of future steps - n_sims : int - Number of simulations to run - use_log : bool, optional - Flag to use log returns, by default True - - Returns - ------- - np.ndarray - Array of predictions. Has shape (n_future, n_sims) - """ - - changes = data.pct_change().dropna() # type: ignore - - if use_log: - changes = np.log(1 + changes) - - dist_mean = changes.mean() - dist_var = changes.var() - dist_drift = dist_mean - 0.5 * dist_var - dist_std = np.sqrt(dist_var) - - random_steps = norm.ppf(np.random.rand(n_future, n_sims)) - predicted_change = np.exp(dist_drift + dist_std * random_steps) - possible_paths = np.zeros_like(predicted_change) - possible_paths[0] = data.values[-1] # type: ignore - - for t in range(1, n_future): - possible_paths[t] = possible_paths[t - 1] * predicted_change[t] - - return possible_paths diff --git a/openbb_terminal/common/prediction_techniques/mc_view.py b/openbb_terminal/common/prediction_techniques/mc_view.py deleted file mode 100644 index f04cc99090c..00000000000 --- a/openbb_terminal/common/prediction_techniques/mc_view.py +++ /dev/null @@ -1,95 +0,0 @@ -"""Monte Carlo View""" -__docformat__ = "numpy" - -import logging -import os -from typing import List, Optional, Union - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import seaborn as sns - -from openbb_terminal.config_terminal import theme -from openbb_terminal.common.prediction_techniques import mc_model -from openbb_terminal.config_plot import PLOT_DPI -from openbb_terminal.decorators import log_start_end -from openbb_terminal.helper_funcs import ( - export_data, - get_next_stock_market_days, - plot_autoscale, - is_valid_axes_count, -) - -logger = logging.getLogger(__name__) - - -@log_start_end(log=logger) -def display_mc_forecast( - data: Union[pd.DataFrame, pd.Series], - n_future: int, - n_sims: int, - use_log=True, - fig_title: str = "", - export: str = "", - time_res: str = "", - external_axes: Optional[List[plt.Axes]] = None, -): - """Display monte carlo forecasting - - Parameters - ---------- - data : Union[pd.Series, np.array] - Data to forecast - n_future : int - Number of days to forecast - n_sims : int - Number of simulations to run - use_log : bool, optional - Flag to use lognormal, by default True - fig_title : str - Figure title - export: str - Format to export data - time_res : str - Resolution for data, allowing for predicting outside of standard market days - external_axes : Optional[List[plt.Axes]], optional - External axes (2 axes are expected in the list), by default None - """ - predicted_values = mc_model.get_mc_brownian(data, n_future, n_sims, use_log) - if not time_res or time_res == "1D": - future_index = get_next_stock_market_days(data.index[-1], n_next_days=n_future) # type: ignore - else: - future_index = pd.date_range(data.index[-1], periods=n_future + 1, freq=time_res)[1:] # type: ignore - - # This plot has 2 axes - if external_axes is None: - _, axes = plt.subplots(1, 2, figsize=plot_autoscale(), dpi=PLOT_DPI) - (ax1, ax2) = axes - elif is_valid_axes_count(external_axes, 2): - (ax1, ax2) = external_axes - else: - return - - ax1.plot(data) - ax1.plot(future_index, predicted_values, alpha=0.3) - start_timestamp = data.index[0] - end_timestamp = future_index[-1] - ax1.set_xlim(start_timestamp, end_timestamp) - ax1.set_title(f"{fig_title} Data Predictions") - - sns.histplot(predicted_values[-1, :], ax=ax2, kde=True) - ax2.set_xlabel("Final Value") - ax2.axvline( - x=data.values[-1], color=theme.down_color, label="Last Value", linestyle="-" - ) - ax2.set_title(f"Distribution of final values after {n_future} steps.") - ax2.set_xlim(np.min(predicted_values[-1, :]), np.max(predicted_values[-1, :])) - ax2.legend() - - theme.style_primary_axis(ax1) - theme.style_primary_axis(ax2) - if external_axes is None: - theme.visualize_output() - - export_data(export, os.path.dirname(os.path.abspath(__file__)), "mc") diff --git a/openbb_terminal/common/prediction_techniques/neural_networks_model.py b/openbb_terminal/common/prediction_techniques/neural_networks_model.py deleted file mode 100644 index 4169ea3aeb9..00000000000 --- a/openbb_terminal/common/prediction_techniques/neural_networks_model.py +++ /dev/null @@ -1,605 +0,0 @@ -"""Neural Network Prediction Models""" -__docformat__ = "numpy" - -import logging -from typing import Any, List, Tuple, Union - -import numpy as np -import pandas as pd -from tensorflow.keras.callbacks import EarlyStopping -from tensorflow.keras.layers import ( - LSTM, - AvgPool1D, - Conv1D, - Dense, - Dropout, - Flatten, - MaxPool1D, - SimpleRNN, -) -from tensorflow.keras.models import Sequential -from tensorflow.keras.optimizers import ( - SGD, - Adadelta, - Adagrad, - Adam, - Adamax, - Ftrl, - Nadam, - RMSprop, -) - -from openbb_terminal import config_neural_network_models as cfg_nn_models -from openbb_terminal.common.prediction_techniques.pred_helper import ( - forecast, - prepare_scale_train_valid_test, -) -from openbb_terminal.decorators import log_start_end -from openbb_terminal.helper_funcs import get_next_stock_market_days -from openbb_terminal.rich_config import console - -logger = logging.getLogger(__name__) - -optimizers = { - "Adam": Adam, - "Adagrad": Adagrad, - "Adadelta": Adadelta, - "Adamax": Adamax, - "Ftrl": Ftrl, - "Nadam": Nadam, - "Rmsprop": RMSprop, - "Sgd": SGD, -} - - -if cfg_nn_models.Early_Stop_Patience: - es = EarlyStopping(monitor="val_loss", patience=cfg_nn_models.Early_Stop_Patience) -else: - # Set patience to very long value - es = EarlyStopping(monitor="val_loss", patience=1000) - -# pylint:disable=too-many-arguments - - -@log_start_end(log=logger) -def build_neural_network_model( - Layers: List[Any], n_inputs: int, n_days: int -) -> Sequential: - """ - Builds neural net from config_neural_network_models.py - Parameters - ---------- - Layers: List[Any] - List of layers with parameters as a dictionary in the file - n_inputs: int - Number of days that will be fed into the NN - n_days: int - Number of days the NN wants to predict - - Returns - ------- - model: Sequential - Keras sequential model with layers from the file - - """ - model = Sequential() - - for idx_layer, d_layer in enumerate(Layers): - # Recurrent Neural Network - if str(*d_layer) == "SimpleRNN": - # Is this the input layer? If so, define input_shape - if idx_layer == 0: - model.add(SimpleRNN(**d_layer["SimpleRNN"], input_shape=(n_inputs, 1))) - # Is this the last output layer? If so, set units to prediction days - elif idx_layer == (len(Layers) - 1): - model.add(SimpleRNN(**d_layer["SimpleRNN"], units=n_days)) - else: - model.add(SimpleRNN(**d_layer["SimpleRNN"])) - - # Long-Short Term-Memory - elif str(*d_layer) == "LSTM": - # Is this the input layer? If so, define input_shape - if idx_layer == 0: - model.add(LSTM(**d_layer["LSTM"], input_shape=(n_inputs, 1))) - # Is this the last output layer? If so, set units to prediction days - elif idx_layer == (len(Layers) - 1): - model.add(LSTM(**d_layer["LSTM"], units=n_days)) - else: - model.add(LSTM(**d_layer["LSTM"])) - - # Dense (Simple Neuron) - elif str(*d_layer) == "Dense": - # Is this the input layer? If so, define input_shape - if idx_layer == 0: - model.add(Dense(**d_layer["Dense"], input_dim=n_inputs)) - # Is this the last output layer? If so, set units to prediction days - elif idx_layer == (len(Layers) - 1): - model.add(Dense(**d_layer["Dense"], units=n_days)) - else: - model.add(Dense(**d_layer["Dense"])) - - # Conv1D Layer - elif str(*d_layer) == "Conv1D": - if idx_layer == 0: - model.add(Conv1D(**d_layer["Conv1D"], input_shape=(n_inputs, 1))) - else: - model.add(Conv1D(**d_layer["Conv1D"])) - # Max Pooling Layer for after Conv Layer - elif str(*d_layer) == "MaxPool1D": - model.add(MaxPool1D(**d_layer["MaxPool1D"])) - # Allow for if user wants to do average pooling - elif str(*d_layer) == "AvgPool1D": - model.add(AvgPool1D(**d_layer["AvgPool1D"])) - # Dropout (Regularization) - elif str(*d_layer) == "Dropout": - model.add(Dropout(**d_layer["Dropout"])) - # Flatten layer for Convolutions - elif str(*d_layer) == "Flatten": - model.add(Flatten()) - else: - console.print(f"Incorrect neuron type: {str(*d_layer)}") - - return model - - -@log_start_end(log=logger) -def mlp_model( - data: Union[pd.Series, pd.DataFrame], - n_input: int, - n_predict: int, - learning_rate: float, - epochs: int, - batch_size: int, - test_size: float, - n_loops: int, - no_shuffle: bool, -) -> Tuple[pd.DataFrame, np.ndarray, np.ndarray, np.ndarray, Any]: - """Train MLP model on data based on config params - - Parameters - ---------- - data : Union[pd.Series, pd.DataFrame] - Data to fit - n_input : int - Length of input sequence - n_predict : int - Length of output to predict - learning_rate : float - Learning rate for optimizer - epochs : int - Number of training epochs - batch_size : int - Model batch size - test_size : float - Fraction of test size - n_loops : int - Number of loops to train model - no_shuffle : bool - Flag to not shuffle data - - Returns - ------- - pd.DataFrame - Dataframe of predictions - np.array - Array of validation predictions - np.array - Array of validation data - np.array - Array of validation x label data - Any - Scaler used for data - """ - ( - X_train, - X_valid, - y_train, - y_valid, - _, - _, - _, - y_dates_valid, - forecast_data_input, - dates_forecast_input, - scaler, - is_error, - ) = prepare_scale_train_valid_test( - data, n_input, n_predict, test_size, "", no_shuffle - ) - if is_error: - return pd.DataFrame(), np.array(0), np.array(0), np.array(0), None - - console.print( - f"Training on {X_train.shape[0]} sequences of length {X_train.shape[1]}. Using {X_valid.shape[0]} sequences " - f" of length {X_valid.shape[1]} for validation. Model will run {n_loops} loops" - ) - - future_dates = get_next_stock_market_days( - dates_forecast_input[-1], n_next_days=n_predict - ) - - preds = np.zeros((n_loops, X_valid.shape[0], n_predict)) - forecast_data = np.zeros((n_loops, n_predict)) - for i in range(n_loops): - # Build Neural Network model - model = build_neural_network_model( - cfg_nn_models.MultiLayer_Perceptron, - n_input, - n_predict, - ) - model.compile( - optimizer=optimizers[cfg_nn_models.Optimizer](learning_rate=learning_rate), - loss=cfg_nn_models.Loss, - ) - model.fit( - X_train.reshape(X_train.shape[0], X_train.shape[1]), - y_train, - epochs=epochs, - verbose=True, - batch_size=batch_size, - validation_data=( - X_valid.reshape(X_valid.shape[0], X_valid.shape[1]), - y_valid, - ), - callbacks=[es], - ) - - preds[i] = model.predict( - X_valid.reshape(X_valid.shape[0], X_valid.shape[1]) - ).reshape(X_valid.shape[0], n_predict) - - if scaler: - future_values = scaler.inverse_transform( - model.predict(forecast_data_input.reshape(1, -1)).reshape(-1, 1) - ) - else: - future_values = model.predict(forecast_data_input.reshape(1, -1)).reshape( - -1, 1 - ) - - forecast_data[i] = future_values.flat - - forecast_data_df = pd.DataFrame(forecast_data.T, index=future_dates) - return forecast_data_df, preds, y_valid, y_dates_valid, scaler - - -@log_start_end(log=logger) -def rnn_model( - data: Union[pd.Series, pd.DataFrame], - n_input: int, - n_predict: int, - learning_rate: float, - epochs: int, - batch_size: int, - test_size: float, - n_loops: int, - no_shuffle: bool, -) -> Tuple[pd.DataFrame, np.ndarray, np.ndarray, np.ndarray, Any]: - """Train RNN model on data based on config params - - Parameters - ---------- - data : Union[pd.Series, pd.DataFrame] - Data to fit - n_input : int - Length of input sequence - n_predict : int - Length of output to predict - learning_rate : float - Learning rate for optimizer - epochs : int - Number of training epochs - batch_size : int - Model batch size - test_size : float - Fraction of test size - n_loops : int - Number of loops to train model - no_shuffle : bool - Flag to not shuffle data - - Returns - ------- - pd.DataFrame - Dataframe of predictions - np.array - Array of validation predictions - np.array - Array of validation data - np.array - Array of validation x label data - Any - Scaler used for data - """ - ( - X_train, - X_valid, - y_train, - y_valid, - _, - _, - _, - y_dates_valid, - forecast_data_input, - dates_forecast_input, - scaler, - is_error, - ) = prepare_scale_train_valid_test( - data, n_input, n_predict, test_size, "", no_shuffle - ) - if is_error: - return pd.DataFrame(), np.array(0), np.array(0), np.array(0), None - - console.print( - f"Training on {X_train.shape[0]} sequences of length {X_train.shape[1]}. Using {X_valid.shape[0]} sequences " - f" of length {X_valid.shape[1]} for validation. Model will run {n_loops} loops" - ) - - future_dates = get_next_stock_market_days( - dates_forecast_input[-1], n_next_days=n_predict - ) - - preds = np.zeros((n_loops, X_valid.shape[0], n_predict)) - forecast_data = np.zeros((n_loops, n_predict)) - for i in range(n_loops): - # Build Neural Network model - model = build_neural_network_model( - cfg_nn_models.Recurrent_Neural_Network, - n_input, - n_predict, - ) - model.compile( - optimizer=optimizers[cfg_nn_models.Optimizer](learning_rate=learning_rate), - loss=cfg_nn_models.Loss, - ) - model.fit( - X_train.reshape(X_train.shape[0], X_train.shape[1], 1), - y_train, - epochs=epochs, - verbose=True, - batch_size=batch_size, - validation_data=( - X_valid.reshape(X_valid.shape[0], X_valid.shape[1], 1), - y_valid, - ), - callbacks=[es], - ) - - preds[i] = model.predict( - X_valid.reshape(X_valid.shape[0], X_valid.shape[1], 1) - ).reshape(X_valid.shape[0], n_predict) - - forecast_data[i] = forecast( - forecast_data_input, future_dates, model, scaler - ).values.flat - - forecast_data_df = pd.DataFrame(forecast_data.T, index=future_dates) - return forecast_data_df, preds, y_valid, y_dates_valid, scaler - - -@log_start_end(log=logger) -def lstm_model( - data: Union[pd.Series, pd.DataFrame], - n_input: int, - n_predict: int, - learning_rate: float, - epochs: int, - batch_size: int, - test_size: float, - n_loops: int, - no_shuffle: bool, -) -> Tuple[pd.DataFrame, np.ndarray, np.ndarray, np.ndarray, Any]: - """Train LSTM model on data based on config params - - Parameters - ---------- - data : Union[pd.Series, pd.DataFrame] - Data to fit - n_input : int - Length of input sequence - n_predict : int - Length of output to predict - learning_rate : float - Learning rate for optimizer - epochs : int - Number of training epochs - batch_size : int - Model batch size - test_size : float - Fraction of test size - n_loops : int - Number of loops to train model - no_shuffle : bool - Flag to not shuffle data - - Returns - ------- - pd.DataFrame - Dataframe of predictions - np.array - Array of validation predictions - np.array - Array of validation data - np.array - Array of validation x label data - Any - Scaler used for data - """ - ( - X_train, - X_valid, - y_train, - y_valid, - _, - _, - _, - y_dates_valid, - forecast_data_input, - dates_forecast_input, - scaler, - is_error, - ) = prepare_scale_train_valid_test( - data, n_input, n_predict, test_size, "", no_shuffle - ) - if is_error: - return pd.DataFrame(), np.array(0), np.array(0), np.array(0), None - - console.print( - f"Training on {X_train.shape[0]} sequences of length {X_train.shape[1]}. Using {X_valid.shape[0]} sequences " - f" of length {X_valid.shape[1]} for validation. Model will run {n_loops} loops" - ) - - future_dates = get_next_stock_market_days( - dates_forecast_input[-1], n_next_days=n_predict - ) - - preds = np.zeros((n_loops, X_valid.shape[0], n_predict)) - forecast_data = np.zeros((n_loops, n_predict)) - for i in range(n_loops): - # Build Neural Network model - model = build_neural_network_model( - cfg_nn_models.Long_Short_Term_Memory, - n_input, - n_predict, - ) - model.compile( - optimizer=optimizers[cfg_nn_models.Optimizer](learning_rate=learning_rate), - loss=cfg_nn_models.Loss, - ) - model.fit( - X_train.reshape(X_train.shape[0], X_train.shape[1], 1), - y_train, - epochs=epochs, - verbose=True, - batch_size=batch_size, - validation_data=( - X_valid.reshape(X_valid.shape[0], X_valid.shape[1], 1), - y_valid, - ), - callbacks=[es], - ) - - preds[i] = model.predict( - X_valid.reshape(X_valid.shape[0], X_valid.shape[1], 1) - ).reshape(X_valid.shape[0], n_predict) - - forecast_data[i] = forecast( - forecast_data_input, future_dates, model, scaler - ).values.flat - - forecast_data_df = pd.DataFrame(forecast_data.T, index=future_dates) - return forecast_data_df, preds, y_valid, y_dates_valid, scaler - - -@log_start_end(log=logger) -def conv1d_model( - data: Union[pd.Series, pd.DataFrame], - n_input: int, - n_predict: int, - learning_rate: float, - epochs: int, - batch_size: int, - test_size: float, - n_loops: int, - no_shuffle: bool, -) -> Tuple[pd.DataFrame, np.ndarray, np.ndarray, np.ndarray, Any]: - """Train Conv1D model on data based on config params - - Parameters - ---------- - data : Union[pd.Series, pd.DataFrame] - Data to fit - n_input : int - Length of input sequence - n_predict : int - Length of output to predict - learning_rate : float - Learning rate for optimizer - epochs : int - Number of training epochs - batch_size : int - Model batch size - test_size : float - Fraction of test size - n_loops : int - Number of loops to train model - no_shuffle : bool - Flag to not shuffle data - - Returns - ------- - pd.DataFrame - Dataframe of predictions - np.array - Array of validation predictions - np.array - Array of validation data - np.array - Array of validation x label data - Any - Scaler used for data - """ - ( - X_train, - X_valid, - y_train, - y_valid, - _, - _, - _, - y_dates_valid, - forecast_data_input, - dates_forecast_input, - scaler, - is_error, - ) = prepare_scale_train_valid_test( - data, n_input, n_predict, test_size, "", no_shuffle - ) - if is_error: - return pd.DataFrame(), np.array(0), np.array(0), np.array(0), None - - console.print( - f"Training on {X_train.shape[0]} sequences of length {X_train.shape[1]}. Using {X_valid.shape[0]} sequences " - f" of length {X_valid.shape[1]} for validation. Model will run {n_loops} loops" - ) - - future_dates = get_next_stock_market_days( - dates_forecast_input[-1], n_next_days=n_predict - ) - - preds = np.zeros((n_loops, X_valid.shape[0], n_predict)) - forecast_data = np.zeros((n_loops, n_predict)) - for i in range(n_loops): - # Build Neural Network model - model = build_neural_network_model( - cfg_nn_models.Convolutional, - n_input, - n_predict, - ) - model.compile( - optimizer=optimizers[cfg_nn_models.Optimizer](learning_rate=learning_rate), - loss=cfg_nn_models.Loss, - ) - model.fit( - X_train.reshape(X_train.shape[0], X_train.shape[1], 1), - y_train, - epochs=epochs, - verbose=True, - batch_size=batch_size, - validation_data=( - X_valid.reshape(X_valid.shape[0], X_valid.shape[1], 1), - y_valid, - ), - callbacks=[es], - ) - - preds[i] = model.predict( - X_valid.reshape(X_valid.shape[0], X_valid.shape[1], 1) - ).reshape(X_valid.shape[0], n_predict) - - forecast_data[i] = forecast( - forecast_data_input, future_dates, model, scaler - ).values.flat - - forecast_data_df = pd.DataFrame(forecast_data.T, index=future_dates) - return forecast_data_df, preds, y_valid, y_dates_valid, scaler diff --git a/openbb_terminal/common/prediction_techniques/neural_networks_view.py b/openbb_terminal/common/prediction_techniques/neural_networks_view.py deleted file mode 100644 index ab40ddb0b06..00000000000 --- a/openbb_terminal/common/prediction_techniques/neural_networks_view.py +++ /dev/null @@ -1,350 +0,0 @@ -""" Neural Networks View""" -__docformat__ = "numpy" - -import logging -from typing import Union - -import numpy as np -import pandas as pd - -from openbb_terminal.common.prediction_techniques import neural_networks_model -from openbb_terminal.common.prediction_techniques.pred_helper import ( - plot_data_predictions, - print_pretty_prediction, -) -from openbb_terminal.decorators import log_start_end -from openbb_terminal.rich_config import console - -# pylint:disable=too-many-arguments - - -logger = logging.getLogger(__name__) - - -@log_start_end(log=logger) -def display_mlp( - dataset: str, - data: Union[pd.Series, pd.DataFrame], - n_input_days: int, - n_predict_days: int, - learning_rate: float, - epochs: int, - batch_size: int, - test_size: float, - n_loops: int, - no_shuffle: bool, - time_res: str = "", -): - """Display trained MLP model - - Parameters - ---------- - dataset : str - Dataset for model - data : Union[pd.Series, pd.DataFrame] - Data to feed to model - n_input_days : int - Number of inputs to train - n_predict_days : int - Number of outputs to predict - learning_rate : float - MLP learning rate - epochs : int - Number of training epochs - batch_size : int - Training batch size - test_size : float - Size of test set - n_loops : int - Number of loops to perform for model - no_shuffle : bool - Flag to not randomly shuffle data - time_res : str - Resolution for data, allowing for predicting outside of standard market days - """ - ( - forecast_data_df, - preds, - y_valid, - y_dates_valid, - scaler, - ) = neural_networks_model.mlp_model( - data, - n_input_days, - n_predict_days, - learning_rate, - epochs, - batch_size, - test_size, - n_loops, - no_shuffle, - ) - - if time_res: - forecast_data_df.index = pd.date_range( - data.index[-1], periods=n_predict_days + 1, freq=time_res - )[1:] - - if n_loops > 1: - forecast_data_df["Median"] = forecast_data_df.median(axis=1) - print_pretty_prediction(forecast_data_df["Median"], data.values[-1]) - else: - print_pretty_prediction(forecast_data_df[0], data.values[-1]) - plot_data_predictions( - data, - np.median(preds, axis=0), - y_valid, - y_dates_valid, - scaler, - f"MLP Model on {dataset}", - forecast_data_df, - n_loops, - time_res, - ) - console.print("") - - -@log_start_end(log=logger) -def display_rnn( - dataset: str, - data: Union[pd.Series, pd.DataFrame], - n_input_days: int, - n_predict_days: int, - learning_rate: float, - epochs: int, - batch_size: int, - test_size: float, - n_loops: int, - no_shuffle: bool, - time_res: str = "", -): - """Display trained RNN model - - Parameters - ---------- - dataset : str - Dataset for model - data : Union[pd.Series, pd.DataFrame] - Data to feed to model - n_input_days : int - Number of inputs to train - n_predict_days : int - Number of outputs to predict - learning_rate : float - MLP learning rate - epochs : int - Number of training epochs - batch_size : int - Training batch size - test_size : float - Size of test set - n_loops : int - Number of loops to perform for model - no_shuffle : bool - Flag to not randomly shuffle data - time_res : str - Resolution for data, allowing for predicting outside of standard market days - """ - - ( - forecast_data_df, - preds, - y_valid, - y_dates_valid, - scaler, - ) = neural_networks_model.rnn_model( - data, - n_input_days, - n_predict_days, - learning_rate, - epochs, - batch_size, - test_size, - n_loops, - no_shuffle, - ) - if time_res: - forecast_data_df.index = pd.date_range( - data.index[-1], periods=n_predict_days + 1, freq=time_res - )[1:] - if n_loops > 1: - forecast_data_df["Median"] = forecast_data_df.median(axis=1) - print_pretty_prediction(forecast_data_df["Median"], data.values[-1]) - else: - print_pretty_prediction(forecast_data_df[0], data.values[-1]) - plot_data_predictions( - data, - np.median(preds, axis=0), - y_valid, - y_dates_valid, - scaler, - f"RNN Model on {dataset}", - forecast_data_df, - n_loops, - time_res, - ) - console.print("") - - -@log_start_end(log=logger) -def display_lstm( - dataset: str, - data: Union[pd.Series, pd.DataFrame], - n_input_days: int, - n_predict_days: int, - learning_rate: float, - epochs: int, - batch_size: int, - test_size: float, - n_loops: int, - no_shuffle: bool, - time_res: str = "", -): - """Display trained LSTM model - - Parameters - ---------- - dataset : str - Dataset for model - data : Union[pd.Series, pd.DataFrame] - Data to feed to model - n_input_days : int - Number of inputs to train - n_predict_days : int - Number of outputs to predict - learning_rate : float - MLP learning rate - epochs : int - Number of training epochs - batch_size : int - Training batch size - test_size : float - Size of test set - n_loops : int - Number of loops to perform for model - no_shuffle : bool - Flag to not randomly shuffle data - time_res : str - Resolution for data, allowing for predicting outside of standard market days - """ - - ( - forecast_data_df, - preds, - y_valid, - y_dates_valid, - scaler, - ) = neural_networks_model.lstm_model( - data, - n_input_days, - n_predict_days, - learning_rate, - epochs, - batch_size, - test_size, - n_loops, - no_shuffle, - ) - if time_res: - forecast_data_df.index = pd.date_range( - data.index[-1], periods=n_predict_days + 1, freq=time_res - )[1:] - if n_loops > 1: - forecast_data_df["Median"] = forecast_data_df.median(axis=1) - print_pretty_prediction(forecast_data_df["Median"], data.values[-1]) - else: - print_pretty_prediction(forecast_data_df[0], data.values[-1]) - plot_data_predictions( - data, - np.median(preds, axis=0), - y_valid, - y_dates_valid, - scaler, - f"LSTM Model on {dataset}", - forecast_data_df, - n_loops, - time_res, - ) - console.print("") - - -@log_start_end(log=logger) -def display_conv1d( - dataset: str, - data: Union[pd.Series, pd.DataFrame], - n_input_days: int, - n_predict_days: int, - learning_rate: float, - epochs: int, - batch_size: int, - test_size: float, - n_loops: int, - no_shuffle: bool, - time_res: str = "", -): - """Display trained Conv1D model - - Parameters - ---------- - dataset : str - Dataset for model - data : Union[pd.Series, pd.DataFrame] - Data to feed to model - n_input_days : int - Number of inputs to train - n_predict_days : int - Number of outputs to predict - learning_rate : float - MLP learning rate - epochs : int - Number of training epochs - batch_size : int - Training batch size - test_size : float - Size of test set - n_loops : int - Number of loops to perform for model - no_shuffle : bool - Flag to not randomly shuffle data - time_res : str - Resolution for data, allowing for predicting outside of standard market days - """ - - ( - forecast_data_df, - preds, - y_valid, - y_dates_valid, - scaler, - ) = neural_networks_model.conv1d_model( - data, - n_input_days, - n_predict_days, - learning_rate, - epochs, - batch_size, - test_size, - n_loops, - no_shuffle, - ) - if time_res: - forecast_data_df.index = pd.date_range( - data.index[-1], periods=n_predict_days + 1, freq=time_res - )[1:] - if n_loops > 1: - forecast_data_df["Median"] = forecast_data_df.median(axis=1) - print_pretty_prediction(forecast_data_df["Median"], data.values[-1]) - else: - print_pretty_prediction(forecast_data_df[0], data.values[-1]) - plot_data_predictions( - data, - np.median(preds, axis=0), - y_valid, - y_dates_valid, - scaler, - f"Conv1D Model on {dataset}", - forecast_data_df, - n_loops, - time_res, - ) - console.print("") diff --git a/openbb_terminal/common/prediction_techniques/pred_helper.py b/openbb_terminal/common/prediction_techniques/pred_helper.py deleted file mode 100644 index e1fe79c4d1b..00000000000 --- a/openbb_terminal/common/prediction_techniques/pred_helper.py +++ /dev/null @@ -1,637 +0,0 @@ -""" Prediction helper functions """ -__docformat__ = "numpy" - -import argparse -from typing import List, Union, Optional -import os -import logging -from warnings import simplefilter -from datetime import timedelta -import numpy as np -import pandas as pd -from pandas.plotting import register_matplotlib_converters -import matplotlib.pyplot as plt -from sklearn.metrics import ( - mean_absolute_error, - r2_score, - mean_squared_error, -) -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler, MinMaxScaler, Normalizer -from tensorflow.keras.models import Sequential -from openbb_terminal.helper_funcs import ( - check_positive, - check_positive_float, - parse_simple_args, - valid_date, - plot_autoscale, - print_rich_table, - is_valid_axes_count, -) -from openbb_terminal import config_neural_network_models as cfg -from openbb_terminal.config_terminal import theme -from openbb_terminal.config_plot import PLOT_DPI -from openbb_terminal.rich_config import console -from openbb_terminal import rich_config - -logger = logging.getLogger(__name__) - -register_matplotlib_converters() - -os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" - -simplefilter(action="ignore", category=FutureWarning) - -# store the user's TensorFlow environment variables -ORIGINAL_TF_XLA_FLAGS = os.environ.get("TF_XLA_FLAGS") -ORIGINAL_TF_FORCE_GPU_ALLOW_GROWTH = os.environ.get("TF_FORCE_GPU_ALLOW_GROWTH") - -PREPROCESSOR = cfg.Preprocess - - -def check_valid_frac(num) -> float: - """Argparse type checker for valid float between 0 and 1""" - if (num < 0) or (num > 1): - raise argparse.ArgumentTypeError(f"{num} is an invalid percentage") - return num - - -def restore_env(): - """Restore environment variables to original values""" - - def restore(key, value): - if value is None: - if key in os.environ: - del os.environ[key] - else: - os.environ[key] = value - - restore("TF_XLA_FLAGS", ORIGINAL_TF_XLA_FLAGS) - restore("TF_FORCE_GPU_ALLOW_GROWTH", ORIGINAL_TF_FORCE_GPU_ALLOW_GROWTH) - - -def parse_args(prog: str, description: str, other_args: List[str]): - """ - Create an argparser and parse other_args. Will print help if user requests it. - Parameters - ---------- - prog: str - Program for argparser - description: str - Description for argparser - other_args - Argparse arguments to pass - Returns - ------- - ns_parser: argparse.Namespace - Parsed argument parser - """ - - parser = argparse.ArgumentParser( - prog=prog, - description=description, - add_help=False, - formatter_class=argparse.RawTextHelpFormatter, # enable multiline help messages - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-i", - "--input", - action="store", - dest="n_inputs", - type=check_positive, - default=40, - help="number of days to use for prediction.", - ) - parser.add_argument( - "--epochs", - action="store", - dest="n_epochs", - type=check_positive, - default=50, - help="number of training epochs.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select - Backtesting", - ) - parser.add_argument( - "--batch_size", - action="store", - dest="n_batch_size", - type=check_positive, - default=None, - help="batch size for model fitting (use a power of 2)", - ) - parser.add_argument( - "--xla_cpu", - action="store_true", - dest="b_xla_cpu", - default=False, - help="enable XLA for CPU (see https://www.tensorflow.org/xla)", - ) - parser.add_argument( - "--xla_gpu", - action="store_true", - dest="b_xla_gpu", - default=False, - help="enable XLA for GPU (see https://www.tensorflow.org/xla)", - ) - parser.add_argument( - "--force_gpu_allow_growth", - action="store", - dest="s_force_gpu_allow_growth", - default="true", - choices=["true", "false", "default"], - help="true: GPU memory will grow as needed. \n" - "false: TensorFlow will allocate 100%% of GPU memory. \n" - "default: usually the same as false, uses env/TensorFlow default", - ) - parser.add_argument( - "-l", - "--loops", - action="store", - dest="n_loops", - type=check_positive, - default=1, - help="number of loops to iterate and train models", - ) - parser.add_argument( - "-v", - "--valid", - type=check_valid_frac, - dest="valid_split", - default=0.1, - help="Validation data split fraction", - ) - - parser.add_argument( - "--lr", - type=check_positive_float, - dest="lr", - default=0.01, - help="Specify learning rate for optimizer.", - ) - - parser.add_argument( - "--no_shuffle", - action="store_false", - dest="no_shuffle", - default=True, - help="Specify if shuffling validation inputs.", - ) - - try: - ns_parser = parse_simple_args(parser, other_args) - if not ns_parser: - return None - - # set xla flags if requested - xla_flags = ( - set(ORIGINAL_TF_XLA_FLAGS.split(" ")) if ORIGINAL_TF_XLA_FLAGS else set() - ) - if ns_parser.b_xla_cpu or ns_parser.b_xla_gpu: - xla_flags.add("--tf_xla_enable_xla_devices") - if ns_parser.b_xla_cpu: - xla_flags.add("--tf_xla_cpu_global_jit") - if ns_parser.b_xla_gpu: - xla_flags.add("--tf_xla_auto_jit=2") - os.environ["TF_XLA_FLAGS"] = " ".join(xla_flags) - - # set GPU memory growth flag - if ns_parser.s_force_gpu_allow_growth == "true": - os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true" - elif ns_parser.s_force_gpu_allow_growth == "false": - os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "false" - - return ns_parser - - except SystemExit: - console.print("") - return None - - -def prepare_scale_train_valid_test( - data: Union[pd.DataFrame, pd.Series], - n_input_days: int, - n_predict_days: int, - test_size: float, - s_end_date: str, - no_shuffle: bool, -): - """ - Prepare and scale train, validate and test data. - Parameters - ---------- - data: pd.DataFrame - Dataframe of stock prices - ns_parser: argparse.Namespace - Parsed arguments - Returns - ------- - X_train: np.ndarray - Array of training data. Shape (# samples, n_inputs, 1) - X_test: np.ndarray - Array of validation data. Shape (total sequences - #samples, n_inputs, 1) - y_train: np.ndarray - Array of training outputs. Shape (#samples, n_days) - y_test: np.ndarray - Array of validation outputs. Shape (total sequences -#samples, n_days) - X_dates_train: np.ndarray - Array of dates for X_train - X_dates_test: np.ndarray - Array of dates for X_test - y_dates_train: np.ndarray - Array of dates for y_train - y_dates_test: np.ndarray - Array of dates for y_test - test_data: np.ndarray - Array of prices after the specified end date - dates_test: np.ndarray - Array of dates after specified end date - scaler: - Fitted PREPROCESSOR - """ - - # Pre-process data - if PREPROCESSOR == "standardization": - scaler = StandardScaler() - - elif PREPROCESSOR == "minmax": - scaler = MinMaxScaler() - - elif PREPROCESSOR == "normalization": - scaler = Normalizer() - - elif (PREPROCESSOR == "none") or (PREPROCESSOR is None): - scaler = None - # Test data is used for forecasting. Takes the last n_input_days data points. - # These points are not fed into training - - if s_end_date: - data = data[data.index <= s_end_date] - if n_input_days + n_predict_days > data.shape[0]: - console.print( - "Cannot train enough input days to predict with loaded dataframe\n" - ) - return ( - None, - None, - None, - None, - None, - None, - None, - None, - None, - None, - None, - True, - ) - - test_data = data.iloc[-n_input_days:] - train_data = data.iloc[:-n_input_days] - - dates = data.index - dates_test = test_data.index - if scaler: - train_data = scaler.fit_transform(data.values.reshape(-1, 1)) - test_data = scaler.transform(test_data.values.reshape(-1, 1)) - else: - train_data = data.values.reshape(-1, 1) - test_data = test_data.values.reshape(-1, 1) - - prices = train_data - - input_dates = [] - input_prices = [] - next_n_day_prices = [] - next_n_day_dates = [] - - for idx in range(len(prices) - n_input_days - n_predict_days): - input_prices.append(prices[idx : idx + n_input_days]) # noqa: E203 - input_dates.append(dates[idx : idx + n_input_days]) # noqa: E203 - next_n_day_prices.append( - prices[ - idx + n_input_days : idx + n_input_days + n_predict_days # noqa: E203 - ] - ) - next_n_day_dates.append( - dates[ - idx + n_input_days : idx + n_input_days + n_predict_days # noqa: E203 - ] - ) - - input_dates = np.asarray(input_dates) # type: ignore - input_prices = np.array(input_prices) # type: ignore - next_n_day_prices = np.array(next_n_day_prices) # type: ignore - next_n_day_dates = np.asarray(next_n_day_dates) # type: ignore - - ( - X_train, - X_valid, - y_train, - y_valid, - X_dates_train, - X_dates_valid, - y_dates_train, - y_dates_valid, - ) = train_test_split( - input_prices, - next_n_day_prices, - input_dates, - next_n_day_dates, - test_size=test_size, - shuffle=no_shuffle, - ) - return ( - X_train, - X_valid, - y_train, - y_valid, - X_dates_train, - X_dates_valid, - y_dates_train, - y_dates_valid, - test_data, - dates_test, - scaler, - False, - ) - - -def forecast( - input_values: np.ndarray, future_dates: List, model: Sequential, scaler -) -> pd.DataFrame: - """ - Forecast the stock movement over future days and rescale - Parameters - ---------- - input_values: np.ndarray - Array of values to be fed into the model - future_dates: List - List of future dates - model: Sequential - Pretrained model - scaler: - Fit scaler to be used to 'un-scale' the data - - Returns - ------- - df_future: pd.DataFrame - Dataframe of predicted values - """ - if scaler: - future_values = scaler.inverse_transform( - model.predict(input_values.reshape(1, -1, 1)).reshape(-1, 1) - ) - else: - future_values = model.predict(input_values.reshape(1, -1, 1)).reshape(-1, 1) - - df_future = pd.DataFrame( - future_values, index=future_dates, columns=["Predicted Price"] - ) - return df_future - - -# pylint:disable=too-many-arguments - - -def plot_data_predictions( - data, - preds, - y_valid, - y_dates_valid, - scaler, - title, - forecast_data, - n_loops, - time_str: str = "", - external_axes: Optional[List[plt.Axes]] = None, -): - """Plots data predictions for the different ML techniques - external_axes : Optional[List[plt.Axes]], optional - External axes (1 axis is expected in the list), by default None - """ - - # This plot has 1 axis - if external_axes is None: - _, ax = plt.subplots( - figsize=plot_autoscale(), - dpi=PLOT_DPI, - ) - elif is_valid_axes_count(external_axes, 1): - (ax,) = external_axes - else: - return - - ax.plot( - data.index, - data.values, - "-o", - ms=2, - label="Real data", - ) - for i in range(len(y_valid) - 1): - - if scaler: - y_pred = scaler.inverse_transform(preds[i].reshape(-1, 1)).ravel() - y_act = scaler.inverse_transform(y_valid[i].reshape(-1, 1)).ravel() - else: - y_pred = preds[i].ravel() - y_act = y_valid[i].ravel() - ax.plot( - y_dates_valid[i], - y_pred, - color=theme.down_color, - ) - ax.fill_between( - y_dates_valid[i], - y_pred, - y_act, - where=(y_pred < y_act), - color=theme.down_color, - alpha=0.2, - ) - ax.fill_between( - y_dates_valid[i], - y_pred, - y_act, - where=(y_pred > y_act), - color=theme.up_color, - alpha=0.2, - ) - - # Leave this one out of the loop so that the legend doesn't get overpopulated with "Predictions" - if scaler: - final_pred = scaler.inverse_transform(preds[-1].reshape(-1, 1)).ravel() - final_valid = scaler.inverse_transform(y_valid[-1].reshape(-1, 1)).ravel() - else: - final_pred = preds[-1].reshape(-1, 1).ravel() - final_valid = y_valid[-1].reshape(-1, 1).ravel() - ax.plot( - y_dates_valid[-1], - final_pred, - color=theme.down_color, - label="Predictions", - ) - ax.fill_between( - y_dates_valid[-1], - final_pred, - final_valid, - alpha=0.2, - ) - - _, _, ymin, ymax = plt.axis() - ax.vlines( - forecast_data.index[0], - ymin, - ymax, - linestyle="--", - ) - if n_loops == 1: - ax.plot( - forecast_data.index, - forecast_data.values, - "-o", - label="Forecast", - ) - else: - ax.plot( - forecast_data.index, - forecast_data.median(axis=1).values, - "-o", - label="Forecast", - ) - ax.fill_between( - forecast_data.index, - forecast_data.quantile(0.25, axis=1).values, - forecast_data.quantile(0.75, axis=1).values, - alpha=0.3, - ) - # Subtracting 1 day only works nicely for daily data. For now if not daily, then start line on last point - if not time_str or time_str == "1D": - ax.axvspan( - forecast_data.index[0] - timedelta(days=1), - forecast_data.index[-1], - alpha=0.2, - ) - ax.set_xlim(data.index[0], forecast_data.index[-1] + timedelta(days=1)) - - else: - ax.axvspan( - forecast_data.index[0], - forecast_data.index[-1], - alpha=0.2, - ) - ax.set_xlim(data.index[0], forecast_data.index[-1]) - ax.set_title(title) - ax.legend() - ax.set_ylabel("Value") - - theme.style_primary_axis(ax) - - if external_axes is None: - theme.visualize_output() - - -def lambda_price_prediction_color(val: float, last_val: float) -> str: - """Set prediction to be a colored string""" - if float(val) > last_val: - return f"[green]{val:.2f} $[/green]" - return f"[red]{val:.2f} $[/red]" - - -def print_pretty_prediction(df_pred: pd.DataFrame, last_price: float): - """Print predictions""" - if rich_config.USE_COLOR: - df_pred = pd.DataFrame(df_pred) - df_pred.columns = ["pred"] - df_pred["pred"] = df_pred["pred"].apply( - lambda x: lambda_price_prediction_color(x, last_val=last_price) - ) - print_rich_table( - df_pred, - show_index=True, - index_name="Datetime", - headers=["Prediction"], - floatfmt=".2f", - title=f"Actual price: [yellow]{last_price:.2f} $[/yellow]", - ) - - else: - df_pred = pd.DataFrame(df_pred) - df_pred.columns = ["pred"] - print_rich_table( - df_pred, - show_index=True, - title=f"Actual price: [yellow]{last_price:.2f} $[/yellow]", - index_name="Datetime", - headers=["Prediction"], - floatfmt=".2f", - ) - - -def print_pretty_prediction_nn(df_pred: pd.DataFrame, last_price: float): - if rich_config.USE_COLOR: - console.print(f"Actual price: [yellow]{last_price:.2f} $[/yellow]\n") - console.print("Prediction:") - console.print( - df_pred.applymap( - lambda x: lambda_price_prediction_color(x, last_val=last_price) - ).to_string() - ) - else: - console.print(f"Actual price: {last_price:.2f} $\n") - console.print("Prediction:") - console.print(df_pred.to_string()) - - -def mean_absolute_percentage_error(y_true: np.ndarray, y_pred: np.ndarray) -> np.number: - """Calculate mean absolute percent error""" - y_true, y_pred = np.array(y_true), np.array(y_pred) - return np.mean(np.abs((y_true - y_pred) / y_true)) * 100 - - -def print_prediction_kpis(real: np.ndarray, pred: np.ndarray): - """Print prediction statistics""" - kpis = { - "MAPE": f"{mean_absolute_percentage_error(real, pred) :.3f} %", - "R2": f"{r2_score(real, pred) :.3f}", - "MAE": f"{mean_absolute_error(real, pred):.3f}", - "MSE": f"{mean_squared_error(real, pred):.3f}", - "RMSE": f"{mean_squared_error(real, pred, squared=False):.3f}", - } - - df = pd.DataFrame.from_dict(kpis, orient="index") - print_rich_table( - df, - show_index=True, - title="KPIs", - floatfmt=".2f", - ) - - -def lambda_price_prediction_backtesting_color(val: list) -> str: - """Add color to backtest data""" - err_pct = 100 * (val[0] - val[1]) / val[1] - if val[0] > val[1]: - s_err_pct = f" [green] +{err_pct:.2f} %" - color = "[/green]" - else: - s_err_pct = f" [red] {err_pct:.2f} %" - color = "[/red]" - return f"{val[1]:.2f} x [yellow]{val[0]:.2f}[/yellow]{s_err_pct}{color}" diff --git a/openbb_terminal/common/prediction_techniques/regression_model.py b/openbb_terminal/common/prediction_techniques/regression_model.py deleted file mode 100644 index d66a3e32eab..00000000000 --- a/openbb_terminal/common/prediction_techniques/regression_model.py +++ /dev/null @@ -1,127 +0,0 @@ -"""Regression Model""" -__docformat__ = "numpy" -import logging -from typing import Any, List, Tuple - -import numpy as np -from sklearn import linear_model, pipeline, preprocessing - -from openbb_terminal.decorators import log_start_end -from openbb_terminal.rich_config import console - -# The tsxv dependence was removed so this fails. Taking from didiers source - - -logger = logging.getLogger(__name__) - - -@log_start_end(log=logger) -def split_train( - sequence: np.ndarray, numInputs: int, numOutputs: int, numJumps: int -) -> Tuple[List, List]: - """Returns sets to train a model - i.e. X[0] = sequence[0], ..., sequence[numInputs] - y[0] = sequence[numInputs+1], ..., sequence[numInputs+numOutputs] - ... - X[k] = sequence[k*numJumps], ..., sequence[k*numJumps+numInputs] - y[k] = sequence[k*numJumps+numInputs+1], ..., sequence[k*numJumps+numInputs+numOutputs] - - Parameters - ---------- - sequence: np.ndarray - Full training dataset - numInputs : int - Number of inputs X used at each training - numOutputs : int - Number of outputs y used at each training - numJumps : int - Number of sequence samples to be ignored between (X,y) sets - Returns - ------- - List - List of numInputs arrays - List - List of numOutputs arrays - """ - - X: List = [] - y: List = [] - - if numInputs + numOutputs > len(sequence): - console.print( - "To have at least one X,y arrays, the sequence size needs to be bigger than numInputs+numOutputs" - ) - return X, y - - for i in range(len(sequence)): - i = numJumps * i - end_ix = i + numInputs - - # Once train data crosses time series length return - if end_ix + numOutputs > len(sequence): - break - - seq_x = sequence[i:end_ix] - X.append(seq_x) - seq_y = sequence[end_ix : end_ix + numOutputs] - y.append(seq_y) - - return X, y - - -@log_start_end(log=logger) -def get_regression_model( - values: List[float], - poly_order: int, - n_input: int, - n_predict: int, - n_jumps: int, -) -> Tuple[List[float], Any]: - """Fit regression model of variable order - - Parameters - ---------- - values : List[float] - Data to fit - poly_order : int - Order of polynomial - n_input : int - Length of input sequence - n_predict : int - Length of prediction sequence - n_jumps : int - Number of jumps in data preparation - - Returns - ------- - List[float] - List of predicted values - Any - Linear model fit to data - """ - # Split training data - stock_x, stock_y = split_train( - values, - n_input, - n_predict, - n_jumps, - ) - - if not stock_x: - console.print("Given the model parameters more training data is needed.\n") - return [], None - - # Machine Learning model - if poly_order == 1: - model = linear_model.LinearRegression(n_jobs=-1) - else: - model = pipeline.make_pipeline( - preprocessing.PolynomialFeatures(poly_order), linear_model.Ridge() - ) - - model.fit(stock_x, stock_y) - l_predictions = [ - i if i > 0 else 0 - for i in model.predict(np.array(values[-n_input:]).reshape(1, -1))[0] - ] - return l_predictions, model diff --git a/openbb_terminal/common/prediction_techniques/regression_view.py b/openbb_terminal/common/prediction_techniques/regression_view.py deleted file mode 100644 index 13a58b854fc..00000000000 --- a/openbb_terminal/common/prediction_techniques/regression_view.py +++ /dev/null @@ -1,272 +0,0 @@ -""" Regression View""" -__docformat__ = "numpy" - -import logging -import os -from typing import Union, Optional, List - -import matplotlib.pyplot as plt -import pandas as pd -from pandas.plotting import register_matplotlib_converters - -from openbb_terminal.common.prediction_techniques import regression_model -from openbb_terminal.common.prediction_techniques.pred_helper import ( - lambda_price_prediction_backtesting_color, - print_prediction_kpis, - print_pretty_prediction, -) -from openbb_terminal.config_plot import PLOT_DPI -from openbb_terminal.config_terminal import theme -from openbb_terminal.decorators import log_start_end -from openbb_terminal.helper_funcs import ( - export_data, - get_next_stock_market_days, - patch_pandas_text_adjustment, - plot_autoscale, - is_valid_axes_count, -) -from openbb_terminal.rich_config import console -from openbb_terminal import rich_config - -logger = logging.getLogger(__name__) - -register_matplotlib_converters() - -# pylint:disable=too-many-arguments - - -@log_start_end(log=logger) -def display_regression( - dataset: str, - values: Union[pd.Series, pd.DataFrame], - poly_order: int, - n_input: int, - n_predict: int, - n_jumps: int, - end_date: str = "", - export: str = "", - time_res: str = "", - external_axes: Optional[List[plt.Axes]] = None, -): - """Display predications for regression models - - Parameters - ---------- - dataset : str - Title for data - values : Union[pd.Series, pd.DataFrame] - Data to fit - poly_order : int - Order of polynomial to fit - n_input : int - Length of input sequence - n_predict : int - Length of prediction sequence - n_jumps : int - Number of jumps in data - end_date : str, optional - Start date for backtesting - export : str, optional - Format for exporting figures - time_res : str - Resolution for data, allowing for predicting outside of standard market days - external_axes : Optional[List[plt.Axes]], optional - External axes (1 axis is expected in the list), by default None - """ - # BACKTESTING - if end_date: - if not time_res: - future_index = get_next_stock_market_days( - last_stock_day=end_date, n_next_days=n_predict - ) - else: - future_index = pd.date_range( - end_date, periods=n_predict + 1, freq=time_res - )[1:] - - df_future = values[future_index[0] : future_index[-1]] # noqa: E203 - values = values[:end_date] # type: ignore - - l_predictions, _ = regression_model.get_regression_model( - list(values.values), poly_order, n_input, n_predict, n_jumps - ) - - # Prediction data - if not time_res: - l_pred_days = get_next_stock_market_days( - last_stock_day=values.index[-1], - n_next_days=n_predict, - ) - else: - l_pred_days = pd.date_range( - values.index[-1], periods=n_predict + 1, freq=time_res - )[1:] - df_pred = pd.Series(l_predictions, index=l_pred_days, name="Price") - - # Plotting - - # This plot has 1 axis - if external_axes is None: - _, ax1 = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI) - elif end_date and not is_valid_axes_count( - external_axes, - 3, - prefix_text="If there is end_date", - suffix_text="when backtesting", - ): - return - elif not end_date and not is_valid_axes_count( - external_axes, - 1, - prefix_text="If there is no end_date", - suffix_text="when backtesting", - ): - return - else: - ax1 = external_axes[0] - - ax1.plot(values.index, values) - # BACKTESTING - if end_date: - ax1.set_title( - f"BACKTESTING: Regression (polynomial {poly_order}) on {dataset} - {n_predict} step prediction", - fontsize=12, - ) - else: - ax1.set_title( - f"Regression (polynomial {poly_order}) on {dataset} - {n_predict} step prediction" - ) - ax1.set_xlim(values.index[0], l_pred_days[-1]) - ax1.set_ylabel("Value") - ax1.plot( - [values.index[-1], df_pred.index[0]], - [values.values[-1], df_pred.values[0]], - color=theme.down_color, - linestyle="--", - ) - ax1.plot(df_pred.index, df_pred, color=theme.down_color) - ax1.axvspan(values.index[-1], df_pred.index[-1], alpha=0.2) - _, _, ymin, ymax = plt.axis() - ax1.vlines(values.index[-1], ymin, ymax, linestyle="--") - - # BACKTESTING - if end_date: - ax1.plot( - df_future.index, - df_future, - color=theme.up_color, - linestyle="--", - ) - ax1.plot( - [values.index[-1], df_future.index[0]], - [ - values.values[-1], - df_future.values[0], - ], - color=theme.up_color, - linestyle="--", - ) - - theme.style_primary_axis(ax1) - if external_axes is None: - theme.visualize_output() - - export_data(export, os.path.dirname(os.path.abspath(__file__)), "regression") - console.print("") - - # BACKTESTING - if end_date: - # This plot has 3 axes - if external_axes is None: - _, axes = plt.subplots( - 2, 1, sharex=True, figsize=plot_autoscale(), dpi=PLOT_DPI - ) - (ax2, ax3) = axes - elif is_valid_axes_count(external_axes, 3): - (_, ax2, ax3) = external_axes - else: - return - - ax2.plot( - df_future.index, - df_future, - color=theme.up_color, - linestyle="--", - ) - ax2.plot(df_pred.index, df_pred, color=theme.down_color, marker="o") - ax2.plot( - [values.index[-1], df_future.index[0]], - [ - values.values[-1], - df_future.values[0], - ], - color=theme.up_color, - linestyle="--", - ) - ax2.plot( - [values.index[-1], df_pred.index[0]], - [values.values[-1], df_pred.values[0]], - color=theme.down_color, - linestyle="--", - marker="o", - ) - ax2.set_title("BACKTESTING: Real data vs Prediction", fontsize=12) - ax2.set_xlim(values.index[-1], df_pred.index[-1]) - ax2.set_ylabel("Value") - ax2.legend(["Real data", "Prediction data"]) - - ax3.axhline(y=0, linestyle="--", color=theme.up_color) - ax3.plot( - df_future.index, - 100 * (df_pred.values - df_future.values) / df_future.values, - color=theme.down_color, - marker="o", - ) - ax3.set_title( - "BACKTESTING: Error between Real data and Prediction [%]", fontsize=12 - ) - ax3.plot( - [values.index[-1], df_future.index[0]], - [ - 0, - 100 * (df_pred.values[0] - df_future.values[0]) / df_future.values[0], - ], - linestyle="--", - color=theme.down_color, - ) - ax3.set_xlim(values.index[-1], df_pred.index[-1]) - ax3.set_xlabel("Time") - ax3.set_ylabel("Error (%)") - ax3.legend(["Real data", "Prediction data"]) - - theme.style_primary_axis(ax2) - theme.style_primary_axis(ax3) - - if external_axes is None: - theme.visualize_output() - - # Refactor prediction dataframe for backtesting print - df_pred.name = "Prediction" - df_pred = df_pred.to_frame() - df_pred["Real"] = df_future - - if rich_config.USE_COLOR: - - patch_pandas_text_adjustment() - - console.print("Time Real [$] x Prediction [$]") - console.print( - df_pred.apply( - lambda_price_prediction_backtesting_color, axis=1 - ).to_string() - ) - else: - console.print(df_pred[["Real", "Prediction"]].round(2).to_string()) - - console.print("") - print_prediction_kpis(df_pred["Real"].values, df_pred["Prediction"].values) - - else: - # Print prediction data - print_pretty_prediction(df_pred, values.values[-1]) - console.print("") diff --git a/openbb_terminal/config_neural_network_models.py b/openbb_terminal/config_neural_network_models.py deleted file mode 100644 index 424f0566aba..00000000000 --- a/openbb_terminal/config_neural_network_models.py +++ /dev/null @@ -1,70 +0,0 @@ -# This configuration file allows to define the layers that constitute each -# of the neural network models used by our MLP, RNN and LSTM models. -# The first layer is the input layer, meaning that to it we add the input shape expected. -# Note that we can still add the number of neurons on the first layer. -# The last layer is the output layer, meaning that from it we have our prediction share price days -# therefore, we don't define units for this layer as this is defined from the terminal directly. -# There are several types of parameters available to configure your Neural Network model, and more on -# these can be found in https://www.tensorflow.org/api_docs/python/tf/keras/layers -# So far the following type of layers are allowed: Dense, SimpleRNN, LSTM, and Dropout - -# This is an on-going work, hence, feel free to request support for more types of layers - -# From the terminal, we are able to define: -# - The number of inputs used by the module -# - The number of training epochs -# - The frequency of data training (i.e. do we use all data, or every other day - may speed up training) -# - The preprocessing type: normalization, standardization, or none -# - The optimizer -# - The loss function - -MultiLayer_Perceptron = [ - {"Dense": {"units": 50, "activation": "relu"}}, - {"Dense": {"units": 100, "activation": "relu"}}, - {"Dense": {"units": 80, "activation": "relu"}}, - {"Dense": {"units": 30, "activation": "relu"}}, - {"Dense": {"activation": "relu"}}, -] - -Recurrent_Neural_Network = [ - {"SimpleRNN": {"units": 20, "activation": "relu", "return_sequences": True}}, - {"SimpleRNN": {"units": 10, "activation": "relu", "return_sequences": True}}, - {"Dropout": {"rate": 0.1}}, - {"SimpleRNN": {"units": 5, "activation": "relu", "return_sequences": False}}, - {"Dense": {"activation": "linear"}}, -] - -Long_Short_Term_Memory = [ - {"LSTM": {"units": 25, "activation": "tanh", "return_sequences": True}}, - {"LSTM": {"units": 15, "activation": "tanh", "return_sequences": False}}, - {"Dense": {"activation": "relu"}}, -] - -Convolutional = [ - {"Conv1D": {"filters": 20, "kernel_size": 10, "activation": "relu"}}, - {"MaxPool1D": {"pool_size": 2}}, - {"Conv1D": {"filters": 10, "kernel_size": 5, "activation": "relu"}}, - {"MaxPool1D": {"pool_size": 2}}, - {"Flatten": {}}, - {"Dense": {"activation": "relu"}}, -] - -# Optimizers : https://www.tensorflow.org/api_docs/python/tf/keras/optimizers -# Adam, Adagrad, Adadelta, Adamax,Ftrl, Nadam, Rmsprop, Sgd -Optimizer = "Adam" - -# Add early stopping to save time training when validation is plateaued -# Can be set to large number or None to ignore -Early_Stop_Patience = 10 - -# Losses https://www.tensorflow.org/api_docs/python/tf/keras/losses -# mae, mape, mse, msle, poisson, logcosh, kld, hinge, squared_hinge, huber -Loss = "mae" - -# Preprocessing technique to use : https://scikit-learn.org/stable/modules/preprocessing.html -# Options are: -# minmax - Adjusts data to be between 0 and 1 - Most Used for Stocks -# standardization - Standardizes data so that it has mean 0 and standard deviation = 1 -# normalization - Normalizes data so that L2 norm (sum x_i^2) = 1 -# none - No preprocessing -Preprocess = "minmax" diff --git a/openbb_terminal/cryptocurrency/crypto_controller.py b/openbb_terminal/cryptocurrency/crypto_controller.py index 57f0936b57d..75bce2051c9 100644 --- a/openbb_terminal/cryptocurrency/crypto_controller.py +++ b/openbb_terminal/cryptocurrency/crypto_controller.py @@ -27,6 +27,7 @@ from openbb_terminal.helper_funcs import ( EXPORT_BOTH_RAW_DATA_AND_FIGURES, EXPORT_ONLY_RAW_DATA_ALLOWED, check_positive, + export_data, ) from openbb_terminal.menu import session from openbb_terminal.parent_classes import CryptoBaseController @@ -67,8 +68,8 @@ class CryptoController(CryptoBaseController): "defi", "tools", "nft", - "pred", "qa", + "forecast", ] DD_VIEWS_MAPPING = { @@ -133,8 +134,8 @@ class CryptoController(CryptoBaseController): mt.add_menu("nft") mt.add_menu("dd", self.symbol) mt.add_menu("ta", self.symbol) - mt.add_menu("pred", self.symbol) mt.add_menu("qa", self.symbol) + mt.add_menu("forecast", self.symbol) console.print(text=mt.menu_text, menu="Cryptocurrency") @log_start_end(log=logger) @@ -249,11 +250,16 @@ class CryptoController(CryptoBaseController): ns_parser = self.parse_known_args_and_warn( parser, other_args, EXPORT_BOTH_RAW_DATA_AND_FIGURES ) - if ns_parser: if not self.symbol: console.print("No coin loaded. First use `load {symbol}`\n") return + export_data( + ns_parser.export, + os.path.join(os.path.dirname(os.path.abspath(__file__))), + f"{self.symbol}", + self.current_df, + ) plot_chart( exchange=self.exchange, @@ -368,7 +374,7 @@ class CryptoController(CryptoBaseController): @log_start_end(log=logger) def call_qa(self, _): - """Process pred command""" + """Process qa command""" if self.symbol: from openbb_terminal.cryptocurrency.quantitative_analysis import ( qa_controller, @@ -521,3 +527,16 @@ class CryptoController(CryptoBaseController): show_all=bool("ALL" in other_args), export=ns_parser.export, ) + + @log_start_end(log=logger) + def call_forecast(self, _): + """Process forecast command""" + from openbb_terminal.forecast import forecast_controller + + console.print(self.symbol) + self.queue = self.load_class( + forecast_controller.ForecastController, + self.symbol, + self.current_df, + self.queue, + ) diff --git a/openbb_terminal/cryptocurrency/prediction_techniques/pred_api.py b/openbb_terminal/cryptocurrency/prediction_techniques/pred_api.py deleted file mode 100644 index ea1d2fe6402..00000000000 --- a/openbb_terminal/cryptocurrency/prediction_techniques/pred_api.py +++ /dev/null @@ -1,33 +0,0 @@ -"""Pred context API.""" - -# flake8: noqa -# pylint: disable=unused-import - -# Menu commands -from openbb_terminal.common.prediction_techniques.ets_view import ( - display_exponential_smoothing as ets, -) -from openbb_terminal.common.prediction_techniques.knn_view import ( - display_k_nearest_neighbors as knn, -) -from openbb_terminal.common.prediction_techniques.regression_view import ( - display_regression as regression, -) -from openbb_terminal.common.prediction_techniques.arima_view import ( - display_arima as arima, -) -from openbb_terminal.common.prediction_techniques.neural_networks_view import ( - display_mlp as mlp, -) -from openbb_terminal.common.prediction_techniques.neural_networks_view import ( - display_rnn as rnn, -) -from openbb_terminal.common.prediction_techniques.neural_networks_view import ( - display_lstm as lstm, -) -from openbb_terminal.common.prediction_techniques.neural_networks_view import ( - display_conv1d as conv1d, -) -from openbb_terminal.common.prediction_techniques.mc_view import ( - display_mc_forecast as mc, -) diff --git a/openbb_terminal/cryptocurrency/prediction_techniques/pred_controller.py b/openbb_terminal/cryptocurrency/prediction_techniques/pred_controller.py deleted file mode 100644 index e241ae6affa..00000000000 --- a/openbb_terminal/cryptocurrency/prediction_techniques/pred_controller.py +++ /dev/null @@ -1,711 +0,0 @@ -"""Crypto Prediction Controller""" -__docformat__ = "numpy" -# pylint: disable=R0902 -import argparse -import logging -from typing import List - -import numpy as np -import pandas as pd -from prompt_toolkit.completion import NestedCompleter - -from openbb_terminal import feature_flags as obbff -from openbb_terminal.common.prediction_techniques import ( - arima_model, - arima_view, - ets_model, - ets_view, - knn_view, - mc_model, - mc_view, - neural_networks_view, - pred_helper, - regression_view, -) -from openbb_terminal.cryptocurrency import cryptocurrency_helpers as c_help -from openbb_terminal.decorators import log_start_end -from openbb_terminal.helper_funcs import ( - EXPORT_ONLY_FIGURES_ALLOWED, - check_positive, - get_next_stock_market_days, - valid_date, -) -from openbb_terminal.menu import session -from openbb_terminal.parent_classes import CryptoBaseController -from openbb_terminal.rich_config import console, MenuText - -logger = logging.getLogger(__name__) - - -class PredictionTechniquesController(CryptoBaseController): - """Prediction Techniques Controller class""" - - CHOICES_COMMANDS = [ - "pick", - "load", - "ets", - "knn", - "regression", - "arima", - "mlp", - "rnn", - "lstm", - "conv1d", - "mc", - ] - - sampling_map = {"H": "Hour", "D": "Day"} - PATH = "/crypto/pred/" - - def __init__( - self, - coin: str, - data: pd.DataFrame, - queue: List[str] = None, - ): - """Constructor""" - super().__init__(queue) - - data["Returns"] = data["Close"].pct_change() - data["LogRet"] = np.log(data["Close"]) - np.log(data["Close"].shift(1)) - data = data.dropna() - - self.data = data - self.coin = coin - self.target = "Close" - - if session and obbff.USE_PROMPT_TOOLKIT: - choices: dict = {c: {} for c in self.controller_choices} - choices["load"]["-r"] = {c: {} for c in c_help.INTERVALS} - choices["pick"] = {c: {} for c in self.data.columns} - choices["ets"]["-t"] = {c: {} for c in ets_model.TRENDS} - choices["ets"]["-s"] = {c: {} for c in ets_model.SEASONS} - choices["arima"]["-i"] = {c: {} for c in arima_model.ICS} - choices["mc"]["--dist"] = {c: {} for c in mc_model.DISTRIBUTIONS} - - choices["support"] = self.SUPPORT_CHOICES - choices["about"] = self.ABOUT_CHOICES - - self.completer = NestedCompleter.from_nested_dict(choices) - - def print_help(self): - """Print help""" - mt = MenuText("crypto/pred/") - mt.add_cmd("load") - mt.add_cmd("pick") - mt.add_raw("\n") - mt.add_param("_ticker", self.coin) - mt.add_param("_target", self.target) - mt.add_raw("\n") - mt.add_info("_models_") - mt.add_cmd("ets") - mt.add_cmd("knn") - mt.add_cmd("regression") - mt.add_cmd("arima") - mt.add_cmd("mlp") - mt.add_cmd("rnn") - mt.add_cmd("lstm") - mt.add_cmd("conv1d") - mt.add_cmd("mc") - console.print(text=mt.menu_text, menu="Cryptocurrency - Prediction Techniques") - - def custom_reset(self): - """Class specific component of reset command""" - if self.coin: - return ["crypto", f"load {self.coin}", "pred"] - return [] - - @log_start_end(log=logger) - def call_pick(self, other_args: List[str]): - """Process pick command""" - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - add_help=False, - prog="pick", - description=""" - Change target variable - """, - ) - parser.add_argument( - "-t", - "--target", - dest="target", - choices=list(self.data.columns), - help="Select variable to analyze", - ) - if other_args and "-t" not in other_args and "-h" not in other_args: - other_args.insert(0, "-t") - - ns_parser = self.parse_known_args_and_warn(parser, other_args) - if ns_parser: - self.target = ns_parser.target - console.print("") - - @log_start_end(log=logger) - def call_ets(self, other_args: List[str]): - """Process ets command""" - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="ets", - description=""" - Exponential Smoothing, see https://otexts.com/fpp2/taxonomy.html - - Trend='N', Seasonal='N': Simple Exponential Smoothing - Trend='N', Seasonal='A': Exponential Smoothing - Trend='N', Seasonal='M': Exponential Smoothing - Trend='A', Seasonal='N': Holt's linear method - Trend='A', Seasonal='A': Additive Holt-Winters' method - Trend='A', Seasonal='M': Multiplicative Holt-Winters' method - Trend='Ad', Seasonal='N': Additive damped trend method - Trend='Ad', Seasonal='A': Exponential Smoothing - Trend='Ad', Seasonal='M': Holt-Winters' damped method - Trend component: N: None, A: Additive, Ad: Additive Damped - Seasonality component: N: None, A: Additive, M: Multiplicative - """, - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-t", - "--trend", - action="store", - dest="trend", - choices=ets_model.TRENDS, - default="N", - help="Trend component: N: None, A: Additive, Ad: Additive Damped.", - ) - parser.add_argument( - "-s", - "--seasonal", - action="store", - dest="seasonal", - choices=ets_model.SEASONS, - default="N", - help="Seasonality component: N: None, A: Additive, M: Multiplicative.", - ) - parser.add_argument( - "-p", - "--periods", - action="store", - dest="seasonal_periods", - type=check_positive, - default=5, - help="Seasonal periods.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select - Backtesting", - ) - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - if ns_parser.s_end_date: - if ns_parser.s_end_date < self.data.index[0]: - console.print( - "Backtesting not allowed, since End Date is older than Start Date of historical data\n" - ) - - if ( - ns_parser.s_end_date - < get_next_stock_market_days( - last_stock_day=self.data.index[0], - n_next_days=5 + ns_parser.n_days, - )[-1] - ): - console.print( - "Backtesting not allowed, since End Date is too close to Start Date to train model\n" - ) - - ets_view.display_exponential_smoothing( - dataset=self.coin, - data=self.data[self.target], - n_predict=ns_parser.n_days, - trend=ns_parser.trend, - seasonal=ns_parser.seasonal, - seasonal_periods=ns_parser.seasonal_periods, - end_date=ns_parser.s_end_date, - export=ns_parser.export, - time_res=self.resolution, - ) - - @log_start_end(log=logger) - def call_knn(self, other_args: List[str]): - """Process knn command""" - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="knn", - description=""" - K nearest neighbors is a simple algorithm that stores all - available cases and predict the numerical target based on a similarity measure - (e.g. distance functions). - """, - ) - parser.add_argument( - "-i", - "--input", - action="store", - dest="n_inputs", - type=check_positive, - default=40, - help="number of days to use as input for prediction.", - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-j", - "--jumps", - action="store", - dest="n_jumps", - type=check_positive, - default=1, - help="number of jumps in training data.", - ) - parser.add_argument( - "-n", - "--neighbors", - action="store", - dest="n_neighbors", - type=check_positive, - default=20, - help="number of neighbors to use on the algorithm.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select for testing", - ) - parser.add_argument( - "-t", - "--test_size", - default=0.2, - dest="valid_split", - type=float, - help="Percentage of data to validate in sample", - ) - parser.add_argument( - "--no_shuffle", - action="store_false", - dest="no_shuffle", - default=True, - help="Specify if shuffling validation inputs.", - ) - ns_parser = self.parse_known_args_and_warn( - parser, other_args, EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - knn_view.display_k_nearest_neighbors( - dataset=self.coin, - data=self.data[self.target], - n_neighbors=ns_parser.n_neighbors, - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - test_size=ns_parser.valid_split, - end_date=ns_parser.s_end_date, - no_shuffle=ns_parser.no_shuffle, - time_res=self.resolution, - ) - - @log_start_end(log=logger) - def call_regression(self, other_args: List[str]): - """Process linear command""" - parser = argparse.ArgumentParser( - add_help=False, - prog="regression", - description=""" - Regression attempts to model the relationship between - two variables by fitting a linear/quadratic/cubic/other equation to - observed data. One variable is considered to be an explanatory variable, - and the other is considered to be a dependent variable. - """, - ) - - parser.add_argument( - "-i", - "--input", - action="store", - dest="n_inputs", - type=check_positive, - default=40, - help="number of days to use for prediction.", - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-j", - "--jumps", - action="store", - dest="n_jumps", - type=check_positive, - default=1, - help="number of jumps in training data.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select - Backtesting", - ) - parser.add_argument( - "-p", - "--polynomial", - action="store", - dest="n_polynomial", - type=check_positive, - default=1, - help="polynomial associated with regression.", - ) - if ( - other_args - and "-h" not in other_args - and ("-p" not in other_args or "--polynomial" not in other_args) - ): - other_args.insert(0, "-p") - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - # BACKTESTING CHECK - if ns_parser.s_end_date: - if ns_parser.s_end_date < self.data.index[0]: - console.print( - "Backtesting not allowed, since End Date is older than Start Date of historical data\n" - ) - - if ( - ns_parser.s_end_date - < get_next_stock_market_days( - last_stock_day=self.data.index[0], - n_next_days=5 + ns_parser.n_days, - )[-1] - ): - console.print( - "Backtesting not allowed, since End Date is too close to Start Date to train model\n" - ) - - regression_view.display_regression( - dataset=self.coin, - values=self.data[self.target], - poly_order=ns_parser.n_polynomial, - n_input=ns_parser.n_inputs, - n_predict=ns_parser.n_days, - n_jumps=ns_parser.n_jumps, - end_date=ns_parser.s_end_date, - export=ns_parser.export, - time_res=self.resolution, - ) - - @log_start_end(log=logger) - def call_arima(self, other_args: List[str]): - """Process arima command""" - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="arima", - description=""" - In statistics and econometrics, and in particular in time series analysis, an - autoregressive integrated moving average (ARIMA) model is a generalization of an - autoregressive moving average (ARMA) model. Both of these models are fitted to time - series data either to better understand the data or to predict future points in the - series (forecasting). ARIMA(p,d,q) where parameters p, d, and q are non-negative - integers, p is the order (number of time lags) of the autoregressive model, d is the - degree of differencing (the number of times the data have had past values subtracted), - and q is the order of the moving-average model. - """, - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-i", - "--ic", - action="store", - dest="s_ic", - type=str, - default="aic", - choices=arima_model.ICS, - help="information criteria.", - ) - parser.add_argument( - "-s", - "--seasonal", - action="store_true", - default=False, - dest="b_seasonal", - help="Use weekly seasonal data.", - ) - parser.add_argument( - "-o", - "--order", - action="store", - dest="s_order", - default="", - type=str, - help="arima model order (p,d,q) in format: p,d,q.", - ) - parser.add_argument( - "-r", - "--results", - action="store_true", - dest="b_results", - default=False, - help="results about ARIMA summary flag.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select - Backtesting", - ) - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - # BACKTESTING CHECK - if ns_parser.s_end_date: - if ns_parser.s_end_date < self.data.index[0]: - console.print( - "Backtesting not allowed, since End Date is older than Start Date of historical data\n" - ) - - if ( - ns_parser.s_end_date - < get_next_stock_market_days( - last_stock_day=self.data.index[0], - n_next_days=5 + ns_parser.n_days, - )[-1] - ): - console.print( - "Backtesting not allowed, since End Date is too close to Start Date to train model\n" - ) - - arima_view.display_arima( - dataset=self.coin, - values=self.data[self.target], - arima_order=ns_parser.s_order, - n_predict=ns_parser.n_days, - seasonal=ns_parser.b_seasonal, - ic=ns_parser.s_ic, - results=ns_parser.b_results, - end_date=ns_parser.s_end_date, - export=ns_parser.export, - time_res=self.resolution, - ) - - @log_start_end(log=logger) - def call_mlp(self, other_args: List[str]): - """Process mlp command""" - try: - ns_parser = pred_helper.parse_args( - prog="mlp", - description="""Multi-Layered-Perceptron. """, - other_args=other_args, - ) - if ns_parser: - neural_networks_view.display_mlp( - dataset=self.coin, - data=self.data[self.target], - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - time_res=self.resolution, - ) - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_rnn(self, other_args: List[str]): - """Process rnn command""" - try: - ns_parser = pred_helper.parse_args( - prog="rnn", - description="""Recurrent Neural Network. """, - other_args=other_args, - ) - if ns_parser: - neural_networks_view.display_rnn( - dataset=self.coin, - data=self.data[self.target], - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - time_res=self.resolution, - ) - - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_lstm(self, other_args: List[str]): - """Process lstm command""" - try: - ns_parser = pred_helper.parse_args( - prog="lstm", - description="""Long-Short Term Memory. """, - other_args=other_args, - ) - if ns_parser: - neural_networks_view.display_lstm( - dataset=self.coin, - data=self.data[self.target], - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - time_res=self.resolution, - ) - - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_conv1d(self, other_args: List[str]): - """Process conv1d command""" - try: - ns_parser = pred_helper.parse_args( - prog="conv1d", - description="""1D CNN.""", - other_args=other_args, - ) - if ns_parser: - neural_networks_view.display_conv1d( - dataset=self.coin, - data=self.data[self.target], - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - time_res=self.resolution, - ) - - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_mc(self, other_args: List[str]): - """Process mc command""" - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - add_help=False, - prog="mc", - description=""" - Perform Monte Carlo forecasting - """, - ) - parser.add_argument( - "-d", - "--days", - help="Days to predict", - dest="n_days", - type=check_positive, - default=30, - ) - parser.add_argument( - "-n", - "--num", - help="Number of simulations to perform", - dest="n_sims", - default=100, - type=check_positive, - ) - parser.add_argument( - "--dist", - choices=mc_model.DISTRIBUTIONS, - default="lognormal", - dest="dist", - help="Whether to model returns or log returns", - ) - - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if self.target != "Close": - console.print("MC Prediction designed for AdjClose prices\n") - - if ns_parser: - mc_view.display_mc_forecast( - data=self.data[self.target], - n_future=ns_parser.n_days, - n_sims=ns_parser.n_sims, - use_log=ns_parser.dist == "lognormal", - export=ns_parser.export, - time_res=self.resolution, - ) diff --git a/openbb_terminal/dashboards/chains.ipynb b/openbb_terminal/dashboards/chains.ipynb deleted file mode 100644 index c17412479ee..00000000000 --- a/openbb_terminal/dashboards/chains.ipynb +++ /dev/null @@ -1,177 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "e9ad60bc", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import ipywidgets as widgets\n", - "import plotly.graph_objs as go\n", - "import yfinance as yf\n", - "from IPython.display import display" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "beb36881", - "metadata": {}, - "outputs": [], - "source": [ - "opts = [\n", - " \"lastTradeDate\",\n", - " \"strike\",\n", - " \"lastPrice\",\n", - " \"bid\",\n", - " \"ask\",\n", - " \"change\",\n", - " \"percentChange\",\n", - " \"volume\",\n", - " \"openInterest\",\n", - " \"impliedVolatility\",\n", - "]\n", - "\n", - "\n", - "def clean_str(string):\n", - " new_str = \"\"\n", - " for letter in string:\n", - " if letter.isupper():\n", - " new_str += \" \"\n", - " new_str += letter\n", - " return new_str.title()\n", - "\n", - "\n", - "def format_plotly(fig, x, y, ticker, expiry, inst):\n", - " fig.update_yaxes(title=clean_str(y))\n", - " fig.update_xaxes(title=clean_str(x))\n", - " expires = \", \".join(expiry)\n", - " title = (\n", - " f\"{clean_str(y)} vs. {clean_str(x)} for {ticker.upper()} {inst}s on {expires}\"\n", - " )\n", - " fig.update_layout(\n", - " autosize=False,\n", - " width=1000,\n", - " height=500,\n", - " title={\n", - " \"text\": title,\n", - " \"y\": 0.95,\n", - " \"x\": 0.5,\n", - " \"xanchor\": \"center\",\n", - " \"yanchor\": \"top\",\n", - " },\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8c2f65c", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "class Chart:\n", - " def __init__(self):\n", - " self.last_ticker = \"\"\n", - " self.dfs = {}\n", - "\n", - " def update(self, ticker, expiry, inst, x, y):\n", - " if ticker:\n", - " stock = yf.Ticker(ticker)\n", - " if ticker != self.last_ticker:\n", - " exp_w.options = stock.options\n", - " self.last_ticker = ticker\n", - "\n", - " if expiry:\n", - " fig = go.Figure()\n", - " for expire in expiry:\n", - " if expire not in self.dfs:\n", - " self.dfs[expire] = stock.option_chain(expire)\n", - " group = self.dfs[expire]\n", - " df = group.calls if inst == \"Call\" else group.puts\n", - "\n", - " plot = go.Scatter(\n", - " x=df[x],\n", - " y=df[y],\n", - " mode=\"lines\",\n", - " connectgaps=True,\n", - " name=expire,\n", - " )\n", - " fig.add_trace(plot)\n", - "\n", - " format_plotly(fig, x, y, ticker, expiry, inst)\n", - "\n", - " if os.environ.get(\"SERVER_SOFTWARE\", \"jupyter\").startswith(\"voila\"):\n", - " fig.show(config={\"showTips\": False}, renderer=\"notebook\")\n", - " else:\n", - " fig.show(config={\"showTips\": False})\n", - "\n", - "\n", - "x_w = widgets.Dropdown(options=opts, value=\"strike\", description=\"X\")\n", - "y_w = widgets.Dropdown(options=opts, value=\"lastPrice\", description=\"Y\")\n", - "tickers_w = widgets.Text(description=\"Ticker\")\n", - "exp_w = widgets.SelectMultiple(description=\"Expiry\")\n", - "inst_w = widgets.Dropdown(options=[\"Put\", \"Call\"], value=\"Call\", description=\"Type\")\n", - "\n", - "select_opt = widgets.VBox([tickers_w, exp_w])\n", - "select_plot = widgets.VBox([inst_w, x_w, y_w])\n", - "controls = widgets.HBox([select_opt, select_plot])\n", - "\n", - "chart = Chart()\n", - "stocks_view = widgets.interactive_output(\n", - " chart.update,\n", - " {\"ticker\": tickers_w, \"expiry\": exp_w, \"inst\": inst_w, \"x\": x_w, \"y\": y_w},\n", - ")\n", - "\n", - "title_html = \"

Option Chain Dashboard

\"\n", - "app_contents = [\n", - " widgets.HTML(title_html),\n", - " controls,\n", - " stocks_view,\n", - "]\n", - "app = widgets.VBox(app_contents)\n", - "display(app)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a84c7390-6b43-4a25-8aa2-31aa84f52605", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - }, - "metadata": { - "interpreter": { - "hash": "e896a00ead8b521528d79ac9ef24990696f2b751eb283ab8e0d078c9c4971ffc" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/dashboards/correlation.ipynb b/openbb_terminal/dashboards/correlation.ipynb deleted file mode 100644 index 38574f5a42d..00000000000 --- a/openbb_terminal/dashboards/correlation.ipynb +++ /dev/null @@ -1,207 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "e9ad60bc", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from datetime import datetime, timedelta\n", - "import ipywidgets as widgets\n", - "import plotly.graph_objs as go\n", - "import yfinance as yf\n", - "import pandas as pd\n", - "from IPython.display import display" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c554cd2-462c-4832-b73c-c214aae11ce4", - "metadata": {}, - "outputs": [], - "source": [ - "interval_opts = [\n", - " \"1m\",\n", - " \"2m\",\n", - " \"5m\",\n", - " \"15m\",\n", - " \"30m\",\n", - " \"60m\",\n", - " \"90m\",\n", - " \"1h\",\n", - " \"1d\",\n", - " \"5d\",\n", - " \"1wk\",\n", - " \"1mo\",\n", - " \"3mo\",\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8c2f65c", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "class Chart:\n", - " def __init__(self):\n", - " self.last_tickers = \"\"\n", - " self.last_interval = \"1d\"\n", - " self.df = pd.DataFrame()\n", - "\n", - " def create(self, data, start, end, interval, tickers):\n", - " global last_tickers, df, last_interval\n", - " if tickers and tickers[-1] == \",\":\n", - " if tickers != self.last_tickers or interval != self.last_interval:\n", - " if interval in [\"1d\", \"5d\", \"1wk\", \"1mo\", \"3mo\"]:\n", - " self.df = yf.download(\n", - " tickers, period=\"max\", interval=interval, progress=False\n", - " )\n", - " else:\n", - " self.df = yf.download(\n", - " tickers, start=start, end=end, interval=interval, progress=False\n", - " )\n", - " self.last_tickers = tickers\n", - " self.last_interval = interval\n", - "\n", - " start_n = datetime(start.year, start.month, start.day)\n", - " end_n = datetime(end.year, end.month, end.day)\n", - "\n", - " df = self.df[data]\n", - "\n", - " if not isinstance(df, pd.Series):\n", - " if interval in [\"1d\", \"5d\", \"1wk\", \"1mo\", \"3mo\"]:\n", - " result = df.loc[(df.index >= start_n) & (df.index <= end_n)].corr()\n", - " else:\n", - " result = df.corr()\n", - "\n", - " base = [\n", - " [\n", - " \"black\" if x == 1 else \"lightgreen\" if x > 0 else \"lightpink\"\n", - " for x in result[y].tolist()\n", - " ]\n", - " for y in result.columns\n", - " ]\n", - " base = [[\"lightgray\" for _ in range(result.shape[0])]] + base\n", - " result = result.reset_index()\n", - " result.rename(columns={\"index\": \"\"}, inplace=True)\n", - "\n", - " fig = go.Figure(\n", - " data=[\n", - " go.Table(\n", - " header=dict(\n", - " values=list(result.columns),\n", - " fill_color=\"lightgray\",\n", - " font=dict(color=\"black\"),\n", - " align=\"left\",\n", - " ),\n", - " cells=dict(\n", - " values=[result[x] for x in result.columns],\n", - " fill_color=base,\n", - " format=[\"\"]\n", - " + [\".2f\" for _ in range(len(df.columns) - 1)],\n", - " font=dict(color=\"black\"),\n", - " align=\"left\",\n", - " ),\n", - " )\n", - " ],\n", - " )\n", - " fig.update_layout(\n", - " autosize=True,\n", - " height=600,\n", - " showlegend=False,\n", - " )\n", - " if os.environ.get(\"SERVER_SOFTWARE\", \"jupyter\").startswith(\"voila\"):\n", - " fig.show(config={\"showTips\": False}, renderer=\"notebook\")\n", - " else:\n", - " fig.show(config={\"showTips\": False})\n", - "\n", - "\n", - "w_auto = widgets.Layout(width=\"auto\")\n", - "data_opts = [\"Open\", \"Close\", \"High\", \"Low\", \"Volume\"]\n", - "data_widget = widgets.Dropdown(\n", - " options=data_opts, value=\"Close\", layout=w_auto, description=\"Data\"\n", - ")\n", - "\n", - "base_date = (datetime.today() - timedelta(days=365)).date()\n", - "start_widget = widgets.DatePicker(value=base_date, layout=w_auto, description=\"Start\")\n", - "end_widget = widgets.DatePicker(\n", - " value=datetime.today().date(), layout=w_auto, description=\"End\"\n", - ")\n", - "interval_widget = widgets.Dropdown(\n", - " options=interval_opts, value=\"1d\", layout=w_auto, description=\"Interval\"\n", - ")\n", - "tickers_widget = widgets.Textarea(value=\"TSLA,AAPL,\", layout=w_auto)\n", - "\n", - "data_box = widgets.VBox([tickers_widget, data_widget])\n", - "date_box = widgets.VBox([start_widget, end_widget, interval_widget])\n", - "controls = widgets.HBox([data_box, date_box], layout=widgets.Layout(width=\"60%\"))\n", - "\n", - "chart = Chart()\n", - "stocks_view = widgets.interactive_output(\n", - " chart.create,\n", - " {\n", - " \"data\": data_widget,\n", - " \"start\": start_widget,\n", - " \"end\": end_widget,\n", - " \"interval\": interval_widget,\n", - " \"tickers\": tickers_widget,\n", - " },\n", - ")\n", - "\n", - "title_html = \"

Correlation Analysis Dashboard

\"\n", - "warning_html = '

Use a comma after EVERY stock typed.

'\n", - "app_contents = [\n", - " widgets.HTML(title_html),\n", - " controls,\n", - " widgets.HTML(warning_html),\n", - " stocks_view,\n", - "]\n", - "app = widgets.VBox(app_contents)\n", - "display(app)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37f46146-a206-4c25-b969-81c5abec4b1d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - }, - "metadata": { - "interpreter": { - "hash": "e896a00ead8b521528d79ac9ef24990696f2b751eb283ab8e0d078c9c4971ffc" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/dashboards/crypto.ipynb b/openbb_terminal/dashboards/crypto.ipynb deleted file mode 100644 index 7972cda6037..00000000000 --- a/openbb_terminal/dashboards/crypto.ipynb +++ /dev/null @@ -1,160 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "6e198e40-0c33-45b3-a79b-2d9ee9eff7b9", - "metadata": { - "tags": [] - }, - "source": [ - "# Cryptocurrency exchange rates to USD ($)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "da655985-c9b7-492e-aefa-fbe3bc1865d5", - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import os\n", - "\n", - "import ipywidgets as ipw\n", - "from IPython.display import display\n", - "import pandas as pd\n", - "\n", - "from openbb_terminal import api as openbb\n", - "\n", - "ipw.HTML(f\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dcbbf867-af50-4d06-ae5c-7c09647aa55d", - "metadata": {}, - "outputs": [], - "source": [ - "COIN_LIST = (\n", - " \"BTC,ETH,USDT,BNB,USDC,XRP,LUNA,ADA,\"\n", - " + \"SOL,AVAX,DOT,BUSD,DOGE,UST,SHIB,WBTC,\"\n", - " + \"MATIC,CRO,DAI,LTC,ATOM,NEAR,LINK,BCH,\"\n", - " + \"UNI,TRX,FTT,ETC,LEO,ALGO,XLM,MANA,\"\n", - " + \"BTCB,HBAR,EGLD,ICP,SAND,XMR,WAVES,VET,\"\n", - " + \"APE,FIL,FTM,AXS,THETA,KLAY,XTZ,RUNE\"\n", - ").split(\",\")\n", - "exchange_rates = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "deeeb80a-3675-44fb-9a1b-2325d8da4e7d", - "metadata": {}, - "outputs": [], - "source": [ - "def get_exchange_rate(coin: str, days: int = 2):\n", - " \"\"\"Get exchange rate for a cryptocurrency agains USD.\"\"\"\n", - " (_, _, _, _, current_df, _,) = openbb.crypto.load(\n", - " coin=coin,\n", - " source=\"cp\",\n", - " days=days,\n", - " vs=\"usd\",\n", - " interval=\"1day\",\n", - " should_load_ta_data=True,\n", - " )\n", - " price_color = \"neutral_color\"\n", - " if current_df[\"Close\"].iloc[-1] > current_df[\"Close\"].iloc[-2]:\n", - " price_color = \"up_color\"\n", - " elif current_df[\"Close\"].iloc[-1] < current_df[\"Close\"].iloc[-2]:\n", - " price_color = \"down_color\"\n", - " price = str(current_df[\"Close\"].iloc[-1])[:7]\n", - "\n", - " return price, price_color" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18acf5cb-6cbf-484d-8042-107c030098de", - "metadata": {}, - "outputs": [], - "source": [ - "def show_rates(btn):\n", - " \"\"\"Show exchange rates in grid box widget.\"\"\"\n", - " grid.children = ()\n", - " for coin in COIN_LIST:\n", - " if coin not in exchange_rates.keys():\n", - " exchange_rates[coin] = {\"price\": None, \"color\": None}\n", - " try:\n", - " price, price_color = get_exchange_rate(coin=coin)\n", - " except:\n", - " price, price_color = \"-------\", \"neutral_color\"\n", - " widgets = list(grid.children)\n", - " widgets.append(\n", - " ipw.HTML(\n", - " openbb.widgets.price_card(\n", - " ticker=coin,\n", - " price=price,\n", - " price_color=price_color,\n", - " )\n", - " )\n", - " )\n", - " grid.children = tuple(widgets)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cf2021e9-de8a-466e-b341-fcd6abda64b5", - "metadata": {}, - "outputs": [], - "source": [ - "update_rates = ipw.Button(description=\"Update rates\")\n", - "update_rates.on_click(show_rates)\n", - "header = ipw.HBox([update_rates])\n", - "\n", - "layout = ipw.Layout(grid_template_columns=\"1fr \" * 8)\n", - "grid = ipw.GridBox(\n", - " [],\n", - " layout=layout,\n", - ")\n", - "\n", - "display(header, grid)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19dfcbfe-eaec-406e-bb3c-72d1b3538a6e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - }, - "voila": { - "theme": "dark" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/dashboards/currencies.ipynb b/openbb_terminal/dashboards/currencies.ipynb deleted file mode 100644 index ad612c6ce8e..00000000000 --- a/openbb_terminal/dashboards/currencies.ipynb +++ /dev/null @@ -1,284 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "da655985-c9b7-492e-aefa-fbe3bc1865d5", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import time\n", - "from datetime import datetime, timedelta\n", - "from typing import Optional\n", - "\n", - "import ipywidgets as ipw\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib_inline.backend_inline\n", - "from ipyflex import FlexLayout\n", - "from widget_helpers import price_card, stylesheet\n", - "\n", - "from openbb_terminal import api as openbb\n", - "\n", - "%matplotlib widget\n", - "openbb.theme.applyMPLstyle()\n", - "ipw.HTML(f\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5905453-4de6-4f5f-8012-9acdfca50987", - "metadata": {}, - "outputs": [], - "source": [ - "def get_exchange_rate(currency_pair: str) -> str:\n", - " \"\"\"Get exchange rate for a currency pair.\"\"\"\n", - " from_symbol, to_symbol = currency_pair.split(\"/\")\n", - " exchange_data = openbb.forex.models.av.get_quote(\n", - " to_symbol=to_symbol, from_symbol=from_symbol\n", - " )\n", - " return exchange_data[\"Realtime Currency Exchange Rate\"][\"5. Exchange Rate\"][:6]\n", - "\n", - "\n", - "def get_candle_widget(\n", - " output: Optional[ipw.Output],\n", - " to_symbol: str,\n", - " from_symbol: str,\n", - ") -> ipw.Output:\n", - " \"\"\"Plot a candle chart for a currency pair.\"\"\"\n", - " start_date = datetime.now() - timedelta(days=1)\n", - " start_date = start_date.strftime(\"%Y-%m-%d\")\n", - "\n", - " exchange_rate_data = openbb.forex.models.av.get_historical(\n", - " to_symbol=to_symbol,\n", - " from_symbol=from_symbol,\n", - " start_date=start_date,\n", - " resolution=\"i\",\n", - " interval=\"15\",\n", - " )\n", - " fig, ax = plt.subplots(1, 1, figsize=(11, 4))\n", - " openbb.forex.candle(\n", - " data=exchange_rate_data,\n", - " to_symbol=to_symbol,\n", - " from_symbol=from_symbol,\n", - " external_axes=[ax],\n", - " )\n", - "\n", - " ax.set_xlim(0, len(exchange_rate_data.index))\n", - " fig.canvas.header_visible = False\n", - " fig.canvas.footer_visible = False\n", - " with output:\n", - " output.clear_output(True)\n", - " fig.canvas.show()\n", - " return output\n", - "\n", - "\n", - "def on_dropdown_change(change):\n", - " \"\"\"Update charts on change of dropdown selection.\"\"\"\n", - " if change[\"type\"] == \"change\" and change[\"name\"] == \"value\":\n", - " output = ipw.Output()\n", - " get_candle_widget(\n", - " to_symbol=to_widget.value,\n", - " from_symbol=from_widget.value,\n", - " )\n", - " dashboard.children[\"Candle\"] = output" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5c768d15-37ad-441b-b069-506c4bdbb9a7", - "metadata": {}, - "outputs": [], - "source": [ - "exchange_rates = {\n", - " \"EUR/USD\": {\"latest\": None, \"previous\": None},\n", - " \"USD/JPY\": {\"latest\": None, \"previous\": None},\n", - " \"GBP/USD\": {\"latest\": None, \"previous\": None},\n", - " \"AUD/USD\": {\"latest\": None, \"previous\": None},\n", - " \"USD/CAD\": {\"latest\": None, \"previous\": None},\n", - " # \"USD/CNY\": {\"latest\": None, \"previous\": None},\n", - " # \"USD/CHF\": {\"latest\": None, \"previous\": None},\n", - " # \"USD/HKD\": {\"latest\": None, \"previous\": None},\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4968ab89-aafe-4234-933c-963bd672715d", - "metadata": {}, - "outputs": [], - "source": [ - "def compose_widgets():\n", - " widgets = {}\n", - " for currency_pair in exchange_rates:\n", - " price = exchange_rates[currency_pair][\"latest\"]\n", - " price_color = \"neutral_color\"\n", - " if exchange_rates[currency_pair][\"previous\"] is not None:\n", - " if (\n", - " exchange_rates[currency_pair][\"latest\"]\n", - " > exchange_rates[currency_pair][\"previous\"]\n", - " ):\n", - " price_color = \"up_color\"\n", - " elif (\n", - " exchange_rates[currency_pair][\"latest\"]\n", - " < exchange_rates[currency_pair][\"previous\"]\n", - " ):\n", - " price_color = \"down_color\"\n", - " widgets[currency_pair] = ipw.HTML(\n", - " price_card(ticker=currency_pair, price=price, price_color=price_color)\n", - " )\n", - " return widgets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f35d1500-a411-4cff-94ee-d65d4d1810f9", - "metadata": {}, - "outputs": [], - "source": [ - "# for currency_code in rates_to_usd:\n", - "# rates_to_usd[currency_code][\"previous\"] = rates_to_usd[currency_code][\"latest\"]\n", - "# rates_to_usd[currency_code][\"latest\"] = get_rate_against_usd(currency_code)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a72837b-4ddd-4c01-a493-ebb430202cbc", - "metadata": {}, - "outputs": [], - "source": [ - "widgets = compose_widgets()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c636543c-42fe-4dd6-a900-1552a3c28c7e", - "metadata": {}, - "outputs": [], - "source": [ - "currency_list = openbb.forex.models.av.get_currency_list()\n", - "from_widget = ipw.Dropdown(\n", - " options=currency_list,\n", - " value=\"EUR\",\n", - " description=\"From:\",\n", - " disabled=False,\n", - " layout=ipw.Layout(margin=\"130\"),\n", - ")\n", - "to_widget = ipw.Dropdown(\n", - " options=currency_list,\n", - " value=\"USD\",\n", - " description=\"To:\",\n", - " disabled=False,\n", - ")\n", - "exchange_selection = ipw.VBox([from_widget, to_widget])\n", - "\n", - "widgets[\"Select\"] = exchange_selection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9d069845-d622-4878-8b18-b385c6cb7a7f", - "metadata": {}, - "outputs": [], - "source": [ - "output = ipw.Output()\n", - "output = get_candle_widget(\n", - " output=output,\n", - " to_symbol=to_widget.value,\n", - " from_symbol=from_widget.value,\n", - ")\n", - "widgets[\"Candle\"] = output" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "81c7e5c7-2e52-45ba-bf7d-aa240f95be2d", - "metadata": {}, - "outputs": [], - "source": [ - "from_widget.observe(on_dropdown_change)\n", - "to_widget.observe(on_dropdown_change)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a0c7d921-e5dc-4ce8-97f1-a59b414a676a", - "metadata": {}, - "outputs": [], - "source": [ - "dashboard = FlexLayout(\n", - " layout_config={\"borderLeft\": False, \"borderRight\": False, \"enableSection\": False},\n", - " style={\n", - " \"height\": \"calc(100vh - 80px)\",\n", - " \"backgroundColor\": \"rgb(0 0 0)\",\n", - " \"fontFamily\": \"Consolas\",\n", - " \"fontWeight\": 800,\n", - " },\n", - " header={\n", - " \"title\": \"Currencies\",\n", - " \"style\": {\n", - " \"backgroundColor\": \"rgb(0 0 0)\",\n", - " \"fontWeight\": 400,\n", - " \"fontSize\": \"28px\",\n", - " },\n", - " \"buttons\": [],\n", - " },\n", - " widgets=widgets,\n", - " template=os.path.join(\"widgets\", \"currencies.json\"),\n", - " editable=False,\n", - ")\n", - "dashboard" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5fedac44-5e09-44f0-bbbe-fbad7c8716cd", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3c850ba2-408b-4061-947e-5a1b8a284d22", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - }, - "voila": { - "theme": "dark" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/dashboards/dashboards_controller.py b/openbb_terminal/dashboards/dashboards_controller.py index 7a2e5748cc7..361e6d3efc9 100644 --- a/openbb_terminal/dashboards/dashboards_controller.py +++ b/openbb_terminal/dashboards/dashboards_controller.py @@ -33,6 +33,8 @@ class DashboardsController(BaseController): "shortdata", "crypto", "futures", + "forecast", + "forecasting", ] PATH = "/dashboards/" @@ -51,6 +53,7 @@ class DashboardsController(BaseController): def print_help(self): """Print help""" mt = MenuText("dashboards/") + mt.add_raw("\nVoila Apps:\n") mt.add_cmd("stocks") mt.add_cmd("correlation") mt.add_cmd("vsurf") @@ -58,45 +61,58 @@ class DashboardsController(BaseController): mt.add_cmd("shortdata") mt.add_cmd("crypto") mt.add_cmd("futures") + mt.add_cmd("forecast") + mt.add_raw("\nStreamlit Apps:\n") + mt.add_cmd("forecasting") console.print(text=mt.menu_text, menu="Dashboards") @log_start_end(log=logger) def call_stocks(self, other_args: List[str]): """Process stocks command""" - create_call(other_args, "stocks", "stocks") + create_call_voila(other_args, "stocks", "stocks") @log_start_end(log=logger) def call_correlation(self, other_args: List[str]): """Process correlation command""" - create_call(other_args, "correlation", "correlation") + create_call_voila(other_args, "correlation", "correlation") @log_start_end(log=logger) def call_vsurf(self, other_args: List[str]): """Process vsurf command""" - create_call(other_args, "vsurf", "") + create_call_voila(other_args, "vsurf", "") @log_start_end(log=logger) def call_chains(self, other_args: List[str]): """Process chains command""" - create_call(other_args, "chains", "") + create_call_voila(other_args, "chains", "") @log_start_end(log=logger) def call_shortdata(self, other_args: List[str]): """Process shortdata command""" - create_call(other_args, "shortdata", "") + create_call_voila(other_args, "shortdata", "") @log_start_end(log=logger) def call_crypto(self, other_args: List[str]): """Process crypto command""" - create_call(other_args, "crypto", "") + create_call_voila(other_args, "crypto", "") @log_start_end(log=logger) def call_futures(self, other_args: List[str]): """Process futures command""" - create_call(other_args, "futures", "") + create_call_voila(other_args, "futures", "") + + @log_start_end(log=logger) + def call_forecast(self, other_args: List[str]): + """Process forecast command""" + create_call_voila(other_args, "forecast", "") + + @log_start_end(log=logger) + def call_forecasting(self, other_args: List[str]): + """Process forecasting command""" + create_call_streamlit(other_args, "forecast") -def create_call(other_args: List[str], name: str, filename: str = None) -> None: +def create_call_voila(other_args: List[str], name: str, filename: str = None) -> None: filename = filename if filename else name parser = argparse.ArgumentParser( @@ -129,14 +145,12 @@ def create_call(other_args: List[str], name: str, filename: str = None) -> None: dest="dark", help="Whether to show voila in dark mode", ) - ns_parser = parse_simple_args(parser, other_args) if ns_parser: cmd = "jupyter-lab" if ns_parser.jupyter else "voila" - file = os.path.join( - os.path.abspath(os.path.dirname(__file__)), f"{filename}.ipynb" - ) + base_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), "voila") + file = os.path.join(base_path, f"{filename}.ipynb") if not ns_parser.input: console.print( f"Warning: opens a port on your computer to run a {cmd} server." @@ -153,5 +167,48 @@ def create_call(other_args: List[str], name: str, filename: str = None) -> None: shell=True, # nosec ) else: - console.print(f"Type: {cmd} {file}\ninto a terminal to run.") + console.print(f"Type: {cmd} voila/{file}\ninto a terminal to run.") + console.print("") + + +def create_call_streamlit( + other_args: List[str], name: str, filename: str = None +) -> None: + filename = filename if filename else name + + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog=name, + description=f"""Shows streamlit {name} dashboard""", + ) + parser.add_argument( + "-n", + "--no-input", + action="store_true", + default=False, + dest="input", + help="Skips confirmation to run server.", + ) + ns_parser = parse_simple_args(parser, other_args) + + if ns_parser: + cmd = "streamlit run" + base_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), "stream") + file = os.path.join(base_path, f"{filename}.py") + if not ns_parser.input: + console.print( + f"Warning: opens a port on your computer to run a {cmd} server." + ) + response = input("Would you like us to run the server for you? y/n\n") + args = "" + if ns_parser.input or response.lower() == "y": + subprocess.Popen( + f"{cmd} {file} {args}", + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + shell=True, + ) + else: + console.print(f"Type: {cmd} stream/{file}\ninto a terminal to run.") console.print("") diff --git a/openbb_terminal/dashboards/futures.ipynb b/openbb_terminal/dashboards/futures.ipynb deleted file mode 100644 index 375ca9da511..00000000000 --- a/openbb_terminal/dashboards/futures.ipynb +++ /dev/null @@ -1,321 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "e9ad60bc", - "metadata": {}, - "outputs": [], - "source": [ - "import os, sys\n", - "import calendar\n", - "from datetime import datetime, timedelta\n", - "import pandas as pd\n", - "import ipywidgets as widgets\n", - "import plotly.graph_objs as go\n", - "import yfinance as yf\n", - "import pandas as pd\n", - "from IPython.display import display\n", - "from pathlib import Path\n", - "\n", - "from openbb_terminal.core.config.paths import REPOSITORY_DIRECTORY" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "01e1841a-ab16-4111-9b45-8ac66b85ab9f", - "metadata": {}, - "outputs": [], - "source": [ - "full_path = REPOSITORY_DIRECTORY / \"custom_imports\" / \"dashboards\" / \"futures.csv\"\n", - "df = pd.read_csv(full_path)\n", - "months = [\"F\", \"G\", \"H\", \"J\", \"K\", \"M\", \"N\", \"Q\", \"U\", \"V\", \"X\", \"Z\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "beb36881", - "metadata": {}, - "outputs": [], - "source": [ - "import os, sys\n", - "\n", - "\n", - "class HiddenPrints:\n", - " def __enter__(self):\n", - " self._original_stdout = sys.stdout\n", - " sys.stdout = open(os.devnull, \"w\")\n", - "\n", - " def __exit__(self, exc_type, exc_val, exc_tb):\n", - " sys.stdout.close()\n", - " sys.stdout = self._original_stdout\n", - "\n", - "\n", - "def format_plotly(fig, data, chart, calc=None):\n", - " fig.update_yaxes(title=None)\n", - " fig.update_xaxes(title=None)\n", - " height = 500 if chart == \"main\" else 300\n", - " fig.update_layout(\n", - " margin=dict(l=0, r=10, t=10, b=10),\n", - " autosize=False,\n", - " width=900,\n", - " height=height,\n", - " legend=dict(orientation=\"h\"),\n", - " title={\n", - " # \"text\": fig_title,\n", - " \"y\": 0.95,\n", - " \"x\": 0.5,\n", - " \"xanchor\": \"center\",\n", - " \"yanchor\": \"top\",\n", - " },\n", - " )\n", - "\n", - "\n", - "def create_line(visual, x, y, name, fig):\n", - " if visual == \"line\":\n", - " plot = go.Scatter(x=x, y=y, mode=\"lines\", name=name) # , connectgaps=True\n", - " if visual == \"scatter\":\n", - " plot = go.Scatter(x=x, y=y, mode=\"markers\", name=name)\n", - " if visual == \"candle\":\n", - " plot = go.Candlestick(\n", - " x=x,\n", - " open=y[\"Open\"],\n", - " close=y[\"Close\"],\n", - " high=y[\"High\"],\n", - " low=y[\"Low\"],\n", - " name=name,\n", - " )\n", - " fig.add_trace(plot)\n", - "\n", - "\n", - "def show_fig(fig):\n", - " config = {\"showTips\": False, \"scrollZoom\": True}\n", - " if os.environ.get(\"SERVER_SOFTWARE\", \"jupyter\").startswith(\"voila\"):\n", - " fig.show(config=config, renderer=\"notebook\")\n", - " else:\n", - " fig.show(config=config)\n", - "\n", - "\n", - "def build_ticker(ticker: str, month: int, year: int) -> str:\n", - " if ticker:\n", - " row = df[df[\"Ticker\"] == f\"{ticker.split(':')[0]}=F\"].iloc[0]\n", - " row = row.to_dict()\n", - " the_tick = row[\"Ticker\"].replace(\"=F\", \"\")\n", - " return f\"{the_tick}{months[month-1]}{str(year)[-2:]}.{row['Exchange']}\"\n", - " return \"\"\n", - "\n", - "\n", - "def next_ticker(ticker: str) -> str:\n", - " symbol, exchange = ticker.split(\".\")\n", - " month = symbol[-3]\n", - " if month == \"Z\":\n", - " new_month = \"F\"\n", - " new_year = int(symbol[-2:]) + 1\n", - " else:\n", - " index = months.index(month)\n", - " new_month = months[index + 1]\n", - " new_year = int(symbol[-2:])\n", - " return f\"{symbol[:-3]}{new_month}{new_year}.{exchange}\"\n", - "\n", - "\n", - "def get_column(df: pd.DataFrame, column: str):\n", - " sub_df = df.xs(column, level=1, axis=1, drop_level=False)\n", - " sub_df.columns = sub_df.columns.to_flat_index().map(lambda x: x[0])\n", - " sub_df = sub_df.dropna(how=\"all\")\n", - " sub_df = sub_df.sort_index()\n", - " if sub_df.empty:\n", - " return None\n", - " return sub_df\n", - "\n", - "\n", - "def get_date(x: str) -> datetime:\n", - " ticker, _ = x.split(\".\")\n", - " month_str = ticker[-3]\n", - " month = months.index(month_str) + 1\n", - " year = int(ticker[-2:]) + 2000\n", - " day = calendar.monthrange(year, month)\n", - " return datetime(year, month, day[1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8c2f65c", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "class Chart:\n", - " def __init__(self):\n", - " self.tickers = {}\n", - " self.last_ticker = \"\"\n", - "\n", - " def create_stock(self, chart_type, contracts, ticker):\n", - " if not ticker:\n", - " return\n", - "\n", - " if self.last_ticker != ticker:\n", - " now = datetime.now()\n", - " clean_ticker = build_ticker(ticker, now.month, now.year)\n", - " if clean_ticker:\n", - " raw_tickers = [clean_ticker]\n", - " for _ in range(36):\n", - " new_ticker = next_ticker(raw_tickers[-1])\n", - " raw_tickers.append(new_ticker)\n", - " raw_ticker = \",\".join(raw_tickers)\n", - " with HiddenPrints():\n", - " dfs = yf.download(raw_ticker, progress=False) # ,period=\"max\"\n", - " self.tickers = {\n", - " x: {\"data\": get_column(dfs, x), \"date\": get_date(x)}\n", - " for x in raw_tickers\n", - " }\n", - " self.last_ticker = ticker\n", - "\n", - " fig = go.Figure()\n", - " i = 0\n", - " if chart_type[0][:4] == \"Hist\":\n", - " for key, item in self.tickers.items():\n", - " if i == contracts:\n", - " break\n", - " result = item[\"data\"]\n", - " if result is not None:\n", - " create_line(\n", - " visual=\"line\",\n", - " x=result.index,\n", - " y=result[\"Adj Close\"],\n", - " name=key,\n", - " fig=fig,\n", - " )\n", - " i += 1\n", - " else:\n", - " x = []\n", - " y = []\n", - " for _, value in self.tickers.items():\n", - " if len(x) > contracts:\n", - " break\n", - " if value[\"data\"] is None:\n", - " continue\n", - " x.append(value[\"date\"])\n", - " y.append(value[\"data\"][\"Adj Close\"].iloc[-1])\n", - " create_line(visual=\"line\", x=x, y=y, name=\"Future Curve\", fig=fig)\n", - "\n", - " format_plotly(fig, \"Close\", \"main\", \"raw\")\n", - " show_fig(fig)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e0d4de31-1b4f-4c1f-8467-ca314a578d56", - "metadata": {}, - "outputs": [], - "source": [ - "category = df[\"Category\"].unique().tolist()\n", - "exchange = df[df[\"Category\"].isin([category[0]])][\"Exchange\"].unique().tolist()\n", - "filt_df = df[df[\"Category\"].isin([category[0]]) & df[\"Exchange\"].isin([exchange[0]])]\n", - "tickers_raw = filt_df[[\"Ticker\", \"Description\"]].values.tolist()\n", - "tickers = [f\"{x.replace('=F', '')}: {y}\" for x, y in tickers_raw]\n", - "\n", - "category.sort()\n", - "exchange.sort()\n", - "tickers.sort()\n", - "\n", - "chart_type = [\"Historical Time Series\", \"Future Curve\"]\n", - "cat_widget = widgets.SelectMultiple(options=category, value=[category[0]])\n", - "exch_widget = widgets.SelectMultiple(options=exchange, value=[exchange[0]])\n", - "tickers_widget = widgets.Select(options=tickers, value=tickers[0])\n", - "chart_widget = widgets.SelectMultiple(options=chart_type, value=[chart_type[0]])\n", - "\n", - "\n", - "def on_change(change):\n", - " cat_value = cat_widget.value\n", - " exch_value = exch_widget.value\n", - "\n", - " # Filter exchange widget\n", - " exch_filtered = df[df[\"Category\"].isin(cat_value)][\"Exchange\"].unique().tolist()\n", - " exch_filtered.sort()\n", - " exch_widget.options = exch_filtered\n", - "\n", - " # Filter ticker widget\n", - " filtered = df[df[\"Category\"].isin(cat_value) & df[\"Exchange\"].isin(exch_value)]\n", - " tick_raw = filtered[[\"Ticker\", \"Description\"]].values.tolist()\n", - " tick = [f\"{x.replace('=F', '')}: {y}\" for x, y in tick_raw]\n", - " tick.sort()\n", - " tickers_widget.options = tick\n", - "\n", - "\n", - "cat_widget.observe(on_change)\n", - "exch_widget.observe(on_change)\n", - "\n", - "contracts_widget = widgets.Dropdown(\n", - " options=list(range(1, 25)), value=6, description=\"Contracts\"\n", - ")\n", - "\n", - "controls = widgets.HBox(\n", - " [cat_widget, exch_widget, tickers_widget, chart_widget, contracts_widget],\n", - " layout=widgets.Layout(width=\"90%\"),\n", - ")\n", - "chart = Chart()\n", - "stocks_view = widgets.interactive_output(\n", - " chart.create_stock,\n", - " {\n", - " \"chart_type\": chart_widget,\n", - " \"contracts\": contracts_widget,\n", - " \"ticker\": tickers_widget,\n", - " },\n", - ")\n", - "\n", - "\n", - "title_html = \"

Futures Analysis Dashboard

\"\n", - "\n", - "app_contents = [widgets.HTML(title_html), controls, stocks_view]\n", - "app = widgets.VBox(app_contents)\n", - "display(app)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6e4c3424-91c9-47c5-b336-fe8e40944905", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - }, - "metadata": { - "interpreter": { - "hash": "e896a00ead8b521528d79ac9ef24990696f2b751eb283ab8e0d078c9c4971ffc" - } - }, - "vscode": { - "interpreter": { - "hash": "ab0e87f2bc73919b75956d1b3e303320699d53da895be1bb46287d7dc67c6497" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/dashboards/shortdata.ipynb b/openbb_terminal/dashboards/shortdata.ipynb deleted file mode 100644 index 4fc17ccc2aa..00000000000 --- a/openbb_terminal/dashboards/shortdata.ipynb +++ /dev/null @@ -1,236 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "import io\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import requests\n", - "from datetime import datetime, timedelta\n", - "import ipywidgets as widgets\n", - "from IPython.display import display\n", - "\n", - "%matplotlib widget" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "outputs_hidden": false - }, - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "class ShortDataDash:\n", - " def __init__(self):\n", - " self.df = pd.DataFrame()\n", - " self.days_slider = widgets.IntSlider(\n", - " value=5,\n", - " min=5,\n", - " max=252,\n", - " step=1,\n", - " description=\"Days Back\",\n", - " style={\"description_width\": \"initial\"},\n", - " )\n", - " self.count_slider = widgets.IntSlider(\n", - " value=10,\n", - " min=1,\n", - " max=25,\n", - " step=1,\n", - " description=\"Number to show.\",\n", - " style={\"description_width\": \"initial\"},\n", - " )\n", - " self.output1 = widgets.Output()\n", - " self.output2 = widgets.Output()\n", - " self.load_button = widgets.Button(\n", - " description=\"Load Data\", layout=widgets.Layout(width=\"200px\", height=\"40px\")\n", - " )\n", - " self.load_button.on_click(self.load_button_click)\n", - " self.show_button = widgets.Button(\n", - " description=\"Change Number Shown\", layout=self.load_button.layout\n", - " )\n", - " self.show_button.on_click(self.show_button_click)\n", - "\n", - " self.slider_box = widgets.HBox([self.days_slider, self.count_slider])\n", - " self.button_box = widgets.VBox([self.load_button, self.show_button])\n", - "\n", - " self.stock_input = widgets.Text(\n", - " value=\"GME\",\n", - " placeholder=\"GME\",\n", - " description=\"Ticker:\",\n", - " )\n", - " self.ticker_button = widgets.Button(description=\"Plot Ticker\")\n", - " self.ticker_button.on_click(self.ticker_button_click)\n", - "\n", - " def show_button_click(self, b):\n", - " self.output1.clear_output()\n", - " with self.output1:\n", - " self.update()\n", - "\n", - " def load_button_click(self, b):\n", - " self.output1.clear_output()\n", - " self.output2.clear_output()\n", - " with self.output1:\n", - " print(f\"Data Loading for {self.days_slider.value} days\")\n", - " self.fetch_new_data()\n", - " self.update()\n", - "\n", - " def ticker_button_click(self, b):\n", - " self.output2.clear_output()\n", - " with self.output2:\n", - " self.ticker_plot()\n", - "\n", - " def fetch_new_data(self):\n", - " self.df = pd.DataFrame()\n", - " today = datetime.now().date()\n", - " idx = 0\n", - " len_df = 0\n", - " while len_df < self.days_slider.value:\n", - " date = today - timedelta(days=idx)\n", - " r = requests.get(\n", - " f\"https://cdn.finra.org/equity/regsho/daily/CNMSshvol{date.strftime('%Y%m%d')}.txt\"\n", - " )\n", - " if r.status_code == 200:\n", - " self.df = pd.concat(\n", - " [self.df, pd.read_csv(io.StringIO(r.text), sep=\"|\")], axis=0\n", - " )\n", - " len_df += 1\n", - " idx += 1\n", - " self.df = self.df[self.df.Date > 20100101]\n", - " self.df.Date = self.df[\"Date\"].apply(\n", - " lambda x: datetime.strptime(str(x), \"%Y%m%d\")\n", - " )\n", - "\n", - " def update(self):\n", - " if not self.df.empty:\n", - " temp = (\n", - " self.df.groupby(\"Symbol\")[[\"ShortVolume\", \"TotalVolume\"]]\n", - " .agg(\"sum\")\n", - " .sort_values(by=\"ShortVolume\", ascending=False)\n", - " .head(self.count_slider.value)[::-1]\n", - " )\n", - " self.fig, self.ax = plt.subplots(figsize=(6, 6))\n", - " self.ax.barh(temp.index, temp.TotalVolume, alpha=0.4, label=\"Total Volume\")\n", - " self.ax.barh(temp.index, temp.ShortVolume, label=\"Short Volume\")\n", - " self.ax.set_title(\n", - " f\"Top {self.count_slider.value} Short Volume in Last {self.days_slider.value} Days\"\n", - " )\n", - " self.ax.legend()\n", - " self.fig.tight_layout()\n", - " plt.show()\n", - "\n", - " def ticker_plot(self):\n", - " stock_data = self.df.copy().loc[\n", - " self.df.Symbol == self.stock_input.value,\n", - " [\"Date\", \"ShortVolume\", \"TotalVolume\"],\n", - " ]\n", - " self.fig2, self.ax2 = plt.subplots(figsize=(6, 6))\n", - " self.ax2.plot(\n", - " stock_data.Date, stock_data.TotalVolume, alpha=0.4, label=\"Total Volume\"\n", - " )\n", - " self.ax2.plot(stock_data.Date, stock_data.ShortVolume, label=\"Short Volume\")\n", - " self.ax2.set_title(\n", - " f\"Stock Volume and Short Volume for {self.stock_input.value.upper()}\"\n", - " )\n", - " self.ax2.legend()\n", - " self.fig2.autofmt_xdate()\n", - " self.fig2.tight_layout()\n", - " plt.show()\n", - "\n", - " def build_app(self):\n", - " title_html = \"\"\"\n", - "

Finra Short Data

\n", - "

This widget downloads the consolidated NMS short data from FINRA and aggregates the data by summing over the entire time period.

\n", - "

Note that clicking the 'Load Data' button will reload all data. This can get time consuming, so if you pick a few hundred days, expect a few minutes for loading time.

\n", - "\"\"\"\n", - " middle_html = \"\"\"\n", - "Here we allow the user to query for a single stock. This will work with the loaded data. Note that if you want to reload the data, this will once again take some time.\n", - " \"\"\"\n", - " return [\n", - " widgets.HTML(\n", - " title_html, layout=widgets.Layout(margin=\"0 0 3em 0\", max_width=\"800px\")\n", - " ),\n", - " self.slider_box,\n", - " self.button_box,\n", - " self.output1,\n", - " widgets.HTML(\n", - " middle_html,\n", - " layout=widgets.Layout(margin=\"0 0 3em 0\", max_width=\"800px\"),\n", - " ),\n", - " self.stock_input,\n", - " self.ticker_button,\n", - " self.output2,\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "outputs_hidden": false - }, - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "dash = ShortDataDash()\n", - "app = widgets.VBox(\n", - " dash.build_app(), layout=widgets.Layout(max_width=\"1024px\", margin=\"0 auto 0 auto\")\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "outputs_hidden": false - }, - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "display(app)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/openbb_terminal/dashboards/stocks.ipynb b/openbb_terminal/dashboards/stocks.ipynb deleted file mode 100644 index dcdafd3df9c..00000000000 --- a/openbb_terminal/dashboards/stocks.ipynb +++ /dev/null @@ -1,404 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "e9ad60bc", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from datetime import datetime, timedelta\n", - "import ipywidgets as widgets\n", - "import plotly.graph_objs as go\n", - "import yfinance as yf\n", - "import pandas as pd\n", - "from IPython.display import display" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "beb36881", - "metadata": {}, - "outputs": [], - "source": [ - "interval_opts = [\n", - " \"1m\",\n", - " \"2m\",\n", - " \"5m\",\n", - " \"15m\",\n", - " \"30m\",\n", - " \"60m\",\n", - " \"90m\",\n", - " \"1h\",\n", - " \"1d\",\n", - " \"5d\",\n", - " \"1wk\",\n", - " \"1mo\",\n", - " \"3mo\",\n", - "]\n", - "\n", - "rows = [\n", - " \"sector\",\n", - " \"marketCap\",\n", - " \"beta\",\n", - " \"fiftyTwoWeekHigh\",\n", - " \"fiftyTwoWeekLow\",\n", - " \"floatShares\",\n", - " \"sharesShort\",\n", - " \"exDividendDate\",\n", - "]\n", - "\n", - "views = {\n", - " \"Raw Data\": lambda x, y: x,\n", - " \"Percent Change\": lambda x, y: x.pct_change(),\n", - " \"Rolling Average\": lambda x, y: x.rolling(y).mean(),\n", - " \"Rolling Variance\": lambda x, y: x.rolling(y).var(),\n", - " \"Rolling Standard Deviation\": lambda x, y: x.rolling(y).var() ** 0.5,\n", - " \"Rolling Coefficient of Variation\": lambda x, y: (x.rolling(y).var() ** 0.5)\n", - " / (x.rolling(y).mean()),\n", - "}\n", - "\n", - "clean_row = {\n", - " \"sector\": \"Sector\",\n", - " \"marketCap\": \"M Cap\",\n", - " \"beta\": \"Beta\",\n", - " \"fiftyTwoWeekHigh\": \"52W High\",\n", - " \"fiftyTwoWeekLow\": \"52W Low\",\n", - " \"floatShares\": \"Floats\",\n", - " \"sharesShort\": \"Shorts\",\n", - " \"exDividendDate\": \"Ex-Div\",\n", - "}\n", - "\n", - "clean_data = {\n", - " \"sector\": lambda x: \"N/A\" if x is None else x,\n", - " \"marketCap\": lambda x: \"N/A\" if x is None else big_num(x),\n", - " \"beta\": lambda x: \"N/A\" if x is None else f\"{round(x,2)}\",\n", - " \"fiftyTwoWeekHigh\": lambda x: \"N/A\" if x is None else f\"${round(x,2)}\",\n", - " \"fiftyTwoWeekLow\": lambda x: \"N/A\" if x is None else f\"${round(x,2)}\",\n", - " \"floatShares\": lambda x: \"N/A\" if x is None else big_num(x),\n", - " \"sharesShort\": lambda x: \"N/A\" if x is None else big_num(x),\n", - " \"exDividendDate\": lambda x: \"N/A\"\n", - " if x is None\n", - " else datetime.fromtimestamp(x).strftime(\"%Y/%m/%d\"),\n", - "}\n", - "\n", - "\n", - "def big_num(num):\n", - " if num > 1_000_000_000_000:\n", - " return f\"{round(num/1_000_000_000_000,2)}T\"\n", - " if num > 1_000_000_000:\n", - " return f\"{round(num/1_000_000_000,2)}B\"\n", - " if num > 1_000_000:\n", - " return f\"{round(num/1_000_000,2)}M\"\n", - " if num > 1_000:\n", - " return f\"{num/round(1_000,2)}K\"\n", - " return f\"{round(num,2)}\"\n", - "\n", - "\n", - "def clean_str(string):\n", - " new_str = \"\"\n", - " for letter in string:\n", - " if letter.isupper():\n", - " new_str += \" \"\n", - " new_str += letter\n", - " return new_str.title()\n", - "\n", - "\n", - "def format_plotly(fig, data, start, end, chart, calc=None):\n", - " fig.update_yaxes(title=None)\n", - " fig.update_xaxes(title=None)\n", - " start_t = start.strftime(\"%Y/%m/%d\")\n", - " end_t = end.strftime(\"%Y/%m/%d\")\n", - " if calc:\n", - " if len(calc) == 1:\n", - " fig_title = f\"{calc[0]} of {data} from {start_t} to {end_t}\"\n", - " else:\n", - " fig_title = f\"{', '.join(calc)} of {data} from {start_t} to {end_t}\"\n", - " else:\n", - " fig_title = \"Volume\"\n", - " height = 500 if chart == \"main\" else 300\n", - " fig.update_layout(\n", - " margin=dict(l=0, r=10, t=10, b=10),\n", - " autosize=False,\n", - " width=900,\n", - " height=height,\n", - " legend=dict(orientation=\"h\"),\n", - " title={\n", - " \"text\": fig_title,\n", - " \"y\": 0.95,\n", - " \"x\": 0.5,\n", - " \"xanchor\": \"center\",\n", - " \"yanchor\": \"top\",\n", - " },\n", - " )\n", - "\n", - "\n", - "def create_line(visual, x, y, name, data, fig):\n", - " if visual == \"line\":\n", - " plot = go.Scatter(x=x, y=y[data], mode=\"lines\", name=name, connectgaps=True)\n", - " if visual == \"scatter\":\n", - " plot = go.Scatter(x=x, y=y[data], mode=\"markers\", name=name)\n", - " if visual == \"candle\":\n", - " plot = go.Candlestick(\n", - " x=x,\n", - " open=y[\"Open\"],\n", - " close=y[\"Close\"],\n", - " high=y[\"High\"],\n", - " low=y[\"Low\"],\n", - " name=name,\n", - " )\n", - " fig.add_trace(plot)\n", - "\n", - "\n", - "def show_fig(fig):\n", - " config = {\"showTips\": False, \"scrollZoom\": True}\n", - " if os.environ.get(\"SERVER_SOFTWARE\", \"jupyter\").startswith(\"voila\"):\n", - " fig.show(config=config, renderer=\"notebook\")\n", - " else:\n", - " fig.show(config=config)\n", - "\n", - "\n", - "def table_data(infos):\n", - " cols = [\"Ticker\"] + list(infos)\n", - " data = pd.DataFrame(columns=cols)\n", - " data[\"Ticker\"] = [clean_row[x] for x in rows]\n", - " for ticker in list(infos):\n", - " data[ticker] = [clean_data[x](infos[ticker].get(x, None)) for x in rows]\n", - " new_cols = {k: clean_str(k) for k in rows}\n", - " return data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8c2f65c", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "class Chart:\n", - " def __init__(self):\n", - " self.last_tickers = \"\"\n", - " self.last_interval = \"1d\"\n", - " self.df = pd.DataFrame()\n", - " self.infos = {}\n", - "\n", - " def create_stock(\n", - " self, calculation, data, rolling, start, end, interval, tickers, chart\n", - " ):\n", - " if tickers and tickers[-1] == \",\":\n", - " if tickers != self.last_tickers or interval != self.last_interval:\n", - " if interval in [\"1d\", \"5d\", \"1wk\", \"1mo\", \"3mo\"]:\n", - " self.df = yf.download(\n", - " tickers, period=\"max\", interval=interval, progress=False\n", - " )\n", - " else:\n", - " end_date = end + timedelta(days=1)\n", - " self.df = yf.download(\n", - " tickers,\n", - " start=start,\n", - " end=end_date,\n", - " interval=interval,\n", - " progress=False,\n", - " )\n", - " self.last_tickers = tickers\n", - " self.last_interval = interval\n", - "\n", - " start_n = datetime(start.year, start.month, start.day)\n", - " end_n = datetime(end.year, end.month, end.day)\n", - " fig = go.Figure()\n", - " for item in calculation:\n", - " calcs = views[item](self.df, rolling)\n", - " if interval in [\"1d\", \"5d\", \"1wk\", \"1mo\", \"3mo\"]:\n", - " result = calcs.loc[\n", - " (calcs.index >= start_n) & (calcs.index <= end_n)\n", - " ]\n", - " else:\n", - " result = calcs\n", - "\n", - " if len(result.columns) == 6:\n", - " name = f\"{tickers.split(',')[0]} {item}\"\n", - " create_line(chart, result.index, result, name, data, fig)\n", - "\n", - " else:\n", - " for val in result.columns.levels[1]:\n", - " vals = result.xs(val, axis=1, level=1, drop_level=True)\n", - " name = f\"{val.upper()} {item}\"\n", - " create_line(chart, result.index, vals, name, data, fig)\n", - "\n", - " format_plotly(fig, data, start, end, \"main\", calculation)\n", - " show_fig(fig)\n", - "\n", - " def create_volume(self, start, end, interval, tickers):\n", - " start_n = datetime(start.year, start.month, start.day)\n", - " end_n = datetime(end.year, end.month, end.day)\n", - " result = self.df.loc[(self.df.index >= start_n) & (self.df.index <= end_n)]\n", - " fig = go.Figure()\n", - " if len(result.columns) == 6:\n", - " name = f\"{tickers.split(',')[0]}\"\n", - " create_line(\"line\", result.index, result, name, \"Volume\", fig)\n", - " else:\n", - " for val in result.columns.levels[1]:\n", - " vals = result.xs(val, axis=1, level=1, drop_level=True)\n", - " name = f\"{val.upper()}\"\n", - " create_line(\"line\", result.index, vals, name, \"Volume\", fig)\n", - " format_plotly(fig, \"Volume\", start, end, \"volume\")\n", - " show_fig(fig)\n", - "\n", - " def create_table(self, tickers):\n", - " if tickers and tickers[-1] == \",\":\n", - " clean_tickers = [x for x in tickers.split(\",\") if x]\n", - " for ticker in clean_tickers:\n", - " if ticker not in self.infos:\n", - " self.infos[ticker] = yf.Ticker(ticker).info\n", - " for ticker in self.infos:\n", - " if ticker not in tickers:\n", - " self.infos.pop(ticker)\n", - " result = table_data(self.infos)\n", - " fig = go.Figure(\n", - " data=[\n", - " go.Table(\n", - " header=dict(\n", - " values=result.columns,\n", - " fill_color=\"lightgray\",\n", - " font=dict(color=\"black\"),\n", - " align=\"left\",\n", - " ),\n", - " cells=dict(\n", - " values=[result[x] for x in result.columns],\n", - " font=dict(color=\"black\"),\n", - " align=\"left\",\n", - " ),\n", - " )\n", - " ],\n", - " )\n", - " fig.update_layout(margin=dict(l=0, r=20, t=0, b=0), width=350)\n", - " show_fig(fig)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e0d4de31-1b4f-4c1f-8467-ca314a578d56", - "metadata": {}, - "outputs": [], - "source": [ - "w_auto = widgets.Layout(width=\"auto\")\n", - "calc_widget = widgets.SelectMultiple(\n", - " options=list(views.keys()), value=[\"Raw Data\"], layout=w_auto\n", - ")\n", - "\n", - "data_opts = [\"Open\", \"Close\", \"High\", \"Low\"]\n", - "data_widget = widgets.Dropdown(\n", - " options=data_opts, value=\"Close\", layout=w_auto, description=\"Data\"\n", - ")\n", - "rolling_widget = widgets.Dropdown(\n", - " options=list(range(2, 101)), value=60, layout=w_auto, description=\"Rolling\"\n", - ")\n", - "\n", - "base_date = (datetime.today() - timedelta(days=365)).date()\n", - "start_widget = widgets.DatePicker(value=base_date, layout=w_auto, description=\"Start\")\n", - "end_widget = widgets.DatePicker(\n", - " value=datetime.today().date(), layout=w_auto, description=\"End\"\n", - ")\n", - "\n", - "interval_widget = widgets.Dropdown(\n", - " options=interval_opts, value=\"1d\", layout=w_auto, description=\"Interval\"\n", - ")\n", - "tickers_widget = widgets.Textarea(\n", - " value=\"TSLA,\", layout=widgets.Layout(width=\"auto\", height=\"100%\")\n", - ")\n", - "chart_opts = [\"line\", \"scatter\", \"candle\"]\n", - "chart_widget = widgets.Dropdown(\n", - " options=chart_opts, value=\"line\", layout=w_auto, description=\"Chart\"\n", - ")\n", - "data_box = widgets.VBox([data_widget, rolling_widget, chart_widget])\n", - "date_box = widgets.VBox([start_widget, end_widget, interval_widget])\n", - "controls = widgets.HBox(\n", - " [tickers_widget, calc_widget, date_box, data_box],\n", - " layout=widgets.Layout(width=\"90%\"),\n", - ")\n", - "chart = Chart()\n", - "stocks_view = widgets.interactive_output(\n", - " chart.create_stock,\n", - " {\n", - " \"calculation\": calc_widget,\n", - " \"data\": data_widget,\n", - " \"rolling\": rolling_widget,\n", - " \"start\": start_widget,\n", - " \"end\": end_widget,\n", - " \"interval\": interval_widget,\n", - " \"tickers\": tickers_widget,\n", - " \"chart\": chart_widget,\n", - " },\n", - ")\n", - "\n", - "volume_view = widgets.interactive_output(\n", - " chart.create_volume,\n", - " {\n", - " \"start\": start_widget,\n", - " \"end\": end_widget,\n", - " \"interval\": interval_widget,\n", - " \"tickers\": tickers_widget,\n", - " },\n", - ")\n", - "\n", - "table_view = widgets.interactive_output(chart.create_table, {\"tickers\": tickers_widget})\n", - "\n", - "charts = widgets.VBox(\n", - " [stocks_view, volume_view],\n", - " layout=widgets.Layout(width=\"100%\", padding=\"0\", margin=\"0\"),\n", - ")\n", - "figures = widgets.HBox(\n", - " [charts, table_view], layout=widgets.Layout(padding=\"0\", margin=\"0\")\n", - ")\n", - "\n", - "title_html = \"

Stock Analysis Dashboard

\"\n", - "warning_html = '

Use a comma after EVERY stock typed.

'\n", - "\n", - "app_contents = [widgets.HTML(title_html), controls, widgets.HTML(warning_html), figures]\n", - "app = widgets.VBox(app_contents)\n", - "display(app)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8f33793c-adc9-4bc1-8fd4-aeff5a00ccba", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - }, - "metadata": { - "interpreter": { - "hash": "e896a00ead8b521528d79ac9ef24990696f2b751eb283ab8e0d078c9c4971ffc" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/cryptocurrency/prediction_techniques/__init__.py b/openbb_terminal/dashboards/stream/__init__.py similarity index 100% rename from openbb_terminal/cryptocurrency/prediction_techniques/__init__.py rename to openbb_terminal/dashboards/stream/__init__.py diff --git a/openbb_terminal/dashboards/stream/forecast.py b/openbb_terminal/dashboards/stream/forecast.py new file mode 100644 index 00000000000..8d96c4a6696 --- /dev/null +++ b/openbb_terminal/dashboards/stream/forecast.py @@ -0,0 +1,303 @@ +from datetime import timedelta, datetime, date +from unittest.mock import patch +from typing import Callable, Any +from inspect import signature +import streamlit as st +import pandas as pd +import yfinance as yf +from openbb_terminal.api import openbb +from openbb_terminal.forecast import helpers +from openbb_terminal.rich_config import console + +st.set_page_config(layout="wide") + +# pylint: disable=E1101 +model_opts = { + "expo": openbb.forecast.expo, # type: ignore + "theta": openbb.forecast.theta, # type: ignore + "linregr": openbb.forecast.linregr, # type: ignore + "regr": openbb.forecast.regr, # type: ignore + "rnn": openbb.forecast.rnn, # type: ignore + "brnn": openbb.forecast.brnn, # type: ignore + "nbeats": openbb.forecast.nbeats, # type: ignore + "tcn": openbb.forecast.tcn, # type: ignore + "trans": openbb.forecast.trans, # type: ignore + "tft": openbb.forecast.tft, # type: ignore +} + +feat_engs = { + "ema": openbb.forecast.ema, # type: ignore + "sto": openbb.forecast.sto, # type: ignore + "rsi": openbb.forecast.rsi, # type: ignore + "roc": openbb.forecast.roc, # type: ignore + "mom": openbb.forecast.mom, # type: ignore + "atr": openbb.forecast.atr, # type: ignore + "delta": openbb.forecast.delta, # type: ignore + "signal": openbb.forecast.signal, # type: ignore +} +# pylint: enable=E1101 + +# Add these: "1m", "2m", "5m", "15m", "30m", "60m", "90m", "1h", + +interval_opts = ["1d", "5d", "1wk", "1mo", "3mo"] + + +def format_df(df: pd.DataFrame) -> pd.DataFrame: + if len(df.columns) != 6: + df.columns = ["_".join(col).strip() for col in df.columns.values] + df.reset_index(inplace=True) + df.columns = [x.lower() for x in df.columns] + return df + + +def has_parameter(func: Callable[..., Any], parameter: str) -> bool: + params = signature(func).parameters + parameters = params.keys() + return parameter in parameters + + +def load_state(name: str, default: Any): + if name not in st.session_state: + st.session_state[name] = default + + +def special_st(text: str): + if "[green]" not in text: + st.write(text) + + +def run_forecast( + data: pd.DataFrame, + model: str, + target_column: str, + past_covariates: list[str], + n_predict: int, +): + if helpers.check_data(data, target_column): + # TODO: let the user choose their own n_predict + kwargs: dict[str, Any] = {} + forecast_model = model_opts[model] + contains_covariates = has_parameter(forecast_model, "past_covariates") + if contains_covariates and past_covariates != []: + kwargs["past_covariates"] = ",".join(past_covariates) + if has_parameter(forecast_model, "output_chunk_length"): + kwargs["output_chunk_length"] = n_predict + + # n_predict and output_chunk_length must be the same if there are past covariates + # run a spinner while we wait for the model to run + with st.spinner("Running model..."): + response = forecast_model( + data=data, + target_column=target_column, + n_predict=n_predict, + **kwargs, + ) + if model == "theta": + ( + ticker_series, + historical_fcast, + predicted_values, + precision, + _, + _model, + ) = response + else: + ( + ticker_series, + historical_fcast, + predicted_values, + precision, + _model, + ) = response + del precision + if model in ["expo", "linregr", "rnn", "tft"]: + predicted_values = predicted_values.quantile_df()[f"{target_column}_0.5"] + else: + predicted_values = predicted_values.pd_dataframe()[target_column] + return ( + historical_fcast.pd_dataframe(), + ticker_series.pd_dataframe(), + pd.DataFrame(predicted_values), + ) + return pd.DataFrame(), pd.DataFrame(), pd.DataFrame() + + +class Handler: + def __init__(self): + load_state("last_tickers", "") + load_state("last_intervals", "1d") + load_state("df", pd.DataFrame()) + default_opts = { + key: [] for key in ["target_widget", "column_widget", "past_covs_widget"] + } + load_state("widget_options", default_opts) + + self.feature_model = None + self.feature_target = None + + # pylint: disable=R0913 + def handle_changes( + self, + past_covariates: list[str], + start, + end, + interval, + tickers, + target_column, + model, + n_predict, + naive, + forecast_only, + ): + del naive, forecast_only + if tickers and target_column: + start_n = datetime(start.year, start.month, start.day) + end_n = datetime(end.year, end.month, end.day) + if interval in ["1d", "5d", "1wk", "1mo", "3mo"]: + result = st.session_state["df"].loc[ + (st.session_state["df"]["date"] >= start_n) + & (st.session_state["df"]["date"] <= end_n) + ] + else: + result = st.session_state["df"] + if not target_column: + target_column = st.session_state["df"].columns[0] + with patch.object(console, "print", special_st): + if helpers.check_data(result, target_column): + final_df = helpers.clean_data(result) + hist_fcast, tick_series, pred_vals = run_forecast( + final_df, model, target_column, past_covariates, n_predict + ) + hist_fcast.columns = ["Historical Forecast"] + tick_series.columns = ["Past Prices"] + pred_vals.columns = ["Prediction Values"] + final = pd.concat( + [hist_fcast, tick_series, pred_vals], axis=1, join="outer" + ) + if not final.empty: + if helpers.check_dates(pred_vals.index.to_series()): + pred_vals.index = pred_vals.index.date + rowc1, rowc2 = st.columns([4, 1]) + # Styled write() with sig figs + with rowc2: + st.write( + pred_vals.style.format({"Prediction Values": "{:.2f}"}) + ) + with rowc1: + st.line_chart(final) + + else: + st.write("There was an error with the data") + + def handle_eng(self, target, feature): + self.feature_target = target + self.feature_model = feat_engs[feature] + + def on_ticker_change(self): + tickers = st.session_state.ticker + if tickers: + interval = st.session_state.interval + start = st.session_state.start + end = st.session_state.end + if ( + tickers != st.session_state["last_tickers"] + or interval != st.session_state["last_interval"] + ): + if interval in ["1d", "5d", "1wk", "1mo", "3mo"]: + df = yf.download( + tickers, period="max", interval=interval, progress=False + ) + else: + end_date = end + timedelta(days=1) + df = yf.download( + tickers, + start=start, + end=end_date, + interval=interval, + progress=False, + ) + df = df.dropna() + st.session_state["df"] = format_df(df) + st.session_state["last_tickers"] = tickers + st.session_state["last_interval"] = interval + st.session_state["widget_options"]["target_widget"] = [ + x for x in st.session_state["df"] if x != "date" + ] + st.session_state["widget_options"]["past_covs_widget"] = [ + x for x in st.session_state["df"] if x != "date" + ] + st.session_state["widget_options"]["column_widget"] = [ + x for x in st.session_state["df"] if x != "date" + ] + + def run(self): + r0c1 = st.columns([4, 1]) + r1c1, r1c2, r1c3, r1c4, r1c5 = st.columns([2, 1, 1, 1, 1]) + r2c1, r2c2, r2c3 = st.columns([1, 1, 1]) + + with r0c1: + st.title("Forecast") + with r1c1: + ticker = st.text_input( + "Ticker", "", key="ticker", on_change=self.on_ticker_change + ) + with r1c2: + start_date = st.date_input( + "Start date", date.today() - timedelta(weeks=104), key="start" + ) + with r1c3: + end_date = st.date_input("End date", date.today(), key="end") + with r1c4: + target_interval = st.selectbox( + "Interval", index=0, key="interval", options=interval_opts + ) + with r1c5: + n_predict = st.selectbox( + "Prediction Days", index=3, key="n_predict", options=list(range(2, 31)) + ) + with r2c1: + # TODO: disable this if the current model does not allow for it + past_covs_widget = st.multiselect( + "Past Covariates", + options=st.session_state["widget_options"]["past_covs_widget"], + ) + with r2c2: + target_widget = st.selectbox( + "Target", options=st.session_state["widget_options"]["target_widget"] + ) + with r2c3: + model_widget = st.selectbox("Model", options=list(model_opts)) + + st.markdown("""---""") + if st.button("Get forecast"): + if ticker: + self.handle_changes( + past_covariates=past_covs_widget, + start=start_date, + end=end_date, + interval=target_interval, + tickers=ticker, + target_column=target_widget, + model=model_widget, + n_predict=n_predict, + naive=False, + forecast_only=False, + ) + else: + st.write("Please select a ticker") + + # TODO need to new add in business days and merge to make new DF before plotting + # create df with 5 next business days from a start date + # next_business_days = pd.date_range(start=end_date, periods=5, freq="B") + + # if st.button("Add Column"): + # kwargs = {} + # if has_parameter(handler.feature_model, "target_column"): + # kwargs["target_column"] = handler.feature_target + # handler.df = handler.feature_model(handler.df, **kwargs) + # st.session_state["widget_options"]["past_covs_widget"] = handler.df.columns + + +if __name__ == "__main__": + handler = Handler() + handler.run() diff --git a/openbb_terminal/dashboards/vsurf.ipynb b/openbb_terminal/dashboards/vsurf.ipynb deleted file mode 100644 index 816c8b52128..00000000000 --- a/openbb_terminal/dashboards/vsurf.ipynb +++ /dev/null @@ -1,162 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "68b664c7-c52d-42c1-b170-575370323d22", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "import sys\n", - "import ipywidgets as widgets\n", - "import plotly.graph_objects as go\n", - "import numpy as np\n", - "import pandas as pd\n", - "from IPython.display import display\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "module_path = os.path.abspath(os.path.join(\"../../..\"))\n", - "\n", - "if module_path not in sys.path:\n", - " sys.path.append(module_path)\n", - "\n", - "from openbb_terminal import api as openbb" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c98dc75-6325-43da-93a2-1b8ec96bd368", - "metadata": {}, - "outputs": [], - "source": [ - "z_dict = {\n", - " \"IV\": \"Implied Volatility\",\n", - " \"OI\": \"Open Interest\",\n", - " \"LP\": \"Last Price\",\n", - "}\n", - "strings = {\n", - " \"IV\": \"DTE: %{x}
Strike: %{y}
IV: %{z}\",\n", - " \"OI\": \"DTE: %{x}
Strike: %{y}
OI: %{z}\",\n", - " \"LP\": \"DTE: %{x}
Strike: %{y}
LP: %{z}\",\n", - "}\n", - "\n", - "\n", - "def create_layout(fig, width, height, z, ticker):\n", - " fig.update_layout(\n", - " title=f\"{z.upper()} Surface for {ticker.upper()}\",\n", - " autosize=False,\n", - " width=width,\n", - " height=height,\n", - " scene=dict(\n", - " xaxis_title=\"Days to Expiration\",\n", - " yaxis_title=\"Strike\",\n", - " zaxis_title=z_dict[z],\n", - " ),\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c1bc5c6a-ccb2-4ef5-9c79-1f619fbf95a6", - "metadata": {}, - "outputs": [], - "source": [ - "class Chart:\n", - " def __init__(self):\n", - " self.last_ticker = \"\"\n", - " self.df = pd.DataFrame()\n", - "\n", - " def create(self, z, ticker, height, width):\n", - " if ticker:\n", - " if ticker != self.last_ticker:\n", - " self.df = openbb.stocks.options.models.yfinance.get_iv_surface(ticker)\n", - " self.last_ticker = ticker\n", - "\n", - " if not self.df.empty:\n", - " z_dict = {\n", - " \"IV\": self.df.impliedVolatility,\n", - " \"OI\": self.df.openInterest,\n", - " \"LP\": self.df.lastPrice,\n", - " }\n", - " fig = go.Figure(\n", - " data=[\n", - " go.Mesh3d(\n", - " z=z_dict[z],\n", - " x=self.df.dte,\n", - " y=self.df.strike,\n", - " intensity=np.sqrt(z_dict[z]),\n", - " colorscale=\"Jet\",\n", - " hovertemplate=strings[z],\n", - " showscale=False,\n", - " )\n", - " ]\n", - " )\n", - "\n", - " create_layout(fig, height, width, z, ticker)\n", - " if os.environ.get(\"SERVER_SOFTWARE\", \"jupyter\").startswith(\"voila\"):\n", - " fig.show(config={\"showTips\": False}, renderer=\"notebook\")\n", - " else:\n", - " fig.show(config={\"showTips\": False})\n", - "\n", - "\n", - "opts = [\"IV\", \"OI\", \"LP\"]\n", - "z_widget = widgets.Dropdown(options=opts, value=\"IV\", description=\"Data\")\n", - "tickers_widget = widgets.Text(description=\"Ticker\", value=\"TSLA\")\n", - "h_widget = widgets.IntText(value=1000, description=\"Height\")\n", - "w_widget = widgets.IntText(value=1000, description=\"Width\")\n", - "\n", - "controls = widgets.VBox([tickers_widget, z_widget])\n", - "size = widgets.VBox([h_widget, w_widget])\n", - "controls = widgets.HBox([controls, size])\n", - "\n", - "chart = Chart()\n", - "stocks_view = widgets.interactive_output(\n", - " chart.create,\n", - " {\"z\": z_widget, \"ticker\": tickers_widget, \"height\": h_widget, \"width\": w_widget},\n", - ")\n", - "\n", - "title_html = \"

Option Surface Dashboard

\"\n", - "app_contents = [\n", - " widgets.HTML(title_html),\n", - " controls,\n", - " stocks_view,\n", - "]\n", - "app = widgets.VBox(app_contents)\n", - "display(app)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "014f267a-92ba-4f23-afb1-9720d8a68cbb", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/econometrics/econometrics_controller.py b/openbb_terminal/econometrics/econometrics_controller.py index eebf94c9605..58830dc5f88 100644 --- a/openbb_terminal/econometrics/econometrics_controller.py +++ b/openbb_terminal/econometrics/econometrics_controller.py @@ -71,7 +71,9 @@ class EconometricsController(BaseController): "granger", "coint", ] - CHOICES_MENUS: List[str] = ["qa", "pred"] + CHOICES_MENUS: List[str] = [ + "qa", + ] pandas_plot_choices = [ "line", "scatter", @@ -316,7 +318,7 @@ class EconometricsController(BaseController): add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="load", - description="Load custom dataset (from previous export, custom imports or StatsModels).", + description="Load dataset (from previous export, custom imports or StatsModels).", ) parser.add_argument( "-f", diff --git a/openbb_terminal/economy/economy_controller.py b/openbb_terminal/economy/economy_controller.py index c0b769f8006..2790a71c7c6 100644 --- a/openbb_terminal/economy/economy_controller.py +++ b/openbb_terminal/economy/economy_controller.py @@ -71,7 +71,9 @@ class EconomyController(BaseController): "edebt", ] - CHOICES_MENUS = ["pred", "qa"] + CHOICES_MENUS = [ + "qa", + ] wsj_sortby_cols_dict = {c: None for c in ["ticker", "last", "change", "prevClose"]} map_period_list = ["1d", "1w", "1m", "3m", "6m", "1y"] @@ -367,7 +369,6 @@ class EconomyController(BaseController): mt.add_raw("\n") mt.add_cmd("plot") mt.add_raw("\n") - mt.add_menu("pred") mt.add_menu("qa") console.print(text=mt.menu_text, menu="Economy") @@ -1626,20 +1627,8 @@ class EconomyController(BaseController): @log_start_end(log=logger) def call_qa(self, _): - """Process pred command""" - - data: Dict = {} - all_datasets_empty = True - for source, _ in self.DATASETS.items(): - if not self.DATASETS[source].empty: - all_datasets_empty = False - if len(self.DATASETS[source].columns) == 1: - data[self.DATASETS[source].columns[0]] = self.DATASETS[source] - else: - for col in list(self.DATASETS[source].columns): - data[col] = self.DATASETS[source][col].to_frame() - - if all_datasets_empty: + """Process qa command""" + if not self.DATASETS: console.print( "There is no data stored yet. Please use either the 'macro', 'fred', 'index' and/or " "'treasury' command in combination with the -st argument to be able to plot data.\n" @@ -1650,4 +1639,13 @@ class EconomyController(BaseController): QaController, ) + data: Dict = {} + for source, _ in self.DATASETS.items(): + if not self.DATASETS[source].empty: + if len(self.DATASETS[source].columns) == 1: + data[self.DATASETS[source].columns[0]] = self.DATASETS[source] + else: + for col in list(self.DATASETS[source].columns): + data[col] = self.DATASETS[source][col].to_frame() + self.queue = self.load_class(QaController, data, self.queue) diff --git a/openbb_terminal/economy/prediction/pred_controller.py b/openbb_terminal/economy/prediction/pred_controller.py deleted file mode 100644 index 9bd8a3240b0..00000000000 --- a/openbb_terminal/economy/prediction/pred_controller.py +++ /dev/null @@ -1,765 +0,0 @@ -"""Fred Series Prediction Controller""" -__docformat__ = "numpy" - -import argparse -import logging -from typing import Dict, List - -from prompt_toolkit.completion import NestedCompleter - -from openbb_terminal import feature_flags as obbff -from openbb_terminal.common.prediction_techniques import ( - arima_model, - arima_view, - ets_model, - ets_view, - knn_view, - mc_model, - mc_view, - neural_networks_view, - pred_helper, - regression_view, -) -from openbb_terminal.decorators import log_start_end -from openbb_terminal.helper_funcs import ( - EXPORT_ONLY_FIGURES_ALLOWED, - check_positive, - get_next_stock_market_days, - valid_date, -) -from openbb_terminal.menu import session -from openbb_terminal.parent_classes import BaseController -from openbb_terminal.rich_config import console, MenuText - -logger = logging.getLogger(__name__) - - -class PredictionTechniquesController(BaseController): - """Prediction Techniques Controller class""" - - CHOICES_COMMANDS = [ - "pick", - "ets", - "knn", - "regression", - "arima", - "mlp", - "rnn", - "lstm", - "conv1d", - "mc", - ] - PATH = "/economy/pred/" - - def __init__( - self, - all_economy_data: Dict, - queue: List[str] = None, - ): - """Constructor""" - super().__init__(queue) - - self.datasets = all_economy_data - self.options = [] - for _, sub_df in all_economy_data.items(): - self.options.extend(list(sub_df.columns)) - self.sources = list(self.datasets.keys()) - self.current_source = self.sources[0] - self.current_source_dataframe = self.datasets[self.current_source] - self.data = self.current_source_dataframe.iloc[:, 0].copy().dropna(axis=0) - self.current_id = self.current_source_dataframe.columns[0].upper() - self.start_date = self.data.index[0] - self.resolution = "" # For the views - - if session and obbff.USE_PROMPT_TOOLKIT: - choices: dict = {c: {} for c in self.controller_choices} - choices["ets"]["-t"] = {c: {} for c in ets_model.TRENDS} - choices["ets"]["-s"] = {c: {} for c in ets_model.SEASONS} - choices["arima"]["-i"] = {c: {} for c in arima_model.ICS} - choices["mc"]["--dist"] = {c: {} for c in mc_model.DISTRIBUTIONS} - choices["pick"] = {c: {} for c in self.options} - choices["pick"]["-c"] = {c: {} for c in self.options} - - choices["support"] = self.SUPPORT_CHOICES - choices["about"] = self.ABOUT_CHOICES - - self.completer = NestedCompleter.from_nested_dict(choices) - - def print_help(self): - """Print help""" - mt = MenuText("economy/pred/") - mt.add_cmd("pick") - mt.add_raw("\n") - mt.add_param("_series", self.current_id) - mt.add_raw("\n") - mt.add_info("_models_") - mt.add_cmd("ets") - mt.add_cmd("knn") - mt.add_cmd("regression") - mt.add_cmd("arima") - mt.add_cmd("mlp") - mt.add_cmd("rnn") - mt.add_cmd("lstm") - mt.add_cmd("conv1d") - mt.add_cmd("mc") - console.print(text=mt.menu_text, menu="Economy - Prediction Techniques") - - def custom_reset(self): - """Class specific component of reset command""" - return ["economy"] - - @log_start_end(log=logger) - def call_pick(self, other_args: List[str]): - """Process add command""" - parser = argparse.ArgumentParser( - add_help=False, - prog="load", - description="Load a FRED series to current selection", - ) - parser.add_argument( - "-c", - "--column", - dest="column", - type=str, - help="Which loaded source to get data from", - choices=self.options, - ) - if other_args and "-" not in other_args[0][0]: - other_args.insert(0, "-c") - ns_parser = self.parse_known_args_and_warn(parser, other_args) - if ns_parser: - for source, sub_df in self.datasets.items(): - if ns_parser.column in sub_df.columns: - self.data = sub_df[ns_parser.column].copy().dropna(axis=0) - self.current_id = ns_parser.column - self.start_date = self.data.index[0] - console.print(f"{ns_parser.column} loaded from {source}.\n") - return - console.print(f"[red]{ns_parser.column} not found in data.[/red]\n") - - @log_start_end(log=logger) - def call_ets(self, other_args: List[str]): - """Process ets command""" - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="ets", - description=""" - Exponential Smoothing, see https://otexts.com/fpp2/taxonomy.html - - Trend='N', Seasonal='N': Simple Exponential Smoothing - Trend='N', Seasonal='A': Exponential Smoothing - Trend='N', Seasonal='M': Exponential Smoothing - Trend='A', Seasonal='N': Holtโ€™s linear method - Trend='A', Seasonal='A': Additive Holt-Wintersโ€™ method - Trend='A', Seasonal='M': Multiplicative Holt-Wintersโ€™ method - Trend='Ad', Seasonal='N': Additive damped trend method - Trend='Ad', Seasonal='A': Exponential Smoothing - Trend='Ad', Seasonal='M': Holt-Wintersโ€™ damped method - Trend component: N: None, A: Additive, Ad: Additive Damped - Seasonality component: N: None, A: Additive, M: Multiplicative - """, - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-t", - "--trend", - action="store", - dest="trend", - choices=ets_model.TRENDS, - default="N", - help="Trend component: N: None, A: Additive, Ad: Additive Damped.", - ) - parser.add_argument( - "-s", - "--seasonal", - action="store", - dest="seasonal", - choices=ets_model.SEASONS, - default="N", - help="Seasonality component: N: None, A: Additive, M: Multiplicative.", - ) - parser.add_argument( - "-p", - "--periods", - action="store", - dest="seasonal_periods", - type=check_positive, - default=5, - help="Seasonal periods.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select - Backtesting", - ) - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - if ns_parser.s_end_date: - if ns_parser.s_end_date < self.data.index[0]: - console.print( - "Backtesting not allowed, since End Date is older than Start Date of historical data\n" - ) - - if ( - ns_parser.s_end_date - < get_next_stock_market_days( - last_stock_day=self.data.index[0], - n_next_days=5 + ns_parser.n_days, - )[-1] - ): - console.print( - "Backtesting not allowed, since End Date is too close to Start Date to train model\n" - ) - - ets_view.display_exponential_smoothing( - dataset=self.current_id, - data=self.data, - n_predict=ns_parser.n_days, - trend=ns_parser.trend, - seasonal=ns_parser.seasonal, - seasonal_periods=ns_parser.seasonal_periods, - end_date=ns_parser.s_end_date, - export=ns_parser.export, - time_res=self.resolution, - ) - - @log_start_end(log=logger) - def call_knn(self, other_args: List[str]): - """Process knn command""" - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="knn", - description=""" - K nearest neighbors is a simple algorithm that stores all - available cases and predict the numerical target based on a similarity measure - (e.g. distance functions). - """, - ) - parser.add_argument( - "-i", - "--input", - action="store", - dest="n_inputs", - type=check_positive, - default=40, - help="number of days to use as input for prediction.", - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-j", - "--jumps", - action="store", - dest="n_jumps", - type=check_positive, - default=1, - help="number of jumps in training data.", - ) - parser.add_argument( - "-n", - "--neighbors", - action="store", - dest="n_neighbors", - type=check_positive, - default=20, - help="number of neighbors to use on the algorithm.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select for testing", - ) - parser.add_argument( - "-t", - "--test_size", - default=0.2, - dest="valid_split", - type=float, - help="Percentage of data to validate in sample", - ) - parser.add_argument( - "--no_shuffle", - action="store_false", - dest="no_shuffle", - default=True, - help="Specify if shuffling validation inputs.", - ) - ns_parser = self.parse_known_args_and_warn( - parser, other_args, EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - if ns_parser.n_inputs > len(self.data): - logger.error("Number of inputs exceeds number of data samples") - console.print( - f"[red]Data only contains {len(self.data)} samples and the model is trying " - f"to use {ns_parser.n_inputs} inputs. Either use less inputs or load with" - f" an earlier start date[/red]\n" - ) - return - try: - knn_view.display_k_nearest_neighbors( - dataset=self.current_id, - data=self.data, - n_neighbors=ns_parser.n_neighbors, - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - test_size=ns_parser.valid_split, - end_date=ns_parser.s_end_date, - no_shuffle=ns_parser.no_shuffle, - time_res=self.resolution, - ) - except ValueError: - logger.exception("The loaded data does not have enough data") - console.print("The loaded data does not have enough data") - - @log_start_end(log=logger) - def call_regression(self, other_args: List[str]): - """Process linear command""" - parser = argparse.ArgumentParser( - add_help=False, - prog="regression", - description=""" - Regression attempts to model the relationship between - two variables by fitting a linear/quadratic/cubic/other equation to - observed data. One variable is considered to be an explanatory variable, - and the other is considered to be a dependent variable. - """, - ) - parser.add_argument( - "-i", - "--input", - action="store", - dest="n_inputs", - type=check_positive, - default=40, - help="number of days to use for prediction.", - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-j", - "--jumps", - action="store", - dest="n_jumps", - type=check_positive, - default=1, - help="number of jumps in training data.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select - Backtesting", - ) - parser.add_argument( - "-p", - "--polynomial", - action="store", - dest="n_polynomial", - type=check_positive, - default=1, - help="polynomial associated with regression.", - ) - if ( - other_args - and "-h" not in other_args - and "-p" not in other_args - and "--polynomial" not in other_args - ): - other_args.insert(0, "-p") - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - # BACKTESTING CHECK - if ns_parser.s_end_date: - if ns_parser.s_end_date < self.data.index[0]: - console.print( - "Backtesting not allowed, since End Date is older than Start Date of historical data\n" - ) - return - - if ( - ns_parser.s_end_date - < get_next_stock_market_days( - last_stock_day=self.data.index[0], - n_next_days=5 + ns_parser.n_days, - )[-1] - ): - console.print( - "Backtesting not allowed, since End Date is too close to Start Date to train model\n" - ) - return - - try: - if ns_parser.n_inputs > len(self.data): - logger.error("Number of inputs exceeds number of data samples") - console.print( - f"[red]Data only contains {len(self.data)} samples and the model is trying " - f"to use {ns_parser.n_inputs} inputs. Either use less inputs or load with" - f" an earlier start date[/red]\n" - ) - return - regression_view.display_regression( - dataset=self.current_id, - values=self.data, - poly_order=ns_parser.n_polynomial, - n_input=ns_parser.n_inputs, - n_predict=ns_parser.n_days, - n_jumps=ns_parser.n_jumps, - end_date=ns_parser.s_end_date, - export=ns_parser.export, - time_res=self.resolution, - ) - except ValueError as e: - logger.exception(str(e)) - console.print(e) - - @log_start_end(log=logger) - def call_arima(self, other_args: List[str]): - """Process arima command""" - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="arima", - description=""" - In statistics and econometrics, and in particular in time series analysis, an - autoregressive integrated moving average (ARIMA) model is a generalization of an - autoregressive moving average (ARMA) model. Both of these models are fitted to time - series data either to better understand the data or to predict future points in the - series (forecasting). ARIMA(p,d,q) where parameters p, d, and q are non-negative - integers, p is the order (number of time lags) of the autoregressive model, d is the - degree of differencing (the number of times the data have had past values subtracted), - and q is the order of the moving-average model. - """, - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-i", - "--ic", - action="store", - dest="s_ic", - type=str, - default="aic", - choices=arima_model.ICS, - help="information criteria.", - ) - parser.add_argument( - "-s", - "--seasonal", - action="store_true", - default=False, - dest="b_seasonal", - help="Use weekly seasonal data.", - ) - parser.add_argument( - "-o", - "--order", - action="store", - dest="s_order", - default="", - type=str, - help="arima model order (p,d,q) in format: p,d,q.", - ) - parser.add_argument( - "-r", - "--results", - action="store_true", - dest="b_results", - default=False, - help="results about ARIMA summary flag.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select - Backtesting", - ) - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - # BACKTESTING CHECK - if ns_parser.s_end_date: - if ns_parser.s_end_date < self.data.index[0]: - console.print( - "Backtesting not allowed, since End Date is older than Start Date of historical data\n" - ) - return - - if ( - ns_parser.s_end_date - < get_next_stock_market_days( - last_stock_day=self.data.index[0], - n_next_days=5 + ns_parser.n_days, - )[-1] - ): - console.print( - "Backtesting not allowed, since End Date is too close to Start Date to train model\n" - ) - return - - arima_view.display_arima( - dataset=self.current_id, - values=self.data, - arima_order=ns_parser.s_order, - n_predict=ns_parser.n_days, - seasonal=ns_parser.b_seasonal, - ic=ns_parser.s_ic, - results=ns_parser.b_results, - end_date=ns_parser.s_end_date, - export=ns_parser.export, - time_res=self.resolution, - ) - - @log_start_end(log=logger) - def call_mlp(self, other_args: List[str]): - """Process mlp command""" - try: - ns_parser = pred_helper.parse_args( - prog="mlp", - description="""Multi-Layered-Perceptron. """, - other_args=other_args, - ) - if ns_parser: - if ns_parser.n_inputs > len(self.data): - logger.error("Number of inputs exceeds number of data samples") - console.print( - f"[red]Data only contains {len(self.data)} samples and the model is trying " - f"to use {ns_parser.n_inputs} inputs. Either use less inputs or load with" - f" an earlier start date[/red]\n" - ) - return - neural_networks_view.display_mlp( - dataset=self.current_id, - data=self.data, - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - time_res=self.resolution, - ) - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_rnn(self, other_args: List[str]): - """Process rnn command""" - try: - ns_parser = pred_helper.parse_args( - prog="rnn", - description="""Recurrent Neural Network. """, - other_args=other_args, - ) - if ns_parser: - if ns_parser.n_inputs > len(self.data): - logger.error("Number of inputs exceeds number of data samples") - console.print( - f"[red]Data only contains {len(self.data)} samples and the model is trying " - f"to use {ns_parser.n_inputs} inputs. Either use less inputs or load with" - f" an earlier start date[/red]\n" - ) - return - neural_networks_view.display_rnn( - dataset=self.current_id, - data=self.data, - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - time_res=self.resolution, - ) - - except Exception as e: - logger.exception(str(e)) - console.print(e) - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_lstm(self, other_args: List[str]): - """Process lstm command""" - try: - ns_parser = pred_helper.parse_args( - prog="lstm", - description="""Long-Short Term Memory. """, - other_args=other_args, - ) - if ns_parser: - if ns_parser.n_inputs > len(self.data): - logger.error("Number of inputs exceeds number of data samples") - console.print( - f"[red]Data only contains {len(self.data)} samples and the model is trying " - f"to use {ns_parser.n_inputs} inputs. Either use less inputs or load with" - f" an earlier start date[/red]\n" - ) - return - neural_networks_view.display_lstm( - dataset=self.current_id, - data=self.data, - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - time_res=self.resolution, - ) - - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_conv1d(self, other_args: List[str]): - """Process conv1d command""" - try: - ns_parser = pred_helper.parse_args( - prog="conv1d", - description="""1D CNN.""", - other_args=other_args, - ) - if ns_parser: - if ns_parser.n_inputs > len(self.data): - logger.error("Number of inputs exceeds number of data samples") - console.print( - f"[red]Data only contains {len(self.data)} samples and the model is trying " - f"to use {ns_parser.n_inputs} inputs. Either use less inputs or load with" - f" an earlier start date[/red]\n" - ) - return - neural_networks_view.display_conv1d( - dataset=self.current_id, - data=self.data, - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - time_res=self.resolution, - ) - - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_mc(self, other_args: List[str]): - """Process mc command""" - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - add_help=False, - prog="mc", - description=""" - Perform Monte Carlo forecasting - """, - ) - parser.add_argument( - "-d", - "--days", - help="Days to predict", - dest="n_days", - type=check_positive, - default=30, - ) - parser.add_argument( - "-n", - "--num", - help="Number of simulations to perform", - dest="n_sims", - type=check_positive, - default=100, - ) - parser.add_argument( - "--dist", - choices=mc_model.DISTRIBUTIONS, - default="lognormal", - dest="dist", - help="Whether to model returns or log returns", - ) - - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - - if ns_parser: - mc_view.display_mc_forecast( - data=self.data, - n_future=ns_parser.n_days, - n_sims=ns_parser.n_sims, - use_log=ns_parser.dist == "lognormal", - export=ns_parser.export, - fig_title=f"Monte Carlo Forecast for {self.current_id}", - ) diff --git a/openbb_terminal/etf/etf_controller.py b/openbb_terminal/etf/etf_controller.py index 9e6fdca1971..a4a7d6c0d5a 100644 --- a/openbb_terminal/etf/etf_controller.py +++ b/openbb_terminal/etf/etf_controller.py @@ -63,7 +63,6 @@ class ETFController(BaseController): ] CHOICES_MENUS = [ "ta", - "pred", "ca", # "scr", "disc", @@ -112,7 +111,6 @@ class ETFController(BaseController): mt.add_cmd("compare", self.etf_name) mt.add_raw("\n") mt.add_menu("ta", self.etf_name) - mt.add_menu("pred", self.etf_name) console.print(text=mt.menu_text, menu="ETF") def custom_reset(self): @@ -635,48 +633,6 @@ class ETFController(BaseController): else: console.print("Use 'load ' prior to this command!", "\n") - @log_start_end(log=logger) - def call_pred(self, _): - """Process pred command""" - # IMPORTANT: 8/11/22 prediction was discontinued on the installer packages - # because forecasting in coming out soon. - # This if statement disallows installer package users from using 'pred' - # even if they turn on the OPENBB_ENABLE_PREDICT feature flag to true - # however it does not prevent users who clone the repo from using it - # if they have ENABLE_PREDICT set to true. - if obbff.PACKAGED_APPLICATION or not obbff.ENABLE_PREDICT: - console.print( - "Predict is disabled. Forecasting coming soon!", - "\n", - ) - else: - if self.etf_name: - try: - from openbb_terminal.etf.prediction_techniques import ( - pred_controller, - ) - - self.queue = self.load_class( - pred_controller.PredictionTechniquesController, - self.etf_name, - self.etf_data.index[0], - "1440min", - self.etf_data, - self.queue, - ) - - except ModuleNotFoundError as e: - logger.exception( - "One of the optional packages seems to be missing: %s", str(e) - ) - console.print( - "One of the optional packages seems to be missing: ", - e, - "\n", - ) - else: - console.print("Use 'load ' prior to this command!", "\n") - @log_start_end(log=logger) def call_ca(self, _): """Process ca command""" diff --git a/openbb_terminal/etf/prediction_techniques/pred_api.py b/openbb_terminal/etf/prediction_techniques/pred_api.py deleted file mode 100644 index ea1d2fe6402..00000000000 --- a/openbb_terminal/etf/prediction_techniques/pred_api.py +++ /dev/null @@ -1,33 +0,0 @@ -"""Pred context API.""" - -# flake8: noqa -# pylint: disable=unused-import - -# Menu commands -from openbb_terminal.common.prediction_techniques.ets_view import ( - display_exponential_smoothing as ets, -) -from openbb_terminal.common.prediction_techniques.knn_view import ( - display_k_nearest_neighbors as knn, -) -from openbb_terminal.common.prediction_techniques.regression_view import ( - display_regression as regression, -) -from openbb_terminal.common.prediction_techniques.arima_view import ( - display_arima as arima, -) -from openbb_terminal.common.prediction_techniques.neural_networks_view import ( - display_mlp as mlp, -) -from openbb_terminal.common.prediction_techniques.neural_networks_view import ( - display_rnn as rnn, -) -from openbb_terminal.common.prediction_techniques.neural_networks_view import ( - display_lstm as lstm, -) -from openbb_terminal.common.prediction_techniques.neural_networks_view import ( - display_conv1d as conv1d, -) -from openbb_terminal.common.prediction_techniques.mc_view import ( - display_mc_forecast as mc, -) diff --git a/openbb_terminal/etf/prediction_techniques/pred_controller.py b/openbb_terminal/etf/prediction_techniques/pred_controller.py deleted file mode 100644 index b68e2d5ba57..00000000000 --- a/openbb_terminal/etf/prediction_techniques/pred_controller.py +++ /dev/null @@ -1,800 +0,0 @@ -""" Prediction Controller """ -__docformat__ = "numpy" - -import argparse -import logging -from datetime import datetime, timedelta -from typing import List - -import numpy as np -import pandas as pd -from prompt_toolkit.completion import NestedCompleter - -from openbb_terminal import feature_flags as obbff -from openbb_terminal.common.prediction_techniques import ( - arima_model, - arima_view, - ets_model, - ets_view, - knn_view, - mc_model, - mc_view, - neural_networks_view, - pred_helper, - regression_view, -) -from openbb_terminal.decorators import log_start_end -from openbb_terminal.helper_funcs import ( - EXPORT_ONLY_FIGURES_ALLOWED, - check_positive, - get_next_stock_market_days, - valid_date, -) -from openbb_terminal.menu import session -from openbb_terminal.parent_classes import BaseController -from openbb_terminal.rich_config import console, MenuText -from openbb_terminal.stocks import stocks_helper - -logger = logging.getLogger(__name__) - - -class PredictionTechniquesController(BaseController): - """Prediction Techniques Controller class""" - - CHOICES_COMMANDS = [ - "load", - "pick", - "ets", - "knn", - "regression", - "arima", - "mlp", - "rnn", - "lstm", - "conv1d", - "mc", - ] - - PATH = "/etf/pred/" - - def __init__( - self, - ticker: str, - start: datetime, - interval: str, - data: pd.DataFrame, - queue: List[str] = None, - ): - """Constructor""" - super().__init__(queue) - - data["Returns"] = data["Adj Close"].pct_change() - data["LogRet"] = np.log(data["Adj Close"]) - np.log(data["Adj Close"].shift(1)) - data = data.rename(columns={"Adj Close": "AdjClose"}) - data = data.dropna() - - self.data = data - self.ticker = ticker - self.start = start - self.interval = interval - self.target = "AdjClose" - - if session and obbff.USE_PROMPT_TOOLKIT: - choices: dict = {c: {} for c in self.controller_choices} - choices["load"]["-r"] = {c: {} for c in stocks_helper.INTERVALS} - choices["pick"] = {c: {} for c in self.data.columns} - choices["ets"]["-t"] = {c: {} for c in ets_model.TRENDS} - choices["ets"]["-s"] = {c: {} for c in ets_model.SEASONS} - choices["arima"]["-i"] = {c: {} for c in arima_model.ICS} - choices["mc"]["--dist"] = {c: {} for c in mc_model.DISTRIBUTIONS} - - choices["support"] = self.SUPPORT_CHOICES - choices["about"] = self.ABOUT_CHOICES - - self.completer = NestedCompleter.from_nested_dict(choices) - - def print_help(self): - """Print help""" - etf_info = f"{self.ticker} (from {self.start.strftime('%Y-%m-%d')})" - mt = MenuText("etf/pred/") - mt.add_cmd("load") - mt.add_cmd("pick") - mt.add_raw("\n") - mt.add_param("_ticker", etf_info) - mt.add_param("_target", self.target) - mt.add_raw("\n") - mt.add_info("_models_") - mt.add_cmd("ets") - mt.add_cmd("knn") - mt.add_cmd("regression") - mt.add_cmd("arima") - mt.add_cmd("mlp") - mt.add_cmd("rnn") - mt.add_cmd("lstm") - mt.add_cmd("conv1d") - mt.add_cmd("mc") - console.print(text=mt.menu_text, menu="ETF - Prediction Techniques") - - def custom_reset(self): - """Class specific component of reset command""" - if self.ticker: - return ["etf", f"load {self.ticker}", "pred"] - return [] - - @log_start_end(log=logger) - def call_load(self, other_args: List[str]): - """Process load command""" - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="load", - description="Load stock ticker to perform analysis on. When the data source is 'yf', an Indian ticker can be" - " loaded by using '.NS' at the end, e.g. 'SBIN.NS'. See available market in" - " https://help.yahoo.com/kb/exchanges-data-providers-yahoo-finance-sln2310.html.", - ) - parser.add_argument( - "-t", - "--ticker", - action="store", - dest="ticker", - required="-h" not in other_args, - help="Stock ticker", - ) - parser.add_argument( - "-s", - "--start", - type=valid_date, - default=(datetime.now() - timedelta(days=366)).strftime("%Y-%m-%d"), - dest="start", - help="The starting date (format YYYY-MM-DD) of the stock", - ) - parser.add_argument( - "-e", - "--end", - type=valid_date, - default=datetime.now().strftime("%Y-%m-%d"), - dest="end", - help="The ending date (format YYYY-MM-DD) of the stock", - ) - parser.add_argument( - "-i", - "--interval", - action="store", - dest="interval", - type=int, - default=1440, - choices=stocks_helper.INTERVALS, - help="Intraday stock minutes", - ) - parser.add_argument( - "-p", - "--prepost", - action="store_true", - default=False, - dest="prepost", - help="Pre/After market hours. Only works for 'yf' source, and intraday data", - ) - - # For the case where a user uses: 'load BB' - if other_args and "-t" not in other_args and "-h" not in other_args: - other_args.insert(0, "-t") - - ns_parser = self.parse_known_args_and_warn( - parser, other_args, EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - df_stock_candidate = stocks_helper.load( - ns_parser.ticker, - ns_parser.start, - ns_parser.interval, - ns_parser.end, - ns_parser.prepost, - ns_parser.source, - ) - - if not df_stock_candidate.empty: - self.data = df_stock_candidate - if "." in ns_parser.ticker: - self.ticker = ns_parser.ticker.upper().split(".")[0] - else: - self.ticker = ns_parser.ticker.upper() - - self.start = ns_parser.start - self.interval = str(ns_parser.interval) + "min" - - self.data["Returns"] = self.data["Adj Close"].pct_change() - self.data["LogRet"] = np.log(self.data["Adj Close"]) - np.log( - self.data["Adj Close"].shift(1) - ) - self.data = self.data.rename(columns={"Adj Close": "AdjClose"}) - self.data = self.data.dropna() - - @log_start_end(log=logger) - def call_pick(self, other_args: List[str]): - """Process pick command""" - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - add_help=False, - prog="pick", - description=""" - Change target variable - """, - ) - parser.add_argument( - "-t", - "--target", - dest="target", - choices=list(self.data.columns), - help="Select variable to analyze", - ) - if other_args and "-t" not in other_args and "-h" not in other_args: - other_args.insert(0, "-t") - - ns_parser = self.parse_known_args_and_warn( - parser, other_args, EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - self.target = ns_parser.target - console.print("") - - @log_start_end(log=logger) - def call_ets(self, other_args: List[str]): - """Process ets command""" - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="ets", - description=""" - Exponential Smoothing, see https://otexts.com/fpp2/taxonomy.html - - Trend='N', Seasonal='N': Simple Exponential Smoothing - Trend='N', Seasonal='A': Exponential Smoothing - Trend='N', Seasonal='M': Exponential Smoothing - Trend='A', Seasonal='N': Holtโ€™s linear method - Trend='A', Seasonal='A': Additive Holt-Wintersโ€™ method - Trend='A', Seasonal='M': Multiplicative Holt-Wintersโ€™ method - Trend='Ad', Seasonal='N': Additive damped trend method - Trend='Ad', Seasonal='A': Exponential Smoothing - Trend='Ad', Seasonal='M': Holt-Wintersโ€™ damped method - Trend component: N: None, A: Additive, Ad: Additive Damped - Seasonality component: N: None, A: Additive, M: Multiplicative - """, - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-t", - "--trend", - action="store", - dest="trend", - choices=ets_model.TRENDS, - default="N", - help="Trend component: N: None, A: Additive, Ad: Additive Damped.", - ) - parser.add_argument( - "-s", - "--seasonal", - action="store", - dest="seasonal", - choices=ets_model.SEASONS, - default="N", - help="Seasonality component: N: None, A: Additive, M: Multiplicative.", - ) - parser.add_argument( - "-p", - "--periods", - action="store", - dest="seasonal_periods", - type=check_positive, - default=5, - help="Seasonal periods.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select - Backtesting", - ) - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - - if ns_parser.s_end_date: - - if ns_parser.s_end_date < self.data.index[0]: - console.print( - "Backtesting not allowed, since End Date is older than Start Date of historical data\n" - ) - - if ( - ns_parser.s_end_date - < get_next_stock_market_days( - last_stock_day=self.data.index[0], - n_next_days=5 + ns_parser.n_days, - )[-1] - ): - console.print( - "Backtesting not allowed, since End Date is too close to Start Date to train model\n" - ) - - ets_view.display_exponential_smoothing( - dataset=self.ticker, - data=self.data[self.target], - n_predict=ns_parser.n_days, - trend=ns_parser.trend, - seasonal=ns_parser.seasonal, - seasonal_periods=ns_parser.seasonal_periods, - end_date=ns_parser.s_end_date, - export=ns_parser.export, - ) - - @log_start_end(log=logger) - def call_knn(self, other_args: List[str]): - """Process knn command""" - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="knn", - description=""" - K nearest neighbors is a simple algorithm that stores all - available cases and predict the numerical target based on a similarity measure - (e.g. distance functions). - """, - ) - parser.add_argument( - "-i", - "--input", - action="store", - dest="n_inputs", - type=check_positive, - default=40, - help="number of days to use as input for prediction.", - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-j", - "--jumps", - action="store", - dest="n_jumps", - type=check_positive, - default=1, - help="number of jumps in training data.", - ) - parser.add_argument( - "-n", - "--neighbors", - action="store", - dest="n_neighbors", - type=check_positive, - default=20, - help="number of neighbors to use on the algorithm.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select for testing", - ) - parser.add_argument( - "-t", - "--test_size", - default=0.2, - dest="valid_split", - type=float, - help="Percentage of data to validate in sample", - ) - parser.add_argument( - "--no_shuffle", - action="store_false", - dest="no_shuffle", - default=True, - help="Specify if shuffling validation inputs.", - ) - ns_parser = self.parse_known_args_and_warn( - parser, other_args, EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - knn_view.display_k_nearest_neighbors( - dataset=self.ticker, - data=self.data[self.target], - n_neighbors=ns_parser.n_neighbors, - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - test_size=ns_parser.valid_split, - end_date=ns_parser.s_end_date, - no_shuffle=ns_parser.no_shuffle, - ) - - @log_start_end(log=logger) - def call_regression(self, other_args: List[str]): - """Process linear command""" - parser = argparse.ArgumentParser( - add_help=False, - prog="regression", - description=""" - Regression attempts to model the relationship between - two variables by fitting a linear/quadratic/cubic/other equation to - observed data. One variable is considered to be an explanatory variable, - and the other is considered to be a dependent variable. - """, - ) - - parser.add_argument( - "-i", - "--input", - action="store", - dest="n_inputs", - type=check_positive, - default=40, - help="number of days to use for prediction.", - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-j", - "--jumps", - action="store", - dest="n_jumps", - type=check_positive, - default=1, - help="number of jumps in training data.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select - Backtesting", - ) - parser.add_argument( - "-p", - "--polynomial", - action="store", - dest="n_polynomial", - type=check_positive, - default=1, - help="polynomial associated with regression.", - ) - if ( - other_args - and "-h" not in other_args - and ("-p" not in other_args or "--polynomial" not in other_args) - ): - other_args.insert(0, "-p") - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - # BACKTESTING CHECK - if ns_parser.s_end_date: - if ns_parser.s_end_date < self.data.index[0]: - console.print( - "Backtesting not allowed, since End Date is older than Start Date of historical data\n" - ) - - if ( - ns_parser.s_end_date - < get_next_stock_market_days( - last_stock_day=self.data.index[0], - n_next_days=5 + ns_parser.n_days, - )[-1] - ): - console.print( - "Backtesting not allowed, since End Date is too close to Start Date to train model\n" - ) - - regression_view.display_regression( - dataset=self.ticker, - values=self.data[self.target], - poly_order=ns_parser.n_polynomial, - n_input=ns_parser.n_inputs, - n_predict=ns_parser.n_days, - n_jumps=ns_parser.n_jumps, - end_date=ns_parser.s_end_date, - export=ns_parser.export, - ) - - @log_start_end(log=logger) - def call_arima(self, other_args: List[str]): - """Process arima command""" - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="arima", - description=""" - In statistics and econometrics, and in particular in time series analysis, an - autoregressive integrated moving average (ARIMA) model is a generalization of an - autoregressive moving average (ARMA) model. Both of these models are fitted to time - series data either to better understand the data or to predict future points in the - series (forecasting). ARIMA(p,d,q) where parameters p, d, and q are non-negative - integers, p is the order (number of time lags) of the autoregressive model, d is the - degree of differencing (the number of times the data have had past values subtracted), - and q is the order of the moving-average model. - """, - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-i", - "--ic", - action="store", - dest="s_ic", - type=str, - default="aic", - choices=arima_model.ICS, - help="information criteria.", - ) - parser.add_argument( - "-s", - "--seasonal", - action="store_true", - default=False, - dest="b_seasonal", - help="Use weekly seasonal data.", - ) - parser.add_argument( - "-o", - "--order", - action="store", - dest="s_order", - default="", - type=str, - help="arima model order (p,d,q) in format: p,d,q.", - ) - parser.add_argument( - "-r", - "--results", - action="store_true", - dest="b_results", - default=False, - help="results about ARIMA summary flag.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select - Backtesting", - ) - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - # BACKTESTING CHECK - if ns_parser.s_end_date: - - if ns_parser.s_end_date < self.data.index[0]: - console.print( - "Backtesting not allowed, since End Date is older than Start Date of historical data\n" - ) - - if ( - ns_parser.s_end_date - < get_next_stock_market_days( - last_stock_day=self.data.index[0], - n_next_days=5 + ns_parser.n_days, - )[-1] - ): - console.print( - "Backtesting not allowed, since End Date is too close to Start Date to train model\n" - ) - - arima_view.display_arima( - dataset=self.ticker, - values=self.data[self.target], - arima_order=ns_parser.s_order, - n_predict=ns_parser.n_days, - seasonal=ns_parser.b_seasonal, - ic=ns_parser.s_ic, - results=ns_parser.b_results, - end_date=ns_parser.s_end_date, - export=ns_parser.export, - ) - - @log_start_end(log=logger) - def call_mlp(self, other_args: List[str]): - """Process mlp command""" - try: - ns_parser = pred_helper.parse_args( - prog="mlp", - description="""Multi-Layered-Perceptron. """, - other_args=other_args, - ) - if ns_parser: - neural_networks_view.display_mlp( - dataset=self.ticker, - data=self.data[self.target], - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - ) - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_rnn(self, other_args: List[str]): - """Process rnn command""" - try: - ns_parser = pred_helper.parse_args( - prog="rnn", - description="""Recurrent Neural Network. """, - other_args=other_args, - ) - if ns_parser: - neural_networks_view.display_rnn( - dataset=self.ticker, - data=self.data[self.target], - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - ) - - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_lstm(self, other_args: List[str]): - """Process lstm command""" - try: - ns_parser = pred_helper.parse_args( - prog="lstm", - description="""Long-Short Term Memory. """, - other_args=other_args, - ) - if ns_parser: - neural_networks_view.display_lstm( - dataset=self.ticker, - data=self.data[self.target], - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - ) - - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_conv1d(self, other_args: List[str]): - """Process conv1d command""" - try: - ns_parser = pred_helper.parse_args( - prog="conv1d", - description="""1D CNN.""", - other_args=other_args, - ) - if ns_parser: - neural_networks_view.display_conv1d( - dataset=self.ticker, - data=self.data[self.target], - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - ) - - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_mc(self, other_args: List[str]): - """Process mc command""" - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - add_help=False, - prog="mc", - description=""" - Perform Monte Carlo forecasting - """, - ) - parser.add_argument( - "-d", - "--days", - help="Days to predict", - dest="n_days", - type=check_positive, - default=30, - ) - parser.add_argument( - "-n", - "--num", - help="Number of simulations to perform", - dest="n_sims", - default=100, - ) - parser.add_argument( - "--dist", - choices=mc_model.DISTRIBUTIONS, - default="lognormal", - dest="dist", - help="Whether to model returns or log returns", - ) - - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - if self.target != "AdjClose": - console.print("MC Prediction designed for AdjClose prices\n") - - mc_view.display_mc_forecast( - data=self.data[self.target], - n_future=ns_parser.n_days, - n_sims=ns_parser.n_sims, - use_log=ns_parser.dist == "lognormal", - export=ns_parser.export, - ) diff --git a/openbb_terminal/feature_flags.py b/openbb_terminal/feature_flags.py index f3e4492e8d0..c369b9dc8cd 100644 --- a/openbb_terminal/feature_flags.py +++ b/openbb_terminal/feature_flags.py @@ -95,6 +95,9 @@ ENABLE_CHECK_API = strtobool(os.getenv("OPENBB_ENABLE_CHECK_API", "True")) # Send logs to data lake LOG_COLLECTION = bool(strtobool(os.getenv("OPENBB_LOG_COLLECT", "True"))) +# Provide export folder path. If empty that means default. +EXPORT_FOLDER_PATH = str(os.getenv("OPENBB_EXPORT_FOLDER_PATH", "")) + # Set a flag if the application is running from a packaged bundle PACKAGED_APPLICATION = strtobool(os.getenv("OPENBB_PACKAGED_APPLICATION", "False")) diff --git a/openbb_terminal/economy/prediction/__init__.py b/openbb_terminal/forecast/__init__.py similarity index 100% rename from openbb_terminal/economy/prediction/__init__.py rename to openbb_terminal/forecast/__init__.py diff --git a/openbb_terminal/forecast/brnn_model.py b/openbb_terminal/forecast/brnn_model.py new file mode 100644 index 00000000000..1219efa2566 --- /dev/null +++ b/openbb_terminal/forecast/brnn_model.py @@ -0,0 +1,164 @@ +# pylint: disable=too-many-arguments +"""RNN Model""" +__docformat__ = "numpy" + +import logging +import warnings +from typing import Any, Union, Optional, List, Tuple + + +import pandas as pd + +from darts import TimeSeries +from darts.models import BlockRNNModel +from darts.utils.likelihood_models import GaussianLikelihood +from openbb_terminal.decorators import log_start_end +from openbb_terminal.forecast import helpers + +logger = logging.getLogger(__name__) + + +@log_start_end(log=logger) +def get_brnn_data( + data: Union[pd.Series, pd.DataFrame], + target_column: str = "close", + n_predict: int = 5, + train_split: float = 0.85, + past_covariates: str = None, + forecast_horizon: int = 5, + input_chunk_length: int = 14, + output_chunk_length: int = 5, + model_type: str = "LSTM", + n_rnn_layers: int = 1, + hidden_size: int = 20, + dropout: float = 0.0, + batch_size: int = 32, + n_epochs: int = 100, + learning_rate: float = 1e-3, + model_save_name: str = "brnn_model", + force_reset: bool = True, + save_checkpoints: bool = True, +) -> Tuple[list[TimeSeries], List[TimeSeries], List[TimeSeries], Optional[float], Any]: + """Performs Block RNN forecasting + + Args: + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + model_type (str, optional): + Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU"). Defaults to "LSTM". + n_rnn_layers (int, optional): + Number of layers in the RNN module. Defaults to 1. + hidden_size (int, optional): + Size for feature maps for each hidden RNN layer. Defaults to 20. + dropout (float, optional): + Fraction of neurons afected by Dropout. Defaults to 0.0. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 100. + learning_rate (float, optional): + Defaults to 1e-3. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset (all checkpoints will be + discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True. + + Returns: + list[TimeSeries] + Adjusted Data series + list[TimeSeries] + Historical forecast by best RNN model + list[TimeSeries] + list of Predictions + Optional[float] + Mean average precision error + Any + Best BRNN Model + """ + + # TODO Check if torch GPU AVAILABLE + + use_scalers = True + probabilistic = False + + scaler, ticker_series = helpers.get_series( + data, target_column, is_scaler=use_scalers + ) + train, val = ticker_series.split_before(train_split) + valid = helpers.check_data_length( + train, val, input_chunk_length, output_chunk_length + ) + if not valid: + return [], [], [], None, None + + ( + past_covariate_whole, + past_covariate_train, + past_covariate_val, + ) = helpers.past_covs(past_covariates, data, train_split, use_scalers) + + # Early Stopping + + brnn_model = BlockRNNModel( + input_chunk_length=input_chunk_length, + output_chunk_length=output_chunk_length, + model=model_type, + n_rnn_layers=n_rnn_layers, + hidden_size=hidden_size, + dropout=dropout, + batch_size=batch_size, + n_epochs=n_epochs, + optimizer_kwargs={"lr": learning_rate}, + model_name=model_save_name, + random_state=42, + pl_trainer_kwargs=helpers.get_pl_kwargs(accelerator="cpu"), + force_reset=force_reset, + save_checkpoints=save_checkpoints, + likelihood=GaussianLikelihood(), + ) + + # fit model on train series for historical forecasting + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + helpers.fit_model( + brnn_model, + train, + val, + past_covariate_train, + past_covariate_val, + ) + best_model = BlockRNNModel.load_from_checkpoint( + model_name=model_save_name, best=True + ) + + # Showing historical backtesting without retraining model (too slow) + return helpers.get_prediction( + "Block RNN", + probabilistic, + use_scalers, + scaler, + past_covariates, + best_model, + ticker_series, + past_covariate_whole, + train_split, + forecast_horizon, + n_predict, + ) diff --git a/openbb_terminal/forecast/brnn_view.py b/openbb_terminal/forecast/brnn_view.py new file mode 100644 index 00000000000..ec1b0727ce5 --- /dev/null +++ b/openbb_terminal/forecast/brnn_view.py @@ -0,0 +1,167 @@ +"""Block RNN View""" +__docformat__ = "numpy" + +import logging +from typing import Union, Optional, List +from datetime import datetime + +import pandas as pd +import matplotlib.pyplot as plt + +from openbb_terminal.forecast import brnn_model +from openbb_terminal.decorators import log_start_end +from openbb_terminal.forecast import helpers + +logger = logging.getLogger(__name__) +# pylint: disable=too-many-arguments + + +@log_start_end(log=logger) +def display_brnn_forecast( + data: Union[pd.Series, pd.DataFrame], + target_column: str = "close", + dataset_name: str = "", + n_predict: int = 5, + past_covariates: str = None, + train_split: float = 0.85, + forecast_horizon: int = 5, + input_chunk_length: int = 14, + output_chunk_length: int = 5, + model_type: str = "LSTM", + n_rnn_layers: int = 1, + hidden_size: int = 20, + dropout: float = 0.0, + batch_size: int = 16, + n_epochs: int = 100, + learning_rate: float = 1e-3, + model_save_name: str = "rnn_model", + force_reset: bool = True, + save_checkpoints: bool = True, + export: str = "", + residuals: bool = False, + forecast_only: bool = False, + start_date: Optional[datetime] = None, + end_date: Optional[datetime] = None, + naive: bool = False, + external_axes: Optional[List[plt.axes]] = None, +): + """Display BRNN forecast + + Parameters + ---------- + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + dataset_name str + The name of the ticker to be predicted + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + model_type (str, optional): + Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU"). Defaults to "LSTM". + n_rnn_layers (int, optional): + Number of layers in the RNN module. Defaults to 1. + hidden_size (int, optional): + Size for feature maps for each hidden RNN layer. Defaults to 20. + dropout (float, optional): + Fraction of neurons afected by Dropout. Defaults to 0.0. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 101. + learning_rate (float, optional): + Defaults to 1e-3. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. + Defaults to True. + export: str + Format to export data + residuals: bool + Whether to show residuals for the model. Defaults to False. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the same + as the previous day's closing price. Defaults to False. + external_axes:Optional[List[plt.axes]] + External axes to plot on + """ + data = helpers.clean_data( + data, start_date, end_date, target_column, past_covariates + ) + if not helpers.check_data(data, target_column, past_covariates): + return + output_chunk_length = helpers.check_output( + output_chunk_length, n_predict, bool(past_covariates) + ) + ( + ticker_series, + historical_fcast, + predicted_values, + precision, + _model, + ) = brnn_model.get_brnn_data( + data=data, + n_predict=n_predict, + target_column=target_column, + past_covariates=past_covariates, + train_split=train_split, + forecast_horizon=forecast_horizon, + input_chunk_length=input_chunk_length, + output_chunk_length=output_chunk_length, + model_type=model_type, + n_rnn_layers=n_rnn_layers, + hidden_size=hidden_size, + dropout=dropout, + batch_size=batch_size, + n_epochs=n_epochs, + learning_rate=learning_rate, + model_save_name=model_save_name, + force_reset=force_reset, + save_checkpoints=save_checkpoints, + ) + if ticker_series == []: + return + + probabilistic = False + helpers.plot_forecast( + "BRNN", + target_column, + historical_fcast, + predicted_values, + ticker_series, + dataset_name, + data, + n_predict, + forecast_horizon, + past_covariates, + precision, + probabilistic, + export, + forecast_only=forecast_only, + naive=naive, + external_axes=external_axes, + ) + if residuals: + helpers.plot_residuals( + _model, past_covariates, ticker_series, forecast_horizon=forecast_horizon + ) diff --git a/openbb_terminal/forecast/expo_model.py b/openbb_terminal/forecast/expo_model.py new file mode 100644 index 00000000000..be595931131 --- /dev/null +++ b/openbb_terminal/forecast/expo_model.py @@ -0,0 +1,148 @@ +# pylint: disable=too-many-arguments +"""Probabilistic Exponential Smoothing Model""" +__docformat__ = "numpy" + +import logging +from typing import Any, Union, Optional, List, Tuple + +import warnings +from statsmodels.tools.sm_exceptions import ConvergenceWarning +import pandas as pd +from darts import TimeSeries +from darts.models import ExponentialSmoothing +from darts.utils.utils import ModelMode, SeasonalityMode +from darts.metrics import mape + +from openbb_terminal.decorators import log_start_end +from openbb_terminal.rich_config import console +from openbb_terminal.forecast import helpers + + +warnings.simplefilter("ignore", ConvergenceWarning) + + +TRENDS = ["N", "A", "M"] +SEASONS = ["N", "A", "M"] +PERIODS = [4, 5, 7] +DAMPEN = ["T", "F"] + +logger = logging.getLogger(__name__) + + +@log_start_end(log=logger) +def get_expo_data( + data: Union[pd.Series, pd.DataFrame], + target_column: str = "close", + trend: str = "A", + seasonal: str = "A", + seasonal_periods: int = 7, + dampen: str = "F", + n_predict: int = 30, + start_window: float = 0.85, + forecast_horizon: int = 5, +) -> Tuple[list[TimeSeries], List[TimeSeries], List[TimeSeries], Optional[float], Any]: + + """Performs Probabilistic Exponential Smoothing forecasting + This is a wrapper around statsmodels Holt-Winters' Exponential Smoothing; + we refer to this link for the original and more complete documentation of the parameters. + + https://unit8co.github.io/darts/generated_api/darts.models.forecasting.exponential_smoothing.html?highlight=exponential + + Parameters + ---------- + data : Union[pd.Series, np.ndarray] + Input data. + target_column (str, optional): + Target column to forecast. Defaults to "close". + trend: str + Trend component. One of [N, A, M] + Defaults to ADDITIVE. + seasonal: str + Seasonal component. One of [N, A, M] + Defaults to ADDITIVE. + seasonal_periods: int + Number of seasonal periods in a year (7 for daily data) + If not set, inferred from frequency of the series. + dampen: str + Dampen the function + n_predict: int + Number of days to forecast + start_window: float + Size of sliding window from start of timeseries and onwards + forecast_horizon: int + Number of days to forecast when backtesting and retraining historical + + Returns + ------- + list[float] + Adjusted Data series + list[float] + List of historical fcast values + list[float] + List of predicted fcast values + Optional[float] + precision + Any + Fit Prob. Expo model object. + """ + + use_scalers = False + _, ticker_series = helpers.get_series(data, target_column, is_scaler=use_scalers) + + if trend == "M": + trend_model = ModelMode.MULTIPLICATIVE + elif trend == "N": + trend_model = ModelMode.NONE + else: # Default + trend_model = ModelMode.ADDITIVE + + if seasonal == "M": + seasonal_model = SeasonalityMode.MULTIPLICATIVE + elif seasonal == "N": + seasonal_model = SeasonalityMode.NONE + else: # Default + seasonal_model = SeasonalityMode.ADDITIVE + + damped = True + if dampen == "F": + damped = False + + # Model Init + model_es = ExponentialSmoothing( + trend=trend_model, + seasonal=seasonal_model, + seasonal_periods=int(seasonal_periods), + damped=damped, + random_state=42, + ) + + # Historical backtesting + historical_fcast_es = model_es.historical_forecasts( + ticker_series, # backtest on entire ts + start=float(start_window), + forecast_horizon=int(forecast_horizon), + verbose=True, + ) + + # train new model on entire timeseries to provide best current forecast + best_model = ExponentialSmoothing( + trend=trend_model, + seasonal=seasonal_model, + seasonal_periods=int(seasonal_periods), + damped=damped, + random_state=42, + ) + + # we have the historical fcast, now lets train on entire set and predict. + best_model.fit(ticker_series) + probabilistic_forecast = best_model.predict(int(n_predict), num_samples=500) + precision = mape(actual_series=ticker_series, pred_series=historical_fcast_es) + console.print(f"Exponential smoothing obtains MAPE: {precision:.2f}% \n") # TODO + + return ( + ticker_series, + historical_fcast_es, + probabilistic_forecast, + precision, + best_model, + ) diff --git a/openbb_terminal/forecast/expo_view.py b/openbb_terminal/forecast/expo_view.py new file mode 100644 index 00000000000..bebb6f8fd46 --- /dev/null +++ b/openbb_terminal/forecast/expo_view.py @@ -0,0 +1,125 @@ +"""Probabilistic Exponential Smoothing View""" +__docformat__ = "numpy" + +import logging +from typing import Union, Optional, List +from datetime import datetime + +import pandas as pd +import matplotlib.pyplot as plt + +from openbb_terminal.forecast import expo_model +from openbb_terminal.decorators import log_start_end +from openbb_terminal.forecast import helpers + +logger = logging.getLogger(__name__) +# pylint: disable=too-many-arguments + + +@log_start_end(log=logger) +def display_expo_forecast( + data: Union[pd.DataFrame, pd.Series], + target_column: str = "close", + dataset_name: str = "", + trend: str = "A", + seasonal: str = "A", + seasonal_periods: int = 7, + dampen: str = "F", + n_predict: int = 30, + start_window: float = 0.85, + forecast_horizon: int = 5, + export: str = "", + residuals: bool = False, + forecast_only: bool = False, + start_date: Optional[datetime] = None, + end_date: Optional[datetime] = None, + naive: bool = False, + external_axes: Optional[List[plt.axes]] = None, +): + """Display Probabilistic Exponential Smoothing forecast + + Parameters + ---------- + data : Union[pd.Series, np.array] + Data to forecast + dataset_name str + The name of the ticker to be predicted + target_column (str, optional): + Target column to forecast. Defaults to "close". + trend: str + Trend component. One of [N, A, M] + Defaults to ADDITIVE. + seasonal: str + Seasonal component. One of [N, A, M] + Defaults to ADDITIVE. + seasonal_periods: int + Number of seasonal periods in a year + If not set, inferred from frequency of the series. + dampen: str + Dampen the function + n_predict: int + Number of days to forecast + start_window: float + Size of sliding window from start of timeseries and onwards + forecast_horizon: int + Number of days to forecast when backtesting and retraining historical + export: str + Format to export data + residuals: bool + Whether to show residuals for the model. Defaults to False. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the same + as the previous day's closing price. Defaults to False. + external_axes:Optional[List[plt.axes]] + External axes to plot on + """ + data = helpers.clean_data(data, start_date, end_date, target_column, None) + if not helpers.check_data(data, target_column, None): + return + + ( + ticker_series, + historical_fcast, + predicted_values, + precision, + _model, + ) = expo_model.get_expo_data( + data=data, + target_column=target_column, + trend=trend, + seasonal=seasonal, + seasonal_periods=seasonal_periods, + dampen=dampen, + n_predict=n_predict, + start_window=start_window, + forecast_horizon=forecast_horizon, + ) + probabilistic = True + helpers.plot_forecast( + "PES", + target_column, + historical_fcast, + predicted_values, + ticker_series, + dataset_name, + data, + n_predict, + forecast_horizon, + None, + precision, + probabilistic, + export, + forecast_only=forecast_only, + naive=naive, + external_axes=external_axes, + ) + if residuals: + helpers.plot_residuals( + _model, None, ticker_series, forecast_horizon=forecast_horizon + ) diff --git a/openbb_terminal/forecast/forecast.ipynb b/openbb_terminal/forecast/forecast.ipynb new file mode 100644 index 00000000000..d7dd2a2cae8 --- /dev/null +++ b/openbb_terminal/forecast/forecast.ipynb @@ -0,0 +1,87 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "a3abc620-1a2c-4c6d-a273-b30ce74a58c9", + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime, timedelta\n", + "from openbb_terminal import api\n", + "import os\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib_inline.backend_inline\n", + "import ipywidgets as widgets\n", + "import plotly.graph_objs as go\n", + "from IPython.display import display\n", + "\n", + "%matplotlib inline\n", + "matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\")\n", + "api.theme.applyMPLstyle()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bad2400-6f8a-4a8a-8161-5e319ae13ba8", + "metadata": {}, + "outputs": [], + "source": [ + "df = api.forecast.load(\"weather.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5eb728df-76e3-4cf2-901b-7275b7ce8f4c", + "metadata": {}, + "outputs": [], + "source": [ + "df = api.forecast.ema(df, target_column=\"MaxTemp\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "261d1eb2-7a0b-4358-8aa5-c1bae1237dab", + "metadata": {}, + "outputs": [], + "source": [ + "api.forecast.expo(df, target_column=\"MaxTemp\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c1d1945-f819-442a-a8e6-66822681b901", + "metadata": {}, + "outputs": [], + "source": [ + "api.forecast.brnn(df, target_column=\"MaxTemp\", past_covariates=\"MinTemp,Rainfall,Evaporation\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openbb_terminal/forecast/forecast_controller.py b/openbb_terminal/forecast/forecast_controller.py new file mode 100644 index 00000000000..94c2e68c55f --- /dev/null +++ b/openbb_terminal/forecast/forecast_controller.py @@ -0,0 +1,2451 @@ +"""Forecast Controller Module""" +__docformat__ = "numpy" + +# pylint: disable=too-many-lines, too-many-branches, too-many-arguments, R0904,R0902,W0707 + +import argparse +import logging +from itertools import chain +from pathlib import Path +from typing import Any, Optional, List, Dict + +try: + import torch + import darts +except ModuleNotFoundError: + raise ModuleNotFoundError( + "Please install the forecast version of the terminal. Instructions " + "are here: https://github.com/OpenBB-finance/OpenBBTerminal/" + "blob/main/openbb_terminal/README.md#anaconda--python" + ) +import pandas as pd +import psutil +from prompt_toolkit.completion import NestedCompleter + +from openbb_terminal import feature_flags as obbff +from openbb_terminal.decorators import log_start_end +from openbb_terminal.helper_funcs import ( + check_positive, + check_positive_float, + NO_EXPORT, + EXPORT_ONLY_FIGURES_ALLOWED, + EXPORT_ONLY_RAW_DATA_ALLOWED, + log_and_raise, + valid_date, +) +from openbb_terminal.helper_funcs import check_list_values +from openbb_terminal.menu import session +from openbb_terminal.parent_classes import BaseController +from openbb_terminal.rich_config import console, MenuText +from openbb_terminal.forecast import ( + forecast_model, + forecast_view, + expo_model, + expo_view, + linregr_view, + nbeats_view, + regr_view, + tcn_view, + theta_view, + rnn_view, + brnn_view, + tft_view, + helpers, + trans_view, +) + +logger = logging.getLogger(__name__) +empty_df = pd.DataFrame() + + +def check_greater_than_one(value) -> int: + """Argparse type to check positive int above 1""" + new_value = int(value) + if new_value <= 1: + log_and_raise( + argparse.ArgumentTypeError( + f"{value} is an invalid positive int value. Must be greater than 1." + ) + ) + return new_value + + +class ForecastController(BaseController): + """Forecast class""" + + CHOICES_COMMANDS: List[str] = [ + "load", + "show", + "plot", + "clean", + "combine", + "desc", + "corr", + "rename", + "delete", + "export", + "ema", + "sto", + "rsi", + "roc", + "mom", + "delta", + "atr", + "signal", + "expo", + "theta", + "rnn", + "brnn", + "nbeats", + "tcn", + "regr", + "linregr", + "trans", + "tft", + "season", + "which", + ] + pandas_plot_choices = [ + "line", + "scatter", + "bar", + "barh", + "hist", + "box", + "kde", + "area", + "pie", + "hexbin", + ] + disclaimer = """ + All models are for educational purposes only. The techniques available in this menu are not + fine tuned or guaranteed to work. Backtesting results is not a guarantee of future accuracy. + Investing involves monetary risk that OpenBB does not take a role in. Please research any + prediction techniques fully before attempting to use. OpenBB is not liable for any loss or + damages.""" + + PATH = "/forecast/" + + loaded_dataset_cols = "\n" + list_dataset_cols: list = list() + + def __init__( + self, ticker: str = "", data: pd.DataFrame = empty_df, queue: List[str] = None + ): + """Constructor""" + super().__init__(queue) + self.files: List[str] = [] + # The full file name with extension, this allows the rest command to work + self.files_full: List[List[str]] = [] + self.datasets: Dict[str, pd.DataFrame] = dict() + + if ticker and not data.empty: + # data["date"] = data.index + data = data.reset_index() # convert date from index to column + data.columns = data.columns.map(lambda x: x.lower().replace(" ", "_")) + + self.files.append(ticker) + self.datasets[ticker] = data + self.loaded_dataset_cols = "\n" + + self.loaded_dataset_cols += ( + f" {ticker} {(20 - len(ticker)) * ' '}: " + f"{', '.join(data.columns)}\n" + ) + + for col in data.columns: + self.list_dataset_cols.append(f"{ticker}.{col}") + + self.DATA_TYPES: List[str] = ["int", "float", "str", "bool", "category", "date"] + + self.signs: Dict[Any, Any] = { + "div": "/", + "mul": "*", + "add": "+", + "sub": "-", + "mod": "%", + "pow": "**", + } + self.file_types = ["csv", "xlsx"] + self.DATA_FILES = { + filepath.name: filepath + for file_type in self.file_types + for filepath in chain( + Path("exports").rglob(f"*.{file_type}"), + Path("custom_imports").rglob(f"*.{file_type}"), + ) + if filepath.is_file() + } + + # setting device on GPU if available, else CPU + self.device = "cuda" if torch.cuda.is_available() else "cpu" + self.comp_ram = f"{round(psutil.virtual_memory().total / (1024.0 ** 3),2)}G" + self.rec_data_size = f"{(psutil.virtual_memory().total / (1024.0 ** 3))//3}G" + self.torch_version = torch.__version__ + self.darts_version = darts.__version__ + + if session and obbff.USE_PROMPT_TOOLKIT: + choices: dict = {c: {} for c in self.controller_choices} + choices["load"] = {c: None for c in self.DATA_FILES.keys()} + choices["show"] = {c: None for c in self.files} + + for feature in ["export", "show"]: + choices[feature] = {c: None for c in self.files} + + for feature in ["plot"]: + choices[feature] = dict() + + self.choices = choices + + # To link to the support HTML + choices["support"] = self.SUPPORT_CHOICES + choices["about"] = self.ABOUT_CHOICES + + self.completer = NestedCompleter.from_nested_dict(choices) + self.update_runtime_choices() + + def update_runtime_choices(self): + if session and obbff.USE_PROMPT_TOOLKIT: + dataset_columns = { + f"{dataset}.{column}": {column: None, dataset: None} + for dataset, dataframe in self.datasets.items() + for column in dataframe.columns + } + + for feature in [ + "plot", + "delete", + ]: + self.choices[feature] = dataset_columns + + for feature in [ + "export", + "show", + "clean", + "desc", + "corr", + "ema", + "sto", + "rsi", + "roc", + "mom", + "delta", + "atr", + "signal", + # "index", + # "remove", + "combine", + "rename", + "expo", + "theta", + "rnn", + "brnn", + "nbeats", + "tcn", + "regr", + "linregr", + "trans", + "tft", + "season", + ]: + self.choices[feature] = {c: None for c in self.files} + + pairs_timeseries = list() + for dataset_col in list(dataset_columns.keys()): + pairs_timeseries += [ + f"{dataset_col},{dataset_col2}" + for dataset_col2 in list(dataset_columns.keys()) + if dataset_col != dataset_col2 + ] + + self.completer = NestedCompleter.from_nested_dict(self.choices) + + def refresh_datasets_on_menu(self): + """Refresh datasets on menu with new columns when adding new features""" + + self.list_dataset_cols = list() + maxfile = max(len(file) for file in self.files) + self.loaded_dataset_cols = "\n" + for dataset, data in self.datasets.items(): + self.loaded_dataset_cols += ( + f" {dataset} {(maxfile - len(dataset)) * ' '}: " + f"{', '.join(data.columns)}\n" + ) + + for col in data.columns: + self.list_dataset_cols.append(f"{dataset}.{col}") + + def print_help(self): + """Print help""" + mt = MenuText("forecast/") + mt.add_param("_disclaimer_", self.disclaimer) + mt.add_raw("\n") + mt.add_param( + "_data_loc", + f"\n\t{obbff.EXPORT_FOLDER_PATH}\n\t{Path('custom_imports').resolve()}/forecast", + ) + mt.add_raw("\n") + mt.add_cmd("load") + mt.add_raw("\n") + mt.add_param("_loaded", self.loaded_dataset_cols) + mt.add_info("_exploration_") + mt.add_cmd("show", self.files) + mt.add_cmd("plot", self.files) + mt.add_cmd("clean", self.files) + mt.add_cmd("combine", self.files) + mt.add_cmd("desc", self.files) + mt.add_cmd("corr", self.files) + mt.add_cmd("season", self.files) + mt.add_cmd("delete", self.files) + mt.add_cmd("rename", self.files) + mt.add_cmd("export", self.files) + mt.add_raw("\n") + mt.add_info("_feateng_") + mt.add_cmd("ema", self.files) + mt.add_cmd("sto", self.files) + mt.add_cmd("rsi", self.files) + mt.add_cmd("roc", self.files) + mt.add_cmd("mom", self.files) + mt.add_cmd("delta", self.files) + mt.add_cmd("atr", self.files) + mt.add_cmd("signal", self.files) + mt.add_raw("\n") + mt.add_info("_tsforecasting_") + mt.add_cmd("expo", self.files) + mt.add_cmd("theta", self.files) + mt.add_cmd("linregr", self.files) + mt.add_cmd("regr", self.files) + mt.add_cmd("rnn", self.files) + mt.add_cmd("brnn", self.files) + mt.add_cmd("nbeats", self.files) + mt.add_cmd("tcn", self.files) + mt.add_cmd("trans", self.files) + mt.add_cmd("tft", self.files) + # mt.add_info("_comingsoon_") + + console.print(text=mt.menu_text, menu="Forecast") + + def custom_reset(self): + """Class specific component of reset command""" + if self.files_full: + queue = ["forecast"] + for file, alias in self.files_full: + load = f"'load {file}" + if alias: + load += f" -a {alias}'" + else: + load += "'" + queue.append(load) + return queue + return [] + + def parse_known_args_and_warn( + self, + parser: argparse.ArgumentParser, + other_args: List[str], + export_allowed: int = NO_EXPORT, + raw: bool = False, + limit: int = 0, + # Custom items + target_dataset: bool = False, + target_column: bool = False, + period: Optional[int] = None, + n_days: bool = False, + seasonal: Optional[str] = None, + periods: bool = False, + window: bool = False, + train_split: bool = False, + input_chunk_length: bool = False, + output_chunk_length: bool = False, + force_reset: bool = False, + save_checkpoints: bool = False, + model_save_name: Optional[str] = None, + n_epochs: bool = False, + model_type: bool = False, + dropout: Optional[float] = None, + batch_size: Optional[int] = None, + learning_rate: bool = False, + past_covariates: bool = False, + all_past_covariates: bool = False, + lags: bool = False, + hidden_size: int = None, + n_jumps: bool = False, + end: bool = False, + start: bool = False, + residuals: bool = False, + forecast_only: bool = False, + naive: bool = False, + ): + if hidden_size: + parser.add_argument( + "--hidden-size", + action="store", + dest="hidden_size", + default=hidden_size, + type=check_positive, + help="Size for feature maps for each hidden RNN layer (h_n)", + ) + if past_covariates: + parser.add_argument( + "--past-covariates", + action="store", + dest="past_covariates", + default=None, + type=str, + help="Past covariates(columns/features) in same dataset. Comma separated.", + ) + if all_past_covariates: + parser.add_argument( + "--all-past-covariates", + action="store_true", + dest="all_past_covariates", + default=False, + help="Adds all rows as past covariates except for date and the target column.", + ) + if naive: + parser.add_argument( + "--naive", + action="store_true", + dest="naive", + default=False, + help="Show the naive baseline for a model.", + ) + if target_dataset: + parser.add_argument( + "-d", + "--target-dataset", + help="The name of the dataset you want to select", + dest="target_dataset", + type=str, + choices=list(self.datasets.keys()), + ) + if target_column: + parser.add_argument( + "-c", + "--target-column", + help="The name of the specific column you want to use", + dest="target_column", + type=str, + default="close", + ) + if period is not None: + parser.add_argument( + "--period", + help="The period to use", + dest="period", + type=check_greater_than_one, + default=period, + ) + if n_days: + parser.add_argument( + "-n", + "--n-days", + action="store", + dest="n_days", + type=check_greater_than_one, + default=5, + help="prediction days.", + ) + if seasonal is not None: + parser.add_argument( + "-s", + "--seasonal", + action="store", + dest="seasonal", + choices=expo_model.SEASONS, + default=seasonal, + help="Seasonality: N: None, A: Additive, M: Multiplicative.", + ) + if periods: + parser.add_argument( + "-p", + "--periods", + action="store", + dest="seasonal_periods", + type=check_positive, + default=7, + help="Seasonal periods: 4: Quarterly, 7: Daily", + ) + if window: + parser.add_argument( + "-w", + "--window", + action="store", + dest="start_window", + default=0.85, + type=check_positive_float, + help="Start point for rolling training and forecast window. 0.0-1.0", + ) + if train_split: + parser.add_argument( + "-t", + "--train-split", + action="store", + dest="train_split", + default=0.85, + type=check_positive_float, + help="Start point for rolling training and forecast window. 0.0-1.0", + ) + if input_chunk_length: + parser.add_argument( + "-i", + "--input-chunk-length", + action="store", + dest="input_chunk_length", + default=14, + type=check_positive, + help="Number of past time steps for forecasting module at prediction time.", + ) + if output_chunk_length: + parser.add_argument( + "-o", + "--output-chunk-length", + action="store", + dest="output_chunk_length", + default=5, + type=check_positive, + help="The length of the forecast of the model.", + ) + if force_reset: + parser.add_argument( + "--force-reset", + action="store", + dest="force_reset", + default=True, + type=bool, + help="""If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded).""", + ) + if save_checkpoints: + parser.add_argument( + "--save-checkpoints", + action="store", + dest="save_checkpoints", + default=True, + type=bool, + help="Whether to automatically save the untrained model and checkpoints.", + ) + if model_save_name is not None: + parser.add_argument( + "--model-save-name", + type=str, + action="store", + dest="model_save_name", + default=model_save_name, + help="Name of the model to save.", + ) + if n_epochs: + parser.add_argument( + "--n-epochs", + action="store", + dest="n_epochs", + default=300, + type=check_positive, + help="Number of epochs over which to train the model.", + ) + if model_type: + parser.add_argument( + "--model-type", + type=str, + action="store", + dest="model_type", + default="LSTM", + help='Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU")', + ) + if dropout is not None: + parser.add_argument( + "--dropout", + action="store", + dest="dropout", + default=dropout, + type=check_positive_float, + help="Fraction of neurons afected by Dropout.", + ) + if batch_size is not None: + parser.add_argument( + "--batch-size", + action="store", + dest="batch_size", + default=batch_size, + type=check_positive, + help="Number of time series (input and output) used in each training pass", + ) + if end: + parser.add_argument( + "--end", + action="store", + type=valid_date, + dest="s_end_date", + default=None, + help="The end date (format YYYY-MM-DD) to select for testing", + ) + if start: + parser.add_argument( + "--start", + action="store", + type=valid_date, + dest="s_start_date", + default=None, + help="The start date (format YYYY-MM-DD) to select for testing", + ) + if learning_rate: + parser.add_argument( + "--learning-rate", + action="store", + dest="learning_rate", + default=1e-3, + type=check_positive_float, + help="Learning rate during training.", + ) + if n_jumps: + parser.add_argument( + "-j", + "--jumps", + action="store", + dest="n_jumps", + type=check_positive, + default=1, + help="number of jumps in training data.", + ) + if lags: + parser.add_argument( + "--lags", + action="store", + dest="lags", + type=check_greater_than_one, + default=14, + help="Lagged target values used to predict the next time step.", + ) + if residuals: + parser.add_argument( + "--residuals", + help="Show the residuals for the model.", + action="store_true", + default=False, + dest="residuals", + ) + if forecast_only: + parser.add_argument( + "--forecast-only", + help="Do not plot the hisotorical data without forecasts.", + action="store_true", + default=False, + dest="forecast_only", + ) + # if user does not put in --target-dataset + return super().parse_known_args_and_warn( + parser, other_args, export_allowed, raw, limit + ) + + def load(self, ticker: str, data: pd.DataFrame): + """Loads news dataframes into memory""" + if not data.empty: + data.columns = data.columns.map(lambda x: x.lower().replace(" ", "_")) + + # If the index is a date, move this into a normal column + if data.index.name == "date": + data = data.reset_index() + # Convert date to datetime + # TODO: for now we drop time, once we add handling for time remove this + if "date" in data.columns: + data["date"] = data["date"].apply(helpers.dt_format) + data["date"] = pd.to_datetime(data["date"]) + + # if we import a custom dataset, remove the old index "unnamed:_0" + if "unnamed:_0" in data.columns: + data = data.drop(columns=["unnamed:_0"]) + + self.files.append(ticker) + self.datasets[ticker] = data + + self.update_runtime_choices() + + # Process new datasets to be updated + self.list_dataset_cols = list() + maxfile = max(len(file) for file in self.files) + self.loaded_dataset_cols = "\n" + for dataset, x_data in self.datasets.items(): + self.loaded_dataset_cols += ( + f" {dataset} {(maxfile - len(dataset)) * ' '}: " + f"{', '.join(x_data.columns)}\n" + ) + + for col in data.columns: + self.list_dataset_cols.append(f"{dataset}.{col}") + + @log_start_end(log=logger) + def call_load(self, other_args: List[str]): + """Process load""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="load", + description="Load custom dataset (from previous export, custom imports).", + ) + parser.add_argument( + "-f", + "--file", + help="File to load data in (can be custom import, may have been exported before.)", + type=str, + ) + parser.add_argument( + "-a", + "--alias", + help="Alias name to give to the dataset", + type=str, + ) + + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "-f") + ns_parser = self.parse_known_args_and_warn(parser, other_args) + + if ns_parser: + if ns_parser.file: + file = ns_parser.file + if ns_parser.alias: + alias = ns_parser.alias + else: + if "." in ns_parser.file: + alias = ".".join(ns_parser.file.split(".")[:-1]) + else: + alias = ns_parser.file + + # check if this dataset has been added already + if alias in self.files: + console.print( + "[red]The file/dataset selected has already been loaded.[/red]\n" + ) + return + + data = forecast_model.load(file, self.file_types, self.DATA_FILES) + self.files_full.append([ns_parser.file, ns_parser.alias]) + + self.load(alias, data) + console.print() + + # Show selected dataframe on console + @log_start_end(log=logger) + def call_which(self, other_args: List[str]): + """Process which command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="which", + description="Show library versions of required packages.", + ) + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + ) + + if ns_parser: + console.print() + console.print( + f"[green]Current Compute Device (CPU or GPU):[/green] {self.device.upper()}" + ) + console.print(f"[green]Current RAM:[/green] {self.comp_ram}") + console.print( + f"[green]Recommended Max dataset size based on current RAM:[/green] {self.rec_data_size}" + ) + console.print(f"[green]Torch version:[/green] {self.torch_version}") + console.print(f"[green]Darts version:[/green] {self.darts_version}") + console.print() + + # Show selected dataframe on console + @log_start_end(log=logger) + def call_show(self, other_args: List[str]): + """Process show command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="show", + description="Show a portion of the DataFrame", + ) + parser.add_argument( + "-n", + "--name", + type=str, + choices=self.files, + dest="name", + help="The name of the database you want to show data for", + ) + parser.add_argument( + "-s", + "--sortcol", + help="Sort based on a column in the DataFrame", + nargs="+", + type=str, + dest="sortcol", + default="", + ) + parser.add_argument( + "-a", + "--ascend", + help="Use this argument to sort in a descending order", + action="store_true", + default=False, + dest="ascend", + ) + + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "-n") + ns_parser = self.parse_known_args_and_warn( + parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED, limit=10 + ) + + if ns_parser: + if not ns_parser.name: + dataset_names = list(self.datasets.keys()) + else: + dataset_names = [ns_parser.name] + + for name in dataset_names: + df = self.datasets[name] + + if name in self.datasets and self.datasets[name].empty: + console.print( + f"[red]No data available for {ns_parser.name}.[/red]\n" + ) + elif ns_parser.sortcol: + sort_column = " ".join(ns_parser.sortcol) + if sort_column not in self.datasets[name].columns: + console.print( + f"[red]{sort_column} not a valid column." + "Showing without sorting.\n[/red]" + ) + else: + df = df.sort_values(by=sort_column, ascending=ns_parser.ascend) + + forecast_view.show_df(df, ns_parser.limit, name, ns_parser.export) + + @log_start_end(log=logger) + def call_rename(self, other_args: List[str]): + """Process rename""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="rename", + description="The column you want to rename from a dataset.", + ) + parser.add_argument( + "-d", + "--dataset", + help="Dataset that will get a column renamed", + dest="dataset", + choices=self.choices["rename"], + type=str, + ) + parser.add_argument( + "-o", + "--oldcol", + help="Old column from dataset to be renamed", + dest="oldcol", + type=str, + required="-h" not in other_args, + ) + parser.add_argument( + "-n", + "--newcol", + help="New column from dataset to be renamed", + dest="newcol", + type=str, + required="-h" not in other_args, + ) + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "-d") + ns_parser = self.parse_known_args_and_warn(parser, other_args, NO_EXPORT) + + if ns_parser: + dataset = ns_parser.dataset + column_old = ns_parser.oldcol + column_new = ns_parser.newcol + + if dataset not in self.datasets: + console.print( + f"Not able to find the dataset {dataset}. Please choose one of " + f"the following: {', '.join(self.datasets)}" + ) + else: + self.datasets[dataset] = forecast_model.rename_column( + self.datasets[dataset], column_old, column_new + ) + self.update_runtime_choices() + + console.print() + + # Show selected dataframe on console + @log_start_end(log=logger) + def call_desc(self, other_args: List[str]): + """Process descriptive stats command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="desc", + description="Show descriptive statistics of a dataset", + ) + + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + EXPORT_ONLY_RAW_DATA_ALLOWED, + target_dataset=True, + ) + + if ns_parser: + # check proper file name is provided + if not ns_parser.target_dataset: + console.print("[red]Please enter valid dataset.\n[/red]") + return + + df = self.datasets[ns_parser.target_dataset] + forecast_view.describe_df(df, ns_parser.target_dataset, ns_parser.export) + + @log_start_end(log=logger) + def call_plot(self, other_args: List[str]): + """Process plot command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="plot", + description="Plot data based on the index", + ) + parser.add_argument( + "-v", + "--values", + help="Dataset.column values to be displayed in a plot", + dest="values", + type=check_list_values(self.choices["plot"]), + ) + + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "-v") + ns_parser = self.parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED + ) + if not ns_parser: + return + + if not ns_parser.values: + console.print("[red]Please enter valid dataset.\n[/red]") + return + + data: dict = {} + for datasetcol in ns_parser.values: + dataset, col = datasetcol.split(".") + df = self.datasets[dataset] + if "date" in df.columns: + df = df.set_index("date") + data[datasetcol] = df[col] + + forecast_view.display_plot( + data, + ns_parser.export, + ) + + @log_start_end(log=logger) + def call_season(self, other_args: List[str]): + """Process season command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="season", + description="The seasonality for a given column", + ) + parser.add_argument( + "-v", + "--values", + help="Dataset.column values to be displayed in a plot", + dest="values", + type=str, + ) + parser.add_argument( + "-m", + help="A time lag to highlight on the plot", + dest="m", + type=int, + default=None, + ) + parser.add_argument( + "--max_lag", + help="The maximal lag order to consider", + dest="max_lag", + type=int, + default=24, + ) + parser.add_argument( + "-a", + "--alpha", + help="The confidence interval to display", + dest="alpha", + type=float, + default=0.05, + ) + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "-v") + ns_parser = self.parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED + ) + + if not ns_parser: + return + + if not ns_parser.values: + console.print("[red]Please enter valid dataset.\n[/red]") + return + + try: + dataset, col = ns_parser.values.split(".") + data = self.datasets[dataset] + except ValueError: + console.print("[red]Please enter 'dataset'.'column'.[/red]\n") + return + + forecast_view.display_seasonality( + data=data, + column=col, + export=ns_parser.export, + m=ns_parser.m, + max_lag=ns_parser.max_lag, + alpha=ns_parser.alpha, + ) + + @log_start_end(log=logger) + def call_corr(self, other_args: List[str]): + """Process correlation command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="corr", + description="Plot correlation coefficients.", + ) + + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + EXPORT_ONLY_FIGURES_ALLOWED, + target_dataset=True, + ) + + if ns_parser: + # check proper file name is provided + if not ns_parser.target_dataset: + console.print("[red]Please enter valid dataset.\n[/red]") + return + + data = self.datasets[ns_parser.target_dataset] + + forecast_view.display_corr( + data, + ns_parser.export, + ) + + @log_start_end(log=logger) + def call_combine(self, other_args: List[str]): + """Process combine""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="combine", + description="Combine two entire datasets, or add specific columns. Add specific" + "columns with the syntax: ,", + ) + parser.add_argument( + "--dataset", + help="Dataset to add columns to", + dest="dataset", + choices=self.choices["combine"], + ) + parser.add_argument( + "-c", + "--columns", + help="The columns we want to add ,", + dest="columns", + type=check_list_values( + list(self.choices["delete"].keys()) + + list(self.choices["combine"].keys()) + ), + ) + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--dataset") + ns_parser = self.parse_known_args_and_warn(parser, other_args, NO_EXPORT) + + if ns_parser: + if ns_parser.dataset not in self.datasets: + console.print( + f"Not able to find the dataset {ns_parser.dataset}. Please choose one of " + f"the following: {', '.join(self.datasets)}" + ) + return + + data = self.datasets[ns_parser.dataset] + + for option in ns_parser.columns: + if "." in option: + dataset, col = option.split(".") + columns = [col] + else: + dataset = option + columns = [x for x in self.datasets[dataset].columns if x != "date"] + + if dataset not in self.datasets: + console.print( + f"Not able to find the dataset {dataset}. Please choose one of " + f"the following: {', '.join(self.datasets)}" + ) + continue + for column in columns: + data = forecast_model.combine_dfs( + data, self.datasets[dataset], column, dataset + ) + + self.datasets[ns_parser.dataset] = data + self.update_runtime_choices() + console.print() + + @log_start_end(log=logger) + def call_clean(self, other_args: List[str]): + """Process clean""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="clean", + description="Clean a dataset by filling and dropping NaN values.", + ) + parser.add_argument( + "-f", + "--fill", + help="The method of filling NaNs. This has options to fill rows (rfill, rbfill, rffill) or fill " + "columns (cfill, cbfill, cffill). Furthermore, it has the option to forward fill and backward fill " + "(up to --limit) which refer to how many rows/columns can be set equal to the last non-NaN value", + dest="fill", + choices=["rfill", "cfill", "rbfill", "cbfill", "rffill", "bffill"], + default="", + ) + parser.add_argument( + "--drop", + help="The method of dropping NaNs. This either has the option rdrop (drop rows that contain NaNs) " + "or cdrop (drop columns that contain NaNs)", + dest="drop", + choices=["rdrop", "cdrop"], + default="", + ) + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + NO_EXPORT, + target_dataset=True, + limit=5, + ) + if ns_parser: + # check proper file name is provided + if not ns_parser.target_dataset: + console.print("[red]Please enter valid dataset.\n[/red]") + return + + ( + self.datasets[ns_parser.target_dataset], + clean_status, + ) = forecast_model.clean( + self.datasets[ns_parser.target_dataset], + ns_parser.fill, + ns_parser.drop, + ns_parser.limit, + ) + if not clean_status: + console.print( + f"Successfully cleaned '{ns_parser.target_dataset}' dataset" + ) + else: + console.print(f"[red]{ns_parser.name} still contains NaNs.[/red]") + + console.print() + + @log_start_end(log=logger) + def call_ema(self, other_args: List[str]): + """Process EMA""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="ema", + description="Add exponential moving average to dataset based on specific column.", + ) + + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + NO_EXPORT, + period=10, + target_dataset=True, + target_column=True, + ) + if ns_parser: + # check proper file name is provided + if not helpers.check_parser_input(ns_parser, self.datasets): + return + + self.datasets[ns_parser.target_dataset] = forecast_model.add_ema( + self.datasets[ns_parser.target_dataset], + ns_parser.target_column, + ns_parser.period, + ) + console.print( + f"Successfully added 'EMA_{ns_parser.period}' to '{ns_parser.target_dataset}' dataset" + ) + + # update forecast menu with new column on modified dataset + self.refresh_datasets_on_menu() + + self.update_runtime_choices() + console.print() + + @log_start_end(log=logger) + def call_sto(self, other_args: List[str]): + """Process Stoch Oscill""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="sto", + description="Add in Stochastic Oscillator %K and %D", + ) + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, other_args, NO_EXPORT, limit=5, period=10, target_dataset=True + ) + if ns_parser: + # check proper file name is provided + if not ns_parser.target_dataset: + console.print("[red]Please enter valid dataset.\n[/red]") + return + + self.datasets[ns_parser.target_dataset] = forecast_model.add_sto( + self.datasets[ns_parser.target_dataset], + ns_parser.period, + ) + console.print( + f"Successfully added 'STOK&D_{ns_parser.period}' to '{ns_parser.target_dataset}' dataset" + ) + + # update forecast menu with new column on modified dataset + self.refresh_datasets_on_menu() + + self.update_runtime_choices() + console.print() + + @log_start_end(log=logger) + def call_delete(self, other_args: List[str]): + """Process delete""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="delete", + description="The column you want to delete from a dataset.", + ) + parser.add_argument( + "--delete", + help="The columns you want to delete from a dataset. Use format: or" + " multiple with ,", + dest="delete", + type=check_list_values(self.choices["delete"]), + ) + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--delete") + ns_parser = self.parse_known_args_and_warn(parser, other_args, NO_EXPORT) + + if ns_parser: + for option in ns_parser.delete: + dataset, column = option.split(".", 1) + + if dataset not in self.datasets: + console.print( + f"Not able to find the dataset {dataset}. Please choose one of " + f"the following: {', '.join(self.datasets)}" + ) + else: + forecast_model.delete_column(self.datasets[dataset], column) + + self.update_runtime_choices() + console.print() + + @log_start_end(log=logger) + def call_rsi(self, other_args: List[str]): + """Process RSI""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="rsi", + description="Add rsi to dataset based on specific column.", + ) + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + NO_EXPORT, + target_dataset=True, + target_column=True, + period=10, + ) + if ns_parser: + # check proper file name is provided + if not ns_parser.target_dataset: + console.print("[red]Please enter valid dataset.\n[/red]") + return + + self.datasets[ns_parser.target_dataset] = forecast_model.add_rsi( + self.datasets[ns_parser.target_dataset], + ns_parser.target_column, + ns_parser.period, + ) + console.print( + f"Successfully added 'RSI_{ns_parser.period}' to '{ns_parser.target_dataset}' dataset" + ) + + # update forecast menu with new column on modified dataset + self.refresh_datasets_on_menu() + + self.update_runtime_choices() + console.print() + + @log_start_end(log=logger) + def call_roc(self, other_args: List[str]): + """Process ROC""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="roc", + description="Add rate of change to dataset based on specific column.", + ) + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + NO_EXPORT, + target_dataset=True, + target_column=True, + period=10, + ) + if ns_parser: + # check proper file name is provided + if not helpers.check_parser_input(ns_parser, self.datasets): + return + + self.datasets[ns_parser.target_dataset] = forecast_model.add_roc( + self.datasets[ns_parser.target_dataset], + ns_parser.target_column, + ns_parser.period, + ) + console.print( + f"Successfully added 'ROC_{ns_parser.period}' to '{ns_parser.target_dataset}' dataset" + ) + + # update forecast menu with new column on modified dataset + self.refresh_datasets_on_menu() + + self.update_runtime_choices() + console.print() + + @log_start_end(log=logger) + def call_mom(self, other_args: List[str]): + """Process Momentum""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="mom", + description="Add momentum to dataset based on specific column.", + ) + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + NO_EXPORT, + target_dataset=True, + target_column=True, + period=10, + ) + if ns_parser: + # check proper file name is provided + if not helpers.check_parser_input(ns_parser, self.datasets): + return + + self.datasets[ns_parser.target_dataset] = forecast_model.add_momentum( + self.datasets[ns_parser.target_dataset], + ns_parser.target_column, + ns_parser.period, + ) + console.print( + f"Successfully added 'Momentum_{ns_parser.period}' to '{ns_parser.target_dataset}' dataset" + ) + + # update forecast menu with new column on modified dataset + self.refresh_datasets_on_menu() + + self.update_runtime_choices() + console.print() + + @log_start_end(log=logger) + def call_delta(self, other_args: List[str]): + """Process %Change (Delta)""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="delta", + description="Add %Change (Delta) to dataset based on specific column.", + ) + + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + NO_EXPORT, + target_dataset=True, + target_column=True, + ) + if ns_parser: + # check proper file name is provided + if not helpers.check_parser_input(ns_parser, self.datasets): + return + + self.datasets[ns_parser.target_dataset] = forecast_model.add_delta( + self.datasets[ns_parser.target_dataset], ns_parser.target_column + ) + console.print( + f"Successfully added 'Delta_{ns_parser.target_column}' to '{ns_parser.target_dataset}' dataset" + ) + + # update forecast menu with new column on modified dataset + self.refresh_datasets_on_menu() + + self.update_runtime_choices() + console.print() + + @log_start_end(log=logger) + def call_atr(self, other_args: List[str]): + """Process Average True Range""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="atr", + description="Add Average True Range to dataset of specific stock ticker.", + ) + + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + NO_EXPORT, + target_dataset=True, + target_column=True, + ) + if ns_parser: + # check proper file name is provided + if not helpers.check_parser_input(ns_parser, self.datasets): + return + + check = False + self.datasets[ns_parser.target_dataset], check = forecast_model.add_atr( + self.datasets[ns_parser.target_dataset] + ) + if check: + console.print( + f"Successfully added 'Average True Range' to '{ns_parser.target_dataset}' dataset" + ) + else: + console.print( + "Could not add 'Average True Range' as it does not have one/all specific columns (low/close/high)" + ) + + # update forecast menu with new column on modified dataset + self.refresh_datasets_on_menu() + + self.update_runtime_choices() + console.print() + + @log_start_end(log=logger) + def call_signal(self, other_args: List[str]): + """Process Price Signal""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="signal", + description="""Add price signal to dataset based on closing price. + 1 if the signal is that short term price will go up as compared to the long term. + 0 if the signal is that short term price will go down as compared to the long term. + """, + ) + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + NO_EXPORT, + target_dataset=True, + target_column=True, + ) + if ns_parser: + # check proper file name is provided + if not helpers.check_parser_input(ns_parser, self.datasets): + return + + self.datasets[ns_parser.target_dataset] = forecast_model.add_signal( + self.datasets[ns_parser.target_dataset] + ) + console.print( + f"Successfully added 'Price Signal' to '{ns_parser.target_dataset}' dataset" + ) + + # update forecast menu with new column on modified dataset + self.refresh_datasets_on_menu() + + self.update_runtime_choices() + console.print() + + @log_start_end(log=logger) + def call_export(self, other_args: List[str]): + """Process export command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="export", + description="Export dataset to Excel", + ) + parser.add_argument( + "-t", + "--type", + help="The file type you wish to export to", + dest="type", + choices=self.file_types, + type=str, + default="xlsx", + ) + + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + export_allowed=NO_EXPORT, + target_dataset=True, + ) + + if not helpers.check_parser_input(ns_parser, self.datasets, "ignore_column"): + return + + forecast_view.export_df( + self.datasets[ns_parser.target_dataset], + ns_parser.type, + ns_parser.target_dataset, + ) + console.print() + + # EXPO Model + @log_start_end(log=logger) + def call_expo(self, other_args: List[str]): + """Process expo command""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="expo", + description=""" + Perform Probabilistic Exponential Smoothing forecast + Trend: N: None, A: Additive, M: Multiplicative + Seasonality: N: None, A: Additive, M: Multiplicative + Dampen: T: True, F: False + """, + ) + parser.add_argument( + "--trend", + action="store", + dest="trend", + choices=expo_model.TRENDS, + default="A", + help="Trend: N: None, A: Additive, M: Multiplicative.", + ) + parser.add_argument( + "--dampen", + action="store", + dest="dampen", + default="F", + help="Dampening", + ) + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + export_allowed=EXPORT_ONLY_FIGURES_ALLOWED, + target_dataset=True, + target_column=True, + n_days=True, + seasonal="A", + periods=True, + window=True, + residuals=True, + forecast_only=True, + start=True, + end=True, + naive=True, + ) + # TODO Convert this to multi series + if ns_parser: + if not helpers.check_parser_input(ns_parser, self.datasets): + return + + expo_view.display_expo_forecast( + data=self.datasets[ns_parser.target_dataset], + dataset_name=ns_parser.target_dataset, + n_predict=ns_parser.n_days, + target_column=ns_parser.target_column, + trend=ns_parser.trend, + seasonal=ns_parser.seasonal, + seasonal_periods=ns_parser.seasonal_periods, + dampen=ns_parser.dampen, + start_window=ns_parser.start_window, + forecast_horizon=ns_parser.n_days, + export=ns_parser.export, + residuals=ns_parser.residuals, + forecast_only=ns_parser.forecast_only, + start_date=ns_parser.s_start_date, + end_date=ns_parser.s_end_date, + naive=ns_parser.naive, + ) + + @log_start_end(log=logger) + def call_theta(self, other_args: List[str]): + """Process theta command""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="theta", + description=""" + Perform Theta forecast + """, + ) + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + export_allowed=EXPORT_ONLY_FIGURES_ALLOWED, + target_dataset=True, + n_days=True, + target_column=True, + seasonal="M", + periods=True, + window=True, + residuals=True, + forecast_only=True, + start=True, + end=True, + naive=True, + ) + + if ns_parser: + if not helpers.check_parser_input(ns_parser, self.datasets): + return + + theta_view.display_theta_forecast( + data=self.datasets[ns_parser.target_dataset], + dataset_name=ns_parser.target_dataset, + n_predict=ns_parser.n_days, + target_column=ns_parser.target_column, + seasonal=ns_parser.seasonal, + seasonal_periods=ns_parser.seasonal_periods, + start_window=ns_parser.start_window, + forecast_horizon=ns_parser.n_days, + export=ns_parser.export, + residuals=ns_parser.residuals, + forecast_only=ns_parser.forecast_only, + start_date=ns_parser.s_start_date, + end_date=ns_parser.s_end_date, + naive=ns_parser.naive, + ) + + @log_start_end(log=logger) + def call_rnn(self, other_args: List[str]): + """Process RNN command""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="rnn", + description=""" + Perform RNN forecast (Vanilla RNN, LSTM, GRU) + """, + ) + # RNN Hyperparameters + parser.add_argument( + "--hidden-dim", + action="store", + dest="hidden_dim", + default=20, + type=check_positive, + help="Size for feature maps for each hidden RNN layer (h_n)", + ) + parser.add_argument( + "--training_length", + action="store", + dest="training_length", + default=20, + type=check_positive, + help="""The length of both input (target and covariates) and output (target) time series used during training. + Generally speaking, training_length should have a higher value than input_chunk_length because otherwise + during training the RNN is never run for as many iterations as it will during training.""", + ) + + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + export_allowed=EXPORT_ONLY_FIGURES_ALLOWED, + target_dataset=True, + n_days=True, + target_column=True, + train_split=True, + input_chunk_length=True, + force_reset=True, + save_checkpoints=True, + model_save_name="rnn_model", + n_epochs=True, + model_type=True, + dropout=0, + batch_size=32, + learning_rate=True, + residuals=True, + forecast_only=True, + start=True, + end=True, + naive=True, + ) + + if ns_parser: + if not helpers.check_parser_input(ns_parser, self.datasets): + return + + rnn_view.display_rnn_forecast( + data=self.datasets[ns_parser.target_dataset], + dataset_name=ns_parser.target_dataset, + n_predict=ns_parser.n_days, + target_column=ns_parser.target_column, + train_split=ns_parser.train_split, + forecast_horizon=ns_parser.n_days, + model_type=ns_parser.model_type, + hidden_dim=ns_parser.hidden_dim, + dropout=ns_parser.dropout, + batch_size=ns_parser.batch_size, + n_epochs=ns_parser.n_epochs, + learning_rate=ns_parser.learning_rate, + model_save_name=ns_parser.model_save_name, + training_length=ns_parser.training_length, + input_chunk_size=ns_parser.input_chunk_length, + force_reset=ns_parser.force_reset, + save_checkpoints=ns_parser.save_checkpoints, + export=ns_parser.export, + residuals=ns_parser.residuals, + forecast_only=ns_parser.forecast_only, + start_date=ns_parser.s_start_date, + end_date=ns_parser.s_end_date, + naive=ns_parser.naive, + ) + + @log_start_end(log=logger) + def call_nbeats(self, other_args: List[str]): + """Process NBEATS command""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="nbeats", + description=""" + Perform NBEATS forecast (Neural Bayesian Estimation of Time Series). + """, + ) + # NBEATS Hyperparameters + parser.add_argument( + "--num_stacks", + action="store", + dest="num_stacks", + default=10, + type=check_positive_float, + help="The number of stacks that make up the whole model.", + ) + parser.add_argument( + "--num_blocks", + action="store", + dest="num_blocks", + default=3, + type=check_positive, + help="The number of blocks making up every stack.", + ) + parser.add_argument( + "--num_layers", + action="store", + dest="num_layers", + default=4, + type=check_positive, + help="""The number of fully connected layers preceding the final forking layers + in each block of every stack.""", + ) + parser.add_argument( + "--layer_widths", + action="store", + dest="layer_widths", + default=512, + type=check_positive, + help="""Determines the number of neurons that make up each fully connected layer + in each block of every stack""", + ) + + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + export_allowed=EXPORT_ONLY_FIGURES_ALLOWED, + target_dataset=True, + n_days=True, + target_column=True, + train_split=True, + input_chunk_length=True, + output_chunk_length=True, + save_checkpoints=True, + force_reset=True, + model_save_name="nbeats_model", + learning_rate=True, + n_epochs=True, + batch_size=800, + past_covariates=True, + all_past_covariates=True, + residuals=True, + forecast_only=True, + start=True, + end=True, + naive=True, + ) + + if ns_parser: + if not helpers.check_parser_input(ns_parser, self.datasets): + return + + covariates = helpers.clean_covariates( + ns_parser, self.datasets[ns_parser.target_dataset] + ) + + nbeats_view.display_nbeats_forecast( + data=self.datasets[ns_parser.target_dataset], + dataset_name=ns_parser.target_dataset, + n_predict=ns_parser.n_days, + target_column=ns_parser.target_column, + past_covariates=covariates, + train_split=ns_parser.train_split, + forecast_horizon=ns_parser.n_days, + input_chunk_length=ns_parser.input_chunk_length, + output_chunk_length=ns_parser.output_chunk_length, + num_stacks=ns_parser.num_stacks, + num_blocks=ns_parser.num_blocks, + num_layers=ns_parser.num_layers, + layer_widths=ns_parser.layer_widths, + batch_size=ns_parser.batch_size, + n_epochs=ns_parser.n_epochs, + learning_rate=ns_parser.learning_rate, + model_save_name=ns_parser.model_save_name, + force_reset=ns_parser.force_reset, + save_checkpoints=ns_parser.save_checkpoints, + export=ns_parser.export, + residuals=ns_parser.residuals, + forecast_only=ns_parser.forecast_only, + start_date=ns_parser.s_start_date, + end_date=ns_parser.s_end_date, + naive=ns_parser.naive, + ) + + @log_start_end(log=logger) + def call_tcn(self, other_args: List[str]): + """Process TCN command""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="tcn", + description=""" + Perform TCN forecast. + """, + ) + # TCN Hyperparameters + parser.add_argument( + "--num-filters", + action="store", + dest="num_filters", + default=3, + type=check_positive, + help="The number of filters in a convolutional layer of the TCN", + ) + parser.add_argument( + "--weight-norm", + action="store", + dest="weight_norm", + default=True, + type=bool, + help="Boolean value indicating whether to use weight normalization.", + ) + parser.add_argument( + "--dilation-base", + action="store", + dest="dilation_base", + default=2, + type=check_positive, + help="The base of the exponent that will determine the dilation on every level.", + ) + + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + export_allowed=EXPORT_ONLY_FIGURES_ALLOWED, + target_dataset=True, + n_days=True, + target_column=True, + train_split=True, + past_covariates=True, + all_past_covariates=True, + save_checkpoints=True, + force_reset=True, + model_save_name="tcn_model", + learning_rate=True, + n_epochs=True, + dropout=0.1, + batch_size=32, + input_chunk_length=True, + output_chunk_length=True, + residuals=True, + forecast_only=True, + start=True, + end=True, + naive=True, + ) + + if ns_parser: + # check proper file name is provided + if not helpers.check_parser_input(ns_parser, self.datasets): + return + + covariates = helpers.clean_covariates( + ns_parser, self.datasets[ns_parser.target_dataset] + ) + + tcn_view.display_tcn_forecast( + data=self.datasets[ns_parser.target_dataset], + dataset_name=ns_parser.target_dataset, + n_predict=ns_parser.n_days, + target_column=ns_parser.target_column, + past_covariates=covariates, + train_split=ns_parser.train_split, + forecast_horizon=ns_parser.n_days, + input_chunk_length=ns_parser.input_chunk_length, + output_chunk_length=ns_parser.output_chunk_length, + dropout=ns_parser.dropout, + num_filters=ns_parser.num_filters, + weight_norm=ns_parser.weight_norm, + dilation_base=ns_parser.dilation_base, + batch_size=ns_parser.batch_size, + n_epochs=ns_parser.n_epochs, + learning_rate=ns_parser.learning_rate, + model_save_name=ns_parser.model_save_name, + force_reset=ns_parser.force_reset, + save_checkpoints=ns_parser.save_checkpoints, + export=ns_parser.export, + residuals=ns_parser.residuals, + forecast_only=ns_parser.forecast_only, + start_date=ns_parser.s_start_date, + end_date=ns_parser.s_end_date, + naive=ns_parser.naive, + ) + + @log_start_end(log=logger) + def call_regr(self, other_args: List[str]): + """Process REGR command""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="regr", + description=""" + Perform a regression forecast + """, + ) + + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + export_allowed=EXPORT_ONLY_FIGURES_ALLOWED, + output_chunk_length=True, + train_split=True, + past_covariates=True, + all_past_covariates=True, + n_days=True, + target_dataset=True, + target_column=True, + lags=True, + residuals=True, + forecast_only=True, + start=True, + end=True, + naive=True, + ) + + if ns_parser: + # check proper file name is provided + if not helpers.check_parser_input(ns_parser, self.datasets): + return + + covariates = helpers.clean_covariates( + ns_parser, self.datasets[ns_parser.target_dataset] + ) + + regr_view.display_regression( + data=self.datasets[ns_parser.target_dataset], + dataset_name=ns_parser.target_dataset, + n_predict=ns_parser.n_days, + target_column=ns_parser.target_column, + past_covariates=covariates, + train_split=ns_parser.train_split, + forecast_horizon=ns_parser.n_days, + output_chunk_length=ns_parser.output_chunk_length, + lags=ns_parser.lags, + export=ns_parser.export, + residuals=ns_parser.residuals, + forecast_only=ns_parser.forecast_only, + start_date=ns_parser.s_start_date, + end_date=ns_parser.s_end_date, + naive=ns_parser.naive, + ) + + @log_start_end(log=logger) + def call_linregr(self, other_args: List[str]): + """Process LINREGR command""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="linregr", + description=""" + Perform a linear regression forecast + """, + ) + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + export_allowed=EXPORT_ONLY_FIGURES_ALLOWED, + lags=True, + output_chunk_length=True, + train_split=True, + past_covariates=True, + all_past_covariates=True, + target_column=True, + n_days=True, + target_dataset=True, + residuals=True, + forecast_only=True, + start=True, + end=True, + naive=True, + ) + + if ns_parser: + if not helpers.check_parser_input(ns_parser, self.datasets): + return + + covariates = helpers.clean_covariates( + ns_parser, self.datasets[ns_parser.target_dataset] + ) + + linregr_view.display_linear_regression( + data=self.datasets[ns_parser.target_dataset], + dataset_name=ns_parser.target_dataset, + n_predict=ns_parser.n_days, + target_column=ns_parser.target_column, + past_covariates=covariates, + train_split=ns_parser.train_split, + forecast_horizon=ns_parser.n_days, + output_chunk_length=ns_parser.output_chunk_length, + lags=ns_parser.lags, + export=ns_parser.export, + residuals=ns_parser.residuals, + forecast_only=ns_parser.forecast_only, + start_date=ns_parser.s_start_date, + end_date=ns_parser.s_end_date, + naive=ns_parser.naive, + ) + + @log_start_end(log=logger) + def call_brnn(self, other_args: List[str]): + """Process BRNN command""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="brnn", + description=""" + Perform BRNN forecast (Vanilla RNN, LSTM, GRU) + """, + ) + # BRNN Hyperparameters + parser.add_argument( + "--n-rnn-layers", + action="store", + dest="n_rnn_layers", + default=1, + type=check_positive, + help="Number of layers in the RNN module.", + ) + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + export_allowed=EXPORT_ONLY_FIGURES_ALLOWED, + save_checkpoints=True, + force_reset=True, + model_save_name="brnn_model", + learning_rate=True, + n_epochs=True, + batch_size=32, + dropout=0, + input_chunk_length=True, + output_chunk_length=True, + model_type=True, + train_split=True, + past_covariates=True, + all_past_covariates=True, + target_dataset=True, + n_days=True, + target_column=True, + hidden_size=20, + residuals=True, + forecast_only=True, + start=True, + end=True, + naive=True, + ) + + if ns_parser: + if not helpers.check_parser_input(ns_parser, self.datasets): + return + + covariates = helpers.clean_covariates( + ns_parser, self.datasets[ns_parser.target_dataset] + ) + + brnn_view.display_brnn_forecast( + data=self.datasets[ns_parser.target_dataset], + dataset_name=ns_parser.target_dataset, + n_predict=ns_parser.n_days, + target_column=ns_parser.target_column, + past_covariates=covariates, + train_split=ns_parser.train_split, + forecast_horizon=ns_parser.n_days, + input_chunk_length=ns_parser.input_chunk_length, + output_chunk_length=ns_parser.output_chunk_length, + model_type=ns_parser.model_type, + n_rnn_layers=ns_parser.n_rnn_layers, + hidden_size=ns_parser.hidden_size, + dropout=ns_parser.dropout, + batch_size=ns_parser.batch_size, + n_epochs=ns_parser.n_epochs, + learning_rate=ns_parser.learning_rate, + model_save_name=ns_parser.model_save_name, + force_reset=ns_parser.force_reset, + save_checkpoints=ns_parser.save_checkpoints, + export=ns_parser.export, + residuals=ns_parser.residuals, + forecast_only=ns_parser.forecast_only, + start_date=ns_parser.s_start_date, + end_date=ns_parser.s_end_date, + naive=ns_parser.naive, + ) + + @log_start_end(log=logger) + def call_trans(self, other_args: List[str]): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="trans", + description=""" + Perform Transformer Forecast. + """, + ) + parser.add_argument( + "--d-model", + action="store", + dest="d_model", + default=64, + type=check_positive, + help="Number of expected features in inputs.", + ) + parser.add_argument( + "--nhead", + action="store", + dest="nhead", + default=4, + type=check_positive, + help="Number of head in the attention mechanism.", + ) + parser.add_argument( + "--num_encoder_layers", + action="store", + dest="num_encoder_layers", + default=3, + type=check_positive, + help="The number of encoder leayers in the encoder.", + ) + parser.add_argument( + "--num_decoder_layers", + action="store", + dest="num_decoder_layers", + default=3, + type=check_positive, + help="The number of decoder leayers in the encoder.", + ) + parser.add_argument( + "--dim_feedforward", + action="store", + dest="dim_feedforward", + default=512, + type=check_positive, + help="The dimension of the feedforward model.", + ) + parser.add_argument( + "--activation", + action="store", + dest="activation", + default="relu", + choices=["relu", "gelu"], + type=str, + help="Number of LSTM layers.", + ) + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + export_allowed=EXPORT_ONLY_FIGURES_ALLOWED, + n_days=True, + target_column=True, + target_dataset=True, + past_covariates=True, + all_past_covariates=True, + train_split=True, + input_chunk_length=True, + output_chunk_length=True, + dropout=0, + batch_size=32, + n_epochs=True, + model_save_name="trans_model", + force_reset=True, + save_checkpoints=True, + residuals=True, + forecast_only=True, + start=True, + end=True, + naive=True, + ) + if ns_parser: + if not helpers.check_parser_input(ns_parser, self.datasets): + return + + covariates = helpers.clean_covariates( + ns_parser, self.datasets[ns_parser.target_dataset] + ) + + trans_view.display_trans_forecast( + data=self.datasets[ns_parser.target_dataset], + dataset_name=ns_parser.target_dataset, + n_predict=ns_parser.n_days, + target_column=ns_parser.target_column, + past_covariates=covariates, + train_split=ns_parser.train_split, + forecast_horizon=ns_parser.n_days, + input_chunk_length=ns_parser.input_chunk_length, + output_chunk_length=ns_parser.output_chunk_length, + d_model=ns_parser.d_model, + nhead=ns_parser.nhead, + num_encoder_layers=ns_parser.num_encoder_layers, + num_decoder_layers=ns_parser.num_decoder_layers, + dim_feedforward=ns_parser.dim_feedforward, + activation=ns_parser.activation, + dropout=ns_parser.dropout, + n_epochs=ns_parser.n_epochs, + batch_size=ns_parser.batch_size, + model_save_name=ns_parser.model_save_name, + force_reset=ns_parser.force_reset, + save_checkpoints=ns_parser.save_checkpoints, + export=ns_parser.export, + residuals=ns_parser.residuals, + forecast_only=ns_parser.forecast_only, + start_date=ns_parser.s_start_date, + end_date=ns_parser.s_end_date, + naive=ns_parser.naive, + ) + + # Below this is ports to the old pred menu + + @log_start_end(log=logger) + def call_tft(self, other_args: List[str]): + """Process TFT command""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + add_help=False, + prog="tft", + description=""" + Perform TFT forecast (Temporal Fusion Transformer). + """, + ) + parser.add_argument( + "--lstm-layers", + action="store", + dest="lstm_layers", + default=1, + type=check_positive, + help="Number of LSTM layers.", + ) + parser.add_argument( + "--num-attention-heads", + action="store", + dest="num_attention_heads", + default=4, + type=check_positive, + help="Number of attention heads.", + ) + parser.add_argument( + "--full-attention", + action="store_true", + dest="full_attention", + default=False, + help="Whether to apply a multi-head attention query.", + ) + parser.add_argument( + "--hidden-continuous-size", + action="store", + dest="hidden_continuous_size", + default=8, + type=check_positive, + help="Default hidden size for processing continuous variables.", + ) + + # if user does not put in --target-dataset + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "--target-dataset") + + ns_parser = self.parse_known_args_and_warn( + parser, + other_args, + export_allowed=EXPORT_ONLY_FIGURES_ALLOWED, + save_checkpoints=True, + target_dataset=True, + n_days=True, + force_reset=True, + model_save_name="tft_model", + train_split=True, + hidden_size=16, + batch_size=32, + n_epochs=True, + dropout=0.1, + input_chunk_length=True, + output_chunk_length=True, + past_covariates=True, + all_past_covariates=True, + target_column=True, + residuals=True, + forecast_only=True, + start=True, + end=True, + naive=True, + ) + + if ns_parser: + if not helpers.check_parser_input(ns_parser, self.datasets): + return + + covariates = helpers.clean_covariates( + ns_parser, self.datasets[ns_parser.target_dataset] + ) + + tft_view.display_tft_forecast( + data=self.datasets[ns_parser.target_dataset], + dataset_name=ns_parser.target_dataset, + n_predict=ns_parser.n_days, + target_column=ns_parser.target_column, + past_covariates=covariates, + train_split=ns_parser.train_split, + forecast_horizon=ns_parser.n_days, + input_chunk_length=ns_parser.input_chunk_length, + output_chunk_length=ns_parser.output_chunk_length, + hidden_size=ns_parser.hidden_size, + lstm_layers=ns_parser.lstm_layers, + num_attention_heads=ns_parser.num_attention_heads, + full_attention=ns_parser.full_attention, + dropout=ns_parser.dropout, + hidden_continuous_size=ns_parser.hidden_continuous_size, + n_epochs=ns_parser.n_epochs, + batch_size=ns_parser.batch_size, + model_save_name=ns_parser.model_save_name, + force_reset=ns_parser.force_reset, + save_checkpoints=ns_parser.save_checkpoints, + export=ns_parser.export, + residuals=ns_parser.residuals, + forecast_only=ns_parser.forecast_only, + start_date=ns_parser.s_start_date, + end_date=ns_parser.s_end_date, + naive=ns_parser.naive, + ) diff --git a/openbb_terminal/forecast/forecast_model.py b/openbb_terminal/forecast/forecast_model.py new file mode 100644 index 00000000000..79f70b7cb64 --- /dev/null +++ b/openbb_terminal/forecast/forecast_model.py @@ -0,0 +1,544 @@ +"""Forecast Model""" +__docformat__ = "numpy" + +# pylint: disable=eval-used + +import logging +from pathlib import Path +from typing import Dict, Union, Any, Optional, List +from itertools import chain + +import pandas as pd +from pandas import DataFrame +import numpy as np + +from openbb_terminal.decorators import log_start_end +from openbb_terminal.rich_config import console + +logger = logging.getLogger(__name__) + + +default_files = { + filepath.name: filepath + for file_type in ["csv", "xlsx"] + for filepath in chain( + Path("exports").rglob(f"*.{file_type}"), + Path("custom_imports").rglob(f"*.{file_type}"), + ) + if filepath.is_file() +} + + +@log_start_end(log=logger) +def load( + file: str, + file_types: Optional[List[str]] = None, + data_files: Optional[Dict[Any, Any]] = None, + add_extension: bool = False, +) -> pd.DataFrame: + """Load custom file into dataframe. + + Parameters + ---------- + file: str + Path to file + file_types: list + Supported file types + data_files: dict + Contains all available data files within the Export folder + add_extension: + Takes a file name and tries loading with csv or xlsx extension + + Returns + ------- + pd.DataFrame: + Dataframe with custom data + """ + if file_types is None: + file_types = ["csv", "xlsx"] + if data_files is None: + data_files = default_files + + if file in data_files: + full_file = data_files[file] + else: + full_file = file + + if add_extension: + for ext in ["xlsx", "csv"]: + tmp = f"{full_file}.{ext}" + if tmp in data_files: + full_file = data_files[tmp] + + if not Path(full_file).exists(): + console.print(f"[red]Cannot find the file {full_file}[/red]\n") + + return pd.DataFrame() + + file_type = Path(full_file).suffix + + if file_type == ".xlsx": + data = pd.read_excel(full_file) + elif file_type == ".csv": + data = pd.read_csv(full_file) + else: + return console.print( + f"The file type {file_type} is not supported. Please choose one of the following: " + f"{', '.join(file_types)}" + ) + + return data + + +@log_start_end(log=logger) +def get_options( + datasets: Dict[str, pd.DataFrame], dataset_name: str = None +) -> Dict[Union[str, Any], DataFrame]: + """Obtain columns-dataset combinations from loaded in datasets that can be used in other commands + + Parameters + ---------- + datasets: dict + The available datasets. + dataset_name: str + The dataset you wish to show the options for. + + Returns + ------- + option_tables: dict + A dictionary with a DataFrame for each option. With dataset_name set, only shows one + options table. + """ + option_tables = {} + + if dataset_name: + columns = datasets[dataset_name].columns + option_tables[dataset_name] = pd.DataFrame( + { + "column": columns, + "option": [f"{dataset_name}.{column}" for column in columns], + } + ) + else: + for dataset, data_values in datasets.items(): + columns = data_values.columns + option_tables[dataset] = pd.DataFrame( + { + "column": columns, + "option": [f"{dataset}.{column}" for column in columns], + } + ) + + return option_tables + + +@log_start_end(log=logger) +def clean( + dataset: pd.DataFrame, + fill: Optional[str] = None, + drop: Optional[str] = None, + limit: Optional[int] = None, +) -> pd.DataFrame: + """Clean up NaNs from the dataset + + Parameters + ---------- + dataset : pd.DataFrame + The dataset you wish to clean + fill : Optional[str] + The method of filling NaNs + drop : Optional[str] + The method of dropping NaNs + limit : Optional[int] + The maximum limit you wish to apply that can be forward or backward filled + + Returns + ------- + pd.DataFrame: + Dataframe with cleaned up data + """ + kwargs = {} + if limit: + kwargs["limit"] = limit + + if fill: + if fill == "rfill": + dataset = dataset.fillna(axis="index", value=0) + if fill == "cfill": + dataset = dataset.fillna(axis="columns", value=0) + elif fill == "rbfill": + dataset = dataset.fillna(axis="index", method="bfill", **kwargs) + elif fill == "cbfill": + dataset = dataset.fillna(axis="columns", method="bfill", **kwargs) + elif fill == "rffill": + dataset = dataset.fillna(axis="index", method="ffill", **kwargs) + elif fill == "cffill": + dataset = dataset.fillna(axis="columns", method="ffill", **kwargs) + + if drop: + if drop == "rdrop": + dataset = dataset.dropna(how="any", axis="index") + elif drop == "cdrop": + dataset = dataset.dropna(how="any", axis="columns") + + # TODO - think about if we want to always interpolate and remove above options + if fill == "" and drop == "": + # drop all rows with na + dataset = dataset.dropna(axis=0) + + dataset = dataset.reset_index(drop=True) + + return dataset, dataset.isnull().values.any() + + +@log_start_end(log=logger) +def add_ema( + dataset: pd.DataFrame, target_column: str = "close", period: int = 10 +) -> pd.DataFrame: + """A moving average provides an indication of the trend of the price movement + by cut down the amount of "noise" on a price chart. + + Parameters + ---------- + dataset : pd.DataFrame + The dataset you wish to clean + target_column : str + The column you wish to add the EMA to + period : int + Time Span + + Returns + ------- + pd.DataFrame: + Dataframe with added EMA column + """ + dataset[f"EMA_{period}"] = ( + dataset[target_column].ewm(span=period, adjust=False).mean() + ) + + return dataset + + +@log_start_end(log=logger) +def add_sto(dataset: pd.DataFrame, period: int = 10) -> pd.DataFrame: + """Stochastic Oscillator %K and %D : A stochastic oscillator is a momentum indicator comparing a particular closing + price of a security to a range of its prices over a certain period of time. %K and %D are slow and fast indicators. + + Requires Low/High/Close columns. + Note: This will drop first rows equal to period due to how this metric is calculated. + + Parameters + ---------- + dataset : pd.DataFrame + The dataset you wish to calculate for + period : int + Span + + Returns + ------- + pd.DataFrame: + Dataframe with added STO K & D columns + """ + + # check if columns exist + if ( + "low" in dataset.columns + and "high" in dataset.columns + and "close" in dataset.columns + ): + STOK = ( + (dataset["close"] - dataset["low"].rolling(period).min()) + / ( + dataset["high"].rolling(period).max() + - dataset["low"].rolling(period).min() + ) + ) * 100 + + dataset[f"SO%K_{period}"] = STOK + dataset[f"SO%D_{period}"] = STOK.rolling(3).mean() + + return dataset + + console.print("[red]Missing Low/High/Close columns[/red]\n") + return dataset + + +@log_start_end(log=logger) +def add_rsi( + dataset: pd.DataFrame, target_column: str = "close", period: int = 10 +) -> pd.DataFrame: + """A momentum indicator that measures the magnitude of recent price changes to evaluate + overbought or oversold conditions in the price of a stock or other asset. + + Parameters + ---------- + dataset : pd.DataFrame + The dataset you wish to calculate for + target_column : str + The column you wish to add the RSI to + period : int + Time Span + + Returns + ------- + pd.DataFrame: + Dataframe with added RSI column + """ + + delta = dataset[target_column].diff().dropna() + u = delta * 0 + d = u.copy() + u[delta > 0] = delta[delta > 0] + d[delta < 0] = -delta[delta < 0] + u[u.index[period - 1]] = np.mean(u[:period]) # first value is sum of avg gains + u = u.drop(u.index[: (period - 1)]) + d[d.index[period - 1]] = np.mean(d[:period]) # first value is sum of avg losses + d = d.drop(d.index[: (period - 1)]) + rs = ( + u.ewm(com=period - 1, adjust=False).mean() + / d.ewm(com=period - 1, adjust=False).mean() + ) + dataset[f"RSI_{period}"] = 100 - 100 / (1 + rs) + + return dataset + + +@log_start_end(log=logger) +def add_roc( + dataset: pd.DataFrame, target_column: str = "close", period: int = 10 +) -> pd.DataFrame: + """A momentum oscillator, which measures the percentage change between the current + value and the n period past value. + + Parameters + ---------- + dataset : pd.DataFrame + The dataset you wish to calculate with + target_column : str + The column you wish to add the ROC to + period : int + Time Span + + Returns + ------- + pd.DataFrame: + Dataframe with added ROC column + """ + M = dataset[target_column].diff(period - 1) + N = dataset[target_column].shift(period - 1) + + dataset[f"ROC_{period}"] = (M / N) * 100 + + return dataset + + +@log_start_end(log=logger) +def add_momentum( + dataset: pd.DataFrame, target_column: str = "close", period: int = 10 +) -> pd.DataFrame: + """A momentum oscillator, which measures the percentage change between the current + value and the n period past value. + + Parameters + ---------- + dataset : pd.DataFrame + The dataset you wish to calculate with + target_column : str + The column you wish to add the MOM to + period : int + Time Span + + Returns + ------- + pd.DataFrame: + Dataframe with added MOM column + """ + + dataset[f"Momentum_{period}"] = dataset[target_column].diff(period) + + return dataset + + +@log_start_end(log=logger) +def add_delta( + dataset: pd.DataFrame, + target_column: str = "close", +) -> pd.DataFrame: + """ + Calculate the %change of a variable based on a specific column + """ + + dataset[f"delta_{target_column}"] = dataset[target_column].pct_change().fillna(0) + + return dataset + + +@log_start_end(log=logger) +def add_atr( + dataset: pd.DataFrame, +) -> pd.DataFrame: + """ + Calculate the Average True Range of a variable based on a a specific stock ticker. + """ + + if "high" in dataset and "low" in dataset and "close" in dataset: + dataset["ATR1"] = abs(dataset["high"] - dataset["low"]) + dataset["ATR2"] = abs(dataset["high"] - dataset["close"].shift()) + dataset["ATR3"] = abs(dataset["low"] - dataset["close"].shift()) + dataset["true_range"] = dataset[["ATR1", "ATR2", "ATR3"]].max(axis=1) + + # drop ATR1, ATR2, ATR3 + dataset = dataset.drop(["ATR1", "ATR2", "ATR3"], axis=1) + + return dataset, True + + return dataset, False + + +@log_start_end(log=logger) +def add_signal(dataset: pd.DataFrame) -> pd.DataFrame: + """A price signal based on short/long term price. + + 1 if the signal is that short term price will go up as compared to the long term. + 0 if the signal is that short term price will go down as compared to the long term. + + Parameters + ---------- + dataset : pd.DataFrame + The dataset you wish to calculate with + + Returns + ------- + pd.DataFrame: + Dataframe with added signal column + """ + + # Create short simple moving average over the short window + # > Create long simple moving average over the long window + + # Create signals + dataset["signal"] = np.where( + dataset["close"].rolling(window=10, min_periods=1, center=False).mean() + > dataset["close"].rolling(window=60, min_periods=1, center=False).mean(), + 1.0, + 0.0, + ) + + return dataset + + +@log_start_end(log=logger) +def combine_dfs( + df1: pd.DataFrame, df2: pd.DataFrame, column: str, dataset: str = "" +) -> pd.DataFrame: + """Adds the given column of df2 to df1 + + Parameters + ---------- + df1: pd.DataFrame + The dataframe to add a column to + df2: pd.DataFrame + The dataframe to lose a column + column: str + The column to transfer + dataset: str + A name for df2 (shows in name of new column) + + Returns + ---------- + data: pd.DataFrame + The new dataframe + """ + if column not in df2: + console.print( + f"Not able to find the column {column}. Please choose one of " + f"the following: {', '.join(df2.columns)}" + ) + return df1 + + if "date" in df1.columns and "date" in df2.columns: + selected = df2[[column, "date"]] + new_cols = [f"{dataset}_{x}" if x != "date" else "date" for x in selected] + selected.columns = new_cols + return df1.merge(selected, on="date", how="left") + + console.print( + "[red]Not all dataframes have a date column so we are combining" + " on index, this may results in data mismatching.[/red]\n" + ) + selected = df2[[column]] + new_cols = [f"{dataset}_{x}" for x in selected] + selected.columns = new_cols + return df1.merge(selected, left_index=True, right_index=True, how="left") + + +@log_start_end(log=logger) +def delete_column(df: pd.DataFrame, column: str) -> None: + if column not in df: + console.print( + f"Not able to find the column {column}. Please choose one of " + f"the following: {', '.join(df.columns)}" + ) + else: + del df[column] + + +@log_start_end(log=logger) +def rename_column(df: pd.DataFrame, old_column: str, new_column: str) -> pd.DataFrame: + """Rename a column in a dataframe + + Parameters + ---------- + df: pd.DataFrame + The dataframe to have a column renamed + old_column: str + The column that will have its name changed + new_column: str + The name to update to + + Returns + ---------- + new_df: pd.DataFrame + The dataframe with the renamed column + """ + if old_column not in df: + console.print( + f"Not able to find the column {old_column}. Please choose one of " + f"the following: {', '.join(df.columns)}" + ) + return df + return df.rename(columns={old_column: new_column}) + + +@log_start_end(log=logger) +def describe_df(df: pd.DataFrame) -> pd.DataFrame: + """Returns statistics for a given df + + Parameters + ---------- + df: pd.DataFrame + The df to produce statistics for + + Returns + ---------- + df: pd.DataFrame + The df with the new data + """ + return df.describe() + + +@log_start_end(log=logger) +def corr_df(df: pd.DataFrame) -> pd.DataFrame: + """Returns correlation for a given df + + Parameters + ---------- + df: pd.DataFrame + The df to produce statistics for + + Returns + ---------- + df: pd.DataFrame + The df with the new data + """ + return df.corr() diff --git a/openbb_terminal/forecast/forecast_view.py b/openbb_terminal/forecast/forecast_view.py new file mode 100644 index 00000000000..8b31c84c3fc --- /dev/null +++ b/openbb_terminal/forecast/forecast_view.py @@ -0,0 +1,254 @@ +"""Forecast View""" +__docformat__ = "numpy" + +import logging +import os +from typing import Dict, Optional, List, Union + +import matplotlib.pyplot as plt +import pandas as pd +from pandas.plotting import register_matplotlib_converters +import seaborn as sns +from darts.utils.statistics import plot_acf + +from openbb_terminal.config_plot import PLOT_DPI +from openbb_terminal.decorators import log_start_end +from openbb_terminal.helper_funcs import ( + export_data, + plot_autoscale, +) +from openbb_terminal.helper_funcs import ( + print_rich_table, +) +from openbb_terminal.rich_config import console +from openbb_terminal.forecast import forecast_model +from openbb_terminal.config_terminal import theme +from openbb_terminal.forecast import helpers + +logger = logging.getLogger(__name__) + +register_matplotlib_converters() + + +@log_start_end(log=logger) +def show_options( + datasets: Dict[str, pd.DataFrame], + dataset_name: str = None, + export: str = "", +): + """Plot custom data + + Parameters + ---------- + datasets: dict + The loaded in datasets + dataset_name: str + The name of the dataset you wish to show options for + export: str + Format to export image + """ + if not datasets: + console.print( + "Please load in a dataset by using the 'load' command before using this feature." + ) + else: + option_tables = forecast_model.get_options(datasets, dataset_name) + + for dataset, data_values in option_tables.items(): + print_rich_table( + data_values, + headers=list(data_values.columns), + show_index=False, + title=f"Options for dataset: '{dataset}'", + ) + + export_data( + export, + os.path.dirname(os.path.abspath(__file__)), + f"{dataset}_options", + data_values.set_index("column"), + ) + + +@log_start_end(log=logger) +def display_plot( + data: Union[pd.DataFrame, Dict[str, pd.DataFrame]], + export: str = "", + external_axes: Optional[List[plt.axes]] = None, +): + """Plot data from a dataset + Parameters + ---------- + data: Dict[str: pd.DataFrame] + Dictionary with key being dataset.column and dataframes being values + export: str + Format to export image + external_axes:Optional[List[plt.axes]] + External axes to plot on + """ + if isinstance(data, pd.DataFrame): + data = {col: data[col] for col in data.columns} + + for dataset_col in list(data): + if isinstance(data[dataset_col].index, pd.MultiIndex): + console.print( + "The index appears to be a multi-index. " + "Therefore, it is not possible to plot the data." + ) + del data[dataset_col] + + # Check that there's at least a valid dataframe + if data: + if external_axes is None: + _, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI) + else: + ax = external_axes[0] + + for dataset_col in data: + try: + if isinstance(data[dataset_col], pd.Series): + ax.plot(data[dataset_col].index, data[dataset_col].values) + elif isinstance(data[dataset_col], pd.DataFrame): + ax.plot(data[dataset_col]) + except TypeError: + print(f"Unable to plot column: {dataset_col}") + + theme.style_primary_axis(ax) + if external_axes is None: + theme.visualize_output() + + ax.legend(list(data.keys())) + + export_data( + export, + os.path.dirname(os.path.abspath(__file__)), + "plot", + ) + + +@log_start_end(log=logger) +def display_seasonality( + data: pd.DataFrame, + column: str = "close", + export: str = "", + m: Optional[int] = None, + max_lag: int = 24, + alpha: float = 0.05, + external_axes: Optional[List[plt.axes]] = None, +): + """Plot seasonality from a dataset + + Parameters + ---------- + data: pd.DataFrame + The dataframe to plot + column: str + The column of the dataframe to analyze + export: str + Format to export image + m: Optional[int] + Optionally, a time lag to highlight on the plot. Default is none. + max_lag: int + The maximal lag order to consider. Default is 24. + alpha: float + The confidence interval to display. Default is 0.05. + external_axes:Optional[List[plt.axes]] + External axes to plot on + """ + + if not data.empty: + _, series = helpers.get_series(data, column) + if external_axes is None: + _, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI) + else: + ax = external_axes[0] + + # TODO: Add darts check_seasonality here + plot_acf( + series, m=m, max_lag=max_lag, alpha=alpha, axis=ax, default_formatting=False + ) + + theme.style_primary_axis(ax) + + if external_axes is None: + theme.visualize_output() + + export_data(export, os.path.dirname(os.path.abspath(__file__)), "plot") + + +@log_start_end(log=logger) +def display_corr( + dataset: pd.DataFrame, + export: str = "", + external_axes: Optional[List[plt.axes]] = None, +): + """Plot correlation coefficients for dataset features + + Parameters + ---------- + dataset : pd.DataFrame + The dataset fore calculating correlation coefficients + export: str + Format to export image + external_axes:Optional[List[plt.axes]] + External axes to plot on + """ + + if external_axes is None: + _, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI) + else: + ax = external_axes[0] + + # correlation + correlation = forecast_model.corr_df(dataset) + sns.heatmap(correlation, vmax=1, square=True, annot=True, cmap="cubehelix") + ax.set_title("Correlation Matrix") + theme.style_primary_axis(ax) + + if external_axes is None: + theme.visualize_output() + + export_data(export, os.path.dirname(os.path.abspath(__file__)), "plot") + + +@log_start_end(log=logger) +def show_df(data: pd.DataFrame, limit: int = 15, name: str = "", export: str = ""): + console.print( + f"[green]{name} has following shape (rowxcolumn): {data.shape}[/green]" + ) + if len(data.columns) > 10: + console.print( + "[red]Dataframe has more than 10 columns. Please export" + " to see all of the data.[/red]\n" + ) + data = data.iloc[:, :10] + print_rich_table( + data.head(limit), + headers=list(data.columns), + show_index=True, + title=f"Dataset {name} | Showing {limit} of {len(data)} rows", + ) + + export_data( + export, os.path.dirname(os.path.abspath(__file__)), f"{name}_show", data + ) + + +@log_start_end(log=logger) +def describe_df(data: pd.DataFrame, name: str = "", export: str = ""): + new_df = forecast_model.describe_df(data) + print_rich_table( + new_df, + headers=list(data.describe().columns), + show_index=True, + title=f"Showing Descriptive Statistics for Dataset {name}", + ) + + export_data(export, os.path.dirname(os.path.abspath(__file__)), f"{name}_show") + + +@log_start_end(log=logger) +def export_df(data: pd.DataFrame, export: str, name: str = "") -> None: + export_data( + export, os.path.dirname(os.path.abspath(__file__)), f"{name}_show", data + ) diff --git a/openbb_terminal/forecast/helpers.py b/openbb_terminal/forecast/helpers.py new file mode 100644 index 00000000000..ab1ec0d1266 --- /dev/null +++ b/openbb_terminal/forecast/helpers.py @@ -0,0 +1,878 @@ +# pylint: disable=too-many-arguments +import os +import argparse +from typing import Any, Union, Optional, List, Dict +from datetime import timedelta, datetime, time +import logging +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler, MinMaxScaler, MaxAbsScaler, Normalizer +from sklearn.metrics import ( + mean_absolute_error, + r2_score, + mean_squared_error, +) +from darts.dataprocessing.transformers import MissingValuesFiller, Scaler +from darts.utils.statistics import plot_residuals_analysis +from darts import TimeSeries +from darts.metrics import mape +from pytorch_lightning.callbacks.early_stopping import EarlyStopping +from openbb_terminal.rich_config import console +from openbb_terminal.config_terminal import theme +from openbb_terminal.helper_funcs import ( + export_data, + plot_autoscale, + print_rich_table, + is_valid_axes_count, +) +from openbb_terminal.config_plot import PLOT_DPI +from openbb_terminal import rich_config + +logger = logging.getLogger(__name__) +logging.getLogger("pytorch_lightning").setLevel(logging.CRITICAL) # No needed for now + + +def mean_absolute_percentage_error(y_true: np.ndarray, y_pred: np.ndarray) -> np.number: + """Calculate mean absolute percent error""" + y_true, y_pred = np.array(y_true), np.array(y_pred) + return np.mean(np.abs((y_true - y_pred) / y_true)) * 100 + + +def print_prediction_kpis(real: np.ndarray, pred: np.ndarray): + """Print prediction statistics""" + kpis = { + "MAPE": f"{mean_absolute_percentage_error(real, pred) :.3f} %", + "R2": f"{r2_score(real, pred) :.3f}", + "MAE": f"{mean_absolute_error(real, pred):.3f}", + "MSE": f"{mean_squared_error(real, pred):.3f}", + "RMSE": f"{mean_squared_error(real, pred, squared=False):.3f}", + } + df = pd.DataFrame.from_dict(kpis, orient="index") + print_rich_table( + df, + show_index=True, + title="KPIs", + floatfmt=".2f", + ) + + +def plot_data_predictions( + data, + preds, + y_valid, + y_dates_valid, + scaler, + title, + forecast_data, + n_loops, + time_str: str = "", + external_axes: Optional[List[plt.Axes]] = None, +): + """Plots data predictions for the different ML techniques + external_axes : Optional[List[plt.Axes]], optional + External axes (1 axis is expected in the list), by default None + """ + + # This plot has 1 axis + if external_axes is None: + _, ax = plt.subplots( + figsize=plot_autoscale(), + dpi=PLOT_DPI, + ) + elif is_valid_axes_count(external_axes, 1): + (ax,) = external_axes + else: + return + + ax.plot( + data.index, + data.values, + "-o", + ms=2, + label="Real data", + ) + for i in range(len(y_valid) - 1): + + if scaler: + y_pred = scaler.inverse_transform(preds[i].reshape(-1, 1)).ravel() + y_act = scaler.inverse_transform(y_valid[i].reshape(-1, 1)).ravel() + else: + y_pred = preds[i].ravel() + y_act = y_valid[i].ravel() + ax.plot( + y_dates_valid[i], + y_pred, + color=theme.down_color, + ) + ax.fill_between( + y_dates_valid[i], + y_pred, + y_act, + where=(y_pred < y_act), + color=theme.down_color, + alpha=0.2, + ) + ax.fill_between( + y_dates_valid[i], + y_pred, + y_act, + where=(y_pred > y_act), + color=theme.up_color, + alpha=0.2, + ) + + # Leave this out of the loop so that the legend doesn't get overpopulated with "Predictions" + if scaler: + final_pred = scaler.inverse_transform(preds[-1].reshape(-1, 1)).ravel() + final_valid = scaler.inverse_transform(y_valid[-1].reshape(-1, 1)).ravel() + else: + final_pred = preds[-1].reshape(-1, 1).ravel() + final_valid = y_valid[-1].reshape(-1, 1).ravel() + ax.plot( + y_dates_valid[-1], + final_pred, + color=theme.down_color, + label="Predictions", + ) + ax.fill_between( + y_dates_valid[-1], + final_pred, + final_valid, + alpha=0.2, + ) + + _, _, ymin, ymax = plt.axis() + ax.vlines( + forecast_data.index[0], + ymin, + ymax, + linestyle="--", + ) + if n_loops == 1: + ax.plot( + forecast_data.index, + forecast_data.values, + "-o", + label="Forecast", + ) + else: + ax.plot( + forecast_data.index, + forecast_data.median(axis=1).values, + "-o", + label="Forecast", + ) + ax.fill_between( + forecast_data.index, + forecast_data.quantile(0.25, axis=1).values, + forecast_data.quantile(0.75, axis=1).values, + alpha=0.3, + ) + # Subtracting 1 day only for daily data. For now if not daily, then start line on last point + if (not time_str or time_str == "1D") and isinstance( + forecast_data.index[0], datetime + ): + ax.axvspan( + forecast_data.index[0] - timedelta(days=1), + forecast_data.index[-1], + alpha=0.2, + ) + ax.set_xlim(data.index[0], forecast_data.index[-1] + timedelta(days=1)) + + else: + ax.axvspan( + forecast_data.index[0], + forecast_data.index[-1], + alpha=0.2, + ) + ax.set_xlim(data.index[0], forecast_data.index[-1]) + ax.set_title(title) + ax.legend() + ax.set_ylabel("Value") + + theme.style_primary_axis(ax) + + if external_axes is None: + theme.visualize_output() + + +def prepare_scale_train_valid_test( + data: Union[pd.DataFrame, pd.Series], + n_input_days: int, + n_predict_days: int, + test_size: float, + s_start_date: Optional[datetime] = None, + s_end_date: Optional[datetime] = None, + no_shuffle: bool = True, + Preprocess: Optional[str] = "standardization", +): + """ + Prepare and scale train, validate and test data. This is an old function for models + imported from the previous pred menu + + Parameters + ---------- + data: pd.DataFrame + Dataframe of stock prices + ns_parser: argparse.Namespace + Parsed arguments + Returns + ------- + X_train: np.ndarray + Array of training data. Shape (# samples, n_inputs, 1) + X_test: np.ndarray + Array of validation data. Shape (total sequences - #samples, n_inputs, 1) + y_train: np.ndarray + Array of training outputs. Shape (#samples, n_days) + y_test: np.ndarray + Array of validation outputs. Shape (total sequences -#samples, n_days) + X_dates_train: np.ndarray + Array of dates for X_train + X_dates_test: np.ndarray + Array of dates for X_test + y_dates_train: np.ndarray + Array of dates for y_train + y_dates_test: np.ndarray + Array of dates for y_test + test_data: np.ndarray + Array of prices after the specified end date + dates_test: np.ndarray + Array of dates after specified end date + scaler: + Fitted PREPROCESSOR + Preprocess: Optional[str] + The method to preprocess data. Choose: standardization, minmax, normalization, or None. + Default is standardization. + """ + + pre_dict = { + "standardization": StandardScaler(), + "minmax": MinMaxScaler(), + "normalization": Normalizer(), + } + if Preprocess is None: + scaler = None + else: + scaler = pre_dict.get(Preprocess, None) + + if s_end_date: + data = data[data.index <= s_end_date] + if n_input_days + n_predict_days > data.shape[0]: + console.print( + "Cannot train enough input days to predict with loaded dataframe\n" + ) + return ( + None, + None, + None, + None, + None, + None, + None, + None, + None, + None, + None, + True, + ) + if s_start_date: + data = data[data.index >= s_start_date] + if n_input_days + n_predict_days > data.shape[0]: + console.print( + "Cannot train enough input days to predict with loaded dataframe\n" + ) + return ( + None, + None, + None, + None, + None, + None, + None, + None, + None, + None, + None, + True, + ) + + test_data = data.iloc[-n_input_days:] + train_data = data.iloc[:-n_input_days] + + dates = data.index + dates_test = test_data.index + if scaler: + train_data = scaler.fit_transform(data.values.reshape(-1, 1)) + test_data = scaler.transform(test_data.values.reshape(-1, 1)) + else: + train_data = data.values.reshape(-1, 1) + test_data = test_data.values.reshape(-1, 1) + + prices = train_data + + input_dates = [] + input_prices = [] + next_n_day_prices = [] + next_n_day_dates = [] + + for idx in range(len(prices) - n_input_days - n_predict_days): + input_prices.append(prices[idx : idx + n_input_days]) # noqa: E203 + input_dates.append(dates[idx : idx + n_input_days]) # noqa: E203 + next_n_day_prices.append( + prices[ + idx + n_input_days : idx + n_input_days + n_predict_days # noqa: E203 + ] + ) + next_n_day_dates.append( + dates[ + idx + n_input_days : idx + n_input_days + n_predict_days # noqa: E203 + ] + ) + + input_dates = np.asarray(input_dates) # type: ignore + input_prices = np.array(input_prices) # type: ignore + next_n_day_prices = np.array(next_n_day_prices) # type: ignore + next_n_day_dates = np.asarray(next_n_day_dates) # type: ignore + + ( + X_train, + X_valid, + y_train, + y_valid, + X_dates_train, + X_dates_valid, + y_dates_train, + y_dates_valid, + ) = train_test_split( + input_prices, + next_n_day_prices, + input_dates, + next_n_day_dates, + test_size=test_size, + shuffle=no_shuffle, + ) + return ( + X_train, + X_valid, + y_train, + y_valid, + X_dates_train, + X_dates_valid, + y_dates_train, + y_dates_valid, + test_data, + dates_test, + scaler, + False, + ) + + +def lambda_price_prediction_color(val: float) -> str: + """Set prediction to be a colored string. This was changed to always shows blue""" + return f"[#00AAFF]{val:.2f} [/#00AAFF]" + + +def print_pretty_prediction(df_pred: pd.DataFrame, last_price: float): + """Print predictions""" + if rich_config.USE_COLOR: + df_pred = pd.DataFrame(df_pred) + df_pred.columns = ["pred"] + df_pred["pred"] = df_pred["pred"].apply( + lambda x: lambda_price_prediction_color(x) + ) + if check_dates(df_pred.index.to_series()): + df_pred.index = df_pred.index.date + print_rich_table( + df_pred, + show_index=True, + index_name="Datetime", + headers=["Prediction"], + floatfmt=".2f", + title=f"Actual price: [yellow]{last_price:.2f} [/yellow]", + ) + + +def past_covs(past_covariates, data, train_split, is_scaler=True): + if past_covariates is not None: + + target_covariates_names = past_covariates.split(",") + + # create first covariate to then stack + console.print(f"[green]Covariate #0: {target_covariates_names[0]}[/green]") + _, past_covariate_whole = get_series( + data, target_column=target_covariates_names[0], is_scaler=is_scaler + ) + + if len(target_covariates_names) > 1: + for i, column in enumerate(target_covariates_names[1:]): + console.print(f"[green]Covariate #{i+1}: {column}[/green]") + _, _temp_new_covariate = get_series( + data, + target_column=target_covariates_names[i + 1], + is_scaler=is_scaler, + ) + + # continually stack covariates based on column names + past_covariate_whole = past_covariate_whole.stack(_temp_new_covariate) + + # Split the full scale covariate to train and val + ( + past_covariate_train, + past_covariate_val, + ) = past_covariate_whole.split_before(train_split) + return ( + past_covariate_whole, + past_covariate_train, + past_covariate_val, + ) + return None, None, None + + +def early_stopper(patience: int, monitor: str = "val_loss"): + my_stopper = EarlyStopping( + monitor=monitor, + patience=patience, + min_delta=0.01, + mode="min", + ) + return my_stopper + + +def get_pl_kwargs( + patience: int = 20, monitor: str = "val_loss", accelerator: str = "cpu" +) -> Dict[str, Any]: + my_stopper = early_stopper(patience, monitor) + pl_trainer_kwargs = { + "callbacks": [my_stopper], + "accelerator": accelerator, + "logger": False, + # "enable_progress_bar": False, + "enable_model_summary": False, + } + return pl_trainer_kwargs + + +def plot_forecast( + name: str, + target_col: str, + historical_fcast, + predicted_values, + ticker_series, + ticker_name: str, + data, + n_predict: int, + forecast_horizon, + past_covariates, + precision, + probabilistic, + export: str, + low_quantile: float = None, + high_quantile: float = None, + forecast_only: bool = False, + naive: bool = False, + external_axes: Optional[List[plt.axes]] = None, +): + quant_kwargs = {} + if low_quantile: + quant_kwargs["low_quantile"] = low_quantile + if high_quantile: + quant_kwargs["high_quantile"] = high_quantile + if not external_axes: + _, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI) + else: + ax = external_axes[0] + + # ax = fig.get_axes()[0] # fig gives list of axes (only one for this case) + naive_fcast = ticker_series.shift(1) + if forecast_only: + ticker_series = ticker_series.drop_before(historical_fcast.start_time()) + ticker_series.plot(label=target_col, ax=ax) + historical_fcast.plot( + label=f"Backtest {forecast_horizon}-Steps ahead forecast", + ax=ax, + **quant_kwargs, + ) + + if naive: + # show naive forecast shift timeseries by 1 + naive_fcast = naive_fcast.drop_before(historical_fcast.start_time()) + naive_precision = mape(ticker_series, naive_fcast) + + naive_fcast.plot( + label=f"Naive+1: {naive_precision:.2f}%", + ax=ax, + **quant_kwargs, + ) + + pred_label = f"{name} Forecast" + if past_covariates: + pred_label += " w/ past covs" + predicted_values.plot(label=pred_label, ax=ax, **quant_kwargs, color="#00AAFF") + ax.set_title( + f"{name} for ${ticker_name} for next [{n_predict}] days (MAPE={precision:.2f}%)" + ) + ax.set_ylabel(target_col) + ax.set_xlabel("Date") + theme.style_primary_axis(ax) + + if not external_axes: + theme.visualize_output() + + if probabilistic: + numeric_forecast = predicted_values.quantile_df()[f"{target_col}_0.5"].tail( + n_predict + ) + else: + numeric_forecast = predicted_values.pd_dataframe()[target_col].tail(n_predict) + + print_pretty_prediction(numeric_forecast, data[target_col].iloc[-1]) + + export_data(export, os.path.dirname(os.path.abspath(__file__)), "expo") + + +def dt_format(x) -> str: + """Convert any Timestamp to YYYY-MM-DD + Args: + x: Pandas Timestamp of any length + Returns: + x: formatted string + """ + x = pd.to_datetime(x) + x = x.strftime("%Y-%m-%d") + return x + + +def get_series( + data: pd.DataFrame, target_column: str = None, is_scaler: bool = True +) -> tuple[Optional[Scaler], TimeSeries]: + filler = MissingValuesFiller() + filler_kwargs = dict( + df=data, + time_col="date", + value_cols=[target_column], + freq="B", + fill_missing_dates=True, + ) + try: + ticker_series = TimeSeries.from_dataframe(**filler_kwargs) + except ValueError: + # remove business days to allow base lib to assume freq + filler_kwargs.pop("freq") + ticker_series = TimeSeries.from_dataframe(**filler_kwargs) + except AttributeError: + # Uses index if no date column + console.print( + "[red]Warning: no date found, assuming items evenly spaced.[/red]\n" + ) + filler_kwargs.pop("time_col") + ticker_series = TimeSeries.from_dataframe(**filler_kwargs) + + if is_scaler: + scaler = Scaler(scaler=MaxAbsScaler()) + scaled_ticker_series = scaler.fit_transform( + filler.transform(ticker_series) + ).astype(np.float32) + return scaler, scaled_ticker_series + ticker_series = filler.transform(ticker_series).astype(np.float32) + scaler = None + return scaler, ticker_series + + +def fit_model( + model, + series, + val_series=None, + past_covariates=None, + val_past_covariates=None, + **kwargs, +): + fit_kwargs = dict( + series=series, + ) + fit_kwargs.update(kwargs) + if val_series is not None: + fit_kwargs["val_series"] = val_series + if past_covariates is not None: + fit_kwargs["past_covariates"] = past_covariates + fit_kwargs["val_past_covariates"] = val_past_covariates + + model.fit(**fit_kwargs) + + +def get_prediction( + model_name, + probabilistic, + use_scalers, + scaler, + past_covariates, + best_model, + ticker_series, + past_covariate_whole, + train_split, + forecast_horizon, + n_predict: int, +): + print(f"Predicting {model_name} for {n_predict} days") + if model_name not in ["Regression", "Logistic Regression"]: + # need to create a new pytorch trainer for historical backtesting to remove progress bar + best_model.trainer = None + best_model.trainer_params["enable_progress_bar"] = False + + # Historical backtest if with covariates + if past_covariates is not None: + historical_fcast = best_model.historical_forecasts( + ticker_series, + past_covariates=past_covariate_whole, + start=train_split, + forecast_horizon=forecast_horizon, + retrain=False, + verbose=True, + ) + # historical backtest without covariates + else: + historical_fcast = best_model.historical_forecasts( + ticker_series, + start=train_split, + forecast_horizon=forecast_horizon, + retrain=False, + verbose=True, + ) + + # now predict N days in the future + if past_covariates is not None: + if probabilistic: + prediction = best_model.predict( + series=ticker_series, + past_covariates=past_covariate_whole, + n=n_predict, + num_samples=500, + ) + else: + prediction = best_model.predict( + series=ticker_series, + past_covariates=past_covariate_whole, + n=n_predict, + ) + else: + if probabilistic: + prediction = best_model.predict( + series=ticker_series, n=n_predict, num_samples=500 + ) + else: + prediction = best_model.predict(series=ticker_series, n=n_predict) + + precision = mape( + actual_series=ticker_series, pred_series=historical_fcast + ) # mape = mean average precision error + console.print(f"{model_name} model obtains MAPE: {precision:.2f}% \n") + + # scale back + if use_scalers: + ticker_series = scaler.inverse_transform(ticker_series) + historical_fcast = scaler.inverse_transform(historical_fcast) + prediction = scaler.inverse_transform(prediction) + + return ticker_series, historical_fcast, prediction, precision, best_model + + +def check_parser_input(parser: argparse.ArgumentParser, datasets, *args) -> bool: + # check proper file name is provided + if not hasattr(parser, "target_dataset"): + return False + if not parser.target_dataset: # type: ignore + console.print("[red]Please enter valid dataset.\n[/red]") + return False + if "ignore_column" in args: + return True + if not hasattr(parser, "target_column"): + return False + + # must check that data["date"] col is within target series + if parser.target_column not in datasets[parser.target_dataset].columns: # type: ignore + console.print( + f"[red]The column {parser.target_column} does not exist.\n[/red]" # type: ignore + ) + return False + return True + + +def plot_residuals( + model, + past_covariates, + series, + forecast_horizon: int = 1, + num_bins: int = 20, + default_formatting: bool = False, +): + del default_formatting + if past_covariates: + console.print( + "[red]Cannot calculate and plot residuals if there are past covariates.[/red]" + ) + else: + console.print( + "[green]Calculating and plotting residuals... This may take a few moments.[/green]" + ) + my_stopper = early_stopper(5, "val_loss") + pl_trainer_kwargs = {"callbacks": [my_stopper], "accelerator": "cpu"} + model.trainer_params = pl_trainer_kwargs + residuals = model.residuals( + series=series, + forecast_horizon=forecast_horizon, + retrain=False, + verbose=True, + ) + plot_residuals_analysis( + residuals=residuals, + num_bins=num_bins, + fill_nan=True, + # TODO: uncomment this once darts pr #1224 becomes a part of the package + # default_formatting=default_formatting, + ) + + +def check_data_length( + train, test, input_chunk_length: int, output_chunk_length: int +) -> bool: + if input_chunk_length + output_chunk_length > len(train): + base = "[red]Not enough train data. Please increase the " + console.print( + base + "train test split or decrease input or output chunk length.[/red]\n" + ) + return False + if input_chunk_length + output_chunk_length > len(test): + base = "[red]Not enough test data. Please decrease the " + console.print( + base + + "train test split or decrease the input or output chunk length.[/red]\n" + ) + return False + return True + + +def filter_dates( + data: Union[pd.DataFrame, pd.Series], + start_date: Optional[datetime], + end_date: Optional[datetime], +) -> Union[pd.DataFrame, pd.Series]: + if start_date and end_date and start_date >= end_date: + console.print("[red]The start date must be before the end date.[/red]\n") + return data + if end_date: + data = data[data["date"] <= end_date] + if start_date: + data = data[data["date"] >= start_date] + return data + + +def clean_data( + data: Union[pd.DataFrame, pd.Series], + start_date: Optional[datetime] = None, + end_date: Optional[datetime] = None, + target_column: Optional[str] = None, + past_covariates: Optional[str] = None, +) -> Union[pd.DataFrame, pd.Series]: + # check if target column is in data and if the target_column has any inf + # replace all inf with nan. This is because darts does not handle inf + # Creating a timeseries with fillna=True will replace all nan with interoplated values + if target_column and target_column in data.columns: + if data[target_column].isin([np.inf, -np.inf]).any(): + console.print( + f"[red]The target column [{target_column}] has inf values. Cleaning...[/red]\n" + ) + data = data.replace([np.inf, -np.inf], np.nan) + + # check if past covariates are in data and if they have any inf + if past_covariates: + covariates = past_covariates.split(",") + for covariate in covariates: + if covariate in data.columns: + if data[covariate].isin([np.inf, -np.inf]).any(): + console.print( + f"[red]The covariate:{covariate} has inf values. Cleaning...[/red]\n" + ) + data = data.replace([np.inf, -np.inf], np.nan) + + if isinstance(data, pd.Series): + col = data.name + columns = ["date", col] + data = pd.DataFrame(data).reset_index() + data.columns = columns + data = filter_dates(data, start_date, end_date) + data = data.set_index("date") + data = data[col] + elif "date" in data.columns: + data = filter_dates(data, start_date, end_date) + elif end_date or start_date: + console.print( + "[red]No 'date' column specified, ignoring start end end date[/red]\n" + ) + return data + + +def clean_covariates(parser, dataset: pd.DataFrame) -> Optional[str]: + forecast_column: str = parser.target_column + if parser.all_past_covariates: + data = dataset + covs = [x for x in data.columns if x not in ["date", parser.target_column]] + return ",".join(covs) + covariates = parser.past_covariates + if not covariates: + return covariates + covs_list = covariates.split(",") + for covariate in covs_list: + if covariate == forecast_column: + covs_list.remove(covariate) + console.print( + f"[red]Forecast target '{forecast_column}' cannot be within the past" + " covariates. Removed the covariates from analysis[/red]\n" + ) + if len(covariates) == 0: + return None + covariates = ",".join(covs_list) + return covariates + + +def check_data( + data: pd.DataFrame, target_column: str, past_covariates: Optional[str] = None +) -> bool: + if target_column not in data.columns: + console.print( + f"[red]Column {target_column} is not in the dataframe." + " Change the 'target_column' parameter.[/red]\n" + ) + return False + if past_covariates is not None: + covariates = past_covariates.split(",") + for covariate in covariates: + if covariate not in data.columns: + console.print( + f"[red]Column {covariate} is not in the dataframe." + " Change the 'past_covariates' parameter.[/red]\n" + ) + return False + return True + + +def check_output( + output_chunk_length: int, n_predict: int, past_covariates: bool +) -> int: + if past_covariates: + if output_chunk_length != n_predict: + console.print( + "[red]Warning: when using past covariates n_predict must equal" + f" output_chunk_length. We have changed your output_chunk_length to {n_predict}" + " to match your n_predict" + ) + return n_predict + return output_chunk_length + + +def check_dates(s: pd.Series) -> bool: + """Checks whether all hours, minutes, seconds and milliseconds are 0""" "" + for _, value in s.items(): + if value.time() != time(0, 0, 0, 0): + return False + return True diff --git a/openbb_terminal/forecast/linregr_model.py b/openbb_terminal/forecast/linregr_model.py new file mode 100644 index 00000000000..ae5053d768f --- /dev/null +++ b/openbb_terminal/forecast/linregr_model.py @@ -0,0 +1,113 @@ +# pylint: disable=too-many-arguments +"""Linear Regression Model""" +__docformat__ = "numpy" + +import logging +from typing import Any, Tuple, Union, List, Optional +import warnings + + +import pandas as pd + +from darts import TimeSeries +from darts.models import LinearRegressionModel +from openbb_terminal.decorators import log_start_end + +from openbb_terminal.forecast import helpers + +logger = logging.getLogger(__name__) + + +@log_start_end(log=logger) +def get_linear_regression_data( + data: Union[pd.Series, pd.DataFrame], + target_column: str = "close", + n_predict: int = 5, + past_covariates: str = None, + train_split: float = 0.85, + forecast_horizon: int = 5, + output_chunk_length: int = 5, + lags: Union[int, List[int]] = 14, + random_state: Optional[int] = None, +) -> Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries], float, Any]: + """Perform Linear Regression Forecasting + + Args: + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 1. + lags (int, list) + lagged target values to predict the next time step + random_state (int, optional): + The state for the model + + Returns: + List[TimeSeries] + Adjusted Data series + List[TimeSeries] + Historical forecast by best RNN model + List[TimeSeries] + list of Predictions + float + Mean average precision error + Any + Best Linear Regression Model + """ + use_scalers = False + probabilistic = True + + scaler, ticker_series = helpers.get_series( + data, target_column, is_scaler=use_scalers + ) + + past_covariate_whole, _, _ = helpers.past_covs( + past_covariates, data, train_split, use_scalers + ) + + if past_covariates is not None: + lags_past_covariates = lags + else: + lags_past_covariates = None + + lin_reg_model = LinearRegressionModel( + output_chunk_length=output_chunk_length, + lags=lags, + lags_past_covariates=lags_past_covariates, + likelihood="quantile", + quantiles=[0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95], + random_state=random_state, + ) + + with warnings.catch_warnings(): + warnings.simplefilter(action="ignore", category=FutureWarning) + if past_covariates is not None: + lin_reg_model.fit( + series=ticker_series, past_covariates=past_covariate_whole + ) + else: + lin_reg_model.fit(series=ticker_series) + + return helpers.get_prediction( + "Logistic Regression", + probabilistic, + use_scalers, + scaler, + past_covariates, + lin_reg_model, + ticker_series, + past_covariate_whole, + train_split, + forecast_horizon, + n_predict, + ) diff --git a/openbb_terminal/forecast/linregr_view.py b/openbb_terminal/forecast/linregr_view.py new file mode 100644 index 00000000000..27b21a12c61 --- /dev/null +++ b/openbb_terminal/forecast/linregr_view.py @@ -0,0 +1,122 @@ +"""Linear Regression View""" +__docformat__ = "numpy" + +import logging +from typing import Union, List, Optional +from datetime import datetime + +import pandas as pd +import matplotlib.pyplot as plt + +from openbb_terminal.forecast import linregr_model +from openbb_terminal.decorators import log_start_end +from openbb_terminal.forecast import helpers + +logger = logging.getLogger(__name__) +# pylint: disable=too-many-arguments + + +@log_start_end(log=logger) +def display_linear_regression( + data: Union[pd.Series, pd.DataFrame], + target_column: str = "close", + dataset_name: str = "", + n_predict: int = 5, + past_covariates: str = None, + train_split: float = 0.85, + forecast_horizon: int = 5, + output_chunk_length: int = 1, + lags: Union[int, List[int]] = 72, + export: str = "", + residuals: bool = False, + forecast_only: bool = False, + start_date: Optional[datetime] = None, + end_date: Optional[datetime] = None, + naive: bool = False, + external_axes: Optional[List[plt.axes]] = None, +): + """Display Linear Regression Forecasting + + Args: + data (Union[pd.Series, pd.DataFrame]): + Input Data + dataset_name str + The name of the ticker to be predicted + n_predict (int, optional): + Days to predict. Defaults to 5. + target_col (str, optional): + Target column to forecast. Defaults to "close". + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 1. + lags (int, list) + lagged target values to predict the next time step + export: str + Format to export data + residuals: bool + Whether to show residuals for the model. Defaults to False. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the + same as the previous day's closing price. Defaults to False. + external_axes:Optional[List[plt.axes]] + External axes to plot on + """ + data = helpers.clean_data( + data, start_date, end_date, target_column, past_covariates + ) + if not helpers.check_data(data, target_column, past_covariates): + return + output_chunk_length = helpers.check_output( + output_chunk_length, n_predict, bool(past_covariates) + ) + ( + ticker_series, + historical_fcast, + predicted_values, + precision, + _model, + ) = linregr_model.get_linear_regression_data( + data=data, + n_predict=n_predict, + target_column=target_column, + past_covariates=past_covariates, + train_split=train_split, + forecast_horizon=forecast_horizon, + output_chunk_length=output_chunk_length, + lags=lags, + ) + + probabilistic = True + helpers.plot_forecast( + "LR", + target_column, + historical_fcast, + predicted_values, + ticker_series, + dataset_name, + data, + n_predict, + forecast_horizon, + past_covariates, + precision, + probabilistic, + export, + forecast_only=forecast_only, + naive=naive, + external_axes=external_axes, + ) + if residuals: + helpers.plot_residuals( + _model, past_covariates, ticker_series, forecast_horizon=forecast_horizon + ) diff --git a/openbb_terminal/forecast/nbeats_model.py b/openbb_terminal/forecast/nbeats_model.py new file mode 100644 index 00000000000..8cede203f53 --- /dev/null +++ b/openbb_terminal/forecast/nbeats_model.py @@ -0,0 +1,161 @@ +# pylint: disable=too-many-arguments +"""NBEATS Model""" +__docformat__ = "numpy" + +import logging +import warnings +from typing import Any, Tuple, Union, List, Optional + +import pandas as pd + +from darts import TimeSeries +from darts.models import NBEATSModel +from openbb_terminal.decorators import log_start_end + +from openbb_terminal.forecast import helpers + +logger = logging.getLogger(__name__) + + +@log_start_end(log=logger) +def get_NBEATS_data( + data: Union[pd.Series, pd.DataFrame], + target_column: str = "close", + n_predict: int = 5, + past_covariates: str = None, + train_split: float = 0.85, + forecast_horizon: int = 5, + input_chunk_length: int = 14, + output_chunk_length: int = 5, + num_stacks: int = 10, + num_blocks: int = 3, + num_layers: int = 4, + layer_widths: int = 512, + batch_size: int = 800, + n_epochs: int = 100, + learning_rate: float = 1e-3, + model_save_name: str = "nbeats_model", + force_reset: bool = True, + save_checkpoints: bool = True, +) -> Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries], Optional[float], Any]: + """Perform NBEATS Forecasting + + Args: + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + num_stacks (int, optional): + The number of stacks that make up the whole model. Defaults to 10. + num_blocks (int, optional): + The number of blocks making up every stack. Defaults to 3. + num_layers (int, optional): + The number of fully connected layers preceding the final forking layers in each block + of every stack. Defaults to 4. + layer_widths (int, optional): + Determines the number of neurons that make up each fully connected layer in each block + of every stack. Defaults to 512. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 100. + learning_rate (float, optional): + Defaults to 1e-3. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. + Defaults to True. + + Returns: + List[TimeSeries] + Adjusted Data series + List[TimeSeries] + Historical forecast by best RNN model + List[TimeSeries] + list of Predictions + Optional[float] + Mean average precision error + Any + Best NBEATS Model + """ + + use_scalers = True + probabilistic = False + + scaler, ticker_series = helpers.get_series( + data, target_column, is_scaler=use_scalers + ) + train, val = ticker_series.split_before(train_split) + valid = helpers.check_data_length( + train, val, input_chunk_length, output_chunk_length + ) + if not valid: + return [], [], [], None, None + + ( + past_covariate_whole, + past_covariate_train, + past_covariate_val, + ) = helpers.past_covs(past_covariates, data, train_split, use_scalers) + + nbeats_model = NBEATSModel( + input_chunk_length=input_chunk_length, + output_chunk_length=output_chunk_length, + generic_architecture=True, + num_stacks=num_stacks, + num_blocks=num_blocks, + num_layers=num_layers, + layer_widths=layer_widths, + n_epochs=n_epochs, + nr_epochs_val_period=1, + batch_size=batch_size, + optimizer_kwargs={"lr": learning_rate}, + model_name=model_save_name, + force_reset=force_reset, + save_checkpoints=save_checkpoints, + random_state=42, + pl_trainer_kwargs=helpers.get_pl_kwargs(accelerator="cpu"), + ) + + # fit model on train series for historical forecasting + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + helpers.fit_model( + nbeats_model, + train, + val, + past_covariate_train, + past_covariate_val, + ) + best_model = NBEATSModel.load_from_checkpoint(model_name=model_save_name, best=True) + + # Showing historical backtesting without retraining model (too slow) + return helpers.get_prediction( + "NBEATS", + probabilistic, + use_scalers, + scaler, + past_covariates, + best_model, + ticker_series, + past_covariate_whole, + train_split, + forecast_horizon, + n_predict, + ) diff --git a/openbb_terminal/forecast/nbeats_view.py b/openbb_terminal/forecast/nbeats_view.py new file mode 100644 index 00000000000..166c6bf4699 --- /dev/null +++ b/openbb_terminal/forecast/nbeats_view.py @@ -0,0 +1,171 @@ +"""NBEATS View""" +__docformat__ = "numpy" + +import logging +from typing import Union, Optional, List +from datetime import datetime + +import pandas as pd +import matplotlib.pyplot as plt + +from openbb_terminal.forecast import nbeats_model +from openbb_terminal.decorators import log_start_end +from openbb_terminal.forecast import helpers + +logger = logging.getLogger(__name__) +# pylint: disable=too-many-arguments + + +@log_start_end(log=logger) +def display_nbeats_forecast( + data: Union[pd.DataFrame, pd.Series], + target_column: str = "close", + dataset_name: str = "", + n_predict: int = 5, + past_covariates: str = None, + train_split: float = 0.85, + forecast_horizon: int = 5, + input_chunk_length: int = 14, + output_chunk_length: int = 5, + num_stacks: int = 10, + num_blocks: int = 3, + num_layers: int = 4, + layer_widths: int = 512, + n_epochs: int = 100, + learning_rate: float = 1e-3, + batch_size: int = 800, + model_save_name: str = "nbeats_model", + force_reset: bool = True, + save_checkpoints: bool = True, + export: str = "", + residuals: bool = False, + forecast_only: bool = False, + start_date: Optional[datetime] = None, + end_date: Optional[datetime] = None, + naive: bool = False, + external_axes: Optional[List[plt.axes]] = None, +): + """Display NBEATS forecast + + Parameters + ---------- + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + dataset_name str + The name of the ticker to be predicted + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + num_stacks (int, optional): + The number of stacks that make up the whole model. Defaults to 10. + num_blocks (int, optional): + The number of blocks making up every stack. Defaults to 3. + num_layers (int, optional): + The number of fully connected layers preceding the final forking layers in each block + of every stack. Defaults to 4. + layer_widths (int, optional): + Determines the number of neurons that make up each fully connected layer in each block + of every stack. Defaults to 512. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults + to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 100. + learning_rate (float, optional): + Defaults to 1e-3. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset (all + checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. + Defaults to True. + export: str + Format to export data + residuals: bool + Whether to show residuals for the model. Defaults to False. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the same + as the previous day's closing price. Defaults to False. + external_axes:Optional[List[plt.axes]] + External axes to plot on + """ + + data = helpers.clean_data( + data, start_date, end_date, target_column, past_covariates + ) + if not helpers.check_data(data, target_column, past_covariates): + return + output_chunk_length = helpers.check_output( + output_chunk_length, n_predict, bool(past_covariates) + ) + ( + ticker_series, + historical_fcast, + predicted_values, + precision, + _model, + ) = nbeats_model.get_NBEATS_data( + data=data, + n_predict=n_predict, + target_column=target_column, + past_covariates=past_covariates, + train_split=train_split, + forecast_horizon=forecast_horizon, + input_chunk_length=input_chunk_length, + output_chunk_length=output_chunk_length, + num_stacks=num_stacks, + num_blocks=num_blocks, + num_layers=num_layers, + layer_widths=layer_widths, + batch_size=batch_size, + n_epochs=n_epochs, + learning_rate=learning_rate, + model_save_name=model_save_name, + force_reset=force_reset, + save_checkpoints=save_checkpoints, + ) + if ticker_series == []: + return + + probabilistic = False + helpers.plot_forecast( + "NBEATS", + target_column, + historical_fcast, + predicted_values, + ticker_series, + dataset_name, + data, + n_predict, + forecast_horizon, + past_covariates, + precision, + probabilistic, + export, + forecast_only=forecast_only, + naive=naive, + external_axes=external_axes, + ) + if residuals: + helpers.plot_residuals( + _model, past_covariates, ticker_series, forecast_horizon=forecast_horizon + ) diff --git a/openbb_terminal/forecast/regr_model.py b/openbb_terminal/forecast/regr_model.py new file mode 100644 index 00000000000..9a431b5ca7f --- /dev/null +++ b/openbb_terminal/forecast/regr_model.py @@ -0,0 +1,104 @@ +# pylint: disable=too-many-arguments +"""Regression Model""" +__docformat__ = "numpy" + +import logging +from typing import Any, Tuple, Union, List +import warnings + + +import pandas as pd + +from darts import TimeSeries +from darts.models import RegressionModel +from openbb_terminal.decorators import log_start_end + +from openbb_terminal.forecast import helpers + +logger = logging.getLogger(__name__) + + +@log_start_end(log=logger) +def get_regression_data( + data: Union[pd.Series, pd.DataFrame], + target_column: str = "close", + n_predict: int = 5, + past_covariates: str = None, + train_split: float = 0.85, + forecast_horizon: int = 5, + output_chunk_length: int = 1, + lags: Union[int, List[int]] = 72, +) -> Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries], float, Any]: + """Perform Regression Forecasting + + Args: + data (Union[pd.Series, pd.DataFrame]): + Input Data + n_predict (int, optional): + Days to predict. Defaults to 5. + target_column (str, optional): + Target column to forecast. Defaults to "close". + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 1. + lags (int, list) + lagged target values to predict the next time step + + Returns: + List[TimeSeries] + Adjusted Data series + List[TimeSeries] + Historical forecast by best RNN model + List[TimeSeries] + list of Predictions + float + Mean average precision error + Any + Best Regression Model + """ + + use_scalers = False + probabilistic = False + + scaler, ticker_series = helpers.get_series( + data, target_column, is_scaler=use_scalers + ) + + past_covariate_whole, _, _ = helpers.past_covs( + past_covariates, data, train_split, use_scalers + ) + + if past_covariates is not None: + lags_past_covariates = lags + else: + lags_past_covariates = None + + reg_model = RegressionModel( + output_chunk_length=output_chunk_length, + lags=lags, + lags_past_covariates=lags_past_covariates, + ) + + with warnings.catch_warnings(): + warnings.simplefilter(action="ignore", category=FutureWarning) + reg_model.fit(ticker_series, past_covariate_whole) + + # Showing historical backtesting without retraining model (too slow) + return helpers.get_prediction( + "Regression", + probabilistic, + use_scalers, + scaler, + past_covariates, + reg_model, + ticker_series, + past_covariate_whole, + train_split, + forecast_horizon, + n_predict, + ) diff --git a/openbb_terminal/forecast/regr_view.py b/openbb_terminal/forecast/regr_view.py new file mode 100644 index 00000000000..8ef6bdb14c5 --- /dev/null +++ b/openbb_terminal/forecast/regr_view.py @@ -0,0 +1,121 @@ +""" Regression View""" +__docformat__ = "numpy" + +import logging +from typing import Union, List, Optional +from datetime import datetime + +import pandas as pd +import matplotlib.pyplot as plt + +from openbb_terminal.forecast import regr_model +from openbb_terminal.decorators import log_start_end +from openbb_terminal.forecast import helpers + +logger = logging.getLogger(__name__) +# pylint: disable=too-many-arguments + + +@log_start_end(log=logger) +def display_regression( + data: Union[pd.Series, pd.DataFrame], + target_column: str = "close", + dataset_name: str = "", + n_predict: int = 5, + past_covariates: str = None, + train_split: float = 0.85, + forecast_horizon: int = 5, + output_chunk_length: int = 1, + lags: Union[int, List[int]] = 72, + export: str = "", + residuals: bool = False, + forecast_only: bool = False, + start_date: Optional[datetime] = None, + end_date: Optional[datetime] = None, + naive: bool = False, + external_axes: Optional[List[plt.axes]] = None, +): + """Display Regression Forecasting + + Args: + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + dataset_name str + The name of the ticker to be predicted + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 1. + lags (int, list) + lagged target values to predict the next time step + export: str + Format to export data + residuals: bool + Whether to show residuals for the model. Defaults to False. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the same + as the previous day's closing price. Defaults to False. + external_axes:Optional[List[plt.axes]] + External axes to plot on + """ + data = helpers.clean_data( + data, start_date, end_date, target_column, past_covariates + ) + if not helpers.check_data(data, target_column, past_covariates): + return + output_chunk_length = helpers.check_output( + output_chunk_length, n_predict, bool(past_covariates) + ) + ( + ticker_series, + historical_fcast, + predicted_values, + precision, + _model, + ) = regr_model.get_regression_data( + data=data, + n_predict=n_predict, + target_column=target_column, + past_covariates=past_covariates, + train_split=train_split, + forecast_horizon=forecast_horizon, + output_chunk_length=output_chunk_length, + lags=lags, + ) + probabilistic = False + helpers.plot_forecast( + "REGR", + target_column, + historical_fcast, + predicted_values, + ticker_series, + dataset_name, + data, + n_predict, + forecast_horizon, + past_covariates, + precision, + probabilistic, + export, + forecast_only=forecast_only, + naive=naive, + external_axes=external_axes, + ) + if residuals: + helpers.plot_residuals( + _model, past_covariates, ticker_series, forecast_horizon=forecast_horizon + ) diff --git a/openbb_terminal/forecast/rnn_model.py b/openbb_terminal/forecast/rnn_model.py new file mode 100644 index 00000000000..a78281ddf2c --- /dev/null +++ b/openbb_terminal/forecast/rnn_model.py @@ -0,0 +1,137 @@ +# pylint: disable=too-many-arguments +"""RNN Model""" +__docformat__ = "numpy" + +import logging +import warnings +from typing import Any, Tuple, Union, List, Optional + +import pandas as pd + +from darts import TimeSeries +from darts.models import RNNModel +from darts.utils.likelihood_models import GaussianLikelihood +from openbb_terminal.decorators import log_start_end +from openbb_terminal.forecast import helpers + +logger = logging.getLogger(__name__) + + +@log_start_end(log=logger) +def get_rnn_data( + data: Union[pd.Series, pd.DataFrame], + target_column: str = "close", + n_predict: int = 5, + train_split: float = 0.85, + forecast_horizon: int = 5, + model_type: str = "LSTM", + hidden_dim: int = 20, + dropout: float = 0.0, + batch_size: int = 16, + n_epochs: int = 100, + learning_rate: float = 1e-3, + model_save_name: str = "rnn_model", + training_length: int = 20, + input_chunk_size: int = 14, + force_reset: bool = True, + save_checkpoints: bool = True, +) -> Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries], Optional[float], Any]: + """Perform RNN forecasting + + Args: + data (Union[pd.Series, pd.DataFrame]): + Input Data + n_predict (int, optional): + Days to predict. Defaults to 5. + target_column (str, optional): + Target column to forecast. Defaults to "close". + train_split (float, optional): + Train/val split. Defaults to 0.85. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + model_type (str, optional): + Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU"). Defaults to "LSTM". + hidden_dim (int, optional): + Size for feature maps for each hidden RNN layer.. Defaults to 20. + dropout (float, optional): + Fraction of neurons afected by Dropout. Defaults to 0.0. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 100. + learning_rate (float, optional): + Defaults to 1e-3. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. + Defaults to True. + + Returns: + List[TimeSeries] + Adjusted Data series + List[TimeSeries] + Historical forecast by best RNN model + List[TimeSeries] + list of Predictions + float + Mean average precision error + Any + Best RNN Model + """ + + # TODO Check if torch GPU AVAILABLE + + use_scalers = True + probabilistic = True + past_covariates = None + past_covariate_whole = None + + scaler, ticker_series = helpers.get_series( + data, target_column, is_scaler=use_scalers + ) + train, val = ticker_series.split_before(train_split) + valid = helpers.check_data_length(train, val, input_chunk_size, 0) + if not valid: + return [], [], [], None, None + + rnn_model = RNNModel( + model=model_type, + hidden_dim=hidden_dim, + dropout=dropout, + batch_size=batch_size, + n_epochs=n_epochs, + optimizer_kwargs={"lr": learning_rate}, + model_name=model_save_name, + random_state=42, + training_length=training_length, + input_chunk_length=input_chunk_size, + pl_trainer_kwargs=helpers.get_pl_kwargs(accelerator="cpu"), + force_reset=force_reset, + save_checkpoints=save_checkpoints, + likelihood=GaussianLikelihood(), + ) + + # fit model on train series for historical forecasting + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + helpers.fit_model(rnn_model, train, val) + best_model = RNNModel.load_from_checkpoint(model_name=model_save_name, best=True) + + # Showing historical backtesting without retraining model (too slow) + return helpers.get_prediction( + "RNN", + probabilistic, + use_scalers, + scaler, + past_covariates, + best_model, + ticker_series, + past_covariate_whole, + train_split, + forecast_horizon, + n_predict, + ) diff --git a/openbb_terminal/forecast/rnn_view.py b/openbb_terminal/forecast/rnn_view.py new file mode 100644 index 00000000000..2c7bf8ee478 --- /dev/null +++ b/openbb_terminal/forecast/rnn_view.py @@ -0,0 +1,151 @@ +"""RNN View""" +__docformat__ = "numpy" + +import logging +from typing import Union, Optional, List +from datetime import datetime + +import pandas as pd +import matplotlib.pyplot as plt + +from openbb_terminal.forecast import rnn_model +from openbb_terminal.decorators import log_start_end +from openbb_terminal.forecast import helpers + +logger = logging.getLogger(__name__) +# pylint: disable=too-many-arguments + + +@log_start_end(log=logger) +def display_rnn_forecast( + data: Union[pd.DataFrame, pd.Series], + target_column: str = "close", + dataset_name: str = "", + n_predict: int = 5, + train_split: float = 0.85, + forecast_horizon: int = 5, + model_type: str = "LSTM", + hidden_dim: int = 20, + dropout: float = 0.0, + batch_size: int = 16, + n_epochs: int = 100, + learning_rate: float = 1e-3, + model_save_name: str = "rnn_model", + training_length: int = 20, + input_chunk_size: int = 14, + force_reset: bool = True, + save_checkpoints: bool = True, + export: str = "", + residuals: bool = False, + forecast_only: bool = False, + start_date: Optional[datetime] = None, + end_date: Optional[datetime] = None, + naive: bool = False, + external_axes: Optional[List[plt.axes]] = None, +): + """Display RNN forecast + + Parameters + ---------- + data (Union[pd.Series, pd.DataFrame]): + Input Data + dataset_name str + The name of the ticker to be predicted + n_predict (int, optional): + Days to predict. Defaults to 5. + target_column (str, optional): + Target column to forecast. Defaults to "close". + train_split (float, optional): + Train/val split. Defaults to 0.85. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + model_type (str, optional): + Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU"). Defaults to "LSTM". + hidden_dim (int, optional): + Size for feature maps for each hidden RNN layer.. Defaults to 20. + dropout (float, optional): + Fraction of neurons afected by Dropout. Defaults to 0.0. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 100. + learning_rate (float, optional): + Defaults to 1e-3. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + export: str + Format to export data + residuals: bool + Whether to show residuals for the model. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the same + as the previous day's closing price. Defaults to False. + external_axes:Optional[List[plt.axes]] + External axes to plot on + """ + + data = helpers.clean_data(data, start_date, end_date, target_column, None) + if not helpers.check_data(data, target_column, None): + return + ( + ticker_series, + historical_fcast, + predicted_values, + precision, + _model, + ) = rnn_model.get_rnn_data( + data=data, + n_predict=n_predict, + target_column=target_column, + train_split=train_split, + forecast_horizon=forecast_horizon, + model_type=model_type, + hidden_dim=hidden_dim, + dropout=dropout, + batch_size=batch_size, + n_epochs=n_epochs, + learning_rate=learning_rate, + model_save_name=model_save_name, + training_length=training_length, + input_chunk_size=input_chunk_size, + force_reset=force_reset, + save_checkpoints=save_checkpoints, + ) + if ticker_series == []: + return + + past_covariates = None + probabilistic = True + helpers.plot_forecast( + "RNN", + target_column, + historical_fcast, + predicted_values, + ticker_series, + dataset_name, + data, + n_predict, + forecast_horizon, + past_covariates, + precision, + probabilistic, + export, + forecast_only=forecast_only, + naive=naive, + external_axes=external_axes, + ) + if residuals: + helpers.plot_residuals( + _model, past_covariates, ticker_series, forecast_horizon=forecast_horizon + ) diff --git a/openbb_terminal/forecast/tcn_model.py b/openbb_terminal/forecast/tcn_model.py new file mode 100644 index 00000000000..65bba9b8093 --- /dev/null +++ b/openbb_terminal/forecast/tcn_model.py @@ -0,0 +1,159 @@ +# pylint: disable=too-many-arguments +"""TCN Model""" +__docformat__ = "numpy" + +import logging +from typing import Any, Tuple, Union, List, Optional +import warnings + +import pandas as pd + +from darts import TimeSeries +from darts.models import TCNModel +from openbb_terminal.decorators import log_start_end +from openbb_terminal.forecast import helpers + +logger = logging.getLogger(__name__) + + +@log_start_end(log=logger) +def get_tcn_data( + data: Union[pd.Series, pd.DataFrame], + target_column: str = "close", + n_predict: int = 5, + past_covariates: str = None, + train_split: float = 0.85, + forecast_horizon: int = 5, + input_chunk_length: int = 14, + output_chunk_length: int = 5, + dropout: float = 0.1, + num_filters: int = 6, + weight_norm: bool = True, + dilation_base: int = 2, + n_epochs: int = 100, + learning_rate: float = 1e-3, + batch_size: int = 800, + model_save_name: str = "tcn_model", + force_reset: bool = True, + save_checkpoints: bool = True, +) -> Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries], Optional[float], Any]: + """Perform TCN forecasting + + Args: + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + dropout (float, optional): + Fraction of neurons afected by Dropout. Defaults to 0.1. + num_filters (int, optional): + The number of filters in a convolutional layer of the TCN. Defaults to 6. + weight_norm (bool, optional): + Boolean value indicating whether to use weight normalization. Defaults to True. + dilation_base (int, optional): + The base of the exponent that will determine the dilation on every level. Defaults to 2. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 100. + learning_rate (float, optional): + Defaults to 1e-3. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. + Defaults to True. + + Returns: + List[TimeSeries] + Adjusted Data series + List[TimeSeries] + Historical forecast by best RNN model + List[TimeSeries] + list of Predictions + Optional[float] + Mean average precision error + Any + Best TCN Model + """ + + # TODO Check if torch GPU AVAILABLE + + use_scalers = True + probabilistic = False + + scaler, ticker_series = helpers.get_series( + data, target_column, is_scaler=use_scalers + ) + train, val = ticker_series.split_before(train_split) + valid = helpers.check_data_length( + train, val, input_chunk_length, output_chunk_length + ) + if not valid: + return [], [], [], None, None + + ( + past_covariate_whole, + past_covariate_train, + past_covariate_val, + ) = helpers.past_covs(past_covariates, data, train_split, use_scalers) + + tcn_model = TCNModel( + input_chunk_length=input_chunk_length, + output_chunk_length=output_chunk_length, + dropout=dropout, + dilation_base=dilation_base, + weight_norm=weight_norm, + num_filters=num_filters, + n_epochs=n_epochs, + nr_epochs_val_period=1, + batch_size=batch_size, + optimizer_kwargs={"lr": learning_rate}, + model_name=model_save_name, + force_reset=force_reset, + save_checkpoints=save_checkpoints, + random_state=42, + pl_trainer_kwargs=helpers.get_pl_kwargs(accelerator="cpu"), + ) + + # fit model on train series for historical forecasting + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + helpers.fit_model( + tcn_model, + train, + val, + past_covariate_train, + past_covariate_val, + ) + best_model = TCNModel.load_from_checkpoint(model_name=model_save_name, best=True) + + # Showing historical backtesting without retraining model (too slow) + return helpers.get_prediction( + "TCN", + probabilistic, + use_scalers, + scaler, + past_covariates, + best_model, + ticker_series, + past_covariate_whole, + train_split, + forecast_horizon, + n_predict, + ) diff --git a/openbb_terminal/forecast/tcn_view.py b/openbb_terminal/forecast/tcn_view.py new file mode 100644 index 00000000000..bdb54e5db1b --- /dev/null +++ b/openbb_terminal/forecast/tcn_view.py @@ -0,0 +1,167 @@ +"""TCN View""" +__docformat__ = "numpy" + +import logging +from typing import Union, Optional, List +from datetime import datetime + +import pandas as pd +import matplotlib.pyplot as plt + +from openbb_terminal.forecast import tcn_model +from openbb_terminal.decorators import log_start_end +from openbb_terminal.forecast import helpers + +logger = logging.getLogger(__name__) +# pylint: disable=too-many-arguments + + +@log_start_end(log=logger) +def display_tcn_forecast( + data: Union[pd.DataFrame, pd.Series], + target_column: str = "close", + dataset_name: str = "", + n_predict: int = 5, + past_covariates: str = None, + train_split: float = 0.85, + forecast_horizon: int = 5, + input_chunk_length: int = 14, + output_chunk_length: int = 5, + dropout: float = 0.1, + num_filters: int = 6, + weight_norm: bool = True, + dilation_base: int = 2, + n_epochs: int = 100, + learning_rate: float = 1e-3, + batch_size: int = 800, + model_save_name: str = "tcn_model", + force_reset: bool = True, + save_checkpoints: bool = True, + export: str = "", + residuals: bool = False, + forecast_only: bool = False, + start_date: Optional[datetime] = None, + end_date: Optional[datetime] = None, + naive: bool = False, + external_axes: Optional[List[plt.axes]] = None, +): + """Display TCN forecast + + Parameters + ---------- + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + dataset_name str + The name of the ticker to be predicted + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + dropout (float, optional): + Fraction of neurons afected by Dropout. Defaults to 0.1. + num_filters (int, optional): + The number of filters in a convolutional layer of the TCN. Defaults to 6. + weight_norm (bool, optional): + Boolean value indicating whether to use weight normalization. Defaults to True. + dilation_base (int, optional): + The base of the exponent that will determine the dilation on every level. Defaults to 2. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 100. + learning_rate (float, optional): + Defaults to 1e-3. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True. + export: str + Format to export data + residuals: bool + Whether to show residuals for the model. Defaults to False. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the same + as the previous day's closing price. Defaults to False. + external_axes:Optional[List[plt.axes]] + External axes to plot on + """ + + data = helpers.clean_data( + data, start_date, end_date, target_column, past_covariates + ) + if not helpers.check_data(data, target_column, past_covariates): + return + output_chunk_length = helpers.check_output( + output_chunk_length, n_predict, bool(past_covariates) + ) + ( + ticker_series, + historical_fcast, + predicted_values, + precision, + _model, + ) = tcn_model.get_tcn_data( + data=data, + n_predict=n_predict, + target_column=target_column, + past_covariates=past_covariates, + train_split=train_split, + forecast_horizon=forecast_horizon, + input_chunk_length=input_chunk_length, + output_chunk_length=output_chunk_length, + dropout=dropout, + num_filters=num_filters, + weight_norm=weight_norm, + dilation_base=dilation_base, + batch_size=batch_size, + n_epochs=n_epochs, + learning_rate=learning_rate, + model_save_name=model_save_name, + force_reset=force_reset, + save_checkpoints=save_checkpoints, + ) + if ticker_series == []: + return + + probabilistic = False + helpers.plot_forecast( + "TCN", + target_column, + historical_fcast, + predicted_values, + ticker_series, + dataset_name, + data, + n_predict, + forecast_horizon, + past_covariates, + precision, + probabilistic, + export, + forecast_only=forecast_only, + naive=naive, + external_axes=external_axes, + ) + if residuals: + helpers.plot_residuals( + _model, past_covariates, ticker_series, forecast_horizon=forecast_horizon + ) diff --git a/openbb_terminal/forecast/tft_model.py b/openbb_terminal/forecast/tft_model.py new file mode 100644 index 00000000000..607724ee663 --- /dev/null +++ b/openbb_terminal/forecast/tft_model.py @@ -0,0 +1,202 @@ +# pylint: disable=too-many-arguments +"""Temporal Fusion Transformer Model""" +__docformat__ = "numpy" + +import logging +from typing import Any, Tuple, Union, List, Optional + +import warnings +from statsmodels.tools.sm_exceptions import ConvergenceWarning +import pandas as pd +from darts import TimeSeries +from darts.models import TFTModel +from darts.utils.likelihood_models import QuantileRegression + +from openbb_terminal.decorators import log_start_end +from openbb_terminal.forecast import helpers + + +warnings.simplefilter("ignore", ConvergenceWarning) + + +logger = logging.getLogger(__name__) + + +@log_start_end(log=logger) +def get_tft_data( + data: Union[pd.Series, pd.DataFrame], + target_column: str = "close", + n_predict: int = 30, + past_covariates: str = None, + train_split: float = 0.85, + forecast_horizon: int = 5, + input_chunk_length: int = 14, + output_chunk_length: int = 5, + hidden_size: int = 16, + lstm_layers: int = 1, + num_attention_heads: int = 4, + full_attention: bool = False, + dropout: float = 0.1, + hidden_continuous_size: int = 8, + n_epochs: int = 200, + batch_size: int = 32, + model_save_name: str = "tft_model", + force_reset: bool = True, + save_checkpoints: bool = True, +) -> Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries], Optional[float], Any]: + + """Performs Temporal Fusion Transformer forecasting + The TFT applies multi-head attention queries on future inputs from mandatory future_covariates. + Specifying future encoders with add_encoders (read below) can automatically generate future + covariates and allows to use the model without having to pass any future_covariates to fit() + and predict(). + + https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html + + Parameters + ---------- + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. + Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + hidden_size (int, optional): + Hidden state size of the TFT. Defaults to 16. + lstm_layers (int, optional): + Number of layers for the Long Short Term Memory Encoder and Decoder. Defaults to 16. + num_attention_headers (int, optional): + Number of attention heads. Defaults to 4. + full_attention (bool, optional): + Whether to apply a multi-head attention query. Defaults to False> + dropout (float, optional): + Fraction of neurons affected by dropout. Defaults to 0.1. + hidden_continuous_size (int, optional): + Default hidden size for processing continuous variables. Defaults to 8. + n_epochs (int, optional): + Number of epochs to run during training. Defaults to 200. + batch_size (int, optional): + Number of samples to pass through network during a single epoch. Defaults to 32. + model_save_name (str, optional): + The name for the model. Defaults to tft_model + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. + Defaults to True. + + + + Returns + ------- + List[float] + Adjusted Data series + List[float] + List of historical fcast values + List[float] + List of predicted fcast values + Optional[float] + precision + Any + Fit Prob. TFT model object. + """ + + use_scalers = True + probabilistic = True + + scaler, ticker_series = helpers.get_series( + data, target_column, is_scaler=use_scalers + ) + train, val = ticker_series.split_before(train_split) + valid = helpers.check_data_length( + train, val, input_chunk_length, output_chunk_length + ) + if not valid: + return [], [], [], None, None + + ( + past_covariate_whole, + past_covariate_train, + past_covariate_val, + ) = helpers.past_covs(past_covariates, data, train_split, use_scalers) + + quantiles = [ + 0.01, + 0.05, + 0.1, + 0.15, + 0.2, + 0.25, + 0.3, + 0.4, + 0.5, + 0.6, + 0.7, + 0.75, + 0.8, + 0.85, + 0.9, + 0.95, + 0.99, + ] + + tft_model = TFTModel( + input_chunk_length=input_chunk_length, + output_chunk_length=output_chunk_length, + hidden_size=hidden_size, + lstm_layers=lstm_layers, + num_attention_heads=num_attention_heads, + full_attention=full_attention, + dropout=dropout, + hidden_continuous_size=hidden_continuous_size, + model_name=model_save_name, + force_reset=force_reset, + save_checkpoints=save_checkpoints, + random_state=42, + n_epochs=n_epochs, + batch_size=batch_size, + pl_trainer_kwargs=helpers.get_pl_kwargs(accelerator="cpu"), + likelihood=QuantileRegression( + quantiles=quantiles + ), # QuantileRegression is set per default + add_relative_index=True, # TODO There is a bug with this. Must fix. Should be false + ) + + # fit model on train series for historical forecasting + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + helpers.fit_model( + tft_model, + train, + val, + past_covariate_train, + past_covariate_val, + ) + best_model = TFTModel.load_from_checkpoint(model_name=model_save_name, best=True) + + # Showing historical backtesting without retraining model (too slow) + return helpers.get_prediction( + "TFT", + probabilistic, + use_scalers, + scaler, + past_covariates, + best_model, + ticker_series, + past_covariate_whole, + train_split, + forecast_horizon, + n_predict, + ) diff --git a/openbb_terminal/forecast/tft_view.py b/openbb_terminal/forecast/tft_view.py new file mode 100644 index 00000000000..010ea16274f --- /dev/null +++ b/openbb_terminal/forecast/tft_view.py @@ -0,0 +1,171 @@ +"""Temporal Fusion Transformer View""" +__docformat__ = "numpy" + +import logging +from typing import Union, Optional, List +from datetime import datetime + +import pandas as pd +import matplotlib.pyplot as plt + +from openbb_terminal.forecast import tft_model +from openbb_terminal.decorators import log_start_end +from openbb_terminal.forecast import helpers + +logger = logging.getLogger(__name__) +# pylint: disable=too-many-arguments + + +@log_start_end(log=logger) +def display_tft_forecast( + data: Union[pd.Series, pd.DataFrame], + target_column: str = "close", + dataset_name: str = "", + n_predict: int = 30, + past_covariates: str = None, + train_split: float = 0.85, + forecast_horizon: int = 5, + input_chunk_length: int = 14, + output_chunk_length: int = 5, + hidden_size: int = 16, + lstm_layers: int = 1, + num_attention_heads: int = 4, + full_attention: bool = False, + dropout: float = 0.1, + hidden_continuous_size: int = 8, + n_epochs: int = 200, + batch_size: int = 32, + model_save_name: str = "tft_model", + force_reset: bool = True, + save_checkpoints: bool = True, + export: str = "", + residuals: bool = False, + forecast_only: bool = False, + start_date: Optional[datetime] = None, + end_date: Optional[datetime] = None, + naive: bool = False, + external_axes: Optional[List[plt.axes]] = None, +): + """Display Temporal Fusion Transformer forecast + + Parameters + ---------- + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + dataset_name str + The name of the ticker to be predicted + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. + Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + hidden_size (int, optional): + Hidden state size of the TFT. Defaults to 16. + lstm_layers (int, optional): + Number of layers for the Long Short Term Memory Encoder and Decoder. Defaults to 16. + num_attention_headers (int, optional): + Number of attention heads. Defaults to 4. + full_attention (bool, optional): + Whether to apply a multi-head attention query. Defaults to False> + dropout (float, optional): + Fraction of neurons affected by dropout. Defaults to 0.1. + hidden_continuous_size (int, optional): + Default hidden size for processing continuous variables. Defaults to 8. + n_epochs (int, optional): + Number of epochs to run during training. Defaults to 200. + batch_size (int, optional): + Number of samples to pass through network during a single epoch. Defaults to 32. + model_save_name (str, optional): + The name for the model. Defaults to tft_model + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. + Defaults to True. + residuals: bool + Whether to show residuals for the model. Defaults to False. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the same + as the previous day's closing price. Defaults to False. + external_axes:Optional[List[plt.axes]] + External axes to plot on + """ + + data = helpers.clean_data( + data, start_date, end_date, target_column, past_covariates + ) + if not helpers.check_data(data, target_column, past_covariates): + return + output_chunk_length = helpers.check_output( + output_chunk_length, n_predict, bool(past_covariates) + ) + ( + ticker_series, + historical_fcast, + predicted_values, + precision, + _model, + ) = tft_model.get_tft_data( + data=data, + n_predict=n_predict, + target_column=target_column, + past_covariates=past_covariates, + train_split=train_split, + forecast_horizon=forecast_horizon, + input_chunk_length=input_chunk_length, + output_chunk_length=output_chunk_length, + hidden_size=hidden_size, + lstm_layers=lstm_layers, + num_attention_heads=num_attention_heads, + full_attention=full_attention, + dropout=dropout, + hidden_continuous_size=hidden_continuous_size, + n_epochs=n_epochs, + batch_size=batch_size, + model_save_name=model_save_name, + force_reset=force_reset, + save_checkpoints=save_checkpoints, + ) + if ticker_series == []: + return + + probabilistic = True + helpers.plot_forecast( + "TFT", + target_column, + historical_fcast, + predicted_values, + ticker_series, + dataset_name, + data, + n_predict, + forecast_horizon, + past_covariates, + precision, + probabilistic, + export, + forecast_only=forecast_only, + naive=naive, + external_axes=external_axes, + ) + if residuals: + helpers.plot_residuals( + _model, past_covariates, ticker_series, forecast_horizon=forecast_horizon + ) diff --git a/openbb_terminal/forecast/theta_model.py b/openbb_terminal/forecast/theta_model.py new file mode 100644 index 00000000000..a800d20e98f --- /dev/null +++ b/openbb_terminal/forecast/theta_model.py @@ -0,0 +1,141 @@ +"""Theta Model""" +__docformat__ = "numpy" + +import logging +from typing import Any, Tuple, Union, List + +import warnings +from statsmodels.tools.sm_exceptions import ConvergenceWarning +import numpy as np +import pandas as pd +from darts import TimeSeries +from darts.models import Theta +from darts.utils.utils import SeasonalityMode +from darts.metrics import mape + +from openbb_terminal.decorators import log_start_end +from openbb_terminal.rich_config import console +from openbb_terminal.forecast import helpers + +warnings.simplefilter("ignore", ConvergenceWarning) + +SEASONS = ["N", "A", "M"] +PERIODS = [4, 5, 7] + +logger = logging.getLogger(__name__) + + +@log_start_end(log=logger) +def get_theta_data( + data: Union[pd.Series, pd.DataFrame], + target_column: str = "close", + seasonal: str = "M", + seasonal_periods: int = 7, + n_predict: int = 30, + start_window: float = 0.85, + forecast_horizon: int = 5, +) -> Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries], float, float, Any]: + + """Performs Theta forecasting + An implementation of the 4Theta method with configurable theta parameter. + + https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html + + Parameters + ---------- + data : Union[pd.Series, np.ndarray] + Input data. + target_column (str, optional): + Target column to forecast. Defaults to "close". + seasonal: str + Seasonal component. One of [N, A, M] + Defaults to MULTIPLICATIVE. + seasonal_periods: int + Number of seasonal periods in a year + If not set, inferred from frequency of the series. + n_predict: int + Number of days to forecast + start_window: float + Size of sliding window from start of timeseries and onwards + forecast_horizon: int + Number of days to forecast when backtesting and retraining historical + + Returns + ------- + List[TimeSeries] + Adjusted Data series + List[TimeSeries] + Historical forecast by best theta + List[TimeSeries] + list of Predictions + float + Mean average precision error + float + Best Theta + Any + Theta Model + """ + + use_scalers = False + _, ticker_series = helpers.get_series(data, target_column, is_scaler=use_scalers) + train, val = ticker_series.split_before(start_window) + + if seasonal == "A": + seasonal = SeasonalityMode.ADDITIVE + elif seasonal == "N": + seasonal = SeasonalityMode.NONE + else: # Default + seasonal = SeasonalityMode.MULTIPLICATIVE + + thetas = np.linspace(-10, 10, 50) + best_mape = float("inf") + best_theta = 0 + for theta in thetas: + model = Theta( + theta=theta, + season_mode=seasonal, + seasonality_period=seasonal_periods, + ) + model.fit(train) + pred_theta = model.predict(len(val)) + res = mape(val, pred_theta) + if res < best_mape: + best_mape = res + best_theta = theta + + best_theta_model = Theta( + best_theta, + season_mode=seasonal, + seasonality_period=seasonal_periods, + ) + + # Training model based on historical backtesting + historical_fcast_theta = best_theta_model.historical_forecasts( + ticker_series, + start=float(start_window), + forecast_horizon=int(forecast_horizon), + verbose=True, + ) + + best_theta_model_final = Theta( + best_theta, + season_mode=seasonal, + seasonality_period=seasonal_periods, + ) + + # fit model on entire series for final prediction + best_theta_model_final.fit(ticker_series) + prediction = best_theta_model_final.predict(int(n_predict)) + precision = mape( + actual_series=ticker_series, pred_series=historical_fcast_theta + ) # mape = mean average precision error + console.print(f"Theta Model obtains MAPE: {precision:.2f}% \n") + + return ( + ticker_series, + historical_fcast_theta, + prediction, + precision, + best_theta, + best_theta_model, + ) diff --git a/openbb_terminal/forecast/theta_view.py b/openbb_terminal/forecast/theta_view.py new file mode 100644 index 00000000000..02fae7b66f8 --- /dev/null +++ b/openbb_terminal/forecast/theta_view.py @@ -0,0 +1,119 @@ +"""Theta View""" +__docformat__ = "numpy" + +import logging +from typing import Union, Optional, List +from datetime import datetime + +import pandas as pd +import matplotlib.pyplot as plt + +from openbb_terminal.forecast import theta_model +from openbb_terminal.decorators import log_start_end +from openbb_terminal.forecast import helpers +from openbb_terminal.rich_config import console + +logger = logging.getLogger(__name__) +# pylint: disable=too-many-arguments + + +@log_start_end(log=logger) +def display_theta_forecast( + data: Union[pd.DataFrame, pd.Series], + target_column: str = "close", + dataset_name: str = "", + seasonal: str = "M", + seasonal_periods: int = 7, + n_predict: int = 30, + start_window: float = 0.85, + forecast_horizon: int = 5, + export: str = "", + residuals: bool = False, + forecast_only: bool = False, + start_date: Optional[datetime] = None, + end_date: Optional[datetime] = None, + naive: bool = False, + external_axes: Optional[List[plt.axes]] = None, +): + """Display Theta forecast + + Parameters + ---------- + data : Union[pd.Series, np.array] + Data to forecast + target_column (str, optional): + Target column to forecast. Defaults to "close". + dataset_name str + The name of the ticker to be predicted + seasonal: str + Seasonal component. One of [N, A, M] + Defaults to MULTIPLICATIVE. + seasonal_periods: int + Number of seasonal periods in a year + If not set, inferred from frequency of the series. + n_predict: int + Number of days to forecast + start_window: float + Size of sliding window from start of timeseries and onwards + forecast_horizon: int + Number of days to forecast when backtesting and retraining historical + export: str + Format to export data + residuals: bool + Whether to show residuals for the model. Defaults to False. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the same + as the previous day's closing price. Defaults to False. + external_axes:Optional[List[plt.axes]] + External axes to plot on + """ + + data = helpers.clean_data(data, start_date, end_date, target_column, None) + if not helpers.check_data(data, target_column, None): + return + ( + ticker_series, + historical_fcast, + predicted_values, + precision, + best_theta, + _model, + ) = theta_model.get_theta_data( + data=data, + seasonal=seasonal, + seasonal_periods=seasonal_periods, + n_predict=n_predict, + target_column=target_column, + start_window=start_window, + forecast_horizon=forecast_horizon, + ) + probabilistic = False + helpers.plot_forecast( + f"Theta {best_theta:.2f}", + target_column, + historical_fcast, + predicted_values, + ticker_series, + dataset_name, + data, + n_predict, + forecast_horizon, + None, + precision, + probabilistic, + export, + forecast_only=forecast_only, + naive=naive, + external_axes=external_axes, + ) + if residuals: + # TODO: Figure out why residuals do not work with Theta + console.print( + "[red]Theta residual is currently not supported. Please stay tuned![/red]" + ) diff --git a/openbb_terminal/forecast/trans_model.py b/openbb_terminal/forecast/trans_model.py new file mode 100644 index 00000000000..54665c6d248 --- /dev/null +++ b/openbb_terminal/forecast/trans_model.py @@ -0,0 +1,171 @@ +# pylint: disable=too-many-arguments +"""Transformer Model""" +__docformat__ = "numpy" + +import logging +import warnings +from typing import Any, Tuple, Union, List, Optional + +import pandas as pd + +from darts import TimeSeries +from darts.models import TransformerModel +from openbb_terminal.decorators import log_start_end +from openbb_terminal.forecast import helpers + +logger = logging.getLogger(__name__) + + +@log_start_end(log=logger) +def get_trans_data( + data: Union[pd.Series, pd.DataFrame], + target_column: str = "close", + n_predict: int = 5, + train_split: float = 0.85, + past_covariates: str = None, + forecast_horizon: int = 5, + input_chunk_length: int = 14, + output_chunk_length: int = 5, + d_model: int = 64, + nhead: int = 4, + num_encoder_layers: int = 3, + num_decoder_layers: int = 3, + dim_feedforward: int = 512, + activation: str = "relu", + dropout: float = 0.0, + batch_size: int = 32, + n_epochs: int = 100, + learning_rate: float = 1e-3, + model_save_name: str = "trans_model", + force_reset: bool = True, + save_checkpoints: bool = True, +) -> Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries], Optional[float], Any]: + """Performs Transformer forecasting + + Args: + data (Union[pd.Series, pd.DataFrame]): + Input Data + n_predict (int, optional): + Days to predict. Defaults to 5. + target_column (str, optional): + Target column to forecast. Defaults to "close". + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + d_model (int): + The number of expected features in the encoder/decoder inputs. Defaults to 64. + nhead (int): + The number of heads in the multi-head attention mechanism. Defaults to 4. + num_encoder_layers (int): + The number of encoder layers in the encoder. Defaults to 3. + num_decoder_layers (int): + The number of decoder layers in the encoder. Defaults to 3. + dim_feedforward (int): + The dimension of the feedforward network model. Defaults to 512. + activation (str): + The activation function of encoder/decoder intermediate layer, โ€˜reluโ€™ or โ€˜geluโ€™. Defaults to 'relu'. + dropout (float, optional): + Fraction of neurons afected by Dropout. Defaults to 0.0. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 100. + learning_rate (float, optional): + Defaults to 1e-3. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset (all checkpoints will be + discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True. + + Returns: + List[TimeSeries] + Adjusted Data series + List[TimeSeries] + Historical forecast by best RNN model + List[TimeSeries] + list of Predictions + Optional[float] + Mean average precision error + Any + Best transformer Model + """ + + # TODO Check if torch GPU AVAILABLE + + use_scalers = True + probabilistic = False + + scaler, ticker_series = helpers.get_series( + data, target_column, is_scaler=use_scalers + ) + train, val = ticker_series.split_before(train_split) + valid = helpers.check_data_length( + train, val, input_chunk_length, output_chunk_length + ) + if not valid: + return [], [], [], None, None + + ( + past_covariate_whole, + past_covariate_train, + past_covariate_val, + ) = helpers.past_covs(past_covariates, data, train_split, use_scalers) + + brnn_model = TransformerModel( + input_chunk_length=input_chunk_length, + output_chunk_length=output_chunk_length, + d_model=d_model, + nhead=nhead, + num_encoder_layers=num_encoder_layers, + num_decoder_layers=num_decoder_layers, + dim_feedforward=dim_feedforward, + activation=activation, + dropout=dropout, + batch_size=batch_size, + n_epochs=n_epochs, + optimizer_kwargs={"lr": learning_rate}, + model_name=model_save_name, + random_state=42, + pl_trainer_kwargs=helpers.get_pl_kwargs(accelerator="cpu"), + force_reset=force_reset, + save_checkpoints=save_checkpoints, + ) + + # fit model on train series for historical forecasting + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + helpers.fit_model( + brnn_model, + train, + val, + past_covariate_train, + past_covariate_val, + ) + best_model = TransformerModel.load_from_checkpoint( + model_name=model_save_name, best=True + ) + + # Showing historical backtesting without retraining model (too slow) + return helpers.get_prediction( + "Transformer", + probabilistic, + use_scalers, + scaler, + past_covariates, + best_model, + ticker_series, + past_covariate_whole, + train_split, + forecast_horizon, + n_predict, + ) diff --git a/openbb_terminal/forecast/trans_view.py b/openbb_terminal/forecast/trans_view.py new file mode 100644 index 00000000000..074c9de546f --- /dev/null +++ b/openbb_terminal/forecast/trans_view.py @@ -0,0 +1,178 @@ +"""Transformer View""" +__docformat__ = "numpy" + +import logging +from typing import Union, Optional, List +from datetime import datetime + +import pandas as pd +import matplotlib.pyplot as plt + +from openbb_terminal.forecast import trans_model +from openbb_terminal.decorators import log_start_end +from openbb_terminal.forecast import helpers + +logger = logging.getLogger(__name__) +# pylint: disable=too-many-arguments + + +@log_start_end(log=logger) +def display_trans_forecast( + data: Union[pd.Series, pd.DataFrame], + target_column: str = "close", + dataset_name: str = "", + n_predict: int = 5, + past_covariates: str = None, + train_split: float = 0.85, + forecast_horizon: int = 5, + input_chunk_length: int = 14, + output_chunk_length: int = 5, + d_model: int = 64, + nhead: int = 4, + num_encoder_layers: int = 3, + num_decoder_layers: int = 3, + dim_feedforward: int = 512, + activation: str = "relu", + dropout: float = 0.1, + batch_size: int = 16, + n_epochs: int = 100, + learning_rate: float = 1e-3, + model_save_name: str = "trans_model", + force_reset: bool = True, + save_checkpoints: bool = True, + export: str = "", + residuals: bool = False, + forecast_only: bool = False, + start_date: Optional[datetime] = None, + end_date: Optional[datetime] = None, + naive: bool = False, + external_axes: Optional[List[plt.axes]] = None, +): + """Display Transformer forecast + + Parameters + ---------- + data (Union[pd.Series, pd.DataFrame]): + Input Data + dataset_name str + The name of the ticker to be predicted + n_predict (int, optional): + Days to predict. Defaults to 5. + target_column (str, optional): + Target column to forecast. Defaults to "close". + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + d_model (int): + The number of expected features in the encoder/decoder inputs. Defaults to 64. + nhead (int): + The number of heads in the multi-head attention mechanism. Defaults to 4. + num_encoder_layers (int): + The number of encoder layers in the encoder. Defaults to 3. + num_decoder_layers (int): + The number of decoder layers in the encoder. Defaults to 3. + dim_feedforward (int): + The dimension of the feedforward network model. Defaults to 512. + activation (str): + The activation function of encoder/decoder intermediate layer, โ€˜reluโ€™ or โ€˜geluโ€™. Defaults to 'relu'. + dropout (float, optional): + Fraction of neurons afected by Dropout. Defaults to 0.1. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 100. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. + Defaults to True. + export: str + Format to export data + residuals: bool + Whether to show residuals for the model. Defaults to False. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the same + as the previous day's closing price. Defaults to False. + external_axes:Optional[List[plt.axes]] + External axes to plot on + """ + + data = helpers.clean_data( + data, start_date, end_date, target_column, past_covariates + ) + if not helpers.check_data(data, target_column, past_covariates): + return + output_chunk_length = helpers.check_output( + output_chunk_length, n_predict, bool(past_covariates) + ) + ( + ticker_series, + historical_fcast, + predicted_values, + precision, + _model, + ) = trans_model.get_trans_data( + data=data, + n_predict=n_predict, + target_column=target_column, + past_covariates=past_covariates, + train_split=train_split, + forecast_horizon=forecast_horizon, + input_chunk_length=input_chunk_length, + output_chunk_length=output_chunk_length, + d_model=d_model, + nhead=nhead, + num_encoder_layers=num_encoder_layers, + num_decoder_layers=num_decoder_layers, + dim_feedforward=dim_feedforward, + activation=activation, + dropout=dropout, + batch_size=batch_size, + n_epochs=n_epochs, + learning_rate=learning_rate, + model_save_name=model_save_name, + force_reset=force_reset, + save_checkpoints=save_checkpoints, + ) + if ticker_series == []: + return + + probabilistic = False + helpers.plot_forecast( + "Transformer", + target_column, + historical_fcast, + predicted_values, + ticker_series, + dataset_name, + data, + n_predict, + forecast_horizon, + past_covariates, + precision, + probabilistic, + export, + forecast_only=forecast_only, + naive=naive, + external_axes=external_axes, + ) + if residuals: + helpers.plot_residuals( + _model, past_covariates, ticker_series, forecast_horizon=forecast_horizon + ) diff --git a/openbb_terminal/forex/forex_controller.py b/openbb_terminal/forex/forex_controller.py index 0e23b0d7150..aecf2f01e4e 100644 --- a/openbb_terminal/forex/forex_controller.py +++ b/openbb_terminal/forex/forex_controller.py @@ -44,7 +44,7 @@ class ForexController(BaseController): """Forex Controller class.""" CHOICES_COMMANDS = ["load", "quote", "candle", "resources", "fwd"] - CHOICES_MENUS = ["ta", "qa", "Oanda", "pred"] + CHOICES_MENUS = ["ta", "qa", "Oanda"] PATH = "/forex/" FILE_PATH = os.path.join(os.path.dirname(__file__), "README.md") @@ -83,7 +83,6 @@ class ForexController(BaseController): mt.add_raw("\n") mt.add_menu("ta", self.fx_pair) mt.add_menu("qa", self.fx_pair) - mt.add_menu("pred", self.fx_pair) mt.add_raw("\n") mt.add_info("forex") mt.add_menu("oanda") @@ -325,49 +324,6 @@ class ForexController(BaseController): else: console.print("No currency pair data is loaded. Use 'load' to load data.\n") - @log_start_end(log=logger) - def call_pred(self, _): - """Process pred command""" - # IMPORTANT: 8/11/22 prediction was discontinued on the installer packages - # because forecasting in coming out soon. - # This if statement disallows installer package users from using 'pred' - # even if they turn on the OPENBB_ENABLE_PREDICT feature flag to true - # however it does not prevent users who clone the repo from using it - # if they have ENABLE_PREDICT set to true. - if obbff.PACKAGED_APPLICATION or not obbff.ENABLE_PREDICT: - console.print( - "Predict is disabled. Forecasting coming soon!", - "\n", - ) - else: - if self.from_symbol and self.to_symbol: - if self.data.empty: - console.print( - "No currency pair data is loaded. Use 'load' to load data.\n" - ) - else: - try: - from openbb_terminal.forex.prediction_techniques import ( - pred_controller, - ) - - self.queue = self.load_class( - pred_controller.PredictionTechniquesController, - self.from_symbol, - self.to_symbol, - self.data.index[0], - "1440min", - self.data, - self.queue, - ) - except ImportError: - logger.exception("Tensorflow not available") - console.print( - "[red]Run pip install tensorflow to continue[/red]\n" - ) - else: - console.print("No pair selected.\n") - @log_start_end(log=logger) def call_qa(self, _): """Process qa command""" diff --git a/openbb_terminal/forex/prediction_techniques/pred_api.py b/openbb_terminal/forex/prediction_techniques/pred_api.py deleted file mode 100644 index ea1d2fe6402..00000000000 --- a/openbb_terminal/forex/prediction_techniques/pred_api.py +++ /dev/null @@ -1,33 +0,0 @@ -"""Pred context API.""" - -# flake8: noqa -# pylint: disable=unused-import - -# Menu commands -from openbb_terminal.common.prediction_techniques.ets_view import ( - display_exponential_smoothing as ets, -) -from openbb_terminal.common.prediction_techniques.knn_view import ( - display_k_nearest_neighbors as knn, -) -from openbb_terminal.common.prediction_techniques.regression_view import ( - display_regression as regression, -) -from openbb_terminal.common.prediction_techniques.arima_view import ( - display_arima as arima, -) -from openbb_terminal.common.prediction_techniques.neural_networks_view import ( - display_mlp as mlp, -) -from openbb_terminal.common.prediction_techniques.neural_networks_view import ( - display_rnn as rnn, -) -from openbb_terminal.common.prediction_techniques.neural_networks_view import ( - display_lstm as lstm, -) -from openbb_terminal.common.prediction_techniques.neural_networks_view import ( - display_conv1d as conv1d, -) -from openbb_terminal.common.prediction_techniques.mc_view import ( - display_mc_forecast as mc, -) diff --git a/openbb_terminal/forex/prediction_techniques/pred_controller.py b/openbb_terminal/forex/prediction_techniques/pred_controller.py deleted file mode 100644 index ab5280f52df..00000000000 --- a/openbb_terminal/forex/prediction_techniques/pred_controller.py +++ /dev/null @@ -1,716 +0,0 @@ -""" Prediction Controller """ -__docformat__ = "numpy" - -import argparse -import logging -from datetime import datetime -from typing import List - -import numpy as np -import pandas as pd -from prompt_toolkit.completion import NestedCompleter - -from openbb_terminal import feature_flags as obbff -from openbb_terminal.common.prediction_techniques import ( - arima_model, - arima_view, - ets_model, - ets_view, - knn_view, - mc_model, - mc_view, - neural_networks_view, - pred_helper, - regression_view, -) -from openbb_terminal.decorators import log_start_end -from openbb_terminal.helper_funcs import ( - EXPORT_ONLY_FIGURES_ALLOWED, - check_positive, - get_next_stock_market_days, - valid_date, -) -from openbb_terminal.menu import session -from openbb_terminal.parent_classes import BaseController -from openbb_terminal.rich_config import console, MenuText - -logger = logging.getLogger(__name__) - - -class PredictionTechniquesController(BaseController): - """Prediction Techniques Controller class""" - - CHOICES_COMMANDS = [ - "pick", - "ets", - "knn", - "regression", - "arima", - "mlp", - "rnn", - "lstm", - "conv1d", - "mc", - ] - - PATH = "/forex/pred/" - - def __init__( - self, - from_symbol: str, - to_symbol: str, - start: datetime, - interval: str, - data: pd.DataFrame, - queue: List[str] = None, - ): - """Constructor""" - super().__init__(queue) - - data["Returns"] = data["Adj Close"].pct_change() - data["LogRet"] = np.log(data["Adj Close"]) - np.log(data["Adj Close"].shift(1)) - data = data.rename(columns={"Adj Close": "AdjClose"}) - data = data.dropna() - - self.data = data - self.from_symbol = from_symbol - self.to_symbol = to_symbol - self.ticker = f"{from_symbol}/{to_symbol}" - self.start = start - self.interval = interval - self.target = "AdjClose" - - if session and obbff.USE_PROMPT_TOOLKIT: - choices: dict = {c: {} for c in self.controller_choices} - choices["pick"] = {c: {} for c in self.data.columns} - choices["ets"]["-t"] = {c: {} for c in ets_model.TRENDS} - choices["ets"]["-s"] = {c: {} for c in ets_model.SEASONS} - choices["arima"]["-i"] = {c: {} for c in arima_model.ICS} - choices["mc"]["--dist"] = {c: {} for c in mc_model.DISTRIBUTIONS} - - choices["support"] = self.SUPPORT_CHOICES - choices["about"] = self.ABOUT_CHOICES - - self.completer = NestedCompleter.from_nested_dict(choices) - - def print_help(self): - """Print help""" - mt = MenuText("forex/pred/") - mt.add_cmd("pick") - mt.add_raw("\n") - mt.add_param("_pair", f"{self.ticker} (from {self.start.strftime('%Y-%m-%d')})") - mt.add_param("_target", self.target) - mt.add_raw("\n") - mt.add_info("_models_") - mt.add_cmd("ets") - mt.add_cmd("knn") - mt.add_cmd("regression") - mt.add_cmd("arima") - mt.add_cmd("mlp") - mt.add_cmd("rnn") - mt.add_cmd("lstm") - mt.add_cmd("conv1d") - mt.add_cmd("mc") - console.print(text=mt.menu_text, menu="Forex - Prediction Techniques") - - def custom_reset(self): - """Class specific component of reset command""" - if self.ticker: - return [ - "forex", - f"from {self.from_symbol}", - f"to {self.to_symbol}", - "load", - "pred", - ] - return [] - - @log_start_end(log=logger) - def call_pick(self, other_args: List[str]): - """Process pick command""" - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - add_help=False, - prog="pick", - description=""" - Change target variable - """, - ) - parser.add_argument( - "-t", - "--target", - dest="target", - choices=list(self.data.columns), - help="Select variable to analyze", - ) - if other_args and "-t" not in other_args and "-h" not in other_args: - other_args.insert(0, "-t") - - ns_parser = self.parse_known_args_and_warn( - parser, other_args, EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - self.target = ns_parser.target - console.print("") - - @log_start_end(log=logger) - def call_ets(self, other_args: List[str]): - """Process ets command""" - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="ets", - description=""" - Exponential Smoothing, see https://otexts.com/fpp2/taxonomy.html - - Trend='N', Seasonal='N': Simple Exponential Smoothing - Trend='N', Seasonal='A': Exponential Smoothing - Trend='N', Seasonal='M': Exponential Smoothing - Trend='A', Seasonal='N': Holtโ€™s linear method - Trend='A', Seasonal='A': Additive Holt-Wintersโ€™ method - Trend='A', Seasonal='M': Multiplicative Holt-Wintersโ€™ method - Trend='Ad', Seasonal='N': Additive damped trend method - Trend='Ad', Seasonal='A': Exponential Smoothing - Trend='Ad', Seasonal='M': Holt-Wintersโ€™ damped method - Trend component: N: None, A: Additive, Ad: Additive Damped - Seasonality component: N: None, A: Additive, M: Multiplicative - """, - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-t", - "--trend", - action="store", - dest="trend", - choices=ets_model.TRENDS, - default="N", - help="Trend component: N: None, A: Additive, Ad: Additive Damped.", - ) - parser.add_argument( - "-s", - "--seasonal", - action="store", - dest="seasonal", - choices=ets_model.SEASONS, - default="N", - help="Seasonality component: N: None, A: Additive, M: Multiplicative.", - ) - parser.add_argument( - "-p", - "--periods", - action="store", - dest="seasonal_periods", - type=check_positive, - default=5, - help="Seasonal periods.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select - Backtesting", - ) - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - - if ns_parser.s_end_date: - - if ns_parser.s_end_date < self.data.index[0]: - console.print( - "Backtesting not allowed, since End Date is older than Start Date of historical data\n" - ) - - if ( - ns_parser.s_end_date - < get_next_stock_market_days( - last_stock_day=self.data.index[0], - n_next_days=5 + ns_parser.n_days, - )[-1] - ): - console.print( - "Backtesting not allowed, since End Date is too close to Start Date to train model\n" - ) - - ets_view.display_exponential_smoothing( - dataset=self.ticker, - data=self.data[self.target], - n_predict=ns_parser.n_days, - trend=ns_parser.trend, - seasonal=ns_parser.seasonal, - seasonal_periods=ns_parser.seasonal_periods, - end_date=ns_parser.s_end_date, - export=ns_parser.export, - ) - - @log_start_end(log=logger) - def call_knn(self, other_args: List[str]): - """Process knn command""" - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="knn", - description=""" - K nearest neighbors is a simple algorithm that stores all - available cases and predict the numerical target based on a similarity measure - (e.g. distance functions). - """, - ) - parser.add_argument( - "-i", - "--input", - action="store", - dest="n_inputs", - type=check_positive, - default=40, - help="number of days to use as input for prediction.", - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-j", - "--jumps", - action="store", - dest="n_jumps", - type=check_positive, - default=1, - help="number of jumps in training data.", - ) - parser.add_argument( - "-n", - "--neighbors", - action="store", - dest="n_neighbors", - type=check_positive, - default=20, - help="number of neighbors to use on the algorithm.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select for testing", - ) - parser.add_argument( - "-t", - "--test_size", - default=0.2, - dest="valid_split", - type=float, - help="Percentage of data to validate in sample", - ) - parser.add_argument( - "--no_shuffle", - action="store_false", - dest="no_shuffle", - default=True, - help="Specify if shuffling validation inputs.", - ) - ns_parser = self.parse_known_args_and_warn( - parser, other_args, EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - knn_view.display_k_nearest_neighbors( - dataset=self.ticker, - data=self.data[self.target], - n_neighbors=ns_parser.n_neighbors, - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - test_size=ns_parser.valid_split, - end_date=ns_parser.s_end_date, - no_shuffle=ns_parser.no_shuffle, - ) - - @log_start_end(log=logger) - def call_regression(self, other_args: List[str]): - """Process linear command""" - parser = argparse.ArgumentParser( - add_help=False, - prog="regression", - description=""" - Regression attempts to model the relationship between - two variables by fitting a linear/quadratic/cubic/other equation to - observed data. One variable is considered to be an explanatory variable, - and the other is considered to be a dependent variable. - """, - ) - - parser.add_argument( - "-i", - "--input", - action="store", - dest="n_inputs", - type=check_positive, - default=40, - help="number of days to use for prediction.", - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-j", - "--jumps", - action="store", - dest="n_jumps", - type=check_positive, - default=1, - help="number of jumps in training data.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select - Backtesting", - ) - parser.add_argument( - "-p", - "--polynomial", - action="store", - dest="n_polynomial", - type=check_positive, - default=1, - help="polynomial associated with regression.", - ) - if ( - other_args - and "-h" not in other_args - and ("-p" not in other_args or "--polynomial" not in other_args) - ): - other_args.insert(0, "-p") - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - # BACKTESTING CHECK - if ns_parser.s_end_date: - if ns_parser.s_end_date < self.data.index[0]: - console.print( - "Backtesting not allowed, since End Date is older than Start Date of historical data\n" - ) - - if ( - ns_parser.s_end_date - < get_next_stock_market_days( - last_stock_day=self.data.index[0], - n_next_days=5 + ns_parser.n_days, - )[-1] - ): - console.print( - "Backtesting not allowed, since End Date is too close to Start Date to train model\n" - ) - - regression_view.display_regression( - dataset=self.ticker, - values=self.data[self.target], - poly_order=ns_parser.n_polynomial, - n_input=ns_parser.n_inputs, - n_predict=ns_parser.n_days, - n_jumps=ns_parser.n_jumps, - end_date=ns_parser.s_end_date, - export=ns_parser.export, - ) - - @log_start_end(log=logger) - def call_arima(self, other_args: List[str]): - """Process arima command""" - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="arima", - description=""" - In statistics and econometrics, and in particular in time series analysis, an - autoregressive integrated moving average (ARIMA) model is a generalization of an - autoregressive moving average (ARMA) model. Both of these models are fitted to time - series data either to better understand the data or to predict future points in the - series (forecasting). ARIMA(p,d,q) where parameters p, d, and q are non-negative - integers, p is the order (number of time lags) of the autoregressive model, d is the - degree of differencing (the number of times the data have had past values subtracted), - and q is the order of the moving-average model. - """, - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-i", - "--ic", - action="store", - dest="s_ic", - type=str, - default="aic", - choices=arima_model.ICS, - help="information criteria.", - ) - parser.add_argument( - "-s", - "--seasonal", - action="store_true", - default=False, - dest="b_seasonal", - help="Use weekly seasonal data.", - ) - parser.add_argument( - "-o", - "--order", - action="store", - dest="s_order", - default="", - type=str, - help="arima model order (p,d,q) in format: p,d,q.", - ) - parser.add_argument( - "-r", - "--results", - action="store_true", - dest="b_results", - default=False, - help="results about ARIMA summary flag.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select - Backtesting", - ) - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - # BACKTESTING CHECK - if ns_parser.s_end_date: - - if ns_parser.s_end_date < self.data.index[0]: - console.print( - "Backtesting not allowed, since End Date is older than Start Date of historical data\n" - ) - - if ( - ns_parser.s_end_date - < get_next_stock_market_days( - last_stock_day=self.data.index[0], - n_next_days=5 + ns_parser.n_days, - )[-1] - ): - console.print( - "Backtesting not allowed, since End Date is too close to Start Date to train model\n" - ) - - arima_view.display_arima( - dataset=self.ticker, - values=self.data[self.target], - arima_order=ns_parser.s_order, - n_predict=ns_parser.n_days, - seasonal=ns_parser.b_seasonal, - ic=ns_parser.s_ic, - results=ns_parser.b_results, - end_date=ns_parser.s_end_date, - export=ns_parser.export, - ) - - @log_start_end(log=logger) - def call_mlp(self, other_args: List[str]): - """Process mlp command""" - try: - ns_parser = pred_helper.parse_args( - prog="mlp", - description="""Multi-Layered-Perceptron. """, - other_args=other_args, - ) - if ns_parser: - neural_networks_view.display_mlp( - dataset=self.ticker, - data=self.data[self.target], - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - ) - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_rnn(self, other_args: List[str]): - """Process rnn command""" - try: - ns_parser = pred_helper.parse_args( - prog="rnn", - description="""Recurrent Neural Network. """, - other_args=other_args, - ) - if ns_parser: - neural_networks_view.display_rnn( - dataset=self.ticker, - data=self.data[self.target], - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - ) - - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_lstm(self, other_args: List[str]): - """Process lstm command""" - try: - ns_parser = pred_helper.parse_args( - prog="lstm", - description="""Long-Short Term Memory. """, - other_args=other_args, - ) - if ns_parser: - neural_networks_view.display_lstm( - dataset=self.ticker, - data=self.data[self.target], - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - ) - - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_conv1d(self, other_args: List[str]): - """Process conv1d command""" - try: - ns_parser = pred_helper.parse_args( - prog="conv1d", - description="""1D CNN.""", - other_args=other_args, - ) - if ns_parser: - neural_networks_view.display_conv1d( - dataset=self.ticker, - data=self.data[self.target], - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - ) - - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_mc(self, other_args: List[str]): - """Process mc command""" - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - add_help=False, - prog="mc", - description=""" - Perform Monte Carlo forecasting - """, - ) - parser.add_argument( - "-d", - "--days", - help="Days to predict", - dest="n_days", - type=check_positive, - default=30, - ) - parser.add_argument( - "-n", - "--num", - help="Number of simulations to perform", - dest="n_sims", - default=100, - ) - parser.add_argument( - "--dist", - choices=mc_model.DISTRIBUTIONS, - default="lognormal", - dest="dist", - help="Whether to model returns or log returns", - ) - - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - if self.target != "AdjClose": - console.print("MC Prediction designed for AdjClose prices\n") - - mc_view.display_mc_forecast( - data=self.data[self.target], - n_future=ns_parser.n_days, - n_sims=ns_parser.n_sims, - use_log=ns_parser.dist == "lognormal", - export=ns_parser.export, - ) diff --git a/openbb_terminal/glossary.json b/openbb_terminal/glossary.json new file mode 100644 index 00000000000..0d3511306a1 --- /dev/null +++ b/openbb_terminal/glossary.json @@ -0,0 +1,4 @@ +{ + "option":"A financial asset that is based on the value of an underlying asset.", + "forex":"Foreign Exchange trading is trading currencie of one country for currency in another country. For example. trading United Stated Dollars (USD) for Great British Pounds (GBP) you are forex trading" +} diff --git a/openbb_terminal/helper_funcs.py b/openbb_terminal/helper_funcs.py index 80f494a65c1..40b00241ba7 100644 --- a/openbb_terminal/helper_funcs.py +++ b/openbb_terminal/helper_funcs.py @@ -5,7 +5,9 @@ import argparse import logging from pathlib import Path from typing import List, Union, Optional, Dict -from datetime import datetime, timedelta, date as d +from functools import lru_cache +from datetime import datetime, timedelta +from datetime import date as d import types from collections.abc import Iterable import os @@ -241,7 +243,6 @@ def print_rich_table( show_header: bool Whether to show the header row. """ - if obbff.USE_TABULATE_DF: table = Table(title=title, show_lines=True, show_header=show_header) @@ -272,6 +273,7 @@ def print_rich_table( floatfmt = [floatfmt for _ in range(len(df.columns))] for idx, values in zip(df.index.tolist(), df.values.tolist()): + # remove hour/min/sec from timestamp index - Format: YYYY-MM-DD # make better row = [str(idx)] if show_index else [] row += [ str(x) @@ -777,21 +779,26 @@ def get_next_stock_market_days(last_stock_day, n_next_days) -> list: l_pred_days = [] years: list = [] holidays: list = [] - while n_days < n_next_days: - last_stock_day += timedelta(hours=24) - year = last_stock_day.date().year - if year not in years: - years.append(year) - holidays += us_market_holidays(year) - # Check if it is a weekend - if last_stock_day.date().weekday() > 4: - continue - # Check if it is a holiday - if last_stock_day.strftime("%Y-%m-%d") in holidays: - continue - # Otherwise stock market is open - n_days += 1 - l_pred_days.append(last_stock_day) + if isinstance(last_stock_day, datetime): + while n_days < n_next_days: + last_stock_day += timedelta(hours=24) + year = last_stock_day.date().year + if year not in years: + years.append(year) + holidays += us_market_holidays(year) + # Check if it is a weekend + if last_stock_day.date().weekday() > 4: + continue + # Check if it is a holiday + if last_stock_day.strftime("%Y-%m-%d") in holidays: + continue + # Otherwise stock market is open + n_days += 1 + l_pred_days.append(last_stock_day) + else: + while n_days < n_next_days: + l_pred_days.append(last_stock_day + 1 + n_days) + n_days += 1 return l_pred_days @@ -1662,3 +1669,29 @@ def search_wikipedia(expression: str) -> None: show_index=False, title=f"Wikipedia results for {expression}", ) + + +@lru_cache +def load_json(path: str) -> Dict[str, str]: + """Loads a dictionary from a json file path + + Parameter + ---------- + path : str + The path for the json file + + Returns + ---------- + Dict[str, str] + The dictionary loaded from json + """ + try: + with open(path) as file: + return json.load(file) + except Exception as e: + console.print( + f"[red]Failed to load preferred source from file: " + f"{obbff.PREFERRED_DATA_SOURCE_FILE}[/red]" + ) + console.print(f"[red]{e}[/red]") + return {} diff --git a/openbb_terminal/keys_model.py b/openbb_terminal/keys_model.py index bccfae7c044..3c61c47c86a 100644 --- a/openbb_terminal/keys_model.py +++ b/openbb_terminal/keys_model.py @@ -1,6 +1,8 @@ """Keys model""" __docformat__ = "numpy" +# pylint: disable=too-many-lines + import sys import logging import os @@ -28,8 +30,6 @@ from openbb_terminal.rich_config import console from openbb_terminal.terminal_helper import suppress_stdout -# pylint: disable=too-many-lines - logger = logging.getLogger(__name__) sys.tracebacklimit = 0 diff --git a/openbb_terminal/parent_classes.py b/openbb_terminal/parent_classes.py index 93e550e837d..57621e465cf 100644 --- a/openbb_terminal/parent_classes.py +++ b/openbb_terminal/parent_classes.py @@ -39,6 +39,7 @@ from openbb_terminal.helper_funcs import ( support_message, check_file_type_saved, check_positive, + load_json, parse_and_split_input, search_wikipedia, ) @@ -87,6 +88,7 @@ class BaseController(metaclass=ABCMeta): "r", "reset", "support", + "glossary", "wiki", "record", "stop", @@ -187,6 +189,13 @@ class BaseController(metaclass=ABCMeta): # goes into "TA", the "TSLA" ticker will appear. If that condition doesn't exist # the previous class will be loaded and even if the user changes the ticker on # the stocks context it will not impact the one of TA menu - unless changes are done. + # An exception is made for forecasting because it is built to handle multiple loaded + # tickers. + if class_ins.PATH in controllers and class_ins.PATH == "/forecast/": + old_class = controllers[class_ins.PATH] + old_class.queue = self.queue + old_class.load(*args[:-1], **kwargs) + return old_class.menu() if class_ins.PATH in controllers and arguments == 1 and obbff.REMEMBER_CONTEXTS: old_class = controllers[class_ins.PATH] old_class.queue = self.queue @@ -477,6 +486,42 @@ class BaseController(metaclass=ABCMeta): path=self.PATH, ) + @log_start_end(log=logger) + def call_glossary(self, other_args: List[str]) -> None: + """Process glossary command""" + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="support", + description="Submit your support request", + ) + parser.add_argument( + "-w", + "--word", + action="store", + dest="word", + type=str, + required="-h" not in other_args, + help="Word that you want defined", + ) + if other_args and "-" not in other_args[0][0]: + other_args.insert(0, "-w") + + ns_parser = parse_simple_args(parser, other_args) + + glossary_file = os.path.join(os.path.dirname(__file__), "glossary.json") + glossary_dict = load_json(glossary_file) + + if ns_parser: + word = glossary_dict.get(ns_parser.word, "") + word = word.lower() + word = word.replace("--", "") + word = word.replace("-", " ") + if word: + console.print(word + "\n") + else: + console.print("Word is not in the glossary.\n") + @log_start_end(log=logger) def call_wiki(self, other_args: List[str]) -> None: """Process wiki command""" @@ -696,6 +741,7 @@ class BaseController(metaclass=ABCMeta): if self.queue and len(self.queue) > 0: # If the command is quitting the menu we want to return in here if self.queue[0] in ("q", "..", "quit"): + self.save_class() # Go back to the root in order to go to the right directory because # there was a jump between indirect menus if custom_path_menu_above: diff --git a/openbb_terminal/reports/equity.ipynb b/openbb_terminal/reports/equity.ipynb index 3d1b9c2942e..eaeb2c89c2e 100644 --- a/openbb_terminal/reports/equity.ipynb +++ b/openbb_terminal/reports/equity.ipynb @@ -10,19 +10,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "driven-billion", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING! Breaking changes incoming! Especially avoid using kwargs, since some of them will change.\n", - "For more information see the official documentation at: https://openbb-finance.github.io/OpenBBTerminal/api/\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "import io\n", @@ -65,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "c3fe7db5-ec6a-42cf-9e66-52dc1de22370", "metadata": {}, "outputs": [], @@ -85,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "intended-sherman", "metadata": { "tags": [ @@ -116,39 +107,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "1be26dae-cafe-4a22-80aa-eff296fc1a9b", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n",
-       "Loading Daily MSFT stock with starting period 2021-04-01 for analysis.\n",
-       "
\n" - ], - "text/plain": [ - "\n", - "Loading Daily MSFT stock with starting period 2021-04-01 for analysis.\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "('Investment Research Report on MSFT',\n", - " '23 September, 2022',\n", - " '12:22',\n", - " datetime.timezone(datetime.timedelta(seconds=14400), 'Arabian Standard Time'))" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ticker_data = openbb.stocks.load(\n", " symbol=symbol, start_date=datetime.datetime.now() - datetime.timedelta(days=18 * 30)\n", @@ -165,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "e3b15d6f-dc9e-4f97-aa07-37d949a4e4bb", "metadata": {}, "outputs": [], @@ -188,114 +150,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "aca4b6ef", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Previous QuarterCurrent QuarterNext QuarterCurrent YearNext Year
QUARTER REVENUES ESTIMATES
Date--ending 12/31/22ending 06/30/23ending 06/30/24
No. of Analysts--283736
Average Estimate--56,497 USD220,468 USD251,499 USD
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Publish Date--7/26/2022--
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CategoryAnalystRatingPrice Target
date
2022-08-17UpgradeCredit SuisseNeutral -> Outperform201.0
2022-07-26InitiatedItau BBAUnderperform136.0
2022-07-20ReiteratedWells FargoOverweightNaN
2022-07-20ReiteratedMorgan StanleyOverweightNaN
2022-07-06ReiteratedGoldmanNeutralNaN
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" - ], - "text/plain": [ - " Category Analyst Rating Price Target\n", - "date \n", - "2022-08-17 Upgrade Credit Suisse Neutral -> Outperform 201.0\n", - "2022-07-26 Initiated Itau BBA Underperform 136.0\n", - "2022-07-20 Reiterated Wells Fargo Overweight NaN\n", - "2022-07-20 Reiterated Morgan Stanley Overweight NaN\n", - "2022-07-06 Reiterated Goldman Neutral NaN" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df_analyst = openbb.stocks.dd.analyst(symbol=symbol)\n", "print(df_analyst.columns)\n", @@ -1303,7 +1065,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "28ce0827", "metadata": {}, "outputs": [], diff --git a/openbb_terminal/reports/etf.ipynb b/openbb_terminal/reports/etf.ipynb index 4feba7cf197..84237e19cc9 100644 --- a/openbb_terminal/reports/etf.ipynb +++ b/openbb_terminal/reports/etf.ipynb @@ -288,7 +288,7 @@ "metadata": { "celltoolbar": "Tags", "kernelspec": { - "display_name": "Python 3.9.13 ('obb')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -302,7 +302,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.9.6" }, "vscode": { "interpreter": { diff --git a/openbb_terminal/reports/forecast.ipynb b/openbb_terminal/reports/forecast.ipynb new file mode 100644 index 00000000000..e9a14ca9431 --- /dev/null +++ b/openbb_terminal/reports/forecast.ipynb @@ -0,0 +1,300 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "70ba2c8a", + "metadata": {}, + "source": [ + "## Notebook setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "driven-billion", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import io\n", + "import warnings\n", + "import datetime\n", + "import numpy as np\n", + "import base64\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib_inline.backend_inline\n", + "\n", + "from IPython.display import HTML\n", + "\n", + "from openbb_terminal.api import widgets\n", + "from openbb_terminal.api import openbb\n", + "from openbb_terminal.helper_classes import TerminalStyle\n", + "\n", + "%matplotlib inline\n", + "matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\")\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3fe7db5-ec6a-42cf-9e66-52dc1de22370", + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " theme = TerminalStyle(\"light\", \"light\", \"light\")\n", + "except:\n", + " pass\n", + "stylesheet = widgets.html_report_stylesheet()\n", + "try:\n", + " with open(\"openbb_terminal/reports/OpenBB_reports_logo.png\", \"rb\") as image_file:\n", + " openbb_image_encoded = base64.b64encode(image_file.read())\n", + "except FileNotFoundError:\n", + " openbb_image_encoded = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c3ae3e3-2b91-43a6-aa44-7b10986fd369", + "metadata": {}, + "outputs": [], + "source": [ + "def get_image(func, title, *args, **kwargs):\n", + " local_fig, ax = plt.subplots(dpi=20)\n", + " kwargs[\"chart\"] = True\n", + " kwargs[\"external_axes\"] = [ax]\n", + " if title:\n", + " ax.set_title(title)\n", + " ax.set_xlabel(\"Lag\")\n", + " ax.set_ylabel(\"Autocorrelation\")\n", + " func(*args, **kwargs)\n", + " local_fig.tight_layout()\n", + " f = io.BytesIO()\n", + " local_fig.savefig(f, format=\"svg\")\n", + " return f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "markdown", + "id": "807fda4e", + "metadata": {}, + "source": [ + "## Select Ticker" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "intended-sherman", + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "# Parameters that will be replaced when calling this notebook\n", + "# Do not leave parameters blank as notebook will not run otherwise\n", + "symbol = \"VTI\"\n", + "report_name = \"Forecast Report for VTI\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1f87190", + "metadata": {}, + "outputs": [], + "source": [ + "ticker = symbol" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a441ddbc-d75b-4a18-84d4-e01b677674d3", + "metadata": {}, + "outputs": [], + "source": [ + "if \".\" in ticker:\n", + " import sys\n", + "\n", + " sys.exit(0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1be26dae-cafe-4a22-80aa-eff296fc1a9b", + "metadata": {}, + "outputs": [], + "source": [ + "ticker_data = openbb.stocks.load(\n", + " symbol=ticker, start_date=datetime.datetime.now() - datetime.timedelta(days=400)\n", + ")\n", + "ticker_data = ticker_data.reset_index()\n", + "ticker_data.columns = [x.lower() for x in ticker_data.columns]\n", + "\n", + "author = \"OpenBB\"\n", + "report_title = f\"FORECAST RESEARCH REPORT ON {ticker.upper()}\"\n", + "report_date = datetime.datetime.now().strftime(\"%d %B, %Y\")\n", + "report_time = datetime.datetime.now().strftime(\"%H:%M\")\n", + "report_timezone = datetime.datetime.now(datetime.timezone.utc).astimezone().tzinfo\n", + "report_title, report_date, report_time, report_timezone" + ] + }, + { + "cell_type": "markdown", + "id": "f8a0d747", + "metadata": {}, + "source": [ + "## Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb31e9ee-e7ae-4e1b-89b4-bb6f571cfdfa", + "metadata": {}, + "outputs": [], + "source": [ + "local_fig, ax = plt.subplots(dpi=20)\n", + "column = \"close\"\n", + "ticker_data.plot(x=\"date\", y=column, ax=ax)\n", + "ax.set_title(f\"{column.title()} for {ticker}\")\n", + "ax.set_xlabel(\"Date\")\n", + "ax.set_ylabel(\"Price\")\n", + "local_fig.tight_layout()\n", + "f = io.BytesIO()\n", + "local_fig.savefig(f, format=\"svg\")\n", + "plot_chart = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8bb53c1-147e-4342-af61-0d0a9cc56e59", + "metadata": {}, + "outputs": [], + "source": [ + "corr_chart = get_image(openbb.forecast.corr, None, ticker_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74b914a1-ed32-4a17-a943-7879bc8f00df", + "metadata": {}, + "outputs": [], + "source": [ + "season_chart = get_image(openbb.forecast.season, \"Seasonality Data\", ticker_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53568c9b-aa06-4ba0-ad86-ede6732e3187", + "metadata": {}, + "outputs": [], + "source": [ + "expo_chart = get_image(openbb.forecast.expo, None, ticker_data, chart=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a919f67-ac9f-4b75-a3b3-102e0fbe5862", + "metadata": {}, + "outputs": [], + "source": [ + "tft_chart = get_image(openbb.forecast.tft, None, ticker_data, train_split=0.5)" + ] + }, + { + "cell_type": "markdown", + "id": "ac501a32-124d-4d21-aae1-a8d147bb1071", + "metadata": {}, + "source": [ + "## Render the report template to a file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "abfe37e4-ea38-42c3-8fb5-7cceb233fb12", + "metadata": {}, + "outputs": [], + "source": [ + "body = \"\"\n", + "\n", + "img = \"./openbb_terminal/reports/OpenBB_reports_logo.png\"\n", + "floppy_disk_img = (\"./openbb_terminal/reports/floppy-disc.png\",)\n", + "body += widgets.header(\n", + " img,\n", + " floppy_disk_img,\n", + " author,\n", + " report_date,\n", + " report_time,\n", + " report_timezone,\n", + " f\"FORECAST REPORT ON: {ticker.upper()}\",\n", + ")\n", + "\n", + "body += widgets.tablinks([\"SUMMARY\"])\n", + "\n", + "htmlcode = widgets.h(3, \"Summary\")\n", + "htmlcode += widgets.row([widgets.h(3, \"Understand the data\")])\n", + "htmlcode += widgets.row([ticker_data.head(10).to_html(), plot_chart])\n", + "htmlcode += widgets.row([widgets.h(3, \"Analyzing the data\")])\n", + "htmlcode += widgets.row([corr_chart, season_chart])\n", + "htmlcode += widgets.row([widgets.h(3, \"Creating predictions\")])\n", + "htmlcode += widgets.row([expo_chart, tft_chart])\n", + "\n", + "body += widgets.add_tab(\"SUMMARY\", htmlcode)\n", + "body += widgets.tab_clickable_and_save_evt()\n", + "report = widgets.html_report(title=report_name, stylesheet=stylesheet, body=body)\n", + "\n", + "# to save the results\n", + "with open(report_name + \".html\", \"w\", encoding=\"utf-8\") as fh:\n", + " fh.write(report)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7f52d81-8362-4a68-9830-fee24c16fa08", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + }, + "vscode": { + "interpreter": { + "hash": "ab0e87f2bc73919b75956d1b3e303320699d53da895be1bb46287d7dc67c6497" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openbb_terminal/reports/portfolio.ipynb b/openbb_terminal/reports/portfolio.ipynb index e49c5c4f872..a5f56373bd6 100644 --- a/openbb_terminal/reports/portfolio.ipynb +++ b/openbb_terminal/reports/portfolio.ipynb @@ -10,19 +10,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "driven-billion", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING! Breaking changes incoming! Especially avoid using kwargs, since some of them will change.\n", - "You can try branch with the latest changes.\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "import io\n", @@ -75,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "c3fe7db5-ec6a-42cf-9e66-52dc1de22370", "metadata": {}, "outputs": [], @@ -87,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "daef64ec", "metadata": { "tags": [ @@ -104,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "ee7112da", "metadata": {}, "outputs": [], @@ -117,24 +108,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "1be26dae-cafe-4a22-80aa-eff296fc1a9b", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('Portfolio report for Public_Equity_Orderbook.xlsx',\n", - " '15 September, 2022',\n", - " '06:55',\n", - " )" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "orderbook_path = holdings + orderbook\n", "_, orderbook_file = os.path.split(orderbook_path)\n", @@ -163,272 +140,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "b139556d", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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PortfolioBenchmark
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Industrials---%8.35%
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Realestate---%2.89%
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(10, 4), dpi=150)\n", "openbb.portfolio.yret(P, chart=True, external_axes=[ax])\n", diff --git a/openbb_terminal/stocks/prediction_techniques/__init__.py b/openbb_terminal/stocks/prediction_techniques/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/openbb_terminal/stocks/prediction_techniques/pred_api.py b/openbb_terminal/stocks/prediction_techniques/pred_api.py deleted file mode 100644 index 8f263cd300b..00000000000 --- a/openbb_terminal/stocks/prediction_techniques/pred_api.py +++ /dev/null @@ -1,39 +0,0 @@ -"""Inference API.""" -import os -from openbb_terminal.helper_classes import ModelsNamespace as _models -from openbb_terminal.common import prediction_techniques - -# flake8: noqa -# pylint: disable=unused-import - -# Menu commands -from openbb_terminal.common.prediction_techniques.ets_view import ( - display_exponential_smoothing as ets, -) -from openbb_terminal.common.prediction_techniques.knn_view import ( - display_k_nearest_neighbors as knn, -) -from openbb_terminal.common.prediction_techniques.regression_view import ( - display_regression as regression, -) -from openbb_terminal.common.prediction_techniques.arima_view import ( - display_arima as arima, -) -from openbb_terminal.common.prediction_techniques.neural_networks_view import ( - display_mlp as mlp, -) -from openbb_terminal.common.prediction_techniques.neural_networks_view import ( - display_rnn as rnn, -) -from openbb_terminal.common.prediction_techniques.neural_networks_view import ( - display_lstm as lstm, -) -from openbb_terminal.common.prediction_techniques.neural_networks_view import ( - display_conv1d as conv1d, -) -from openbb_terminal.common.prediction_techniques.mc_view import ( - display_mc_forecast as mc, -) - -# Models -models = _models(os.path.abspath(os.path.dirname(prediction_techniques.__file__))) diff --git a/openbb_terminal/stocks/prediction_techniques/pred_controller.py b/openbb_terminal/stocks/prediction_techniques/pred_controller.py deleted file mode 100644 index d8ba2853ee8..00000000000 --- a/openbb_terminal/stocks/prediction_techniques/pred_controller.py +++ /dev/null @@ -1,714 +0,0 @@ -""" Prediction Controller """ -__docformat__ = "numpy" - -import argparse -import logging -from datetime import datetime -from typing import List - -import numpy as np -import pandas as pd -from prompt_toolkit.completion import NestedCompleter - -from openbb_terminal import feature_flags as obbff -from openbb_terminal.common.prediction_techniques import ( - arima_model, - arima_view, - ets_model, - ets_view, - knn_view, - mc_model, - mc_view, - neural_networks_view, - pred_helper, - regression_view, -) -from openbb_terminal.decorators import log_start_end -from openbb_terminal.helper_funcs import ( - EXPORT_ONLY_FIGURES_ALLOWED, - check_positive, - get_next_stock_market_days, - valid_date, -) -from openbb_terminal.menu import session -from openbb_terminal.parent_classes import BaseController -from openbb_terminal.rich_config import console, MenuText -from openbb_terminal.stocks import stocks_helper - -logger = logging.getLogger(__name__) - - -class PredictionTechniquesController(BaseController): - """Prediction Techniques Controller class""" - - CHOICES_COMMANDS = [ - "load", - "pick", - "ets", - "knn", - "regression", - "arima", - "mlp", - "rnn", - "lstm", - "conv1d", - "mc", - ] - - PATH = "/stocks/pred/" - - def __init__( - self, - ticker: str, - start: datetime, - interval: str, - stock: pd.DataFrame, - queue: List[str] = None, - ): - """Constructor""" - super().__init__(queue) - - stock["Returns"] = stock["Adj Close"].pct_change() - stock["LogRet"] = np.log(stock["Adj Close"]) - np.log( - stock["Adj Close"].shift(1) - ) - stock = stock.rename(columns={"Adj Close": "AdjClose"}) - stock = stock.dropna() - - self.stock = stock - self.ticker = ticker - self.start = start - self.interval = interval - self.target = "AdjClose" - - if session and obbff.USE_PROMPT_TOOLKIT: - choices: dict = {c: {} for c in self.controller_choices} - choices["load"]["-r"] = {c: {} for c in stocks_helper.INTERVALS} - choices["pick"] = {c: {} for c in stock.columns} - choices["ets"]["-t"] = {c: {} for c in ets_model.TRENDS} - choices["ets"]["-s"] = {c: {} for c in ets_model.SEASONS} - choices["arima"]["-i"] = {c: {} for c in arima_model.ICS} - choices["mc"]["--dist"] = {c: {} for c in mc_model.DISTRIBUTIONS} - self.completer = NestedCompleter.from_nested_dict(choices) - - def print_help(self): - """Print help""" - s_intraday = (f"Intraday {self.interval}", "Daily")[self.interval == "1440min"] - if self.start: - stock_info = ( - f"{s_intraday} {self.ticker} (from {self.start.strftime('%Y-%m-%d')})" - ) - else: - stock_info = f"{s_intraday} {self.ticker}" - - mt = MenuText("stocks/pred/") - mt.add_cmd("load") - mt.add_cmd("pick") - mt.add_raw("\n") - mt.add_param("_ticker", stock_info) - mt.add_param("_target", self.target) - mt.add_raw("\n") - mt.add_info("_models_") - mt.add_cmd("ets") - mt.add_cmd("knn") - mt.add_cmd("regression") - mt.add_cmd("arima") - mt.add_cmd("mlp") - mt.add_cmd("rnn") - mt.add_cmd("lstm") - mt.add_cmd("conv1d") - mt.add_cmd("mc") - console.print(text=mt.menu_text, menu="Stocks - Prediction Techniques") - - def custom_reset(self): - """Class specific component of reset command""" - if self.ticker: - return ["stocks", f"load {self.ticker}", "pred"] - return [] - - @log_start_end(log=logger) - def call_pick(self, other_args: List[str]): - """Process pick command""" - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - add_help=False, - prog="pick", - description=""" - Change target variable - """, - ) - parser.add_argument( - "-t", - "--target", - dest="target", - choices=list(self.stock.columns), - help="Select variable to analyze", - ) - if other_args and "-t" not in other_args and "-h" not in other_args: - other_args.insert(0, "-t") - - ns_parser = self.parse_known_args_and_warn( - parser, other_args, EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - self.target = ns_parser.target - console.print("") - - @log_start_end(log=logger) - def call_ets(self, other_args: List[str]): - """Process ets command""" - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="ets", - description=""" - Exponential Smoothing, see https://otexts.com/fpp2/taxonomy.html - - Trend='N', Seasonal='N': Simple Exponential Smoothing - Trend='N', Seasonal='A': Exponential Smoothing - Trend='N', Seasonal='M': Exponential Smoothing - Trend='A', Seasonal='N': Holtโ€™s linear method - Trend='A', Seasonal='A': Additive Holt-Wintersโ€™ method - Trend='A', Seasonal='M': Multiplicative Holt-Wintersโ€™ method - Trend='Ad', Seasonal='N': Additive damped trend method - Trend='Ad', Seasonal='A': Exponential Smoothing - Trend='Ad', Seasonal='M': Holt-Wintersโ€™ damped method - Trend component: N: None, A: Additive, Ad: Additive Damped - Seasonality component: N: None, A: Additive, M: Multiplicative - """, - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-t", - "--trend", - action="store", - dest="trend", - choices=ets_model.TRENDS, - default="N", - help="Trend component: N: None, A: Additive, Ad: Additive Damped.", - ) - parser.add_argument( - "-s", - "--seasonal", - action="store", - dest="seasonal", - choices=ets_model.SEASONS, - default="N", - help="Seasonality component: N: None, A: Additive, M: Multiplicative.", - ) - parser.add_argument( - "-p", - "--periods", - action="store", - dest="seasonal_periods", - type=check_positive, - default=5, - help="Seasonal periods.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select - Backtesting", - ) - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - - if ns_parser.s_end_date: - - if ns_parser.s_end_date < self.stock.index[0]: - console.print( - "Backtesting not allowed, since End Date is older than Start Date of historical data\n" - ) - - if ( - ns_parser.s_end_date - < get_next_stock_market_days( - last_stock_day=self.stock.index[0], - n_next_days=5 + ns_parser.n_days, - )[-1] - ): - console.print( - "Backtesting not allowed, since End Date is too close to Start Date to train model\n" - ) - - ets_view.display_exponential_smoothing( - dataset=self.ticker, - data=self.stock[self.target], - n_predict=ns_parser.n_days, - trend=ns_parser.trend, - seasonal=ns_parser.seasonal, - seasonal_periods=ns_parser.seasonal_periods, - end_date=ns_parser.s_end_date, - export=ns_parser.export, - ) - - @log_start_end(log=logger) - def call_knn(self, other_args: List[str]): - """Process knn command""" - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="knn", - description=""" - K nearest neighbors is a simple algorithm that stores all - available cases and predict the numerical target based on a similarity measure - (e.g. distance functions). - """, - ) - parser.add_argument( - "-i", - "--input", - action="store", - dest="n_inputs", - type=check_positive, - default=40, - help="number of days to use as input for prediction.", - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-j", - "--jumps", - action="store", - dest="n_jumps", - type=check_positive, - default=1, - help="number of jumps in training data.", - ) - parser.add_argument( - "-n", - "--neighbors", - action="store", - dest="n_neighbors", - type=check_positive, - default=20, - help="number of neighbors to use on the algorithm.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select for testing", - ) - parser.add_argument( - "-t", - "--test_size", - default=0.2, - dest="valid_split", - type=float, - help="Percentage of data to validate in sample", - ) - parser.add_argument( - "--no_shuffle", - action="store_false", - dest="no_shuffle", - default=True, - help="Specify if shuffling validation inputs.", - ) - ns_parser = self.parse_known_args_and_warn( - parser, other_args, EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - knn_view.display_k_nearest_neighbors( - dataset=self.ticker, - data=self.stock[self.target], - n_neighbors=ns_parser.n_neighbors, - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - test_size=ns_parser.valid_split, - end_date=ns_parser.s_end_date, - no_shuffle=ns_parser.no_shuffle, - ) - - @log_start_end(log=logger) - def call_regression(self, other_args: List[str]): - """Process linear command""" - parser = argparse.ArgumentParser( - add_help=False, - prog="regression", - description=""" - Regression attempts to model the relationship between - two variables by fitting a linear/quadratic/cubic/other equation to - observed data. One variable is considered to be an explanatory variable, - and the other is considered to be a dependent variable. - """, - ) - - parser.add_argument( - "-i", - "--input", - action="store", - dest="n_inputs", - type=check_positive, - default=40, - help="number of days to use for prediction.", - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-j", - "--jumps", - action="store", - dest="n_jumps", - type=check_positive, - default=1, - help="number of jumps in training data.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select - Backtesting", - ) - parser.add_argument( - "-p", - "--polynomial", - action="store", - dest="n_polynomial", - type=check_positive, - default=1, - help="polynomial associated with regression.", - ) - if other_args and "-" not in other_args[0][0]: - other_args.insert(0, "-p") - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - # BACKTESTING CHECK - if ns_parser.s_end_date: - if ns_parser.s_end_date < self.stock.index[0]: - console.print( - "Backtesting not allowed, since End Date is older than Start Date of historical data\n" - ) - - if ( - ns_parser.s_end_date - < get_next_stock_market_days( - last_stock_day=self.stock.index[0], - n_next_days=5 + ns_parser.n_days, - )[-1] - ): - console.print( - "Backtesting not allowed, since End Date is too close to Start Date to train model\n" - ) - - regression_view.display_regression( - dataset=self.ticker, - values=self.stock[self.target], - poly_order=ns_parser.n_polynomial, - n_input=ns_parser.n_inputs, - n_predict=ns_parser.n_days, - n_jumps=ns_parser.n_jumps, - end_date=ns_parser.s_end_date, - export=ns_parser.export, - ) - - @log_start_end(log=logger) - def call_arima(self, other_args: List[str]): - """Process arima command""" - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="arima", - description=""" - In statistics and econometrics, and in particular in time series analysis, an - autoregressive integrated moving average (ARIMA) model is a generalization of an - autoregressive moving average (ARMA) model. Both of these models are fitted to time - series data either to better understand the data or to predict future points in the - series (forecasting). ARIMA(p,d,q) where parameters p, d, and q are non-negative - integers, p is the order (number of time lags) of the autoregressive model, d is the - degree of differencing (the number of times the data have had past values subtracted), - and q is the order of the moving-average model. - """, - ) - parser.add_argument( - "-d", - "--days", - action="store", - dest="n_days", - type=check_positive, - default=5, - help="prediction days.", - ) - parser.add_argument( - "-i", - "--ic", - action="store", - dest="s_ic", - type=str, - default="aic", - choices=arima_model.ICS, - help="information criteria.", - ) - parser.add_argument( - "-s", - "--seasonal", - action="store_true", - default=False, - dest="b_seasonal", - help="Use weekly seasonal data.", - ) - parser.add_argument( - "-o", - "--order", - action="store", - dest="s_order", - default="", - type=str, - help="arima model order (p,d,q) in format: p,d,q.", - ) - parser.add_argument( - "-r", - "--results", - action="store_true", - dest="b_results", - default=False, - help="results about ARIMA summary flag.", - ) - parser.add_argument( - "-e", - "--end", - action="store", - type=valid_date, - dest="s_end_date", - default=None, - help="The end date (format YYYY-MM-DD) to select - Backtesting", - ) - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - # BACKTESTING CHECK - if ns_parser.s_end_date: - - if ns_parser.s_end_date < self.stock.index[0]: - console.print( - "Backtesting not allowed, since End Date is older than Start Date of historical data\n" - ) - - if ( - ns_parser.s_end_date - < get_next_stock_market_days( - last_stock_day=self.stock.index[0], - n_next_days=5 + ns_parser.n_days, - )[-1] - ): - console.print( - "Backtesting not allowed, since End Date is too close to Start Date to train model\n" - ) - - arima_view.display_arima( - dataset=self.ticker, - values=self.stock[self.target], - arima_order=ns_parser.s_order, - n_predict=ns_parser.n_days, - seasonal=ns_parser.b_seasonal, - ic=ns_parser.s_ic, - results=ns_parser.b_results, - end_date=ns_parser.s_end_date, - export=ns_parser.export, - ) - - @log_start_end(log=logger) - def call_mlp(self, other_args: List[str]): - """Process mlp command""" - try: - ns_parser = pred_helper.parse_args( - prog="mlp", - description="""Multi-Layered-Perceptron. """, - other_args=other_args, - ) - if ns_parser: - neural_networks_view.display_mlp( - dataset=self.ticker, - data=self.stock[self.target], - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - ) - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_rnn(self, other_args: List[str]): - """Process rnn command""" - try: - ns_parser = pred_helper.parse_args( - prog="rnn", - description="""Recurrent Neural Network. """, - other_args=other_args, - ) - if ns_parser: - neural_networks_view.display_rnn( - dataset=self.ticker, - data=self.stock[self.target], - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - ) - - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_lstm(self, other_args: List[str]): - """Process lstm command""" - try: - ns_parser = pred_helper.parse_args( - prog="lstm", - description="""Long-Short Term Memory. """, - other_args=other_args, - ) - if ns_parser: - neural_networks_view.display_lstm( - dataset=self.ticker, - data=self.stock[self.target], - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - ) - - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_conv1d(self, other_args: List[str]): - """Process conv1d command""" - try: - ns_parser = pred_helper.parse_args( - prog="conv1d", - description="""1D CNN.""", - other_args=other_args, - ) - if ns_parser: - neural_networks_view.display_conv1d( - dataset=self.ticker, - data=self.stock[self.target], - n_input_days=ns_parser.n_inputs, - n_predict_days=ns_parser.n_days, - learning_rate=ns_parser.lr, - epochs=ns_parser.n_epochs, - batch_size=ns_parser.n_batch_size, - test_size=ns_parser.valid_split, - n_loops=ns_parser.n_loops, - no_shuffle=ns_parser.no_shuffle, - ) - - except Exception as e: - logger.exception(str(e)) - console.print(e, "\n") - - finally: - pred_helper.restore_env() - - @log_start_end(log=logger) - def call_mc(self, other_args: List[str]): - """Process mc command""" - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - add_help=False, - prog="mc", - description=""" - Perform Monte Carlo forecasting - """, - ) - parser.add_argument( - "-d", - "--days", - help="Days to predict", - dest="n_days", - type=check_positive, - default=30, - ) - parser.add_argument( - "-n", - "--num", - help="Number of simulations to perform", - dest="n_sims", - default=100, - type=check_positive, - ) - parser.add_argument( - "--dist", - choices=mc_model.DISTRIBUTIONS, - default="lognormal", - dest="dist", - help="Whether to model returns or log returns", - ) - - ns_parser = self.parse_known_args_and_warn( - parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED - ) - if ns_parser: - if self.target != "AdjClose": - console.print("MC Prediction designed for AdjClose prices\n") - - mc_view.display_mc_forecast( - data=self.stock[self.target], - n_future=ns_parser.n_days, - n_sims=ns_parser.n_sims, - use_log=ns_parser.dist == "lognormal", - export=ns_parser.export, - ) diff --git a/openbb_terminal/stocks/stocks_controller.py b/openbb_terminal/stocks/stocks_controller.py index fff4108e2d3..f794e276b2d 100644 --- a/openbb_terminal/stocks/stocks_controller.py +++ b/openbb_terminal/stocks/stocks_controller.py @@ -49,7 +49,6 @@ class StocksController(StockBaseController): "ta", "ba", "qa", - "pred", "disc", "dps", "scr", @@ -63,6 +62,7 @@ class StocksController(StockBaseController): "ca", "options", "th", + "forecast", ] PATH = "/stocks/" @@ -138,7 +138,7 @@ class StocksController(StockBaseController): mt.add_menu("bt", self.ticker) mt.add_menu("ta", self.ticker) mt.add_menu("qa", self.ticker) - mt.add_menu("pred", self.ticker) + mt.add_menu("forecast", self.ticker) console.print(text=mt.menu_text, menu="Stocks") def custom_reset(self): @@ -688,48 +688,13 @@ class StocksController(StockBaseController): console.print("Use 'load ' prior to this command!", "\n") @log_start_end(log=logger) - def call_pred(self, _): - """Process pred command""" - # IMPORTANT: 8/11/22 prediction was discontinued on the installer packages - # because forecasting in coming out soon. - # This if statement disallows installer package users from using 'pred' - # even if they turn on the OPENBB_ENABLE_PREDICT feature flag to true - # however it does not prevent users who clone the repo from using it - # if they have ENABLE_PREDICT set to true. - if obbff.PACKAGED_APPLICATION or not obbff.ENABLE_PREDICT: - console.print( - "Predict is disabled. Forecasting coming soon!", - "\n", - ) - else: - if self.ticker: - if self.interval == "1440min": - try: - from openbb_terminal.stocks.prediction_techniques import ( - pred_controller, - ) + def call_forecast(self, _): + """Process forecast command""" + from openbb_terminal.forecast import forecast_controller - self.queue = self.load_class( - pred_controller.PredictionTechniquesController, - self.ticker, - self.start, - self.interval, - self.stock, - self.queue, - ) - except ModuleNotFoundError as e: - logger.exception( - "One of the optional packages seems to be missing: %s", - str(e), - ) - console.print( - "One of the optional packages seems to be missing: ", - e, - "\n", - ) - - # TODO: This menu should work regardless of data being daily or not! - else: - console.print("Load daily data to use this menu!", "\n") - else: - console.print("Use 'load ' prior to this command!", "\n") + self.queue = self.load_class( + forecast_controller.ForecastController, + self.ticker, + self.stock, + self.queue, + ) diff --git a/poetry.lock b/poetry.lock index 00ddb661dbc..e757f583929 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,11 +1,3 @@ -[[package]] -name = "absl-py" -version = "1.2.0" -description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." -category = "main" -optional = true -python-versions = ">=3.6" - [[package]] name = "aiodns" version = "3.0.0" @@ -68,6 +60,25 @@ python-versions = "*" aiohttp = "*" requests = "*" +[[package]] +name = "altair" +version = "4.2.0" +description = "Altair: A declarative statistical visualization library for Python." +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +entrypoints = "*" +jinja2 = "*" +jsonschema = ">=3.0" +numpy = "*" +pandas = ">=0.18" +toolz = "*" + +[package.extras] +dev = ["black", "docutils", "ipython", "flake8", "pytest", "sphinx", "mistune (<2.0.0)", "m2r", "vega-datasets", "recommonmark"] + [[package]] name = "altgraph" version = "0.17.3" @@ -215,17 +226,6 @@ six = "*" [package.extras] test = ["pytest", "astroid (<=2.5.3)"] -[[package]] -name = "astunparse" -version = "1.6.3" -description = "An AST unparser for Python" -category = "main" -optional = true -python-versions = "*" - -[package.dependencies] -six = ">=1.6.1,<2.0" - [[package]] name = "async-generator" version = "1.10" @@ -285,7 +285,7 @@ python-versions = "*" [[package]] name = "backoff" -version = "2.1.2" +version = "2.2.1" description = "Function decoration for backoff and retry" category = "main" optional = false @@ -363,11 +363,11 @@ lxml = ["lxml"] [[package]] name = "black" -version = "22.8.0" +version = "22.10.0" description = "The uncompromising code formatter." category = "main" optional = false -python-versions = ">=3.6.2" +python-versions = ">=3.7" [package.dependencies] click = ">=8.0.0" @@ -401,6 +401,14 @@ webencodings = "*" css = ["tinycss2 (>=1.1.0,<1.2)"] dev = ["build (==0.8.0)", "flake8 (==4.0.1)", "hashin (==0.17.0)", "pip-tools (==6.6.2)", "pytest (==7.1.2)", "Sphinx (==4.3.2)", "tox (==3.25.0)", "twine (==4.0.1)", "wheel (==0.37.1)", "black (==22.3.0)", "mypy (==0.961)"] +[[package]] +name = "blinker" +version = "1.5" +description = "Fast, simple object-to-object and broadcast signaling" +category = "main" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" + [[package]] name = "bs4" version = "0.0.1" @@ -432,12 +440,12 @@ name = "cachetools" version = "5.2.0" description = "Extensible memoizing collections and decorators" category = "main" -optional = true +optional = false python-versions = "~=3.7" [[package]] name = "ccxt" -version = "1.94.2" +version = "1.95.25" description = "A JavaScript / Python / PHP cryptocurrency trading library with support for 130+ exchanges" category = "main" optional = false @@ -449,7 +457,7 @@ aiohttp = {version = ">=3.8", markers = "python_version >= \"3.5.2\""} certifi = ">=2018.1.18" cryptography = ">=2.6.1" requests = ">=2.18.4" -yarl = {version = "1.7.2", markers = "python_version >= \"3.5.2\""} +yarl = {version = ">=1.7.2", markers = "python_version >= \"3.5.2\""} [package.extras] doc = ["Sphinx (==4.0)", "mistune (==0.8.4)", "m2r2 (==0.2.7)", "sphinx-rtd-theme (==0.5.2)", "readthedocs-sphinx-search (==0.1.0)"] @@ -900,7 +908,7 @@ python-versions = "*" [[package]] name = "finnhub-python" -version = "2.4.14" +version = "2.4.15" description = "Finnhub API" category = "main" optional = false @@ -956,17 +964,9 @@ mccabe = ">=0.6.0,<0.7.0" pycodestyle = ">=2.7.0,<2.8.0" pyflakes = ">=2.3.0,<2.4.0" -[[package]] -name = "flatbuffers" -version = "22.9.24" -description = "The FlatBuffers serialization format for Python" -category = "main" -optional = true -python-versions = "*" - [[package]] name = "fonttools" -version = "4.37.3" +version = "4.37.4" description = "Tools to manipulate font files" category = "main" optional = false @@ -1060,14 +1060,6 @@ category = "main" optional = false python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*" -[[package]] -name = "gast" -version = "0.4.0" -description = "Python AST that abstracts the underlying Python version" -category = "main" -optional = true -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" - [[package]] name = "gitdb" version = "4.0.9" @@ -1081,7 +1073,7 @@ smmap = ">=3.0.1,<6" [[package]] name = "gitpython" -version = "3.1.27" +version = "3.1.28" description = "GitPython is a python library used to interact with Git repositories" category = "main" optional = false @@ -1090,52 +1082,6 @@ python-versions = ">=3.7" [package.dependencies] gitdb = ">=4.0.1,<5" -[[package]] -name = "google-auth" -version = "2.12.0" -description = "Google Authentication Library" -category = "main" -optional = true -python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*" - -[package.dependencies] -cachetools = ">=2.0.0,<6.0" -pyasn1-modules = ">=0.2.1" -rsa = {version = ">=3.1.4,<5", markers = "python_version >= \"3.6\""} -six = ">=1.9.0" - -[package.extras] -aiohttp = ["requests (>=2.20.0,<3.0.0dev)", "aiohttp (>=3.6.2,<4.0.0dev)"] -enterprise_cert = ["cryptography (==36.0.2)", "pyopenssl (==22.0.0)"] -pyopenssl = ["pyopenssl (>=20.0.0)"] -reauth = ["pyu2f (>=0.1.5)"] - -[[package]] -name = "google-auth-oauthlib" -version = "0.4.6" -description = "Google Authentication Library" -category = "main" -optional = true -python-versions = ">=3.6" - -[package.dependencies] -google-auth = ">=1.0.0" -requests-oauthlib = ">=0.7.0" - -[package.extras] -tool = ["click (>=6.0.0)"] - -[[package]] -name = "google-pasta" -version = "0.2.0" -description = "pasta is an AST-based Python refactoring library" -category = "main" -optional = true -python-versions = "*" - -[package.dependencies] -six = "*" - [[package]] name = "grpcio" version = "1.49.1" @@ -1158,17 +1104,6 @@ category = "main" optional = false python-versions = ">=3.7" -[[package]] -name = "h5py" -version = "3.7.0" -description = "Read and write HDF5 files from Python" -category = "main" -optional = true -python-versions = ">=3.7" - -[package.dependencies] -numpy = ">=1.14.5" - [[package]] name = "hijri-converter" version = "2.2.4" @@ -1179,11 +1114,11 @@ python-versions = ">=3.6" [[package]] name = "holidays" -version = "0.11.3.1" +version = "0.14.2" description = "Generate and work with holidays in Python" category = "main" optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" [package.dependencies] convertdate = ">=2.3.0" @@ -1211,7 +1146,7 @@ lxml = ["lxml"] [[package]] name = "identify" -version = "2.5.5" +version = "2.5.6" description = "File identification library for Python" category = "dev" optional = false @@ -1238,7 +1173,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" [[package]] name = "importlib-metadata" -version = "4.12.0" +version = "5.0.0" description = "Read metadata from Python packages" category = "main" optional = false @@ -1248,9 +1183,9 @@ python-versions = ">=3.7" zipp = ">=0.5" [package.extras] -docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)"] +docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "jaraco.tidelift (>=1.4)"] perf = ["ipython"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.3)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "importlib-resources (>=1.3)"] +testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "importlib-resources (>=1.3)"] [[package]] name = "inflection" @@ -1299,6 +1234,21 @@ Unidecode = ">=1.1.1" docs = ["sphinx (==3.4.3)", "recommonmark (==0.7.1)", "furo (==2021.4.11b34)", "investpy (==1.0.8)"] tests = ["pytest (==6.2.5)"] +[[package]] +name = "ipyflex" +version = "0.2.4" +description = "Jupyter Widget Flex Layout" +category = "main" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +ipywidgets = ">=7.0.0" + +[package.extras] +test = ["nbval", "pytest-cov", "pytest (>=4.6)"] +docs = ["sphinx-rtd-theme", "sphinx (>=1.5)", "recommonmark", "pypandoc", "pytest-check-links", "nbsphinx-link", "nbsphinx", "jupyter-sphinx"] + [[package]] name = "ipykernel" version = "6.16.0" @@ -1616,7 +1566,7 @@ test = ["coverage", "ipykernel", "pre-commit", "pytest-console-scripts", "pytest [[package]] name = "jupyterlab" -version = "3.4.7" +version = "3.4.8" description = "JupyterLab computational environment" category = "main" optional = false @@ -1700,30 +1650,13 @@ optional = false python-versions = ">=3.6" [[package]] -name = "keras" -version = "2.10.0" -description = "Deep learning for humans." +name = "kaleido" +version = "0.2.1" +description = "Static image export for web-based visualization libraries with zero dependencies" category = "main" optional = true python-versions = "*" -[[package]] -name = "keras-preprocessing" -version = "1.1.2" -description = "Easy data preprocessing and data augmentation for deep learning models" -category = "main" -optional = true -python-versions = "*" - -[package.dependencies] -numpy = ">=1.9.1" -six = ">=1.9.0" - -[package.extras] -image = ["scipy (>=0.14)", "Pillow (>=5.2.0)"] -pep8 = ["flake8"] -tests = ["pandas", "pillow", "tensorflow", "keras", "pytest", "pytest-xdist", "pytest-cov"] - [[package]] name = "kiwisolver" version = "1.4.4" @@ -1748,14 +1681,6 @@ category = "dev" optional = false python-versions = ">=3.6" -[[package]] -name = "libclang" -version = "14.0.6" -description = "Clang Python Bindings, mirrored from the official LLVM repo: https://github.com/llvm/llvm-project/tree/main/clang/bindings/python, to make the installation process easier." -category = "main" -optional = true -python-versions = "*" - [[package]] name = "linearmodels" version = "4.27" @@ -1816,20 +1741,6 @@ python-versions = "*" [package.dependencies] altgraph = ">=0.17" -[[package]] -name = "markdown" -version = "3.4.1" -description = "Python implementation of Markdown." -category = "main" -optional = true -python-versions = ">=3.7" - -[package.dependencies] -importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""} - -[package.extras] -testing = ["coverage", "pyyaml"] - [[package]] name = "markdown-it-py" version = "1.1.0" @@ -2017,7 +1928,7 @@ testing = ["beautifulsoup4", "coverage", "docutils (>=0.17.0,<0.18.0)", "pytest [[package]] name = "nbclassic" -version = "0.4.4" +version = "0.4.5" description = "A web-based notebook environment for interactive computing" category = "main" optional = false @@ -2043,9 +1954,9 @@ tornado = ">=6.1" traitlets = ">=4.2.1" [package.extras] -test = ["requests-unixsocket", "pytest-tornasync", "pytest-cov", "selenium (==4.1.5)", "nbval", "testpath", "requests", "coverage", "pytest"] +docs = ["sphinx", "nbsphinx", "sphinxcontrib-github-alt", "sphinx-rtd-theme", "myst-parser"] json-logging = ["json-logging"] -docs = ["myst-parser", "sphinx-rtd-theme", "sphinxcontrib-github-alt", "nbsphinx", "sphinx"] +test = ["pytest", "coverage", "requests", "testpath", "nbval", "selenium (==4.1.5)", "pytest-cov", "pytest-tornasync", "requests-unixsocket"] [[package]] name = "nbclient" @@ -2116,9 +2027,25 @@ traitlets = ">=5.1" [package.extras] test = ["check-manifest", "pep440", "pre-commit", "pytest", "testpath"] +[[package]] +name = "nbmake" +version = "1.3.0" +description = "Pytest plugin for testing notebooks" +category = "dev" +optional = false +python-versions = ">=3.7.0,<4.0.0" + +[package.dependencies] +ipykernel = ">=5.4.0" +nbclient = ">=0.5.13,<0.6.0" +nbformat = ">=5.0.8,<6.0.0" +pydantic = ">=1.7.2,<2.0.0" +Pygments = ">=2.7.3,<3.0.0" +pytest = ">=6.1.0" + [[package]] name = "nest-asyncio" -version = "1.5.5" +version = "1.5.6" description = "Patch asyncio to allow nested event loops" category = "main" optional = false @@ -2126,7 +2053,7 @@ python-versions = ">=3.5" [[package]] name = "networkx" -version = "2.8.6" +version = "2.8.7" description = "Python package for creating and manipulating graphs and networks" category = "main" optional = false @@ -2134,7 +2061,7 @@ python-versions = ">=3.8" [package.extras] default = ["numpy (>=1.19)", "scipy (>=1.8)", "matplotlib (>=3.4)", "pandas (>=1.3)"] -developer = ["pre-commit (>=2.20)", "mypy (>=0.961)"] +developer = ["pre-commit (>=2.20)", "mypy (>=0.981)"] doc = ["sphinx (>=5)", "pydata-sphinx-theme (>=0.9)", "sphinx-gallery (>=0.10)", "numpydoc (>=1.4)", "pillow (>=9.1)", "nb2plots (>=0.6)", "texext (>=0.6.6)"] extra = ["lxml (>=4.6)", "pygraphviz (>=1.9)", "pydot (>=1.4.2)", "sympy (>=1.10)"] test = ["pytest (>=7.1)", "pytest-cov (>=3.0)", "codecov (>=2.1)"] @@ -2242,21 +2169,6 @@ python-versions = ">=3.6" [package.dependencies] et-xmlfile = "*" -[[package]] -name = "opt-einsum" -version = "3.3.0" -description = "Optimizing numpys einsum function" -category = "main" -optional = true -python-versions = ">=3.5" - -[package.dependencies] -numpy = ">=1.7" - -[package.extras] -docs = ["sphinx (==1.2.3)", "sphinxcontrib-napoleon", "sphinx-rtd-theme", "numpydoc"] -tests = ["pytest", "pytest-cov", "pytest-pep8"] - [[package]] name = "osqp" version = "0.6.2.post5" @@ -2584,11 +2496,11 @@ python-versions = ">= 3.5" [[package]] name = "protobuf" -version = "3.19.6" +version = "3.20.3" description = "Protocol Buffers" category = "main" optional = false -python-versions = ">=3.5" +python-versions = ">=3.7" [[package]] name = "psaw" @@ -2654,23 +2566,15 @@ requests = "*" requests-oauthlib = "*" [[package]] -name = "pyasn1" -version = "0.4.8" -description = "ASN.1 types and codecs" +name = "pyarrow" +version = "9.0.0" +description = "Python library for Apache Arrow" category = "main" -optional = true -python-versions = "*" - -[[package]] -name = "pyasn1-modules" -version = "0.2.8" -description = "A collection of ASN.1-based protocols modules." -category = "main" -optional = true -python-versions = "*" +optional = false +python-versions = ">=3.7" [package.dependencies] -pyasn1 = ">=0.4.6,<0.5.0" +numpy = ">=1.16.6" [[package]] name = "pycares" @@ -2718,7 +2622,7 @@ name = "pydantic" version = "1.8.2" description = "Data validation and settings management using python 3.6 type hinting" category = "main" -optional = true +optional = false python-versions = ">=3.6.1" [package.dependencies] @@ -2728,6 +2632,22 @@ typing-extensions = ">=3.7.4.3" dotenv = ["python-dotenv (>=0.10.4)"] email = ["email-validator (>=1.0.3)"] +[[package]] +name = "pydeck" +version = "0.8.0b3" +description = "Widget for deck.gl maps" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +jinja2 = ">=2.10.1" +numpy = ">=1.16.4" + +[package.extras] +carto = ["pydeck-carto"] +jupyter = ["ipywidgets (>=7,<8)", "traitlets (>=4.3.2)", "ipython (>=5.8.0)", "ipykernel (>=5.1.2)"] + [[package]] name = "pyerfa" version = "2.0.0.1" @@ -2905,6 +2825,14 @@ srv = ["dnspython (>=1.16.0,<1.17.0)"] tls = ["ipaddress"] zstd = ["zstandard"] +[[package]] +name = "pympler" +version = "1.0.1" +description = "A development tool to measure, monitor and analyze the memory behavior of Python objects." +category = "main" +optional = false +python-versions = ">=3.6" + [[package]] name = "pyobjc-core" version = "8.5.1" @@ -3022,7 +2950,7 @@ testing = ["fields", "hunter", "process-tests", "six", "pytest-xdist", "virtuale [[package]] name = "pytest-mock" -version = "3.9.0" +version = "3.10.0" description = "Thin-wrapper around the mock package for easier use with pytest" category = "dev" optional = false @@ -3140,7 +3068,7 @@ requests = ">=2.0" [[package]] name = "pytz" -version = "2022.2.1" +version = "2022.4" description = "World timezone definitions, modern and historical" category = "main" optional = false @@ -3160,14 +3088,14 @@ tzdata = {version = "*", markers = "python_version >= \"3.6\""} [[package]] name = "pyupgrade" -version = "2.38.2" +version = "2.38.4" description = "A tool to automatically upgrade syntax for newer versions." category = "dev" optional = false python-versions = ">=3.7" [package.dependencies] -tokenize-rt = ">=3.2.0" +tokenize-rt = "<5" [[package]] name = "pywin32" @@ -3249,7 +3177,7 @@ test = ["flaky", "pytest", "pytest-qt"] [[package]] name = "qtpy" -version = "2.2.0" +version = "2.2.1" description = "Provides an abstraction layer on top of the various Qt bindings (PyQt5/6 and PySide2/6)." category = "main" optional = false @@ -3259,7 +3187,7 @@ python-versions = ">=3.7" packaging = "*" [package.extras] -test = ["pytest-qt", "pytest-cov (>=3.0.0)", "pytest (>=6,!=7.0.0,!=7.0.1)"] +test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"] [[package]] name = "quandl" @@ -3395,17 +3323,6 @@ pyotp = "*" python-dotenv = "*" requests = "*" -[[package]] -name = "rsa" -version = "4.9" -description = "Pure-Python RSA implementation" -category = "main" -optional = true -python-versions = ">=3.6,<4" - -[package.dependencies] -pyasn1 = ">=0.1.3" - [[package]] name = "ruamel.yaml" version = "0.17.21" @@ -3512,6 +3429,14 @@ trio = ">=0.17,<1.0" trio-websocket = ">=0.9,<1.0" urllib3 = {version = ">=1.26,<2.0", extras = ["socks"]} +[[package]] +name = "semver" +version = "2.13.0" +description = "Python helper for Semantic Versioning (http://semver.org/)" +category = "main" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" + [[package]] name = "send2trash" version = "1.8.0" @@ -3793,13 +3718,47 @@ python-versions = ">=3.8" [package.dependencies] pbr = ">=2.0.0,<2.1.0 || >2.1.0" +[[package]] +name = "streamlit" +version = "1.13.0" +description = "The fastest way to build data apps in Python" +category = "main" +optional = false +python-versions = ">=3.7, !=3.9.7" + +[package.dependencies] +altair = ">=3.2.0" +blinker = ">=1.0.0" +cachetools = ">=4.0" +click = ">=7.0" +gitpython = "!=3.1.19" +importlib-metadata = ">=1.4" +numpy = "*" +packaging = ">=14.1" +pandas = ">=0.21.0" +pillow = ">=6.2.0" +protobuf = ">=3.12,<3.20.2 || >3.20.2,<4" +pyarrow = ">=4.0" +pydeck = ">=0.1.dev5" +pympler = ">=0.9" +python-dateutil = "*" +requests = ">=2.4" +rich = ">=10.11.0" +semver = "*" +toml = "*" +tornado = ">=5.0" +typing-extensions = ">=3.10.0.0" +tzlocal = ">=1.1" +validators = ">=0.2" +watchdog = {version = "*", markers = "platform_system != \"Darwin\""} + [[package]] name = "tabulate" -version = "0.8.10" +version = "0.9.0" description = "Pretty-print tabular data" category = "main" optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +python-versions = ">=3.7" [package.extras] widechars = ["wcwidth"] @@ -3830,149 +3789,6 @@ python-versions = ">=3.6" [package.extras] doc = ["reno", "sphinx", "tornado (>=4.5)"] -[[package]] -name = "tensorboard" -version = "2.10.1" -description = "TensorBoard lets you watch Tensors Flow" -category = "main" -optional = true -python-versions = ">=3.6" - -[package.dependencies] -absl-py = ">=0.4" -google-auth = ">=1.6.3,<3" -google-auth-oauthlib = ">=0.4.1,<0.5" -grpcio = ">=1.24.3" -markdown = ">=2.6.8" -numpy = ">=1.12.0" -protobuf = ">=3.9.2,<3.20" -requests = ">=2.21.0,<3" -tensorboard-data-server = ">=0.6.0,<0.7.0" -tensorboard-plugin-wit = ">=1.6.0" -werkzeug = ">=1.0.1" - -[[package]] -name = "tensorboard-data-server" -version = "0.6.1" -description = "Fast data loading for TensorBoard" -category = "main" -optional = true -python-versions = ">=3.6" - -[[package]] -name = "tensorboard-plugin-wit" -version = "1.8.1" -description = "What-If Tool TensorBoard plugin." -category = "main" -optional = true -python-versions = "*" - -[[package]] -name = "tensorflow" -version = "2.10.0" -description = "TensorFlow is an open source machine learning framework for everyone." -category = "main" -optional = true -python-versions = ">=3.7" - -[package.dependencies] -absl-py = ">=1.0.0" -astunparse = ">=1.6.0" -flatbuffers = ">=2.0" -gast = ">=0.2.1,<=0.4.0" -google-pasta = ">=0.1.1" -grpcio = ">=1.24.3,<2.0" -h5py = ">=2.9.0" -keras = ">=2.10.0,<2.11" -keras-preprocessing = ">=1.1.1" -libclang = ">=13.0.0" -numpy = ">=1.20" -opt-einsum = ">=2.3.2" -packaging = "*" -protobuf = ">=3.9.2,<3.20" -six = ">=1.12.0" -tensorboard = ">=2.10,<2.11" -tensorflow-estimator = ">=2.10.0,<2.11" -tensorflow-io-gcs-filesystem = ">=0.23.1" -termcolor = ">=1.1.0" -typing-extensions = ">=3.6.6" -wrapt = ">=1.11.0" - -[[package]] -name = "tensorflow-estimator" -version = "2.10.0" -description = "TensorFlow Estimator." -category = "main" -optional = true -python-versions = ">=3.7" - -[[package]] -name = "tensorflow-io-gcs-filesystem" -version = "0.27.0" -description = "TensorFlow IO" -category = "main" -optional = true -python-versions = ">=3.7, <3.11" - -[package.extras] -tensorflow = ["tensorflow (>=2.10.0,<2.11.0)"] -tensorflow-aarch64 = ["tensorflow-aarch64 (>=2.10.0,<2.11.0)"] -tensorflow-cpu = ["tensorflow-cpu (>=2.10.0,<2.11.0)"] -tensorflow-gpu = ["tensorflow-gpu (>=2.10.0,<2.11.0)"] -tensorflow-rocm = ["tensorflow-rocm (>=2.10.0,<2.11.0)"] - -[[package]] -name = "tensorflow-macos" -version = "2.10.0" -description = "TensorFlow is an open source machine learning framework for everyone." -category = "main" -optional = true -python-versions = ">=3.7" - -[package.dependencies] -absl-py = ">=1.0.0" -astunparse = ">=1.6.0" -flatbuffers = ">=2.0" -gast = ">=0.2.1,<=0.4.0" -google-pasta = ">=0.1.1" -grpcio = ">=1.24.3,<2.0" -h5py = ">=2.9.0" -keras = ">=2.10.0,<2.11" -keras-preprocessing = ">=1.1.1" -libclang = ">=13.0.0" -numpy = ">=1.20" -opt-einsum = ">=2.3.2" -packaging = "*" -protobuf = ">=3.9.2,<3.20" -six = ">=1.12.0" -tensorboard = ">=2.10,<2.11" -tensorflow-estimator = ">=2.10.0,<2.11" -termcolor = ">=1.1.0" -typing-extensions = ">=3.6.6" -wrapt = ">=1.11.0" - -[[package]] -name = "tensorflow-metal" -version = "0.5.1" -description = "TensorFlow acceleration for Mac 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= "^3.8,<3.11, !=3.9.7" scipy = "1.8.1" iso8601 = "^0.1.14" FundamentalAnalysis = "^0.2.6" @@ -20,7 +20,7 @@ alpha-vantage = "^2.3.1" finviz = "^1.3.4" bs4 = "^0.0.1" rapidfuzz = "^1.1.1" -yfinance = "^0.1.63" +yfinance = "0.1.74" psaw = "^0.0.12" praw = "^7.1.4" Quandl = "^3.6.0" @@ -55,7 +55,7 @@ sentiment-investor = "^2.0.0" fred = "^3.1" financedatabase = "1.0.2" reportlab = "^3.6.2" -holidays = "^0.11.3" +holidays = "^0.14.2" degiro-connector = "^2.0.3" python-binance = "^1.0.15" python-i18n = "^0.3.9" @@ -87,15 +87,17 @@ Riskfolio-Lib = "^3.1.1" ccxt = "^1.91.60" html5lib = "^1.1" feedparser = "^6.0.10" -tensorflow = {version = "^2.8.2", optional = true} -tensorflow-macos = {version = "^2.9.2", optional = true} -tensorflow-metal = {version = "^0.5.0", optional = true} pyinstaller = {version = "^4.10", optional = true} jupyterlab-code-formatter = {version = "^1.4.10", optional = true} jupyterlab-lsp = {version = "^3.10.1", optional = true} jedi-language-server = {version = "^0.35.1", optional = true} +kaleido = {version = "<=0.2.1", optional = true} +torch = {version = "1.11.0", optional = true} +streamlit = "^1.12.2" charset-normalizer = "2.1.0" +watchdog = "^2.1.9" pythclient = "^0.1.2" +ipyflex = "^0.2.4" "ruamel.yaml" = "^0.17.21" [tool.poetry.dev-dependencies] @@ -122,10 +124,10 @@ types-setuptools = "^57.4.7" pre-commit = "^2.16.0" pytest-cov = "^3.0.0" types-six = "^1.16.12" +nbmake = ">0.5.0" [tool.poetry.extras] -prediction = ["tensorflow", "u8darts"] -prediction-m1-mac = ["tensorflow-macos", "tensorflow-metal"] +prediction = ["torch"] jupyterlab = ["jupyterlab-code-formatter", "jupyterlab-lsp", "jedi-language-server"] installer = ["pyinstaller"] diff --git a/pytest.ini b/pytest.ini index e53ac5f9281..cd259c2202c 100644 --- a/pytest.ini +++ b/pytest.ini @@ -1,5 +1,5 @@ [pytest] addopts = -p no:warnings markers = - prediction: mark tests requiring prediction dependencies, like tensorflow + prediction: mark tests requiring prediction dependencies linux: tests that are not stable on Windows diff --git a/requirements-full.txt b/requirements-full.txt index 0d68f864a29..6da8f48b95d 100644 --- a/requirements-full.txt +++ b/requirements-full.txt @@ -1,11 +1,11 @@ -absl-py==1.2.0; python_version >= "3.7" aiodns==3.0.0; python_version >= "3.5.2" and python_full_version >= "3.7.0" aiohttp==3.8.3; python_version >= "3.6" and python_full_version >= "3.7.0" aiosignal==1.2.0; python_version >= "3.6" and python_full_version >= "3.7.0" alabaster==0.7.12; python_version >= "3.6" alpha-vantage==2.3.1 +altair==4.2.0; python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7" anyio==3.6.1; python_full_version >= "3.6.2" and python_version >= "3.7" -appnope==0.1.3; platform_system == "Darwin" and python_version >= "3.8" and sys_platform == "darwin" and python_full_version >= "3.6.2" +appnope==0.1.3; platform_system == "Darwin" and python_version >= "3.8" and python_full_version >= "3.7.0" and python_full_version < "4.0.0" and sys_platform == "darwin" arch==5.3.1; python_version >= "3.7" argon2-cffi-bindings==21.2.0; python_version >= "3.7" argon2-cffi==21.3.0; python_version >= "3.7" @@ -13,34 +13,34 @@ ascii-magic==1.6; python_version >= "3.5" astor==0.8.1; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.8" astroid==2.12.10; python_full_version >= "3.7.2" astropy==5.1; python_version >= "3.8" -asttokens==2.0.8; python_full_version >= "3.6.2" and python_version >= "3.8" -astunparse==1.6.3; python_version >= "3.7" +asttokens==2.0.8; python_version >= "3.8" and python_full_version >= "3.7.0" and python_full_version < "4.0.0" async-generator==1.10; python_version >= "3.7" and python_version < "4.0" async-timeout==4.0.2; python_version >= "3.6" and python_full_version >= "3.7.0" -atomicwrites==1.4.1; python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.4.0" -attrs==21.4.0; python_full_version >= "3.7.0" and python_version >= "3.7" and (python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.5.0") and (python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.6" and python_version < "4.0" and python_full_version >= "3.5.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7") +atomicwrites==1.4.1; sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.7.0" and python_full_version < "4.0.0" +attrs==21.4.0; python_full_version >= "3.7.0" and python_version >= "3.7" and (python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.5.0") and (python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.6" and python_version < "4.0" and python_full_version >= "3.5.0") and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.7.0" or python_full_version > "3.9.7" and python_version >= "3.7" and python_full_version < "4.0.0") babel==2.10.3; python_version >= "3.7" -backcall==0.2.0; python_full_version >= "3.6.2" and python_version >= "3.8" -backoff==2.1.2; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.7.0" -backports.zoneinfo==0.2.1; python_version < "3.9" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6") +backcall==0.2.0; python_version >= "3.8" and python_full_version >= "3.7.0" and python_full_version < "4.0.0" +backoff==2.2.1; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.7.0" +backports.zoneinfo==0.2.1; python_version >= "3.7" and python_full_version < "3.9.7" and python_version < "3.9" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.6.0" or python_full_version > "3.9.7" and python_version >= "3.6") or python_full_version > "3.9.7" and python_version >= "3.6" and python_version < "3.9" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.6.0" or python_full_version > "3.9.7" and python_version >= "3.6") bandit==1.7.4; python_version >= "3.7" base58==2.1.1; python_version >= "3.5" and python_full_version >= "3.7.0" beartype==0.7.1; python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.6.0" beautifulsoup4==4.11.1; python_full_version >= "3.6.0" and python_version >= "3.7" -black==22.8.0; python_full_version >= "3.6.2" +black==22.10.0; python_version >= "3.7" bleach==5.0.1; python_version >= "3.7" +blinker==1.5; python_version >= "3.7" and python_full_version < "3.0.0" or python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.5.0" or python_full_version > "3.9.7" bs4==0.0.1 bt==0.2.9; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.6.0") -cachetools==5.2.0; python_version >= "3.7" and python_version < "4.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") -ccxt==1.94.2 -certifi==2022.9.24; python_version >= "3.8" and python_version < "4" and python_full_version >= "3.7.1" and python_full_version < "4.0.0" +cachetools==5.2.0; python_version >= "3.7" and python_version < "4.0" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7") +ccxt==1.95.25 +certifi==2022.9.24; python_version >= "3.8" and python_version < "4" and python_full_version >= "3.7.1" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7") cffi==1.15.1 cfgv==3.3.1; python_full_version >= "3.6.1" and python_version >= "3.7" charset-normalizer==2.1.0; python_full_version >= "3.6.0" -click==8.1.3; python_version >= "3.7" and python_full_version >= "3.6.2" +click==8.1.3; python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7" codespell==2.2.1; python_version >= "3.6" -colorama==0.4.5; python_full_version >= "3.7.2" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and (python_version >= "3.6" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.6" and python_full_version >= "3.5.0") and sys_platform == "win32" and (python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" or platform_system == "Windows" and python_version >= "3.7" and python_full_version >= "3.5.0") and (python_version >= "2.7" and python_full_version < "3.0.0" and platform_system == "Windows" or python_full_version >= "3.5.0" and platform_system == "Windows") and platform_system == "Windows" and python_version >= "3.8" and (python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.5.0") -commonmark==0.9.1; python_full_version >= "3.6.2" and python_full_version < "4.0.0" +colorama==0.4.5; python_full_version >= "3.7.2" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and (python_version >= "3.6" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.6" and python_full_version >= "3.5.0") and sys_platform == "win32" and (python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" or platform_system == "Windows" and python_version >= "3.7" and python_full_version >= "3.5.0") and (python_version >= "2.7" and python_full_version < "3.0.0" and platform_system == "Windows" or python_full_version >= "3.5.0" and platform_system == "Windows") and (python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7") or platform_system == "Windows" and python_version >= "3.7" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7") and python_full_version >= "3.5.0") and python_version >= "3.8" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7") +commonmark==0.9.1; python_full_version >= "3.6.2" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7") convertdate==2.4.0; python_version >= "3.7" and python_version < "4" coverage==6.5.0; python_version >= "3.7" cryptography==38.0.1; python_version >= "3.6" @@ -50,8 +50,8 @@ cycler==0.11.0; python_version >= "3.7" cython==0.29.32; python_version >= "3.8" and python_full_version < "3.0.0" and sys_platform == "darwin" or python_full_version >= "3.3.0" and sys_platform == "darwin" and python_version >= "3.8" dateparser==1.1.1; python_version >= "3.5" datetime==4.7; python_version >= "3.5" -debugpy==1.6.3; python_version >= "3.7" -decorator==5.1.1; python_full_version >= "3.6.2" and python_version >= "3.8" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.5") +debugpy==1.6.3; python_full_version >= "3.7.0" and python_full_version < "4.0.0" and python_version >= "3.7" +decorator==5.1.1; python_version >= "3.8" and python_full_version >= "3.7.0" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.5") and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.5") defusedxml==0.7.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7" degiro-connector==2.0.21; python_full_version >= "3.7.1" and python_full_version < "4.0.0" deprecation==2.1.0 @@ -61,22 +61,21 @@ distlib==0.3.6; python_version >= "3.7" dnspython==2.2.1; python_version >= "3.6" and python_version < "4.0" docutils==0.17.1; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6" ecos==2.0.10; python_version >= "3.8" and python_version < "3.11" -entrypoints==0.4; python_version >= "3.7" +entrypoints==0.4; python_full_version >= "3.7.0" and python_full_version < "4.0.0" and python_version >= "3.7" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7") et-xmlfile==1.1.0; python_version >= "3.6" exceptiongroup==1.0.0rc9; python_version >= "3.7" and python_version < "3.11" exchange-calendars==4.2.3; python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.7.0" -executing==1.1.0; python_full_version >= "3.6.2" and python_version >= "3.8" -fastjsonschema==2.16.2; python_full_version >= "3.7.0" and python_version >= "3.7" +executing==1.1.0; python_version >= "3.8" and python_full_version >= "3.7.0" and python_full_version < "4.0.0" +fastjsonschema==2.16.2; python_full_version >= "3.7.0" and python_full_version < "4.0.0" and python_version >= "3.7" feedparser==6.0.10; python_version >= "3.6" ffn==0.3.6; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" filelock==3.8.0; python_version >= "3.7" financedatabase==1.0.2 -finnhub-python==2.4.14 +finnhub-python==2.4.15 finviz==1.4.4 finvizfinance==0.14.3; python_version >= "3.5" flake8==3.9.2; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0") -flatbuffers==22.9.24; python_version >= "3.7" -fonttools==4.37.3; python_version >= "3.7" +fonttools==4.37.4; python_version >= "3.7" formulaic==0.3.4; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.8" fred==3.1 fredapi==0.4.3 @@ -84,60 +83,52 @@ frozendict==2.3.4; python_version >= "3.6" frozenlist==1.3.1; python_version >= "3.7" and python_full_version >= "3.7.0" fundamentalanalysis==0.2.14 future==0.18.2; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" -gast==0.4.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" -gitdb==4.0.9; python_version >= "3.7" -gitpython==3.1.27; python_version >= "3.7" -google-auth-oauthlib==0.4.6; python_version >= "3.7" -google-auth==2.12.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" -google-pasta==0.2.0; python_version >= "3.7" +gitdb==4.0.9; python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7" +gitpython==3.1.28; python_version >= "3.7" grpcio==1.49.1; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.7" h11==0.14.0; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.7.0" -h5py==3.7.0; python_version >= "3.7" -hijri-converter==2.2.4; python_version >= "3.6" -holidays==0.11.3.1; python_version >= "3.6" +hijri-converter==2.2.4; python_version >= "3.7" +holidays==0.14.2; python_version >= "3.7" html5lib==1.1; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0") -identify==2.5.5; python_version >= "3.7" -idna==3.4; python_version >= "3.8" and python_version < "4" and python_full_version >= "3.7.1" and python_full_version < "4.0.0" +identify==2.5.6; python_version >= "3.7" +idna==3.4; python_version >= "3.8" and python_version < "4" and python_full_version >= "3.7.1" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7") imagesize==1.4.1; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6" -importlib-metadata==4.12.0; python_version < "3.10" and python_version >= "3.7" +importlib-metadata==5.0.0; python_version >= "3.7" and python_full_version < "3.9.7" and python_version < "3.10" or python_full_version > "3.9.7" and python_version >= "3.7" and python_version < "3.10" inflection==0.5.1; python_version >= "3.6" -iniconfig==1.1.1; python_version >= "3.7" +iniconfig==1.1.1; python_full_version >= "3.7.0" and python_full_version < "4.0.0" and python_version >= "3.7" interface-meta==1.3.0; python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.7.1" and python_full_version < "4.0.0" investpy==1.0.8; python_version >= "3.7" -ipykernel==6.16.0; python_version >= "3.7" +ipyflex==0.2.4; python_version >= "3.6" +ipykernel==6.16.0; python_full_version >= "3.7.0" and python_full_version < "4.0.0" and python_version >= "3.7" ipympl==0.8.8 ipython-genutils==0.2.0; python_version >= "3.7" -ipython==8.5.0; python_full_version >= "3.6.2" and python_version >= "3.8" +ipython==8.5.0; python_version >= "3.8" and python_full_version >= "3.7.0" and python_full_version < "4.0.0" ipywidgets==7.7.2 iso8601==0.1.16 isort==5.10.1; python_full_version >= "3.7.2" and python_version < "4.0" -jedi==0.18.1; python_full_version >= "3.6.2" and python_version >= "3.8" +jedi==0.18.1; python_version >= "3.8" and python_full_version >= "3.7.0" and python_full_version < "4.0.0" jinja2==3.1.2; python_version >= "3.7" joblib==1.2.0; python_version >= "3.8" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.8" json5==0.9.10; python_version >= "3.7" jsonschema==3.2.0 -jupyter-client==7.3.5; python_full_version >= "3.7.0" and python_version >= "3.7" +jupyter-client==7.3.5; python_full_version >= "3.7.0" and python_full_version < "4.0.0" and python_version >= "3.7" jupyter-console==6.4.4; python_version >= "3.7" -jupyter-core==4.11.1; python_full_version >= "3.7.0" and python_version >= "3.7" +jupyter-core==4.11.1; python_full_version >= "3.7.0" and python_full_version < "4.0.0" and python_version >= "3.7" jupyter-server==1.19.1; python_version >= "3.7" jupyter==1.0.0 jupyterlab-pygments==0.2.2; python_version >= "3.7" jupyterlab-server==2.15.2; python_version >= "3.7" jupyterlab-widgets==1.1.1; python_version >= "3.6" -jupyterlab==3.4.7; python_version >= "3.7" -keras-preprocessing==1.1.2; python_version >= "3.7" -keras==2.10.0; python_version >= "3.7" +jupyterlab==3.4.8; python_version >= "3.7" kiwisolver==1.4.4; python_version >= "3.7" korean-lunar-calendar==0.3.1; python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.7.0" lazy-object-proxy==1.7.1; python_version >= "3.6" and python_full_version >= "3.7.2" -libclang==14.0.6; python_version >= "3.7" linearmodels==4.27; python_version >= "3.8" loguru==0.6.0; python_version >= "3.5" and python_full_version >= "3.7.0" lxml==4.9.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" markdown-it-py==1.1.0; python_version >= "3.6" and python_version < "4.0" -markdown==3.4.1; python_version >= "3.7" markupsafe==2.1.1; python_version >= "3.7" -matplotlib-inline==0.1.6; python_full_version >= "3.6.2" and python_version >= "3.8" +matplotlib-inline==0.1.6; python_full_version >= "3.7.0" and python_full_version < "4.0.0" and python_version >= "3.8" matplotlib==3.5.3; python_version >= "3.7" mccabe==0.6.1; python_full_version >= "3.7.2" mdit-py-plugins==0.2.8; python_version >= "3.6" and python_version < "4.0" @@ -147,15 +138,16 @@ more-itertools==8.14.0; python_version >= "3.6" mplfinance==0.12.9b1 multidict==6.0.2; python_version >= "3.7" and python_full_version >= "3.7.0" multitasking==0.0.11 -mypy-extensions==0.4.3; python_version >= "3.8" and python_full_version >= "3.6.2" +mypy-extensions==0.4.3; python_version >= "3.8" mypy==0.930; python_version >= "3.6" myst-parser==0.15.2; python_version >= "3.6" -nbclassic==0.4.4; python_version >= "3.7" -nbclient==0.5.13; python_full_version >= "3.7.0" and python_version >= "3.7" +nbclassic==0.4.5; python_version >= "3.7" +nbclient==0.5.13; python_full_version >= "3.7.0" and python_version >= "3.7" and python_full_version < "4.0.0" nbconvert==6.5.4; python_version >= "3.7" -nbformat==5.6.1; python_full_version >= "3.7.0" and python_version >= "3.7" -nest-asyncio==1.5.5; python_full_version >= "3.7.0" and python_version >= "3.7" -networkx==2.8.6; python_version >= "3.8" +nbformat==5.6.1; python_full_version >= "3.7.0" and python_full_version < "4.0.0" and python_version >= "3.7" +nbmake==1.3.0; python_full_version >= "3.7.0" and python_full_version < "4.0.0" +nest-asyncio==1.5.6; python_full_version >= "3.7.0" and python_full_version < "4.0.0" and python_version >= "3.7" +networkx==2.8.7; python_version >= "3.8" nodeenv==1.7.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.7.0" and python_version >= "3.7" notebook-shim==0.1.0; python_version >= "3.7" notebook==6.4.12; python_version >= "3.7" @@ -164,25 +156,24 @@ oandapyv20==0.6.3 oauthlib==3.2.1; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6" onetimepass==1.0.1; python_full_version >= "3.7.1" and python_full_version < "4.0.0" openpyxl==3.0.10; python_version >= "3.6" -opt-einsum==3.3.0; python_version >= "3.7" osqp==0.6.2.post5; python_version >= "3.8" and python_version < "3.11" outcome==1.2.0; python_version >= "3.7" and python_version < "4.0" -packaging==21.3; python_version >= "3.8" +packaging==21.3; python_version >= "3.8" and python_full_version < "3.9.7" and python_full_version >= "3.7.0" or python_full_version > "3.9.7" and python_version >= "3.8" and python_full_version < "4.0.0" pandas-datareader==0.10.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6" pandas-market-calendars==3.2; python_full_version >= "3.7.0" pandas-ta==0.3.14b pandas==1.5.0; python_version >= "3.8" pandocfilters==1.5.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" -parso==0.8.3; python_full_version >= "3.6.2" and python_version >= "3.8" -pathspec==0.10.1; python_version >= "3.7" and python_full_version >= "3.6.2" +parso==0.8.3; python_version >= "3.8" and python_full_version >= "3.7.0" and python_full_version < "4.0.0" +pathspec==0.10.1; python_version >= "3.7" patsy==0.5.2; python_version >= "3.8" pbr==5.10.0; python_version >= "3.8" -pexpect==4.8.0; sys_platform != "win32" and python_version >= "3.8" and python_full_version >= "3.6.2" -pickleshare==0.7.5; python_full_version >= "3.6.2" and python_version >= "3.8" -pillow==9.2.0; python_version >= "3.7" and python_version < "4" +pexpect==4.8.0; sys_platform != "win32" and python_version >= "3.8" and python_full_version >= "3.7.0" and python_full_version < "4.0.0" +pickleshare==0.7.5; python_version >= "3.8" and python_full_version >= "3.7.0" and python_full_version < "4.0.0" +pillow==9.2.0; python_version >= "3.7" and python_version < "4" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7") platformdirs==2.5.2; python_version >= "3.7" and python_full_version >= "3.7.2" plotly==5.10.0; python_version >= "3.6" -pluggy==1.0.0; python_version >= "3.7" +pluggy==1.0.0; python_full_version >= "3.7.0" and python_full_version < "4.0.0" and python_version >= "3.7" pmdarima==1.8.5; python_version >= "3.7" praw==7.6.0; python_version >= "3.7" and python_version < "4.0" prawcore==2.3.0; python_version >= "3.7" and python_version < "4.0" @@ -190,19 +181,20 @@ pre-commit==2.20.0; python_version >= "3.7" prometheus-client==0.14.1; python_version >= "3.7" prompt-toolkit==3.0.31; python_full_version >= "3.6.2" property-cached==1.6.4; python_version >= "3.8" -protobuf==3.19.6; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.7" +protobuf==3.20.3; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.7" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7") psaw==0.0.12; python_version >= "3" -psutil==5.9.2; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" -ptyprocess==0.7.0; os_name != "nt" and python_version >= "3.8" and sys_platform != "win32" and python_full_version >= "3.6.2" -pure-eval==0.2.2; python_full_version >= "3.6.2" and python_version >= "3.8" -py==1.11.0; python_version >= "3.7" and python_full_version < "3.0.0" and implementation_name == "pypy" or python_full_version >= "3.5.0" and python_version >= "3.7" and implementation_name == "pypy" +psutil==5.9.2; python_full_version >= "3.7.0" and python_full_version < "4.0.0" and python_version >= "3.7" +ptyprocess==0.7.0; os_name != "nt" and python_version >= "3.8" and sys_platform != "win32" and python_full_version >= "3.7.0" and python_full_version < "4.0.0" +pure-eval==0.2.2; python_version >= "3.8" and python_full_version >= "3.7.0" and python_full_version < "4.0.0" +py==1.11.0; python_full_version >= "3.7.0" and python_version >= "3.7" and python_full_version < "4.0.0" and implementation_name == "pypy" pyally==1.1.2 -pyasn1-modules==0.2.8; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" -pyasn1==0.4.8; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") or python_full_version >= "3.6.0" and python_version >= "3.7" and python_version < "4" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") +pyarrow==9.0.0; python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7" pycares==4.2.2; python_version >= "3.5.2" and python_full_version >= "3.7.0" pycodestyle==2.7.0; python_full_version >= "3.7.0" pycoingecko==2.3.0 pycparser==2.21 +pydantic==1.8.2; python_full_version >= "3.7.0" and python_full_version < "4.0.0" +pydeck==0.8.0b3; python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7" pyerfa==2.0.0.1; python_version >= "3.8" pyex==0.5.0 pyflakes==2.3.1; python_full_version >= "3.7.0" @@ -212,45 +204,45 @@ pylint==2.15.2; python_full_version >= "3.7.2" pyluach==2.0.1; python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.7.0" pymeeus==0.5.11; python_version >= "3.7" and python_version < "4" pymongo==3.12.3 +pympler==1.0.1; python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.6" pyobjc-core==8.5.1; python_version >= "3.6" and sys_platform == "darwin" pyobjc-framework-cocoa==8.5.1; python_version >= "3.6" and sys_platform == "darwin" pyotp==2.7.0; python_version >= "3.6" -pyparsing==3.0.9; python_full_version >= "3.6.8" and python_version >= "3.7" +pyparsing==3.0.9; python_full_version >= "3.6.8" and python_version >= "3.7" and (python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.6.8" or python_full_version > "3.9.7" and python_version >= "3.7") pyportfolioopt==1.5.3; python_version >= "3.8" and python_version < "3.11" pyprind==2.11.3; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" pyrsistent==0.18.1; python_version >= "3.7" pysocks==1.7.1; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "3.7" pytest-cov==3.0.0; python_version >= "3.6" -pytest-mock==3.9.0; python_version >= "3.7" +pytest-mock==3.10.0; python_version >= "3.7" pytest-recording==0.12.1; python_version >= "3.5" pytest==6.2.5; python_version >= "3.6" pythclient==0.1.2; python_full_version >= "3.7.0" python-binance==1.0.16 python-coinmarketcap==0.2 -python-dateutil==2.8.2; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "3.11" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5") +python-dateutil==2.8.2; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "3.11" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.3.0" or python_full_version > "3.9.7") and (python_version >= "3.8" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.8") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5") python-dotenv==0.19.2; python_version >= "3.5" python-i18n==0.3.9 pytrends==4.8.0 -pytz-deprecation-shim==0.1.0.post0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6" -pytz==2022.2.1; python_version >= "3.8" and python_version < "3.11" and python_full_version >= "3.7.1" and python_full_version < "4.0.0" -pyupgrade==2.38.2; python_version >= "3.7" +pytz-deprecation-shim==0.1.0.post0; python_version >= "3.7" and python_full_version < "3.0.0" or python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.6.0" or python_full_version > "3.9.7" and python_version >= "3.6" +pytz==2022.4; python_version >= "3.8" and python_version < "3.11" and python_full_version >= "3.7.1" and python_full_version < "4.0.0" and (python_version >= "3.8" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.8") +pyupgrade==2.38.4; python_version >= "3.7" pywin32==304; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.7" pywinpty==2.0.8; os_name == "nt" and python_version >= "3.7" pyyaml==6.0; python_version >= "3.8" -pyzmq==24.0.1; python_version >= "3.7" +pyzmq==24.0.1; python_full_version >= "3.7.0" and python_full_version < "4.0.0" and python_version >= "3.7" qdldl==0.1.5.post2; python_version >= "3.8" and python_version < "3.11" qtconsole==5.3.2; python_version >= "3.7" -qtpy==2.2.0; python_version >= "3.7" +qtpy==2.2.1; python_version >= "3.7" quandl==3.7.0; python_version >= "3.6" rapidfuzz==1.9.1; python_version >= "2.7" regex==2022.3.2; python_version >= "3.6" reportlab==3.6.11; python_version >= "3.7" and python_version < "4" -requests-oauthlib==1.3.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" +requests-oauthlib==1.3.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" requests==2.28.1; python_version >= "3.7" and python_version < "4" rich==10.16.2; python_full_version >= "3.6.2" and python_full_version < "4.0.0" riskfolio-lib==3.3.0; python_version >= "3.7" robin-stocks==2.1.0; python_version >= "3" -rsa==4.9; python_version >= "3.6" and python_version < "4" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") ruamel.yaml.clib==0.2.6; platform_python_implementation == "CPython" and python_version < "3.11" and python_version >= "3.5" ruamel.yaml==0.17.21; python_version >= "3" scikit-learn==1.1.2; python_version >= "3.8" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.8" @@ -259,11 +251,12 @@ screeninfo==0.6.7 scs==3.2.0; python_version >= "3.8" and python_version < "3.11" seaborn==0.11.2; python_version >= "3.6" selenium==4.5.0; python_version >= "3.7" and python_version < "4.0" +semver==2.13.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.4.0" or python_full_version > "3.9.7" send2trash==1.8.0; python_version >= "3.7" sentiment-investor==2.1.0; python_version >= "3.8" and python_version < "4.0" setuptools-scm==6.4.2; python_version >= "3.8" sgmllib3k==1.0.0; python_version >= "3.6" -six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "3.11" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") +six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "3.11" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.3.0" or python_full_version > "3.9.7") and (python_version >= "3.8" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.8") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0") and (python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.7.0" or python_full_version > "3.9.7" and python_version >= "3.7" and python_full_version < "4.0.0") smmap==5.0.0; python_version >= "3.7" sniffio==1.3.0; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.6.2" snowballstemmer==2.2.0; python_version >= "3.6" @@ -279,65 +272,61 @@ sphinxcontrib-qthelp==1.0.3; python_version >= "3.6" sphinxcontrib-serializinghtml==1.1.5; python_version >= "3.6" squarify==0.4.3 sseclient==0.0.27 -stack-data==0.5.1; python_full_version >= "3.6.2" and python_version >= "3.8" +stack-data==0.5.1; python_version >= "3.8" and python_full_version >= "3.7.0" and python_full_version < "4.0.0" statsmodels==0.13.2; python_version >= "3.7" stevedore==4.0.0; python_version >= "3.8" -tabulate==0.8.10; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" +streamlit==1.13.0; (python_version >= "3.7" and python_full_version < "3.9.7") or (python_full_version > "3.9.7") +tabulate==0.9.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" temporal-cache==0.1.4 tenacity==8.1.0; python_version >= "3.6" -tensorboard-data-server==0.6.1; python_version >= "3.7" -tensorboard-plugin-wit==1.8.1; python_version >= "3.7" -tensorboard==2.10.1; python_version >= "3.7" -tensorflow-estimator==2.10.0; python_version >= "3.7" -tensorflow-io-gcs-filesystem==0.27.0; python_version >= "3.7" and python_version < "3.11" -tensorflow==2.10.0; python_version >= "3.7" -termcolor==2.0.1; python_version >= "3.7" terminado==0.16.0; python_version >= "3.7" thepassiveinvestor==1.0.10 threadpoolctl==3.1.0; python_version >= "3.8" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.8" tinycss2==1.1.1; python_version >= "3.7" -tokenize-rt==4.2.1; python_full_version >= "3.6.2" and python_version >= "3.7" -toml==0.10.2; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7" -tomli==2.0.1; python_full_version <= "3.11.0a6" and python_full_version >= "3.7.2" and python_version >= "3.8" and python_version < "3.11" +tokenize-rt==4.2.1; python_version >= "3.7" and python_full_version >= "3.6.1" +toml==0.10.2; python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.7.0" or python_full_version > "3.9.7" and python_version >= "3.7" and python_full_version < "4.0.0" +tomli==2.0.1; python_full_version <= "3.11.0a6" and python_version >= "3.8" and python_version < "3.11" and python_full_version >= "3.7.2" tomlkit==0.11.5; python_version >= "3.6" and python_version < "4.0" and python_full_version >= "3.7.2" -toolz==0.12.0; python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.7.0" -tornado==6.2; python_version >= "3.7" +toolz==0.12.0; python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.7.0" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7") +torch==1.11.0; python_full_version >= "3.7.0" +tornado==6.2; python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.7.0" or python_full_version > "3.9.7" and python_version >= "3.7" and python_full_version < "4.0.0" tqdm==4.64.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" -tradingview-ta==3.2.10; python_version >= "3.6" -traitlets==5.4.0; python_full_version >= "3.7.0" and python_version >= "3.8" +tradingview-ta==3.3.0; python_version >= "3.6" +traitlets==5.4.0; python_full_version >= "3.7.0" and python_full_version < "4.0.0" and python_version >= "3.8" trio-websocket==0.9.2; python_version >= "3.7" and python_version < "4.0" trio==0.22.0; python_version >= "3.7" and python_version < "4.0" types-python-dateutil==2.8.19 types-pytz==2021.3.8 types-pyyaml==6.0.12 -types-requests==2.28.11 +types-requests==2.28.11.2 types-setuptools==57.4.18 types-six==1.16.21 types-urllib3==1.26.25 -typing-extensions==4.3.0; python_version < "3.10" and python_full_version >= "3.7.2" and python_version >= "3.7" -tzdata==2022.4; platform_system == "Windows" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6") -tzlocal==4.2; python_version >= "3.6" +typing-extensions==4.4.0; python_version < "3.10" and python_version >= "3.7" and python_full_version >= "3.7.2" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7") and python_full_version < "4.0.0" +tzdata==2022.4; python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" or python_version >= "3.7" and python_full_version < "3.9.7" and platform_system == "Windows" and python_full_version >= "3.6.0" or python_full_version > "3.9.7" and python_version >= "3.6" and platform_system == "Windows" +tzlocal==4.2; python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.6" ujson==5.5.0; python_version >= "3.7" -unidecode==1.3.5; python_version >= "3.7" +unidecode==1.3.6; python_version >= "3.7" update-checker==0.18.0; python_version >= "3.7" and python_version < "4.0" -urllib3==1.26.12; python_full_version >= "3.7.1" and python_version < "4" and python_version >= "3.8" and python_full_version < "4.0.0" +urllib3==1.26.12; python_full_version >= "3.7.1" and python_version < "4" and python_version >= "3.8" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7") user-agent==0.1.10 vadersentiment==3.3.2 +validators==0.20.0; python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.4" valinvest==0.0.2; python_version >= "3.6" vcrpy==4.2.1; python_version >= "3.7" virtualenv==20.16.5; python_version >= "3.7" voila==0.3.6; python_version >= "3.7" -wcwidth==0.2.5; python_full_version >= "3.6.2" and python_version >= "3.8" +watchdog==2.1.9; python_version >= "3.6" +wcwidth==0.2.5; python_full_version >= "3.7.0" and python_version >= "3.8" and python_full_version < "4.0.0" webencodings==0.5.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7" websocket-client==1.4.1; python_version >= "3.8" and python_version < "4.0" websockets==10.3; python_version >= "3.7" -werkzeug==2.2.2; python_version >= "3.7" -widgetsnbextension==3.6.1 +widgetsnbextension==3.6.1; python_version >= "3.6" win32-setctime==1.1.0; python_version >= "3.5" and python_full_version >= "3.7.0" and sys_platform == "win32" wrapt==1.14.1; python_full_version >= "3.7.2" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7") and python_version >= "3.8" and python_version < "3.11" wsproto==1.2.0; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.7.0" xlsxwriter==3.0.3; python_version >= "3.7" -yarl==1.7.2; python_version >= "3.7" and python_full_version >= "3.7.0" +yarl==1.8.1; python_version >= "3.7" and python_full_version >= "3.7.0" yfinance==0.1.74 -zipp==3.8.1; python_version < "3.10" and python_version >= "3.7" +zipp==3.8.1; python_version >= "3.7" and python_full_version < "3.9.7" and python_version < "3.10" or python_full_version > "3.9.7" and python_version >= "3.7" and python_version < "3.10" zope.interface==5.4.0; python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5" diff --git a/requirements.txt b/requirements.txt index e853095a8fd..c737ce87027 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,36 +2,39 @@ aiodns==3.0.0; python_version >= "3.5.2" and python_full_version >= "3.7.0" aiohttp==3.8.3; python_version >= "3.6" and python_full_version >= "3.7.0" aiosignal==1.2.0; python_version >= "3.6" and python_full_version >= "3.7.0" alpha-vantage==2.3.1 +altair==4.2.0; python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7" anyio==3.6.1; python_full_version >= "3.6.2" and python_version >= "3.7" -appnope==0.1.3; platform_system == "Darwin" and python_version >= "3.8" and sys_platform == "darwin" and python_full_version >= "3.6.2" +appnope==0.1.3; platform_system == "Darwin" and python_version >= "3.8" and sys_platform == "darwin" arch==5.3.1; python_version >= "3.7" argon2-cffi-bindings==21.2.0; python_version >= "3.7" argon2-cffi==21.3.0; python_version >= "3.7" ascii-magic==1.6; python_version >= "3.5" astor==0.8.1; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.8" astropy==5.1; python_version >= "3.8" -asttokens==2.0.8; python_full_version >= "3.6.2" and python_version >= "3.8" +asttokens==2.0.8; python_version >= "3.8" async-generator==1.10; python_version >= "3.7" and python_version < "4.0" async-timeout==4.0.2; python_version >= "3.6" and python_full_version >= "3.7.0" -attrs==21.4.0; python_version >= "3.7" and python_full_version >= "3.7.0" and (python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.5.0") +attrs==21.4.0; python_version >= "3.6" and python_full_version >= "3.7.0" and (python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.5.0") and (python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.7.0" or python_full_version > "3.9.7" and python_version >= "3.7") babel==2.10.3; python_version >= "3.7" -backcall==0.2.0; python_full_version >= "3.6.2" and python_version >= "3.8" -backoff==2.1.2; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.7.0" -backports.zoneinfo==0.2.1; python_version < "3.9" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6") +backcall==0.2.0; python_version >= "3.8" +backoff==2.2.1; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.7.0" +backports.zoneinfo==0.2.1; python_version >= "3.7" and python_full_version < "3.9.7" and python_version < "3.9" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.6.0" or python_full_version > "3.9.7" and python_version >= "3.6") or python_full_version > "3.9.7" and python_version >= "3.6" and python_version < "3.9" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.6.0" or python_full_version > "3.9.7" and python_version >= "3.6") base58==2.1.1; python_version >= "3.5" and python_full_version >= "3.7.0" beartype==0.7.1; python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.6.0" beautifulsoup4==4.11.1; python_full_version >= "3.6.0" and python_version >= "3.7" -black==22.8.0; python_full_version >= "3.6.2" +black==22.10.0; python_version >= "3.7" bleach==5.0.1; python_version >= "3.7" +blinker==1.5; python_version >= "3.7" and python_full_version < "3.0.0" or python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.5.0" or python_full_version > "3.9.7" bs4==0.0.1 bt==0.2.9; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.6.0") -ccxt==1.94.2 -certifi==2022.9.24; python_version >= "3.8" and python_version < "4" and python_full_version >= "3.7.1" and python_full_version < "4.0.0" +cachetools==5.2.0; python_version >= "3.7" and python_version < "4.0" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7") +ccxt==1.95.25 +certifi==2022.9.24; python_version >= "3.8" and python_version < "4" and python_full_version >= "3.7.1" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7") cffi==1.15.1 charset-normalizer==2.1.0; python_full_version >= "3.6.0" -click==8.1.3; python_version >= "3.7" and python_full_version >= "3.6.2" -colorama==0.4.5; python_full_version >= "3.7.0" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and (python_version >= "2.7" and python_full_version < "3.0.0" and platform_system == "Windows" or python_full_version >= "3.5.0" and platform_system == "Windows") and platform_system == "Windows" and python_version >= "3.8" and sys_platform == "win32" -commonmark==0.9.1; python_full_version >= "3.6.2" and python_full_version < "4.0.0" +click==8.1.3; python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7" +colorama==0.4.5; python_full_version >= "3.7.0" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and (python_version >= "2.7" and python_full_version < "3.0.0" and platform_system == "Windows" or python_full_version >= "3.5.0" and platform_system == "Windows") and (python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7") or platform_system == "Windows" and python_version >= "3.7" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7") and python_full_version >= "3.5.0") and (python_version >= "3.8" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.8" and python_full_version >= "3.5.0") and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7") and python_version >= "3.5" and sys_platform == "win32" +commonmark==0.9.1; python_full_version >= "3.6.2" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7") convertdate==2.4.0; python_version >= "3.7" and python_version < "4" cryptography==38.0.1; python_version >= "3.6" cssselect==1.1.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" @@ -41,27 +44,27 @@ cython==0.29.32; python_version >= "3.8" and python_full_version < "3.0.0" and s dateparser==1.1.1; python_version >= "3.5" datetime==4.7; python_version >= "3.5" debugpy==1.6.3; python_version >= "3.7" -decorator==5.1.1; python_full_version >= "3.6.2" and python_version >= "3.8" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.5") +decorator==5.1.1; python_version >= "3.8" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.5") and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.5") defusedxml==0.7.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7" degiro-connector==2.0.21; python_full_version >= "3.7.1" and python_full_version < "4.0.0" deprecation==2.1.0 detecta==0.0.5; python_version >= "3.6" dnspython==2.2.1; python_version >= "3.6" and python_version < "4.0" ecos==2.0.10; python_version >= "3.8" and python_version < "3.11" -entrypoints==0.4; python_version >= "3.7" +entrypoints==0.4; python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7" et-xmlfile==1.1.0; python_version >= "3.6" exceptiongroup==1.0.0rc9; python_version >= "3.7" and python_version < "3.11" exchange-calendars==4.2.3; python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.7.0" -executing==1.1.0; python_full_version >= "3.6.2" and python_version >= "3.8" +executing==1.1.0; python_version >= "3.8" fastjsonschema==2.16.2; python_full_version >= "3.7.0" and python_version >= "3.7" feedparser==6.0.10; python_version >= "3.6" ffn==0.3.6; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" financedatabase==1.0.2 -finnhub-python==2.4.14 +finnhub-python==2.4.15 finviz==1.4.4 finvizfinance==0.14.3; python_version >= "3.5" flake8==3.9.2; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0") -fonttools==4.37.3; python_version >= "3.7" +fonttools==4.37.4; python_version >= "3.7" formulaic==0.3.4; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.8" fred==3.1 fredapi==0.4.3 @@ -69,25 +72,26 @@ frozendict==2.3.4; python_version >= "3.6" frozenlist==1.3.1; python_version >= "3.7" and python_full_version >= "3.7.0" fundamentalanalysis==0.2.14 future==0.18.2; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" -gitdb==4.0.9; python_version >= "3.7" -gitpython==3.1.27; python_version >= "3.7" +gitdb==4.0.9; python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7" +gitpython==3.1.28; python_version >= "3.7" grpcio==1.49.1; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.7" h11==0.14.0; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.7.0" -hijri-converter==2.2.4; python_version >= "3.6" -holidays==0.11.3.1; python_version >= "3.6" +hijri-converter==2.2.4; python_version >= "3.7" +holidays==0.14.2; python_version >= "3.7" html5lib==1.1; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0") -idna==3.4; python_version >= "3.8" and python_version < "4" and python_full_version >= "3.7.1" and python_full_version < "4.0.0" -importlib-metadata==4.12.0; python_version < "3.10" and python_version >= "3.7" +idna==3.4; python_version >= "3.8" and python_version < "4" and python_full_version >= "3.7.1" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7") +importlib-metadata==5.0.0; python_version >= "3.7" and python_full_version < "3.9.7" and python_version < "3.10" or python_full_version > "3.9.7" and python_version >= "3.7" and python_version < "3.10" inflection==0.5.1; python_version >= "3.6" interface-meta==1.3.0; python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.7.1" and python_full_version < "4.0.0" investpy==1.0.8; python_version >= "3.7" +ipyflex==0.2.4; python_version >= "3.6" ipykernel==6.16.0; python_version >= "3.7" ipympl==0.8.8 ipython-genutils==0.2.0; python_version >= "3.7" -ipython==8.5.0; python_full_version >= "3.6.2" and python_version >= "3.8" +ipython==8.5.0; python_version >= "3.8" ipywidgets==7.7.2 iso8601==0.1.16 -jedi==0.18.1; python_full_version >= "3.6.2" and python_version >= "3.8" +jedi==0.18.1; python_version >= "3.8" jinja2==3.1.2; python_version >= "3.7" joblib==1.2.0; python_version >= "3.8" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.8" json5==0.9.10; python_version >= "3.7" @@ -100,14 +104,14 @@ jupyter==1.0.0 jupyterlab-pygments==0.2.2; python_version >= "3.7" jupyterlab-server==2.15.2; python_version >= "3.7" jupyterlab-widgets==1.1.1; python_version >= "3.6" -jupyterlab==3.4.7; python_version >= "3.7" +jupyterlab==3.4.8; python_version >= "3.7" kiwisolver==1.4.4; python_version >= "3.7" korean-lunar-calendar==0.3.1; python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.7.0" linearmodels==4.27; python_version >= "3.8" loguru==0.6.0; python_version >= "3.5" and python_full_version >= "3.7.0" lxml==4.9.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" markupsafe==2.1.1; python_version >= "3.7" -matplotlib-inline==0.1.6; python_full_version >= "3.6.2" and python_version >= "3.8" +matplotlib-inline==0.1.6; python_version >= "3.8" matplotlib==3.5.3; python_version >= "3.7" mccabe==0.6.1; python_full_version >= "3.7.0" mistune==0.8.4; python_version >= "3.7" @@ -115,14 +119,14 @@ more-itertools==8.14.0; python_version >= "3.6" mplfinance==0.12.9b1 multidict==6.0.2; python_version >= "3.7" and python_full_version >= "3.7.0" multitasking==0.0.11 -mypy-extensions==0.4.3; python_version >= "3.8" and python_full_version >= "3.6.2" +mypy-extensions==0.4.3; python_version >= "3.8" mypy==0.930; python_version >= "3.6" -nbclassic==0.4.4; python_version >= "3.7" +nbclassic==0.4.5; python_version >= "3.7" nbclient==0.5.13; python_full_version >= "3.7.0" and python_version >= "3.7" nbconvert==6.5.4; python_version >= "3.7" nbformat==5.6.1; python_full_version >= "3.7.0" and python_version >= "3.7" -nest-asyncio==1.5.5; python_full_version >= "3.7.0" and python_version >= "3.7" -networkx==2.8.6; python_version >= "3.8" +nest-asyncio==1.5.6; python_full_version >= "3.7.0" and python_version >= "3.7" +networkx==2.8.7; python_version >= "3.8" notebook-shim==0.1.0; python_version >= "3.7" notebook==6.4.12; python_version >= "3.7" numpy==1.23.3; python_version >= "3.8" @@ -132,19 +136,19 @@ onetimepass==1.0.1; python_full_version >= "3.7.1" and python_full_version < "4. openpyxl==3.0.10; python_version >= "3.6" osqp==0.6.2.post5; python_version >= "3.8" and python_version < "3.11" outcome==1.2.0; python_version >= "3.7" and python_version < "4.0" -packaging==21.3; python_version >= "3.8" +packaging==21.3; python_version >= "3.8" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.8" pandas-datareader==0.10.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6" pandas-market-calendars==3.2; python_full_version >= "3.7.0" pandas-ta==0.3.14b pandas==1.5.0; python_version >= "3.8" pandocfilters==1.5.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" -parso==0.8.3; python_full_version >= "3.6.2" and python_version >= "3.8" -pathspec==0.10.1; python_version >= "3.7" and python_full_version >= "3.6.2" +parso==0.8.3; python_version >= "3.8" +pathspec==0.10.1; python_version >= "3.7" patsy==0.5.2; python_version >= "3.8" -pexpect==4.8.0; sys_platform != "win32" and python_version >= "3.8" and python_full_version >= "3.6.2" -pickleshare==0.7.5; python_full_version >= "3.6.2" and python_version >= "3.8" -pillow==9.2.0; python_version >= "3.7" and python_version < "4" -platformdirs==2.5.2; python_version >= "3.7" and python_full_version >= "3.6.2" +pexpect==4.8.0; sys_platform != "win32" and python_version >= "3.8" +pickleshare==0.7.5; python_version >= "3.8" +pillow==9.2.0; python_version >= "3.7" and python_version < "4" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7") +platformdirs==2.5.2; python_version >= "3.7" plotly==5.10.0; python_version >= "3.6" pmdarima==1.8.5; python_version >= "3.7" praw==7.6.0; python_version >= "3.7" and python_version < "4.0" @@ -152,17 +156,19 @@ prawcore==2.3.0; python_version >= "3.7" and python_version < "4.0" prometheus-client==0.14.1; python_version >= "3.7" prompt-toolkit==3.0.31; python_full_version >= "3.6.2" property-cached==1.6.4; python_version >= "3.8" -protobuf==3.19.6; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.5" +protobuf==3.20.3; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.7" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7") psaw==0.0.12; python_version >= "3" psutil==5.9.2; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" -ptyprocess==0.7.0; os_name != "nt" and python_version >= "3.8" and sys_platform != "win32" and python_full_version >= "3.6.2" -pure-eval==0.2.2; python_full_version >= "3.6.2" and python_version >= "3.8" +ptyprocess==0.7.0; os_name != "nt" and python_version >= "3.8" and sys_platform != "win32" +pure-eval==0.2.2; python_version >= "3.8" py==1.11.0; python_version >= "3.7" and python_full_version < "3.0.0" and implementation_name == "pypy" or implementation_name == "pypy" and python_version >= "3.7" and python_full_version >= "3.5.0" pyally==1.1.2 +pyarrow==9.0.0; python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7" pycares==4.2.2; python_version >= "3.5.2" and python_full_version >= "3.7.0" pycodestyle==2.7.0; python_full_version >= "3.7.0" pycoingecko==2.3.0 pycparser==2.21 +pydeck==0.8.0b3; python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7" pyerfa==2.0.0.1; python_version >= "3.8" pyex==0.5.0 pyflakes==2.3.1; python_full_version >= "3.7.0" @@ -171,10 +177,11 @@ pyhdfe==0.1.0; python_version >= "3.8" pyluach==2.0.1; python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.7.0" pymeeus==0.5.11; python_version >= "3.7" and python_version < "4" pymongo==3.12.3 +pympler==1.0.1; python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.6" pyobjc-core==8.5.1; python_version >= "3.6" and sys_platform == "darwin" pyobjc-framework-cocoa==8.5.1; python_version >= "3.6" and sys_platform == "darwin" pyotp==2.7.0; python_version >= "3.6" -pyparsing==3.0.9; python_full_version >= "3.6.8" and python_version >= "3.7" +pyparsing==3.0.9; python_full_version >= "3.6.8" and python_version >= "3.7" and (python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.6.8" or python_full_version > "3.9.7" and python_version >= "3.7") pyportfolioopt==1.5.3; python_version >= "3.8" and python_version < "3.11" pyprind==2.11.3; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" pyrsistent==0.18.1; python_version >= "3.7" @@ -182,19 +189,19 @@ pysocks==1.7.1; python_version >= "3.7" and python_full_version < "3.0.0" and py pythclient==0.1.2; python_full_version >= "3.7.0" python-binance==1.0.16 python-coinmarketcap==0.2 -python-dateutil==2.8.2; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "3.11" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5") +python-dateutil==2.8.2; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "3.11" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.3.0" or python_full_version > "3.9.7") and (python_version >= "3.8" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.8") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5") python-dotenv==0.19.2; python_version >= "3.5" python-i18n==0.3.9 pytrends==4.8.0 -pytz-deprecation-shim==0.1.0.post0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6" -pytz==2022.2.1; python_version >= "3.8" and python_version < "3.11" and python_full_version >= "3.7.1" and python_full_version < "4.0.0" +pytz-deprecation-shim==0.1.0.post0; python_version >= "3.7" and python_full_version < "3.0.0" or python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.6.0" or python_full_version > "3.9.7" and python_version >= "3.6" +pytz==2022.4; python_version >= "3.8" and python_version < "3.11" and python_full_version >= "3.7.1" and python_full_version < "4.0.0" and (python_version >= "3.8" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.8") pywin32==304; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.7" pywinpty==2.0.8; os_name == "nt" and python_version >= "3.7" pyyaml==6.0; python_version >= "3.8" pyzmq==24.0.1; python_version >= "3.7" qdldl==0.1.5.post2; python_version >= "3.8" and python_version < "3.11" qtconsole==5.3.2; python_version >= "3.7" -qtpy==2.2.0; python_version >= "3.7" +qtpy==2.2.1; python_version >= "3.7" quandl==3.7.0; python_version >= "3.6" rapidfuzz==1.9.1; python_version >= "2.7" regex==2022.3.2; python_version >= "3.6" @@ -212,11 +219,12 @@ screeninfo==0.6.7 scs==3.2.0; python_version >= "3.8" and python_version < "3.11" seaborn==0.11.2; python_version >= "3.6" selenium==4.5.0; python_version >= "3.7" and python_version < "4.0" +semver==2.13.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.4.0" or python_full_version > "3.9.7" send2trash==1.8.0; python_version >= "3.7" sentiment-investor==2.1.0; python_version >= "3.8" and python_version < "4.0" setuptools-scm==6.4.2; python_version >= "3.8" sgmllib3k==1.0.0; python_version >= "3.6" -six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "3.11" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0") +six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "3.11" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.3.0" or python_full_version > "3.9.7") and (python_version >= "3.8" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.8") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0") and (python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.7.0" or python_full_version > "3.9.7" and python_version >= "3.7") and (python_version >= "3.8" and python_full_version < "3.0.0" or python_version >= "3.8" and python_full_version >= "3.3.0") smmap==5.0.0; python_version >= "3.7" sniffio==1.3.0; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.6.2" socketio-client-nexus==0.7.6 @@ -224,44 +232,48 @@ sortedcontainers==2.4.0; python_version >= "3.7" and python_version < "4.0" soupsieve==2.3.2.post1; python_version >= "3.6" and python_full_version >= "3.6.0" squarify==0.4.3 sseclient==0.0.27 -stack-data==0.5.1; python_full_version >= "3.6.2" and python_version >= "3.8" +stack-data==0.5.1; python_version >= "3.8" statsmodels==0.13.2; python_version >= "3.7" -tabulate==0.8.10; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" +streamlit==1.13.0; (python_version >= "3.7" and python_full_version < "3.9.7") or (python_full_version > "3.9.7") +tabulate==0.9.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" temporal-cache==0.1.4 tenacity==8.1.0; python_version >= "3.6" terminado==0.16.0; python_version >= "3.7" thepassiveinvestor==1.0.10 threadpoolctl==3.1.0; python_version >= "3.8" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.8" tinycss2==1.1.1; python_version >= "3.7" -tomli==2.0.1; python_full_version < "3.11.0a7" and python_full_version >= "3.6.2" and python_version >= "3.8" -toolz==0.12.0; python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.7.0" -tornado==6.2; python_version >= "3.7" +toml==0.10.2; python_version >= "3.7" and python_full_version < "3.0.0" or python_version >= "3.7" and python_full_version < "3.9.7" and python_full_version >= "3.3.0" or python_full_version > "3.9.7" +tomli==2.0.1; python_full_version < "3.11.0a7" and python_version >= "3.8" +toolz==0.12.0; python_version >= "3.8" and python_version < "4.0" and python_full_version >= "3.7.0" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7") +tornado==6.2; python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7" tqdm==4.64.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" -tradingview-ta==3.2.10; python_version >= "3.6" -traitlets==5.4.0; python_full_version >= "3.7.0" and python_version >= "3.8" +tradingview-ta==3.3.0; python_version >= "3.6" +traitlets==5.4.0; python_version >= "3.8" and python_full_version >= "3.7.0" trio-websocket==0.9.2; python_version >= "3.7" and python_version < "4.0" trio==0.22.0; python_version >= "3.7" and python_version < "4.0" -typing-extensions==4.3.0; python_version < "3.10" and python_full_version >= "3.7.0" and python_version >= "3.7" -tzdata==2022.4; platform_system == "Windows" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6") -tzlocal==4.2; python_version >= "3.6" +typing-extensions==4.4.0; python_version < "3.10" and python_version >= "3.7" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.7") and python_full_version >= "3.7.0" +tzdata==2022.4; python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" or python_version >= "3.7" and python_full_version < "3.9.7" and platform_system == "Windows" and python_full_version >= "3.6.0" or python_full_version > "3.9.7" and python_version >= "3.6" and platform_system == "Windows" +tzlocal==4.2; python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.6" ujson==5.5.0; python_version >= "3.7" -unidecode==1.3.5; python_version >= "3.7" +unidecode==1.3.6; python_version >= "3.7" update-checker==0.18.0; python_version >= "3.7" and python_version < "4.0" -urllib3==1.26.12; python_full_version >= "3.7.1" and python_version < "4" and python_version >= "3.8" and python_full_version < "4.0.0" +urllib3==1.26.12; python_full_version >= "3.7.1" and python_version < "4" and python_version >= "3.8" and python_full_version < "4.0.0" and (python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7") user-agent==0.1.10 vadersentiment==3.3.2 +validators==0.20.0; python_version >= "3.7" and python_full_version < "3.9.7" or python_full_version > "3.9.7" and python_version >= "3.4" valinvest==0.0.2; python_version >= "3.6" voila==0.3.6; python_version >= "3.7" +watchdog==2.1.9; python_version >= "3.6" wcwidth==0.2.5; python_full_version >= "3.6.2" and python_version >= "3.8" webencodings==0.5.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7" websocket-client==1.4.1; python_version >= "3.8" and python_version < "4.0" websockets==10.3; python_version >= "3.7" -widgetsnbextension==3.6.1 +widgetsnbextension==3.6.1; python_version >= "3.6" win32-setctime==1.1.0; python_version >= "3.5" and python_full_version >= "3.7.0" and sys_platform == "win32" wrapt==1.14.1; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.8" wsproto==1.2.0; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.7.0" xlsxwriter==3.0.3; python_version >= "3.7" -yarl==1.7.2; python_version >= "3.6" and python_full_version >= "3.7.0" +yarl==1.8.1; python_version >= "3.7" and python_full_version >= "3.7.0" yfinance==0.1.74 -zipp==3.8.1; python_version < "3.10" and python_version >= "3.7" +zipp==3.8.1; python_version >= "3.7" and python_full_version < "3.9.7" and python_version < "3.10" or python_full_version > "3.9.7" and python_version >= "3.7" and python_version < "3.10" zope.interface==5.4.0; python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5" diff --git a/scripts/test_forecast.openbb b/scripts/test_forecast.openbb new file mode 100644 index 00000000000..65c6a288f33 --- /dev/null +++ b/scripts/test_forecast.openbb @@ -0,0 +1,29 @@ +stocks +load gme +forecast +show GME +plot GME.close +clean GME +desc GME +corr GME +season GME.close +delete GME.open +rename GME -o volume -n vol +ema GME +sto GME +rsi GME +roc GME +mom GME +delta GME +atr GME +signal GME +expo GME +theta GME +linregr GME +regr GME +rnn GME +brnn GME +nbeats GME +tcn GME +trans GME +tft GME diff --git a/scripts/test_forecast_advanced.openbb b/scripts/test_forecast_advanced.openbb new file mode 100644 index 00000000000..4ffc122185e --- /dev/null +++ b/scripts/test_forecast_advanced.openbb @@ -0,0 +1,13 @@ +stocks +load GME +forecast +q +load TSLA +forecast +show TSLA +q +load AMC +forecast +show AMC +combine GME -c TSLA.close,AMC.close +brnn GME --past-covariates=volume,TSLA_close,AMC_close diff --git a/terminal.py b/terminal.py index ee59b6299ef..6787a409771 100644 --- a/terminal.py +++ b/terminal.py @@ -16,6 +16,7 @@ from prompt_toolkit import PromptSession from prompt_toolkit.completion import NestedCompleter from prompt_toolkit.styles import Style from prompt_toolkit.formatted_text import HTML +import pandas as pd from openbb_terminal import feature_flags as obbff @@ -23,7 +24,7 @@ from openbb_terminal.core.config.paths import ( REPOSITORY_DIRECTORY, USER_ENV_FILE, REPOSITORY_ENV_FILE, - USER_EXPORTS_DIRECTORY, + HOME_DIRECTORY, ROUTINES_DIRECTORY, ) from openbb_terminal.core.log.generation.path_tracking_file_handler import ( @@ -86,6 +87,7 @@ class TerminalController(BaseController): "alternative", "econometrics", "sources", + "forecast", ] PATH = "/" @@ -157,6 +159,7 @@ class TerminalController(BaseController): mt.add_raw("\n") mt.add_info("_others_") mt.add_menu("econometrics") + mt.add_menu("forecast") mt.add_menu("portfolio") mt.add_menu("dashboards") mt.add_menu("reports") @@ -419,6 +422,14 @@ class TerminalController(BaseController): self.queue = EconometricsController(self.queue).menu() + def call_forecast(self, _): + """Process forecast command""" + from openbb_terminal.forecast.forecast_controller import ( + ForecastController, + ) + + self.queue = self.load_class(ForecastController, "", pd.DataFrame(), self.queue) + def call_portfolio(self, _): """Process portfolio command""" from openbb_terminal.portfolio.portfolio_controller import ( @@ -552,10 +563,28 @@ class TerminalController(BaseController): ] if "export" in self.queue[0]: - export_path = USER_EXPORTS_DIRECTORY - console.print( - f"Export data to be saved in the selected folder: '{export_path}'" - ) + export_path = self.queue[0].split(" ")[1] + # If the path selected does not start from the user root, give relative location from root + if export_path[0] == "~": + export_path = export_path.replace( + "~", HOME_DIRECTORY.as_posix() + ) + elif export_path[0] != "/": + export_path = os.path.join( + os.path.dirname(os.path.abspath(__file__)), export_path + ) + + # Check if the directory exists + if os.path.isdir(export_path): + console.print( + f"Export data to be saved in the selected folder: '{export_path}'" + ) + else: + os.makedirs(export_path) + console.print( + f"[green]Folder '{export_path}' successfully created.[/green]" + ) + obbff.EXPORT_FOLDER_PATH = export_path self.queue = self.queue[1:] @@ -573,13 +602,36 @@ def terminal(jobs_cmds: List[str] = None, appName: str = "gst"): if jobs_cmds is not None and jobs_cmds: logger.info("INPUT: %s", "/".join(jobs_cmds)) + export_path = "" if jobs_cmds and "export" in jobs_cmds[0]: + export_path = jobs_cmds[0].split("/")[0].split(" ")[1] jobs_cmds = ["/".join(jobs_cmds[0].split("/")[1:])] ret_code = 1 t_controller = TerminalController(jobs_cmds) an_input = "" + if export_path: + # If the path selected does not start from the user root, give relative location from terminal root + if export_path[0] == "~": + export_path = export_path.replace("~", HOME_DIRECTORY.as_posix()) + elif export_path[0] != "/": + export_path = os.path.join( + os.path.dirname(os.path.abspath(__file__)), export_path + ) + + # Check if the directory exists + if os.path.isdir(export_path): + console.print( + f"Export data to be saved in the selected folder: '{export_path}'" + ) + else: + os.makedirs(export_path) + console.print( + f"[green]Folder '{export_path}' successfully created.[/green]" + ) + obbff.EXPORT_FOLDER_PATH = export_path + bootup() if not jobs_cmds: welcome_message() @@ -776,12 +828,18 @@ def run_scripts( if test_mode and "exit" not in lines[-1]: lines.append("exit") + export_folder = "" if "export" in lines[0]: + export_folder = lines[0].split("export ")[1].rstrip() lines = lines[1:] simulate_argv = f"/{'/'.join([line.rstrip() for line in lines])}" file_cmds = simulate_argv.replace("//", "/home/").split() file_cmds = insert_start_slash(file_cmds) if file_cmds else file_cmds + if export_folder: + file_cmds = [f"export {export_folder}{' '.join(file_cmds)}"] + else: + file_cmds = [" ".join(file_cmds)] if not test_mode: terminal(file_cmds, appName="openbb_script") diff --git a/tests/openbb_terminal/common/prediction_techniques/__init__.py b/tests/openbb_terminal/common/prediction_techniques/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/tests/openbb_terminal/common/prediction_techniques/test_neural_networks_view.py b/tests/openbb_terminal/common/prediction_techniques/test_neural_networks_view.py deleted file mode 100644 index daa909f4534..00000000000 --- a/tests/openbb_terminal/common/prediction_techniques/test_neural_networks_view.py +++ /dev/null @@ -1,29 +0,0 @@ -""" fundamental_analysis/yahoo_finance_api.py tests """ -import unittest - -# try: -# from openbb_terminal.common.prediction_techniques.neural_networks_view import ( -# display_mlp, -# ) -# except ModuleNotFoundError as e_module: -# console.print("One of the optional packages seems to be missing") -# console.print(e_module) -# console.print("Skipping the test") - -# from openbb_terminal import feature_flags as obbff - - -class TestPredNeuralNetworks(unittest.TestCase): - def test_mlp(self): - pass - # if not obbff.ENABLE_PREDICT: - # return - - # Fix: need to come up with a better way of doing this - # try: - # Fix: need to move loading of ticker data into a df somewhere - # display_mlp([], "TLSA", None) - # except NameError as e_name: - # console.print("One of the optional packages seems to be missing") - # console.print(e_name) - # console.print("Skipping the test") diff --git a/tests/openbb_terminal/core/log/collection/test_logging_clock.py b/tests/openbb_terminal/core/log/collection/test_logging_clock.py index c071d14aad3..a859fc1ecf0 100644 --- a/tests/openbb_terminal/core/log/collection/test_logging_clock.py +++ b/tests/openbb_terminal/core/log/collection/test_logging_clock.py @@ -24,7 +24,6 @@ def test_calculate_next_sharp_invalid(): clock.calculate_next_sharp(now, "bad") -# TODO: find a better way to mock the while loop def test_do_action_every_sharp(mocker): mock = mocker.Mock() mock.count = 0 diff --git a/tests/openbb_terminal/cryptocurrency/prediction_techniques/__init__.py b/tests/openbb_terminal/cryptocurrency/prediction_techniques/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/tests/openbb_terminal/cryptocurrency/prediction_techniques/test_pred_controller.py b/tests/openbb_terminal/cryptocurrency/prediction_techniques/test_pred_controller.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/tests/openbb_terminal/cryptocurrency/test_crypto_controller.py b/tests/openbb_terminal/cryptocurrency/test_crypto_controller.py index a17f530eef9..a73efa1e3bb 100644 --- a/tests/openbb_terminal/cryptocurrency/test_crypto_controller.py +++ b/tests/openbb_terminal/cryptocurrency/test_crypto_controller.py @@ -343,7 +343,6 @@ def test_call_func( "call_candle", "call_ta", "call_dd", - "call_pred", ], ) def test_call_func_no_current_coin(tested_func): diff --git a/tests/openbb_terminal/cryptocurrency/txt/test_crypto_controller/test_print_help.txt b/tests/openbb_terminal/cryptocurrency/txt/test_crypto_controller/test_print_help.txt index ce491bfc744..1f60f8998cb 100644 --- a/tests/openbb_terminal/cryptocurrency/txt/test_crypto_controller/test_print_help.txt +++ b/tests/openbb_terminal/cryptocurrency/txt/test_crypto_controller/test_print_help.txt @@ -18,6 +18,6 @@ Interval (min): > nft non-fungible tokens, today drops [unvl]> dd due-diligence for loaded coin, coin information, social media, market stats[/unvl] [unvl]> ta technical analysis for loaded coin, ema, macd, rsi, adx, bbands, obv[/unvl] -[unvl]> pred prediction techniques, regression, arima, rnn, lstm, conv1d, monte carlo[/unvl] [unvl]> qa quantitative analysis decompose, cusum, residuals analysis[/unvl] +[unvl]> forecast forecast techniques, rnn, nbeats, transformer, block rnn[/unvl] diff --git a/tests/openbb_terminal/econometrics/test_regression_model.py b/tests/openbb_terminal/econometrics/test_regression_model.py deleted file mode 100644 index 43d20c63d68..00000000000 --- a/tests/openbb_terminal/econometrics/test_regression_model.py +++ /dev/null @@ -1,371 +0,0 @@ -# IMPORTATION STANDARD -import pandas as pd - -# IMPORTATION THIRDPARTY -import pytest - -# IMPORTATION INTERNAL -from openbb_terminal.econometrics import regression_model, econometrics_model - - -@pytest.mark.vcr() -@pytest.mark.parametrize( - "regression_variables, data", - [ - ( - ["longley.TOTEMP", "longley.GNP", "longley.ARMED", "longley.POP"], - { - "longley": econometrics_model.load( - "longley", ["csv", "xlsx"], {}, {"longley": "longley"} - ) - }, - ) - ], -) -def test_get_regression_data(recorder, regression_variables, data): - ( - regression_df, - dependent_variable, - independent_variables, - ) = regression_model.get_regression_data( - regression_variables=regression_variables, data=data - ) - - recorder.capture( - pd.DataFrame([regression_df, dependent_variable, independent_variables]) - ) - - -@pytest.mark.vcr() -@pytest.mark.parametrize( - "regression_variables, data, show_regression", - [ - ( - ["longley.TOTEMP", "longley.GNP", "longley.ARMED", "longley.POP"], - { - "longley": econometrics_model.load( - "longley", ["csv", "xlsx"], {}, {"longley": "longley"} - ) - }, - True, - ), - ( - ["longley.TOTEMP", "longley.GNP", "longley.ARMED", "longley.POP"], - { - "longley": econometrics_model.load( - "longley", ["csv", "xlsx"], {}, {"longley": "longley"} - ) - }, - False, - ), - ], -) -def test_get_ols(recorder, regression_variables, data, show_regression): - _, _, _, model = regression_model.get_ols( - regression_variables=regression_variables, - data=data, - show_regression=show_regression, - ) - - result = pd.DataFrame([model.params]) - - recorder.capture(result, float_format="%.5f") - - -@pytest.mark.vcr() -@pytest.mark.parametrize( - "regression_variables, data", - [ - ( - [ - "wage_panel.educ", - "wage_panel.married", - "wage_panel.lwage", - "wage_panel.hisp", - "wage_panel.black", - ], - { - "wage_panel": econometrics_model.load( - "wage_panel", ["csv", "xlsx"], {}, {"wage_panel": "wage_panel"} - ).set_index(["nr", "year"]) - }, - ) - ], -) -def test_get_pols(recorder, regression_variables, data): - _, _, _, model = regression_model.get_pols( - regression_variables=regression_variables, - data=data, - ) - - result = pd.DataFrame([model.params]) - - recorder.capture(result, float_format="%.5f") - - -@pytest.mark.vcr() -@pytest.mark.parametrize( - "regression_variables, data", - [ - ( - [ - "wage_panel.married", - "wage_panel.lwage", - "wage_panel.hisp", - "wage_panel.black", - ], - { - "wage_panel": econometrics_model.load( - "wage_panel", ["csv", "xlsx"], {}, {"wage_panel": "wage_panel"} - ).set_index(["nr", "year"]) - }, - ) - ], -) -def test_get_re(recorder, regression_variables, data): - _, _, _, model = regression_model.get_re( - regression_variables=regression_variables, - data=data, - ) - - result = pd.DataFrame([model.params]) - - recorder.capture(result, float_format="%.5f") - - -@pytest.mark.vcr() -@pytest.mark.parametrize( - "regression_variables, data", - [ - ( - [ - "wage_panel.educ", - "wage_panel.married", - "wage_panel.lwage", - "wage_panel.hisp", - "wage_panel.black", - ], - { - "wage_panel": econometrics_model.load( - "wage_panel", ["csv", "xlsx"], {}, {"wage_panel": "wage_panel"} - ).set_index(["nr", "year"]) - }, - ) - ], -) -def test_get_bols(recorder, regression_variables, data): - _, _, _, model = regression_model.get_bols( - regression_variables=regression_variables, - data=data, - ) - - result = pd.DataFrame([model.params]) - - recorder.capture(result, float_format="%.5f") - - -@pytest.mark.vcr() -@pytest.mark.parametrize( - "regression_variables, data", - [ - ( - [ - "wage_panel.educ", - "wage_panel.married", - "wage_panel.lwage", - "wage_panel.hisp", - "wage_panel.black", - ], - { - "wage_panel": econometrics_model.load( - "wage_panel", ["csv", "xlsx"], {}, {"wage_panel": "wage_panel"} - ).set_index(["nr", "year"]) - }, - ) - ], -) -def test_get_fe(recorder, regression_variables, data): - _, _, _, model = regression_model.get_fe( - regression_variables=regression_variables, - data=data, - ) - - result = pd.DataFrame([model.params]) - - recorder.capture(result, float_format="%.5f") - - -@pytest.mark.vcr() -@pytest.mark.parametrize( - "regression_variables, data", - [ - ( - ["wage_panel.lwage", "wage_panel.married"], - { - "wage_panel": econometrics_model.load( - "wage_panel", ["csv", "xlsx"], {}, {"wage_panel": "wage_panel"} - ).set_index(["nr", "year"]) - }, - ) - ], -) -def test_get_fdols(recorder, regression_variables, data): - _, _, _, model = regression_model.get_fdols( - regression_variables=regression_variables, - data=data, - ) - - result = pd.DataFrame([model.params]) - - recorder.capture(result, float_format="%.5f") - - -@pytest.mark.vcr() -@pytest.mark.parametrize( - "regression_variables, data", - [ - ( - [ - "wage_panel.married", - "wage_panel.lwage", - "wage_panel.hisp", - "wage_panel.black", - ], - { - "wage_panel": econometrics_model.load( - "wage_panel", ["csv", "xlsx"], {}, {"wage_panel": "wage_panel"} - ).set_index(["nr", "year"]) - }, - ) - ], -) -def test_get_comparison(recorder, regression_variables, data): - regressions = {"RE": {}, "FE": {}} - - _, _, _, regressions["RE"]["model"] = regression_model.get_re( - regression_variables=regression_variables, - data=data, - ) - - _, _, _, regressions["FE"]["model"] = regression_model.get_fe( - regression_variables=regression_variables, - data=data, - ) - - comparison_result = regression_model.get_comparison(regressions) - - recorder.capture(comparison_result.params, float_format="%.5f") - - -@pytest.mark.vcr() -@pytest.mark.parametrize( - "regression_variables, data, show_regression", - [ - ( - ["longley.TOTEMP", "longley.GNP", "longley.ARMED", "longley.POP"], - { - "longley": econometrics_model.load( - "longley", ["csv", "xlsx"], {}, {"longley": "longley"} - ) - }, - True, - ), - ( - ["longley.GNP", "longley.ARMED", "longley.POP"], - { - "longley": econometrics_model.load( - "longley", ["csv", "xlsx"], {}, {"longley": "longley"} - ) - }, - True, - ), - ], -) -def test_get_dwat(recorder, regression_variables, data, show_regression): - _, _, _, model = regression_model.get_ols( - regression_variables=regression_variables, - data=data, - show_regression=show_regression, - ) - - result = regression_model.get_dwat(model.resid).round(5) - - recorder.capture(result) - - -@pytest.mark.vcr() -@pytest.mark.parametrize( - "regression_variables, data, show_regression, lags", - [ - ( - ["longley.ARMED", "longley.GNP", "longley.TOTEMP", "longley.POP"], - { - "longley": econometrics_model.load( - "longley", ["csv", "xlsx"], {}, {"longley": "longley"} - ) - }, - True, - 3, - ), - ( - ["longley.GNP", "longley.POP"], - { - "longley": econometrics_model.load( - "longley", ["csv", "xlsx"], {}, {"longley": "longley"} - ) - }, - True, - 1, - ), - ], -) -def test_get_bgod(recorder, regression_variables, data, show_regression, lags): - _, _, _, model = regression_model.get_ols( - regression_variables=regression_variables, - data=data, - show_regression=show_regression, - ) - - lm_stat, p_value, f_stat, fp_value = regression_model.get_bgod(model, lags) - - result = pd.DataFrame([lm_stat, p_value, f_stat, fp_value]) - - recorder.capture(result, float_format="%.5f") - - -@pytest.mark.vcr() -@pytest.mark.parametrize( - "regression_variables, data, show_regression", - [ - ( - ["longley.GNP", "longley.TOTEMP", "longley.POP"], - { - "longley": econometrics_model.load( - "longley", ["csv", "xlsx"], {}, {"longley": "longley"} - ) - }, - True, - ), - ( - ["longley.POP", "longley.GNP"], - { - "longley": econometrics_model.load( - "longley", ["csv", "xlsx"], {}, {"longley": "longley"} - ) - }, - True, - ), - ], -) -def test_get_bpag(recorder, regression_variables, data, show_regression): - _, _, _, model = regression_model.get_ols( - regression_variables=regression_variables, - data=data, - show_regression=show_regression, - ) - - lm_stat, p_value, f_stat, fp_value = regression_model.get_bpag(model) - - result = pd.DataFrame([lm_stat, p_value, f_stat, fp_value]) - - recorder.capture(result, float_format="%.5f") diff --git a/tests/openbb_terminal/econometrics/test_regression_view.py b/tests/openbb_terminal/econometrics/test_regression_view.py deleted file mode 100644 index 13363e0ad89..00000000000 --- a/tests/openbb_terminal/econometrics/test_regression_view.py +++ /dev/null @@ -1,81 +0,0 @@ -# IMPORTATION THIRDPARTY -import pytest - -# IMPORTATION INTERNAL -from openbb_terminal.econometrics import ( - regression_model, - econometrics_model, - regression_view, -) - - -@pytest.mark.vcr() -@pytest.mark.record_stdout -@pytest.mark.parametrize( - "regression_variables, data, show_regression, lags", - [ - ( - ["longley.TOTEMP", "longley.ARMED", "longley.POP", "longley.GNP"], - { - "longley": econometrics_model.load( - "longley", ["csv", "xlsx"], {}, {"longley": "longley"} - ) - }, - False, - 1, - ), - ( - ["longley.TOTEMP", "longley.ARMED", "longley.POP", "longley.GNP"], - { - "longley": econometrics_model.load( - "longley", ["csv", "xlsx"], {}, {"longley": "longley"} - ) - }, - False, - 2, - ), - ], -) -def test_display_bgod(regression_variables, data, show_regression, lags): - _, _, _, model = regression_model.get_ols( - regression_variables=regression_variables, - data=data, - show_regression=show_regression, - ) - - regression_view.display_bgod(model=model, lags=lags) - - -@pytest.mark.vcr() -@pytest.mark.record_stdout -@pytest.mark.parametrize( - "regression_variables, data, show_regression", - [ - ( - ["longley.TOTEMP", "longley.ARMED", "longley.POP"], - { - "longley": econometrics_model.load( - "longley", ["csv", "xlsx"], {}, {"longley": "longley"} - ) - }, - False, - ), - ( - ["longley.GNP", "longley.ARMED", "longley.POP"], - { - "longley": econometrics_model.load( - "longley", ["csv", "xlsx"], {}, {"longley": "longley"} - ) - }, - False, - ), - ], -) -def test_display_bpag(regression_variables, data, show_regression): - _, _, _, model = regression_model.get_ols( - regression_variables=regression_variables, - data=data, - show_regression=show_regression, - ) - - regression_view.display_bpag(model=model) diff --git a/tests/openbb_terminal/economy/cassettes/test_alphavantage_view/test_realtime_performance_sector.yaml b/tests/openbb_terminal/economy/cassettes/test_alphavantage_view/test_realtime_performance_sector.yaml index f839118f75e..0e975d65d2b 100644 --- a/tests/openbb_terminal/economy/cassettes/test_alphavantage_view/test_realtime_performance_sector.yaml +++ b/tests/openbb_terminal/economy/cassettes/test_alphavantage_view/test_realtime_performance_sector.yaml @@ -13,29 +13,29 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA+yXTW/UMBCG7/0V1kpFIOHB9vgjzg26BYqohLrtoUdrMWzEboISF6lC/HeU3VZt - 3LXX6okD18zE8bzP+PXk9xEhhMzOfXBk7oKb1eT39tH28Vn7res3LjRdO6vJ7GpBFn4Zup588f02 - 0i49edl7tw7NxpMXZNUMoeubpVu/mr1+WOezGwK58N96P6z813EpwYSgDKnghJmaYa0EuVq8OXVD - 8H072776Z7fC7MK1P8jbmlx4t6aX44cefT65YXLpl6u2W3ffb7cfBG6PH+/ppGuHm43vybwZlr0f - X3H9fWoVpW42N22z3K278P2vZumHMZWDlJPUj6MWK3Li+nFnMwZaTeLnLvi+cevhLsom0bP2680Q - HsXlNP6+aV27fAiLREmL4H6u/X2SmSSNKpLTIbhwt0PBJ/HT1vc7yRiw6fpXoVk3odktTBlYdryH - 1LuacDJ3t/8p/cuUTmqi8pTS0tt4t2mgBiqTEccAz5LRIGQhZAXGpiGquFsiiAqYOEgJoRI5SgJ0 - BgUDJRIMKYKq9lGaj2fpvGvDKs3pYRkDVZVXU5r0/jQg5qqTUOmchBjXNwEgngJI9QyLZU5DZ6Cr - Qr94kjrtRMrA4MEOoAKk3gfqtCZYDgo12Nyx4LENTkDx+NAcMqSoDSgDLQ+XysDyNE/KwfBcu1AF - CgvZUA1WF3YHNaBKPYFyvi31Ka/3Nbn2rqeho3MXPHl5fTl/VYROgBT5PpI648c89ogIHkVgJkvv - ScJeegg2cxqpAtRZehZQltLjDEwxPs6gssX8ELjax+/DaIwjwRJmUsbnKTZuFssRic4toCqskGtg - 1UFAHLcXc/KMYyxp5BAIMjOzWBClvqhB6PzNGG0kg0uCFvtofRzdMU8roycX8fyWqUbGoKZjgAKR - 9UZpQZWWKzUonmEkdTy1xTdmBVYdnjo0IMt1LyIYmW4lwYHJxODBVeI+OxvHw+cSE4wD04XEONOg - bI5JhcBNmqlG0LnLVFlgmO1wBJ2dfJQAmxEYDaAtwShVYWehhAoTyCyg2UfsU004ezYyqVQschoZ - MhtLHiETBkHmzoZg6oDvcL1zkxR2Lqp4QNnrswJBYsZomdgKWsTFSJA2+R/2wOXoz18AAAD//wMA - RRkqKVgRAAA= + H4sIAAAAAAAAAwAAAP//7Jddj9M4FIbv+RVWJVYg0bM+PnZi5w6mwzJoR0LTmQsurWJoRJusEs9K + CPHfV8kMbOO0pxZ3SNzW8bH9Pu/56NcnQgixuA7Ri5WPflGJr+NP489Xzce22/tYt82iEou7tViH + TWw78S5040qzCeJZF/wu1vsg/hDbuo9tV2/87vnixf9x/vZ9FDfhYxf6bfgwhFJSqSXKpbRCUkW2 + Mk7crf+89H0MXbMYt357iLC48c1n8bISN8HvlrfDQQfHTy982YTu05fhgKWEUj09vMRF2/T3+9CJ + dfT/7EI/foVgzOSrq+bDfR+72u++f+CmYe5ivatj/bhfgXST5TeDGFtx4bvw+AFO97+uG99sfsRX + QHqyPrxSXPbRx+8B9PSEax9Dd7Df6OSZ+/19U29GamIdun/rzY/LWn1cklXdb7ow7PDdg3yUxj3w + grgNm23T7tpHqTWgfnqE2atKoFj5L795/Rq8LiphcnkhgS04Eipdn+igAA0jM6YvP/0ahCJXUARd + cvARSJ63oAQtc6lg6qbUjQWxZtKjTHNSqyGzrtsmbnNYaTCKNT1C4TjTmzTA1NMGnGOTsgQqzgtb + pngYC1sgmwvBgqXclLPgLAMMNZBhiWEBrjiG7LISlI9MSSCX7zJJrP4KqOSLmiGGrx6fxPjHpPsZ + ciUgnfeCBcNxcPMgJ5GiTivREWbaHmP2uhLvg++WsV2ufAzi2fvb1fOsjLNgucRHmfpslpKUQj3e + DwlsyTdENz6OgT+7S9KxCJTLpUsSLGb3LARElgwREOaCJjv2wDnGv4ZqOYDMQWdUeqn5iFBYtqhb + ex6cTueIqehIYPiaijqddOZcpczlNtSbMpebUlDyVVBROmdx3Nwo6Jzbm6Fk8txOP0k70NO0n6Qn + PwlQCZLpeIryXY4WNHIkUYHjqjMqKM9XApTpjbmSmE7RSXWCgk9LxLEkzIldDRPkzxJDqdN7nZbV + IDjNIdQ2nUqOyaZMOoge9GCdZvLUBLMWPVERXTo0pdzLtI4k3Mu0cCcYLKDNJK7Gq855va0Eyp8G + RsoCsZMlOgcmO1PcbFBNFKESiPhcUiCZvx3WgD0/4lsCx+RHSaDVCc8gQZkLhUZpHqA8+fYfAAAA + //8DALLJLbSKEQAA headers: Allow: - GET, HEAD, OPTIONS CF-Cache-Status: - DYNAMIC CF-RAY: - - 6ef63417b81a973f-AMS + - 756d38587a6b95b7-LUX Connection: - keep-alive Content-Encoding: @@ -43,9 +43,7 @@ interactions: Content-Type: - application/json Date: - - Mon, 21 Mar 2022 11:03:52 GMT - Expect-CT: - - max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct" + - Sat, 08 Oct 2022 07:38:59 GMT Server: - cloudflare Transfer-Encoding: diff --git a/tests/openbb_terminal/economy/test_alphavantage_view.py b/tests/openbb_terminal/economy/test_alphavantage_view.py index 73ca92085c8..fbd75dc0868 100644 --- a/tests/openbb_terminal/economy/test_alphavantage_view.py +++ b/tests/openbb_terminal/economy/test_alphavantage_view.py @@ -17,7 +17,7 @@ def vcr_config(): @pytest.mark.vcr @pytest.mark.parametrize( "raw", - [True, True, False], + [True, False], ) @pytest.mark.record_stdout def test_realtime_performance_sector(mocker, raw): @@ -27,7 +27,4 @@ def test_realtime_performance_sector(mocker, raw): # MOCK EXPORT_DATA mocker.patch(target="openbb_terminal.economy.alphavantage_view.export_data") - alphavantage_view.realtime_performance_sector( - raw=raw, - export="", - ) + alphavantage_view.realtime_performance_sector(raw=raw) diff --git a/tests/openbb_terminal/economy/txt/test_alphavantage_view/test_realtime_performance_sector[True].txt b/tests/openbb_terminal/economy/txt/test_alphavantage_view/test_realtime_performance_sector[True].txt new file mode 100644 index 00000000000..32785a48e1c --- /dev/null +++ b/tests/openbb_terminal/economy/txt/test_alphavantage_view/test_realtime_performance_sector[True].txt @@ -0,0 +1,12 @@ + Sector % Chg +0 Energy -0.72 +1 Consumer Staples -1.55 +2 Industrials -1.92 +3 Utilities -2.09 +4 Health Care -2.12 +5 Financials -2.34 +6 Real Estate -2.49 +7 Materials -2.54 +8 Communication Services -2.84 +9 Consumer Discretionary -3.54 +10 Information Technology -4.14 diff --git a/tests/openbb_terminal/economy/txt/test_economy_controller/test_print_help.txt b/tests/openbb_terminal/economy/txt/test_economy_controller/test_print_help.txt index 20b85929ee6..0494790c2ec 100644 --- a/tests/openbb_terminal/economy/txt/test_economy_controller/test_print_help.txt +++ b/tests/openbb_terminal/economy/txt/test_economy_controller/test_print_help.txt @@ -21,6 +21,5 @@ Stored datasets: plot plot data from the above commands together -> pred Open the prediction menu to analyse stored data > qa Open quantitative analysis menu with stored data diff --git a/tests/openbb_terminal/etf/prediction_techniques/__init__.py b/tests/openbb_terminal/etf/prediction_techniques/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/tests/openbb_terminal/etf/prediction_techniques/conftest.py b/tests/openbb_terminal/etf/prediction_techniques/conftest.py deleted file mode 100644 index 98189bd3993..00000000000 --- a/tests/openbb_terminal/etf/prediction_techniques/conftest.py +++ /dev/null @@ -1,12 +0,0 @@ -# IMPORTATION STANDARD - -# IMPORTATION THIRDPARTY -import pytest -from _pytest.nodes import Node - -# IMPORTATION INTERNAL - - -def pytest_runtest_setup(item: Node): - if not item.config.getoption("--prediction"): - pytest.skip(msg="Runs only with option : --prediction") diff --git a/tests/openbb_terminal/etf/prediction_techniques/test_pred_controller.py b/tests/openbb_terminal/etf/prediction_techniques/test_pred_controller.py deleted file mode 100644 index 641ef3db2b7..00000000000 --- a/tests/openbb_terminal/etf/prediction_techniques/test_pred_controller.py +++ /dev/null @@ -1,555 +0,0 @@ -# IMPORTATION STANDARD -import os -from datetime import datetime - -# IMPORTATION THIRDPARTY -import pandas as pd -import pytest - -# IMPORTATION INTERNAL -pred_controller = pytest.importorskip( - modname="openbb_terminal.etf.prediction_techniques.pred_controller", - reason="Requires prediction dependencies, like tensorflow", -) - -# pylint: disable=E1101 -# pylint: disable=W0603 - -# pytest.skip(allow_module_level=True) - - -DF_ETF = pd.DataFrame.from_dict( - data={ - pd.Timestamp("2020-11-30 00:00:00"): { - "Open": 75.69999694824219, - "High": 76.08999633789062, - "Low": 75.41999816894531, - "Close": 75.75, - "Adj Close": 71.90919494628906, - "Volume": 5539100, - "date_id": 1, - "OC_High": 75.75, - "OC_Low": 75.69999694824219, - }, - pd.Timestamp("2020-12-01 00:00:00"): { - "Open": 76.0199966430664, - "High": 77.12999725341797, - "Low": 75.69000244140625, - "Close": 77.02999877929688, - "Adj Close": 73.1242904663086, - "Volume": 6791700, - "date_id": 2, - "OC_High": 77.02999877929688, - "OC_Low": 76.0199966430664, - }, - }, - orient="index", -) -EMPTY_DF = pd.DataFrame() - -PRED_CONTROLLER = pred_controller.PredictionTechniquesController( - ticker="MOCK_TICKER", - start=datetime.strptime("2020-12-15", "%Y-%m-%d"), - interval="1440min", - data=DF_ETF, -) - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "queue, expected", - [ - (["pick", "help"], []), - (["quit", "help"], ["help"]), - ], -) -def test_menu_with_queue(expected, mocker, queue): - mocker.patch( - target=( - "openbb_terminal.etf.prediction_techniques.pred_controller." - "PredictionTechniquesController.switch" - ), - return_value=["quit"], - ) - result_menu = pred_controller.PredictionTechniquesController( - ticker="ARKW", - start=datetime.strptime("2020-12-01", "%Y-%m-%d"), - interval="1440min", - data=DF_ETF, - queue=queue, - ).menu() - - assert result_menu == expected - - -@pytest.mark.vcr(record_mode="none") -def test_menu_without_queue_completion(mocker): - # ENABLE AUTO-COMPLETION : HELPER_FUNCS.MENU - mocker.patch( - target="openbb_terminal.feature_flags.USE_PROMPT_TOOLKIT", - new=True, - ) - mocker.patch( - target="openbb_terminal.parent_classes.session", - ) - mocker.patch( - target="openbb_terminal.parent_classes.session.prompt", - return_value="quit", - ) - - # DISABLE AUTO-COMPLETION : CONTROLLER.COMPLETER - mocker.patch.object( - target=pred_controller.obbff, - attribute="USE_PROMPT_TOOLKIT", - new=True, - ) - mocker.patch( - target="openbb_terminal.etf.prediction_techniques.pred_controller.session", - ) - mocker.patch( - target="openbb_terminal.etf.prediction_techniques.pred_controller.session.prompt", - return_value="quit", - ) - - result_menu = pred_controller.PredictionTechniquesController( - ticker="ARKW", - start=datetime.strptime("2020-12-01", "%Y-%m-%d"), - interval="1440min", - data=DF_ETF, - queue=None, - ).menu() - - assert result_menu == ["help"] - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "mock_input", - ["picked", "load help", "mock"], -) -def test_menu_without_queue_sys_exit(mock_input, mocker): - # DISABLE AUTO-COMPLETION - mocker.patch.object( - target=pred_controller.obbff, - attribute="USE_PROMPT_TOOLKIT", - new=False, - ) - mocker.patch( - target="openbb_terminal.etf.prediction_techniques.pred_controller.session", - return_value=None, - ) - - # MOCK USER INPUT - mocker.patch("builtins.input", return_value=mock_input) - - # MOCK SWITCH - class SystemExitSideEffect: - def __init__(self): - self.first_call = True - - def __call__(self, *args, **kwargs): - if self.first_call: - self.first_call = False - raise SystemExit() - return ["quit"] - - mock_switch = mocker.Mock(side_effect=SystemExitSideEffect()) - mocker.patch( - target=( - "openbb_terminal.etf.prediction_techniques.pred_controller." - "PredictionTechniquesController.switch" - ), - new=mock_switch, - ) - - result_menu = pred_controller.PredictionTechniquesController( - ticker="ARKW", - start=datetime.strptime("2020-12-01", "%Y-%m-%d"), - interval="1440min", - data=DF_ETF, - queue=None, - ).menu() - - assert result_menu == ["help"] - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.record_stdout -@pytest.mark.parametrize( - "start", - [ - datetime.strptime("2020-12-01", "%Y-%m-%d"), - ], -) -def test_print_help(start): - controller = pred_controller.PredictionTechniquesController( - ticker="ARKW", - start=start, - interval="1440min", - data=DF_ETF, - ) - controller.print_help() - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "an_input, expected_queue", - [ - ("", []), - ("/help", ["home", "help"]), - ("help/help", ["help", "help"]), - ("q", ["quit"]), - ("h", []), - ("r", ["quit", "quit", "reset", "etf", "load ARKW", "pred"]), - ], -) -def test_switch(an_input, expected_queue): - controller = pred_controller.PredictionTechniquesController( - ticker="ARKW", - start=datetime.strptime("2020-12-01", "%Y-%m-%d"), - interval="1440min", - data=DF_ETF, - queue=None, - ) - queue = controller.switch(an_input=an_input) - - assert queue == expected_queue - - -@pytest.mark.vcr(record_mode="none") -def test_call_cls(mocker): - mocker.patch("os.system") - controller = pred_controller.PredictionTechniquesController( - ticker="ARKW", - start=datetime.strptime("2020-12-01", "%Y-%m-%d"), - interval="1440min", - data=DF_ETF, - ) - controller.call_cls([]) - - assert controller.queue == [] - os.system.assert_called_once_with("cls||clear") - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "func, queue, expected_queue", - [ - ( - "call_exit", - [], - [ - "quit", - "quit", - "quit", - ], - ), - ("call_exit", ["help"], ["quit", "quit", "quit", "help"]), - ("call_home", [], ["quit", "quit"]), - ("call_help", [], []), - ("call_quit", [], ["quit"]), - ("call_quit", ["help"], ["quit", "help"]), - ( - "call_reset", - [], - ["quit", "quit", "reset", "etf", "load ARKW", "pred"], - ), - ( - "call_reset", - ["help"], - ["quit", "quit", "reset", "etf", "load ARKW", "pred", "help"], - ), - ], -) -def test_call_func_expect_queue(expected_queue, queue, func): - controller = pred_controller.PredictionTechniquesController( - ticker="ARKW", - start=datetime.strptime("2020-12-01", "%Y-%m-%d"), - interval="1440min", - data=DF_ETF, - queue=queue, - ) - result = getattr(controller, func)([]) - - assert result is None - assert controller.queue == expected_queue - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "tested_func, mocked_func, other_args, called_with", - [ - ( - "call_ets", - "ets_view.display_exponential_smoothing", - [ - "--day=1", - "--trend=A", - "--seasonal=M", - "--period=2", - "--end=2020-12-15", - "--export=png", - ], - dict( - ticker="MOCK_TICKER", - values=PRED_CONTROLLER.data["AdjClose"], - n_predict=1, - trend="A", - seasonal="M", - seasonal_periods=2, - s_end_date=datetime.strptime("2020-12-15", "%Y-%m-%d"), - export="png", - ), - ), - ( - "call_knn", - "knn_view.display_k_nearest_neighbors", - [ - "--input=1", - "--days=2", - "--jumps=3", - "--neighbors=4", - "--end=2020-12-01", - "--test_size=0.1", - "--no_shuffle", - ], - dict( - ticker="MOCK_TICKER", - data=PRED_CONTROLLER.data["AdjClose"], - n_neighbors=4, - n_input_days=1, - n_predict_days=2, - test_size=0.1, - end_date=datetime.strptime("2020-12-01", "%Y-%m-%d"), - no_shuffle=False, - ), - ), - ( - "call_regression", - "regression_view.display_regression", - [ - "4", - "--input=1", - "--days=2", - "--jumps=3", - "--end=2020-12-15", - "--export=png", - ], - dict( - dataset="MOCK_TICKER", - values=PRED_CONTROLLER.data["AdjClose"], - poly_order=4, - n_input=1, - n_predict=2, - n_jumps=3, - s_end_date=datetime.strptime("2020-12-15", "%Y-%m-%d"), - export="png", - ), - ), - ( - "call_arima", - "arima_view.display_arima", - [ - "--days=1", - "--ic=bic", - "--seasonal", - "--order=p,q,d", - "--results", - "--end=2020-12-15", - "--export=png", - ], - dict( - dataset="MOCK_TICKER", - values=PRED_CONTROLLER.data["AdjClose"], - arima_order="p,q,d", - n_predict=1, - seasonal=True, - ic="bic", - results=True, - s_end_date=datetime.strptime("2020-12-15", "%Y-%m-%d"), - export="png", - ), - ), - ( - "call_mlp", - "neural_networks_view.display_mlp", - [ - "--days=1", - "--input=2", - "--epochs=3", - "--end=2020-12-15", - "--batch_size=4", - "--loops=5", - # "--valid", "0.1", - "--lr=0.01", - "--no_shuffle", - ], - dict( - dataset="MOCK_TICKER", - data=PRED_CONTROLLER.data["AdjClose"], - n_input_days=2, - n_predict_days=1, - learning_rate=0.01, - epochs=3, - batch_size=4, - test_size=0.1, - n_loops=5, - no_shuffle=False, - ), - ), - ( - "call_rnn", - "neural_networks_view.display_rnn", - [ - "--days=1", - "--input=2", - "--epochs=3", - "--end=2020-12-15", - "--batch_size=4", - "--loops=5", - # "--valid", "0.1", - "--lr=0.01", - "--no_shuffle", - ], - dict( - dataset="MOCK_TICKER", - data=PRED_CONTROLLER.data["AdjClose"], - n_input_days=2, - n_predict_days=1, - learning_rate=0.01, - epochs=3, - batch_size=4, - test_size=0.1, - n_loops=5, - no_shuffle=False, - ), - ), - ( - "call_lstm", - "neural_networks_view.display_lstm", - [ - "--days=1", - "--input=2", - "--epochs=3", - "--end=2020-12-15", - "--batch_size=4", - "--loops=5", - # "--valid", "0.1", - "--lr=0.01", - "--no_shuffle", - ], - dict( - dataset="MOCK_TICKER", - data=PRED_CONTROLLER.data["AdjClose"], - n_input_days=2, - n_predict_days=1, - learning_rate=0.01, - epochs=3, - batch_size=4, - test_size=0.1, - n_loops=5, - no_shuffle=False, - ), - ), - ( - "call_conv1d", - "neural_networks_view.display_conv1d", - [ - "--days=1", - "--input=2", - "--epochs=3", - "--end=2020-12-15", - "--batch_size=4", - "--loops=5", - # "--valid", "0.1", - "--lr=0.01", - "--no_shuffle", - ], - dict( - dataset="MOCK_TICKER", - data=PRED_CONTROLLER.data["AdjClose"], - n_input_days=2, - n_predict_days=1, - learning_rate=0.01, - epochs=3, - batch_size=4, - test_size=0.1, - n_loops=5, - no_shuffle=False, - ), - ), - ( - "call_mc", - "mc_view.display_mc_forecast", - [ - "--days=1", - # "--num=2", - "--dist=normal", - "--export=png", - ], - dict( - data=PRED_CONTROLLER.data["AdjClose"], - n_future=1, - n_sims=100, - use_log=False, - export="png", - ), - ), - ], -) -def test_call_func(tested_func, mocked_func, other_args, called_with, mocker): - mock = mocker.Mock() - mocker.patch( - "openbb_terminal.etf.prediction_techniques.pred_controller." + mocked_func, - new=mock, - ) - getattr(PRED_CONTROLLER, tested_func)(other_args=other_args) - - if isinstance(called_with, dict): - mock.assert_called_once_with(**called_with) - elif isinstance(called_with, list): - mock.assert_called_once_with(*called_with) - else: - mock.assert_called_once() - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "func", - [ - "call_load", - "call_pick", - "call_ets", - "call_knn", - "call_regression", - "call_arima", - "call_mlp", - "call_rnn", - "call_lstm", - "call_conv1d", - "call_mc", - ], -) -def test_call_func_no_parser(func, mocker): - mock = mocker.Mock(return_value=None) - mocker.patch.object( - target=pred_controller, - attribute="parse_known_args_and_warn", - new=mock, - ) - mocker.patch.object( - target=pred_controller.pred_helper, - attribute="parse_known_args_and_warn", - new=mock, - ) - controller = pred_controller.PredictionTechniquesController( - ticker="MOCK_TICKER", - start=datetime.strptime("2020-12-01", "%Y-%m-%d"), - interval="1440min", - data=DF_ETF.copy(), - ) - - func_result = getattr(controller, func)(other_args=["PM"]) - assert func_result is None - assert controller.queue == [] - getattr(pred_controller, "parse_known_args_and_warn").assert_called_once() diff --git a/tests/openbb_terminal/etf/prediction_techniques/txt/test_pred_controller/test_call_load.txt b/tests/openbb_terminal/etf/prediction_techniques/txt/test_pred_controller/test_call_load.txt deleted file mode 100644 index 22f11e86f80..00000000000 --- a/tests/openbb_terminal/etf/prediction_techniques/txt/test_pred_controller/test_call_load.txt +++ /dev/null @@ -1,19 +0,0 @@ - - load load new ticker - pick pick new target variable - -Ticker Loaded: ARKW (from 2020-12-01) -Target Column: AdjClose - -Models: - ets exponential smoothing (e.g. Holt-Winters) - knn k-Nearest Neighbors - regression polynomial regression - arima autoregressive integrated moving average - mlp MultiLayer Perceptron - rnn Recurrent Neural Network - lstm Long-Short Term Memory - conv1d 1D Convolutional Neural Network - mc Monte-Carlo simulations - -2022 Jan 20, 09:57 (โœจ) /etf/pred/ $ \ No newline at end of file diff --git a/tests/openbb_terminal/etf/prediction_techniques/txt/test_pred_controller/test_print_help[None].txt b/tests/openbb_terminal/etf/prediction_techniques/txt/test_pred_controller/test_print_help[None].txt deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/tests/openbb_terminal/etf/prediction_techniques/txt/test_pred_controller/test_print_help[start0].txt b/tests/openbb_terminal/etf/prediction_techniques/txt/test_pred_controller/test_print_help[start0].txt deleted file mode 100644 index 3626d514a2c..00000000000 --- a/tests/openbb_terminal/etf/prediction_techniques/txt/test_pred_controller/test_print_help[start0].txt +++ /dev/null @@ -1,18 +0,0 @@ - - load load new ticker - pick pick new target variable - -Ticker Loaded: ARKW (from 2020-12-01) -Target Column: AdjClose - -Models: - ets exponential smoothing (e.g. Holt-Winters) - knn k-Nearest Neighbors - regression polynomial regression - arima autoregressive integrated moving average - mlp MultiLayer Perceptron - rnn Recurrent Neural Network - lstm Long-Short Term Memory - conv1d 1D Convolutional Neural Network - mc Monte-Carlo simulations - diff --git a/tests/openbb_terminal/etf/prediction_techniques/txt/test_pred_controller/test_print_help[start1].txt b/tests/openbb_terminal/etf/prediction_techniques/txt/test_pred_controller/test_print_help[start1].txt deleted file mode 100644 index 3626d514a2c..00000000000 --- a/tests/openbb_terminal/etf/prediction_techniques/txt/test_pred_controller/test_print_help[start1].txt +++ /dev/null @@ -1,18 +0,0 @@ - - load load new ticker - pick pick new target variable - -Ticker Loaded: ARKW (from 2020-12-01) -Target Column: AdjClose - -Models: - ets exponential smoothing (e.g. Holt-Winters) - knn k-Nearest Neighbors - regression polynomial regression - arima autoregressive integrated moving average - mlp MultiLayer Perceptron - rnn Recurrent Neural Network - lstm Long-Short Term Memory - conv1d 1D Convolutional Neural Network - mc Monte-Carlo simulations - diff --git a/tests/openbb_terminal/etf/txt/test_etf_controller/test_print_help.txt b/tests/openbb_terminal/etf/txt/test_etf_controller/test_print_help.txt index 7df575c6b36..69a136e59d7 100644 --- a/tests/openbb_terminal/etf/txt/test_etf_controller/test_print_help.txt +++ b/tests/openbb_terminal/etf/txt/test_etf_controller/test_print_help.txt @@ -18,5 +18,4 @@ Major holdings: [unvl] compare compare multiple different ETFs[/unvl] [StockAnalysis] [unvl]> ta technical analysis, ema, macd, rsi, adx, bbands, obv[/unvl] -[unvl]> pred prediction techniques, regression, arima, rnn, lstm[/unvl] diff --git a/openbb_terminal/forex/prediction_techniques/__init__.py b/tests/openbb_terminal/forecast/__init__.py similarity index 100% rename from openbb_terminal/forex/prediction_techniques/__init__.py rename to tests/openbb_terminal/forecast/__init__.py diff --git a/tests/openbb_terminal/forecast/conftest.py b/tests/openbb_terminal/forecast/conftest.py new file mode 100644 index 00000000000..ebc22a5c451 --- /dev/null +++ b/tests/openbb_terminal/forecast/conftest.py @@ -0,0 +1,38 @@ +import os +import pytest +from _pytest.nodes import Node +import pandas as pd + + +def create_path(*path: str) -> str: + base_path = os.path.dirname(os.path.abspath(__file__)) + in_between = os.path.dirname(base_path) + default_path = os.path.join(in_between, *path) + return default_path + + +@pytest.fixture +def tsla_csv(): + path = create_path("forecast", "data", "TSLA.csv") + df = pd.read_csv(path) + df.columns = [x.lower() for x in df.columns] + df["date"] = df["date"].apply(lambda x: pd.to_datetime(x)) + return df + + +def test_model(model, data, *args, **kwargs): + try: + _, _, predict, MAPE, _ = model(data, *args, **kwargs) + except ValueError: + _, _, predict, MAPE, _, _ = model(data, *args, **kwargs) + try: + predict_list = predict.pd_dataframe()["close"].tolist() + except (AssertionError, KeyError): + predict_list = predict.quantile_df() + predict_list = predict_list[predict_list.columns[0]].tolist() + return predict_list, MAPE + + +def pytest_runtest_setup(item: Node): + if not item.config.getoption("--prediction"): + pytest.skip(msg="Runs only with option : --prediction") diff --git a/tests/openbb_terminal/forecast/data/TSLA.csv b/tests/openbb_terminal/forecast/data/TSLA.csv new file mode 100644 index 00000000000..5ebfc74c3dc --- /dev/null +++ b/tests/openbb_terminal/forecast/data/TSLA.csv @@ -0,0 +1,612 @@ +Date,Open,High,Low,Close,Adj Close,Volume +2020-01-02,84.9000015258789,86.13999938964844,84.34200286865234,86.052001953125,86.052001953125,47660500 +2020-01-03,88.0999984741211,90.80000305175781,87.38400268554688,88.60199737548828,88.60199737548828,88892500 +2020-01-06,88.09400177001953,90.31199645996094,88.0,90.30799865722656,90.30799865722656,50665000 +2020-01-07,92.27999877929688,94.32599639892578,90.6719970703125,93.81199645996094,93.81199645996094,89410500 +2020-01-08,94.73999786376953,99.697998046875,93.64600372314453,98.4280014038086,98.4280014038086,155721500 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+2022-06-03,729.6799926757812,743.3900146484375,700.25,703.5499877929688,703.5499877929688,37348100 diff --git a/tests/openbb_terminal/forecast/test_NBEATS_model.py b/tests/openbb_terminal/forecast/test_NBEATS_model.py new file mode 100644 index 00000000000..72f6d417822 --- /dev/null +++ b/tests/openbb_terminal/forecast/test_NBEATS_model.py @@ -0,0 +1,11 @@ +import pytest +from tests.openbb_terminal.forecast import conftest + +try: + from openbb_terminal.forecast import nbeats_model +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_get_NBEATS_model(tsla_csv): + conftest.test_model(nbeats_model.get_NBEATS_data, tsla_csv, n_epochs=1) diff --git a/tests/openbb_terminal/forecast/test_NBEATS_view.py b/tests/openbb_terminal/forecast/test_NBEATS_view.py new file mode 100644 index 00000000000..be64393f590 --- /dev/null +++ b/tests/openbb_terminal/forecast/test_NBEATS_view.py @@ -0,0 +1,10 @@ +import pytest + +try: + from openbb_terminal.forecast import nbeats_view +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_display_nbeats_forecast(tsla_csv): + nbeats_view.display_nbeats_forecast(tsla_csv, "TSLA", n_epochs=1) diff --git a/tests/openbb_terminal/forecast/test_TCN_model.py b/tests/openbb_terminal/forecast/test_TCN_model.py new file mode 100644 index 00000000000..244029ed9ac --- /dev/null +++ b/tests/openbb_terminal/forecast/test_TCN_model.py @@ -0,0 +1,11 @@ +import pytest +from tests.openbb_terminal.forecast import conftest + +try: + from openbb_terminal.forecast import tcn_model +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_get_TCN_model(tsla_csv): + conftest.test_model(tcn_model.get_tcn_data, tsla_csv, n_epochs=1) diff --git a/tests/openbb_terminal/forecast/test_brnn_model.py b/tests/openbb_terminal/forecast/test_brnn_model.py new file mode 100644 index 00000000000..7e0f57666c6 --- /dev/null +++ b/tests/openbb_terminal/forecast/test_brnn_model.py @@ -0,0 +1,17 @@ +import pytest +from tests.openbb_terminal.forecast import conftest + +try: + from openbb_terminal.forecast import brnn_model +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_get_brnn_model(tsla_csv): + conftest.test_model(brnn_model.get_brnn_data, tsla_csv, n_epochs=1) + + +def test_get_brnn_model_past_covs(tsla_csv): + conftest.test_model( + brnn_model.get_brnn_data, tsla_csv, n_epochs=1, past_covariates="open" + ) diff --git a/tests/openbb_terminal/forecast/test_brnn_view.py b/tests/openbb_terminal/forecast/test_brnn_view.py new file mode 100644 index 00000000000..08df16ceea8 --- /dev/null +++ b/tests/openbb_terminal/forecast/test_brnn_view.py @@ -0,0 +1,12 @@ +import pytest + +try: + from openbb_terminal.forecast import brnn_view +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_display_brnn_forecast(tsla_csv, mocker): + mock = mocker.patch("openbb_terminal.forecast.trans_view.helpers.plot_residuals") + brnn_view.display_brnn_forecast(data=tsla_csv, n_epochs=1, residuals=True) + mock.assert_called_once() diff --git a/tests/openbb_terminal/forecast/test_expo_model.py b/tests/openbb_terminal/forecast/test_expo_model.py new file mode 100644 index 00000000000..f6123595b65 --- /dev/null +++ b/tests/openbb_terminal/forecast/test_expo_model.py @@ -0,0 +1,11 @@ +import pytest +from tests.openbb_terminal.forecast import conftest + +try: + from openbb_terminal.forecast import expo_model +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_get_expo_model(tsla_csv): + conftest.test_model(expo_model.get_expo_data, tsla_csv) diff --git a/tests/openbb_terminal/forecast/test_expo_view.py b/tests/openbb_terminal/forecast/test_expo_view.py new file mode 100644 index 00000000000..1931d9e17dd --- /dev/null +++ b/tests/openbb_terminal/forecast/test_expo_view.py @@ -0,0 +1,22 @@ +import pytest + +try: + from openbb_terminal.forecast import expo_view +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_display_tft_forecast(tsla_csv): + with pytest.raises(AttributeError): + expo_view.display_expo_forecast( + tsla_csv, + target_column="close", + trend="N", + seasonal="N", + seasonal_periods=3, + dampen="F", + n_predict=1, + start_window=0.5, + forecast_horizon=1, + residuals=True, + ) diff --git a/tests/openbb_terminal/forecast/test_forecast_api.py b/tests/openbb_terminal/forecast/test_forecast_api.py new file mode 100644 index 00000000000..48573f60fa7 --- /dev/null +++ b/tests/openbb_terminal/forecast/test_forecast_api.py @@ -0,0 +1,12 @@ +import pytest +from openbb_terminal.helper_classes import ModelsNamespace + +# pylint: enable=E1101 +try: + from openbb_terminal.forecast import forecast_api # type: ignore +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_api(): + assert isinstance(forecast_api.models, ModelsNamespace) diff --git a/tests/openbb_terminal/forecast/test_forecast_controller.py b/tests/openbb_terminal/forecast/test_forecast_controller.py new file mode 100644 index 00000000000..0fb152cb2d2 --- /dev/null +++ b/tests/openbb_terminal/forecast/test_forecast_controller.py @@ -0,0 +1,426 @@ +from typing import List +import argparse +import pandas as pd +import pytest +from prompt_toolkit.completion import NestedCompleter + +try: + from openbb_terminal.forecast import forecast_controller as fc +except ImportError: + pytest.skip(allow_module_level=True) + +# pylint: disable=E1121 +base = "openbb_terminal.forecast.forecast_controller." +df = pd.DataFrame([[1, 2, 3], [2, 3, 4], [3, 4, 5]], columns=["first", "date", "close"]) + + +class Thing: + help = True + + def format_help(self): + return "You need help" + + +def mock_func(_): + return (Thing(), 2) + + +def test_check_greater_than_one(): + assert fc.check_greater_than_one(2) == 2 + + +def test_check_greater_than_one_invalid(): + with pytest.raises(argparse.ArgumentTypeError): + fc.check_greater_than_one(-2) + + +def test_forecast_controller(mocker): + mocker.patch(base + "session", True) + mocker.patch(base + "obbff.USE_PROMPT_TOOLKIT", True) + cont = fc.ForecastController(data=df, ticker="TSLA") + assert isinstance(cont.files, List) + + +def test_fc_update_runtime_choices(mocker): + mocker.patch(base + "session", True) + mocker.patch(base + "obbff.USE_PROMPT_TOOLKIT", True) + cont = fc.ForecastController() + cont.datasets = {"stonks": df} + cont.update_runtime_choices() + assert isinstance(cont.completer, NestedCompleter) + + +def test_fc_refresh_datasets_on_menu(): + cont = fc.ForecastController() + cont.datasets = {"stonks": df} + cont.files = ["file.csv", "fiile.csv"] + cont.refresh_datasets_on_menu() + assert "stonks.first" in cont.list_dataset_cols + + +def test_fc_print_help(capsys): + cont = fc.ForecastController() + cont.print_help() + captured = capsys.readouterr() + assert "forecast" in captured.out + + +def test_fc_custom_reset(): + cont = fc.ForecastController() + val = cont.custom_reset() + assert not val + + +def test_fc_custom_reset_with_files(): + cont = fc.ForecastController() + cont.files_full = [["file1", "file1"]] + val = cont.custom_reset() + assert val == ["forecast", "'load file1 -a file1'"] + + +def test_fc_parse_known_args_and_warn(mocker): + mock = mocker.Mock() + mock2 = mocker.Mock() + mock.parse_known_args = mock_func + mock.format_help = Thing().format_help + mock.add_argument = mock2 + cont = fc.ForecastController() + cont.parse_known_args_and_warn( + parser=mock, + other_args=[], + export_allowed=0, + raw=True, + limit=1, + target_dataset=True, + target_column=True, + period=5, + n_days=True, + seasonal="weekly", + periods=True, + window=True, + train_split=True, + input_chunk_length=True, + output_chunk_length=True, + force_reset=True, + save_checkpoints=True, + model_save_name="the_models", + n_epochs=True, + model_type=True, + dropout=0.05, + batch_size=3, + learning_rate=True, + past_covariates=True, + lags=True, + hidden_size=4, + n_jumps=True, + end=True, + residuals=True, + forecast_only=True, + ) + assert mock2.call_count == 28 + + +def test_fc_call_load(mocker): + mocker.patch(base + "forecast_model.load", return_value=df) + cont = fc.ForecastController() + cont.call_load(["data.csv"]) + + +def test_fc_call_load_alias(): + cont = fc.ForecastController() + cont.files = ["the_data"] + cont.call_load(["data.csv", "-a", "the_data"]) + + +def test_fc_call_load_alias_no_dot(): + cont = fc.ForecastController() + cont.call_load(["datacsv", "-a", "the_data"]) + + +@pytest.mark.parametrize( + "name,loc_df", [("base", df), ("base", pd.DataFrame()), ("", df)] +) +def test_fc_call_show(mocker, name, loc_df): + mock = mocker.MagicMock() + mock.name = name + mock.limit = 4 + mocker.patch( + base + "ForecastController.parse_known_args_and_warn", return_value=mock + ) + cont = fc.ForecastController() + cont.datasets = {"base": loc_df} + cont.call_show(["data"]) + + +@pytest.mark.parametrize("dataset", ["base", None]) +def test_call_desc(mocker, dataset): + mock = mocker.MagicMock() + mock.target_dataset = dataset + mocker.patch( + base + "ForecastController.parse_known_args_and_warn", return_value=mock + ) + cont = fc.ForecastController() + cont.datasets = {"base": df} + cont.call_desc(["data"]) + + +def test_call_plot(mocker): + mock = mocker.MagicMock() + mock.values = ["data.first"] + mocker.patch( + base + "ForecastController.parse_known_args_and_warn", return_value=mock + ) + cont = fc.ForecastController() + cont.choices = {"plot": ["data.first", "data.second", "data.third"]} + cont.datasets = {"data": df} + cont.call_plot(["data.first"]) + + +def test_call_plot_no_parser(mocker): + mocker.patch( + base + "ForecastController.parse_known_args_and_warn", return_value=None + ) + cont = fc.ForecastController() + cont.choices = {"plot": ["data.first", "data.second", "data.third"]} + cont.datasets = {"data": df} + cont.call_plot(["data.first"]) + + +def test_call_plot_no_values(mocker): + mock = mocker.MagicMock() + mock.values = None + mocker.patch( + base + "ForecastController.parse_known_args_and_warn", return_value=mock + ) + cont = fc.ForecastController() + cont.choices = {"plot": ["data.first", "data.second", "data.third"]} + cont.datasets = {"data": df} + cont.call_plot(["data.first"]) + + +def test_call_season(tsla_csv): + cont = fc.ForecastController() + cont.datasets = {"data": tsla_csv} + cont.call_season(["data.close", "--max_lag", "1"]) + + +def test_call_season_error(): + cont = fc.ForecastController() + cont.datasets = {"data": df} + cont.call_season(["datafirst", "--max_lag", "1"]) + + +def test_call_season_no_parser(mocker): + mocker.patch( + base + "ForecastController.parse_known_args_and_warn", return_value=None + ) + cont = fc.ForecastController() + cont.datasets = {"data": df} + cont.call_season(["data.first", "--max_lag", "1"]) + + +def test_call_season_no_values(mocker): + mock = mocker.MagicMock() + mock.values = None + mocker.patch( + base + "ForecastController.parse_known_args_and_warn", return_value=mock + ) + cont = fc.ForecastController() + cont.datasets = {"data": df} + cont.call_season(["data.first", "--max_lag", "1"]) + + +def test_call_corr(): + cont = fc.ForecastController() + cont.datasets = {"data": df} + cont.call_corr(["data"]) + + +def test_call_corr_target(mocker): + mock = mocker.MagicMock() + mock.target_dataset = None + mocker.patch( + base + "ForecastController.parse_known_args_and_warn", return_value=mock + ) + cont = fc.ForecastController() + cont.datasets = {"data": df} + cont.call_corr(["data"]) + + +def test_call_comb_not_in(mocker): + mock = mocker.MagicMock() + mock.values = ["data.first"] + mocker.patch( + base + "ForecastController.parse_known_args_and_warn", return_value=mock + ) + cont = fc.ForecastController() + cont.datasets = {"data": df} + cont.choices = { + "combine": {"data.first": 1, "data.second": 2, "data.third": 3}, + "delete": {"data.first": 1, "data.second": 2, "data.third": 3}, + } + cont.call_combine(["data"]) + + +def test_call_comb(mocker): + mock = mocker.MagicMock() + mock.dataset = "data" + mock.columns = ["data.first", "data.second"] + mocker.patch( + base + "ForecastController.parse_known_args_and_warn", return_value=mock + ) + cont = fc.ForecastController() + cont.datasets = {"data": df} + cont.choices = { + "combine": {"data.first": 1, "data.second": 2, "data.third": 3}, + "delete": {"data.first": 1, "data.second": 2, "data.third": 3}, + } + cont.call_combine(["data"]) + + +def test_call_clean(): + cont = fc.ForecastController() + cont.datasets = {"data": df} + cont.call_clean(["data"]) + + +def test_call_clean_bad(mocker): + mock = mocker.MagicMock() + mock.target_dataset = None + mocker.patch( + base + "ForecastController.parse_known_args_and_warn", return_value=mock + ) + cont = fc.ForecastController() + cont.datasets = {"data": df} + cont.call_clean(["data"]) + + +@pytest.mark.parametrize( + "feature", ["ema", "sto", "rsi", "roc", "mom", "delta", "atr", "signal", "delete"] +) +def test_call_feat_eng_invalid(feature): + cont = fc.ForecastController() + cont.datasets = {"data": df} + cont.files = ["file.csv", "fiile.csv"] + cont.choices = { + "combine": {"data.first": 1, "data.second": 2, "data.third": 3}, + "delete": {"data.first": 1, "data.second": 2, "data.third": 3}, + } + the_list = ["data"] + if feature == "rsi": + the_list.append("--period") + the_list.append("2") + getattr(cont, f"call_{feature}")(the_list) + + +@pytest.mark.parametrize( + "feature", + ["ema", "sto", "rsi", "roc", "mom", "delta", "atr", "signal", "delete", "export"], +) +def test_call_feat_eng_invalid_parser(feature, mocker): + mocker.patch(base + "helpers.check_parser_input", return_value=None) + mock = mocker.MagicMock() + mock.target_dataset = None + mocker.patch( + base + "ForecastController.parse_known_args_and_warn", return_value=mock + ) + cont = fc.ForecastController() + cont.datasets = {"data": df} + cont.files = ["file.csv", "fiile.csv"] + cont.choices = { + "combine": {"data.first": 1, "data.second": 2, "data.third": 3}, + "delete": {"data.first": 1, "data.second": 2, "data.third": 3}, + } + the_list = ["data"] + if feature == "rsi": + the_list.append("--period") + the_list.append("2") + getattr(cont, f"call_{feature}")(the_list) + + +def test_call_ema(mocker): + mock = mocker.MagicMock() + mock.target_dataset = "data" + mock.columns = ["data.first", "data.second"] + mocker.patch( + base + "ForecastController.parse_known_args_and_warn", return_value=mock + ) + cont = fc.ForecastController() + cont.datasets = {"data": df} + cont.call_ema(["data"]) + + +@pytest.mark.parametrize("datasets", [[], ["data"], ["bad"]]) +def test_call_delete(mocker, datasets): + cont = fc.ForecastController() + mock = mocker.MagicMock() + if datasets == ["bad"]: + mock.delete = ["data.lose"] + else: + mock.delete = ["data.close"] + mocker.patch( + base + "ForecastController.parse_known_args_and_warn", return_value=mock + ) + if datasets == ["data"]: + cont.datasets = {"data": df} + cont.choices = { + "combine": ["data.first", "data.second", "data.third"], + "delete": ["data.first", "data.second", "data.third"], + } + cont.call_delete([]) + + +def test_call_export(mocker): + mock = mocker.MagicMock() + mock.target_dataset = "data" + mock.columns = ["data.first", "data.second"] + mocker.patch( + base + "ForecastController.parse_known_args_and_warn", return_value=mock + ) + cont = fc.ForecastController() + cont.datasets = {"data": df} + cont.call_export(["data"]) + + +@pytest.mark.parametrize( + "opt, func", + [ + ("expo", "expo_view.display_expo_forecast"), + ("theta", "theta_view.display_theta_forecast"), + ("rnn", "rnn_view.display_rnn_forecast"), + ("nbeats", "nbeats_view.display_nbeats_forecast"), + ("tcn", "tcn_view.display_tcn_forecast"), + ("regr", "regr_view.display_regression"), + ("linregr", "linregr_view.display_linear_regression"), + ("brnn", "brnn_view.display_brnn_forecast"), + ("trans", "trans_view.display_trans_forecast"), + ("tft", "tft_view.display_tft_forecast"), + ], +) +def test_models(mocker, opt, func): + mocker.patch(base + "helpers.check_parser_input", return_value=True) + mocker.patch(base + func) + cont = fc.ForecastController() + cont.datasets = {"data": df} + getattr(cont, f"call_{opt}")(["data"]) + + +@pytest.mark.parametrize( + "opt", + [ + "expo", + "theta", + "rnn", + "nbeats", + "tcn", + "regr", + "linregr", + "brnn", + "trans", + "tft", + ], +) +def test_models_bad(opt): + cont = fc.ForecastController() + cont.datasets = {"data": df} + getattr(cont, f"call_{opt}")(["data"]) diff --git a/tests/openbb_terminal/forecast/test_forecast_model.py b/tests/openbb_terminal/forecast/test_forecast_model.py new file mode 100644 index 00000000000..dc17f47f7b1 --- /dev/null +++ b/tests/openbb_terminal/forecast/test_forecast_model.py @@ -0,0 +1,56 @@ +from typing import Tuple +import pandas as pd +import pytest +from openbb_terminal.forecast import forecast_model as fm +from tests.openbb_terminal.forecast import conftest + + +@pytest.mark.parametrize("file_type", ["csv", "xlsx", "swp", "juan"]) +def test_load(file_type): + path = conftest.create_path("forecast", "data", f"TSLA.{file_type}") + if not file_type == "xlsx": + fm.load("TSLA", file_type, {"TSLA": path}) + else: + with pytest.raises(ValueError): + fm.load("TSLA", file_type, {"TSLA": path}) + + +@pytest.mark.parametrize("name", ["data", None]) +def test_get_options(tsla_csv, name): + val = fm.get_options({"data": tsla_csv}, name) + assert val["data"].iloc[0]["option"] == "data.date" + + +@pytest.mark.parametrize( + "fill, drop", + [ + (f"{x}fill", f"{y}drop") + for x in ["r", "c", "rb", "cb", "rf", "cf"] + for y in ["r", "c"] + ] + + [("", "")], +) +def test_clean(tsla_csv, fill, drop): + val = fm.clean(tsla_csv, fill, drop, 4) + assert isinstance(val, Tuple) + + +@pytest.mark.parametrize( + "command", ["ema", "sto", "rsi", "roc", "momentum", "delta", "atr", "signal"] +) +def test_add_feature_engineering(tsla_csv, command): + val = getattr(fm, f"add_{command}")(tsla_csv) + if command == "atr": + assert isinstance(val, Tuple) + else: + assert isinstance(val, pd.DataFrame) + + +def test_add_sto_bad(capsys): + fm.add_sto(pd.DataFrame()) + captured = capsys.readouterr() + assert "Missing" in captured.out + + +def test_add_atr_bad(): + fm.add_atr(pd.DataFrame()) diff --git a/tests/openbb_terminal/forecast/test_forecast_view.py b/tests/openbb_terminal/forecast/test_forecast_view.py new file mode 100644 index 00000000000..facba0f274a --- /dev/null +++ b/tests/openbb_terminal/forecast/test_forecast_view.py @@ -0,0 +1,77 @@ +import pandas as pd +import pytest + +try: + from openbb_terminal.forecast import forecast_view as fv +except ImportError: + pytest.skip(allow_module_level=True) +base = "openbb_terminal.forecast.forecast_view." + + +def test_show_options_bad(capsys): + fv.show_options({}) + captured = capsys.readouterr() + assert "Please load" in captured.out + + +def test_show_options(tsla_csv, capsys): + fv.show_options({"name": tsla_csv}, "name") + captured = capsys.readouterr() + assert "close" in captured.out + + +def test_display_plot(tsla_csv, mocker): + mock = mocker.patch(base + "theme.visualize_output") + fv.display_plot({"data": tsla_csv}) + mock.assert_called_once() + + +def test_display_plot_multiindex(tsla_csv, mocker, capsys): + mock = mocker.patch(base + "theme.visualize_output") + tuples = [("1", x) for x in tsla_csv.index] + index = pd.MultiIndex.from_tuples(tuples, names=["first", "second"]) + tsla_csv.index = index + fv.display_plot({"data": tsla_csv}) + captured = capsys.readouterr() + assert "multi-index" in captured.out + mock.assert_not_called() + + +def test_display_plot_external_axes(tsla_csv, mocker): + mock1 = mocker.Mock() + mock = mocker.patch(base + "theme.visualize_output") + fv.display_plot({"data": tsla_csv}, external_axes=[mock1]) + mock.assert_not_called() + + +def test_display_plot_series(tsla_csv, mocker): + mock1 = mocker.Mock() + mock = mocker.patch(base + "theme.visualize_output") + fv.display_plot({"data": tsla_csv["close"]}, external_axes=[mock1]) + mock.assert_not_called() + + +def test_display_seasonality_external_axes(tsla_csv, mocker): + mock1 = mocker.Mock() + mock = mocker.patch(base + "theme.visualize_output") + fv.display_seasonality(tsla_csv, external_axes=[mock1], column="close") + mock.assert_not_called() + + +def test_display_seasonality(tsla_csv, mocker): + mock = mocker.patch(base + "theme.visualize_output") + fv.display_seasonality(tsla_csv, column="close") + mock.assert_called_once() + + +def test_display_corr_external_axes(tsla_csv, mocker): + mock1 = mocker.Mock() + mock = mocker.patch(base + "theme.visualize_output") + fv.display_corr(tsla_csv, external_axes=[mock1]) + mock.assert_not_called() + + +def test_display_corr(tsla_csv, mocker): + mock = mocker.patch(base + "theme.visualize_output") + fv.display_corr(tsla_csv) + mock.assert_called_once() diff --git a/tests/openbb_terminal/forecast/test_helpers.py b/tests/openbb_terminal/forecast/test_helpers.py new file mode 100644 index 00000000000..3d15ff741e6 --- /dev/null +++ b/tests/openbb_terminal/forecast/test_helpers.py @@ -0,0 +1,18 @@ +import numpy as np +import pytest + +try: + from openbb_terminal.forecast import helpers +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_mean_absolute_percentage_error(): + arr = np.array([1, 2, 3, 4]) + assert helpers.mean_absolute_percentage_error(arr, arr) == 0 + + +def test_print_prediction_kpis(): + arr1 = np.array([1, 2, 3, 4]) + arr2 = np.array([2, 3, 4, 5]) + helpers.print_prediction_kpis(arr1, arr2) diff --git a/tests/openbb_terminal/forecast/test_linear_model.py b/tests/openbb_terminal/forecast/test_linear_model.py new file mode 100644 index 00000000000..07b5ff1b6da --- /dev/null +++ b/tests/openbb_terminal/forecast/test_linear_model.py @@ -0,0 +1,13 @@ +import pytest +from tests.openbb_terminal.forecast import conftest + +try: + from openbb_terminal.forecast import linregr_model +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_get_linear_regression_model(tsla_csv): + conftest.test_model( + linregr_model.get_linear_regression_data, tsla_csv, random_state=1 + ) diff --git a/tests/openbb_terminal/forecast/test_linregr_view.py b/tests/openbb_terminal/forecast/test_linregr_view.py new file mode 100644 index 00000000000..ae805f678b8 --- /dev/null +++ b/tests/openbb_terminal/forecast/test_linregr_view.py @@ -0,0 +1,12 @@ +import pytest + +try: + from openbb_terminal.forecast import linregr_view +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_display_linregr_forecast(tsla_csv, mocker): + mock = mocker.patch("openbb_terminal.forecast.trans_view.helpers.plot_residuals") + linregr_view.display_linear_regression(tsla_csv, residuals=True) + mock.assert_called_once() diff --git a/tests/openbb_terminal/forecast/test_regr_model.py b/tests/openbb_terminal/forecast/test_regr_model.py new file mode 100644 index 00000000000..7fd1bf3aecd --- /dev/null +++ b/tests/openbb_terminal/forecast/test_regr_model.py @@ -0,0 +1,11 @@ +import pytest +from tests.openbb_terminal.forecast import conftest + +try: + from openbb_terminal.forecast import regr_model +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_get_regression_model(tsla_csv): + conftest.test_model(regr_model.get_regression_data, tsla_csv) diff --git a/tests/openbb_terminal/forecast/test_regr_view.py b/tests/openbb_terminal/forecast/test_regr_view.py new file mode 100644 index 00000000000..6b18dc1540b --- /dev/null +++ b/tests/openbb_terminal/forecast/test_regr_view.py @@ -0,0 +1,12 @@ +import pytest + +try: + from openbb_terminal.forecast import regr_view +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_display_regr_forecast(tsla_csv, mocker): + mock = mocker.patch("openbb_terminal.forecast.trans_view.helpers.plot_residuals") + regr_view.display_regression(tsla_csv, residuals=True) + mock.assert_called_once() diff --git a/tests/openbb_terminal/forecast/test_rnn_model.py b/tests/openbb_terminal/forecast/test_rnn_model.py new file mode 100644 index 00000000000..bc9be0da103 --- /dev/null +++ b/tests/openbb_terminal/forecast/test_rnn_model.py @@ -0,0 +1,11 @@ +import pytest +from tests.openbb_terminal.forecast import conftest + +try: + from openbb_terminal.forecast import rnn_model +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_get_rnn_model(tsla_csv): + conftest.test_model(rnn_model.get_rnn_data, tsla_csv, n_epochs=1) diff --git a/tests/openbb_terminal/forecast/test_rnn_view.py b/tests/openbb_terminal/forecast/test_rnn_view.py new file mode 100644 index 00000000000..72f3f1930bb --- /dev/null +++ b/tests/openbb_terminal/forecast/test_rnn_view.py @@ -0,0 +1,12 @@ +import pytest + +try: + from openbb_terminal.forecast import rnn_view +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_display_rnn_forecast(tsla_csv, mocker): + mock = mocker.patch("openbb_terminal.forecast.trans_view.helpers.plot_residuals") + rnn_view.display_rnn_forecast(tsla_csv, n_epochs=1, residuals=True) + mock.assert_called_once() diff --git a/tests/openbb_terminal/forecast/test_tcn_view.py b/tests/openbb_terminal/forecast/test_tcn_view.py new file mode 100644 index 00000000000..c9c54b53788 --- /dev/null +++ b/tests/openbb_terminal/forecast/test_tcn_view.py @@ -0,0 +1,12 @@ +import pytest + +try: + from openbb_terminal.forecast import tcn_view +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_display_tcn_forecast(tsla_csv, mocker): + mock = mocker.patch("openbb_terminal.forecast.trans_view.helpers.plot_residuals") + tcn_view.display_tcn_forecast(tsla_csv, n_epochs=1, residuals=True) + mock.assert_called_once() diff --git a/tests/openbb_terminal/forecast/test_tft_model.py b/tests/openbb_terminal/forecast/test_tft_model.py new file mode 100644 index 00000000000..882c4d83b8e --- /dev/null +++ b/tests/openbb_terminal/forecast/test_tft_model.py @@ -0,0 +1,11 @@ +import pytest +from tests.openbb_terminal.forecast import conftest + +try: + from openbb_terminal.forecast import tft_model +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_get_tft_model(tsla_csv): + conftest.test_model(tft_model.get_tft_data, tsla_csv, n_epochs=1) diff --git a/tests/openbb_terminal/forecast/test_tft_view.py b/tests/openbb_terminal/forecast/test_tft_view.py new file mode 100644 index 00000000000..8150906ef7a --- /dev/null +++ b/tests/openbb_terminal/forecast/test_tft_view.py @@ -0,0 +1,12 @@ +import pytest + +try: + from openbb_terminal.forecast import tft_view +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_display_tft_forecast(tsla_csv, mocker): + mock = mocker.patch("openbb_terminal.forecast.trans_view.helpers.plot_residuals") + tft_view.display_tft_forecast(tsla_csv, forecast_horizon=1, residuals=True) + mock.assert_called_once() diff --git a/tests/openbb_terminal/forecast/test_theta_model.py b/tests/openbb_terminal/forecast/test_theta_model.py new file mode 100644 index 00000000000..711bcf449be --- /dev/null +++ b/tests/openbb_terminal/forecast/test_theta_model.py @@ -0,0 +1,11 @@ +import pytest +from tests.openbb_terminal.forecast import conftest + +try: + from openbb_terminal.forecast import theta_model +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_get_theta_model(tsla_csv): + conftest.test_model(theta_model.get_theta_data, tsla_csv) diff --git a/tests/openbb_terminal/forecast/test_theta_view.py b/tests/openbb_terminal/forecast/test_theta_view.py new file mode 100644 index 00000000000..de078e43a2d --- /dev/null +++ b/tests/openbb_terminal/forecast/test_theta_view.py @@ -0,0 +1,18 @@ +import pytest + +try: + from openbb_terminal.forecast import theta_view +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_display_theta_forecast(tsla_csv): + theta_view.display_theta_forecast( + data=tsla_csv, + seasonal="N", + seasonal_periods=3, + n_predict=5, + target_column="close", + start_window=0.3, + forecast_horizon=1, + ) diff --git a/tests/openbb_terminal/forecast/test_trans_model.py b/tests/openbb_terminal/forecast/test_trans_model.py new file mode 100644 index 00000000000..8705831c600 --- /dev/null +++ b/tests/openbb_terminal/forecast/test_trans_model.py @@ -0,0 +1,11 @@ +import pytest +from tests.openbb_terminal.forecast import conftest + +try: + from openbb_terminal.forecast import trans_model +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_get_trans_model(tsla_csv): + conftest.test_model(trans_model.get_trans_data, tsla_csv, n_epochs=1) diff --git a/tests/openbb_terminal/forecast/test_trans_view.py b/tests/openbb_terminal/forecast/test_trans_view.py new file mode 100644 index 00000000000..f3d1d58fa41 --- /dev/null +++ b/tests/openbb_terminal/forecast/test_trans_view.py @@ -0,0 +1,14 @@ +import pytest + +try: + from openbb_terminal.forecast import trans_view +except ImportError: + pytest.skip(allow_module_level=True) + + +def test_display_trans_forecast(tsla_csv, mocker): + mock = mocker.patch("openbb_terminal.forecast.trans_view.helpers.plot_residuals") + trans_view.display_trans_forecast( + tsla_csv, residuals=True, past_covariates="open", n_epochs=1 + ) + mock.assert_called_once() diff --git a/tests/openbb_terminal/forex/test_forex_controller.py b/tests/openbb_terminal/forex/test_forex_controller.py index 9bf52d4580b..092b352501f 100644 --- a/tests/openbb_terminal/forex/test_forex_controller.py +++ b/tests/openbb_terminal/forex/test_forex_controller.py @@ -329,7 +329,6 @@ def test_call_func_no_parser(func, mocker): "call_candle", "call_ta", "call_qa", - "call_pred", ], ) def test_call_func_no_ticker(func, mocker): diff --git a/tests/openbb_terminal/forex/txt/test_forex_controller/test_print_help.txt b/tests/openbb_terminal/forex/txt/test_forex_controller/test_print_help.txt index 2b665ac8872..6a831d0f2c2 100644 --- a/tests/openbb_terminal/forex/txt/test_forex_controller/test_print_help.txt +++ b/tests/openbb_terminal/forex/txt/test_forex_controller/test_print_help.txt @@ -10,7 +10,6 @@ Source: YahooFinance [unvl]> ta technical analysis, ema, macd, rsi, adx, bbands, obv[/unvl] [unvl]> qa quantitative analysis, decompose, cusum, residuals analysis[/unvl] -[unvl]> pred prediction techniques regression, arima, rnn, lstm, conv1d, monte carlo[/unvl] Forex brokerages: > oanda Oanda menu diff --git a/tests/openbb_terminal/stocks/cassettes/test_stocks_controller/test_call_load.yaml b/tests/openbb_terminal/stocks/cassettes/test_stocks_controller/test_call_load.yaml index e028ecac98c..956818a517e 100644 --- a/tests/openbb_terminal/stocks/cassettes/test_stocks_controller/test_call_load.yaml +++ b/tests/openbb_terminal/stocks/cassettes/test_stocks_controller/test_call_load.yaml @@ -12,7 +12,7 @@ interactions: uri: https://query2.finance.yahoo.com/v8/finance/chart/TSLA?events=div%2Csplits&includePrePost=False&interval=1d&period1=MOCK_PERIOD_1&period2=MOCK_PERIOD_2 response: body: - string: '{"chart":{"result":[{"meta":{"currency":"USD","symbol":"TSLA","exchangeName":"NMS","instrumentType":"EQUITY","firstTradeDate":1277818200,"regularMarketTime":1662148804,"gmtoffset":-14400,"timezone":"EDT","exchangeTimezoneName":"America/New_York","regularMarketPrice":270.21,"chartPreviousClose":308.973,"priceHint":2,"currentTradingPeriod":{"pre":{"timezone":"EDT","end":1662125400,"start":1662105600,"gmtoffset":-14400},"regular":{"timezone":"EDT","end":1662148800,"start":1662125400,"gmtoffset":-14400},"post":{"timezone":"EDT","end":1662163200,"start":1662148800,"gmtoffset":-14400}},"dataGranularity":"1d","range":"","validRanges":["1d","5d","1mo","3mo","6mo","1y","2y","5y","10y","ytd","max"]},"timestamp":[1639751400],"indicators":{"quote":[{"open":[304.92333984375],"close":[310.8566589355469],"low":[303.0133361816406],"volume":[100437300],"high":[320.2200012207031]}],"adjclose":[{"adjclose":[310.8566589355469]}]}}],"error":null}}' + string: '{"chart":{"result":[{"meta":{"currency":"USD","symbol":"TSLA","exchangeName":"NMS","instrumentType":"EQUITY","firstTradeDate":1277818200,"regularMarketTime":1663259544,"gmtoffset":-14400,"timezone":"EDT","exchangeTimezoneName":"America/New_York","regularMarketPrice":304.74,"chartPreviousClose":308.973,"priceHint":2,"currentTradingPeriod":{"pre":{"timezone":"EDT","start":1663228800,"end":1663248600,"gmtoffset":-14400},"regular":{"timezone":"EDT","start":1663248600,"end":1663272000,"gmtoffset":-14400},"post":{"timezone":"EDT","start":1663272000,"end":1663286400,"gmtoffset":-14400}},"dataGranularity":"1d","range":"","validRanges":["1d","5d","1mo","3mo","6mo","1y","2y","5y","10y","ytd","max"]},"timestamp":[1639751400],"indicators":{"quote":[{"close":[310.8566589355469],"volume":[100437300],"low":[303.0133361816406],"open":[304.92333984375],"high":[320.2200012207031]}],"adjclose":[{"adjclose":[310.8566589355469]}]}}],"error":null}}' headers: Age: - '0' @@ -37,21 +37,21 @@ interactions: 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finance-nodejs--mtls-production-bf1.finance-k8s.svc.yahoo.local:4080/* x-envoy-upstream-service-time: - - '286' + - '270' status: code: 200 message: OK diff --git a/tests/openbb_terminal/stocks/prediction_techniques/__init__.py b/tests/openbb_terminal/stocks/prediction_techniques/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/tests/openbb_terminal/stocks/prediction_techniques/conftest.py b/tests/openbb_terminal/stocks/prediction_techniques/conftest.py deleted file mode 100644 index 98189bd3993..00000000000 --- a/tests/openbb_terminal/stocks/prediction_techniques/conftest.py +++ /dev/null @@ -1,12 +0,0 @@ -# IMPORTATION STANDARD - -# IMPORTATION THIRDPARTY -import pytest -from _pytest.nodes import Node - -# IMPORTATION INTERNAL - - -def pytest_runtest_setup(item: Node): - if not item.config.getoption("--prediction"): - pytest.skip(msg="Runs only with option : --prediction") diff --git a/tests/openbb_terminal/stocks/prediction_techniques/test_pred_api.py b/tests/openbb_terminal/stocks/prediction_techniques/test_pred_api.py deleted file mode 100644 index 64f44a4d187..00000000000 --- a/tests/openbb_terminal/stocks/prediction_techniques/test_pred_api.py +++ /dev/null @@ -1,16 +0,0 @@ -# IMPORTATION STANDARD - -# IMPORTATION THIRDPARTY -import pytest - -# IMPORTATION INTERNAL -from openbb_terminal.helper_classes import ModelsNamespace as _models - -try: - from openbb_terminal.stocks.prediction_techniques import pred_api -except ImportError: - pytest.skip(allow_module_level=True) - - -def test_models(): - assert isinstance(pred_api.models, _models) diff --git a/tests/openbb_terminal/stocks/prediction_techniques/test_pred_controller.py b/tests/openbb_terminal/stocks/prediction_techniques/test_pred_controller.py deleted file mode 100644 index 3f13cc61dde..00000000000 --- a/tests/openbb_terminal/stocks/prediction_techniques/test_pred_controller.py +++ /dev/null @@ -1,577 +0,0 @@ -# IMPORTATION STANDARD -import os -from datetime import datetime - -# IMPORTATION THIRDPARTY -import pandas as pd -import pytest - -# IMPORTATION INTERNAL -try: - from openbb_terminal.stocks.prediction_techniques import pred_controller -except ImportError: - pytest.skip(allow_module_level=True) - -# pylint: disable=E1101 -# pylint: disable=W0603 - -# pytest.skip(allow_module_level=True) - - -DF_STOCK = pd.DataFrame.from_dict( - data={ - pd.Timestamp("2020-11-30 00:00:00"): { - "Open": 75.69999694824219, - "High": 76.08999633789062, - "Low": 75.41999816894531, - "Close": 75.75, - "Adj Close": 71.90919494628906, - "Volume": 5539100, - "date_id": 1, - "OC_High": 75.75, - "OC_Low": 75.69999694824219, - }, - pd.Timestamp("2020-12-01 00:00:00"): { - "Open": 76.0199966430664, - "High": 77.12999725341797, - "Low": 75.69000244140625, - "Close": 77.02999877929688, - "Adj Close": 73.1242904663086, - "Volume": 6791700, - "date_id": 2, - "OC_High": 77.02999877929688, - "OC_Low": 76.0199966430664, - }, - }, - orient="index", -) -EMPTY_DF = pd.DataFrame() - -PRED_CONTROLLER = pred_controller.PredictionTechniquesController( - ticker="MOCK_TICKER", - start=datetime.strptime("2020-12-15", "%Y-%m-%d"), - interval="1440min", - stock=DF_STOCK, -) - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "queue, expected", - [ - (["pick", "help"], []), - (["quit", "help"], ["help"]), - ], -) -def test_menu_with_queue(expected, mocker, queue): - mocker.patch( - target=( - "openbb_terminal.stocks.prediction_techniques.pred_controller." - "PredictionTechniquesController.switch" - ), - return_value=["quit"], - ) - result_menu = pred_controller.PredictionTechniquesController( - ticker="TSLA", - start=datetime.strptime("2020-12-01", "%Y-%m-%d"), - interval="1440min", - stock=DF_STOCK, - queue=queue, - ).menu() - - assert result_menu == expected - - -@pytest.mark.vcr(record_mode="none") -def test_menu_without_queue_completion(mocker): - # ENABLE AUTO-COMPLETION : HELPER_FUNCS.MENU - mocker.patch( - target="openbb_terminal.feature_flags.USE_PROMPT_TOOLKIT", - new=True, - ) - mocker.patch( - target="openbb_terminal.parent_classes.session", - ) - mocker.patch( - target="openbb_terminal.parent_classes.session.prompt", - return_value="quit", - ) - - # DISABLE AUTO-COMPLETION : CONTROLLER.COMPLETER - mocker.patch.object( - target=pred_controller.obbff, - attribute="USE_PROMPT_TOOLKIT", - new=True, - ) - mocker.patch( - target="openbb_terminal.stocks.prediction_techniques.pred_controller.session", - ) - mocker.patch( - target="openbb_terminal.stocks.prediction_techniques.pred_controller.session.prompt", - return_value="quit", - ) - - result_menu = pred_controller.PredictionTechniquesController( - ticker="TSLA", - start=datetime.strptime("2020-12-01", "%Y-%m-%d"), - interval="1440min", - stock=DF_STOCK, - queue=None, - ).menu() - - assert result_menu == ["help"] - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "mock_input", - ["picked", "load help", "mock"], -) -def test_menu_without_queue_sys_exit(mock_input, mocker): - # DISABLE AUTO-COMPLETION - mocker.patch.object( - target=pred_controller.obbff, - attribute="USE_PROMPT_TOOLKIT", - new=False, - ) - mocker.patch( - target="openbb_terminal.stocks.prediction_techniques.pred_controller.session", - return_value=None, - ) - - # MOCK USER INPUT - mocker.patch("builtins.input", return_value=mock_input) - - # MOCK SWITCH - class SystemExitSideEffect: - def __init__(self): - self.first_call = True - - def __call__(self, *args, **kwargs): - if self.first_call: - self.first_call = False - raise SystemExit() - return ["quit"] - - mock_switch = mocker.Mock(side_effect=SystemExitSideEffect()) - mocker.patch( - target=( - "openbb_terminal.stocks.prediction_techniques.pred_controller." - "PredictionTechniquesController.switch" - ), - new=mock_switch, - ) - - result_menu = pred_controller.PredictionTechniquesController( - ticker="TSLA", - start=datetime.strptime("2020-12-01", "%Y-%m-%d"), - interval="1440min", - stock=DF_STOCK, - queue=None, - ).menu() - - assert result_menu == ["help"] - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.record_stdout -@pytest.mark.parametrize( - "start", - [ - None, - datetime.strptime("2020-12-01", "%Y-%m-%d"), - ], -) -def test_print_help(start): - controller = pred_controller.PredictionTechniquesController( - ticker="TSLA", - start=start, - interval="1440min", - stock=DF_STOCK, - ) - controller.print_help() - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "an_input, expected_queue", - [ - ("", []), - ("/help", ["home", "help"]), - ("help/help", ["help", "help"]), - ("q", ["quit"]), - ("h", []), - ("r", ["quit", "quit", "reset", "stocks", "load TSLA", "pred"]), - ], -) -def test_switch(an_input, expected_queue): - controller = pred_controller.PredictionTechniquesController( - ticker="TSLA", - start=datetime.strptime("2020-12-01", "%Y-%m-%d"), - interval="1440min", - stock=DF_STOCK, - queue=None, - ) - queue = controller.switch(an_input=an_input) - - assert queue == expected_queue - - -@pytest.mark.vcr(record_mode="none") -def test_call_cls(mocker): - mocker.patch("os.system") - controller = pred_controller.PredictionTechniquesController( - ticker="TSLA", - start=datetime.strptime("2020-12-01", "%Y-%m-%d"), - interval="1440min", - stock=DF_STOCK, - ) - controller.call_cls([]) - - assert controller.queue == [] - os.system.assert_called_once_with("cls||clear") - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "func, queue, expected_queue", - [ - ( - "call_exit", - [], - [ - "quit", - "quit", - "quit", - ], - ), - ("call_exit", ["help"], ["quit", "quit", "quit", "help"]), - ("call_home", [], ["quit", "quit"]), - ("call_help", [], []), - ("call_quit", [], ["quit"]), - ("call_quit", ["help"], ["quit", "help"]), - ( - "call_reset", - [], - ["quit", "quit", "reset", "stocks", "load TSLA", "pred"], - ), - ( - "call_reset", - ["help"], - ["quit", "quit", "reset", "stocks", "load TSLA", "pred", "help"], - ), - ], -) -def test_call_func_expect_queue(expected_queue, queue, func): - controller = pred_controller.PredictionTechniquesController( - ticker="TSLA", - start=datetime.strptime("2020-12-01", "%Y-%m-%d"), - interval="1440min", - stock=DF_STOCK, - queue=queue, - ) - result = getattr(controller, func)([]) - - assert result is None - assert controller.queue == expected_queue - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "tested_func, mocked_func, other_args, called_with", - [ - ( - "call_ets", - "ets_view.display_exponential_smoothing", - [ - "--day=1", - "--trend=A", - "--seasonal=M", - "--period=2", - "--end=2020-12-15", - "--export=png", - ], - dict( - ticker="MOCK_TICKER", - values=PRED_CONTROLLER.stock["AdjClose"], - n_predict=1, - trend="A", - seasonal="M", - seasonal_periods=2, - s_end_date=datetime.strptime("2020-12-15", "%Y-%m-%d"), - export="png", - ), - ), - ( - "call_knn", - "knn_view.display_k_nearest_neighbors", - [ - "--input=1", - "--days=2", - "--jumps=3", - "--neighbors=4", - "--end=2020-12-01", - "--test_size=0.1", - "--no_shuffle", - ], - dict( - ticker="MOCK_TICKER", - data=PRED_CONTROLLER.stock["AdjClose"], - n_neighbors=4, - n_input_days=1, - n_predict_days=2, - test_size=0.1, - end_date=datetime.strptime("2020-12-01", "%Y-%m-%d"), - no_shuffle=False, - ), - ), - ( - "call_regression", - "regression_view.display_regression", - [ - "4", - "--input=1", - "--days=2", - "--jumps=3", - "--end=2020-12-15", - "--export=png", - ], - dict( - dataset="MOCK_TICKER", - values=PRED_CONTROLLER.stock["AdjClose"], - poly_order=4, - n_input=1, - n_predict=2, - n_jumps=3, - s_end_date=datetime.strptime("2020-12-15", "%Y-%m-%d"), - export="png", - ), - ), - ( - "call_arima", - "arima_view.display_arima", - [ - "--days=1", - "--ic=bic", - "--seasonal", - "--order=p,q,d", - "--results", - "--end=2020-12-15", - "--export=png", - ], - dict( - dataset="MOCK_TICKER", - values=PRED_CONTROLLER.stock["AdjClose"], - arima_order="p,q,d", - n_predict=1, - seasonal=True, - ic="bic", - results=True, - s_end_date=datetime.strptime("2020-12-15", "%Y-%m-%d"), - export="png", - ), - ), - ( - "call_mlp", - "neural_networks_view.display_mlp", - [ - "--days=1", - "--input=2", - "--epochs=3", - "--end=2020-12-15", - "--batch_size=4", - "--loops=5", - # "--valid", "0.1", - "--lr=0.01", - "--no_shuffle", - ], - dict( - dataset="MOCK_TICKER", - data=PRED_CONTROLLER.stock["AdjClose"], - n_input_days=2, - n_predict_days=1, - learning_rate=0.01, - epochs=3, - batch_size=4, - test_size=0.1, - n_loops=5, - no_shuffle=False, - ), - ), - ( - "call_rnn", - "neural_networks_view.display_rnn", - [ - "--days=1", - "--input=2", - "--epochs=3", - "--end=2020-12-15", - "--batch_size=4", - "--loops=5", - # "--valid", "0.1", - "--lr=0.01", - "--no_shuffle", - ], - dict( - dataset="MOCK_TICKER", - data=PRED_CONTROLLER.stock["AdjClose"], - n_input_days=2, - n_predict_days=1, - learning_rate=0.01, - epochs=3, - batch_size=4, - test_size=0.1, - n_loops=5, - no_shuffle=False, - ), - ), - ( - "call_lstm", - "neural_networks_view.display_lstm", - [ - "--days=1", - "--input=2", - "--epochs=3", - "--end=2020-12-15", - "--batch_size=4", - "--loops=5", - # "--valid", "0.1", - "--lr=0.01", - "--no_shuffle", - ], - dict( - dataset="MOCK_TICKER", - data=PRED_CONTROLLER.stock["AdjClose"], - n_input_days=2, - n_predict_days=1, - learning_rate=0.01, - epochs=3, - batch_size=4, - test_size=0.1, - n_loops=5, - no_shuffle=False, - ), - ), - ( - "call_conv1d", - "neural_networks_view.display_conv1d", - [ - "--days=1", - "--input=2", - "--epochs=3", - "--end=2020-12-15", - "--batch_size=4", - "--loops=5", - # "--valid", "0.1", - "--lr=0.01", - "--no_shuffle", - ], - dict( - dataset="MOCK_TICKER", - data=PRED_CONTROLLER.stock["AdjClose"], - n_input_days=2, - n_predict_days=1, - learning_rate=0.01, - epochs=3, - batch_size=4, - test_size=0.1, - n_loops=5, - no_shuffle=False, - ), - ), - ( - "call_mc", - "mc_view.display_mc_forecast", - [ - "--days=1", - # "--num=2", - "--dist=normal", - "--export=png", - ], - dict( - data=PRED_CONTROLLER.stock["AdjClose"], - n_future=1, - n_sims=100, - use_log=False, - export="png", - ), - ), - ], -) -def test_call_func(tested_func, mocked_func, other_args, called_with, mocker): - mock = mocker.Mock() - mocker.patch( - "openbb_terminal.stocks.prediction_techniques.pred_controller." + mocked_func, - new=mock, - ) - getattr(PRED_CONTROLLER, tested_func)(other_args=other_args) - - if isinstance(called_with, dict): - mock.assert_called_once_with(**called_with) - elif isinstance(called_with, list): - mock.assert_called_once_with(*called_with) - else: - mock.assert_called_once() - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "func", - [ - "call_pick", - "call_ets", - "call_knn", - "call_regression", - "call_arima", - "call_mlp", - "call_rnn", - "call_lstm", - "call_conv1d", - "call_mc", - ], -) -def test_call_func_no_parser(func, mocker): - mock = mocker.Mock(return_value=None) - mocker.patch.object( - target=pred_controller, - attribute="parse_known_args_and_warn", - new=mock, - ) - mocker.patch.object( - target=pred_controller.pred_helper, - attribute="parse_known_args_and_warn", - new=mock, - ) - controller = pred_controller.PredictionTechniquesController( - ticker="MOCK_TICKER", - start=datetime.strptime("2020-12-01", "%Y-%m-%d"), - interval="1440min", - stock=DF_STOCK.copy(), - ) - - func_result = getattr(controller, func)(other_args=["PM"]) - assert func_result is None - assert controller.queue == [] - getattr(pred_controller, "parse_known_args_and_warn").assert_called_once() - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "ticker, expected", - [ - (None, []), - ("MOCK_TICKER", ["stocks", "load MOCK_TICKER", "pred"]), - ], -) -def test_custom_reset(expected, ticker): - controller = pred_controller.PredictionTechniquesController( - ticker=None, - start=datetime.strptime("2020-12-01", "%Y-%m-%d"), - interval="1440min", - stock=DF_STOCK.copy(), - ) - controller.ticker = ticker - - result = controller.custom_reset() - - assert result == expected diff --git a/tests/openbb_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_menu_quick_exit.txt b/tests/openbb_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_menu_quick_exit.txt deleted file mode 100644 index 55a003966aa..00000000000 --- a/tests/openbb_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_menu_quick_exit.txt +++ /dev/null @@ -1,23 +0,0 @@ - -Prediction Techniques: - cls clear screen - ?/help show this menu again - q quit this menu, and shows back to main menu - quit quit to abandon program - load load new ticker - pick pick new target variable - -Daily Stock: TSLA (from 2020-12-01) -Target Column: AdjClose - -Models: - ets exponential smoothing (e.g. Holt-Winters) - knn k-Nearest Neighbors - regression polynomial regression - arima autoregressive integrated moving average - mlp MultiLayer Perceptron - rnn Recurrent Neural Network - lstm Long-Short Term Memory - conv1d 1D Convolutional Neural Network - mc Monte-Carlo simulations - diff --git a/tests/openbb_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_menu_system_exit.txt b/tests/openbb_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_menu_system_exit.txt deleted file mode 100644 index a0c348743e2..00000000000 --- a/tests/openbb_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_menu_system_exit.txt +++ /dev/null @@ -1,27 +0,0 @@ - -Prediction Techniques: - cls clear screen - ?/help show this menu again - q quit this menu, and shows back to main menu - quit quit to abandon program - load load new ticker - pick pick new target variable - -Daily Stock: TSLA (from 2020-12-01) -Target Column: AdjClose - -Models: - ets exponential smoothing (e.g. Holt-Winters) - knn k-Nearest Neighbors - regression polynomial regression - arima autoregressive integrated moving average - mlp MultiLayer Perceptron - rnn Recurrent Neural Network - lstm Long-Short Term Memory - conv1d 1D Convolutional Neural Network - mc Monte-Carlo simulations - -The command selected doesn't exist - -Did you mean 'quit'? - diff --git a/tests/openbb_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_print_help.txt b/tests/openbb_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_print_help.txt deleted file mode 100644 index 55a003966aa..00000000000 --- a/tests/openbb_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_print_help.txt +++ /dev/null @@ -1,23 +0,0 @@ - -Prediction Techniques: - cls clear screen - ?/help show this menu again - q quit this menu, and shows back to main menu - quit quit to abandon program - load load new ticker - pick pick new target variable - -Daily Stock: TSLA (from 2020-12-01) -Target Column: AdjClose - -Models: - ets exponential smoothing (e.g. Holt-Winters) - knn k-Nearest Neighbors - regression polynomial regression - arima autoregressive integrated moving average - mlp MultiLayer Perceptron - rnn Recurrent Neural Network - lstm Long-Short Term Memory - conv1d 1D Convolutional Neural Network - mc Monte-Carlo simulations - diff --git a/tests/openbb_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_print_help[None].txt b/tests/openbb_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_print_help[None].txt deleted file mode 100644 index 239faf3f8d6..00000000000 --- a/tests/openbb_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_print_help[None].txt +++ /dev/null @@ -1,18 +0,0 @@ - - load load new ticker - pick pick new target variable - -Stock: {s_intraday} {self.ticker} -Target Column: AdjClose - -Models: - ets exponential smoothing (e.g. Holt-Winters) - knn k-Nearest Neighbors - regression polynomial regression - arima autoregressive integrated moving average - mlp MultiLayer Perceptron - rnn Recurrent Neural Network - lstm Long-Short Term Memory - conv1d 1D Convolutional Neural Network - mc Monte-Carlo simulations - diff --git a/tests/openbb_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_print_help[start1].txt b/tests/openbb_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_print_help[start1].txt deleted file mode 100644 index e3d899d457e..00000000000 --- a/tests/openbb_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_print_help[start1].txt +++ /dev/null @@ -1,18 +0,0 @@ - - load load new ticker - pick pick new target variable - -Stock: Daily TSLA (from 2020-12-01) -Target Column: AdjClose - -Models: - ets exponential smoothing (e.g. Holt-Winters) - knn k-Nearest Neighbors - regression polynomial regression - arima autoregressive integrated moving average - mlp MultiLayer Perceptron - rnn Recurrent Neural Network - lstm Long-Short Term Memory - conv1d 1D Convolutional Neural Network - mc Monte-Carlo simulations - diff --git a/tests/openbb_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_switch_help.txt b/tests/openbb_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_switch_help.txt deleted file mode 100644 index 55a003966aa..00000000000 --- a/tests/openbb_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_switch_help.txt +++ /dev/null @@ -1,23 +0,0 @@ - -Prediction Techniques: - cls clear screen - ?/help show this menu again - q quit this menu, and shows back to main menu - quit quit to abandon program - load load new ticker - pick pick new target variable - -Daily Stock: TSLA (from 2020-12-01) -Target Column: AdjClose - -Models: - ets exponential smoothing (e.g. Holt-Winters) - knn k-Nearest Neighbors - regression polynomial regression - arima autoregressive integrated moving average - mlp MultiLayer Perceptron - rnn Recurrent Neural Network - lstm Long-Short Term Memory - conv1d 1D Convolutional Neural Network - mc Monte-Carlo simulations - diff --git a/tests/openbb_terminal/stocks/test_stocks_controller.py b/tests/openbb_terminal/stocks/test_stocks_controller.py index 53f267099e0..d0c811cf501 100644 --- a/tests/openbb_terminal/stocks/test_stocks_controller.py +++ b/tests/openbb_terminal/stocks/test_stocks_controller.py @@ -501,7 +501,6 @@ def test_call_func_no_parser(func, mocker): "call_bt", "call_ta", "call_qa", - "call_pred", ], ) def test_call_func_no_ticker(func, mocker): diff --git a/tests/openbb_terminal/stocks/txt/test_stocks_controller/test_print_help.txt b/tests/openbb_terminal/stocks/txt/test_stocks_controller/test_print_help.txt index 94f553ceec7..e2231ac47d3 100644 --- a/tests/openbb_terminal/stocks/txt/test_stocks_controller/test_print_help.txt +++ b/tests/openbb_terminal/stocks/txt/test_stocks_controller/test_print_help.txt @@ -28,5 +28,5 @@ Currency: [unvl]> bt strategy backtester, simple ema, ema cross, rsi strategies[/unvl] [unvl]> ta technical analysis, ema, macd, rsi, adx, bbands, obv[/unvl] [unvl]> qa quantitative analysis, decompose, cusum, residuals analysis[/unvl] -[unvl]> pred prediction techniques, regression, arima, rnn, lstm[/unvl] +[unvl]> forecast forecast techniques, rnn, nbeats, transformer, block rnn[/unvl] diff --git a/website/content/api/crypto/dd/active/_index.md b/website/content/api/crypto/dd/active/_index.md index 74fbe9d2633..ebf88a566a9 100644 --- a/website/content/api/crypto/dd/active/_index.md +++ b/website/content/api/crypto/dd/active/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### crypto.dd.active(symbol: str, interval: str = '24h', start_date: int = 1262304000, end_date: int = 1663943882) -> pandas.core.frame.DataFrame +### crypto.dd.active(symbol: str, interval: str = '24h', start_date: int = 1262300400, end_date: int = 1664172183) -> pandas.core.frame.DataFrame Returns active addresses of a certain symbol [Source: https://glassnode.com] diff --git a/website/content/api/crypto/dd/btcrb/_index.md b/website/content/api/crypto/dd/btcrb/_index.md index 43500efa8eb..8d0d12200ed 100644 --- a/website/content/api/crypto/dd/btcrb/_index.md +++ b/website/content/api/crypto/dd/btcrb/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### crypto.dd.btcrb(symbol: str, start_date: int = 1262304000, end_date: int = 1663943882, print_errors: bool = True) -> pandas.core.frame.DataFrame +### crypto.dd.btcrb(symbol: str, start_date: int = 1262300400, end_date: int = 1664172183, print_errors: bool = True) -> pandas.core.frame.DataFrame Returns the price of a cryptocurrency [Source: https://glassnode.com] @@ -23,7 +23,7 @@ Returns the price of a cryptocurrency price over time ## Getting charts -### crypto.dd.btcrb(start_date: int = 1262304000, end_date: int = 1663943882, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) +### crypto.dd.btcrb(start_date: int = 1262300400, end_date: int = 1664172183, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) Displays bitcoin rainbow chart [Price data from source: https://glassnode.com] diff --git a/website/content/api/crypto/dd/change/_index.md b/website/content/api/crypto/dd/change/_index.md index f8b8f876b4e..468c7da0f62 100644 --- a/website/content/api/crypto/dd/change/_index.md +++ b/website/content/api/crypto/dd/change/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### crypto.dd.change(symbol: str, exchange: str = 'binance', start_date: int = 1262304000, end_date: int = 1663943882) -> pandas.core.frame.DataFrame +### crypto.dd.change(symbol: str, exchange: str = 'binance', start_date: int = 1262300400, end_date: int = 1664172183) -> pandas.core.frame.DataFrame Returns 30d change of the supply held in exchange wallets of a certain symbol. [Source: https://glassnode.com] diff --git a/website/content/api/crypto/dd/eb/_index.md b/website/content/api/crypto/dd/eb/_index.md index fb73c26c9c4..17bac82fe36 100644 --- a/website/content/api/crypto/dd/eb/_index.md +++ b/website/content/api/crypto/dd/eb/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### crypto.dd.eb(symbol: str, exchange: str = 'binance', start_date: int = 1262304000, end_date: int = 1663943882) -> pandas.core.frame.DataFrame +### crypto.dd.eb(symbol: str, exchange: str = 'binance', start_date: int = 1262300400, end_date: int = 1664172183) -> pandas.core.frame.DataFrame Returns the total amount of coins held on exchange addresses in units and percentage. [Source: https://glassnode.com] diff --git a/website/content/api/crypto/dd/gh/_index.md b/website/content/api/crypto/dd/gh/_index.md index 4da600f7b27..50c9ce03ce4 100644 --- a/website/content/api/crypto/dd/gh/_index.md +++ b/website/content/api/crypto/dd/gh/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### crypto.dd.gh(symbol: str, dev_activity: bool = False, interval: str = '1d', start_date: str = '2021-09-23T15:38:02Z', end_date: str = '2022-09-23T15:38:02Z') -> pandas.core.frame.DataFrame +### crypto.dd.gh(symbol: str, dev_activity: bool = False, interval: str = '1d', start_date: str = '2021-09-26T08:03:03Z', end_date: str = '2022-09-26T08:03:03Z') -> pandas.core.frame.DataFrame Returns a list of developer activity for a given coin and time interval. @@ -26,7 +26,7 @@ Returns a list of developer activity for a given coin and time interval. developer activity over time ## Getting charts -### crypto.dd.gh(symbol: str, start_date: str = '2021-09-23T15:38:02Z', dev_activity: bool = False, end_date: str = '2022-09-23T15:38:02Z', interval: str = '1d', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None +### crypto.dd.gh(symbol: str, start_date: str = '2021-09-26T08:03:03Z', dev_activity: bool = False, end_date: str = '2022-09-26T08:03:03Z', interval: str = '1d', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None Returns a list of github activity for a given coin and time interval. diff --git a/website/content/api/crypto/dd/hr/_index.md b/website/content/api/crypto/dd/hr/_index.md index f5c2c19835f..40584919a0a 100644 --- a/website/content/api/crypto/dd/hr/_index.md +++ b/website/content/api/crypto/dd/hr/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### crypto.dd.hr(symbol: str, interval: str = '24h', start_date: int = 1262304000, end_date: int = 1663943882) -> pandas.core.frame.DataFrame +### crypto.dd.hr(symbol: str, interval: str = '24h', start_date: int = 1262300400, end_date: int = 1664172183) -> pandas.core.frame.DataFrame Returns dataframe with mean hashrate of btc or eth blockchain and symbol price [Source: https://glassnode.com] @@ -23,7 +23,7 @@ Returns dataframe with mean hashrate of btc or eth blockchain and symbol price mean hashrate and symbol price over time ## Getting charts -### crypto.dd.hr(symbol: str, start_date: int = 1632407882, end_date: int = 1663943882, interval: str = '24h', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None +### crypto.dd.hr(symbol: str, start_date: int = 1632636183, end_date: int = 1664172183, interval: str = '24h', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None Display dataframe with mean hashrate of btc or eth blockchain and symbol price. [Source: https://glassnode.org] diff --git a/website/content/api/crypto/dd/mcapdom/_index.md b/website/content/api/crypto/dd/mcapdom/_index.md index 57fac386d1b..94d8fa9d0bc 100644 --- a/website/content/api/crypto/dd/mcapdom/_index.md +++ b/website/content/api/crypto/dd/mcapdom/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### crypto.dd.mcapdom(symbol: str, interval: str = '1d', start_date: str = '2021-09-23', end_date: str = '2022-09-23') -> pandas.core.frame.DataFrame +### crypto.dd.mcapdom(symbol: str, interval: str = '1d', start_date: str = '2021-09-26', end_date: str = '2022-09-26') -> pandas.core.frame.DataFrame Returns market dominance of a coin over time [Source: https://messari.io/] @@ -23,7 +23,7 @@ Returns market dominance of a coin over time market dominance percentage over time ## Getting charts -### crypto.dd.mcapdom(symbol: str, start_date: str = '2021-09-23', end_date: str = '2022-09-23', interval: str = '1d', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None +### crypto.dd.mcapdom(symbol: str, start_date: str = '2021-09-26', end_date: str = '2022-09-26', interval: str = '1d', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None Display market dominance of a coin over time [Source: https://messari.io/] diff --git a/website/content/api/crypto/dd/mt/_index.md b/website/content/api/crypto/dd/mt/_index.md index 72bce2afc25..cc3f8438e1a 100644 --- a/website/content/api/crypto/dd/mt/_index.md +++ b/website/content/api/crypto/dd/mt/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### crypto.dd.mt(symbol: str, timeseries_id: str, interval: str = '1d', start_date: str = '2021-09-23', end_date: str = '2022-09-23') -> Tuple[pandas.core.frame.DataFrame, str] +### crypto.dd.mt(symbol: str, timeseries_id: str, interval: str = '1d', start_date: str = '2021-09-26', end_date: str = '2022-09-26') -> Tuple[pandas.core.frame.DataFrame, str] Returns messari timeseries [Source: https://messari.io/] @@ -27,7 +27,7 @@ Returns messari timeseries timeserie title ## Getting charts -### crypto.dd.mt(symbol: str, timeseries_id: str, start_date: str = '2021-09-23', end_date: str = '2022-09-23', interval: str = '1d', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None +### crypto.dd.mt(symbol: str, timeseries_id: str, start_date: str = '2021-09-26', end_date: str = '2022-09-26', interval: str = '1d', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None Display messari timeseries [Source: https://messari.io/] diff --git a/website/content/api/crypto/dd/nonzero/_index.md b/website/content/api/crypto/dd/nonzero/_index.md index eda2764a6c7..5dd581fe018 100644 --- a/website/content/api/crypto/dd/nonzero/_index.md +++ b/website/content/api/crypto/dd/nonzero/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### crypto.dd.nonzero(symbol: str, start_date: int = 1262304000, end_date: int = 1663943882) -> pandas.core.frame.DataFrame +### crypto.dd.nonzero(symbol: str, start_date: int = 1262300400, end_date: int = 1664172183) -> pandas.core.frame.DataFrame Returns addresses with non-zero balance of a certain symbol [Source: https://glassnode.com] diff --git a/website/content/api/crypto/load/_index.md b/website/content/api/crypto/load/_index.md index 4b7c8121652..bdd2fb1cc40 100644 --- a/website/content/api/crypto/load/_index.md +++ b/website/content/api/crypto/load/_index.md @@ -1,5 +1,5 @@ ## Get underlying data -### crypto.load(symbol: 'str', start_date: 'datetime' = datetime.datetime(2019, 9, 19, 15, 38, 2, 624906), interval: 'str' = '1440', exchange: 'str' = 'binance', vs_currency: 'str' = 'usdt', end_date: 'datetime' = datetime.datetime(2022, 9, 23, 15, 38, 2, 624916), source: 'str' = 'ccxt') +### crypto.load(symbol: 'str', start_date: 'datetime' = datetime.datetime(2019, 9, 22, 8, 3, 3, 294791), interval: 'str' = '1440', exchange: 'str' = 'binance', vs_currency: 'str' = 'usdt', end_date: 'datetime' = datetime.datetime(2022, 9, 26, 8, 3, 3, 294804), source: 'str' = 'ccxt') Load crypto currency to perform analysis on CoinGecko is used as source for price and YahooFinance for volume. diff --git a/website/content/api/crypto/onchain/btc_supply/_index.md b/website/content/api/crypto/onchain/btc_supply/_index.md index 57f51ce6585..bb86e0c4331 100644 --- a/website/content/api/crypto/onchain/btc_supply/_index.md +++ b/website/content/api/crypto/onchain/btc_supply/_index.md @@ -11,7 +11,7 @@ Returns BTC circulating supply [Source: https://api.blockchain.info/] BTC circulating supply ## Getting charts -### crypto.onchain.btc_supply(start_date: int = 1262304000, end_date: int = 1663943882, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None +### crypto.onchain.btc_supply(start_date: int = 1262300400, end_date: int = 1664172184, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None Returns BTC circulating supply [Source: https://api.blockchain.info/] diff --git a/website/content/api/crypto/onchain/btc_transac/_index.md b/website/content/api/crypto/onchain/btc_transac/_index.md index 41515914d94..8c690187ead 100644 --- a/website/content/api/crypto/onchain/btc_transac/_index.md +++ b/website/content/api/crypto/onchain/btc_transac/_index.md @@ -11,7 +11,7 @@ Returns BTC confirmed transactions [Source: https://api.blockchain.info/] BTC confirmed transactions ## Getting charts -### crypto.onchain.btc_transac(start_date: int = 1262304000, end_date: int = 1663943882, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None +### crypto.onchain.btc_transac(start_date: int = 1262300400, end_date: int = 1664172184, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None Returns BTC confirmed transactions [Source: https://api.blockchain.info/] diff --git a/website/content/api/crypto/ov/altindex/_index.md b/website/content/api/crypto/ov/altindex/_index.md index ff531052968..12941bec8f4 100644 --- a/website/content/api/crypto/ov/altindex/_index.md +++ b/website/content/api/crypto/ov/altindex/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### crypto.ov.altindex(period: int = 30, start_date: int = 1262304000, end_date: int = 1663943882) -> pandas.core.frame.DataFrame +### crypto.ov.altindex(period: int = 30, start_date: int = 1262300400, end_date: int = 1664172183) -> pandas.core.frame.DataFrame Get altcoin index overtime [Source: https://blockchaincenter.net] @@ -23,7 +23,7 @@ Get altcoin index overtime Date, Value (Altcoin Index) ## Getting charts -### crypto.ov.altindex(period: int = 365, start_date: int = 1262304000, end_date: int = 1663943882, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None +### crypto.ov.altindex(period: int = 365, start_date: int = 1262300400, end_date: int = 1664172183, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None Displays altcoin index overtime [Source: https://blockchaincenter.net] diff --git a/website/content/api/economy/fred_yield_curve/_index.md b/website/content/api/economy/fred_yield_curve/_index.md index 5ad06237402..fa896867c67 100644 --- a/website/content/api/economy/fred_yield_curve/_index.md +++ b/website/content/api/economy/fred_yield_curve/_index.md @@ -18,7 +18,7 @@ Gets yield curve data from FRED Date for which the yield curve is obtained ## Getting charts -### economy.fred_yield_curve(date: datetime.datetime = datetime.datetime(2022, 9, 23, 15, 38, 2, 959751, chart=True), external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, raw: bool = False, export: str = '') +### economy.fred_yield_curve(date: datetime.datetime = datetime.datetime(2022, 9, 26, 8, 3, 4, 555182, chart=True), external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, raw: bool = False, export: str = '') Display yield curve based on US Treasury rates for a specified date. diff --git a/website/content/api/economy/macro/_index.md b/website/content/api/economy/macro/_index.md index 74e1a4aac4d..3853253aacc 100644 --- a/website/content/api/economy/macro/_index.md +++ b/website/content/api/economy/macro/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### economy.macro(parameters: list = None, countries: list = None, transform: str = '', start_date: str = '1900-01-01', end_date=datetime.date(2022, 9, 23), symbol: str = '') -> Tuple[Any, Dict[Any, Dict[Any, Any]], str] +### economy.macro(parameters: list = None, countries: list = None, transform: str = '', start_date: str = '1900-01-01', end_date=datetime.date(2022, 9, 26), symbol: str = '') -> Tuple[Any, Dict[Any, Dict[Any, Any]], str] This functions groups the data queried from the EconDB database [Source: EconDB] @@ -30,7 +30,7 @@ This functions groups the data queried from the EconDB database [Source: EconDB] Denomination which can be Trillions, Billions, Millions, Thousands ## Getting charts -### economy.macro(parameters: list = None, countries: list = None, transform: str = '', start_date: str = '1900-01-01', end_date: str = '2022-09-23', symbol: str = '', raw: bool = False, external_axes: Optional[List[axes]] = None, export: str = '', chart=True) +### economy.macro(parameters: list = None, countries: list = None, transform: str = '', start_date: str = '1900-01-01', end_date: str = '2022-09-26', symbol: str = '', raw: bool = False, external_axes: Optional[List[axes]] = None, export: str = '', chart=True) Show the received macro data about a company [Source: EconDB] diff --git a/website/content/api/economy/treasury/_index.md b/website/content/api/economy/treasury/_index.md index 72f99cca865..4d49c0b336e 100644 --- a/website/content/api/economy/treasury/_index.md +++ b/website/content/api/economy/treasury/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### economy.treasury(instruments: list = None, maturities: list = None, frequency: str = 'monthly', start_date: str = '1900-01-01', end_date: str = '2022-09-23') -> pandas.core.frame.DataFrame +### economy.treasury(instruments: list = None, maturities: list = None, frequency: str = 'monthly', start_date: str = '1900-01-01', end_date: str = '2022-09-26') -> pandas.core.frame.DataFrame Get U.S. Treasury rates [Source: EconDB] @@ -25,7 +25,7 @@ Get U.S. Treasury rates [Source: EconDB] Holds data of the selected types and maturities ## Getting charts -### economy.treasury(instruments: list = None, maturities: list = None, frequency: str = 'monthly', start_date: str = '1900-01-01', end_date: str = '2022-09-23', raw: bool = False, external_axes: Optional[List[axes]] = None, export: str = '', chart=True) +### economy.treasury(instruments: list = None, maturities: list = None, frequency: str = 'monthly', start_date: str = '1900-01-01', end_date: str = '2022-09-26', raw: bool = False, external_axes: Optional[List[axes]] = None, export: str = '', chart=True) Display U.S. Treasury rates [Source: EconDB] diff --git a/website/content/api/etf/candle/_index.md b/website/content/api/etf/candle/_index.md index 489b25b79a0..1196ab7586f 100644 --- a/website/content/api/etf/candle/_index.md +++ b/website/content/api/etf/candle/_index.md @@ -1,5 +1,5 @@ ## Get underlying data -### etf.candle(symbol: str, data: pandas.core.frame.DataFrame = None, use_matplotlib: bool = True, intraday: bool = False, add_trend: bool = False, ma: Optional[Iterable[int]] = None, asset_type: str = '', start_date: datetime.datetime = datetime.datetime(2019, 9, 19, 15, 38, 2, 731895), interval: int = 1440, end_date: datetime.datetime = datetime.datetime(2022, 9, 23, 15, 38, 2, 731896), prepost: bool = False, source: str = 'YahooFinance', iexrange: str = 'ytd', weekly: bool = False, monthly: bool = False, external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, raw: bool = False) +### etf.candle(symbol: str, data: pandas.core.frame.DataFrame = None, use_matplotlib: bool = True, intraday: bool = False, add_trend: bool = False, ma: Optional[Iterable[int]] = None, asset_type: str = '', start_date: datetime.datetime = datetime.datetime(2019, 9, 22, 8, 3, 1, 875559), interval: int = 1440, end_date: datetime.datetime = datetime.datetime(2022, 9, 26, 8, 3, 1, 875573), prepost: bool = False, source: str = 'YahooFinance', iexrange: str = 'ytd', weekly: bool = False, monthly: bool = False, external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, raw: bool = False) Shows candle plot of loaded ticker. [Source: Yahoo Finance, IEX Cloud or Alpha Vantage] diff --git a/website/content/api/etf/load/_index.md b/website/content/api/etf/load/_index.md index 5645e4d8e62..2471c7b5229 100644 --- a/website/content/api/etf/load/_index.md +++ b/website/content/api/etf/load/_index.md @@ -1,5 +1,5 @@ ## Get underlying data -### etf.load(symbol: str, start_date: datetime.datetime = datetime.datetime(2019, 9, 19, 15, 38, 2, 731876), interval: int = 1440, end_date: datetime.datetime = datetime.datetime(2022, 9, 23, 15, 38, 2, 731892), prepost: bool = False, source: str = 'YahooFinance', iexrange: str = 'ytd', weekly: bool = False, monthly: bool = False) +### etf.load(symbol: str, start_date: datetime.datetime = None, interval: int = 1440, end_date: datetime.datetime = None, prepost: bool = False, source: str = 'YahooFinance', iexrange: str = 'ytd', weekly: bool = False, monthly: bool = False) Load a symbol to perform analysis using the string above as a template. diff --git a/website/content/api/etf/news/_index.md b/website/content/api/etf/news/_index.md index 776c460de1b..22726589dc4 100644 --- a/website/content/api/etf/news/_index.md +++ b/website/content/api/etf/news/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### etf.news(query: str, limit: int = 10, start_date: str = '2022-09-16', show_newest: bool = True, sources: str = '') -> List[Tuple[Any, Any]] +### etf.news(query: str, limit: int = 10, start_date: str = '2022-09-19', show_newest: bool = True, sources: str = '') -> List[Tuple[Any, Any]] Get news for a given term. [Source: NewsAPI] @@ -22,7 +22,7 @@ Get news for a given term. [Source: NewsAPI] List of tuples containing news df in first index and dict containing title of news df ## Getting charts -### etf.news(query: str, limit: int = 3, start_date: str = '2022-09-16', show_newest: bool = True, sources: str = '', export: str = '', chart=True) -> None +### etf.news(query: str, limit: int = 3, start_date: str = '2022-09-19', show_newest: bool = True, sources: str = '', export: str = '', chart=True) -> None Display news for a given term. [Source: NewsAPI] diff --git a/website/content/api/forecast/_index.md b/website/content/api/forecast/_index.md new file mode 100644 index 00000000000..026279ed2c9 --- /dev/null +++ b/website/content/api/forecast/_index.md @@ -0,0 +1,6 @@ +--- +title: forecast +keywords: "" +excerpt: "" +geekdocCollapseSection: true +--- diff --git a/website/content/api/forecast/atr/_index.md b/website/content/api/forecast/atr/_index.md new file mode 100644 index 00000000000..11339a9bbfe --- /dev/null +++ b/website/content/api/forecast/atr/_index.md @@ -0,0 +1,5 @@ +## Get underlying data +### forecast.atr(dataset: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame + + + Calculate the Average True Range of a variable based on a a specific stock ticker. diff --git a/website/content/api/forecast/brnn/_index.md b/website/content/api/forecast/brnn/_index.md new file mode 100644 index 00000000000..c0c7b3e249f --- /dev/null +++ b/website/content/api/forecast/brnn/_index.md @@ -0,0 +1,118 @@ +To obtain charts, make sure to add `chart=True` as the last parameter + +## Get underlying data +### forecast.brnn(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', n_predict: int = 5, train_split: float = 0.85, past_covariates: str = None, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, model_type: str = 'LSTM', n_rnn_layers: int = 1, hidden_size: int = 20, dropout: float = 0.0, batch_size: int = 32, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = 'brnn_model', force_reset: bool = True, save_checkpoints: bool = True) -> Tuple[list[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], Optional[float], Any] + +Performs Block RNN forecasting + + Args: + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + model_type (str, optional): + Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU"). Defaults to "LSTM". + n_rnn_layers (int, optional): + Number of layers in the RNN module. Defaults to 1. + hidden_size (int, optional): + Size for feature maps for each hidden RNN layer. Defaults to 20. + dropout (float, optional): + Fraction of neurons afected by Dropout. Defaults to 0.0. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 100. + learning_rate (float, optional): + Defaults to 1e-3. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset (all checkpoints will be + discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True. + + Returns: + list[TimeSeries] + Adjusted Data series + list[TimeSeries] + Historical forecast by best RNN model + list[TimeSeries] + list of Predictions + Optional[float] + Mean average precision error + Any + Best BRNN Model + +## Getting charts +### forecast.brnn(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', dataset_name: str = '', n_predict: int = 5, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, model_type: str = 'LSTM', n_rnn_layers: int = 1, hidden_size: int = 20, dropout: float = 0.0, batch_size: int = 16, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = 'rnn_model', force_reset: bool = True, save_checkpoints: bool = True, export: str = '', residuals: bool = False, forecast_only: bool = False, start_date: Optional[datetime.datetime] = None, end_date: Optional[datetime.datetime] = None, naive: bool = False, chart=True) + +Display BRNN forecast + + Parameters + ---------- + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + dataset_name str + The name of the ticker to be predicted + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + model_type (str, optional): + Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU"). Defaults to "LSTM". + n_rnn_layers (int, optional): + Number of layers in the RNN module. Defaults to 1. + hidden_size (int, optional): + Size for feature maps for each hidden RNN layer. Defaults to 20. + dropout (float, optional): + Fraction of neurons afected by Dropout. Defaults to 0.0. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 101. + learning_rate (float, optional): + Defaults to 1e-3. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. + Defaults to True. + export: str + Format to export data + residuals: bool + Whether to show residuals for the model. Defaults to False. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the same + as the previous day's closing price. Defaults to False. diff --git a/website/content/api/forecast/clean/_index.md b/website/content/api/forecast/clean/_index.md new file mode 100644 index 00000000000..b5397c7ab7f --- /dev/null +++ b/website/content/api/forecast/clean/_index.md @@ -0,0 +1,20 @@ +## Get underlying data +### forecast.clean(dataset: pandas.core.frame.DataFrame, fill: Optional[str] = None, drop: Optional[str] = None, limit: Optional[int] = None) -> pandas.core.frame.DataFrame + +Clean up NaNs from the dataset + + Parameters + ---------- + dataset : pd.DataFrame + The dataset you wish to clean + fill : Optional[str] + The method of filling NaNs + drop : Optional[str] + The method of dropping NaNs + limit : Optional[int] + The maximum limit you wish to apply that can be forward or backward filled + + Returns + ------- + pd.DataFrame: + Dataframe with cleaned up data diff --git a/website/content/api/forecast/combine/_index.md b/website/content/api/forecast/combine/_index.md new file mode 100644 index 00000000000..53f00df6b98 --- /dev/null +++ b/website/content/api/forecast/combine/_index.md @@ -0,0 +1,20 @@ +## Get underlying data +### forecast.combine(df1: pandas.core.frame.DataFrame, df2: pandas.core.frame.DataFrame, column: str, dataset: str = '') -> pandas.core.frame.DataFrame + +Adds the given column of df2 to df1 + + Parameters + ---------- + df1: pd.DataFrame + The dataframe to add a column to + df2: pd.DataFrame + The dataframe to lose a column + column: str + The column to transfer + dataset: str + A name for df2 (shows in name of new column) + + Returns + ---------- + data: pd.DataFrame + The new dataframe diff --git a/website/content/api/forecast/corr/_index.md b/website/content/api/forecast/corr/_index.md new file mode 100644 index 00000000000..8b8f969d6d5 --- /dev/null +++ b/website/content/api/forecast/corr/_index.md @@ -0,0 +1,13 @@ +## Get underlying data +### forecast.corr(dataset: pandas.core.frame.DataFrame, export: str = '', external_axes: Optional[List[axes]] = None) + +Plot correlation coefficients for dataset features + + Parameters + ---------- + dataset : pd.DataFrame + The dataset fore calculating correlation coefficients + export: str + Format to export image + external_axes:Optional[List[plt.axes]] + External axes to plot on diff --git a/website/content/api/forecast/delete/_index.md b/website/content/api/forecast/delete/_index.md new file mode 100644 index 00000000000..017702de3f7 --- /dev/null +++ b/website/content/api/forecast/delete/_index.md @@ -0,0 +1,4 @@ +## Get underlying data +### forecast.delete(df: pandas.core.frame.DataFrame, column: str) -> None + + diff --git a/website/content/api/forecast/delta/_index.md b/website/content/api/forecast/delta/_index.md new file mode 100644 index 00000000000..e4fa2e31253 --- /dev/null +++ b/website/content/api/forecast/delta/_index.md @@ -0,0 +1,5 @@ +## Get underlying data +### forecast.delta(dataset: pandas.core.frame.DataFrame, target_column: str = 'close') -> pandas.core.frame.DataFrame + + + Calculate the %change of a variable based on a specific column diff --git a/website/content/api/forecast/desc/_index.md b/website/content/api/forecast/desc/_index.md new file mode 100644 index 00000000000..c0982063751 --- /dev/null +++ b/website/content/api/forecast/desc/_index.md @@ -0,0 +1,4 @@ +## Get underlying data +### forecast.desc(df: pandas.core.frame.DataFrame, name: str = '', export: str = '') + + diff --git a/website/content/api/forecast/ema/_index.md b/website/content/api/forecast/ema/_index.md new file mode 100644 index 00000000000..91ea19c6d93 --- /dev/null +++ b/website/content/api/forecast/ema/_index.md @@ -0,0 +1,19 @@ +## Get underlying data +### forecast.ema(dataset: pandas.core.frame.DataFrame, target_column: str = 'close', period: int = 10) -> pandas.core.frame.DataFrame + +A moving average provides an indication of the trend of the price movement + by cut down the amount of "noise" on a price chart. + + Parameters + ---------- + dataset : pd.DataFrame + The dataset you wish to clean + target_column : str + The column you wish to add the EMA to + period : int + Time Span + + Returns + ------- + pd.DataFrame: + Dataframe with added EMA column diff --git a/website/content/api/forecast/expo/_index.md b/website/content/api/forecast/expo/_index.md new file mode 100644 index 00000000000..8cf9f99916f --- /dev/null +++ b/website/content/api/forecast/expo/_index.md @@ -0,0 +1,91 @@ +To obtain charts, make sure to add `chart=True` as the last parameter + +## Get underlying data +### forecast.expo(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', trend: str = 'A', seasonal: str = 'A', seasonal_periods: int = 7, dampen: str = 'F', n_predict: int = 30, start_window: float = 0.85, forecast_horizon: int = 5) -> Tuple[list[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], Optional[float], Any] + +Performs Probabilistic Exponential Smoothing forecasting + This is a wrapper around statsmodels Holt-Winters' Exponential Smoothing; + we refer to this link for the original and more complete documentation of the parameters. + + https://unit8co.github.io/darts/generated_api/darts.models.forecasting.exponential_smoothing.html?highlight=exponential + + Parameters + ---------- + data : Union[pd.Series, np.ndarray] + Input data. + target_column (str, optional): + Target column to forecast. Defaults to "close". + trend: str + Trend component. One of [N, A, M] + Defaults to ADDITIVE. + seasonal: str + Seasonal component. One of [N, A, M] + Defaults to ADDITIVE. + seasonal_periods: int + Number of seasonal periods in a year (7 for daily data) + If not set, inferred from frequency of the series. + dampen: str + Dampen the function + n_predict: int + Number of days to forecast + start_window: float + Size of sliding window from start of timeseries and onwards + forecast_horizon: int + Number of days to forecast when backtesting and retraining historical + + Returns + ------- + list[float] + Adjusted Data series + list[float] + List of historical fcast values + list[float] + List of predicted fcast values + Optional[float] + precision + Any + Fit Prob. Expo model object. + +## Getting charts +### forecast.expo(data: Union[pandas.core.frame.DataFrame, pandas.core.series.Series], target_column: str = 'close', dataset_name: str = '', trend: str = 'A', seasonal: str = 'A', seasonal_periods: int = 7, dampen: str = 'F', n_predict: int = 30, start_window: float = 0.85, forecast_horizon: int = 5, export: str = '', residuals: bool = False, forecast_only: bool = False, start_date: Optional[datetime.datetime] = None, end_date: Optional[datetime.datetime] = None, naive: bool = False, chart=True) + +Display Probabilistic Exponential Smoothing forecast + + Parameters + ---------- + data : Union[pd.Series, np.array] + Data to forecast + dataset_name str + The name of the ticker to be predicted + target_column (str, optional): + Target column to forecast. Defaults to "close". + trend: str + Trend component. One of [N, A, M] + Defaults to ADDITIVE. + seasonal: str + Seasonal component. One of [N, A, M] + Defaults to ADDITIVE. + seasonal_periods: int + Number of seasonal periods in a year + If not set, inferred from frequency of the series. + dampen: str + Dampen the function + n_predict: int + Number of days to forecast + start_window: float + Size of sliding window from start of timeseries and onwards + forecast_horizon: int + Number of days to forecast when backtesting and retraining historical + export: str + Format to export data + residuals: bool + Whether to show residuals for the model. Defaults to False. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the same + as the previous day's closing price. Defaults to False. diff --git a/website/content/api/forecast/export/_index.md b/website/content/api/forecast/export/_index.md new file mode 100644 index 00000000000..db7794a4f8a --- /dev/null +++ b/website/content/api/forecast/export/_index.md @@ -0,0 +1,4 @@ +## Get underlying data +### forecast.export(df: pandas.core.frame.DataFrame, export: str, name: str = '') -> None + + diff --git a/website/content/api/forecast/linregr/_index.md b/website/content/api/forecast/linregr/_index.md new file mode 100644 index 00000000000..02c61cf63be --- /dev/null +++ b/website/content/api/forecast/linregr/_index.md @@ -0,0 +1,76 @@ +To obtain charts, make sure to add `chart=True` as the last parameter + +## Get underlying data +### forecast.linregr(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', n_predict: int = 5, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, output_chunk_length: int = 5, lags: Union[int, List[int]] = 14, random_state: Optional[int] = None) -> Tuple[List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], float, Any] + +Perform Linear Regression Forecasting + + Args: + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 1. + lags (int, list) + lagged target values to predict the next time step + random_state (int, optional): + The state for the model + + Returns: + List[TimeSeries] + Adjusted Data series + List[TimeSeries] + Historical forecast by best RNN model + List[TimeSeries] + list of Predictions + float + Mean average precision error + Any + Best Linear Regression Model + +## Getting charts +### forecast.linregr(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', dataset_name: str = '', n_predict: int = 5, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, output_chunk_length: int = 1, lags: Union[int, List[int]] = 72, export: str = '', residuals: bool = False, forecast_only: bool = False, start_date: Optional[datetime.datetime] = None, end_date: Optional[datetime.datetime] = None, naive: bool = False, chart=True) + +Display Linear Regression Forecasting + + Args: + data (Union[pd.Series, pd.DataFrame]): + Input Data + dataset_name str + The name of the ticker to be predicted + n_predict (int, optional): + Days to predict. Defaults to 5. + target_col (str, optional): + Target column to forecast. Defaults to "close". + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 1. + lags (int, list) + lagged target values to predict the next time step + export: str + Format to export data + residuals: bool + Whether to show residuals for the model. Defaults to False. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the + same as the previous day's closing price. Defaults to False. diff --git a/website/content/api/forecast/load/_index.md b/website/content/api/forecast/load/_index.md new file mode 100644 index 00000000000..e0c0d454bc2 --- /dev/null +++ b/website/content/api/forecast/load/_index.md @@ -0,0 +1,20 @@ +## Get underlying data +### forecast.load(file: str, file_types: Optional[List[str]] = None, data_files: Optional[Dict[Any, Any]] = None, add_extension: bool = False) -> pandas.core.frame.DataFrame + +Load custom file into dataframe. + + Parameters + ---------- + file: str + Path to file + file_types: list + Supported file types + data_files: dict + Contains all available data files within the Export folder + add_extension: + Takes a file name and tries loading with csv or xlsx extension + + Returns + ------- + pd.DataFrame: + Dataframe with custom data diff --git a/website/content/api/forecast/mom/_index.md b/website/content/api/forecast/mom/_index.md new file mode 100644 index 00000000000..6daa474a157 --- /dev/null +++ b/website/content/api/forecast/mom/_index.md @@ -0,0 +1,19 @@ +## Get underlying data +### forecast.mom(dataset: pandas.core.frame.DataFrame, target_column: str = 'close', period: int = 10) -> pandas.core.frame.DataFrame + +A momentum oscillator, which measures the percentage change between the current + value and the n period past value. + + Parameters + ---------- + dataset : pd.DataFrame + The dataset you wish to calculate with + target_column : str + The column you wish to add the MOM to + period : int + Time Span + + Returns + ------- + pd.DataFrame: + Dataframe with added MOM column diff --git a/website/content/api/forecast/nbeats/_index.md b/website/content/api/forecast/nbeats/_index.md new file mode 100644 index 00000000000..ca0f6c2d50d --- /dev/null +++ b/website/content/api/forecast/nbeats/_index.md @@ -0,0 +1,124 @@ +To obtain charts, make sure to add `chart=True` as the last parameter + +## Get underlying data +### forecast.nbeats(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', n_predict: int = 5, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, num_stacks: int = 10, num_blocks: int = 3, num_layers: int = 4, layer_widths: int = 512, batch_size: int = 800, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = 'nbeats_model', force_reset: bool = True, save_checkpoints: bool = True) -> Tuple[List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], Optional[float], Any] + +Perform NBEATS Forecasting + + Args: + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + num_stacks (int, optional): + The number of stacks that make up the whole model. Defaults to 10. + num_blocks (int, optional): + The number of blocks making up every stack. Defaults to 3. + num_layers (int, optional): + The number of fully connected layers preceding the final forking layers in each block + of every stack. Defaults to 4. + layer_widths (int, optional): + Determines the number of neurons that make up each fully connected layer in each block + of every stack. Defaults to 512. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 100. + learning_rate (float, optional): + Defaults to 1e-3. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. + Defaults to True. + + Returns: + List[TimeSeries] + Adjusted Data series + List[TimeSeries] + Historical forecast by best RNN model + List[TimeSeries] + list of Predictions + Optional[float] + Mean average precision error + Any + Best NBEATS Model + +## Getting charts +### forecast.nbeats(data: Union[pandas.core.frame.DataFrame, pandas.core.series.Series], target_column: str = 'close', dataset_name: str = '', n_predict: int = 5, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, num_stacks: int = 10, num_blocks: int = 3, num_layers: int = 4, layer_widths: int = 512, n_epochs: int = 100, learning_rate: float = 0.001, batch_size: int = 800, model_save_name: str = 'nbeats_model', force_reset: bool = True, save_checkpoints: bool = True, export: str = '', residuals: bool = False, forecast_only: bool = False, start_date: Optional[datetime.datetime] = None, end_date: Optional[datetime.datetime] = None, naive: bool = False, chart=True) + +Display NBEATS forecast + + Parameters + ---------- + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + dataset_name str + The name of the ticker to be predicted + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + num_stacks (int, optional): + The number of stacks that make up the whole model. Defaults to 10. + num_blocks (int, optional): + The number of blocks making up every stack. Defaults to 3. + num_layers (int, optional): + The number of fully connected layers preceding the final forking layers in each block + of every stack. Defaults to 4. + layer_widths (int, optional): + Determines the number of neurons that make up each fully connected layer in each block + of every stack. Defaults to 512. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults + to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 100. + learning_rate (float, optional): + Defaults to 1e-3. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset (all + checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. + Defaults to True. + export: str + Format to export data + residuals: bool + Whether to show residuals for the model. Defaults to False. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the same + as the previous day's closing price. Defaults to False. diff --git a/website/content/api/forecast/plot/_index.md b/website/content/api/forecast/plot/_index.md new file mode 100644 index 00000000000..8045389eea9 --- /dev/null +++ b/website/content/api/forecast/plot/_index.md @@ -0,0 +1,27 @@ +To obtain charts, make sure to add `chart=True` as the last parameter + +## Get underlying data +### forecast.plot(data: Union[pandas.core.frame.DataFrame, Dict[str, pandas.core.frame.DataFrame]], export: str = '', external_axes: Optional[List[axes]] = None) + +Plot data from a dataset + Parameters + ---------- + data: Dict[str: pd.DataFrame] + Dictionary with key being dataset.column and dataframes being values + export: str + Format to export image + external_axes:Optional[List[plt.axes]] + External axes to plot on + +## Getting charts +### forecast.plot(data: Union[pandas.core.frame.DataFrame, Dict[str, pandas.core.frame.DataFrame]], export: str = '', external_axes: Optional[List[axes]] = None, chart=True) + +Plot data from a dataset + Parameters + ---------- + data: Dict[str: pd.DataFrame] + Dictionary with key being dataset.column and dataframes being values + export: str + Format to export image + external_axes:Optional[List[plt.axes]] + External axes to plot on diff --git a/website/content/api/forecast/regr/_index.md b/website/content/api/forecast/regr/_index.md new file mode 100644 index 00000000000..c0619e04ed6 --- /dev/null +++ b/website/content/api/forecast/regr/_index.md @@ -0,0 +1,74 @@ +To obtain charts, make sure to add `chart=True` as the last parameter + +## Get underlying data +### forecast.regr(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', n_predict: int = 5, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, output_chunk_length: int = 1, lags: Union[int, List[int]] = 72) -> Tuple[List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], float, Any] + +Perform Regression Forecasting + + Args: + data (Union[pd.Series, pd.DataFrame]): + Input Data + n_predict (int, optional): + Days to predict. Defaults to 5. + target_column (str, optional): + Target column to forecast. Defaults to "close". + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 1. + lags (int, list) + lagged target values to predict the next time step + + Returns: + List[TimeSeries] + Adjusted Data series + List[TimeSeries] + Historical forecast by best RNN model + List[TimeSeries] + list of Predictions + float + Mean average precision error + Any + Best Regression Model + +## Getting charts +### forecast.regr(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', dataset_name: str = '', n_predict: int = 5, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, output_chunk_length: int = 1, lags: Union[int, List[int]] = 72, export: str = '', residuals: bool = False, forecast_only: bool = False, start_date: Optional[datetime.datetime] = None, end_date: Optional[datetime.datetime] = None, naive: bool = False, chart=True) + +Display Regression Forecasting + + Args: + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + dataset_name str + The name of the ticker to be predicted + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 1. + lags (int, list) + lagged target values to predict the next time step + export: str + Format to export data + residuals: bool + Whether to show residuals for the model. Defaults to False. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the same + as the previous day's closing price. Defaults to False. diff --git a/website/content/api/forecast/rename/_index.md b/website/content/api/forecast/rename/_index.md new file mode 100644 index 00000000000..8f9667585d9 --- /dev/null +++ b/website/content/api/forecast/rename/_index.md @@ -0,0 +1,18 @@ +## Get underlying data +### forecast.rename(df: pandas.core.frame.DataFrame, old_column: str, new_column: str) -> pandas.core.frame.DataFrame + +Rename a column in a dataframe + + Parameters + ---------- + df: pd.DataFrame + The dataframe to have a column renamed + old_column: str + The column that will have its name changed + new_column: str + The name to update to + + Returns + ---------- + new_df: pd.DataFrame + The dataframe with the renamed column diff --git a/website/content/api/forecast/rnn/_index.md b/website/content/api/forecast/rnn/_index.md new file mode 100644 index 00000000000..599b67db46d --- /dev/null +++ b/website/content/api/forecast/rnn/_index.md @@ -0,0 +1,102 @@ +To obtain charts, make sure to add `chart=True` as the last parameter + +## Get underlying data +### forecast.rnn(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', n_predict: int = 5, train_split: float = 0.85, forecast_horizon: int = 5, model_type: str = 'LSTM', hidden_dim: int = 20, dropout: float = 0.0, batch_size: int = 16, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = 'rnn_model', training_length: int = 20, input_chunk_size: int = 14, force_reset: bool = True, save_checkpoints: bool = True) -> Tuple[List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], Optional[float], Any] + +Perform RNN forecasting + + Args: + data (Union[pd.Series, pd.DataFrame]): + Input Data + n_predict (int, optional): + Days to predict. Defaults to 5. + target_column (str, optional): + Target column to forecast. Defaults to "close". + train_split (float, optional): + Train/val split. Defaults to 0.85. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + model_type (str, optional): + Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU"). Defaults to "LSTM". + hidden_dim (int, optional): + Size for feature maps for each hidden RNN layer.. Defaults to 20. + dropout (float, optional): + Fraction of neurons afected by Dropout. Defaults to 0.0. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 100. + learning_rate (float, optional): + Defaults to 1e-3. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. + Defaults to True. + + Returns: + List[TimeSeries] + Adjusted Data series + List[TimeSeries] + Historical forecast by best RNN model + List[TimeSeries] + list of Predictions + float + Mean average precision error + Any + Best RNN Model + +## Getting charts +### forecast.rnn(data: Union[pandas.core.frame.DataFrame, pandas.core.series.Series], target_column: str = 'close', dataset_name: str = '', n_predict: int = 5, train_split: float = 0.85, forecast_horizon: int = 5, model_type: str = 'LSTM', hidden_dim: int = 20, dropout: float = 0.0, batch_size: int = 16, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = 'rnn_model', training_length: int = 20, input_chunk_size: int = 14, force_reset: bool = True, save_checkpoints: bool = True, export: str = '', residuals: bool = False, forecast_only: bool = False, start_date: Optional[datetime.datetime] = None, end_date: Optional[datetime.datetime] = None, naive: bool = False, chart=True) + +Display RNN forecast + + Parameters + ---------- + data (Union[pd.Series, pd.DataFrame]): + Input Data + dataset_name str + The name of the ticker to be predicted + n_predict (int, optional): + Days to predict. Defaults to 5. + target_column (str, optional): + Target column to forecast. Defaults to "close". + train_split (float, optional): + Train/val split. Defaults to 0.85. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + model_type (str, optional): + Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU"). Defaults to "LSTM". + hidden_dim (int, optional): + Size for feature maps for each hidden RNN layer.. Defaults to 20. + dropout (float, optional): + Fraction of neurons afected by Dropout. Defaults to 0.0. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 100. + learning_rate (float, optional): + Defaults to 1e-3. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + export: str + Format to export data + residuals: bool + Whether to show residuals for the model. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the same + as the previous day's closing price. Defaults to False. diff --git a/website/content/api/forecast/roc/_index.md b/website/content/api/forecast/roc/_index.md new file mode 100644 index 00000000000..9a8f16d8538 --- /dev/null +++ b/website/content/api/forecast/roc/_index.md @@ -0,0 +1,19 @@ +## Get underlying data +### forecast.roc(dataset: pandas.core.frame.DataFrame, target_column: str = 'close', period: int = 10) -> pandas.core.frame.DataFrame + +A momentum oscillator, which measures the percentage change between the current + value and the n period past value. + + Parameters + ---------- + dataset : pd.DataFrame + The dataset you wish to calculate with + target_column : str + The column you wish to add the ROC to + period : int + Time Span + + Returns + ------- + pd.DataFrame: + Dataframe with added ROC column diff --git a/website/content/api/forecast/rsi/_index.md b/website/content/api/forecast/rsi/_index.md new file mode 100644 index 00000000000..337c5a31120 --- /dev/null +++ b/website/content/api/forecast/rsi/_index.md @@ -0,0 +1,19 @@ +## Get underlying data +### forecast.rsi(dataset: pandas.core.frame.DataFrame, target_column: str = 'close', period: int = 10) -> pandas.core.frame.DataFrame + +A momentum indicator that measures the magnitude of recent price changes to evaluate + overbought or oversold conditions in the price of a stock or other asset. + + Parameters + ---------- + dataset : pd.DataFrame + The dataset you wish to calculate for + target_column : str + The column you wish to add the RSI to + period : int + Time Span + + Returns + ------- + pd.DataFrame: + Dataframe with added RSI column diff --git a/website/content/api/forecast/season/_index.md b/website/content/api/forecast/season/_index.md new file mode 100644 index 00000000000..82317177557 --- /dev/null +++ b/website/content/api/forecast/season/_index.md @@ -0,0 +1,21 @@ +## Get underlying data +### forecast.season(data: pandas.core.frame.DataFrame, column: str = 'close', export: str = '', m: Optional[int] = None, max_lag: int = 24, alpha: float = 0.05, external_axes: Optional[List[axes]] = None) + +Plot seasonality from a dataset + + Parameters + ---------- + data: pd.DataFrame + The dataframe to plot + column: str + The column of the dataframe to analyze + export: str + Format to export image + m: Optional[int] + Optionally, a time lag to highlight on the plot. Default is none. + max_lag: int + The maximal lag order to consider. Default is 24. + alpha: float + The confidence interval to display. Default is 0.05. + external_axes:Optional[List[plt.axes]] + External axes to plot on diff --git a/website/content/api/forecast/show/_index.md b/website/content/api/forecast/show/_index.md new file mode 100644 index 00000000000..cc1cabc916a --- /dev/null +++ b/website/content/api/forecast/show/_index.md @@ -0,0 +1,11 @@ +To obtain charts, make sure to add `chart=True` as the last parameter + +## Get underlying data +### forecast.show(df: pandas.core.frame.DataFrame, limit: int = 15, name: str = '', export: str = '') + + + +## Getting charts +### forecast.show(df: pandas.core.frame.DataFrame, limit: int = 15, name: str = '', export: str = '', chart=True) + + diff --git a/website/content/api/forecast/signal/_index.md b/website/content/api/forecast/signal/_index.md new file mode 100644 index 00000000000..1184c20ec5f --- /dev/null +++ b/website/content/api/forecast/signal/_index.md @@ -0,0 +1,17 @@ +## Get underlying data +### forecast.signal(dataset: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame + +A price signal based on short/long term price. + + 1 if the signal is that short term price will go up as compared to the long term. + 0 if the signal is that short term price will go down as compared to the long term. + + Parameters + ---------- + dataset : pd.DataFrame + The dataset you wish to calculate with + + Returns + ------- + pd.DataFrame: + Dataframe with added signal column diff --git a/website/content/api/forecast/sto/_index.md b/website/content/api/forecast/sto/_index.md new file mode 100644 index 00000000000..8beeae1ba1f --- /dev/null +++ b/website/content/api/forecast/sto/_index.md @@ -0,0 +1,20 @@ +## Get underlying data +### forecast.sto(dataset: pandas.core.frame.DataFrame, period: int = 10) -> pandas.core.frame.DataFrame + +Stochastic Oscillator %K and %D : A stochastic oscillator is a momentum indicator comparing a particular closing + price of a security to a range of its prices over a certain period of time. %K and %D are slow and fast indicators. + + Requires Low/High/Close columns. + Note: This will drop first rows equal to period due to how this metric is calculated. + + Parameters + ---------- + dataset : pd.DataFrame + The dataset you wish to calculate for + period : int + Span + + Returns + ------- + pd.DataFrame: + Dataframe with added STO K & D columns diff --git a/website/content/api/forecast/tcn/_index.md b/website/content/api/forecast/tcn/_index.md new file mode 100644 index 00000000000..58822707044 --- /dev/null +++ b/website/content/api/forecast/tcn/_index.md @@ -0,0 +1,118 @@ +To obtain charts, make sure to add `chart=True` as the last parameter + +## Get underlying data +### forecast.tcn(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', n_predict: int = 5, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, dropout: float = 0.1, num_filters: int = 6, weight_norm: bool = True, dilation_base: int = 2, n_epochs: int = 100, learning_rate: float = 0.001, batch_size: int = 800, model_save_name: str = 'tcn_model', force_reset: bool = True, save_checkpoints: bool = True) -> Tuple[List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], Optional[float], Any] + +Perform TCN forecasting + + Args: + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + dropout (float, optional): + Fraction of neurons afected by Dropout. Defaults to 0.1. + num_filters (int, optional): + The number of filters in a convolutional layer of the TCN. Defaults to 6. + weight_norm (bool, optional): + Boolean value indicating whether to use weight normalization. Defaults to True. + dilation_base (int, optional): + The base of the exponent that will determine the dilation on every level. Defaults to 2. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 100. + learning_rate (float, optional): + Defaults to 1e-3. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. + Defaults to True. + + Returns: + List[TimeSeries] + Adjusted Data series + List[TimeSeries] + Historical forecast by best RNN model + List[TimeSeries] + list of Predictions + Optional[float] + Mean average precision error + Any + Best TCN Model + +## Getting charts +### forecast.tcn(data: Union[pandas.core.frame.DataFrame, pandas.core.series.Series], target_column: str = 'close', dataset_name: str = '', n_predict: int = 5, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, dropout: float = 0.1, num_filters: int = 6, weight_norm: bool = True, dilation_base: int = 2, n_epochs: int = 100, learning_rate: float = 0.001, batch_size: int = 800, model_save_name: str = 'tcn_model', force_reset: bool = True, save_checkpoints: bool = True, export: str = '', residuals: bool = False, forecast_only: bool = False, start_date: Optional[datetime.datetime] = None, end_date: Optional[datetime.datetime] = None, naive: bool = False, chart=True) + +Display TCN forecast + + Parameters + ---------- + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + dataset_name str + The name of the ticker to be predicted + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + dropout (float, optional): + Fraction of neurons afected by Dropout. Defaults to 0.1. + num_filters (int, optional): + The number of filters in a convolutional layer of the TCN. Defaults to 6. + weight_norm (bool, optional): + Boolean value indicating whether to use weight normalization. Defaults to True. + dilation_base (int, optional): + The base of the exponent that will determine the dilation on every level. Defaults to 2. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 100. + learning_rate (float, optional): + Defaults to 1e-3. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True. + export: str + Format to export data + residuals: bool + Whether to show residuals for the model. Defaults to False. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the same + as the previous day's closing price. Defaults to False. diff --git a/website/content/api/forecast/tft/_index.md b/website/content/api/forecast/tft/_index.md new file mode 100644 index 00000000000..ab22b350708 --- /dev/null +++ b/website/content/api/forecast/tft/_index.md @@ -0,0 +1,133 @@ +To obtain charts, make sure to add `chart=True` as the last parameter + +## Get underlying data +### forecast.tft(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', n_predict: int = 30, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, hidden_size: int = 16, lstm_layers: int = 1, num_attention_heads: int = 4, full_attention: bool = False, dropout: float = 0.1, hidden_continuous_size: int = 8, n_epochs: int = 200, batch_size: int = 32, model_save_name: str = 'tft_model', force_reset: bool = True, save_checkpoints: bool = True) -> Tuple[List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], Optional[float], Any] + +Performs Temporal Fusion Transformer forecasting + The TFT applies multi-head attention queries on future inputs from mandatory future_covariates. + Specifying future encoders with add_encoders (read below) can automatically generate future + covariates and allows to use the model without having to pass any future_covariates to fit() + and predict(). + + https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html + + Parameters + ---------- + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. + Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + hidden_size (int, optional): + Hidden state size of the TFT. Defaults to 16. + lstm_layers (int, optional): + Number of layers for the Long Short Term Memory Encoder and Decoder. Defaults to 16. + num_attention_headers (int, optional): + Number of attention heads. Defaults to 4. + full_attention (bool, optional): + Whether to apply a multi-head attention query. Defaults to False> + dropout (float, optional): + Fraction of neurons affected by dropout. Defaults to 0.1. + hidden_continuous_size (int, optional): + Default hidden size for processing continuous variables. Defaults to 8. + n_epochs (int, optional): + Number of epochs to run during training. Defaults to 200. + batch_size (int, optional): + Number of samples to pass through network during a single epoch. Defaults to 32. + model_save_name (str, optional): + The name for the model. Defaults to tft_model + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. + Defaults to True. + + + + Returns + ------- + List[float] + Adjusted Data series + List[float] + List of historical fcast values + List[float] + List of predicted fcast values + Optional[float] + precision + Any + Fit Prob. TFT model object. + +## Getting charts +### forecast.tft(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', dataset_name: str = '', n_predict: int = 30, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, hidden_size: int = 16, lstm_layers: int = 1, num_attention_heads: int = 4, full_attention: bool = False, dropout: float = 0.1, hidden_continuous_size: int = 8, n_epochs: int = 200, batch_size: int = 32, model_save_name: str = 'tft_model', force_reset: bool = True, save_checkpoints: bool = True, export: str = '', residuals: bool = False, forecast_only: bool = False, start_date: Optional[datetime.datetime] = None, end_date: Optional[datetime.datetime] = None, naive: bool = False, chart=True) + +Display Temporal Fusion Transformer forecast + + Parameters + ---------- + data (Union[pd.Series, pd.DataFrame]): + Input Data + target_column (str, optional): + Target column to forecast. Defaults to "close". + dataset_name str + The name of the ticker to be predicted + n_predict (int, optional): + Days to predict. Defaults to 5. + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. + Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + hidden_size (int, optional): + Hidden state size of the TFT. Defaults to 16. + lstm_layers (int, optional): + Number of layers for the Long Short Term Memory Encoder and Decoder. Defaults to 16. + num_attention_headers (int, optional): + Number of attention heads. Defaults to 4. + full_attention (bool, optional): + Whether to apply a multi-head attention query. Defaults to False> + dropout (float, optional): + Fraction of neurons affected by dropout. Defaults to 0.1. + hidden_continuous_size (int, optional): + Default hidden size for processing continuous variables. Defaults to 8. + n_epochs (int, optional): + Number of epochs to run during training. Defaults to 200. + batch_size (int, optional): + Number of samples to pass through network during a single epoch. Defaults to 32. + model_save_name (str, optional): + The name for the model. Defaults to tft_model + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. + Defaults to True. + residuals: bool + Whether to show residuals for the model. Defaults to False. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the same + as the previous day's closing price. Defaults to False. diff --git a/website/content/api/forecast/theta/_index.md b/website/content/api/forecast/theta/_index.md new file mode 100644 index 00000000000..39517edb0d8 --- /dev/null +++ b/website/content/api/forecast/theta/_index.md @@ -0,0 +1,82 @@ +To obtain charts, make sure to add `chart=True` as the last parameter + +## Get underlying data +### forecast.theta(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', seasonal: str = 'M', seasonal_periods: int = 7, n_predict: int = 30, start_window: float = 0.85, forecast_horizon: int = 5) -> Tuple[List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], float, float, Any] + +Performs Theta forecasting + An implementation of the 4Theta method with configurable theta parameter. + + https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html + + Parameters + ---------- + data : Union[pd.Series, np.ndarray] + Input data. + target_column (str, optional): + Target column to forecast. Defaults to "close". + seasonal: str + Seasonal component. One of [N, A, M] + Defaults to MULTIPLICATIVE. + seasonal_periods: int + Number of seasonal periods in a year + If not set, inferred from frequency of the series. + n_predict: int + Number of days to forecast + start_window: float + Size of sliding window from start of timeseries and onwards + forecast_horizon: int + Number of days to forecast when backtesting and retraining historical + + Returns + ------- + List[TimeSeries] + Adjusted Data series + List[TimeSeries] + Historical forecast by best theta + List[TimeSeries] + list of Predictions + float + Mean average precision error + float + Best Theta + Any + Theta Model + +## Getting charts +### forecast.theta(data: Union[pandas.core.frame.DataFrame, pandas.core.series.Series], target_column: str = 'close', dataset_name: str = '', seasonal: str = 'M', seasonal_periods: int = 7, n_predict: int = 30, start_window: float = 0.85, forecast_horizon: int = 5, export: str = '', residuals: bool = False, forecast_only: bool = False, start_date: Optional[datetime.datetime] = None, end_date: Optional[datetime.datetime] = None, naive: bool = False, chart=True) + +Display Theta forecast + + Parameters + ---------- + data : Union[pd.Series, np.array] + Data to forecast + target_column (str, optional): + Target column to forecast. Defaults to "close". + dataset_name str + The name of the ticker to be predicted + seasonal: str + Seasonal component. One of [N, A, M] + Defaults to MULTIPLICATIVE. + seasonal_periods: int + Number of seasonal periods in a year + If not set, inferred from frequency of the series. + n_predict: int + Number of days to forecast + start_window: float + Size of sliding window from start of timeseries and onwards + forecast_horizon: int + Number of days to forecast when backtesting and retraining historical + export: str + Format to export data + residuals: bool + Whether to show residuals for the model. Defaults to False. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the same + as the previous day's closing price. Defaults to False. diff --git a/website/content/api/forecast/trans/_index.md b/website/content/api/forecast/trans/_index.md new file mode 100644 index 00000000000..0e1cffa8d02 --- /dev/null +++ b/website/content/api/forecast/trans/_index.md @@ -0,0 +1,128 @@ +To obtain charts, make sure to add `chart=True` as the last parameter + +## Get underlying data +### forecast.trans(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', n_predict: int = 5, train_split: float = 0.85, past_covariates: str = None, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, d_model: int = 64, nhead: int = 4, num_encoder_layers: int = 3, num_decoder_layers: int = 3, dim_feedforward: int = 512, activation: str = 'relu', dropout: float = 0.0, batch_size: int = 32, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = 'trans_model', force_reset: bool = True, save_checkpoints: bool = True) -> Tuple[List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], Optional[float], Any] + +Performs Transformer forecasting + + Args: + data (Union[pd.Series, pd.DataFrame]): + Input Data + n_predict (int, optional): + Days to predict. Defaults to 5. + target_column (str, optional): + Target column to forecast. Defaults to "close". + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + d_model (int): + The number of expected features in the encoder/decoder inputs. Defaults to 64. + nhead (int): + The number of heads in the multi-head attention mechanism. Defaults to 4. + num_encoder_layers (int): + The number of encoder layers in the encoder. Defaults to 3. + num_decoder_layers (int): + The number of decoder layers in the encoder. Defaults to 3. + dim_feedforward (int): + The dimension of the feedforward network model. Defaults to 512. + activation (str): + The activation function of encoder/decoder intermediate layer, โ€˜reluโ€™ or โ€˜geluโ€™. Defaults to 'relu'. + dropout (float, optional): + Fraction of neurons afected by Dropout. Defaults to 0.0. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 100. + learning_rate (float, optional): + Defaults to 1e-3. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset (all checkpoints will be + discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True. + + Returns: + List[TimeSeries] + Adjusted Data series + List[TimeSeries] + Historical forecast by best RNN model + List[TimeSeries] + list of Predictions + Optional[float] + Mean average precision error + Any + Best transformer Model + +## Getting charts +### forecast.trans(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', dataset_name: str = '', n_predict: int = 5, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, d_model: int = 64, nhead: int = 4, num_encoder_layers: int = 3, num_decoder_layers: int = 3, dim_feedforward: int = 512, activation: str = 'relu', dropout: float = 0.1, batch_size: int = 16, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = 'trans_model', force_reset: bool = True, save_checkpoints: bool = True, export: str = '', residuals: bool = False, forecast_only: bool = False, start_date: Optional[datetime.datetime] = None, end_date: Optional[datetime.datetime] = None, naive: bool = False, chart=True) + +Display Transformer forecast + + Parameters + ---------- + data (Union[pd.Series, pd.DataFrame]): + Input Data + dataset_name str + The name of the ticker to be predicted + n_predict (int, optional): + Days to predict. Defaults to 5. + target_column (str, optional): + Target column to forecast. Defaults to "close". + train_split (float, optional): + Train/val split. Defaults to 0.85. + past_covariates (str, optional): + Multiple secondary columns to factor in when forecasting. Defaults to None. + forecast_horizon (int, optional): + Forecast horizon when performing historical forecasting. Defaults to 5. + input_chunk_length (int, optional): + Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. + output_chunk_length (int, optional): + The length of the forecast of the model. Defaults to 5. + d_model (int): + The number of expected features in the encoder/decoder inputs. Defaults to 64. + nhead (int): + The number of heads in the multi-head attention mechanism. Defaults to 4. + num_encoder_layers (int): + The number of encoder layers in the encoder. Defaults to 3. + num_decoder_layers (int): + The number of decoder layers in the encoder. Defaults to 3. + dim_feedforward (int): + The dimension of the feedforward network model. Defaults to 512. + activation (str): + The activation function of encoder/decoder intermediate layer, โ€˜reluโ€™ or โ€˜geluโ€™. Defaults to 'relu'. + dropout (float, optional): + Fraction of neurons afected by Dropout. Defaults to 0.1. + batch_size (int, optional): + Number of time series (input and output sequences) used in each training pass. Defaults to 32. + n_epochs (int, optional): + Number of epochs over which to train the model. Defaults to 100. + model_save_name (str, optional): + Name for model. Defaults to "brnn_model". + force_reset (bool, optional): + If set to True, any previously-existing model with the same name will be reset + (all checkpoints will be discarded). Defaults to True. + save_checkpoints (bool, optional): + Whether or not to automatically save the untrained model and checkpoints from training. + Defaults to True. + export: str + Format to export data + residuals: bool + Whether to show residuals for the model. Defaults to False. + forecast_only: bool + Whether to only show dates in the forecasting range. Defaults to False. + start_date: Optional[datetime] + The starting date to perform analysis, data before this is trimmed. Defaults to None. + end_date: Optional[datetime] + The ending date to perform analysis, data after this is trimmed. Defaults to None. + naive: bool + Whether to show the naive baseline. This just assumes the closing price will be the same + as the previous day's closing price. Defaults to False. diff --git a/website/content/api/forex/load/_index.md b/website/content/api/forex/load/_index.md index 96d7173cd35..66f0f2cd4d4 100644 --- a/website/content/api/forex/load/_index.md +++ b/website/content/api/forex/load/_index.md @@ -1,5 +1,5 @@ ## Get underlying data -### forex.load(to_symbol: str, from_symbol: str, resolution: str = 'd', interval: str = '1day', start_date: str = '2021-09-23', source: str = 'YahooFinance') -> pandas.core.frame.DataFrame +### forex.load(to_symbol: str, from_symbol: str, resolution: str = 'd', interval: str = '1day', start_date: str = '2021-09-26', source: str = 'YahooFinance') -> pandas.core.frame.DataFrame Loads forex for two given symbols diff --git a/website/content/api/stocks/ba/cnews/_index.md b/website/content/api/stocks/ba/cnews/_index.md index e0a87cabee1..366e1655038 100644 --- a/website/content/api/stocks/ba/cnews/_index.md +++ b/website/content/api/stocks/ba/cnews/_index.md @@ -1,5 +1,5 @@ ## Get underlying data -### stocks.ba.cnews(symbol: str, start_date: str = '2022-08-24', end_date: str = '2022-09-23') -> List[Dict] +### stocks.ba.cnews(symbol: str, start_date: str = '2022-08-27', end_date: str = '2022-09-26') -> List[Dict] Get news from a company. [Source: Finnhub] diff --git a/website/content/api/stocks/ba/hist/_index.md b/website/content/api/stocks/ba/hist/_index.md index d20c33c8a67..885537fde09 100644 --- a/website/content/api/stocks/ba/hist/_index.md +++ b/website/content/api/stocks/ba/hist/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### stocks.ba.hist(symbol: str, start_date: str = '2022-09-16', end_date: str = '2022-09-23', number: int = 100) -> pandas.core.frame.DataFrame +### stocks.ba.hist(symbol: str, start_date: str = '2022-09-19', end_date: str = '2022-09-26', number: int = 100) -> pandas.core.frame.DataFrame Get hour-level sentiment data for the chosen symbol @@ -25,7 +25,7 @@ Get hour-level sentiment data for the chosen symbol Dataframe of historical sentiment ## Getting charts -### stocks.ba.hist(symbol: str, start_date: str = '2022-09-16', end_date: str = '2022-09-23', number: int = 100, raw: bool = False, limit: int = 10, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) +### stocks.ba.hist(symbol: str, start_date: str = '2022-09-19', end_date: str = '2022-09-26', number: int = 100, raw: bool = False, limit: int = 10, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) Display historical sentiment data of a ticker, and plot a chart with RHI and AHI. diff --git a/website/content/api/stocks/ba/trend/_index.md b/website/content/api/stocks/ba/trend/_index.md index 0a2632b4e67..d0ed3eb72ca 100644 --- a/website/content/api/stocks/ba/trend/_index.md +++ b/website/content/api/stocks/ba/trend/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### stocks.ba.trend(start_date: datetime.datetime = datetime.datetime(2022, 9, 23, 15, 38, 2, 184449), hour: int = 0, number: int = 10) -> pandas.core.frame.DataFrame +### stocks.ba.trend(start_date: datetime.datetime = datetime.datetime(2022, 9, 26, 8, 3, 2, 310220), hour: int = 0, number: int = 10) -> pandas.core.frame.DataFrame Get sentiment data on the most talked about tickers within the last hour @@ -24,7 +24,7 @@ Get sentiment data on the most talked about tickers Dataframe of trending data ## Getting charts -### stocks.ba.trend(start_date: datetime.datetime = datetime.datetime(2022, 9, 23, 15, 38, 2, 184662, chart=True), hour: int = 0, number: int = 10, limit: int = 10, export: str = '') +### stocks.ba.trend(start_date: datetime.datetime = datetime.datetime(2022, 9, 26, 8, 3, 2, 310261, chart=True), hour: int = 0, number: int = 10, limit: int = 10, export: str = '') Display most talked about tickers within the last hour together with their sentiment data. diff --git a/website/content/api/stocks/ca/hcorr/_index.md b/website/content/api/stocks/ca/hcorr/_index.md index 44eb5fb0b22..9d2e29bfee1 100644 --- a/website/content/api/stocks/ca/hcorr/_index.md +++ b/website/content/api/stocks/ca/hcorr/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### stocks.ca.hcorr(similar: List[str], start_date: str = '2021-09-22', candle_type: str = 'a') +### stocks.ca.hcorr(similar: List[str], start_date: str = '2021-09-25', candle_type: str = 'a') Get historical price correlation. [Source: Yahoo Finance] @@ -18,7 +18,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter OHLCA column to use for candles or R for returns, by default "a" for Adjusted Close ## Getting charts -### stocks.ca.hcorr(similar: List[str], start_date: str = '2021-09-22', candle_type: str = 'a', display_full_matrix: bool = False, raw: bool = False, external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, export: str = '', chart=True) +### stocks.ca.hcorr(similar: List[str], start_date: str = '2021-09-25', candle_type: str = 'a', display_full_matrix: bool = False, raw: bool = False, external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, export: str = '', chart=True) Correlation heatmap based on historical price comparison diff --git a/website/content/api/stocks/ca/hist/_index.md b/website/content/api/stocks/ca/hist/_index.md index 2239ee754ec..32e9d210823 100644 --- a/website/content/api/stocks/ca/hist/_index.md +++ b/website/content/api/stocks/ca/hist/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### stocks.ca.hist(similar: List[str], start_date: str = '2021-09-22', candle_type: str = 'a') -> pandas.core.frame.DataFrame +### stocks.ca.hist(similar: List[str], start_date: str = '2021-09-25', candle_type: str = 'a') -> pandas.core.frame.DataFrame Get historical prices for all comparison stocks @@ -22,7 +22,7 @@ Get historical prices for all comparison stocks Dataframe containing candle type variable for each ticker ## Getting charts -### stocks.ca.hist(similar: List[str], start_date: str = '2021-09-22', candle_type: str = 'a', normalize: bool = True, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) +### stocks.ca.hist(similar: List[str], start_date: str = '2021-09-25', candle_type: str = 'a', normalize: bool = True, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) Display historical stock prices. [Source: Yahoo Finance] diff --git a/website/content/api/stocks/ca/volume/_index.md b/website/content/api/stocks/ca/volume/_index.md index bf8f8f0c023..5358aa224a6 100644 --- a/website/content/api/stocks/ca/volume/_index.md +++ b/website/content/api/stocks/ca/volume/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### stocks.ca.volume(similar: List[str], start_date: str = '2021-09-22') -> pandas.core.frame.DataFrame +### stocks.ca.volume(similar: List[str], start_date: str = '2021-09-25') -> pandas.core.frame.DataFrame Get stock volume. [Source: Yahoo Finance] @@ -15,7 +15,7 @@ Get stock volume. [Source: Yahoo Finance] Start date of comparison, by default 1 year ago ## Getting charts -### stocks.ca.volume(similar: List[str], start_date: str = '2021-09-22', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) +### stocks.ca.volume(similar: List[str], start_date: str = '2021-09-25', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) Display stock volume. [Source: Yahoo Finance] diff --git a/website/content/api/stocks/candle/_index.md b/website/content/api/stocks/candle/_index.md index 7a928acdb63..4e4f7902e8f 100644 --- a/website/content/api/stocks/candle/_index.md +++ b/website/content/api/stocks/candle/_index.md @@ -1,5 +1,5 @@ ## Get underlying data -### stocks.candle(symbol: str, data: pandas.core.frame.DataFrame = None, use_matplotlib: bool = True, intraday: bool = False, add_trend: bool = False, ma: Optional[Iterable[int]] = None, asset_type: str = '', start_date: datetime.datetime = datetime.datetime(2019, 9, 19, 15, 38, 2, 731895), interval: int = 1440, end_date: datetime.datetime = datetime.datetime(2022, 9, 23, 15, 38, 2, 731896), prepost: bool = False, source: str = 'YahooFinance', iexrange: str = 'ytd', weekly: bool = False, monthly: bool = False, external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, raw: bool = False) +### stocks.candle(symbol: str, data: pandas.core.frame.DataFrame = None, use_matplotlib: bool = True, intraday: bool = False, add_trend: bool = False, ma: Optional[Iterable[int]] = None, asset_type: str = '', start_date: datetime.datetime = datetime.datetime(2019, 9, 22, 8, 3, 1, 875559), interval: int = 1440, end_date: datetime.datetime = datetime.datetime(2022, 9, 26, 8, 3, 1, 875573), prepost: bool = False, source: str = 'YahooFinance', iexrange: str = 'ytd', weekly: bool = False, monthly: bool = False, external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, raw: bool = False) Shows candle plot of loaded ticker. [Source: Yahoo Finance, IEX Cloud or Alpha Vantage] diff --git a/website/content/api/stocks/dd/pt/_index.md b/website/content/api/stocks/dd/pt/_index.md index 2043a723488..b90e94ff4da 100644 --- a/website/content/api/stocks/dd/pt/_index.md +++ b/website/content/api/stocks/dd/pt/_index.md @@ -16,7 +16,7 @@ Get analysts' price targets for a given stock. [Source: Business Insider] Analysts data ## Getting charts -### stocks.dd.pt(symbol: str, data: pandas.core.frame.DataFrame, start_date: str = '2022-09-23', limit: int = 10, raw: bool = False, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) +### stocks.dd.pt(symbol: str, data: pandas.core.frame.DataFrame, start_date: str = '2022-09-26', limit: int = 10, raw: bool = False, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) Display analysts' price targets for a given stock. [Source: Business Insider] diff --git a/website/content/api/stocks/disc/dividends/_index.md b/website/content/api/stocks/disc/dividends/_index.md index fd5bbd330c2..fa0ee2a234c 100644 --- a/website/content/api/stocks/disc/dividends/_index.md +++ b/website/content/api/stocks/disc/dividends/_index.md @@ -1,5 +1,5 @@ ## Get underlying data -### stocks.disc.dividends(date: str = '2022-09-23') -> pandas.core.frame.DataFrame +### stocks.disc.dividends(date: str = '2022-09-26') -> pandas.core.frame.DataFrame Gets dividend calendar for given date. Date represents Ex-Dividend Date diff --git a/website/content/api/stocks/disc/ipo/_index.md b/website/content/api/stocks/disc/ipo/_index.md index 593a9bf03b2..1accd9d1c89 100644 --- a/website/content/api/stocks/disc/ipo/_index.md +++ b/website/content/api/stocks/disc/ipo/_index.md @@ -1,5 +1,5 @@ ## Get underlying data -### stocks.disc.ipo(start_date: str = '2022-09-18', end_date: str = '2022-09-23') -> pandas.core.frame.DataFrame +### stocks.disc.ipo(start_date: str = '2022-09-21', end_date: str = '2022-09-26') -> pandas.core.frame.DataFrame Get IPO calendar diff --git a/website/content/api/stocks/dps/ftd/_index.md b/website/content/api/stocks/dps/ftd/_index.md index 6156ff06859..7b203879430 100644 --- a/website/content/api/stocks/dps/ftd/_index.md +++ b/website/content/api/stocks/dps/ftd/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### stocks.dps.ftd(symbol: str, start_date: str = '2022-07-25', end_date: str = '2022-09-23', limit: int = 0) -> pandas.core.frame.DataFrame +### stocks.dps.ftd(symbol: str, start_date: str = '2022-07-28', end_date: str = '2022-09-26', limit: int = 0) -> pandas.core.frame.DataFrame Display fails-to-deliver data for a given ticker. [Source: SEC] @@ -22,7 +22,7 @@ Display fails-to-deliver data for a given ticker. [Source: SEC] Fail to deliver data ## Getting charts -### stocks.dps.ftd(symbol: str, data: pandas.core.frame.DataFrame, start_date: str = '2022-07-25', end_date: str = '2022-09-23', limit: int = 0, raw: bool = False, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) +### stocks.dps.ftd(symbol: str, data: pandas.core.frame.DataFrame, start_date: str = '2022-07-28', end_date: str = '2022-09-26', limit: int = 0, raw: bool = False, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) Display fails-to-deliver data for a given ticker. [Source: SEC] diff --git a/website/content/api/stocks/fa/analysis/_index.md b/website/content/api/stocks/fa/analysis/_index.md index 53414f8691c..c0512877c4a 100644 --- a/website/content/api/stocks/fa/analysis/_index.md +++ b/website/content/api/stocks/fa/analysis/_index.md @@ -1,5 +1,5 @@ ## Get underlying data -### stocks.fa.analysis(symbol: str) -> str +### stocks.fa.analysis(symbol: str) -> pandas.core.frame.DataFrame Save time reading SEC filings with the help of machine learning. [Source: https://eclect.us] diff --git a/website/content/api/stocks/fa/mktcap/_index.md b/website/content/api/stocks/fa/mktcap/_index.md index e7d04fcdbf9..afe6cb2f267 100644 --- a/website/content/api/stocks/fa/mktcap/_index.md +++ b/website/content/api/stocks/fa/mktcap/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### stocks.fa.mktcap(symbol: str, start_date: str = '2019-09-21') -> Tuple[pandas.core.frame.DataFrame, str] +### stocks.fa.mktcap(symbol: str, start_date: str = '2019-09-24') -> Tuple[pandas.core.frame.DataFrame, str] Get market cap over time for ticker. [Source: Yahoo Finance] @@ -20,7 +20,7 @@ Get market cap over time for ticker. [Source: Yahoo Finance] Currency of ticker ## Getting charts -### stocks.fa.mktcap(symbol: str, start_date: str = '2019-09-21', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) +### stocks.fa.mktcap(symbol: str, start_date: str = '2019-09-24', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) Display market cap over time. [Source: Yahoo Finance] diff --git a/website/content/api/stocks/ins/act/_index.md b/website/content/api/stocks/ins/act/_index.md index 11601611c0e..fd2c827538d 100644 --- a/website/content/api/stocks/ins/act/_index.md +++ b/website/content/api/stocks/ins/act/_index.md @@ -16,7 +16,7 @@ Get insider activity. [Source: Business Insider] Get insider activity data ## Getting charts -### stocks.ins.act(data: pandas.core.frame.DataFrame, symbol: str, start_date: str = '2019-09-19', interval: str = '1440min', limit: int = 10, raw: bool = False, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) +### stocks.ins.act(data: pandas.core.frame.DataFrame, symbol: str, start_date: str = '2019-09-22', interval: str = '1440min', limit: int = 10, raw: bool = False, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) Display insider activity. [Source: Business Insider] diff --git a/website/content/api/stocks/load/_index.md b/website/content/api/stocks/load/_index.md index 48a4dff40ac..fbcf84b7e7b 100644 --- a/website/content/api/stocks/load/_index.md +++ b/website/content/api/stocks/load/_index.md @@ -1,5 +1,5 @@ ## Get underlying data -### stocks.load(symbol: str, start_date: datetime.datetime = datetime.datetime(2019, 9, 19, 15, 38, 2, 731876), interval: int = 1440, end_date: datetime.datetime = datetime.datetime(2022, 9, 23, 15, 38, 2, 731892), prepost: bool = False, source: str = 'YahooFinance', iexrange: str = 'ytd', weekly: bool = False, monthly: bool = False) +### stocks.load(symbol: str, start_date: datetime.datetime = None, interval: int = 1440, end_date: datetime.datetime = None, prepost: bool = False, source: str = 'YahooFinance', iexrange: str = 'ytd', weekly: bool = False, monthly: bool = False) Load a symbol to perform analysis using the string above as a template. diff --git a/website/content/api/stocks/options/pcr/_index.md b/website/content/api/stocks/options/pcr/_index.md index 583862ad116..b0d89fc9879 100644 --- a/website/content/api/stocks/options/pcr/_index.md +++ b/website/content/api/stocks/options/pcr/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### stocks.options.pcr(symbol: str, window: int = 30, start_date: str = '2021-09-22') -> pandas.core.frame.DataFrame +### stocks.options.pcr(symbol: str, window: int = 30, start_date: str = '2021-09-25') -> pandas.core.frame.DataFrame Gets put call ratio over last time window [Source: AlphaQuery.com] @@ -15,7 +15,7 @@ Gets put call ratio over last time window [Source: AlphaQuery.com] Start date to plot, by default last 366 days ## Getting charts -### stocks.options.pcr(symbol: str, window: int = 30, start_date: str = '2021-09-22', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) +### stocks.options.pcr(symbol: str, window: int = 30, start_date: str = '2021-09-25', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) Display put call ratio [Source: AlphaQuery.com] diff --git a/website/content/api/stocks/screener/historical/_index.md b/website/content/api/stocks/screener/historical/_index.md index b127c858119..d2c74f4f0d6 100644 --- a/website/content/api/stocks/screener/historical/_index.md +++ b/website/content/api/stocks/screener/historical/_index.md @@ -1,7 +1,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter ## Get underlying data -### stocks.screener.historical(preset_loaded: str = 'top_gainers', limit: int = 10, start_date: str = '2022-03-27', type_candle: str = 'a', normalize: bool = True) +### stocks.screener.historical(preset_loaded: str = 'top_gainers', limit: int = 10, start_date: str = '2022-03-30', type_candle: str = 'a', normalize: bool = True) View historical price of stocks that meet preset @@ -28,7 +28,7 @@ View historical price of stocks that meet preset Whether some random stock selection due to limitations ## Getting charts -### stocks.screener.historical(preset_loaded: str = 'top_gainers', limit: int = 10, start_date: str = '2022-03-27', type_candle: str = 'a', normalize: bool = True, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> List[str] +### stocks.screener.historical(preset_loaded: str = 'top_gainers', limit: int = 10, start_date: str = '2022-03-30', type_candle: str = 'a', normalize: bool = True, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> List[str] View historical price of stocks that meet preset diff --git a/website/content/terminal/common/pred/_index.md b/website/content/terminal/common/pred/_index.md deleted file mode 100755 index f893c9c6fd2..00000000000 --- a/website/content/terminal/common/pred/_index.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -geekdocCollapseSection: true ---- - -**Prediction Techniques** are methods of using historical data to train machine learning models that can predict future outcomes based on statistical computations. Predictive analytics is being used by businesses to tackle difficulties such as choosing which items to advertise to specific client segments and on which platforms, as well as uncover new insights from existing data. Predictive modeling is defined as the process of approximating a mapping function (f) from input variables (X) to output variables (Y). - -**Available commands** -{{< toc-tree >}} diff --git a/website/content/terminal/common/pred/arima/_index.md b/website/content/terminal/common/pred/arima/_index.md deleted file mode 100755 index da254c55279..00000000000 --- a/website/content/terminal/common/pred/arima/_index.md +++ /dev/null @@ -1,29 +0,0 @@ -``` -usage: arima [-d N_DAYS] [-i {aic,aicc,bic,hqic,oob}] [-s] [-o S_ORDER] [-r] [-e S_END_DATE] [-h] - [--export {png,jpg,pdf,svg}] -``` - -In statistics and econometrics, and in particular in time series analysis, an autoregressive integrated moving average -(ARIMA) model is a generalization of an autoregressive moving average (ARMA) model. Both of these models are fitted to -time series data either to better understand the data or to predict future points in the series (forecasting). -ARIMA(p,d,q) where parameters p, d, and q are non-negative integers, p is the order (number of time lags) of the -autoregressive model, d is the degree of differencing (the number of times the data have had past values subtracted), and -q is the order of the moving-average model. - -``` -optional arguments: - -d N_DAYS, --days N_DAYS - prediction days. (default: 5) - -i {aic,aicc,bic,hqic,oob}, --ic {aic,aicc,bic,hqic,oob} - information criteria. (default: aic) - -s, --seasonal Use weekly seasonal data. (default: False) - -o S_ORDER, --order S_ORDER - arima model order (p,d,q) in format: p,d,q. (default: ) - -r, --results results about ARIMA summary flag. (default: False) - -e S_END_DATE, --end S_END_DATE - The end date (format YYYY-MM-DD) to select - Backtesting (default: None) - -h, --help show this help message (default: False) - --export {png,jpg,pdf,svg} - Export figure into png, jpg, pdf, svg (default: ) -``` -![ArimaImg](https://user-images.githubusercontent.com/18151143/154813853-8cd8f9e4-9d99-4d53-8fe0-8110c13eac21.png) diff --git a/website/content/terminal/common/pred/conv1d/_index.md b/website/content/terminal/common/pred/conv1d/_index.md deleted file mode 100755 index 07bde85feea..00000000000 --- a/website/content/terminal/common/pred/conv1d/_index.md +++ /dev/null @@ -1,33 +0,0 @@ -``` -usage: conv1d [-d N_DAYS] [-i N_INPUTS] [--epochs N_EPOCHS] [-e S_END_DATE] [--batch_size N_BATCH_SIZE] [--xla_cpu] [--xla_gpu] - [--force_gpu_allow_growth {true,false,default}] [--loops N_LOOPS] [-v VALID_SPLIT] [--lr LR] [--no_shuffle] [-h] -``` - -1D Convolutional Neural Net. - -``` -optional arguments: - -d N_DAYS, --days N_DAYS - prediction days. - -i N_INPUTS, --input N_INPUTS - number of days to use for prediction. - --epochs N_EPOCHS number of training epochs. - -e S_END_DATE, --end S_END_DATE - The end date (format YYYY-MM-DD) to select - Backtesting - --batch_size N_BATCH_SIZE - batch size for model fitting (use a power of 2) - --xla_cpu enable XLA for CPU (see https://www.tensorflow.org/xla) - --xla_gpu enable XLA for GPU (see https://www.tensorflow.org/xla) - --force_gpu_allow_growth {true,false,default} - true: GPU memory will grow as needed. - false: TensorFlow will allocate 100% of GPU memory. - default: usually the same as false, uses env/TensorFlow default - --loops N_LOOPS number of loops to iterate and train models - -v VALID_SPLIT, --valid VALID_SPLIT - Validation data split fraction - --lr LR Specify learning rate for optimizer. - --no_shuffle Specify if shuffling validation inputs. - -h, --help show this help message -``` -Feature Screenshot - conv1d - diff --git a/website/content/terminal/common/pred/ets/_index.md b/website/content/terminal/common/pred/ets/_index.md deleted file mode 100755 index fc60e1b103c..00000000000 --- a/website/content/terminal/common/pred/ets/_index.md +++ /dev/null @@ -1,26 +0,0 @@ -``` -usage: ets [-d N_DAYS] [-t TREND] [-s SEASONAL] [-p SEASONAL_PERIODS] [-e S_END_DATE] [-h] -``` - -Exponential Smoothing, see https://otexts.com/fpp2/taxonomy.html Trend='N', Seasonal='N': Simple Exponential Smoothing Trend='N', Seasonal='A': -Exponential Smoothing Trend='N', Seasonal='M': Exponential Smoothing Trend='A', Seasonal='N': Holtโ€™s linear method Trend='A', Seasonal='A': Additive -Holt-Wintersโ€™ method Trend='A', Seasonal='M': Multiplicative Holt-Wintersโ€™ method Trend='Ad', Seasonal='N': Additive damped trend method Trend='Ad', -Seasonal='A': Exponential Smoothing Trend='Ad', Seasonal='M': Holt-Wintersโ€™ damped method Trend component: N: None, A: Additive, Ad: Additive Damped -Seasonality component: N: None, A: Additive, M: Multiplicative - -``` -optional arguments: - -d N_DAYS, --days N_DAYS - prediction days. - -t TREND, --trend TREND - Trend component: N: None, A: Additive, Ad: Additive Damped. - -s SEASONAL, --seasonal SEASONAL - Seasonality component: N: None, A: Additive, M: Multiplicative. - -p SEASONAL_PERIODS, --periods SEASONAL_PERIODS - Seasonal periods. - -e S_END_DATE, --end S_END_DATE - The end date (format YYYY-MM-DD) to select - Backtesting - -h, --help show this help message -``` - -![ETS](https://user-images.githubusercontent.com/18151143/154813959-6868dcce-cd35-4b89-b298-12954de21541.png) diff --git a/website/content/terminal/common/pred/knn/_index.md b/website/content/terminal/common/pred/knn/_index.md deleted file mode 100755 index 7790fa89131..00000000000 --- a/website/content/terminal/common/pred/knn/_index.md +++ /dev/null @@ -1,27 +0,0 @@ -``` -usage: knn [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS] [-n N_NEIGHBORS] [-e S_END_DATE] [-t VALID_SPLIT] [-p {normalization,standardization,minmax,none}] [--no_shuffle] [-h] -``` - -K nearest neighbors is a simple algorithm that stores all available cases and predict the numerical target based on a similarity measure (e.g. distance functions). - -``` -optional arguments: - -i N_INPUTS, --input N_INPUTS - number of days to use as input for prediction. - -d N_DAYS, --days N_DAYS - prediction days. - -j N_JUMPS, --jumps N_JUMPS - number of jumps in training data. - -n N_NEIGHBORS, --neighbors N_NEIGHBORS - number of neighbors to use on the algorithm. - -e S_END_DATE, --end S_END_DATE - The end date (format YYYY-MM-DD) to select for testing - -t VALID_SPLIT, --test_size VALID_SPLIT - Percentage of data to validate in sample - -p {normalization,standardization,minmax,none}, --pp {normalization,standardization,minmax,none} - pre-processing data. - --no_shuffle Specify if shuffling validation inputs. - -h, --help show this help message -``` -![KNN](https://user-images.githubusercontent.com/18151143/154814016-f9589028-38c0-4468-a97d-b4cc7f2a7366.png) - diff --git a/website/content/terminal/common/pred/lstm/_index.md b/website/content/terminal/common/pred/lstm/_index.md deleted file mode 100755 index 54c69c8268e..00000000000 --- a/website/content/terminal/common/pred/lstm/_index.md +++ /dev/null @@ -1,45 +0,0 @@ -``` -usage: lstm [-d N_DAYS] [-i N_INPUTS] [--epochs N_EPOCHS] [-e S_END_DATE] [--batch_size N_BATCH_SIZE] [--xla_cpu] [--xla_gpu] - [--force_gpu_allow_growth {true,false,default}] [--loops N_LOOPS] [-v VALID_SPLIT] [--lr LR] [--no_shuffle] [-h] -``` - -Long-Short Term Memory - -Due to the complexity of defining a model through command line, one can define it in: [config_neural_network_models.py](/config_neural_network_models.py) -``` -Long_Short_Term_Memory \ - = [ {'LSTM': - {'units':25, 'activation':'tanh', 'return_sequences':True} }, - {'LSTM': - {'units':15, 'activation':'tanh', 'return_sequences':False} }, - {'Dense': - {'activation':'relu'} }] -``` - -``` -optional arguments: - -d N_DAYS, --days N_DAYS - prediction days. - -i N_INPUTS, --input N_INPUTS - number of days to use for prediction. - --epochs N_EPOCHS number of training epochs. - -e S_END_DATE, --end S_END_DATE - The end date (format YYYY-MM-DD) to select - Backtesting - --batch_size N_BATCH_SIZE - batch size for model fitting (use a power of 2) - --xla_cpu enable XLA for CPU (see https://www.tensorflow.org/xla) - --xla_gpu enable XLA for GPU (see https://www.tensorflow.org/xla) - --force_gpu_allow_growth {true,false,default} - true: GPU memory will grow as needed. - false: TensorFlow will allocate 100% of GPU memory. - default: usually the same as false, uses env/TensorFlow default - --loops N_LOOPS number of loops to iterate and train models - -v VALID_SPLIT, --valid VALID_SPLIT - Validation data split fraction - --lr LR Specify learning rate for optimizer. - --no_shuffle Specify if shuffling validation inputs. - -h, --help show this help message -``` -![LSTM](https://user-images.githubusercontent.com/18151143/154814043-79fc59f2-635b-4264-8f74-215bb2621201.png) - - diff --git a/website/content/terminal/common/pred/mc/_index.md b/website/content/terminal/common/pred/mc/_index.md deleted file mode 100644 index 1bd343c57b7..00000000000 --- a/website/content/terminal/common/pred/mc/_index.md +++ /dev/null @@ -1,20 +0,0 @@ -``` -usage: mc [-d N_DAYS] [-n N_SIMS] [--dist {normal,lognormal}] [-h] [--export {png,jpg,pdf,svg}] -``` - -Perform Monte Carlo forecasting - -``` -optional arguments: - -d N_DAYS, --days N_DAYS - Days to predict (default: 30) - -n N_SIMS, --num N_SIMS - Number of simulations to perform (default: 100) - --dist {normal,lognormal} - Whether to model returns or log returns (default: lognormal) - -h, --help show this help message (default: False) - --export {png,jpg,pdf,svg} - Export figure into png, jpg, pdf, svg (default: ) -``` - -![MC](https://user-images.githubusercontent.com/18151143/154814179-0cfe9c43-9395-41df-8d15-b8a1de1a8121.png) diff --git a/website/content/terminal/common/pred/mlp/_index.md b/website/content/terminal/common/pred/mlp/_index.md deleted file mode 100755 index c5902f4cc98..00000000000 --- a/website/content/terminal/common/pred/mlp/_index.md +++ /dev/null @@ -1,48 +0,0 @@ -``` -usage: mlp [-d N_DAYS] [-i N_INPUTS] [--epochs N_EPOCHS] [-e S_END_DATE] [--batch_size N_BATCH_SIZE] [--xla_cpu] [--xla_gpu] - [--force_gpu_allow_growth {true,false,default}] [--loops N_LOOPS] [-v VALID_SPLIT] [--lr LR] [--no_shuffle] [-h] -``` - -Multi-Layered-Perceptron. - -Due to the complexity of defining a model through command line, one can define it in: [config_neural_network_models.txt](/config_neural_network_models.py) -``` -MultiLayer_Perceptron \ - = [ {'Dense': - {'units':50, 'activation':'relu'} }, - {'Dense': - {'units':100, 'activation':'relu'} }, - {'Dense': - {'units':80, 'activation':'relu'} }, - {'Dense': - {'units':30, 'activation':'relu'} }, - {'Dense': - {'activation':'relu'} }] -``` - -``` -optional arguments: - -d N_DAYS, --days N_DAYS - prediction days. - -i N_INPUTS, --input N_INPUTS - number of days to use for prediction. - --epochs N_EPOCHS number of training epochs. - -e S_END_DATE, --end S_END_DATE - The end date (format YYYY-MM-DD) to select - Backtesting - --batch_size N_BATCH_SIZE - batch size for model fitting (use a power of 2) - --xla_cpu enable XLA for CPU (see https://www.tensorflow.org/xla) - --xla_gpu enable XLA for GPU (see https://www.tensorflow.org/xla) - --force_gpu_allow_growth {true,false,default} - true: GPU memory will grow as needed. - false: TensorFlow will allocate 100% of GPU memory. - default: usually the same as false, uses env/TensorFlow default - --loops N_LOOPS number of loops to iterate and train models - -v VALID_SPLIT, --valid VALID_SPLIT - Validation data split fraction - --lr LR Specify learning rate for optimizer. - --no_shuffle Specify if shuffling validation inputs. - -h, --help show this help message -``` - -![MLP](https://user-images.githubusercontent.com/18151143/154814230-5799aa66-7e2c-42bf-a0b9-8b207b9f464c.png) diff --git a/website/content/terminal/common/pred/regression/_index.md b/website/content/terminal/common/pred/regression/_index.md deleted file mode 100755 index f8d94e2d9e5..00000000000 --- a/website/content/terminal/common/pred/regression/_index.md +++ /dev/null @@ -1,27 +0,0 @@ -``` -usage: regression [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS] [-e S_END_DATE] [-p N_POLYNOMIAL] [-h] - [--export {png,jpg,pdf,svg}] -``` - -Regression attempts to model the relationship between two variables by fitting a linear/quadratic/cubic/other equation to -observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent -variable. - -``` -optional arguments: - -i N_INPUTS, --input N_INPUTS - number of days to use for prediction. - -d N_DAYS, --days N_DAYS - prediction days. - -j N_JUMPS, --jumps N_JUMPS - number of jumps in training data. - -e S_END_DATE, --end S_END_DATE - The end date (format YYYY-MM-DD) to select - Backtesting - -p N_POLYNOMIAL, --polynomial N_POLYNOMIAL - polynomial associated with regression. - -h, --help show this help message - --export {png,jpg,pdf,svg} - Export figure into png, jpg, pdf, svg -``` - -![Regression](https://user-images.githubusercontent.com/18151143/154814693-779e8610-7694-43d8-b1cc-a853458fee2f.png) diff --git a/website/content/terminal/common/pred/rnn/_index.md b/website/content/terminal/common/pred/rnn/_index.md deleted file mode 100755 index d0093728973..00000000000 --- a/website/content/terminal/common/pred/rnn/_index.md +++ /dev/null @@ -1,48 +0,0 @@ -``` -usage: rnn [-d N_DAYS] [-i N_INPUTS] [--epochs N_EPOCHS] [-e S_END_DATE] [--batch_size N_BATCH_SIZE] [--xla_cpu] [--xla_gpu] - [--force_gpu_allow_growth {true,false,default}] [--loops N_LOOPS] [-v VALID_SPLIT] [--lr LR] [--no_shuffle] [-h] -``` - -Recurrent Neural Network - -Due to the complexity of defining a model through command line, one can define it in: [config_neural_network_models.txt](/config_neural_network_models.py) -``` -Recurrent_Neural_Network \ - = [ {'SimpleRNN': - {'units':100, 'activation':'linear', 'return_sequences':True} }, - {'SimpleRNN': - {'units':50, 'activation':'linear', 'return_sequences':True} }, - {'Dropout': - {'rate':0.2} }, - {'SimpleRNN': - {'units':21, 'activation':'linear', 'return_sequences':False} }, - {'Dense': - {'activation':'relu'} }] -``` - -``` -optional arguments: - -d N_DAYS, --days N_DAYS - prediction days. - -i N_INPUTS, --input N_INPUTS - number of days to use for prediction. - --epochs N_EPOCHS number of training epochs. - -e S_END_DATE, --end S_END_DATE - The end date (format YYYY-MM-DD) to select - Backtesting - --batch_size N_BATCH_SIZE - batch size for model fitting (use a power of 2) - --xla_cpu enable XLA for CPU (see https://www.tensorflow.org/xla) - --xla_gpu enable XLA for GPU (see https://www.tensorflow.org/xla) - --force_gpu_allow_growth {true,false,default} - true: GPU memory will grow as needed. - false: TensorFlow will allocate 100% of GPU memory. - default: usually the same as false, uses env/TensorFlow default - --loops N_LOOPS number of loops to iterate and train models - -v VALID_SPLIT, --valid VALID_SPLIT - Validation data split fraction - --lr LR Specify learning rate for optimizer. - --no_shuffle Specify if shuffling validation inputs. - -h, --help show this help message -``` - -![RNN](https://user-images.githubusercontent.com/18151143/154814291-4bb741d3-8eea-4020-8224-1d42d0adc77c.png) diff --git a/website/content/terminal/forecast/_index.md b/website/content/terminal/forecast/_index.md new file mode 100644 index 00000000000..378a6ab88af --- /dev/null +++ b/website/content/terminal/forecast/_index.md @@ -0,0 +1,559 @@ +--- +title: Introduction to Forecast +keywords: "machine learning, statistics, deep learning, neural networks, time series, research, feature engineering, hyperparameters" +excerpt: "The introduction to Forecast explains how to use the menu" +geekdocCollapseSection: true +--- + +The forecasting menu is a machine learning toolkit that provides practitioners with high-level components that can quickly provide state-of-the-art results, be it with with classical or deep learning models, while also providing researchers with low-level components that can be mixed, matched and fine tuned to build new approaches and custom tuned models. Bring in multiple datasets and train machine learning models with unlimited external factors to see how underlying data may change future forecasting predictions and accuracy. + +## Accessing the Forecast menu + +The Forecast menu is called upon by typing `forecast` which opens the following menu: +``` +(๐Ÿฆ‹) / $ forecast +``` +image + +## How to Use + +To begin any machine learning, you must first load in data. The menu supports importing both terminal datasets found in `stocks` and `cryptocurrency`, along with external datasets in the form of `.csv` that can be placed in the following location: `.../OpenBBTerminal/custom_imports/forecast/` + +It is important to note, if you ever have troubles on running a command, please read the help commands to guide you on what is available. You can do this by trailing any command with a `-h` + +``` +(๐Ÿฆ‹) /forecast/ $ -h +``` + +### Loading Data + +Importing data from within the terminal can be simply done as follows: + +``` +(๐Ÿฆ‹) / $ stocks +(๐Ÿฆ‹) /stocks/ $ load AAPL +(๐Ÿฆ‹) /stocks/ $ forecast +``` +The menu can support loading in unlimited datasets. Once the first one is loaded, the menu options should turn blue to allow you to begin working through the menu options. + +To load external data in the form as `.csv`, please place them into the folder shown within your terminal menu and then load them in as follows: + +``` +(๐Ÿฆ‹) / $ forecast +(๐Ÿฆ‹) /forecast/ $ load msft.csv +(๐Ÿฆ‹) /forecast/ $ load btc.csv +``` +image + +### Understanding the structure of the menu + +The menu is broken up into three distinct sections. These sections represent the typical order of operations a machine learning professional would have to take in order to complete a project. + +Once data is loaded in, begin with `Exploration`. + +`Exploration`: Explore the datasets loaded into the menu to further understand your data and create unique new datasets by combining and analyzing features. Functions to note: `plot`, `combine`, `desc`, `corr` +image + + +`Feature Engineering`: Manuipulate datasets (addition, deletion, combination, mutation) of your data set to potentially improve machine learning model training, by providing new features and covariates that may leading to better performance and greater accuracy. +image + + +`TimeSeries Forecasting`: Train state of the art models on custom datasets and experiment tuning hyperparameters. For more information on specific model implementations, please see [Darts Models](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.html) for in depth documentation. +image + + +## Sample workflow #1 (beginner) + +Let's begin by using one of the datasets we loaded in previously : `AAPL` + +We will be forecasting `5 Business days` ahead for the remaider of these workflows unless specified. + +Note: All models automatically perform Historical backtesting on the test split before providing a prediction. + +Note: `MAPE` = [mean average percentage error](https://en.wikipedia.org/wiki/Mean_absolute_percentage_error). We use MAPE as it is quite convenient and scale independent since it calculates error as a percentage instead of an absolute value. THere are many more metrics to compare time series. The metrics will compare only common slices of series when the two series are not aligned, and parallelize computation over a large number of pairs of series. More metrics will be released in future versions of the menu. + +``` +(๐Ÿฆ‹) /forecast/ $ plot AAPL.close +``` +image + +``` +(๐Ÿฆ‹) /forecast/ $ desc AAPL + + Showing Descriptive Statistics for Dataset AAPL +โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ count โ”‚ 758.00 โ”‚ 758.00 โ”‚ 758.00 โ”‚ 758.00 โ”‚ 758.00 โ”‚ 758.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ mean โ”‚ 121.57 โ”‚ 123.07 โ”‚ 120.15 โ”‚ 121.68 โ”‚ 120.72 โ”‚ 113864153.69 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ std โ”‚ 36.37 โ”‚ 36.74 โ”‚ 35.95 โ”‚ 36.34 โ”‚ 36.61 โ”‚ 56098731.72 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ min โ”‚ 53.47 โ”‚ 54.11 โ”‚ 52.77 โ”‚ 53.54 โ”‚ 52.38 โ”‚ 41000000.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 25% โ”‚ 83.10 โ”‚ 86.41 โ”‚ 83.04 โ”‚ 84.16 โ”‚ 83.00 โ”‚ 77032650.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 50% โ”‚ 128.18 โ”‚ 129.64 โ”‚ 126.63 โ”‚ 127.86 โ”‚ 126.70 โ”‚ 98135650.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 75% โ”‚ 149.05 โ”‚ 150.38 โ”‚ 147.69 โ”‚ 149.14 โ”‚ 148.50 โ”‚ 131152875.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ max โ”‚ 182.63 โ”‚ 182.94 โ”‚ 179.12 โ”‚ 182.01 โ”‚ 181.26 โ”‚ 426510000.00 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` + +Let's use a simple **Probabilistic Exponential Smoothing Model** to predict the close price. Keep in mind all models are perform automatic historical backtesting before providing future forecasts. + +Note: All models forecaste `close` by default. + +``` +(๐Ÿฆ‹) /forecast/ $ expo AAPL +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 115/115 [00:17<00:00, 6.44it/s] +Exponential smoothing obtains MAPE: 3.86% + + + Actual price: 157.37 +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Datetime โ”ƒ Prediction โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 2022-09-12 โ”‚ 157.99 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-13 โ”‚ 157.89 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-14 โ”‚ 158.32 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-15 โ”‚ 158.25 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-16 โ”‚ 158.52 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` +image + +That looks great, but we might want to see it a little more up close. Lets set the flag for `--forecast-only`. + +image + + +We can also play with some models that are bit more advanced. As we go down the list, models begin to become larger in parameter size and complexity. This will play a key role later on when we want to train models with `past_covariates` (aka. external factors). + +This time lets test with a **Recurrent Neural Network** which by default uses an `LSTM` backbone. We can also choose to test out a `GRU` backbone to experiment. Let's do both and see if we can improve our accuracy and reduce the overall MAPE. + +``` +(๐Ÿฆ‹) /forecast/ $ rnn AAPL --forecast-only +Epoch 193: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 25/25 [00:00<00:00, 129.49it/s, loss=-2.74, train_loss=-2.75, val_loss=-2.22] +Predicting RNN for 5 days +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 115/115 [00:01<00:00, 86.89it/s] +RNN model obtains MAPE: 3.69% + + + Actual price: 157.37 +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Datetime โ”ƒ Prediction โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 2022-09-12 โ”‚ 155.71 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-13 โ”‚ 155.42 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-14 โ”‚ 155.11 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-15 โ”‚ 154.81 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-16 โ”‚ 154.51 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` +image + +Seems like we improved the performance and reduced MAPE! + +Now for the second task, we would like to change the model type from `LSTM` --> `GRU`. Use the `-h` flag to understand the particular parameters one can change for RNN. (Please note that the parameters are different for each model). + +``` +(๐Ÿฆ‹) /forecast/ $ rnn -h +usage: rnn [--hidden-dim HIDDEN_DIM] [--training_length TRAINING_LENGTH] [--naive] [-d {AAPL,msft}] [-c TARGET_COLUMN] [-n N_DAYS] [-t TRAIN_SPLIT] + [-i INPUT_CHUNK_LENGTH] [--force-reset FORCE_RESET] [--save-checkpoints SAVE_CHECKPOINTS] [--model-save-name MODEL_SAVE_NAME] + [--n-epochs N_EPOCHS] [--model-type MODEL_TYPE] [--dropout DROPOUT] [--batch-size BATCH_SIZE] [--end S_END_DATE] [--start S_START_DATE] + [--learning-rate LEARNING_RATE] [--residuals] [--forecast-only] [-h] [--export EXPORT] + +Perform RNN forecast (Vanilla RNN, LSTM, GRU) + +optional arguments: + --hidden-dim HIDDEN_DIM + Size for feature maps for each hidden RNN layer (h_n) (default: 20) + --training_length TRAINING_LENGTH + The length of both input (target and covariates) and output (target) time series used during training. Generally speaking, + training_length should have a higher value than input_chunk_length because otherwise during training the RNN is never run for as + many iterations as it will during training. (default: 20) + --naive Show the naive baseline for a model. (default: False) + -d {AAPL,msft}, --target-dataset {AAPL,msft} + The name of the dataset you want to select (default: None) + -c TARGET_COLUMN, --target-column TARGET_COLUMN + The name of the specific column you want to use (default: close) + -n N_DAYS, --n-days N_DAYS + prediction days. (default: 5) + -t TRAIN_SPLIT, --train-split TRAIN_SPLIT + Start point for rolling training and forecast window. 0.0-1.0 (default: 0.85) + -i INPUT_CHUNK_LENGTH, --input-chunk-length INPUT_CHUNK_LENGTH + Number of past time steps for forecasting module at prediction time. (default: 14) + --force-reset FORCE_RESET + If set to True, any previously-existing model with the same name will be reset (all checkpoints will be discarded). (default: + True) + --save-checkpoints SAVE_CHECKPOINTS + Whether to automatically save the untrained model and checkpoints. (default: True) + --model-save-name MODEL_SAVE_NAME + Name of the model to save. (default: rnn_model) + --n-epochs N_EPOCHS Number of epochs over which to train the model. (default: 300) + --model-type MODEL_TYPE + Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU") (default: LSTM) + --dropout DROPOUT Fraction of neurons afected by Dropout. (default: 0) + --batch-size BATCH_SIZE + Number of time series (input and output) used in each training pass (default: 32) + --end S_END_DATE The end date (format YYYY-MM-DD) to select for testing (default: None) + --start S_START_DATE The start date (format YYYY-MM-DD) to select for testing (default: None) + --learning-rate LEARNING_RATE + Learning rate during training. (default: 0.001) + --residuals Show the residuals for the model. (default: False) + --forecast-only Do not plot the hisotorical data without forecasts. (default: False) + -h, --help show this help message (default: False) + --export EXPORT Export figure into png, jpg, pdf, svg (default: ) +``` + +Lets change the `--model-type` parameter to `GRU` and rerun. + +``` +(๐Ÿฆ‹) /forecast/ $ rnn AAPL --model-type GRU --forecast-only +Epoch 35: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 25/25 [00:00<00:00, 125.85it/s, loss=-2.72, train_loss=-2.74, val_loss=-2.13] +Predicting RNN for 5 days +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 115/115 [00:01<00:00, 85.14it/s] +RNN model obtains MAPE: 3.64% + + + Actual price: 157.37 +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Datetime โ”ƒ Prediction โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 2022-09-12 โ”‚ 155.28 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-13 โ”‚ 155.02 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-14 โ”‚ 154.65 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-15 โ”‚ 154.30 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-16 โ”‚ 153.96 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` +image + +Looks like we squeezed out a little bit more accuracy! Good work. + +The take away for this is that all models should work out of the box when forecasting for a particular Timeseries. One can switch the target by specifying a `-c` for `TARGET_COLUMN` and test out performance with multiple different models with a single command. + +## Sample workflow #2 (advanced) + +To build successful models and improve accuracy over time, it is important to capture external data related to the timeseries you are training on. +This can be seen in everyday applications: +- Observed rainfalls and known weather forecasts can help to predict hydro and solar electricity production +- Recently-observed activity on an e-commerce website can help predict future sales. +- Making the model aware of up-coming holidays can help sales forecasting. + +_In fact, more often than not, strictly relying on the history of a time series to predict its future is missing a lot of valuable information._ + +**Past covariates** are time series whose past values are known at prediction time. Those series often contain values that have to be observed to be known. + +![image](https://user-images.githubusercontent.com/105685594/190244764-ce8cf01f-c959-4827-a326-62b0e172332d.png) + +If you would like to explore this topic more, please read the [blog post](https://medium.com/unit8-machine-learning-publication/time-series-forecasting-using-past-and-future-external-data-with-darts-1f0539585993) written by the authors of Darts. + +Note: Only the following models can handle `past_covariates`: `BlockRNNModel`, `NBEATSModel`, `TCNModel`, `TransformerModel`, `RegressionModel` (incl. `LinearRegressionModel`), `Temporal Fusion Transformer` + +In this work flow lets do the following: + +- add in some correlation analysis +- combine datasets +- perform feature engineering +- train models with `past_covariates` + +Let's begin by loading in our datasets of AAPL and MSFT once again. In this work flow we are going to test if `close` price of MSFT is at all affected by the `close` of AAPL. + +For a refresher, we will grab data from the `stocks` menu found on the terminal. + +``` +(๐Ÿฆ‹) / $ stocks +(๐Ÿฆ‹) /stocks/ $ load AAPL +(๐Ÿฆ‹) /stocks/ $ forecast +(๐Ÿฆ‹) /forecast/ $ .. +(๐Ÿฆ‹) /stocks/ $ load MSFT +(๐Ÿฆ‹) /stocks/ $ forecast +``` + +image + +Before we go combining them, let's train a simple `Block RNN` model on MSFT `close` price to see how to use `past_covariates` + +Make sure to always check your current data set to know the column names: +``` +(๐Ÿฆ‹) /forecast/ $ show MSFT +MSFT has following shape (rowxcolumn): (759, 7) + + Dataset MSFT | Showing 10 of 759 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2019-09-10 00:00:00 โ”‚ 136.80 โ”‚ 136.89 โ”‚ 134.51 โ”‚ 136.08 โ”‚ 132.22 โ”‚ 28903400 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2019-09-11 00:00:00 โ”‚ 135.91 โ”‚ 136.27 โ”‚ 135.09 โ”‚ 136.12 โ”‚ 132.26 โ”‚ 24726100 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2019-09-12 00:00:00 โ”‚ 137.85 โ”‚ 138.42 โ”‚ 136.87 โ”‚ 137.52 โ”‚ 133.62 โ”‚ 27010000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2019-09-13 00:00:00 โ”‚ 137.78 โ”‚ 138.06 โ”‚ 136.57 โ”‚ 137.32 โ”‚ 133.42 โ”‚ 23363100 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2019-09-16 00:00:00 โ”‚ 135.83 โ”‚ 136.70 โ”‚ 135.66 โ”‚ 136.33 โ”‚ 132.46 โ”‚ 16731400 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2019-09-17 00:00:00 โ”‚ 136.96 โ”‚ 137.52 โ”‚ 136.43 โ”‚ 137.39 โ”‚ 133.49 โ”‚ 17814200 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2019-09-18 00:00:00 โ”‚ 137.36 โ”‚ 138.67 โ”‚ 136.53 โ”‚ 138.52 โ”‚ 134.59 โ”‚ 23982100 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2019-09-19 00:00:00 โ”‚ 140.30 โ”‚ 142.37 โ”‚ 140.07 โ”‚ 141.07 โ”‚ 137.07 โ”‚ 35772100 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2019-09-20 00:00:00 โ”‚ 141.01 โ”‚ 141.65 โ”‚ 138.25 โ”‚ 139.44 โ”‚ 135.48 โ”‚ 39167300 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2019-09-23 00:00:00 โ”‚ 139.23 โ”‚ 139.63 โ”‚ 138.44 โ”‚ 139.14 โ”‚ 135.19 โ”‚ 17139300 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` + +Without any covariates: + +``` +(๐Ÿฆ‹) /forecast/ $ brnn MSFT --forecast-only +Epoch 87: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 25/25 [00:00<00:00, 33.84it/s, loss=-2.06, train_loss=-2.27, val_loss=-1.82] +Predicting Block RNN for 5 days +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 115/115 [00:06<00:00, 18.76it/s] +Block RNN model obtains MAPE: 4.62% + + + Actual price: 251.99 +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Datetime โ”ƒ Prediction โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 2022-09-14 โ”‚ 258.52 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-15 โ”‚ 258.01 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-16 โ”‚ 258.95 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-19 โ”‚ 257.20 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-20 โ”‚ 257.66 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` +image + + +With covariates: + +To use any covariates, you have 2 options: +- specify specific columns with `--past-covariates` +- specify all columns as past covariates except the one you are forecasting `--all-past-covariates` + + +``` +(๐Ÿฆ‹) /forecast/ $ brnn MSFT --forecast-only --past-covariates volume +Covariate #0: volume +Epoch 37: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 25/25 [00:00<00:00, 149.03it/s, loss=-2.16, train_loss=-2.08, val_loss=-1.44] +Predicting Block RNN for 5 days +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 115/115 [00:01<00:00, 88.94it/s] +Block RNN model obtains MAPE: 5.10% + + + Actual price: 251.99 +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Datetime โ”ƒ Prediction โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 2022-09-14 โ”‚ 257.02 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-15 โ”‚ 255.82 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-16 โ”‚ 260.13 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-19 โ”‚ 258.32 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-20 โ”‚ 258.52 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` +image + +You can see here that adding in the external variable of `volume` negatively affected the accuracy. + +Let's add in all remaining columns from our dataset as covariates and see what happens to the accuracy. + +``` +(๐Ÿฆ‹) /forecast/ $ brnn MSFT --forecast-only --all-past-covariates +Covariate #0: open +Covariate #1: high +Covariate #2: low +Covariate #3: adj_close +Covariate #4: volume +Epoch 50: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 25/25 [00:00<00:00, 149.29it/s, loss=-2.41, train_loss=-2.53, val_loss=-1.71] +Predicting Block RNN for 5 days +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 115/115 [00:01<00:00, 89.43it/s] +Block RNN model obtains MAPE: 4.26% + + + Actual price: 251.99 +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Datetime โ”ƒ Prediction โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 2022-09-14 โ”‚ 259.28 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-15 โ”‚ 259.13 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-16 โ”‚ 259.42 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-19 โ”‚ 257.73 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-20 โ”‚ 258.64 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` +image + +**Final Result:** Using `open`, `high`, `low`, `adj_close`, `volume` as `past_covariates` improved MAPE from 4.62 --> 4.26. + +Now that we know how to use covariates and understand their effect, why don't we also use `AAPL`'s ticker data as `past_covariates` to check whether this correlates and improves our forecasting accuracy. + +**Remember: You can use unlimited number of `past_covariates` but they must all be combined into a single dataframe with the target forecast timeseries before training.** + +We will combine `MSFT` and `AAPL`. + +``` +(๐Ÿฆ‹) /forecast/ $ combine MSFT -c AAPL +(๐Ÿฆ‹) /forecast/ $ show MSFT +MSFT has following shape (rowxcolumn): (759, 13) +Dataframe has more than 10 columns. Please export to see all of the data. + + + Dataset MSFT | Showing 10 of 759 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ AAPL_open โ”ƒ AAPL_high โ”ƒ AAPL_low โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2019-09-10 00:00:00 โ”‚ 136.80 โ”‚ 136.89 โ”‚ 134.51 โ”‚ 136.08 โ”‚ 132.22 โ”‚ 28903400 โ”‚ 53.47 โ”‚ 54.19 โ”‚ 52.93 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2019-09-11 00:00:00 โ”‚ 135.91 โ”‚ 136.27 โ”‚ 135.09 โ”‚ 136.12 โ”‚ 132.26 โ”‚ 24726100 โ”‚ 54.52 โ”‚ 55.93 โ”‚ 54.43 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2019-09-12 00:00:00 โ”‚ 137.85 โ”‚ 138.42 โ”‚ 136.87 โ”‚ 137.52 โ”‚ 133.62 โ”‚ 27010000 โ”‚ 56.20 โ”‚ 56.60 โ”‚ 55.72 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2019-09-13 00:00:00 โ”‚ 137.78 โ”‚ 138.06 โ”‚ 136.57 โ”‚ 137.32 โ”‚ 133.42 โ”‚ 23363100 โ”‚ 55.00 โ”‚ 55.20 โ”‚ 54.26 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2019-09-16 00:00:00 โ”‚ 135.83 โ”‚ 136.70 โ”‚ 135.66 โ”‚ 136.33 โ”‚ 132.46 โ”‚ 16731400 โ”‚ 54.43 โ”‚ 55.03 โ”‚ 54.39 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2019-09-17 00:00:00 โ”‚ 136.96 โ”‚ 137.52 โ”‚ 136.43 โ”‚ 137.39 โ”‚ 133.49 โ”‚ 17814200 โ”‚ 54.99 โ”‚ 55.21 โ”‚ 54.78 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2019-09-18 00:00:00 โ”‚ 137.36 โ”‚ 138.67 โ”‚ 136.53 โ”‚ 138.52 โ”‚ 134.59 โ”‚ 23982100 โ”‚ 55.26 โ”‚ 55.71 โ”‚ 54.86 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2019-09-19 00:00:00 โ”‚ 140.30 โ”‚ 142.37 โ”‚ 140.07 โ”‚ 141.07 โ”‚ 137.07 โ”‚ 35772100 โ”‚ 55.50 โ”‚ 55.94 โ”‚ 55.09 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2019-09-20 00:00:00 โ”‚ 141.01 โ”‚ 141.65 โ”‚ 138.25 โ”‚ 139.44 โ”‚ 135.48 โ”‚ 39167300 โ”‚ 55.35 โ”‚ 55.64 โ”‚ 54.37 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2019-09-23 00:00:00 โ”‚ 139.23 โ”‚ 139.63 โ”‚ 138.44 โ”‚ 139.14 โ”‚ 135.19 โ”‚ 17139300 โ”‚ 54.74 โ”‚ 54.96 โ”‚ 54.41 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` + +Now we can run the same `BRNN` model with all `past_covariates` of both tickers. The output will show the model grabbing all covariates to bring into training to predict `close` of `MSFT`. + +``` +(๐Ÿฆ‹) /forecast/ $ brnn MSFT --forecast-only --all-past-covariates +Covariate #0: open +Covariate #1: high +Covariate #2: low +Covariate #3: adj_close +Covariate #4: volume +Covariate #5: AAPL_open +Covariate #6: AAPL_high +Covariate #7: AAPL_low +Covariate #8: AAPL_close +Covariate #9: AAPL_adj_close +Covariate #10: AAPL_volume +Epoch 116: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 25/25 [00:00<00:00, 149.26it/s, loss=-2.5, train_loss=-2.61, val_loss=-1.93] +Predicting Block RNN for 5 days +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 115/115 [00:01<00:00, 90.26it/s] +Block RNN model obtains MAPE: 3.93% + + + Actual price: 251.99 +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Datetime โ”ƒ Prediction โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 2022-09-14 โ”‚ 259.78 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-15 โ”‚ 258.15 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-16 โ”‚ 261.44 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-19 โ”‚ 257.57 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-20 โ”‚ 257.99 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` +image + +For one last experiment, we can perform some other feature engineering on `MSFT` and add it to our `past_covariates` to train on. + +In this case, let's add in `Momentum` over past 10 days of `MSFT` and append it to our `past_covariates` + +``` +(๐Ÿฆ‹) /forecast/ $ mom MSFT +Successfully added 'Momentum_10' to 'MSFT' dataset + +(๐Ÿฆ‹) /forecast/ $ brnn MSFT --forecast-only --all-past-covariates +The data contains inf or nan values. They will be removed. + +Covariate #0: open +Covariate #1: high +Covariate #2: low +Covariate #3: adj_close +Covariate #4: volume +Covariate #5: AAPL_open +Covariate #6: AAPL_high +Covariate #7: AAPL_low +Covariate #8: AAPL_close +Covariate #9: AAPL_adj_close +Covariate #10: AAPL_volume +Covariate #11: Momentum_10 +Epoch 71: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 24/24 [00:00<00:00, 147.87it/s, loss=-2.52, train_loss=-2.57, val_loss=-1.82] +Predicting Block RNN for 5 days +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 114/114 [00:01<00:00, 90.05it/s] +Block RNN model obtains MAPE: 3.72% + + + Actual price: 251.99 +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Datetime โ”ƒ Prediction โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 2022-09-14 โ”‚ 255.14 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-15 โ”‚ 256.20 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-16 โ”‚ 256.67 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-19 โ”‚ 258.63 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-09-20 โ”‚ 256.62 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` +image + + +There we have it. Bringing in another ticker has allowed us to further improve the model accuracy. Furthermore, adding in a new feature to the dataset allowed us to improve the accuracy further. + +**MAPE = 4.62%** (no past covariates) + +**MAPE = 4.26%** (`open`,`high`,`low`,`adj_close`,`volume`) + +**MAPE = 3.93%** (`open`,`high`,`low`,`adj_close`,`volume`,`AAPL_open`,`AAPL_high`,`APPL_low`,`APPL_adj_close`,`APPL_volume`,`APPL_close`) + +**MAPE = 3.72%** (`open`,`high`,`low`,`adj_close`,`volume`,`AAPL_open`,`AAPL_high`,`APPL_low`,`APPL_adj_close`,`APPL_volume`,`APPL_close`,`Momentum_10`) + + +More workflows coming soon! + +If you have any questions or would like to request for new feature engineering or model additions, please join us on discord. Happy hacking! diff --git a/website/content/terminal/forecast/atr/_index.md b/website/content/terminal/forecast/atr/_index.md new file mode 100644 index 00000000000..ecd9e036dc3 --- /dev/null +++ b/website/content/terminal/forecast/atr/_index.md @@ -0,0 +1,52 @@ +``` +usage: atr [-d {}] [-c TARGET_COLUMN] [-h] +``` + +Add Average True Range to dataset of specific stock ticker. + +``` +optional arguments: + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -c TARGET_COLUMN, --target-column TARGET_COLUMN + The name of the specific column you want to use (default: close) + -h, --help show this help message (default: False) + +For more information and examples, use 'about rsi' to access the related guide. +``` + +Example: +``` +(๐Ÿฆ‹) /forecast/ $ load aapl_20220628_113243.csv + +(๐Ÿฆ‹) /forecast/ $ atr aapl_20220628_113243 +Successfully added 'Average True Range' to 'aapl_20220628_113243' dataset + +(๐Ÿฆ‹) /forecast/ $ show aapl_20220628_113243 +aapl_20220628_113243 has following shape (rowxcolumn): (759, 9) + + Dataset aapl_20220628_113243 | Showing 10 of 759 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ unnamed:_0 โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ true_range โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 0 โ”‚ 2019-06-10 โ”‚ 47.95 โ”‚ 48.84 โ”‚ 47.90 โ”‚ 48.15 โ”‚ 46.99 โ”‚ 104883600 โ”‚ 0.94 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 1 โ”‚ 2019-06-11 โ”‚ 48.72 โ”‚ 49.00 โ”‚ 48.40 โ”‚ 48.70 โ”‚ 47.53 โ”‚ 107731600 โ”‚ 0.85 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2 โ”‚ 2019-06-12 โ”‚ 48.49 โ”‚ 48.99 โ”‚ 48.35 โ”‚ 48.55 โ”‚ 47.38 โ”‚ 73012800 โ”‚ 0.65 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 3 โ”‚ 2019-06-13 โ”‚ 48.67 โ”‚ 49.20 โ”‚ 48.40 โ”‚ 48.54 โ”‚ 47.37 โ”‚ 86698400 โ”‚ 0.80 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 4 โ”‚ 2019-06-14 โ”‚ 47.89 โ”‚ 48.40 โ”‚ 47.58 โ”‚ 48.19 โ”‚ 47.03 โ”‚ 75046000 โ”‚ 0.96 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 5 โ”‚ 2019-06-17 โ”‚ 48.22 โ”‚ 48.74 โ”‚ 48.04 โ”‚ 48.47 โ”‚ 47.31 โ”‚ 58676400 โ”‚ 0.70 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 6 โ”‚ 2019-06-18 โ”‚ 49.01 โ”‚ 50.07 โ”‚ 48.80 โ”‚ 49.61 โ”‚ 48.42 โ”‚ 106204000 โ”‚ 1.60 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 7 โ”‚ 2019-06-19 โ”‚ 49.92 โ”‚ 49.97 โ”‚ 49.33 โ”‚ 49.47 โ”‚ 48.28 โ”‚ 84496800 โ”‚ 0.64 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 8 โ”‚ 2019-06-20 โ”‚ 50.09 โ”‚ 50.15 โ”‚ 49.51 โ”‚ 49.87 โ”‚ 48.67 โ”‚ 86056000 โ”‚ 0.69 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 9 โ”‚ 2019-06-21 โ”‚ 49.70 โ”‚ 50.21 โ”‚ 49.54 โ”‚ 49.69 โ”‚ 48.50 โ”‚ 191202400 โ”‚ 0.68 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` diff --git a/website/content/terminal/forecast/brnn/_index.md b/website/content/terminal/forecast/brnn/_index.md new file mode 100644 index 00000000000..734be44f831 --- /dev/null +++ b/website/content/terminal/forecast/brnn/_index.md @@ -0,0 +1,86 @@ +``` +usage: brnn [--n-rnn-layers N_RNN_LAYERS] [--hidden-size HIDDEN_SIZE] [--past-covariates PAST_COVARIATES] + [--all-past-covariates] [--naive] [-d {}] [-c TARGET_COLUMN] [-n N_DAYS] [-t TRAIN_SPLIT] + [-i INPUT_CHUNK_LENGTH] [-o OUTPUT_CHUNK_LENGTH] [--force-reset FORCE_RESET] + [--save-checkpoints SAVE_CHECKPOINTS] [--model-save-name MODEL_SAVE_NAME] [--n-epochs N_EPOCHS] + [--model-type MODEL_TYPE] [--dropout DROPOUT] [--batch-size BATCH_SIZE] [--end S_END_DATE] + [--start S_START_DATE] [--learning-rate LEARNING_RATE] [--residuals] [--forecast-only] [-h] + [--export EXPORT] +``` + +Perform BRNN forecast (Vanilla RNN, LSTM, GRU) + +``` +optional arguments: + --n-rnn-layers N_RNN_LAYERS + Number of layers in the RNN module. (default: 1) + --hidden-size HIDDEN_SIZE + Size for feature maps for each hidden RNN layer (h_n) (default: 20) + --past-covariates PAST_COVARIATES + Past covariates(columns/features) in same dataset. Comma separated. (default: None) + --all-past-covariates + Adds all rows as past covariates except for date and the target column. (default: False) + --naive Show the naive baseline for a model. (default: False) + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -c TARGET_COLUMN, --target-column TARGET_COLUMN + The name of the specific column you want to use (default: close) + -n N_DAYS, --n-days N_DAYS + prediction days. (default: 5) + -t TRAIN_SPLIT, --train-split TRAIN_SPLIT + Start point for rolling training and forecast window. 0.0-1.0 (default: 0.85) + -i INPUT_CHUNK_LENGTH, --input-chunk-length INPUT_CHUNK_LENGTH + Number of past time steps for forecasting module at prediction time. (default: 14) + -o OUTPUT_CHUNK_LENGTH, --output-chunk-length OUTPUT_CHUNK_LENGTH + The length of the forecast of the model. (default: 5) + --force-reset FORCE_RESET + If set to True, any previously-existing model with the same name will be reset (all checkpoints + will be discarded). (default: True) + --save-checkpoints SAVE_CHECKPOINTS + Whether to automatically save the untrained model and checkpoints. (default: True) + --model-save-name MODEL_SAVE_NAME + Name of the model to save. (default: brnn_model) + --n-epochs N_EPOCHS Number of epochs over which to train the model. (default: 300) + --model-type MODEL_TYPE + Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU") (default: LSTM) + --dropout DROPOUT Fraction of neurons afected by Dropout. (default: 0) + --batch-size BATCH_SIZE + Number of time series (input and output) used in each training pass (default: 32) + --end S_END_DATE The end date (format YYYY-MM-DD) to select for testing (default: None) + --start S_START_DATE The start date (format YYYY-MM-DD) to select for testing (default: None) + --learning-rate LEARNING_RATE + Learning rate during training. (default: 0.001) + --residuals Show the residuals for the model. (default: False) + --forecast-only Do not plot the hisotorical data without forecasts. (default: False) + -h, --help show this help message (default: False) + --export EXPORT Export figure into png, jpg, pdf, svg (default: ) + +For more information and examples, use 'about export' to access the related guide. +``` + +Example: +``` +2022 Jul 23, 10:36 (๐Ÿฆ‹) /forecast/ $ load GME_20220719_123734.csv -a GME + +2022 Jul 23, 11:03 (๐Ÿฆ‹) /forecast/ $ brnn GME +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 115/115 [00:07<00:00, 15.10it/s] +Block RNN model obtains MAPE: 17.09% + + + + Actual price: $ 146.64 +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Datetime โ”ƒ Prediction โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 2022-07-19 00:00:00 โ”‚ $ 147.89 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-20 00:00:00 โ”‚ $ 149.11 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-21 00:00:00 โ”‚ $ 149.59 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-22 00:00:00 โ”‚ $ 141.46 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-25 00:00:00 โ”‚ $ 144.99 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` +![brnn](https://user-images.githubusercontent.com/72827203/180615365-1644ce7a-050e-4ee1-92c2-4518db14f53d.png) diff --git a/website/content/terminal/forecast/clean/_index.md b/website/content/terminal/forecast/clean/_index.md new file mode 100644 index 00000000000..8e995c644c9 --- /dev/null +++ b/website/content/terminal/forecast/clean/_index.md @@ -0,0 +1,121 @@ +``` +usage: clean [-f {rfill,cfill,rbfill,cbfill,rffill,bffill}] [--drop {rdrop,cdrop}] [-d {}] [-h] [-l LIMIT] +``` + +Clean a dataset by filling and/or dropping NaN values. + +``` +optional arguments: + -f {rfill,cfill,rbfill,cbfill,rffill,bffill}, --fill {rfill,cfill,rbfill,cbfill,rffill,bffill} + The method of filling NaNs. This has options to fill rows (rfill, rbfill, rffill) or fill columns (cfill, cbfill, cffill). + Furthermore, it has the option to forward fill and backward fill (up to --limit) which refer to how many rows/columns can be + set equal to the last non-NaN value (default: ) + --drop {rdrop,cdrop} The method of dropping NaNs. This either has the option rdrop (drop rows that contain NaNs) or cdrop (drop columns that contain + NaNs) (default: ) + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -h, --help show this help message (default: False) + -l LIMIT, --limit LIMIT + Number of entries to show in data. (default: 5) + +For more information and examples, use 'about clean' to access the related guide. +``` + +Example: +``` +(๐Ÿฆ‹) /forecast/ $ show TSLA + +TSLA has following shape (rowxcolumn): (611, 7) + + Dataset TSLA | Showing 10 of 611 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-02 โ”‚ 84.90 โ”‚ 86.14 โ”‚ 84.34 โ”‚ 86.05 โ”‚ 86.05 โ”‚ 47660500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-03 โ”‚ 88.10 โ”‚ 90.80 โ”‚ 87.38 โ”‚ 88.60 โ”‚ 88.60 โ”‚ 88892500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-06 โ”‚ 88.09 โ”‚ 90.31 โ”‚ 88.00 โ”‚ 90.31 โ”‚ 90.31 โ”‚ 50665000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-07 โ”‚ 92.28 โ”‚ 94.33 โ”‚ 90.67 โ”‚ 93.81 โ”‚ 93.81 โ”‚ 89410500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-08 โ”‚ 94.74 โ”‚ 99.70 โ”‚ 93.65 โ”‚ 98.43 โ”‚ 98.43 โ”‚ 155721500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-09 โ”‚ 99.42 โ”‚ 99.76 โ”‚ 94.57 โ”‚ 96.27 โ”‚ 96.27 โ”‚ 142202000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-10 โ”‚ 96.36 โ”‚ 96.99 โ”‚ 94.74 โ”‚ 95.63 โ”‚ 95.63 โ”‚ 64797500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-13 โ”‚ 98.70 โ”‚ 105.13 โ”‚ 98.40 โ”‚ 104.97 โ”‚ 104.97 โ”‚ 132588000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-14 โ”‚ 108.85 โ”‚ 109.48 โ”‚ 104.98 โ”‚ 107.58 โ”‚ 107.58 โ”‚ 144981000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-15 โ”‚ 105.95 โ”‚ 107.57 โ”‚ 103.36 โ”‚ 103.70 โ”‚ 103.70 โ”‚ 86844000 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + +``` +``` +(๐Ÿฆ‹) /forecast/ $ rsi TSLA --period 30 +Successfully added 'RSI_30' to 'TSLA' dataset + +(๐Ÿฆ‹) /forecast/ $ show TSLA +TSLA has following shape (rowxcolumn): (611, 8) + + Dataset TSLA | Showing 10 of 611 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ RSI_30 โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-02 โ”‚ 84.90 โ”‚ 86.14 โ”‚ 84.34 โ”‚ 86.05 โ”‚ 86.05 โ”‚ 47660500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-03 โ”‚ 88.10 โ”‚ 90.80 โ”‚ 87.38 โ”‚ 88.60 โ”‚ 88.60 โ”‚ 88892500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-06 โ”‚ 88.09 โ”‚ 90.31 โ”‚ 88.00 โ”‚ 90.31 โ”‚ 90.31 โ”‚ 50665000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-07 โ”‚ 92.28 โ”‚ 94.33 โ”‚ 90.67 โ”‚ 93.81 โ”‚ 93.81 โ”‚ 89410500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-08 โ”‚ 94.74 โ”‚ 99.70 โ”‚ 93.65 โ”‚ 98.43 โ”‚ 98.43 โ”‚ 155721500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-09 โ”‚ 99.42 โ”‚ 99.76 โ”‚ 94.57 โ”‚ 96.27 โ”‚ 96.27 โ”‚ 142202000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-10 โ”‚ 96.36 โ”‚ 96.99 โ”‚ 94.74 โ”‚ 95.63 โ”‚ 95.63 โ”‚ 64797500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-13 โ”‚ 98.70 โ”‚ 105.13 โ”‚ 98.40 โ”‚ 104.97 โ”‚ 104.97 โ”‚ 132588000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-14 โ”‚ 108.85 โ”‚ 109.48 โ”‚ 104.98 โ”‚ 107.58 โ”‚ 107.58 โ”‚ 144981000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-15 โ”‚ 105.95 โ”‚ 107.57 โ”‚ 103.36 โ”‚ 103.70 โ”‚ 103.70 โ”‚ 86844000 โ”‚ nan โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + +(๐Ÿฆ‹) /forecast/ $ clean TSLA -f rfill +Namespace(drop='', fill='rfill', help=False, limit=5, target_dataset='TSLA') +Successfully cleaned 'TSLA' dataset + +(๐Ÿฆ‹) /forecast/ $ show TSLA +2022 Jul 11, 14:20 (๐Ÿฆ‹) /forecast/ $ show TSLA +TSLA has following shape (rowxcolumn): (611, 8) + + Dataset TSLA | Showing 10 of 611 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ RSI_30 โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-02 โ”‚ 84.90 โ”‚ 86.14 โ”‚ 84.34 โ”‚ 86.05 โ”‚ 86.05 โ”‚ 47660500 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-03 โ”‚ 88.10 โ”‚ 90.80 โ”‚ 87.38 โ”‚ 88.60 โ”‚ 88.60 โ”‚ 88892500 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-06 โ”‚ 88.09 โ”‚ 90.31 โ”‚ 88.00 โ”‚ 90.31 โ”‚ 90.31 โ”‚ 50665000 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-07 โ”‚ 92.28 โ”‚ 94.33 โ”‚ 90.67 โ”‚ 93.81 โ”‚ 93.81 โ”‚ 89410500 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-08 โ”‚ 94.74 โ”‚ 99.70 โ”‚ 93.65 โ”‚ 98.43 โ”‚ 98.43 โ”‚ 155721500 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-09 โ”‚ 99.42 โ”‚ 99.76 โ”‚ 94.57 โ”‚ 96.27 โ”‚ 96.27 โ”‚ 142202000 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-10 โ”‚ 96.36 โ”‚ 96.99 โ”‚ 94.74 โ”‚ 95.63 โ”‚ 95.63 โ”‚ 64797500 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-13 โ”‚ 98.70 โ”‚ 105.13 โ”‚ 98.40 โ”‚ 104.97 โ”‚ 104.97 โ”‚ 132588000 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-14 โ”‚ 108.85 โ”‚ 109.48 โ”‚ 104.98 โ”‚ 107.58 โ”‚ 107.58 โ”‚ 144981000 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-15 โ”‚ 105.95 โ”‚ 107.57 โ”‚ 103.36 โ”‚ 103.70 โ”‚ 103.70 โ”‚ 86844000 โ”‚ 0.00 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + +``` diff --git a/website/content/terminal/forecast/combine/_index.md b/website/content/terminal/forecast/combine/_index.md new file mode 100644 index 00000000000..20eb56ee5a4 --- /dev/null +++ b/website/content/terminal/forecast/combine/_index.md @@ -0,0 +1,52 @@ +``` +usage: combine [--dataset {}] [-c COLUMNS] [-h] +``` + +The columns you want to add to a dataset. The first argument is the dataset to add columns in, the remaining could be: , + +``` +optional arguments: + --dataset {} Dataset to add columns to (default: None) + -c COLUMNS, --columns COLUMNS + The columns we want to add , (default: None) + -h, --help show this help message (default: False) + +For more information and examples, use 'about combine' to access the related guide. +``` + +Example: +``` +(๐Ÿฆ‹) /forecast/ $ load TSLA.csv + +(๐Ÿฆ‹) /forecast/ $ load MSFT.csv + +(๐Ÿฆ‹) /forecast/ $ combine --dataset TSLA --columns MSFT.volume + +(๐Ÿฆ‹) /forecast/ $ show TSLA +TSLA has following shape (rowxcolumn): (611, 8) + + Dataset TSLA | Showing 10 of 611 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ MSFT_volume โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-02 โ”‚ 84.90 โ”‚ 86.14 โ”‚ 84.34 โ”‚ 86.05 โ”‚ 86.05 โ”‚ 47660500 โ”‚ 1359650900.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-03 โ”‚ 88.10 โ”‚ 90.80 โ”‚ 87.38 โ”‚ 88.60 โ”‚ 88.60 โ”‚ 88892500 โ”‚ 1074643300.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-06 โ”‚ 88.09 โ”‚ 90.31 โ”‚ 88.00 โ”‚ 90.31 โ”‚ 90.31 โ”‚ 50665000 โ”‚ 1110237200.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-07 โ”‚ 92.28 โ”‚ 94.33 โ”‚ 90.67 โ”‚ 93.81 โ”‚ 93.81 โ”‚ 89410500 โ”‚ 1319029500.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-08 โ”‚ 94.74 โ”‚ 99.70 โ”‚ 93.65 โ”‚ 98.43 โ”‚ 98.43 โ”‚ 155721500 โ”‚ 1720130200.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-09 โ”‚ 99.42 โ”‚ 99.76 โ”‚ 94.57 โ”‚ 96.27 โ”‚ 96.27 โ”‚ 142202000 โ”‚ 1671811600.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-10 โ”‚ 96.36 โ”‚ 96.99 โ”‚ 94.74 โ”‚ 95.63 โ”‚ 95.63 โ”‚ 64797500 โ”‚ 1408590600.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-13 โ”‚ 98.70 โ”‚ 105.13 โ”‚ 98.40 โ”‚ 104.97 โ”‚ 104.97 โ”‚ 132588000 โ”‚ 1279372100.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-14 โ”‚ 108.85 โ”‚ 109.48 โ”‚ 104.98 โ”‚ 107.58 โ”‚ 107.58 โ”‚ 144981000 โ”‚ 1273139500.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-15 โ”‚ 105.95 โ”‚ 107.57 โ”‚ 103.36 โ”‚ 103.70 โ”‚ 103.70 โ”‚ 86844000 โ”‚ 1281432800.00 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` diff --git a/website/content/terminal/forecast/corr/_index.md b/website/content/terminal/forecast/corr/_index.md new file mode 100644 index 00000000000..a4a92a9091c --- /dev/null +++ b/website/content/terminal/forecast/corr/_index.md @@ -0,0 +1,24 @@ +``` +usage: corr [-d {}] [-h] [--export EXPORT] +``` + +Plot correlation coefficients of a dataset. + +``` +optional arguments: + -d {TSLA}, --target-dataset {TSLA} + The name of the dataset you want to select (default: None) + -h, --help show this help message (default: False) + --export EXPORT Export figure into png, jpg, pdf, svg (default: ) + +For more information and examples, use 'about corr' to access the related guide. +``` + +Example: +``` +(๐Ÿฆ‹) /forecast/ $ load TSLA.csv + +(๐Ÿฆ‹) /forecast/ $ corr TSLA +TODO: screen shot + +``` diff --git a/website/content/terminal/forecast/delete/_index.md b/website/content/terminal/forecast/delete/_index.md new file mode 100644 index 00000000000..93f264da65e --- /dev/null +++ b/website/content/terminal/forecast/delete/_index.md @@ -0,0 +1,78 @@ +``` +usage: delete [-d DELETE] [-h] +``` + +Allows you to remove any column from your dataset. For example `delete dataset.column` + +``` +optional arguments: + -d DELETE, --delete DELETE + The columns you want to delete from a dataset. Use format: or multiple with , (default: None) + -h, --help show this help message (default: False) +``` + +Example: +``` +(๐Ÿฆ‹) /forecast/ $ load TSLA.csv + +(๐Ÿฆ‹) /forecast/ $ roc TSLA +Successfully added 'ROC_10' to 'TSLA' dataset + +(๐Ÿฆ‹) /forecast/ $ show +TSLA has following shape (rowxcolumn): (611, 8) + + Dataset TSLA | Showing 10 of 611 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ ROC_10 โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-02 โ”‚ 84.90 โ”‚ 86.14 โ”‚ 84.34 โ”‚ 86.05 โ”‚ 86.05 โ”‚ 47660500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-03 โ”‚ 88.10 โ”‚ 90.80 โ”‚ 87.38 โ”‚ 88.60 โ”‚ 88.60 โ”‚ 88892500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-06 โ”‚ 88.09 โ”‚ 90.31 โ”‚ 88.00 โ”‚ 90.31 โ”‚ 90.31 โ”‚ 50665000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-07 โ”‚ 92.28 โ”‚ 94.33 โ”‚ 90.67 โ”‚ 93.81 โ”‚ 93.81 โ”‚ 89410500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-08 โ”‚ 94.74 โ”‚ 99.70 โ”‚ 93.65 โ”‚ 98.43 โ”‚ 98.43 โ”‚ 155721500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-09 โ”‚ 99.42 โ”‚ 99.76 โ”‚ 94.57 โ”‚ 96.27 โ”‚ 96.27 โ”‚ 142202000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-10 โ”‚ 96.36 โ”‚ 96.99 โ”‚ 94.74 โ”‚ 95.63 โ”‚ 95.63 โ”‚ 64797500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-13 โ”‚ 98.70 โ”‚ 105.13 โ”‚ 98.40 โ”‚ 104.97 โ”‚ 104.97 โ”‚ 132588000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-14 โ”‚ 108.85 โ”‚ 109.48 โ”‚ 104.98 โ”‚ 107.58 โ”‚ 107.58 โ”‚ 144981000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-15 โ”‚ 105.95 โ”‚ 107.57 โ”‚ 103.36 โ”‚ 103.70 โ”‚ 103.70 โ”‚ 86844000 โ”‚ 20.51 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + +(๐Ÿฆ‹) /forecast/ $ delete TSLA.ROC_10 + +(๐Ÿฆ‹) /forecast/ $ show TSLA +TSLA has following shape (rowxcolumn): (611, 7) + + Dataset TSLA | Showing 10 of 611 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-02 โ”‚ 84.90 โ”‚ 86.14 โ”‚ 84.34 โ”‚ 86.05 โ”‚ 86.05 โ”‚ 47660500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-03 โ”‚ 88.10 โ”‚ 90.80 โ”‚ 87.38 โ”‚ 88.60 โ”‚ 88.60 โ”‚ 88892500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-06 โ”‚ 88.09 โ”‚ 90.31 โ”‚ 88.00 โ”‚ 90.31 โ”‚ 90.31 โ”‚ 50665000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-07 โ”‚ 92.28 โ”‚ 94.33 โ”‚ 90.67 โ”‚ 93.81 โ”‚ 93.81 โ”‚ 89410500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-08 โ”‚ 94.74 โ”‚ 99.70 โ”‚ 93.65 โ”‚ 98.43 โ”‚ 98.43 โ”‚ 155721500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-09 โ”‚ 99.42 โ”‚ 99.76 โ”‚ 94.57 โ”‚ 96.27 โ”‚ 96.27 โ”‚ 142202000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-10 โ”‚ 96.36 โ”‚ 96.99 โ”‚ 94.74 โ”‚ 95.63 โ”‚ 95.63 โ”‚ 64797500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-13 โ”‚ 98.70 โ”‚ 105.13 โ”‚ 98.40 โ”‚ 104.97 โ”‚ 104.97 โ”‚ 132588000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-14 โ”‚ 108.85 โ”‚ 109.48 โ”‚ 104.98 โ”‚ 107.58 โ”‚ 107.58 โ”‚ 144981000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-15 โ”‚ 105.95 โ”‚ 107.57 โ”‚ 103.36 โ”‚ 103.70 โ”‚ 103.70 โ”‚ 86844000 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` diff --git a/website/content/terminal/forecast/delta/_index.md b/website/content/terminal/forecast/delta/_index.md new file mode 100644 index 00000000000..6c3c72f292a --- /dev/null +++ b/website/content/terminal/forecast/delta/_index.md @@ -0,0 +1,55 @@ +``` +usage: delta [-d {}] [-c TARGET_COLUMN] [-h] +``` + +Add %Change (Delta) to dataset based on specific column. + +``` +optional arguments: + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -c TARGET_COLUMN, --target-column TARGET_COLUMN + The name of the specific column you want to use (default: close) + -h, --help show this help message (default: False) + +For more information and examples, use 'about rsi' to access the related guide. +``` + +Example: +``` +(๐Ÿฆ‹) /forecast/ $ load aapl_20220628_113243.csv + +(๐Ÿฆ‹) /forecast/ $ delta aapl_20220628_113243 +Successfully added 'Delta_close' to 'aapl_20220628_113243' dataset + +(๐Ÿฆ‹) /forecast/ $ delta aapl_20220628_113243 -c volume +Successfully added 'Delta_volume' to 'aapl_20220628_113243' dataset + +(๐Ÿฆ‹) /forecast/ $ show aapl_20220628_113243 +aapl_20220628_113243 has following shape (rowxcolumn): (759, 10) + + Dataset aapl_20220628_113243 | Showing 10 of 759 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ unnamed:_0 โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ delta_close โ”ƒ delta_volume โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 0 โ”‚ 2019-06-10 โ”‚ 47.95 โ”‚ 48.84 โ”‚ 47.90 โ”‚ 48.15 โ”‚ 46.99 โ”‚ 104883600 โ”‚ 0.00 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 1 โ”‚ 2019-06-11 โ”‚ 48.72 โ”‚ 49.00 โ”‚ 48.40 โ”‚ 48.70 โ”‚ 47.53 โ”‚ 107731600 โ”‚ 0.01 โ”‚ 0.03 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2 โ”‚ 2019-06-12 โ”‚ 48.49 โ”‚ 48.99 โ”‚ 48.35 โ”‚ 48.55 โ”‚ 47.38 โ”‚ 73012800 โ”‚ -0.00 โ”‚ -0.32 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 3 โ”‚ 2019-06-13 โ”‚ 48.67 โ”‚ 49.20 โ”‚ 48.40 โ”‚ 48.54 โ”‚ 47.37 โ”‚ 86698400 โ”‚ -0.00 โ”‚ 0.19 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 4 โ”‚ 2019-06-14 โ”‚ 47.89 โ”‚ 48.40 โ”‚ 47.58 โ”‚ 48.19 โ”‚ 47.03 โ”‚ 75046000 โ”‚ -0.01 โ”‚ -0.13 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 5 โ”‚ 2019-06-17 โ”‚ 48.22 โ”‚ 48.74 โ”‚ 48.04 โ”‚ 48.47 โ”‚ 47.31 โ”‚ 58676400 โ”‚ 0.01 โ”‚ -0.22 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 6 โ”‚ 2019-06-18 โ”‚ 49.01 โ”‚ 50.07 โ”‚ 48.80 โ”‚ 49.61 โ”‚ 48.42 โ”‚ 106204000 โ”‚ 0.02 โ”‚ 0.81 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 7 โ”‚ 2019-06-19 โ”‚ 49.92 โ”‚ 49.97 โ”‚ 49.33 โ”‚ 49.47 โ”‚ 48.28 โ”‚ 84496800 โ”‚ -0.00 โ”‚ -0.20 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 8 โ”‚ 2019-06-20 โ”‚ 50.09 โ”‚ 50.15 โ”‚ 49.51 โ”‚ 49.87 โ”‚ 48.67 โ”‚ 86056000 โ”‚ 0.01 โ”‚ 0.02 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 9 โ”‚ 2019-06-21 โ”‚ 49.70 โ”‚ 50.21 โ”‚ 49.54 โ”‚ 49.69 โ”‚ 48.50 โ”‚ 191202400 โ”‚ -0.00 โ”‚ 1.22 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` diff --git a/website/content/terminal/forecast/desc/_index.md b/website/content/terminal/forecast/desc/_index.md new file mode 100644 index 00000000000..402694533e4 --- /dev/null +++ b/website/content/terminal/forecast/desc/_index.md @@ -0,0 +1,42 @@ +``` +usage: desc [-n {}] [-h] [--export {csv,json,xlsx}] +``` + +Show the descriptive statistics of a dataset. + +``` +optional arguments: + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -h, --help show this help message (default: False) + --export EXPORT Export raw data into csv, json, xlsx (default: ) +``` + +Example: +``` +(๐Ÿฆ‹) /forecast/ $ load TSLA.csv + +(๐Ÿฆ‹) /forecast/ $ desc TSLA + + Showing Descriptive Statistics for Dataset TSLA +โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ count โ”‚ 611.00 โ”‚ 611.00 โ”‚ 611.00 โ”‚ 611.00 โ”‚ 611.00 โ”‚ 611.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ mean โ”‚ 599.50 โ”‚ 613.65 โ”‚ 583.85 โ”‚ 599.19 โ”‚ 599.19 โ”‚ 47275047.14 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ std โ”‚ 308.73 โ”‚ 315.33 โ”‚ 300.77 โ”‚ 307.95 โ”‚ 307.95 โ”‚ 35945322.23 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ min โ”‚ 74.94 โ”‚ 80.97 โ”‚ 70.10 โ”‚ 72.24 โ”‚ 72.24 โ”‚ 9800600.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 25% โ”‚ 325.09 โ”‚ 332.00 โ”‚ 304.80 โ”‚ 316.07 โ”‚ 316.07 โ”‚ 23386000.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 50% โ”‚ 661.42 โ”‚ 672.00 โ”‚ 644.60 โ”‚ 659.58 โ”‚ 659.58 โ”‚ 33337300.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 75% โ”‚ 830.71 โ”‚ 846.82 โ”‚ 801.99 โ”‚ 822.97 โ”‚ 822.97 โ”‚ 62445150.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ max โ”‚ 1234.41 โ”‚ 1243.49 โ”‚ 1217.00 โ”‚ 1229.91 โ”‚ 1229.91 โ”‚ 304694000.00 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + +``` diff --git a/website/content/terminal/forecast/ema/_index.md b/website/content/terminal/forecast/ema/_index.md new file mode 100644 index 00000000000..040157bec49 --- /dev/null +++ b/website/content/terminal/forecast/ema/_index.md @@ -0,0 +1,82 @@ +``` +usage: ema [-d {}] [-c TARGET_COLUMN] [--period PERIOD] [-h] +``` + +Add exponential moving average to dataset based on specific column. + +``` +optional arguments: + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -c TARGET_COLUMN, --target-column TARGET_COLUMN + The name of the specific column you want to use (default: close) + --period PERIOD The period to use (default: 10) + -h, --help show this help message (default: False) + +For more information and examples, use 'about ema' to access the related guide. +``` + +Example: +``` +(๐Ÿฆ‹) /forecast/ $ load TSLA.csv + +(๐Ÿฆ‹) /forecast/ $ show TSLA +TSLA has following shape (rowxcolumn): (611, 7) + + Dataset TSLA | Showing 10 of 611 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-02 โ”‚ 84.90 โ”‚ 86.14 โ”‚ 84.34 โ”‚ 86.05 โ”‚ 86.05 โ”‚ 47660500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-03 โ”‚ 88.10 โ”‚ 90.80 โ”‚ 87.38 โ”‚ 88.60 โ”‚ 88.60 โ”‚ 88892500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-06 โ”‚ 88.09 โ”‚ 90.31 โ”‚ 88.00 โ”‚ 90.31 โ”‚ 90.31 โ”‚ 50665000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-07 โ”‚ 92.28 โ”‚ 94.33 โ”‚ 90.67 โ”‚ 93.81 โ”‚ 93.81 โ”‚ 89410500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-08 โ”‚ 94.74 โ”‚ 99.70 โ”‚ 93.65 โ”‚ 98.43 โ”‚ 98.43 โ”‚ 155721500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-09 โ”‚ 99.42 โ”‚ 99.76 โ”‚ 94.57 โ”‚ 96.27 โ”‚ 96.27 โ”‚ 142202000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-10 โ”‚ 96.36 โ”‚ 96.99 โ”‚ 94.74 โ”‚ 95.63 โ”‚ 95.63 โ”‚ 64797500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-13 โ”‚ 98.70 โ”‚ 105.13 โ”‚ 98.40 โ”‚ 104.97 โ”‚ 104.97 โ”‚ 132588000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-14 โ”‚ 108.85 โ”‚ 109.48 โ”‚ 104.98 โ”‚ 107.58 โ”‚ 107.58 โ”‚ 144981000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-15 โ”‚ 105.95 โ”‚ 107.57 โ”‚ 103.36 โ”‚ 103.70 โ”‚ 103.70 โ”‚ 86844000 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + +(๐Ÿฆ‹) /forecast/ $ ema TSLA +Successfully added 'EMA_10' to 'TSLA' dataset + +(๐Ÿฆ‹) /forecast/ $ show TSLA +TSLA has following shape (rowxcolumn): (611, 8) + + Dataset TSLA | Showing 10 of 611 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ EMA_10 โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-02 โ”‚ 84.90 โ”‚ 86.14 โ”‚ 84.34 โ”‚ 86.05 โ”‚ 86.05 โ”‚ 47660500 โ”‚ 86.05 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-03 โ”‚ 88.10 โ”‚ 90.80 โ”‚ 87.38 โ”‚ 88.60 โ”‚ 88.60 โ”‚ 88892500 โ”‚ 86.52 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-06 โ”‚ 88.09 โ”‚ 90.31 โ”‚ 88.00 โ”‚ 90.31 โ”‚ 90.31 โ”‚ 50665000 โ”‚ 87.21 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-07 โ”‚ 92.28 โ”‚ 94.33 โ”‚ 90.67 โ”‚ 93.81 โ”‚ 93.81 โ”‚ 89410500 โ”‚ 88.41 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-08 โ”‚ 94.74 โ”‚ 99.70 โ”‚ 93.65 โ”‚ 98.43 โ”‚ 98.43 โ”‚ 155721500 โ”‚ 90.23 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-09 โ”‚ 99.42 โ”‚ 99.76 โ”‚ 94.57 โ”‚ 96.27 โ”‚ 96.27 โ”‚ 142202000 โ”‚ 91.33 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-10 โ”‚ 96.36 โ”‚ 96.99 โ”‚ 94.74 โ”‚ 95.63 โ”‚ 95.63 โ”‚ 64797500 โ”‚ 92.11 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-13 โ”‚ 98.70 โ”‚ 105.13 โ”‚ 98.40 โ”‚ 104.97 โ”‚ 104.97 โ”‚ 132588000 โ”‚ 94.45 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-14 โ”‚ 108.85 โ”‚ 109.48 โ”‚ 104.98 โ”‚ 107.58 โ”‚ 107.58 โ”‚ 144981000 โ”‚ 96.84 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-15 โ”‚ 105.95 โ”‚ 107.57 โ”‚ 103.36 โ”‚ 103.70 โ”‚ 103.70 โ”‚ 86844000 โ”‚ 98.08 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + +``` diff --git a/website/content/terminal/forecast/expo/_index.md b/website/content/terminal/forecast/expo/_index.md new file mode 100644 index 00000000000..12b435a7924 --- /dev/null +++ b/website/content/terminal/forecast/expo/_index.md @@ -0,0 +1,62 @@ +``` +usage: expo [--trend {N,A,M}] [--dampen DAMPEN] [--naive] [-d {}] [-c TARGET_COLUMN] [-n N_DAYS] [-s {N,A,M}] + [-p SEASONAL_PERIODS] [-w START_WINDOW] [--end S_END_DATE] [--start S_START_DATE] [--residuals] + [--forecast-only] [-h] [--export EXPORT] +``` + +Perform Probabilistic Exponential Smoothing forecast Trend: N: None, A: Additive, M: Multiplicative Seasonality: N: None, A: Additive, M: Multiplicative Dampen: T: True, F: False + +``` +optional arguments: + --trend {N,A,M} Trend: N: None, A: Additive, M: Multiplicative. (default: A) + --dampen DAMPEN Dampening (default: F) + --naive Show the naive baseline for a model. (default: False) + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -c TARGET_COLUMN, --target-column TARGET_COLUMN + The name of the specific column you want to use (default: close) + -n N_DAYS, --n-days N_DAYS + prediction days. (default: 5) + -s {N,A,M}, --seasonal {N,A,M} + Seasonality: N: None, A: Additive, M: Multiplicative. (default: A) + -p SEASONAL_PERIODS, --periods SEASONAL_PERIODS + Seasonal periods: 4: Quarterly, 7: Daily (default: 7) + -w START_WINDOW, --window START_WINDOW + Start point for rolling training and forecast window. 0.0-1.0 (default: 0.85) + --end S_END_DATE The end date (format YYYY-MM-DD) to select for testing (default: None) + --start S_START_DATE The start date (format YYYY-MM-DD) to select for testing (default: None) + --residuals Show the residuals for the model. (default: False) + --forecast-only Do not plot the hisotorical data without forecasts. (default: False) + -h, --help show this help message (default: False) + --export EXPORT Export figure into png, jpg, pdf, svg (default: ) + + +For more information and examples, use 'about export' to access the related guide. +``` + +Example: +``` +2022 Jul 23, 10:36 (๐Ÿฆ‹) /forecast/ $ load GME_20220719_123734.csv -a GME + +2022 Jul 23, 10:52 (๐Ÿฆ‹) /forecast/ $ expo GME +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 115/115 [00:16<00:00, 6.80it/s] +Exponential smoothing obtains MAPE: 12.88% + + + + Actual price: $ 146.64 +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Datetime โ”ƒ Prediction โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 2022-07-19 00:00:00 โ”‚ $ 146.35 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-20 00:00:00 โ”‚ $ 148.63 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-21 00:00:00 โ”‚ $ 148.86 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-22 00:00:00 โ”‚ $ 151.76 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-25 00:00:00 โ”‚ $ 149.74 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` +![expo](https://user-images.githubusercontent.com/72827203/180615313-e45d6cb3-06a8-45aa-ae4e-505df07e7210.png) diff --git a/website/content/terminal/forecast/export/_index.md b/website/content/terminal/forecast/export/_index.md new file mode 100644 index 00000000000..26d83b5b69c --- /dev/null +++ b/website/content/terminal/forecast/export/_index.md @@ -0,0 +1,27 @@ +``` +usage: export [-t {csv,xlsx}] [-d {}] [-h] +``` + +Export dataset to Excel + +``` +optional arguments: + -t {csv,xlsx}, --type {csv,xlsx} + The file type you wish to export to (default: xlsx) + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -h, --help show this help message (default: False) + +For more information and examples, use 'about export' to access the related guide. +``` + +Example: +``` +(๐Ÿฆ‹) /forecast/ $ load aapl + +(๐Ÿฆ‹) /forecast/ $ ema aapl +Successfully added 'EMA_10' to 'aapl' dataset + +(๐Ÿฆ‹) /forecast/ $ export aapl +Saved file: .../OpenBBTerminal/exports/forecast/aapl_20220711_151219.xlsx +``` diff --git a/website/content/terminal/forecast/linregr/_index.md b/website/content/terminal/forecast/linregr/_index.md new file mode 100644 index 00000000000..d7b8521cf5d --- /dev/null +++ b/website/content/terminal/forecast/linregr/_index.md @@ -0,0 +1,62 @@ +``` +usage: linregr [--past-covariates PAST_COVARIATES] [--all-past-covariates] [--naive] [-d {}] [-c TARGET_COLUMN] + [-n N_DAYS] [-t TRAIN_SPLIT] [-o OUTPUT_CHUNK_LENGTH] [--end S_END_DATE] [--start S_START_DATE] + [--lags LAGS] [--residuals] [--forecast-only] [-h] [--export EXPORT] +``` + +Perform a linear regression forecast. + +``` +optional arguments: + --past-covariates PAST_COVARIATES + Past covariates(columns/features) in same dataset. Comma separated. (default: None) + --all-past-covariates + Adds all rows as past covariates except for date and the target column. (default: False) + --naive Show the naive baseline for a model. (default: False) + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -c TARGET_COLUMN, --target-column TARGET_COLUMN + The name of the specific column you want to use (default: close) + -n N_DAYS, --n-days N_DAYS + prediction days. (default: 5) + -t TRAIN_SPLIT, --train-split TRAIN_SPLIT + Start point for rolling training and forecast window. 0.0-1.0 (default: 0.85) + -o OUTPUT_CHUNK_LENGTH, --output-chunk-length OUTPUT_CHUNK_LENGTH + The length of the forecast of the model. (default: 5) + --end S_END_DATE The end date (format YYYY-MM-DD) to select for testing (default: None) + --start S_START_DATE The start date (format YYYY-MM-DD) to select for testing (default: None) + --lags LAGS Lagged target values used to predict the next time step. (default: 14) + --residuals Show the residuals for the model. (default: False) + --forecast-only Do not plot the hisotorical data without forecasts. (default: False) + -h, --help show this help message (default: False) + --export EXPORT Export figure into png, jpg, pdf, svg (default: ) + +For more information and examples, use 'about export' to access the related guide. +``` + +Example: +``` +2022 Jul 23, 10:36 (๐Ÿฆ‹) /forecast/ $ load GME_20220719_123734.csv -a GME + +2022 Jul 23, 11:03 (๐Ÿฆ‹) /forecast/ $ linregr GME +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 115/115 [00:07<00:00, 15.10it/s] +Logistic Regression model obtains MAPE: 10.85% + + + + Actual price: $ 146.64 +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Datetime โ”ƒ Prediction โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 2022-07-19 00:00:00 โ”‚ $ 144.41 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-20 00:00:00 โ”‚ $ 142.69 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-21 00:00:00 โ”‚ $ 140.94 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-22 00:00:00 โ”‚ $ 139.89 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-25 00:00:00 โ”‚ $ 136.04 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` +![linregr](https://user-images.githubusercontent.com/72827203/180615335-26395da8-3848-40f4-a68b-d2c14475db95.png) diff --git a/website/content/terminal/forecast/load/_index.md b/website/content/terminal/forecast/load/_index.md new file mode 100644 index 00000000000..08bfe1f6896 --- /dev/null +++ b/website/content/terminal/forecast/load/_index.md @@ -0,0 +1,47 @@ +``` +usage: load [-f FILE] [-a ALIAS] [-h] +``` + +Load custom data set into a DataFrame. If a column exists titled "date", "time", "timestamp", "quarter" is +automatically converted to a date format and is set as index of the DataFrame. + + +``` +optional arguments: + -f FILE, --file FILE File to load data in (can be custom import, may have been exported before.) (default: None) + -a ALIAS, --alias ALIAS + Alias name to give to the dataset (default: None) + -h, --help show this help message (default: False) + +``` + +Example: +``` +(๐Ÿฆ‹) /forecast/ $ load aapl.csv + +(๐Ÿฆ‹) /forecast/ $ show aapl + Dataset aapl | Showing 10 of 759 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2019-06-10 โ”‚ 47.95 โ”‚ 48.84 โ”‚ 47.90 โ”‚ 48.15 โ”‚ 46.99 โ”‚ 104883600 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2019-06-11 โ”‚ 48.72 โ”‚ 49.00 โ”‚ 48.40 โ”‚ 48.70 โ”‚ 47.53 โ”‚ 107731600 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2019-06-12 โ”‚ 48.49 โ”‚ 48.99 โ”‚ 48.35 โ”‚ 48.55 โ”‚ 47.38 โ”‚ 73012800 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2019-06-13 โ”‚ 48.67 โ”‚ 49.20 โ”‚ 48.40 โ”‚ 48.54 โ”‚ 47.37 โ”‚ 86698400 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2019-06-14 โ”‚ 47.89 โ”‚ 48.40 โ”‚ 47.58 โ”‚ 48.19 โ”‚ 47.03 โ”‚ 75046000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2019-06-17 โ”‚ 48.22 โ”‚ 48.74 โ”‚ 48.04 โ”‚ 48.47 โ”‚ 47.31 โ”‚ 58676400 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2019-06-18 โ”‚ 49.01 โ”‚ 50.07 โ”‚ 48.80 โ”‚ 49.61 โ”‚ 48.42 โ”‚ 106204000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2019-06-19 โ”‚ 49.92 โ”‚ 49.97 โ”‚ 49.33 โ”‚ 49.47 โ”‚ 48.28 โ”‚ 84496800 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2019-06-20 โ”‚ 50.09 โ”‚ 50.15 โ”‚ 49.51 โ”‚ 49.87 โ”‚ 48.67 โ”‚ 86056000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2019-06-21 โ”‚ 49.70 โ”‚ 50.21 โ”‚ 49.54 โ”‚ 49.69 โ”‚ 48.50 โ”‚ 191202400 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` diff --git a/website/content/terminal/forecast/mom/_index.md b/website/content/terminal/forecast/mom/_index.md new file mode 100644 index 00000000000..dbd8ebb7319 --- /dev/null +++ b/website/content/terminal/forecast/mom/_index.md @@ -0,0 +1,111 @@ +``` +usage: mom [-d {}] [-c TARGET_COLUMN] [--period PERIOD] [-h] +``` + +Add momentum to dataset based on specific column. + +``` +optional arguments: + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -c TARGET_COLUMN, --target-column TARGET_COLUMN + The name of the specific column you want to use (default: close) + --period PERIOD The period to use (default: 10) + -h, --help show this help message (default: False) + +For more information and examples, use 'about mom' to access the related guide. +``` + +Example: +``` +(๐Ÿฆ‹) /forecast/ $ show TSLA +TSLA has following shape (rowxcolumn): (611, 7) + + Dataset TSLA | Showing 10 of 611 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-02 โ”‚ 84.90 โ”‚ 86.14 โ”‚ 84.34 โ”‚ 86.05 โ”‚ 86.05 โ”‚ 47660500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-03 โ”‚ 88.10 โ”‚ 90.80 โ”‚ 87.38 โ”‚ 88.60 โ”‚ 88.60 โ”‚ 88892500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-06 โ”‚ 88.09 โ”‚ 90.31 โ”‚ 88.00 โ”‚ 90.31 โ”‚ 90.31 โ”‚ 50665000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-07 โ”‚ 92.28 โ”‚ 94.33 โ”‚ 90.67 โ”‚ 93.81 โ”‚ 93.81 โ”‚ 89410500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-08 โ”‚ 94.74 โ”‚ 99.70 โ”‚ 93.65 โ”‚ 98.43 โ”‚ 98.43 โ”‚ 155721500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-09 โ”‚ 99.42 โ”‚ 99.76 โ”‚ 94.57 โ”‚ 96.27 โ”‚ 96.27 โ”‚ 142202000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-10 โ”‚ 96.36 โ”‚ 96.99 โ”‚ 94.74 โ”‚ 95.63 โ”‚ 95.63 โ”‚ 64797500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-13 โ”‚ 98.70 โ”‚ 105.13 โ”‚ 98.40 โ”‚ 104.97 โ”‚ 104.97 โ”‚ 132588000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-14 โ”‚ 108.85 โ”‚ 109.48 โ”‚ 104.98 โ”‚ 107.58 โ”‚ 107.58 โ”‚ 144981000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-15 โ”‚ 105.95 โ”‚ 107.57 โ”‚ 103.36 โ”‚ 103.70 โ”‚ 103.70 โ”‚ 86844000 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + +(๐Ÿฆ‹) /forecast/ $ mom TSLA +Successfully added 'Momentum_10' to 'TSLA' dataset + +(๐Ÿฆ‹) /forecast/ $ show TSLA +TSLA has following shape (rowxcolumn): (611, 8) + + Dataset TSLA | Showing 10 of 611 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ Momentum_10 โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-02 โ”‚ 84.90 โ”‚ 86.14 โ”‚ 84.34 โ”‚ 86.05 โ”‚ 86.05 โ”‚ 47660500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-03 โ”‚ 88.10 โ”‚ 90.80 โ”‚ 87.38 โ”‚ 88.60 โ”‚ 88.60 โ”‚ 88892500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-06 โ”‚ 88.09 โ”‚ 90.31 โ”‚ 88.00 โ”‚ 90.31 โ”‚ 90.31 โ”‚ 50665000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-07 โ”‚ 92.28 โ”‚ 94.33 โ”‚ 90.67 โ”‚ 93.81 โ”‚ 93.81 โ”‚ 89410500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-08 โ”‚ 94.74 โ”‚ 99.70 โ”‚ 93.65 โ”‚ 98.43 โ”‚ 98.43 โ”‚ 155721500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-09 โ”‚ 99.42 โ”‚ 99.76 โ”‚ 94.57 โ”‚ 96.27 โ”‚ 96.27 โ”‚ 142202000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-10 โ”‚ 96.36 โ”‚ 96.99 โ”‚ 94.74 โ”‚ 95.63 โ”‚ 95.63 โ”‚ 64797500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-13 โ”‚ 98.70 โ”‚ 105.13 โ”‚ 98.40 โ”‚ 104.97 โ”‚ 104.97 โ”‚ 132588000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-14 โ”‚ 108.85 โ”‚ 109.48 โ”‚ 104.98 โ”‚ 107.58 โ”‚ 107.58 โ”‚ 144981000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-15 โ”‚ 105.95 โ”‚ 107.57 โ”‚ 103.36 โ”‚ 103.70 โ”‚ 103.70 โ”‚ 86844000 โ”‚ nan โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + +(๐Ÿฆ‹) /forecast/ $ clean TSLA +Namespace(drop='', fill='', help=False, limit=5, target_dataset='TSLA') +Successfully cleaned 'TSLA' dataset + +(๐Ÿฆ‹) /forecast/ $ show TSLA +TSLA has following shape (rowxcolumn): (601, 8) + + Dataset TSLA | Showing 10 of 601 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ Momentum_10 โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-16 โ”‚ 98.75 โ”‚ 102.89 โ”‚ 98.43 โ”‚ 102.70 โ”‚ 102.70 โ”‚ 108683500 โ”‚ 16.65 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-17 โ”‚ 101.52 โ”‚ 103.13 โ”‚ 100.63 โ”‚ 102.10 โ”‚ 102.10 โ”‚ 68145500 โ”‚ 13.50 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-21 โ”‚ 106.05 โ”‚ 109.72 โ”‚ 105.68 โ”‚ 109.44 โ”‚ 109.44 โ”‚ 89017500 โ”‚ 19.13 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-22 โ”‚ 114.38 โ”‚ 118.90 โ”‚ 111.82 โ”‚ 113.91 โ”‚ 113.91 โ”‚ 156845000 โ”‚ 20.10 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-23 โ”‚ 112.85 โ”‚ 116.40 โ”‚ 111.12 โ”‚ 114.44 โ”‚ 114.44 โ”‚ 98255000 โ”‚ 16.01 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-24 โ”‚ 114.13 โ”‚ 114.77 โ”‚ 110.85 โ”‚ 112.96 โ”‚ 112.96 โ”‚ 71768000 โ”‚ 16.70 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-27 โ”‚ 108.40 โ”‚ 112.89 โ”‚ 107.86 โ”‚ 111.60 โ”‚ 111.60 โ”‚ 68040500 โ”‚ 15.97 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-28 โ”‚ 113.70 โ”‚ 115.36 โ”‚ 111.62 โ”‚ 113.38 โ”‚ 113.38 โ”‚ 58942500 โ”‚ 8.41 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-29 โ”‚ 115.14 โ”‚ 117.96 โ”‚ 113.49 โ”‚ 116.20 โ”‚ 116.20 โ”‚ 89007500 โ”‚ 8.61 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-30 โ”‚ 126.48 โ”‚ 130.18 โ”‚ 123.60 โ”‚ 128.16 โ”‚ 128.16 โ”‚ 145028500 โ”‚ 24.46 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` diff --git a/website/content/terminal/forecast/nbeats/_index.md b/website/content/terminal/forecast/nbeats/_index.md new file mode 100644 index 00000000000..87aeb61125f --- /dev/null +++ b/website/content/terminal/forecast/nbeats/_index.md @@ -0,0 +1,82 @@ +``` +usage: nbeats [--num_stacks NUM_STACKS] [--num_blocks NUM_BLOCKS] [--num_layers NUM_LAYERS] [--layer_widths LAYER_WIDTHS] [--past-covariates PAST_COVARIATES] [--all-past-covariates] [--naive] [-d {}] [-c TARGET_COLUMN] [-n N_DAYS] + [-t TRAIN_SPLIT] [-i INPUT_CHUNK_LENGTH] [-o OUTPUT_CHUNK_LENGTH] [--force-reset FORCE_RESET] [--save-checkpoints SAVE_CHECKPOINTS] [--model-save-name MODEL_SAVE_NAME] [--n-epochs N_EPOCHS] [--batch-size BATCH_SIZE] + [--end S_END_DATE] [--start S_START_DATE] [--learning-rate LEARNING_RATE] [--residuals] [--forecast-only] [-h] [--export EXPORT] +``` + +Perform NBEATS forecast (Neural Bayesian Estimation of Time Series). + +``` +optional arguments: + --num_stacks NUM_STACKS + The number of stacks that make up the whole model. (default: 10) + --num_blocks NUM_BLOCKS + The number of blocks making up every stack. (default: 3) + --num_layers NUM_LAYERS + The number of fully connected layers preceding the final forking layers in each block of every stack. (default: 4) + --layer_widths LAYER_WIDTHS + Determines the number of neurons that make up each fully connected layer in each block of every stack (default: 512) + --past-covariates PAST_COVARIATES + Past covariates(columns/features) in same dataset. Comma separated. (default: None) + --all-past-covariates + Adds all rows as past covariates except for date and the target column. (default: False) + --naive Show the naive baseline for a model. (default: False) + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -c TARGET_COLUMN, --target-column TARGET_COLUMN + The name of the specific column you want to use (default: close) + -n N_DAYS, --n-days N_DAYS + prediction days. (default: 5) + -t TRAIN_SPLIT, --train-split TRAIN_SPLIT + Start point for rolling training and forecast window. 0.0-1.0 (default: 0.85) + -i INPUT_CHUNK_LENGTH, --input-chunk-length INPUT_CHUNK_LENGTH + Number of past time steps for forecasting module at prediction time. (default: 14) + -o OUTPUT_CHUNK_LENGTH, --output-chunk-length OUTPUT_CHUNK_LENGTH + The length of the forecast of the model. (default: 5) + --force-reset FORCE_RESET + If set to True, any previously-existing model with the same name will be reset (all checkpoints will be discarded). (default: True) + --save-checkpoints SAVE_CHECKPOINTS + Whether to automatically save the untrained model and checkpoints. (default: True) + --model-save-name MODEL_SAVE_NAME + Name of the model to save. (default: nbeats_model) + --n-epochs N_EPOCHS Number of epochs over which to train the model. (default: 300) + --batch-size BATCH_SIZE + Number of time series (input and output) used in each training pass (default: 800) + --end S_END_DATE The end date (format YYYY-MM-DD) to select for testing (default: None) + --start S_START_DATE The start date (format YYYY-MM-DD) to select for testing (default: None) + --learning-rate LEARNING_RATE + Learning rate during training. (default: 0.001) + --residuals Show the residuals for the model. (default: False) + --forecast-only Do not plot the hisotorical data without forecasts. (default: False) + -h, --help show this help message (default: False) + --export EXPORT Export figure into png, jpg, pdf, svg (default: ) + +For more information and examples, use 'about export' to access the related guide. +``` + +Example: +``` +2022 Jul 23, 10:36 (๐Ÿฆ‹) /forecast/ $ load GME_20220719_123734.csv -a GME + +2022 Jul 23, 11:03 (๐Ÿฆ‹) /forecast/ $ nbeats GME +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 115/115 [00:07<00:00, 15.10it/s] +NBEATS model obtains MAPE: 23.53% + + + + Actual price: $ 146.64 +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Datetime โ”ƒ Prediction โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 2022-07-19 00:00:00 โ”‚ $ 158.52 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-20 00:00:00 โ”‚ $ 172.21 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-21 00:00:00 โ”‚ $ 67.46 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-22 00:00:00 โ”‚ $ 97.63 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-25 00:00:00 โ”‚ $ 154.24 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` +![nbeats](https://user-images.githubusercontent.com/72827203/180615396-d29126ae-ad75-4f84-9f67-2121dc4e695a.png) diff --git a/website/content/terminal/forecast/plot/_index.md b/website/content/terminal/forecast/plot/_index.md new file mode 100644 index 00000000000..8e0d8fc8abe --- /dev/null +++ b/website/content/terminal/forecast/plot/_index.md @@ -0,0 +1,22 @@ +``` +usage: plot [-v VALUES] [-h] [--export EXPORT] +``` + +Plot data based on the index. + +``` +optional arguments: + -v VALUES, --values VALUES + Dataset.column values to be displayed in a plot (default: None) + -h, --help show this help message (default: False) + --export EXPORT Export figure into png, jpg, pdf, svg (default: ) + +``` + +Example: +``` +(๐Ÿฆ‹) /forecast/ $ load aapl.csv + +(๐Ÿฆ‹) /forecast/ $ plot appl.close +``` +![close](TODO) diff --git a/website/content/terminal/forecast/regr/_index.md b/website/content/terminal/forecast/regr/_index.md new file mode 100644 index 00000000000..5d8d0b79028 --- /dev/null +++ b/website/content/terminal/forecast/regr/_index.md @@ -0,0 +1,62 @@ +``` +usage: regr [--past-covariates PAST_COVARIATES] [--all-past-covariates] [--naive] [-d {}] [-c TARGET_COLUMN] [-n N_DAYS] + [-t TRAIN_SPLIT] [-o OUTPUT_CHUNK_LENGTH] [--end S_END_DATE] [--start S_START_DATE] [--lags LAGS] + [--residuals] [--forecast-only] [-h] [--export EXPORT] +``` + +Perform a regression forecast. + +``` +optional arguments: + --past-covariates PAST_COVARIATES + Past covariates(columns/features) in same dataset. Comma separated. (default: None) + --all-past-covariates + Adds all rows as past covariates except for date and the target column. (default: False) + --naive Show the naive baseline for a model. (default: False) + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -c TARGET_COLUMN, --target-column TARGET_COLUMN + The name of the specific column you want to use (default: close) + -n N_DAYS, --n-days N_DAYS + prediction days. (default: 5) + -t TRAIN_SPLIT, --train-split TRAIN_SPLIT + Start point for rolling training and forecast window. 0.0-1.0 (default: 0.85) + -o OUTPUT_CHUNK_LENGTH, --output-chunk-length OUTPUT_CHUNK_LENGTH + The length of the forecast of the model. (default: 5) + --end S_END_DATE The end date (format YYYY-MM-DD) to select for testing (default: None) + --start S_START_DATE The start date (format YYYY-MM-DD) to select for testing (default: None) + --lags LAGS Lagged target values used to predict the next time step. (default: 14) + --residuals Show the residuals for the model. (default: False) + --forecast-only Do not plot the hisotorical data without forecasts. (default: False) + -h, --help show this help message (default: False) + --export EXPORT Export figure into png, jpg, pdf, svg (default: ) + +For more information and examples, use 'about export' to access the related guide. +``` + +Example: +``` +2022 Jul 23, 10:36 (๐Ÿฆ‹) /forecast/ $ load GME_20220719_123734.csv -a GME + +2022 Jul 23, 11:08 (๐Ÿฆ‹) /forecast/ $ regr GME +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 115/115 [00:01<00:00, 111.85it/s] +Regression model obtains MAPE: 12.34% + + + + Actual price: $ 146.64 +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Datetime โ”ƒ Prediction โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 2022-07-19 00:00:00 โ”‚ $ 143.74 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-20 00:00:00 โ”‚ $ 144.90 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-21 00:00:00 โ”‚ $ 142.41 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-22 00:00:00 โ”‚ $ 133.50 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-25 00:00:00 โ”‚ $ 134.24 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` +![regr](https://user-images.githubusercontent.com/72827203/180615346-82fae367-d0cc-4d78-be30-b947a83df909.png) diff --git a/website/content/terminal/forecast/rename/_index.md b/website/content/terminal/forecast/rename/_index.md new file mode 100644 index 00000000000..adeccc4933c --- /dev/null +++ b/website/content/terminal/forecast/rename/_index.md @@ -0,0 +1,76 @@ +``` +usage: rename [-d {}] [-o OLDCOL] [-n NEWCOL] [-h] +``` + +The column you want to rename from a dataset. + +``` +optional arguments: + -d {}, --dataset {} Dataset that will get a column renamed (default: None) + -o OLDCOL, --oldcol OLDCOL + Old column from dataset to be renamed (default: None) + -n NEWCOL, --newcol NEWCOL + New column from dataset to be renamed (default: None) + -h, --help show this help message (default: False) +``` + +Example: +``` +2022 Jul 23, 10:16 (๐Ÿฆ‹) /forecast/ $ show AAPL +AAPL has following shape (rowxcolumn): (759, 7) + + Dataset AAPL | Showing 10 of 759 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2019-07-15 โ”‚ 51.02 โ”‚ 51.47 โ”‚ 51.00 โ”‚ 51.30 โ”‚ 50.07 โ”‚ 67789600 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2019-07-16 โ”‚ 51.15 โ”‚ 51.53 โ”‚ 50.88 โ”‚ 51.12 โ”‚ 49.90 โ”‚ 67467200 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2019-07-17 โ”‚ 51.01 โ”‚ 51.27 โ”‚ 50.82 โ”‚ 50.84 โ”‚ 49.62 โ”‚ 56430000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2019-07-18 โ”‚ 51.00 โ”‚ 51.47 โ”‚ 50.92 โ”‚ 51.42 โ”‚ 50.18 โ”‚ 74162400 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2019-07-19 โ”‚ 51.45 โ”‚ 51.62 โ”‚ 50.59 โ”‚ 50.65 โ”‚ 49.43 โ”‚ 83717200 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2019-07-22 โ”‚ 50.91 โ”‚ 51.81 โ”‚ 50.90 โ”‚ 51.81 โ”‚ 50.56 โ”‚ 89111600 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2019-07-23 โ”‚ 52.12 โ”‚ 52.23 โ”‚ 51.82 โ”‚ 52.21 โ”‚ 50.96 โ”‚ 73420800 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2019-07-24 โ”‚ 51.92 โ”‚ 52.29 โ”‚ 51.79 โ”‚ 52.17 โ”‚ 50.92 โ”‚ 59966400 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2019-07-25 โ”‚ 52.22 โ”‚ 52.31 โ”‚ 51.68 โ”‚ 51.76 โ”‚ 50.51 โ”‚ 55638400 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2019-07-26 โ”‚ 51.87 โ”‚ 52.43 โ”‚ 51.78 โ”‚ 51.94 โ”‚ 50.69 โ”‚ 70475600 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + +2022 Jul 23, 10:16 (๐Ÿฆ‹) /forecast/ $ rename AAPL -o close -n new_name + +2022 Jul 23, 10:16 (๐Ÿฆ‹) /forecast/ $ show AAPL +AAPL has following shape (rowxcolumn): (759, 7) + + Dataset AAPL | Showing 10 of 759 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ new_name โ”ƒ adj_close โ”ƒ volume โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2019-07-15 โ”‚ 51.02 โ”‚ 51.47 โ”‚ 51.00 โ”‚ 51.30 โ”‚ 50.07 โ”‚ 67789600 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2019-07-16 โ”‚ 51.15 โ”‚ 51.53 โ”‚ 50.88 โ”‚ 51.12 โ”‚ 49.90 โ”‚ 67467200 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2019-07-17 โ”‚ 51.01 โ”‚ 51.27 โ”‚ 50.82 โ”‚ 50.84 โ”‚ 49.62 โ”‚ 56430000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2019-07-18 โ”‚ 51.00 โ”‚ 51.47 โ”‚ 50.92 โ”‚ 51.42 โ”‚ 50.18 โ”‚ 74162400 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2019-07-19 โ”‚ 51.45 โ”‚ 51.62 โ”‚ 50.59 โ”‚ 50.65 โ”‚ 49.43 โ”‚ 83717200 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2019-07-22 โ”‚ 50.91 โ”‚ 51.81 โ”‚ 50.90 โ”‚ 51.81 โ”‚ 50.56 โ”‚ 89111600 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2019-07-23 โ”‚ 52.12 โ”‚ 52.23 โ”‚ 51.82 โ”‚ 52.21 โ”‚ 50.96 โ”‚ 73420800 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2019-07-24 โ”‚ 51.92 โ”‚ 52.29 โ”‚ 51.79 โ”‚ 52.17 โ”‚ 50.92 โ”‚ 59966400 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2019-07-25 โ”‚ 52.22 โ”‚ 52.31 โ”‚ 51.68 โ”‚ 51.76 โ”‚ 50.51 โ”‚ 55638400 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2019-07-26 โ”‚ 51.87 โ”‚ 52.43 โ”‚ 51.78 โ”‚ 51.94 โ”‚ 50.69 โ”‚ 70475600 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` diff --git a/website/content/terminal/forecast/rnn/_index.md b/website/content/terminal/forecast/rnn/_index.md new file mode 100644 index 00000000000..f2eb7221aab --- /dev/null +++ b/website/content/terminal/forecast/rnn/_index.md @@ -0,0 +1,81 @@ +``` +usage: rnn [--hidden-dim HIDDEN_DIM] [--training_length TRAINING_LENGTH] [--naive] [-d {}] [-c TARGET_COLUMN] + [-n N_DAYS] [-t TRAIN_SPLIT] [-i INPUT_CHUNK_LENGTH] [--force-reset FORCE_RESET] + [--save-checkpoints SAVE_CHECKPOINTS] [--model-save-name MODEL_SAVE_NAME] [--n-epochs N_EPOCHS] + [--model-type MODEL_TYPE] [--dropout DROPOUT] [--batch-size BATCH_SIZE] [--end S_END_DATE] + [--start S_START_DATE] [--learning-rate LEARNING_RATE] [--residuals] [--forecast-only] [-h] [--export EXPORT] +``` + +Perform RNN forecast (Vanilla RNN, LSTM, GRU) + +``` +optional arguments: + --hidden-dim HIDDEN_DIM + Size for feature maps for each hidden RNN layer (h_n) (default: 20) + --training_length TRAINING_LENGTH + The length of both input (target and covariates) and output (target) time series used during + training. Generally speaking, training_length should have a higher value than input_chunk_length + because otherwise during training the RNN is never run for as many iterations as it will during + training. (default: 20) + --naive Show the naive baseline for a model. (default: False) + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -c TARGET_COLUMN, --target-column TARGET_COLUMN + The name of the specific column you want to use (default: close) + -n N_DAYS, --n-days N_DAYS + prediction days. (default: 5) + -t TRAIN_SPLIT, --train-split TRAIN_SPLIT + Start point for rolling training and forecast window. 0.0-1.0 (default: 0.85) + -i INPUT_CHUNK_LENGTH, --input-chunk-length INPUT_CHUNK_LENGTH + Number of past time steps for forecasting module at prediction time. (default: 14) + --force-reset FORCE_RESET + If set to True, any previously-existing model with the same name will be reset (all checkpoints + will be discarded). (default: True) + --save-checkpoints SAVE_CHECKPOINTS + Whether to automatically save the untrained model and checkpoints. (default: True) + --model-save-name MODEL_SAVE_NAME + Name of the model to save. (default: rnn_model) + --n-epochs N_EPOCHS Number of epochs over which to train the model. (default: 300) + --model-type MODEL_TYPE + Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU") (default: LSTM) + --dropout DROPOUT Fraction of neurons afected by Dropout. (default: 0) + --batch-size BATCH_SIZE + Number of time series (input and output) used in each training pass (default: 32) + --end S_END_DATE The end date (format YYYY-MM-DD) to select for testing (default: None) + --start S_START_DATE The start date (format YYYY-MM-DD) to select for testing (default: None) + --learning-rate LEARNING_RATE + Learning rate during training. (default: 0.001) + --residuals Show the residuals for the model. (default: False) + --forecast-only Do not plot the hisotorical data without forecasts. (default: False) + -h, --help show this help message (default: False) + --export EXPORT Export figure into png, jpg, pdf, svg (default: ) + +For more information and examples, use 'about export' to access the related guide. +``` + +Example: +``` +2022 Jul 23, 10:36 (๐Ÿฆ‹) /forecast/ $ load GME_20220719_123734.csv -a GME + +2022 Jul 23, 11:03 (๐Ÿฆ‹) /forecast/ $ rnn GME +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 115/115 [00:07<00:00, 15.10it/s] +RNN model obtains MAPE: 14.67% + + + + Actual price: $ 146.64 +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Datetime โ”ƒ Prediction โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 2022-07-19 00:00:00 โ”‚ $ 146.89 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-20 00:00:00 โ”‚ $ 148.58 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-21 00:00:00 โ”‚ $ 150.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-22 00:00:00 โ”‚ $ 151.23 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-25 00:00:00 โ”‚ $ 152.29 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` +![rnn](https://user-images.githubusercontent.com/72827203/180615355-5c30635a-be63-4b9a-836d-9feb3d3ac263.png) diff --git a/website/content/terminal/forecast/roc/_index.md b/website/content/terminal/forecast/roc/_index.md new file mode 100644 index 00000000000..bff13bc2c55 --- /dev/null +++ b/website/content/terminal/forecast/roc/_index.md @@ -0,0 +1,79 @@ +``` +usage: roc [-d {}] [-c TARGET_COLUMN] [--period PERIOD] [-h] +``` + +Add rate of change to dataset based on specific column. + +``` +optional arguments: + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -c TARGET_COLUMN, --target-column TARGET_COLUMN + The name of the specific column you want to use (default: close) + --period PERIOD The period to use (default: 10) + -h, --help show this help message (default: False) + +For more information and examples, use 'about roc' to access the related guide. +``` + +Example: +``` +(๐Ÿฆ‹) /forecast/ $ show TSLA +TSLA has following shape (rowxcolumn): (611, 7) + + Dataset TSLA | Showing 10 of 611 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-02 โ”‚ 84.90 โ”‚ 86.14 โ”‚ 84.34 โ”‚ 86.05 โ”‚ 86.05 โ”‚ 47660500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-03 โ”‚ 88.10 โ”‚ 90.80 โ”‚ 87.38 โ”‚ 88.60 โ”‚ 88.60 โ”‚ 88892500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-06 โ”‚ 88.09 โ”‚ 90.31 โ”‚ 88.00 โ”‚ 90.31 โ”‚ 90.31 โ”‚ 50665000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-07 โ”‚ 92.28 โ”‚ 94.33 โ”‚ 90.67 โ”‚ 93.81 โ”‚ 93.81 โ”‚ 89410500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-08 โ”‚ 94.74 โ”‚ 99.70 โ”‚ 93.65 โ”‚ 98.43 โ”‚ 98.43 โ”‚ 155721500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-09 โ”‚ 99.42 โ”‚ 99.76 โ”‚ 94.57 โ”‚ 96.27 โ”‚ 96.27 โ”‚ 142202000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-10 โ”‚ 96.36 โ”‚ 96.99 โ”‚ 94.74 โ”‚ 95.63 โ”‚ 95.63 โ”‚ 64797500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-13 โ”‚ 98.70 โ”‚ 105.13 โ”‚ 98.40 โ”‚ 104.97 โ”‚ 104.97 โ”‚ 132588000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-14 โ”‚ 108.85 โ”‚ 109.48 โ”‚ 104.98 โ”‚ 107.58 โ”‚ 107.58 โ”‚ 144981000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-15 โ”‚ 105.95 โ”‚ 107.57 โ”‚ 103.36 โ”‚ 103.70 โ”‚ 103.70 โ”‚ 86844000 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + +(๐Ÿฆ‹) /forecast/ $ roc TSLA +Successfully added 'ROC_10' to 'TSLA' dataset + +(๐Ÿฆ‹) /forecast/ $ show TSLA +TSLA has following shape (rowxcolumn): (611, 8) + + Dataset TSLA | Showing 10 of 611 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ ROC_10 โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-02 โ”‚ 84.90 โ”‚ 86.14 โ”‚ 84.34 โ”‚ 86.05 โ”‚ 86.05 โ”‚ 47660500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-03 โ”‚ 88.10 โ”‚ 90.80 โ”‚ 87.38 โ”‚ 88.60 โ”‚ 88.60 โ”‚ 88892500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-06 โ”‚ 88.09 โ”‚ 90.31 โ”‚ 88.00 โ”‚ 90.31 โ”‚ 90.31 โ”‚ 50665000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-07 โ”‚ 92.28 โ”‚ 94.33 โ”‚ 90.67 โ”‚ 93.81 โ”‚ 93.81 โ”‚ 89410500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-08 โ”‚ 94.74 โ”‚ 99.70 โ”‚ 93.65 โ”‚ 98.43 โ”‚ 98.43 โ”‚ 155721500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-09 โ”‚ 99.42 โ”‚ 99.76 โ”‚ 94.57 โ”‚ 96.27 โ”‚ 96.27 โ”‚ 142202000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-10 โ”‚ 96.36 โ”‚ 96.99 โ”‚ 94.74 โ”‚ 95.63 โ”‚ 95.63 โ”‚ 64797500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-13 โ”‚ 98.70 โ”‚ 105.13 โ”‚ 98.40 โ”‚ 104.97 โ”‚ 104.97 โ”‚ 132588000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-14 โ”‚ 108.85 โ”‚ 109.48 โ”‚ 104.98 โ”‚ 107.58 โ”‚ 107.58 โ”‚ 144981000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-15 โ”‚ 105.95 โ”‚ 107.57 โ”‚ 103.36 โ”‚ 103.70 โ”‚ 103.70 โ”‚ 86844000 โ”‚ 20.51 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` diff --git a/website/content/terminal/forecast/rsi/_index.md b/website/content/terminal/forecast/rsi/_index.md new file mode 100644 index 00000000000..af3f28c4418 --- /dev/null +++ b/website/content/terminal/forecast/rsi/_index.md @@ -0,0 +1,112 @@ +``` +usage: rsi [-d {}] [-c TARGET_COLUMN] [--period PERIOD] [-h] +``` + +Add rsi to dataset based on specific column. + +``` +optional arguments: + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -c TARGET_COLUMN, --target-column TARGET_COLUMN + The name of the specific column you want to use (default: close) + --period PERIOD The period to use (default: 10) + -h, --help show this help message (default: False) + +For more information and examples, use 'about rsi' to access the related guide. +``` + +Example: +``` +(๐Ÿฆ‹) /forecast/ $ show TSLA +TSLA has following shape (rowxcolumn): (611, 7) + + Dataset TSLA | Showing 10 of 611 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-02 โ”‚ 84.90 โ”‚ 86.14 โ”‚ 84.34 โ”‚ 86.05 โ”‚ 86.05 โ”‚ 47660500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-03 โ”‚ 88.10 โ”‚ 90.80 โ”‚ 87.38 โ”‚ 88.60 โ”‚ 88.60 โ”‚ 88892500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-06 โ”‚ 88.09 โ”‚ 90.31 โ”‚ 88.00 โ”‚ 90.31 โ”‚ 90.31 โ”‚ 50665000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-07 โ”‚ 92.28 โ”‚ 94.33 โ”‚ 90.67 โ”‚ 93.81 โ”‚ 93.81 โ”‚ 89410500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-08 โ”‚ 94.74 โ”‚ 99.70 โ”‚ 93.65 โ”‚ 98.43 โ”‚ 98.43 โ”‚ 155721500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-09 โ”‚ 99.42 โ”‚ 99.76 โ”‚ 94.57 โ”‚ 96.27 โ”‚ 96.27 โ”‚ 142202000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-10 โ”‚ 96.36 โ”‚ 96.99 โ”‚ 94.74 โ”‚ 95.63 โ”‚ 95.63 โ”‚ 64797500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-13 โ”‚ 98.70 โ”‚ 105.13 โ”‚ 98.40 โ”‚ 104.97 โ”‚ 104.97 โ”‚ 132588000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-14 โ”‚ 108.85 โ”‚ 109.48 โ”‚ 104.98 โ”‚ 107.58 โ”‚ 107.58 โ”‚ 144981000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-15 โ”‚ 105.95 โ”‚ 107.57 โ”‚ 103.36 โ”‚ 103.70 โ”‚ 103.70 โ”‚ 86844000 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + +(๐Ÿฆ‹) /forecast/ $ rsi TSLA +Successfully added 'RSI_10' to 'TSLA' dataset + +(๐Ÿฆ‹) /forecast/ $ show TSLA +TSLA has following shape (rowxcolumn): (611, 8) + + Dataset TSLA | Showing 10 of 611 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ RSI_10 โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-02 โ”‚ 84.90 โ”‚ 86.14 โ”‚ 84.34 โ”‚ 86.05 โ”‚ 86.05 โ”‚ 47660500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-03 โ”‚ 88.10 โ”‚ 90.80 โ”‚ 87.38 โ”‚ 88.60 โ”‚ 88.60 โ”‚ 88892500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-06 โ”‚ 88.09 โ”‚ 90.31 โ”‚ 88.00 โ”‚ 90.31 โ”‚ 90.31 โ”‚ 50665000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-07 โ”‚ 92.28 โ”‚ 94.33 โ”‚ 90.67 โ”‚ 93.81 โ”‚ 93.81 โ”‚ 89410500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-08 โ”‚ 94.74 โ”‚ 99.70 โ”‚ 93.65 โ”‚ 98.43 โ”‚ 98.43 โ”‚ 155721500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-09 โ”‚ 99.42 โ”‚ 99.76 โ”‚ 94.57 โ”‚ 96.27 โ”‚ 96.27 โ”‚ 142202000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-10 โ”‚ 96.36 โ”‚ 96.99 โ”‚ 94.74 โ”‚ 95.63 โ”‚ 95.63 โ”‚ 64797500 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-13 โ”‚ 98.70 โ”‚ 105.13 โ”‚ 98.40 โ”‚ 104.97 โ”‚ 104.97 โ”‚ 132588000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-14 โ”‚ 108.85 โ”‚ 109.48 โ”‚ 104.98 โ”‚ 107.58 โ”‚ 107.58 โ”‚ 144981000 โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-15 โ”‚ 105.95 โ”‚ 107.57 โ”‚ 103.36 โ”‚ 103.70 โ”‚ 103.70 โ”‚ 86844000 โ”‚ nan โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + +(๐Ÿฆ‹) /forecast/ $ clean TSLA +Namespace(drop='', fill='', help=False, limit=5, target_dataset='TSLA') +Successfully cleaned 'TSLA' dataset + +(๐Ÿฆ‹) /forecast/ $ show TSLA +TSLA has following shape (rowxcolumn): (601, 8) + + Dataset TSLA | Showing 10 of 601 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ RSI_10 โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-16 โ”‚ 98.75 โ”‚ 102.89 โ”‚ 98.43 โ”‚ 102.70 โ”‚ 102.70 โ”‚ 108683500 โ”‚ 76.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-17 โ”‚ 101.52 โ”‚ 103.13 โ”‚ 100.63 โ”‚ 102.10 โ”‚ 102.10 โ”‚ 68145500 โ”‚ 74.45 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-21 โ”‚ 106.05 โ”‚ 109.72 โ”‚ 105.68 โ”‚ 109.44 โ”‚ 109.44 โ”‚ 89017500 โ”‚ 80.00 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-22 โ”‚ 114.38 โ”‚ 118.90 โ”‚ 111.82 โ”‚ 113.91 โ”‚ 113.91 โ”‚ 156845000 โ”‚ 82.56 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-23 โ”‚ 112.85 โ”‚ 116.40 โ”‚ 111.12 โ”‚ 114.44 โ”‚ 114.44 โ”‚ 98255000 โ”‚ 82.85 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-24 โ”‚ 114.13 โ”‚ 114.77 โ”‚ 110.85 โ”‚ 112.96 โ”‚ 112.96 โ”‚ 71768000 โ”‚ 78.80 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-27 โ”‚ 108.40 โ”‚ 112.89 โ”‚ 107.86 โ”‚ 111.60 โ”‚ 111.60 โ”‚ 68040500 โ”‚ 75.05 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-28 โ”‚ 113.70 โ”‚ 115.36 โ”‚ 111.62 โ”‚ 113.38 โ”‚ 113.38 โ”‚ 58942500 โ”‚ 76.66 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-29 โ”‚ 115.14 โ”‚ 117.96 โ”‚ 113.49 โ”‚ 116.20 โ”‚ 116.20 โ”‚ 89007500 โ”‚ 79.05 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-30 โ”‚ 126.48 โ”‚ 130.18 โ”‚ 123.60 โ”‚ 128.16 โ”‚ 128.16 โ”‚ 145028500 โ”‚ 85.87 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + +``` diff --git a/website/content/terminal/forecast/season/_index.md b/website/content/terminal/forecast/season/_index.md new file mode 100644 index 00000000000..1549323026f --- /dev/null +++ b/website/content/terminal/forecast/season/_index.md @@ -0,0 +1,28 @@ +``` +usage: season [-v VALUES] [-m M] [--max_lag MAX_LAG] [-a ALPHA] [-h] [--export EXPORT] +``` + +Plot the seasonality for a given column + +``` +optional arguments: + -v VALUES, --values VALUES + Dataset.column values to be displayed in a plot (default: None) + -m M A time lag to highlight on the plot (default: None) + --max_lag MAX_LAG The maximal lag order to consider (default: 24) + -a ALPHA, --alpha ALPHA + The confidence interval to display (default: 0.05) + -h, --help show this help message (default: False) + --export EXPORT Export figure into png, jpg, pdf, svg (default: ) + +For more information and examples, use 'about season' to access the related guide. +``` + +Example: +``` +(๐Ÿฆ‹) /forecast/ $ load TSLA.csv + +(๐Ÿฆ‹) /forecast/ $ season TSLA.volume +TODO: screen shot + +``` diff --git a/website/content/terminal/forecast/show/_index.md b/website/content/terminal/forecast/show/_index.md new file mode 100644 index 00000000000..c0a1c27a72a --- /dev/null +++ b/website/content/terminal/forecast/show/_index.md @@ -0,0 +1,53 @@ +``` +usage: show [-n {}] [-s SORTCOL [SORTCOL ...]] [-a] [-h] [--export EXPORT] [-l LIMIT] +``` + +Show a portion of the, with `--name` defined, dataset or show a portion of all datasets when +`--name` is not defined. + +``` +optional arguments: + -n {}, --name {} The name of the database you want to show data for (default: None) + -s SORTCOL [SORTCOL ...], --sortcol SORTCOL [SORTCOL ...] + Sort based on a column in the DataFrame (default: ) + -a, --ascend Use this argument to sort in a descending order (default: False) + -h, --help show this help message (default: False) + --export EXPORT Export raw data into csv, json, xlsx (default: ) + -l LIMIT, --limit LIMIT + Number of entries to show in data. (default: 10) +``` + +Example: +``` +(๐Ÿฆ‹) /forecast/ $ load aapl.csv + +(๐Ÿฆ‹) /forecast/ $ show aapl + +aapl has following shape (rowxcolumn): (759, 7) + + Dataset aapl | Showing 10 of 759 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2019-06-10 โ”‚ 47.95 โ”‚ 48.84 โ”‚ 47.90 โ”‚ 48.15 โ”‚ 46.99 โ”‚ 104883600 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2019-06-11 โ”‚ 48.72 โ”‚ 49.00 โ”‚ 48.40 โ”‚ 48.70 โ”‚ 47.53 โ”‚ 107731600 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2019-06-12 โ”‚ 48.49 โ”‚ 48.99 โ”‚ 48.35 โ”‚ 48.55 โ”‚ 47.38 โ”‚ 73012800 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2019-06-13 โ”‚ 48.67 โ”‚ 49.20 โ”‚ 48.40 โ”‚ 48.54 โ”‚ 47.37 โ”‚ 86698400 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2019-06-14 โ”‚ 47.89 โ”‚ 48.40 โ”‚ 47.58 โ”‚ 48.19 โ”‚ 47.03 โ”‚ 75046000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2019-06-17 โ”‚ 48.22 โ”‚ 48.74 โ”‚ 48.04 โ”‚ 48.47 โ”‚ 47.31 โ”‚ 58676400 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2019-06-18 โ”‚ 49.01 โ”‚ 50.07 โ”‚ 48.80 โ”‚ 49.61 โ”‚ 48.42 โ”‚ 106204000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2019-06-19 โ”‚ 49.92 โ”‚ 49.97 โ”‚ 49.33 โ”‚ 49.47 โ”‚ 48.28 โ”‚ 84496800 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2019-06-20 โ”‚ 50.09 โ”‚ 50.15 โ”‚ 49.51 โ”‚ 49.87 โ”‚ 48.67 โ”‚ 86056000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2019-06-21 โ”‚ 49.70 โ”‚ 50.21 โ”‚ 49.54 โ”‚ 49.69 โ”‚ 48.50 โ”‚ 191202400 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + +``` diff --git a/website/content/terminal/forecast/signal/_index.md b/website/content/terminal/forecast/signal/_index.md new file mode 100644 index 00000000000..ca8c0275cd0 --- /dev/null +++ b/website/content/terminal/forecast/signal/_index.md @@ -0,0 +1,119 @@ +``` +usage: signal [-d {}] [-c TARGET_COLUMN] [-h] +``` + +Add price signal to dataset based on closing price. 1 if the signal is that short term price will go up as compared to the long term. 0 if the signal is that short term price will go down as compared to the long term. + +``` +optional arguments: + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -c TARGET_COLUMN, --target-column TARGET_COLUMN + The name of the specific column you want to use (default: close) + --period PERIOD The period to use (default: 10) + -h, --help show this help message (default: False) + +For more information and examples, use 'about signal' to access the related guide. +``` + +Example: +``` +(๐Ÿฆ‹) /forecast/ $ show TSLA +TSLA has following shape (rowxcolumn): (611, 7) + + Dataset TSLA | Showing 10 of 611 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-02 โ”‚ 84.90 โ”‚ 86.14 โ”‚ 84.34 โ”‚ 86.05 โ”‚ 86.05 โ”‚ 47660500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-03 โ”‚ 88.10 โ”‚ 90.80 โ”‚ 87.38 โ”‚ 88.60 โ”‚ 88.60 โ”‚ 88892500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-06 โ”‚ 88.09 โ”‚ 90.31 โ”‚ 88.00 โ”‚ 90.31 โ”‚ 90.31 โ”‚ 50665000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-07 โ”‚ 92.28 โ”‚ 94.33 โ”‚ 90.67 โ”‚ 93.81 โ”‚ 93.81 โ”‚ 89410500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-08 โ”‚ 94.74 โ”‚ 99.70 โ”‚ 93.65 โ”‚ 98.43 โ”‚ 98.43 โ”‚ 155721500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-09 โ”‚ 99.42 โ”‚ 99.76 โ”‚ 94.57 โ”‚ 96.27 โ”‚ 96.27 โ”‚ 142202000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-10 โ”‚ 96.36 โ”‚ 96.99 โ”‚ 94.74 โ”‚ 95.63 โ”‚ 95.63 โ”‚ 64797500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-13 โ”‚ 98.70 โ”‚ 105.13 โ”‚ 98.40 โ”‚ 104.97 โ”‚ 104.97 โ”‚ 132588000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-14 โ”‚ 108.85 โ”‚ 109.48 โ”‚ 104.98 โ”‚ 107.58 โ”‚ 107.58 โ”‚ 144981000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-15 โ”‚ 105.95 โ”‚ 107.57 โ”‚ 103.36 โ”‚ 103.70 โ”‚ 103.70 โ”‚ 86844000 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + +(๐Ÿฆ‹) /forecast/ $ signal TSLA +Successfully added 'Price Signal' to 'TSLA' dataset + +(๐Ÿฆ‹) /forecast/ $ show TSLA -l 30 +TSLA has following shape (rowxcolumn): (611, 8) + + Dataset TSLA | Showing 30 of 611 rows +โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ signal โ”ƒ +โ”กโ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-02 โ”‚ 84.90 โ”‚ 86.14 โ”‚ 84.34 โ”‚ 86.05 โ”‚ 86.05 โ”‚ 47660500 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-03 โ”‚ 88.10 โ”‚ 90.80 โ”‚ 87.38 โ”‚ 88.60 โ”‚ 88.60 โ”‚ 88892500 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-06 โ”‚ 88.09 โ”‚ 90.31 โ”‚ 88.00 โ”‚ 90.31 โ”‚ 90.31 โ”‚ 50665000 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-07 โ”‚ 92.28 โ”‚ 94.33 โ”‚ 90.67 โ”‚ 93.81 โ”‚ 93.81 โ”‚ 89410500 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-08 โ”‚ 94.74 โ”‚ 99.70 โ”‚ 93.65 โ”‚ 98.43 โ”‚ 98.43 โ”‚ 155721500 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-09 โ”‚ 99.42 โ”‚ 99.76 โ”‚ 94.57 โ”‚ 96.27 โ”‚ 96.27 โ”‚ 142202000 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-10 โ”‚ 96.36 โ”‚ 96.99 โ”‚ 94.74 โ”‚ 95.63 โ”‚ 95.63 โ”‚ 64797500 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-13 โ”‚ 98.70 โ”‚ 105.13 โ”‚ 98.40 โ”‚ 104.97 โ”‚ 104.97 โ”‚ 132588000 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-14 โ”‚ 108.85 โ”‚ 109.48 โ”‚ 104.98 โ”‚ 107.58 โ”‚ 107.58 โ”‚ 144981000 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-15 โ”‚ 105.95 โ”‚ 107.57 โ”‚ 103.36 โ”‚ 103.70 โ”‚ 103.70 โ”‚ 86844000 โ”‚ 0.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 10 โ”‚ 2020-01-16 โ”‚ 98.75 โ”‚ 102.89 โ”‚ 98.43 โ”‚ 102.70 โ”‚ 102.70 โ”‚ 108683500 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 11 โ”‚ 2020-01-17 โ”‚ 101.52 โ”‚ 103.13 โ”‚ 100.63 โ”‚ 102.10 โ”‚ 102.10 โ”‚ 68145500 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 12 โ”‚ 2020-01-21 โ”‚ 106.05 โ”‚ 109.72 โ”‚ 105.68 โ”‚ 109.44 โ”‚ 109.44 โ”‚ 89017500 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 13 โ”‚ 2020-01-22 โ”‚ 114.38 โ”‚ 118.90 โ”‚ 111.82 โ”‚ 113.91 โ”‚ 113.91 โ”‚ 156845000 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 14 โ”‚ 2020-01-23 โ”‚ 112.85 โ”‚ 116.40 โ”‚ 111.12 โ”‚ 114.44 โ”‚ 114.44 โ”‚ 98255000 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 15 โ”‚ 2020-01-24 โ”‚ 114.13 โ”‚ 114.77 โ”‚ 110.85 โ”‚ 112.96 โ”‚ 112.96 โ”‚ 71768000 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 16 โ”‚ 2020-01-27 โ”‚ 108.40 โ”‚ 112.89 โ”‚ 107.86 โ”‚ 111.60 โ”‚ 111.60 โ”‚ 68040500 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 17 โ”‚ 2020-01-28 โ”‚ 113.70 โ”‚ 115.36 โ”‚ 111.62 โ”‚ 113.38 โ”‚ 113.38 โ”‚ 58942500 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 18 โ”‚ 2020-01-29 โ”‚ 115.14 โ”‚ 117.96 โ”‚ 113.49 โ”‚ 116.20 โ”‚ 116.20 โ”‚ 89007500 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 19 โ”‚ 2020-01-30 โ”‚ 126.48 โ”‚ 130.18 โ”‚ 123.60 โ”‚ 128.16 โ”‚ 128.16 โ”‚ 145028500 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 20 โ”‚ 2020-01-31 โ”‚ 128.00 โ”‚ 130.60 โ”‚ 126.50 โ”‚ 130.11 โ”‚ 130.11 โ”‚ 78596500 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 21 โ”‚ 2020-02-03 โ”‚ 134.74 โ”‚ 157.23 โ”‚ 134.70 โ”‚ 156.00 โ”‚ 156.00 โ”‚ 235325000 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 22 โ”‚ 2020-02-04 โ”‚ 176.59 โ”‚ 193.80 โ”‚ 166.78 โ”‚ 177.41 โ”‚ 177.41 โ”‚ 304694000 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 23 โ”‚ 2020-02-05 โ”‚ 164.65 โ”‚ 169.20 โ”‚ 140.82 โ”‚ 146.94 โ”‚ 146.94 โ”‚ 242119000 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 24 โ”‚ 2020-02-06 โ”‚ 139.98 โ”‚ 159.17 โ”‚ 137.40 โ”‚ 149.79 โ”‚ 149.79 โ”‚ 199404000 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 25 โ”‚ 2020-02-07 โ”‚ 146.11 โ”‚ 153.95 โ”‚ 146.00 โ”‚ 149.61 โ”‚ 149.61 โ”‚ 85317500 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 26 โ”‚ 2020-02-10 โ”‚ 160.00 โ”‚ 164.00 โ”‚ 150.48 โ”‚ 154.26 โ”‚ 154.26 โ”‚ 123446000 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 27 โ”‚ 2020-02-11 โ”‚ 153.76 โ”‚ 156.70 โ”‚ 151.60 โ”‚ 154.88 โ”‚ 154.88 โ”‚ 58487500 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 28 โ”‚ 2020-02-12 โ”‚ 155.57 โ”‚ 157.95 โ”‚ 152.67 โ”‚ 153.46 โ”‚ 153.46 โ”‚ 60112500 โ”‚ 1.00 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 29 โ”‚ 2020-02-13 โ”‚ 148.37 โ”‚ 163.60 โ”‚ 147.00 โ”‚ 160.80 โ”‚ 160.80 โ”‚ 131446500 โ”‚ 1.00 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` diff --git a/website/content/terminal/forecast/sto/_index.md b/website/content/terminal/forecast/sto/_index.md new file mode 100644 index 00000000000..6a12cbab5fb --- /dev/null +++ b/website/content/terminal/forecast/sto/_index.md @@ -0,0 +1,85 @@ +``` +usage: sto [-d {}] [--period PERIOD] [-h] [-l LIMIT] +``` + +Add in Stochastic Oscillator %K and %D + +``` +optional arguments: + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + --period PERIOD The period to use (default: 10) + -h, --help show this help message (default: False) + -l LIMIT, --limit LIMIT + Number of entries to show in data. (default: 5) + +For more information and examples, use 'about sto' to access the related guide. +``` + +Example: +``` +(๐Ÿฆ‹) /forecast/ $ load TSLA.csv + +(๐Ÿฆ‹) /forecast/ $ show TSLA +TSLA has following shape (rowxcolumn): (611, 7) + + Dataset TSLA | Showing 10 of 611 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-02 โ”‚ 84.90 โ”‚ 86.14 โ”‚ 84.34 โ”‚ 86.05 โ”‚ 86.05 โ”‚ 47660500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-03 โ”‚ 88.10 โ”‚ 90.80 โ”‚ 87.38 โ”‚ 88.60 โ”‚ 88.60 โ”‚ 88892500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-06 โ”‚ 88.09 โ”‚ 90.31 โ”‚ 88.00 โ”‚ 90.31 โ”‚ 90.31 โ”‚ 50665000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-07 โ”‚ 92.28 โ”‚ 94.33 โ”‚ 90.67 โ”‚ 93.81 โ”‚ 93.81 โ”‚ 89410500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-08 โ”‚ 94.74 โ”‚ 99.70 โ”‚ 93.65 โ”‚ 98.43 โ”‚ 98.43 โ”‚ 155721500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-09 โ”‚ 99.42 โ”‚ 99.76 โ”‚ 94.57 โ”‚ 96.27 โ”‚ 96.27 โ”‚ 142202000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-10 โ”‚ 96.36 โ”‚ 96.99 โ”‚ 94.74 โ”‚ 95.63 โ”‚ 95.63 โ”‚ 64797500 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-13 โ”‚ 98.70 โ”‚ 105.13 โ”‚ 98.40 โ”‚ 104.97 โ”‚ 104.97 โ”‚ 132588000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-14 โ”‚ 108.85 โ”‚ 109.48 โ”‚ 104.98 โ”‚ 107.58 โ”‚ 107.58 โ”‚ 144981000 โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-15 โ”‚ 105.95 โ”‚ 107.57 โ”‚ 103.36 โ”‚ 103.70 โ”‚ 103.70 โ”‚ 86844000 โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + +(๐Ÿฆ‹) /forecast/ $ sto TSLA +Successfully added 'STOK&D_10' to 'TSLA' dataset + +(๐Ÿฆ‹) /forecast/ $ show TSLA +TSLA has following shape (rowxcolumn): (611, 9) + + Dataset TSLA | Showing 10 of 611 rows +โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ โ”ƒ date โ”ƒ open โ”ƒ high โ”ƒ low โ”ƒ close โ”ƒ adj_close โ”ƒ volume โ”ƒ SO%K_10 โ”ƒ SO%D_10 โ”ƒ +โ”กโ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 0 โ”‚ 2020-01-02 โ”‚ 84.90 โ”‚ 86.14 โ”‚ 84.34 โ”‚ 86.05 โ”‚ 86.05 โ”‚ 47660500 โ”‚ nan โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 1 โ”‚ 2020-01-03 โ”‚ 88.10 โ”‚ 90.80 โ”‚ 87.38 โ”‚ 88.60 โ”‚ 88.60 โ”‚ 88892500 โ”‚ nan โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2 โ”‚ 2020-01-06 โ”‚ 88.09 โ”‚ 90.31 โ”‚ 88.00 โ”‚ 90.31 โ”‚ 90.31 โ”‚ 50665000 โ”‚ nan โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 3 โ”‚ 2020-01-07 โ”‚ 92.28 โ”‚ 94.33 โ”‚ 90.67 โ”‚ 93.81 โ”‚ 93.81 โ”‚ 89410500 โ”‚ nan โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 4 โ”‚ 2020-01-08 โ”‚ 94.74 โ”‚ 99.70 โ”‚ 93.65 โ”‚ 98.43 โ”‚ 98.43 โ”‚ 155721500 โ”‚ nan โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 5 โ”‚ 2020-01-09 โ”‚ 99.42 โ”‚ 99.76 โ”‚ 94.57 โ”‚ 96.27 โ”‚ 96.27 โ”‚ 142202000 โ”‚ nan โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 6 โ”‚ 2020-01-10 โ”‚ 96.36 โ”‚ 96.99 โ”‚ 94.74 โ”‚ 95.63 โ”‚ 95.63 โ”‚ 64797500 โ”‚ nan โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 7 โ”‚ 2020-01-13 โ”‚ 98.70 โ”‚ 105.13 โ”‚ 98.40 โ”‚ 104.97 โ”‚ 104.97 โ”‚ 132588000 โ”‚ nan โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 8 โ”‚ 2020-01-14 โ”‚ 108.85 โ”‚ 109.48 โ”‚ 104.98 โ”‚ 107.58 โ”‚ 107.58 โ”‚ 144981000 โ”‚ nan โ”‚ nan โ”‚ +โ”œโ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 9 โ”‚ 2020-01-15 โ”‚ 105.95 โ”‚ 107.57 โ”‚ 103.36 โ”‚ 103.70 โ”‚ 103.70 โ”‚ 86844000 โ”‚ 77.00 โ”‚ nan โ”‚ +โ””โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + +(๐Ÿฆ‹) /forecast/ $ clean TSLA +Namespace(drop='', fill='', help=False, limit=5, target_dataset='TSLA') +Successfully cleaned 'TSLA' dataset +``` diff --git a/website/content/terminal/forecast/tcn/_index.md b/website/content/terminal/forecast/tcn/_index.md new file mode 100644 index 00000000000..193647b8e7f --- /dev/null +++ b/website/content/terminal/forecast/tcn/_index.md @@ -0,0 +1,85 @@ +``` +usage: tcn [--num-filters NUM_FILTERS] [--weight-norm WEIGHT_NORM] [--dilation-base DILATION_BASE] + [--past-covariates PAST_COVARIATES] [--all-past-covariates] [--naive] [-d {}] [-c TARGET_COLUMN] [-n N_DAYS] + [-t TRAIN_SPLIT] [-i INPUT_CHUNK_LENGTH] [-o OUTPUT_CHUNK_LENGTH] [--force-reset FORCE_RESET] + [--save-checkpoints SAVE_CHECKPOINTS] [--model-save-name MODEL_SAVE_NAME] [--n-epochs N_EPOCHS] + [--dropout DROPOUT] [--batch-size BATCH_SIZE] [--end S_END_DATE] [--start S_START_DATE] + [--learning-rate LEARNING_RATE] [--residuals] [--forecast-only] [-h] [--export EXPORT] +``` + +Perform TCN forecast. + +``` +optional arguments: + --num-filters NUM_FILTERS + The number of filters in a convolutional layer of the TCN (default: 3) + --weight-norm WEIGHT_NORM + Boolean value indicating whether to use weight normalization. (default: True) + --dilation-base DILATION_BASE + The base of the exponent that will determine the dilation on every level. (default: 2) + --past-covariates PAST_COVARIATES + Past covariates(columns/features) in same dataset. Comma separated. (default: None) + --all-past-covariates + Adds all rows as past covariates except for date and the target column. (default: False) + --naive Show the naive baseline for a model. (default: False) + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -c TARGET_COLUMN, --target-column TARGET_COLUMN + The name of the specific column you want to use (default: close) + -n N_DAYS, --n-days N_DAYS + prediction days. (default: 5) + -t TRAIN_SPLIT, --train-split TRAIN_SPLIT + Start point for rolling training and forecast window. 0.0-1.0 (default: 0.85) + -i INPUT_CHUNK_LENGTH, --input-chunk-length INPUT_CHUNK_LENGTH + Number of past time steps for forecasting module at prediction time. (default: 14) + -o OUTPUT_CHUNK_LENGTH, --output-chunk-length OUTPUT_CHUNK_LENGTH + The length of the forecast of the model. (default: 5) + --force-reset FORCE_RESET + If set to True, any previously-existing model with the same name will be reset (all checkpoints + will be discarded). (default: True) + --save-checkpoints SAVE_CHECKPOINTS + Whether to automatically save the untrained model and checkpoints. (default: True) + --model-save-name MODEL_SAVE_NAME + Name of the model to save. (default: tcn_model) + --n-epochs N_EPOCHS Number of epochs over which to train the model. (default: 300) + --dropout DROPOUT Fraction of neurons afected by Dropout. (default: 0.1) + --batch-size BATCH_SIZE + Number of time series (input and output) used in each training pass (default: 32) + --end S_END_DATE The end date (format YYYY-MM-DD) to select for testing (default: None) + --start S_START_DATE The start date (format YYYY-MM-DD) to select for testing (default: None) + --learning-rate LEARNING_RATE + Learning rate during training. (default: 0.001) + --residuals Show the residuals for the model. (default: False) + --forecast-only Do not plot the hisotorical data without forecasts. (default: False) + -h, --help show this help message (default: False) + --export EXPORT Export figure into png, jpg, pdf, svg (default: ) + +For more information and examples, use 'about export' to access the related guide. +``` + +Example: +``` +2022 Jul 23, 10:36 (๐Ÿฆ‹) /forecast/ $ load GME_20220719_123734.csv -a GME + +2022 Jul 23, 11:08 (๐Ÿฆ‹) /forecast/ $ tcn GME +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 115/115 [00:01<00:00, 111.85it/s] +TCN model obtains MAPE: 19.12% + + + + Actual price: $ 146.64 +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Datetime โ”ƒ Prediction โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 2022-07-19 00:00:00 โ”‚ $ 135.73 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-20 00:00:00 โ”‚ $ 142.42 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-21 00:00:00 โ”‚ $ 140.68 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-22 00:00:00 โ”‚ $ 152.98 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-25 00:00:00 โ”‚ $ 154.55 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` +![tcn](https://user-images.githubusercontent.com/72827203/180615408-ac6f9289-c3e9-486f-b262-701ef1906373.png) diff --git a/website/content/terminal/forecast/tft/_index.md b/website/content/terminal/forecast/tft/_index.md new file mode 100644 index 00000000000..06f97c69b11 --- /dev/null +++ b/website/content/terminal/forecast/tft/_index.md @@ -0,0 +1,87 @@ +``` +usage: tft [--lstm-layers LSTM_LAYERS] [--num-attention-heads NUM_ATTENTION_HEADS] [--full-attention] + [--hidden-continuous-size HIDDEN_CONTINUOUS_SIZE] [--hidden-size HIDDEN_SIZE] + [--past-covariates PAST_COVARIATES] [--all-past-covariates] [--naive] [-d {}] [-c TARGET_COLUMN] [-n N_DAYS] + [-t TRAIN_SPLIT] [-i INPUT_CHUNK_LENGTH] [-o OUTPUT_CHUNK_LENGTH] [--force-reset FORCE_RESET] + [--save-checkpoints SAVE_CHECKPOINTS] [--model-save-name MODEL_SAVE_NAME] [--n-epochs N_EPOCHS] + [--dropout DROPOUT] [--batch-size BATCH_SIZE] [--end S_END_DATE] [--start S_START_DATE] [--residuals] + [--forecast-only] [-h] [--export EXPORT] +``` + +Perform TFT forecast (Temporal Fusion Transformer). + +``` +optional arguments: + --lstm-layers LSTM_LAYERS + Number of LSTM layers. (default: 1) + --num-attention-heads NUM_ATTENTION_HEADS + Number of attention heads. (default: 4) + --full-attention Whether to apply a multi-head attention query. (default: False) + --hidden-continuous-size HIDDEN_CONTINUOUS_SIZE + Default hidden size for processing continuous variables. (default: 8) + --hidden-size HIDDEN_SIZE + Size for feature maps for each hidden RNN layer (h_n) (default: 16) + --past-covariates PAST_COVARIATES + Past covariates(columns/features) in same dataset. Comma separated. (default: None) + --all-past-covariates + Adds all rows as past covariates except for date and the target column. (default: False) + --naive Show the naive baseline for a model. (default: False) + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -c TARGET_COLUMN, --target-column TARGET_COLUMN + The name of the specific column you want to use (default: close) + -n N_DAYS, --n-days N_DAYS + prediction days. (default: 5) + -t TRAIN_SPLIT, --train-split TRAIN_SPLIT + Start point for rolling training and forecast window. 0.0-1.0 (default: 0.85) + -i INPUT_CHUNK_LENGTH, --input-chunk-length INPUT_CHUNK_LENGTH + Number of past time steps for forecasting module at prediction time. (default: 14) + -o OUTPUT_CHUNK_LENGTH, --output-chunk-length OUTPUT_CHUNK_LENGTH + The length of the forecast of the model. (default: 5) + --force-reset FORCE_RESET + If set to True, any previously-existing model with the same name will be reset (all checkpoints + will be discarded). (default: True) + --save-checkpoints SAVE_CHECKPOINTS + Whether to automatically save the untrained model and checkpoints. (default: True) + --model-save-name MODEL_SAVE_NAME + Name of the model to save. (default: tft_model) + --n-epochs N_EPOCHS Number of epochs over which to train the model. (default: 300) + --dropout DROPOUT Fraction of neurons afected by Dropout. (default: 0.1) + --batch-size BATCH_SIZE + Number of time series (input and output) used in each training pass (default: 32) + --end S_END_DATE The end date (format YYYY-MM-DD) to select for testing (default: None) + --start S_START_DATE The start date (format YYYY-MM-DD) to select for testing (default: None) + --residuals Show the residuals for the model. (default: False) + --forecast-only Do not plot the hisotorical data without forecasts. (default: False) + -h, --help show this help message (default: False) + --export EXPORT Export figure into png, jpg, pdf, svg (default: ) + +For more information and examples, use 'about export' to access the related guide. +``` + +Example: +``` +2022 Jul 23, 10:36 (๐Ÿฆ‹) /forecast/ $ load GME_20220719_123734.csv -a GME + +2022 Jul 23, 11:03 (๐Ÿฆ‹) /forecast/ $ tft GME +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 115/115 [00:07<00:00, 15.10it/s] +TFT model obtains MAPE: 44.60% + + + + Actual price: $ 146.64 +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Datetime โ”ƒ Prediction โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 2022-07-19 00:00:00 โ”‚ $ 169.69 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-20 00:00:00 โ”‚ $ 168.53 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-21 00:00:00 โ”‚ $ 167.33 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-22 00:00:00 โ”‚ $ 167.23 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-25 00:00:00 โ”‚ $ 165.82 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` +![tft](https://user-images.githubusercontent.com/72827203/180615444-47bcdd54-0693-4415-9617-ed3a571b26c6.png) diff --git a/website/content/terminal/forecast/theta/_index.md b/website/content/terminal/forecast/theta/_index.md new file mode 100644 index 00000000000..c66896fae47 --- /dev/null +++ b/website/content/terminal/forecast/theta/_index.md @@ -0,0 +1,58 @@ +``` +usage: theta [--naive] [-d {}] [-c TARGET_COLUMN] [-n N_DAYS] [-s {N,A,M}] [-p SEASONAL_PERIODS] [-w START_WINDOW] + [--end S_END_DATE] [--start S_START_DATE] [--residuals] [--forecast-only] [-h] [--export EXPORT] +``` + +Perform Theta forecast. + +``` +optional arguments: + --naive Show the naive baseline for a model. (default: False) + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -c TARGET_COLUMN, --target-column TARGET_COLUMN + The name of the specific column you want to use (default: close) + -n N_DAYS, --n-days N_DAYS + prediction days. (default: 5) + -s {N,A,M}, --seasonal {N,A,M} + Seasonality: N: None, A: Additive, M: Multiplicative. (default: M) + -p SEASONAL_PERIODS, --periods SEASONAL_PERIODS + Seasonal periods: 4: Quarterly, 7: Daily (default: 7) + -w START_WINDOW, --window START_WINDOW + Start point for rolling training and forecast window. 0.0-1.0 (default: 0.85) + --end S_END_DATE The end date (format YYYY-MM-DD) to select for testing (default: None) + --start S_START_DATE The start date (format YYYY-MM-DD) to select for testing (default: None) + --residuals Show the residuals for the model. (default: False) + --forecast-only Do not plot the hisotorical data without forecasts. (default: False) + -h, --help show this help message (default: False) + --export EXPORT Export figure into png, jpg, pdf, svg (default: ) + +For more information and examples, use 'about export' to access the related guide. +``` + +Example: +``` +2022 Jul 23, 10:36 (๐Ÿฆ‹) /forecast/ $ load GME_20220719_123734.csv -a GME + +2022 Jul 23, 11:01 (๐Ÿฆ‹) /forecast/ $ theta GME +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 115/115 [00:23<00:00, 4.88it/s] +Theta Model obtains MAPE: 12.71% + + + + Actual price: $ 146.64 +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Datetime โ”ƒ Prediction โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 2022-07-19 00:00:00 โ”‚ $ 145.41 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-20 00:00:00 โ”‚ $ 147.28 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-21 00:00:00 โ”‚ $ 147.28 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-22 00:00:00 โ”‚ $ 148.66 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-25 00:00:00 โ”‚ $ 147.18 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` +![theta](https://user-images.githubusercontent.com/72827203/180615324-5b50445c-cc95-4efa-84a6-85e85ddc2d28.png) diff --git a/website/content/terminal/forecast/trans/_index.md b/website/content/terminal/forecast/trans/_index.md new file mode 100644 index 00000000000..2e0e64e2a4d --- /dev/null +++ b/website/content/terminal/forecast/trans/_index.md @@ -0,0 +1,89 @@ +``` +usage: trans [--d-model D_MODEL] [--nhead NHEAD] [--num_encoder_layers NUM_ENCODER_LAYERS] + [--num_decoder_layers NUM_DECODER_LAYERS] [--dim_feedforward DIM_FEEDFORWARD] [--activation {relu,gelu}] + [--past-covariates PAST_COVARIATES] [--all-past-covariates] [--naive] [-d {}] [-c TARGET_COLUMN] + [-n N_DAYS] [-t TRAIN_SPLIT] [-i INPUT_CHUNK_LENGTH] [-o OUTPUT_CHUNK_LENGTH] [--force-reset FORCE_RESET] + [--save-checkpoints SAVE_CHECKPOINTS] [--model-save-name MODEL_SAVE_NAME] [--n-epochs N_EPOCHS] + [--dropout DROPOUT] [--batch-size BATCH_SIZE] [--end S_END_DATE] [--start S_START_DATE] [--residuals] + [--forecast-only] [-h] [--export EXPORT] +``` + +Perform Transformer forecast. + +``` +optional arguments: + --d-model D_MODEL Number of expected features in inputs. (default: 64) + --nhead NHEAD Number of head in the attention mechanism. (default: 4) + --num_encoder_layers NUM_ENCODER_LAYERS + The number of encoder leayers in the encoder. (default: 3) + --num_decoder_layers NUM_DECODER_LAYERS + The number of decoder leayers in the encoder. (default: 3) + --dim_feedforward DIM_FEEDFORWARD + The dimension of the feedforward model. (default: 512) + --activation {relu,gelu} + Number of LSTM layers. (default: relu) + --past-covariates PAST_COVARIATES + Past covariates(columns/features) in same dataset. Comma separated. (default: None) + --all-past-covariates + Adds all rows as past covariates except for date and the target column. (default: False) + --naive Show the naive baseline for a model. (default: False) + -d {}, --target-dataset {} + The name of the dataset you want to select (default: None) + -c TARGET_COLUMN, --target-column TARGET_COLUMN + The name of the specific column you want to use (default: close) + -n N_DAYS, --n-days N_DAYS + prediction days. (default: 5) + -t TRAIN_SPLIT, --train-split TRAIN_SPLIT + Start point for rolling training and forecast window. 0.0-1.0 (default: 0.85) + -i INPUT_CHUNK_LENGTH, --input-chunk-length INPUT_CHUNK_LENGTH + Number of past time steps for forecasting module at prediction time. (default: 14) + -o OUTPUT_CHUNK_LENGTH, --output-chunk-length OUTPUT_CHUNK_LENGTH + The length of the forecast of the model. (default: 5) + --force-reset FORCE_RESET + If set to True, any previously-existing model with the same name will be reset (all checkpoints + will be discarded). (default: True) + --save-checkpoints SAVE_CHECKPOINTS + Whether to automatically save the untrained model and checkpoints. (default: True) + --model-save-name MODEL_SAVE_NAME + Name of the model to save. (default: trans_model) + --n-epochs N_EPOCHS Number of epochs over which to train the model. (default: 300) + --dropout DROPOUT Fraction of neurons afected by Dropout. (default: 0) + --batch-size BATCH_SIZE + Number of time series (input and output) used in each training pass (default: 32) + --end S_END_DATE The end date (format YYYY-MM-DD) to select for testing (default: None) + --start S_START_DATE The start date (format YYYY-MM-DD) to select for testing (default: None) + --residuals Show the residuals for the model. (default: False) + --forecast-only Do not plot the hisotorical data without forecasts. (default: False) + -h, --help show this help message (default: False) + --export EXPORT Export figure into png, jpg, pdf, svg (default: ) + +For more information and examples, use 'about export' to access the related guide. +``` + +Example: +``` +2022 Jul 23, 10:36 (๐Ÿฆ‹) /forecast/ $ load GME_20220719_123734.csv -a GME + +2022 Jul 23, 11:01 (๐Ÿฆ‹) /forecast/ $ trans GME +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 115/115 [00:23<00:00, 4.88it/s] +Transformer model obtains MAPE: 13.11% + + + + Actual price: $ 146.64 +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Datetime โ”ƒ Prediction โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ 2022-07-19 00:00:00 โ”‚ $ 145.63 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-20 00:00:00 โ”‚ $ 142.28 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-21 00:00:00 โ”‚ $ 137.67 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-22 00:00:00 โ”‚ $ 137.33 โ”‚ +โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค +โ”‚ 2022-07-25 00:00:00 โ”‚ $ 130.62 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` +![trans](https://user-images.githubusercontent.com/72827203/180615423-948cc67c-cead-4e13-9cab-c348bc4c86ab.png) + diff --git a/website/content/terminal/forecast/which/_index.md b/website/content/terminal/forecast/which/_index.md new file mode 100644 index 00000000000..5a4a1bfbb7b --- /dev/null +++ b/website/content/terminal/forecast/which/_index.md @@ -0,0 +1,23 @@ +``` +usage: which [-h] +``` + +Show library versions of required packages. + + +``` +optional arguments: + -h, --help show this help message (default: False) +``` + +Example: +``` +2022 Sep 15, 00:04 (๐Ÿฆ‹) /forecast/ $ which +which + +Current Compute Device (CPU or GPU): CPU +Current RAM: 16.0G +Recommended Max dataset size based on current RAM: 5.0G +Torch version: 1.11.0 +Darts version: 0.21.0 +``` diff --git a/website/content/terminal/jupyter/dashboards/forecast/_index.md b/website/content/terminal/jupyter/dashboards/forecast/_index.md new file mode 100644 index 00000000000..d8c1ff14abc --- /dev/null +++ b/website/content/terminal/jupyter/dashboards/forecast/_index.md @@ -0,0 +1,13 @@ +``` +usage: forecast [-j] [-n] [-d] [-h] +``` + +Shows forecast dashboard + +``` +optional arguments: + -j, --jupyter Shows dashboard in jupyter-lab. (default: False) + -n, --no-input Skips confirmation to run server. (default: False) + -d, --dark Whether to show voila in dark mode (default: False) + -h, --help show this help message (default: False) +``` diff --git a/website/content/terminal/jupyter/dashboards/forecasting/_index.md b/website/content/terminal/jupyter/dashboards/forecasting/_index.md new file mode 100644 index 00000000000..e9226261aa9 --- /dev/null +++ b/website/content/terminal/jupyter/dashboards/forecasting/_index.md @@ -0,0 +1,11 @@ +``` +usage: forecast [-n] [-h] +``` + +Shows streamlit forecast dashboard + +``` +optional arguments: + -n, --no-input Skips confirmation to run server. (default: False) + -h, --help show this help message (default: False) +``` diff --git a/website/data/menu/main.yml b/website/data/menu/main.yml index 92c80bc3d69..fc20b2579f3 100755 --- a/website/data/menu/main.yml +++ b/website/data/menu/main.yml @@ -3,6 +3,70 @@ main: - name: OpenBB Terminal ref: "/" sub: + - name: forecast + ref: "/terminal/forecast/" + sub: + - name: atr + ref: "terminal/forecast/atr" + - name: brnn + ref: "terminal/forecast/brnn" + - name: clean + ref: "terminal/forecast/clean" + - name: combine + ref: "terminal/forecast/combine" + - name: corr + ref: "terminal/forecast/corr" + - name: delete + ref: "terminal/forecast/delete" + - name: delta + ref: "terminal/forecast/delta" + - name: desc + ref: "terminal/forecast/desc" + - name: ema + ref: "terminal/forecast/ema" + - name: expo + ref: "terminal/forecast/expo" + - name: export + ref: "terminal/forecast/export" + - name: linregr + ref: "terminal/forecast/linregr" + - name: load + ref: "terminal/forecast/load" + - name: mom + ref: "terminal/forecast/mom" + - name: nbeats + ref: "terminal/forecast/nbeats" + - name: plot + ref: "terminal/forecast/plot" + - name: regr + ref: "terminal/forecast/regr" + - name: rename + ref: "terminal/forecast/rename" + - name: rnn + ref: "terminal/forecast/rnn" + - name: roc + ref: "terminal/forecast/roc" + - name: rsi + ref: "terminal/forecast/rsi" + - name: season + ref: "terminal/forecast/season" + - name: show + ref: "terminal/forecast/show" + - name: signal + ref: "terminal/forecast/signal" + - name: sto + ref: "terminal/forecast/sto" + - name: tcn + ref: "terminal/forecast/tcn" + - name: tft + ref: "terminal/forecast/tft" + - name: theta + ref: "terminal/forecast/theta" + - name: trans + ref: "terminal/forecast/trans" + - name: which + ref: "terminal/forecast/which" + - name: stocks ref: "/terminal/stocks" sub: @@ -624,27 +688,6 @@ main: ref: "/terminal/common/qa/line" - name: raw ref: "/terminal/common/qa/raw" - - name: prediction techniques - ref: "/terminal/common/pred" - sub: - - name: ets - ref: "/terminal/common/pred/ets" - - name: knn - ref: "/terminal/common/pred/knn" - - name: regression - ref: "/terminal/common/pred/regression" - - name: arima - ref: "/terminal/common/pred/arima" - - name: mlp - ref: "/terminal/common/pred/mlp" - - name: rnn - ref: "/terminal/common/pred/rnn" - - name: lstm - ref: "/terminal/common/pred/lstm" - - name: conv1d - ref: "/terminal/common/pred/conv1d" - - name: mc - ref: "/terminal/common/pred/mc" - name: Cryptocurrency ref: "/terminal/crypto" sub: @@ -1007,27 +1050,6 @@ main: ref: "/terminal/economy/performance" - name: spectrum ref: "/terminal/economy/spectrum" - - name: prediction techniques - ref: "/terminal/common/pred" - sub: - - name: ets - ref: "/terminal/common/pred/ets" - - name: knn - ref: "/terminal/common/pred/knn" - - name: regression - ref: "/terminal/common/pred/regression" - - name: arima - ref: "/terminal/common/pred/arima" - - name: mlp - ref: "/terminal/common/pred/mlp" - - name: rnn - ref: "/terminal/common/pred/rnn" - - name: lstm - ref: "/terminal/common/pred/lstm" - - name: conv1d - ref: "/terminal/common/pred/conv1d" - - name: mc - ref: "/terminal/common/pred/mc" - name: quantitative analysis ref: "/terminal/common/qa" sub: @@ -1165,27 +1187,6 @@ main: ref: "/terminal/common/ta/fib" - name: clenow ref: "/terminal/common/ta/clenow" - - name: prediction techniques - ref: "/terminal/common/pred" - sub: - - name: ets - ref: "/terminal/common/pred/ets" - - name: knn - ref: "/terminal/common/pred/knn" - - name: regression - ref: "/terminal/common/pred/regression" - - name: arima - ref: "/terminal/common/pred/arima" - - name: mlp - ref: "/terminal/common/pred/mlp" - - name: rnn - ref: "/terminal/common/pred/rnn" - - name: lstm - ref: "/terminal/common/pred/lstm" - - name: conv1d - ref: "/terminal/common/pred/conv1d" - - name: mc - ref: "/terminal/common/pred/mc" - name: funds ref: "/terminal/funds" sub: @@ -1499,6 +1500,10 @@ main: ref: "/terminal/jupyter/dashboards/chains" - name: "shortdata" ref: "/terminal/jupyter/dashboards/shortdata" + - name: "forecast" + ref: "/terminal/jupyter/dashboards/forecast" + - name: "forecasting" + ref: "/terminal/jupyter/dashboards/forecasting" - name: "reports" ref: "/terminal/jupyter/reports" - name: common @@ -1653,27 +1658,6 @@ main: ref: "/terminal/common/qa/line" - name: raw ref: "/terminal/common/qa/raw" - - name: prediction techniques - ref: "/terminal/common/pred" - sub: - - name: ets - ref: "/terminal/common/pred/ets" - - name: knn - ref: "/terminal/common/pred/knn" - - name: regression - ref: "/terminal/common/pred/regression" - - name: arima - ref: "/terminal/common/pred/arima" - - name: mlp - ref: "/terminal/common/pred/mlp" - - name: rnn - ref: "/terminal/common/pred/rnn" - - name: lstm - ref: "/terminal/common/pred/lstm" - - name: conv1d - ref: "/terminal/common/pred/conv1d" - - name: mc - ref: "/terminal/common/pred/mc" - name: scripts ref: "/terminal/scripts" sub: @@ -1683,6 +1667,67 @@ main: - name: api ref: /api sub: + - name: forecast + ref: /api/forecast + sub: + - name: delta + ref: /api/forecast/delta + - name: ema + ref: /api/forecast/ema + - name: tcn + ref: /api/forecast/tcn + - name: plot + ref: /api/forecast/plot + - name: combine + ref: /api/forecast/combine + - name: brnn + ref: /api/forecast/brnn + - name: delete + ref: /api/forecast/delete + - name: desc + ref: /api/forecast/desc + - name: rsi + ref: /api/forecast/rsi + - name: linregr + ref: /api/forecast/linregr + - name: regr + ref: /api/forecast/regr + - name: show + ref: /api/forecast/show + - name: nbeats + ref: /api/forecast/nbeats + - name: expo + ref: /api/forecast/expo + - name: roc + ref: /api/forecast/roc + - name: clean + ref: /api/forecast/clean + - name: load + ref: /api/forecast/load + - name: tft + ref: /api/forecast/tft + - name: corr + ref: /api/forecast/corr + - name: rename + ref: /api/forecast/rename + - name: atr + ref: /api/forecast/atr + - name: season + ref: /api/forecast/season + - name: trans + ref: /api/forecast/trans + - name: export + ref: /api/forecast/export + - name: theta + ref: /api/forecast/theta + - name: signal + ref: /api/forecast/signal + - name: sto + ref: /api/forecast/sto + - name: rnn + ref: /api/forecast/rnn + - name: mom + ref: /api/forecast/mom - name: crypto ref: /api/crypto sub: