jerry/OpenBB
1
0
Fork 0
mirror of https://github.com/OpenBB-finance/OpenBB.git synced 2026-11-02 23:41:27 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'main' into docker_viz

Browse Source
This commit is contained in:
Jason Kai
2021-03-18 14:56:06 -04:00
parent b3bfcc9b90 245b34de62
commit 3ba9ab6b88
37 changed files with 1173 additions and 233 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
.github/workflows/test.yml vendored
View File

@@ -23,7 +23,7 @@ jobs:
- run: pip install bandit black codespell flake8 mypy pyupgrade safety pylint
- run: bandit -r . || true
- run: black --check .
- run: codespell --ignore-words-list="mape" --quiet-level=2 --skip=".git"
- run: codespell --ignore-words-list="mape","ba" --quiet-level=2 --skip=".git"
- run: flake8 . --count --ignore=E203,W503 --max-line-length=122 --show-source --statistics
- run: mypy --ignore-missing-imports .
- run: shopt -s globstar && pyupgrade --py36-plus **/*.py

17
ROADMAP.md
View File

@@ -19,7 +19,7 @@
* [Frontend](#Frontend)
* [Developer Experience](#Developer-Experience)
* [User Experience](#User-Experience)
* [Options](#Options)
---
## Discovery
@@ -152,6 +152,7 @@
### Short term
- [ ] Add Coingecko
- [ ] Add [Coinpaprika](https://coinpaprika.com/api/)
- [ ] Add crypto charts
### Long term
@@ -204,3 +205,17 @@
### Long term
- [ ] Add a WHL installation
---
## Options
### Short term
- [ ] Add Volume graph (@lolrenx)
- [ ] Add Open Interest graph (@lolrenx)
### Long term
- [ ] Add max pain graph

4
gamestonk_terminal/README.md
View File

@@ -2,7 +2,7 @@
### Table of contents
* [Discover Stocks](#Discover-Stocks-)
* [Market Sentiment](#Market-Sentiment-)
* [Behavioural Analysis](#Behavioural-Analysis-)
* [Research Web pages](#Research-Web-pages)
* [Fundamental Analysis](#Fundamental-Analysis-)
* [Technical Analysis](#Technical-Analysis-)
@@ -66,7 +66,7 @@ Command|Description|Source
 
## Market Sentiment [»](sentiment/README.md)
## Behavioural Analysis [»](behavioural_analysis/README.md)
Command|Description
----|----
[Reddit](https://reddit.com)|

2
gamestonk_terminal/__init__.py
View File

@@ -4,5 +4,5 @@ from . import due_diligence
from . import fundamental_analysis
from . import papermill
from . import prediction_techniques
from . import sentiment
from . import behavioural_analysis
from . import technical_analysis

27
gamestonk_terminal/sentiment/README.md → gamestonk_terminal/behavioural_analysis/README.md
View File

@@ -1,6 +1,8 @@
# MARKET SENTIMENT
# BEHAVIOURAL ANALYSIS
This menu aims to extrapolate market's sentiment regarding a pre-loaded ticker, and the usage of the following commands along with an example will be exploited below.
This menu aims to extrapolate behavioural analysis regarding a pre-loaded ticker, and the usage of the following commands along with an example will be exploited below.
* [finbrain](#finbrain)
[REDDIT](#REDDIT)
* [wsb](#wsb)
@@ -12,11 +14,11 @@ This menu aims to extrapolate market's sentiment regarding a pre-loaded ticker,
* [spac_c](#spac_c)
- show other users spacs announcements from subreddit SPACs community
* [spac](#spac)
- show other users spacs announcements from other subs
- show other users spacs announcements from other subs
[STOCKTWITS](#STOCKTWITS)
* [bullbear](#bullbear)
- estimate quick sentiment from last 30 messages on board
- estimate quick sentiment from last 30 messages on board
* [messages](#messages)
- output up to the 30 last messages on the board
* [trending](#trending)
@@ -29,7 +31,7 @@ This menu aims to extrapolate market's sentiment regarding a pre-loaded ticker,
- infer about stock's sentiment from latest tweets
* [sentiment](#sentiment)
- in-depth sentiment prediction from tweets over time
[GOOGLE](#GOOGLE)
* [mentions](#mentions)
- interest over time based on stock's mentions
@@ -41,6 +43,17 @@ This menu aims to extrapolate market's sentiment regarding a pre-loaded ticker,
- top rising related queries with stock
## finbrain <a name="finbrain"></a>
```
usage: finbrain
```
FinBrain collects the news headlines from 15+ major financial news sources on a daily basis and analyzes them to generate sentiment scores for more than 4500 US stocks. FinBrain Technologies develops deep learning algorithms for financial analysis and prediction, which currently serves traders from more than 150 countries all around the world. [Source: See https://finbrain.tech]
![finbrain2](https://user-images.githubusercontent.com/25267873/111629515-49c97000-87e9-11eb-92a6-a9eebb4b4bd9.png)
<img width="986" alt="Captura de ecrã 2021-03-18, às 12 53 31" src="https://user-images.githubusercontent.com/25267873/111629542-4fbf5100-87e9-11eb-8d5d-96faa8d3fca4.png">
# REDDIT <a name="REDDIT"></a>
## wsb <a name="wsb"></a>
@@ -106,7 +119,7 @@ usage: bullbear [-t S_TICKER]
```
Print bullbear sentiment based on last 30 messages on the board. Also prints the watchlist_count. [Source: Stocktwits]
* -t : ticker to gather sentiment from.
<img width="934" alt="sentiment" src="https://user-images.githubusercontent.com/25267873/108612307-42c46300-73df-11eb-9cec-253c8fb6d62f.png">
@@ -156,7 +169,7 @@ Print quick sentiment inference from last tweets that contain the ticker. This m
```
usage: sentiment [-n N_NUM] [-d N_DAYS_PAST]
```
Plot in-depth sentiment extracted from tweets from last days that contain pre-defined ticker. This model splits the text into character-level tokens and uses the DistilBERT model to make predictions. DistilBERT is a distilled version of the powerful BERT transformer model. Note that a big num of tweets extracted per hour in conjunction with a high number of days in the past, will make the algorithm take a long period of time to estimate sentiment. Inspired by https://towardsdatascience.com/sentiment-analysis-for-stock-price-prediction-in-python-bed40c65d178. [Source: Twitter]
Plot in-depth sentiment extracted from tweets from last days that contain pre-defined ticker. This model splits the text into character-level tokens and uses the DistilBERT model to make predictions. DistilBERT is a distilled version of the powerful BERT transformer model. Note that a big num of tweets extracted per hour in conjunction with a high number of days in the past, will make the algorithm take a long period of time to estimate sentiment. Inspired by https://towardsdatascience.com/sentiment-analysis-for-stock-price-prediction-in-python-bed40c65d178. [Source: Twitter]
* -n : num of tweets to extract per hour. Default 100.
* -d : num of days in the past to extract tweets. Default 7.

0
gamestonk_terminal/sentiment/__init__.py → gamestonk_terminal/behavioural_analysis/__init__.py
View File

249
gamestonk_terminal/behavioural_analysis/ba_controller.py Normal file
View File

@@ -0,0 +1,249 @@
"""Behavioural Analysis Controller Module"""
__docformat__ = "numpy"
import argparse
from typing import List
from datetime import datetime
from gamestonk_terminal import feature_flags as gtff
from gamestonk_terminal.helper_funcs import get_flair
from gamestonk_terminal.menu import session
from gamestonk_terminal.behavioural_analysis import (
google_view,
reddit_view,
stocktwits_view,
finbrain_view,
)
from prompt_toolkit.completion import NestedCompleter
class BehaviouralAnalysisController:
"""Behavioural Analysis Controller class"""
# Command choices
CHOICES = [
"help",
"q",
"quit",
"watchlist",
"spac",
"spac_c",
"wsb",
"popular",
"bullbear",
"messages",
"trending",
"stalker",
"infer",
"sentiment",
"mentions",
"regions",
"queries",
"rise",
"finbrain",
]
def __init__(self, ticker: str, start: datetime):
"""Constructor"""
self.ticker = ticker
self.start = start
self.ba_parser = argparse.ArgumentParser(add_help=False, prog="ba")
self.ba_parser.add_argument(
"cmd",
choices=self.CHOICES,
)
@staticmethod
def print_help():
"""Print help"""
print("\nBehavioural Analysis:")
print(" help show this behavioural analysis menu again")
print(" q quit this menu, and shows back to main menu")
print(" quit quit to abandon program")
print("")
print(" finbrain sentiment from 15+ major news headlines")
print("")
print("Reddit:")
print(
" wsb show what WSB gang is up to in subreddit wallstreetbets"
)
print(" watchlist show other users watchlist")
print(" popular show popular tickers")
print(
" spac_c show other users spacs announcements from subreddit SPACs community"
)
print(" spac show other users spacs announcements from other subs")
print("")
print("Stocktwits:")
print(
" bullbear estimate quick sentiment from last 30 messages on board"
)
print(" messages output up to the 30 last messages on the board")
print(" trending trending stocks")
print(" stalker stalk stocktwits user's last messages")
print("")
print("Twitter:")
print(" infer infer about stock's sentiment from latest tweets")
print(" sentiment in-depth sentiment prediction from tweets over time")
print("")
print("Google:")
print(" mentions interest over time based on stock's mentions")
print(" regions regions that show highest interest in stock")
print(" queries top related queries with this stock")
print(" rise top rising related queries with stock")
print("")
def switch(self, an_input: str):
"""Process and dispatch input
Returns
-------
True, False or None
False - quit the menu
True - quit the program
None - continue in the menu
"""
(known_args, other_args) = self.ba_parser.parse_known_args(an_input.split())
return getattr(
self, "call_" + known_args.cmd, lambda: "Command not recognized!"
)(other_args)
def call_help(self, _):
"""Process Help command"""
self.print_help()
def call_q(self, _):
"""Process Q command - quit the menu"""
return False
def call_quit(self, _):
"""Process Quit command - quit the program"""
return True
def call_watchlist(self, other_args: List[str]):
"""Process watchlist command"""
reddit_view.watchlist(other_args)
def call_spac(self, other_args: List[str]):
"""Process spac command"""
reddit_view.spac(other_args)
def call_spac_c(self, other_args: List[str]):
"""Process spac_c command"""
reddit_view.spac_community(other_args)
def call_wsb(self, other_args: List[str]):
"""Process wsb command"""
reddit_view.wsb_community(other_args)
def call_popular(self, other_args: List[str]):
"""Process popular command"""
reddit_view.popular_tickers(other_args)
def call_bullbear(self, other_args: List[str]):
"""Process bullbear command"""
stocktwits_view.bullbear(other_args, self.ticker)
def call_messages(self, other_args: List[str]):
"""Process messages command"""
stocktwits_view.messages(other_args, self.ticker)
def call_trending(self, other_args: List[str]):
"""Process trending command"""
stocktwits_view.trending(other_args)
def call_stalker(self, other_args: List[str]):
"""Process stalker command"""
stocktwits_view.stalker(other_args)
def call_mentions(self, other_args: List[str]):
"""Process mentions command"""
google_view.mentions(other_args, self.ticker, self.start)
def call_regions(self, other_args: List[str]):
"""Process regions command"""
google_view.regions(other_args, self.ticker)
def call_queries(self, other_args: List[str]):
"""Process queries command"""
google_view.queries(other_args, self.ticker)
def call_rise(self, other_args: List[str]):
"""Process rise command"""
google_view.rise(other_args, self.ticker)
def call_infer(self, other_args: List[str]):
"""Process infer command"""
if not gtff.ENABLE_PREDICT:
print("Predict is not enabled in feature_flags.py")
print("Twitter inference menu is disabled", "\n")
return
try:
# pylint: disable=import-outside-toplevel
from gamestonk_terminal.behavioural_analysis import twitter_view
except ModuleNotFoundError as e:
print("Optional packages need to be installed")
print(e, "\n")
return
except Exception as e:
print(e, "\n")
return
twitter_view.inference(other_args, self.ticker)
def call_sentiment(self, other_args: List[str]):
"""Process sentiment command"""
if not gtff.ENABLE_PREDICT:
print("Predict is not enabled in feature_flags.py")
print("Twitter inference menu is disabled", "\n")
return
try:
# pylint: disable=import-outside-toplevel
from gamestonk_terminal.behavioural_analysis import twitter_view
except ModuleNotFoundError as e:
print("Optional packages need to be installed")
print(e, "\n")
return
except Exception as e:
print(e, "\n")
return
twitter_view.sentiment(other_args, self.ticker)
def call_finbrain(self, other_args: List[str]):
"""Process finbrain command"""
finbrain_view.sentiment_analysis(other_args, self.ticker)
def menu(ticker: str, start: datetime):
"""Behavioural Analysis Menu"""
ba_controller = BehaviouralAnalysisController(ticker, start)
ba_controller.call_help(None)
while True:
# Get input command from user
if session and gtff.USE_PROMPT_TOOLKIT:
completer = NestedCompleter.from_nested_dict(
{c: None for c in ba_controller.CHOICES}
)
an_input = session.prompt(
f"{get_flair()} (ba)> ",
completer=completer,
)
else:
an_input = input(f"{get_flair()} (ba)> ")
try:
process_input = ba_controller.switch(an_input)
if process_input is not None:
return process_input
except SystemExit:
print("The command selected doesn't exist\n")
continue

161
gamestonk_terminal/behavioural_analysis/finbrain_view.py Normal file
View File

@@ -0,0 +1,161 @@
"""Finbrain View Module"""
__docformat__ = "numpy"
import argparse
from typing import List
import requests
from matplotlib import pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
from pandas.plotting import register_matplotlib_converters
import numpy as np
from gamestonk_terminal.helper_funcs import (
parse_known_args_and_warn,
plot_autoscale,
)
from gamestonk_terminal.config_plot import PLOT_DPI
from gamestonk_terminal import feature_flags as gtff
from colorama import Fore, Style
register_matplotlib_converters()
def sentiment_coloring(val: float, last_val: float) -> str:
if float(val) > last_val:
color = Fore.GREEN
else:
color = Fore.RED
return f"{color}{val}{Style.RESET_ALL}"
def get_sentiment(ticker: str) -> pd.DataFrame:
"""Gets Sentiment analysis provided by FinBrain's API
Parameters
----------
ticker : str
Ticker to get the sentiment analysis from
Returns
-------
DataFrame()
Empty if there was an issue with data retrieval
"""
result = requests.get(f"https://api.finbrain.tech/v0/sentiments/{ticker}")
sentiment = pd.DataFrame()
if result.status_code == 200:
if "sentimentAnalysis" in result.json():
sentiment = pd.DataFrame.from_dict(
result.json()["sentimentAnalysis"], orient="index"
)
sentiment.index = pd.to_datetime(sentiment.index).to_pydatetime()
sentiment.index.name = "date"
sentiment.columns = ["Sentiment Analysis"]
else:
print("Unexpected data format from FinBrain API")
else:
print("Request error in retrieving sentiment from FinBrain API")
return sentiment
def plot_sentiment(sentiment: pd.DataFrame, ticker: str):
"""Plot Sentiment analysis provided by FinBrain's API
Parameters
----------
sentiment : pd.DataFrame
Dataframe with sentiment data to plot
ticker : str
Ticker to get the sentiment analysis from
"""
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
for index, row in sentiment.iterrows():
if float(row["Sentiment Analysis"]) >= 0:
plt.scatter(index, float(row["Sentiment Analysis"]), s=100, c="green")
else:
plt.scatter(index, float(row["Sentiment Analysis"]), s=100, c="red")
plt.axhline(y=0, color="k", linestyle="--", lw=2)
plt.xlabel("Time")
plt.ylabel("Sentiment")
plt.grid(b=True, which="major", color="#666666", linestyle="-")
plt.minorticks_on()
plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
start_date = sentiment.index[-1].strftime("%Y/%m/%d")
plt.title(f"FinBrain's Sentiment Analysis for {ticker.upper()} since {start_date}")
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter("%Y/%m/%d"))
plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=1))
plt.gcf().autofmt_xdate()
plt.ylim([-1.1, 1.1])
senValues = np.array(pd.to_numeric(sentiment["Sentiment Analysis"].values))
senNone = np.array(0 * len(sentiment))
plt.fill_between(
sentiment.index,
pd.to_numeric(sentiment["Sentiment Analysis"].values),
0,
where=(senValues < senNone),
alpha=0.30,
color="red",
interpolate=True,
)
plt.fill_between(
sentiment.index,
pd.to_numeric(sentiment["Sentiment Analysis"].values),
0,
where=(senValues >= senNone),
alpha=0.30,
color="green",
interpolate=True,
)
if gtff.USE_ION:
plt.ion()
plt.show()
def sentiment_analysis(other_args: List[str], ticker: str):
"""Sentiment analysis from FinBrain
Parameters
----------
other_args : List[str]
Command line arguments to be processed with argparse
ticker : str
Ticker to get the sentiment analysis from
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="finbrain",
description="""FinBrain collects the news headlines from 15+ major financial news
sources on a daily basis and analyzes them to generate sentiment scores
for more than 4500 US stocks.FinBrain Technologies develops deep learning
algorithms for financial analysis and prediction, which currently serves
traders from more than 150 countries all around the world.
[Source: https://finbrain.tech]""",
)
try:
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
df_sentiment = get_sentiment(ticker)
if not df_sentiment.empty:
plot_sentiment(df_sentiment, ticker)
df_sentiment.sort_index(ascending=True, inplace=True)
if gtff.USE_COLOR:
print(
df_sentiment["Sentiment Analysis"]
.apply(sentiment_coloring, last_val=0)
.to_string()
)
else:
print(df_sentiment.to_string())
print("")
except Exception as e:
print(e, "\n")
print("")

0
gamestonk_terminal/sentiment/google_api.py → gamestonk_terminal/behavioural_analysis/google_view.py
View File

0
gamestonk_terminal/sentiment/reddit_api.py → gamestonk_terminal/behavioural_analysis/reddit_view.py
View File

0
gamestonk_terminal/sentiment/stocktwits_api.py → gamestonk_terminal/behavioural_analysis/stocktwits_view.py
View File

1
gamestonk_terminal/sentiment/twitter_api.py → gamestonk_terminal/behavioural_analysis/twitter_view.py
View File

@@ -357,7 +357,6 @@ def sentiment(l_args, s_ticker):
df_tweets["created_at"].values[0], "%Y-%m-%d %H:%M:%S"
).day
n_idx = 0
n_next_idx = 0
for n_next_idx, dt_created in enumerate(df_tweets["created_at"]):
if datetime.strptime(dt_created, "%Y-%m-%d %H:%M:%S").day > n_day:
l_xticks.append(n_next_idx)

6
gamestonk_terminal/comparison_analysis/yahoo_finance_api.py
View File

@@ -6,11 +6,11 @@ import matplotlib.pyplot as plt
import yfinance as yf
import seaborn as sns
from gamestonk_terminal.helper_funcs import parse_known_args_and_warn
from gamestonk_terminal.helper_funcs import parse_known_args_and_warn, plot_autoscale
from gamestonk_terminal.config_plot import PLOT_DPI
register_matplotlib_converters()
d_candle_types = {
"o": "Open",
"h": "High",
@@ -79,7 +79,7 @@ def historical(l_args, df_stock, s_ticker, s_start, s_interval, similar):
similar += ns_parser.l_also
plt.figure()
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.title(f"Similar companies to {s_ticker}")
df_stock = yf.download(s_ticker, start=s_start, progress=False)
plt.plot(

3
gamestonk_terminal/config_terminal.py
View File

@@ -25,3 +25,6 @@ API_POLYGON_KEY = os.getenv("GT_API_POLYGON_KEY") or "REPLACE_ME"
API_TWITTER_KEY = os.getenv("GT_API_TWITTER_KEY") or "REPLACE_ME"
API_TWITTER_SECRET_KEY = os.getenv("GT_API_TWITTER_SECRET_KEY") or "REPLACE_ME"
API_TWITTER_BEARER_TOKEN = os.getenv("GT_API_TWITTER_BEARER_TOKEN") or "REPLACE_ME"
# https://fred.stlouisfed.org/docs/api/api_key.html
API_FRED_KEY = os.getenv("GT_FRED_API_KEY") or "REPLACE_ME"

124
gamestonk_terminal/fred/README.md Normal file
View File

@@ -0,0 +1,124 @@
# FRED
https://fred.stlouisfed.org
The purpose of fred is to have the ability to look at economic data. This page offers 2 options:
* [predefined](#gdp)
* Get predefined data
* [cust](#cust)
* Get custom fred data
The current predefined options are
* GDP
* 1,5,10,30 Year Treasury Rates
* 30 Year Mortgage Rates
* Unemployment Rate
* Effective Federal Funds rate
* Moody's AAA Corporate Bond
* CAD to USD Conversion.
## predefined <a name="predefined"></a>
```text
usage: selected [-n N_TO_GET] [-s START_DATE] [--noplot] [--hidedata]
```
Gets predefined data. Arguments
* selected : Key from the predefined list.
* -s : Start Date. Format YYYY-mm-dd. First point to get. Defaults to Jan 1 2019
* --noplot : Flag to hide plot
* --hidedata : Flag to suppress printing output. Useful for data released daily/weekly.
To plot the 30-Year Mortgage rates since 2018-01-01 suppressing the output.
````
mort30 -s 2018-01-01 --hidedata
````
![mort30](https://user-images.githubusercontent.com/18151143/111521185-3d80dc80-872f-11eb-989d-c2bb256aa213.png)
To show all GDP data since 1/1/2016, with no plot.
````
gdp -s 2016-01-01 --noplot
````
Output:
````
GDP
Date
2016-01-01 18470.156
2016-04-01 18656.207
2016-07-01 18821.359
2016-10-01 19032.580
2017-01-01 19237.435
2017-04-01 19379.232
2017-07-01 19617.288
2017-10-01 19937.963
2018-01-01 20242.215
2018-04-01 20552.653
2018-07-01 20742.723
2018-10-01 20909.853
2019-01-01 21115.309
2019-04-01 21329.877
2019-07-01 21540.325
2019-10-01 21747.394
2020-01-01 21561.139
2020-04-01 19520.114
2020-07-01 21170.252
2020-10-01 21487.896
````
## cust <a name="cust"></a>
The fred database has many different datasets freely available. They each have their own custom SERIES_ID which can be obtained through the website.
````
usage: cust [-i --id] [-s START_DATE] [--noplot] [--hidedata]
````
* -i/--id : Series ID for FRED data. Required argument
* -s : Start Date. Format YYYY-mm-dd. First point to get. Defaults to Jan 1 2015.
* --noplot : Flag to suppress output plot.
* --hidedata : Flag to suppress data output
An example of custom data could be the USD/EURO exchange rate.
According to https://fred.stlouisfed.org/series/DEXUSEU , the series ID is DEXUSEU. So to plot this from the start of February 2021 to now (3/17)
````
cust --id DEXUSEU -s 2021-02-01
````
This will plot and display:
![cust](https://user-images.githubusercontent.com/18151143/111521481-92bcee00-872f-11eb-8a02-e7346e56e462.png)
````
DEXUSEU
Date
2021-02-01 1.2070
2021-02-02 1.2020
2021-02-03 1.2025
2021-02-04 1.1974
2021-02-05 1.2035
2021-02-08 1.2045
2021-02-09 1.2106
2021-02-10 1.2132
2021-02-11 1.2127
2021-02-12 1.2126
2021-02-15 NaN
2021-02-16 1.2107
2021-02-17 1.2042
2021-02-18 1.2078
2021-02-19 1.2136
2021-02-22 1.2155
2021-02-23 1.2142
2021-02-24 1.2143
2021-02-25 1.2229
2021-02-26 1.2093
2021-03-01 1.2054
2021-03-02 1.2079
2021-03-03 1.2073
2021-03-04 1.2045
2021-03-05 1.1914
2021-03-08 1.1849
2021-03-09 1.1885
2021-03-10 1.1900
2021-03-11 1.1978
````
Note the NaN on 2-15, which corresponds to President's Day.

0
gamestonk_terminal/fred/__init__.py Normal file
View File

191
gamestonk_terminal/fred/fred_api.py Normal file
View File

@@ -0,0 +1,191 @@
import argparse
import pandas as pd
from pandas.plotting import register_matplotlib_converters
import matplotlib.pyplot as plt
from fredapi import Fred
from gamestonk_terminal.helper_funcs import (
parse_known_args_and_warn,
valid_date,
plot_autoscale,
)
from gamestonk_terminal.config_plot import PLOT_DPI
from gamestonk_terminal.config_terminal import API_FRED_KEY
from gamestonk_terminal import feature_flags as gtff
register_matplotlib_converters()
api_map = {
"gdp": "GDP",
"t10": "DGS10",
"t1": "DGS1",
"t5": "DGS5",
"t30": "DGS30",
"mort30": "MORTGAGE30US",
"fedrate": "FEDFUNDS",
"moodAAA": "AAA",
"usdcad": "DEXCAUS",
"unemp": "UNRATE",
}
title_map = {
"gdp": "Gross Domestic Product",
"t10": "10-Year Treasury Constant Maturity Rate",
"t1": "1-Year Treasury Constant Maturity Rate",
"t5": "5-Year Treasury Constant Maturity Rate",
"t30": "30-Year Treasury Constant Maturity Rate",
"mort30": "30-Year Mortgage Rate",
"fedrate": "Effective Federal Funds Rate",
"moodAAA": "Moody's Seasoned AAA Corporate Bond Yield",
"usdcad": "Canada / U.S. Foreign Exchange Rate",
"unemp": "Unemployment Rate",
}
def get_fred_data(l_args, choice):
fred = Fred(api_key=API_FRED_KEY)
parser = argparse.ArgumentParser(
add_help=False,
prog="Custom",
description="""
Custom Data
""",
)
parser.add_argument(
"-s",
dest="start_date",
type=valid_date,
default="2019-01-01",
required=False,
help="Starting date (YYYY-MM-DD) of data",
)
parser.add_argument(
"--noplot",
action="store_false",
default=True,
dest="noplot",
help="Suppress output plot",
)
parser.add_argument(
"--hidedata",
action="store_false",
default=True,
dest="hidedata",
help="Suppress data display plot",
)
try:
ns_parser = parse_known_args_and_warn(parser, l_args)
if not ns_parser:
return
string_to_get = api_map[choice]
title = title_map[choice]
data = fred.get_series(string_to_get, ns_parser.start_date)
data = pd.DataFrame(data, columns=[f"{string_to_get}"])
data.index.name = "Date"
if ns_parser.hidedata:
print(data)
print("")
if ns_parser.noplot:
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.plot(data.index, data.iloc[:, 0], "-ok")
plt.xlabel("Time")
plt.xlim(data.index[0], data.index[-1])
plt.ylabel(f"{string_to_get}")
plt.grid(b=True, which="major", color="#666666", linestyle="-")
plt.minorticks_on()
plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
plt.title(title)
plt.show()
print("")
if gtff.USE_ION:
plt.ion()
except SystemExit:
print("")
except Exception as e:
print(e)
print("")
return
def custom_data(l_args):
fred = Fred(api_key=API_FRED_KEY)
parser = argparse.ArgumentParser(
add_help=False,
prog="Custom",
description="""
Custom Data
""",
)
parser.add_argument(
"-i", "--id", dest="series_id", required=True, type=str, help="FRED Series ID"
)
parser.add_argument(
"-s",
dest="start_date",
type=valid_date,
default="2019-01-01",
required=False,
help="Starting date (YYYY-MM-DD) of data",
)
parser.add_argument(
"--noplot",
action="store_false",
default=True,
dest="noplot",
help="Suppress output plot",
)
parser.add_argument(
"--hidedata",
action="store_false",
default=True,
dest="hidedata",
help="Suppress data display plot",
)
try:
ns_parser = parse_known_args_and_warn(parser, l_args)
if not ns_parser:
return
data = fred.get_series(ns_parser.series_id, ns_parser.start_date)
data = pd.DataFrame(data, columns=[f"{ns_parser.series_id}"])
data.index.name = "Date"
if ns_parser.hidedata:
print(data)
print("")
if ns_parser.noplot:
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.plot(data.index, data.iloc[:, 0], "-ok")
plt.xlabel("Time")
plt.xlim(data.index[0], data.index[-1])
plt.ylabel(f"{ns_parser.series_id}")
plt.grid(b=True, which="major", color="#666666", linestyle="-")
plt.minorticks_on()
plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
plt.title(f"FRED {ns_parser.series_id} Series")
plt.show()
print("")
if gtff.USE_ION:
plt.ion()
except SystemExit:
print("")
except Exception as e:
print(e)
print("")
return

90
gamestonk_terminal/fred/fred_menu.py Normal file
View File

@@ -0,0 +1,90 @@
import argparse
from matplotlib import pyplot as plt
from prompt_toolkit.completion import NestedCompleter
from gamestonk_terminal import feature_flags as gtff
from gamestonk_terminal.helper_funcs import get_flair
from gamestonk_terminal.menu import session
from gamestonk_terminal.fred import fred_api
def print_fred():
""" Print help """
print("\nFred Economic Data:")
print(" help show this menu again")
print(" q quit this menu, and shows back to main menu")
print(" quit quit to abandon program")
print(" ")
print(" gdp GDP")
print(" unemp Unemployment Rate")
print(" t1 1-Year Treasury Constant Maturity Rate")
print(" t5 5-Year Treasury Constant Maturity Rate")
print(" t10 10-Year Treasury Constant Maturity Rate")
print(" t30 30-Year Treasury Constant Maturity Rate")
print(" mort30 30-Year Fixed Rate Mortgage Average")
print(" fedrate Effective Federal Funds Rate")
print(" moodAAA Moody's Seasoned AAA Corporate Bond Yield")
print(" usdcad Canada / U.S. Foreign Exchange Rate (CAD per 1 USD)")
print("")
print(" cust User Specified FRED Data - Please Specify --id")
print("")
return
def fred_menu():
plt.close("all")
fred_parser = argparse.ArgumentParser(prog="fred", add_help=False)
defined_choices = [
"gdp",
"unemp",
"t1",
"t5",
"t10",
"t30",
"mort30",
"fedrate",
"moodAAA",
"usdcad",
]
choices = ["help", "q", "quit", "cust"] + defined_choices
fred_parser.add_argument("cmd", choices=choices)
completer = NestedCompleter.from_nested_dict({c: None for c in choices})
should_print_help = True
while True:
if should_print_help:
print_fred()
should_print_help = False
# Get input command from user
if session and gtff.USE_PROMPT_TOOLKIT:
as_input = session.prompt(
f"{get_flair()} (fred)> ",
completer=completer,
)
else:
as_input = input(f"{get_flair()} (fred)> ")
try:
(ns_known_args, l_args) = fred_parser.parse_known_args(as_input.split())
except SystemExit:
print("The command selected doesn't exist\n")
continue
if ns_known_args.cmd == "help":
should_print_help = True
elif ns_known_args.cmd == "q":
# Leave the fred menu
return False
elif ns_known_args.cmd == "quit":
# Abandon the program
return True
elif ns_known_args.cmd in defined_choices:
fred_api.get_fred_data(l_args, ns_known_args.cmd)
elif ns_known_args.cmd == "cust":
fred_api.custom_data(l_args)

10
gamestonk_terminal/helper_funcs.py
View File

@@ -357,6 +357,16 @@ def get_flair() -> str:
return ""
def str_to_bool(value):
if isinstance(value, bool):
return value
if value.lower() in {"false", "f", "0", "no", "n"}:
return False
if value.lower() in {"true", "t", "1", "yes", "y"}:
return True
raise ValueError(f"{value} is not a valid boolean value")
def get_screeninfo():
screens = get_monitors() # Get all available monitors
if len(screens) - 1 < cfgPlot.MONITOR: # Check to see if chosen monitor is detected

7
gamestonk_terminal/main_helper.py
View File

@@ -51,9 +51,13 @@ def print_help(s_ticker, s_start, s_interval, b_is_market_open):
)
print(" mill papermill menu, \t\t\t menu to generate notebook reports")
print(
" sen sentiment of the market, \t from: reddit, stocktwits, twitter"
" fred economic data, \t\t\t from: Federal Reserve Bank of St. Louis "
)
if s_ticker:
print(
" ba behavioural analysis, \t from: reddit, stocktwits, twitter"
)
print(
" res research web page, \t e.g.: macroaxis, yahoo finance, fool"
)
@@ -72,6 +76,7 @@ def print_help(s_ticker, s_start, s_interval, b_is_market_open):
print(
" pred prediction techniques, \t e.g.: regression, arima, rnn, lstm, prophet"
)
print(" op options info, \t e.g.: volume")
print("")

18
gamestonk_terminal/options/README.md Normal file
View File

@@ -0,0 +1,18 @@
# OPTIONS DATA
This menu aims to give insight on options. options can cause significant share price movement, and option pricing gives info about market sentiment for a ticker.
* [volume](#volume)
* Show traded options for a given expiry [Yahoo Finance]
## volume <a name="get"></a>
```text
usage: volume -d 'yyyy-mm-dd'
```
Display volume graph for a date. [Source: Yahoo Finance]
* -d : expiration date.

59
gamestonk_terminal/options/op_menu.py Normal file
View File

@@ -0,0 +1,59 @@
import argparse
from prompt_toolkit.completion import NestedCompleter
from gamestonk_terminal import feature_flags as gtff
from gamestonk_terminal.helper_funcs import get_flair
from gamestonk_terminal.options import volume as vol
from gamestonk_terminal.menu import session
def opt_menu(s_ticker):
# Add list of arguments that the options parser accepts
opt_parser = argparse.ArgumentParser(prog="opt", add_help=False)
choices = ["help", "q", "quit", "volume", "oi"]
opt_parser.add_argument("cmd", choices=choices)
completer = NestedCompleter.from_nested_dict({c: None for c in choices})
# print_options(s_ticker, s_start, s_interval)
# Loop forever and ever
while True:
# Get input command from user
if session and gtff.USE_PROMPT_TOOLKIT:
as_input = session.prompt(
f"{get_flair()} (opt)> ",
completer=completer,
)
else:
as_input = input(f"{get_flair()} (opt)> ")
# Parse fundamental analysis command of the list of possible commands
try:
(ns_known_args, l_args) = opt_parser.parse_known_args(as_input.split())
except SystemExit:
print("The command selected doesn't exist\n")
continue
if ns_known_args.cmd == "help":
pass
# print_options(s_ticker)
elif ns_known_args.cmd == "q":
# Just leave the options menu
return False
elif ns_known_args.cmd == "quit":
# Abandon the program
return True
elif ns_known_args.cmd == "volume":
# call the volume graph
vol.volume_graph(l_args, s_ticker)
elif ns_known_args.cmd == "oi":
# call the volume graph
vol.open_interest_graph(l_args, s_ticker)
else:
print("Command not recognized!")

144
gamestonk_terminal/options/volume.py Normal file
View File

@@ -0,0 +1,144 @@
import argparse
import yfinance as yf
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px
from gamestonk_terminal.helper_funcs import (
parse_known_args_and_warn,
valid_date,
)
def open_interest_graph(l_args, s_ticker):
""" Show traded options volume. [Source: Yahoo Finance] """
parser = argparse.ArgumentParser(
add_help=False,
prog="volume",
description="""Display volume graph for a date. [Source: Yahoo Finance].""",
)
parser.add_argument(
"-e",
"--expiry",
type=valid_date,
dest="s_expiry_date",
help="The expiry date (format YYYY-MM-DD) for the option chain",
required=True,
)
try:
ns_parser = parse_known_args_and_warn(parser, l_args)
if not ns_parser:
return
__get_open_interest_graph(
s_ticker, ns_parser.s_expiry_date.strftime("%Y-%m-%d")
)
except SystemExit:
print("")
except Exception as e:
print(e)
def volume_graph(l_args, s_ticker):
""" Show traded options volume. [Source: Yahoo Finance] """
parser = argparse.ArgumentParser(
add_help=False,
prog="volume",
description="""Display volume graph for a date. [Source: Yahoo Finance].""",
)
parser.add_argument(
"-e",
"--expiry",
type=valid_date,
dest="s_expiry_date",
help="The expiry date (format YYYY-MM-DD) for the option chain",
required=True,
)
try:
ns_parser = parse_known_args_and_warn(parser, l_args)
if not ns_parser:
return
__get_volume_graph(s_ticker, ns_parser.s_expiry_date.strftime("%Y-%m-%d"))
except SystemExit:
print("")
except Exception as e:
print(e)
def __get_open_interest_graph(ticker_name, exp_date):
df = __get_volume_data(ticker_name, exp_date)
__generate_graph_sns(df, ticker_name, exp_date, True)
# __generate_graph_plotly(df, ticker_name, exp_date)
def __get_volume_graph(ticker_name, exp_date):
df = __get_volume_data(ticker_name, exp_date)
__generate_graph_sns(df, ticker_name, exp_date)
# __generate_graph_plotly(df, ticker_name, exp_date)
def __pull_call_put_data(call_put, flag):
df = call_put.pivot_table(
index="strike", values=["volume", "openInterest"], aggfunc="sum"
)
df.reindex()
df["strike"] = df.index
df["type"] = flag
return df
def __get_volume_data(ticker_name, exp_date):
option_chain = yf.Ticker(ticker_name).option_chain(exp_date)
calls = __pull_call_put_data(option_chain.calls, "calls")
puts = __pull_call_put_data(option_chain.puts, "puts")
volume_data = pd.concat([calls, puts], axis=0)
# dataframe
return volume_data
def __generate_graph_plotly(df, ticker_name, exp_date, for_open_interest=False):
# version with plotly express
if for_open_interest:
op_type = "openInterest"
else:
op_type = "volume"
fig = px.line(
df,
x="strike",
y=op_type,
title=f"{ticker_name} options {op_type} for {exp_date}",
color="type",
)
fig.show()
return
def __generate_graph_sns(df, ticker_name, exp_date, for_open_interest=False):
# version with seaborn express
if for_open_interest:
op_type = "openInterest"
else:
op_type = "volume"
plt.figure(figsize=(12, 6))
fig = sns.lineplot(
data=df, x="strike", y=op_type, hue="type", palette=["limegreen", "tomato"]
)
plt.title(f"{ticker_name} options {op_type} for {exp_date}")
plt.show()
return fig

6
gamestonk_terminal/prediction_techniques/arima.py
View File

@@ -11,6 +11,7 @@ from gamestonk_terminal.helper_funcs import (
valid_date,
patch_pandas_text_adjustment,
get_next_stock_market_days,
plot_autoscale,
)
from gamestonk_terminal.prediction_techniques.pred_helper import (
print_pretty_prediction,
@@ -18,6 +19,7 @@ from gamestonk_terminal.prediction_techniques.pred_helper import (
print_prediction_kpis,
)
from gamestonk_terminal.config_plot import PLOT_DPI
from gamestonk_terminal import feature_flags as gtff
register_matplotlib_converters()
@@ -168,7 +170,7 @@ def arima(l_args, s_ticker, df_stock):
print("")
# Plotting
plt.figure()
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=2)
if ns_parser.s_order:
# BACKTESTING
@@ -241,7 +243,7 @@ def arima(l_args, s_ticker, df_stock):
# BACKTESTING
if ns_parser.s_end_date:
plt.figure()
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.subplot(211)
plt.plot(
df_future.index,

6
gamestonk_terminal/prediction_techniques/ets.py
View File

@@ -11,6 +11,7 @@ from gamestonk_terminal.helper_funcs import (
valid_date,
patch_pandas_text_adjustment,
get_next_stock_market_days,
plot_autoscale,
)
from gamestonk_terminal.prediction_techniques.pred_helper import (
print_pretty_prediction,
@@ -18,6 +19,7 @@ from gamestonk_terminal.prediction_techniques.pred_helper import (
print_prediction_kpis,
)
from gamestonk_terminal.config_plot import PLOT_DPI
from gamestonk_terminal import feature_flags as gtff
register_matplotlib_converters()
@@ -179,7 +181,7 @@ def exponential_smoothing(l_args, s_ticker, df_stock):
print(f"SSE: {round(model.sse, 2)}\n")
# Plotting
plt.figure()
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=2)
# BACKTESTING
if ns_parser.s_end_date:
@@ -249,7 +251,7 @@ def exponential_smoothing(l_args, s_ticker, df_stock):
# BACKTESTING
if ns_parser.s_end_date:
plt.figure()
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.subplot(211)
plt.plot(
df_future.index,

6
gamestonk_terminal/prediction_techniques/fbprophet.py
View File

@@ -11,12 +11,14 @@ from gamestonk_terminal.helper_funcs import (
parse_known_args_and_warn,
valid_date,
patch_pandas_text_adjustment,
plot_autoscale,
)
from gamestonk_terminal.prediction_techniques.pred_helper import (
price_prediction_backtesting_color,
print_prediction_kpis,
)
from gamestonk_terminal.config_plot import PLOT_DPI
from gamestonk_terminal import feature_flags as gtff
register_matplotlib_converters()
@@ -114,7 +116,7 @@ def fbprophet(l_args, s_ticker, df_stock):
] # .apply(lambda x: f"{x:.2f} $")
df_pred.index = l_pred_days
_, ax = plt.subplots()
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
model.plot(
forecast[: -ns_parser.n_days],
ax=ax,
@@ -177,7 +179,7 @@ def fbprophet(l_args, s_ticker, df_stock):
# BACKTESTING
if ns_parser.s_end_date:
plt.figure()
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.subplot(211)
plt.plot(
df_future.index,

7
gamestonk_terminal/prediction_techniques/knn.py
View File

@@ -11,6 +11,7 @@ from gamestonk_terminal.helper_funcs import (
valid_date,
patch_pandas_text_adjustment,
get_next_stock_market_days,
plot_autoscale,
)
from gamestonk_terminal.prediction_techniques.pred_helper import (
print_pretty_prediction,
@@ -18,7 +19,7 @@ from gamestonk_terminal.prediction_techniques.pred_helper import (
print_prediction_kpis,
)
from gamestonk_terminal.config_plot import PLOT_DPI
from gamestonk_terminal import feature_flags as gtff
register_matplotlib_converters()
@@ -143,7 +144,7 @@ def k_nearest_neighbors(l_args, s_ticker, df_stock):
df_pred = pd.Series(l_predictions, index=l_pred_days, name="Price")
# Plotting
plt.figure()
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=2)
s_knn = f"{ns_parser.n_neighbors}-Nearest Neighbors on {s_ticker}"
# BACKTESTING
@@ -202,7 +203,7 @@ def k_nearest_neighbors(l_args, s_ticker, df_stock):
# BACKTESTING
if ns_parser.s_end_date:
plt.figure()
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.subplot(211)
plt.plot(
df_future.index,

6
gamestonk_terminal/prediction_techniques/neural_networks.py
View File

@@ -18,6 +18,7 @@ from gamestonk_terminal.helper_funcs import (
valid_date,
patch_pandas_text_adjustment,
get_next_stock_market_days,
plot_autoscale,
)
from gamestonk_terminal.prediction_techniques.pred_helper import (
@@ -28,6 +29,7 @@ from gamestonk_terminal.prediction_techniques.pred_helper import (
from gamestonk_terminal import feature_flags as gtff
from gamestonk_terminal.config_plot import PLOT_DPI
from gamestonk_terminal import config_neural_network_models as cfg_nn_models
@@ -348,7 +350,7 @@ def _plot_and_print_results(
):
"""Plot and print the results. """
# Plotting
plt.figure()
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=3)
# BACKTESTING
@@ -413,7 +415,7 @@ def _plot_and_print_results(
# BACKTESTING
if ns_parser.s_end_date:
plt.figure()
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.subplot(211)
plt.plot(
df_future.index,

7
gamestonk_terminal/prediction_techniques/regression.py
View File

@@ -14,6 +14,7 @@ from gamestonk_terminal.helper_funcs import (
valid_date,
patch_pandas_text_adjustment,
get_next_stock_market_days,
plot_autoscale,
)
from gamestonk_terminal.prediction_techniques.pred_helper import (
@@ -22,7 +23,7 @@ from gamestonk_terminal.prediction_techniques.pred_helper import (
print_prediction_kpis,
)
from gamestonk_terminal.config_plot import PLOT_DPI
from gamestonk_terminal import feature_flags as gtff
register_matplotlib_converters()
@@ -163,7 +164,7 @@ def regression(l_args, s_ticker, df_stock, polynomial):
df_pred = pd.Series(l_predictions, index=l_pred_days, name="Price")
# Plotting
plt.figure()
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=2)
# BACKTESTING
if ns_parser.s_end_date:
@@ -225,7 +226,7 @@ def regression(l_args, s_ticker, df_stock, polynomial):
# BACKTESTING
if ns_parser.s_end_date:
plt.figure()
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.subplot(211)
plt.plot(
df_future.index,

7
gamestonk_terminal/prediction_techniques/sma.py
View File

@@ -11,6 +11,7 @@ from gamestonk_terminal.helper_funcs import (
valid_date,
patch_pandas_text_adjustment,
get_next_stock_market_days,
plot_autoscale,
)
from gamestonk_terminal.prediction_techniques.pred_helper import (
@@ -19,7 +20,7 @@ from gamestonk_terminal.prediction_techniques.pred_helper import (
print_prediction_kpis,
)
from gamestonk_terminal.config_plot import PLOT_DPI
from gamestonk_terminal import feature_flags as gtff
register_matplotlib_converters()
@@ -125,7 +126,7 @@ def simple_moving_average(l_args, s_ticker, df_stock):
df_pred = pd.Series(l_predictions, index=l_pred_days, name="Price")
# Plotting
plt.figure()
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=2)
# BACKTESTING
if ns_parser.s_end_date:
@@ -189,7 +190,7 @@ def simple_moving_average(l_args, s_ticker, df_stock):
# BACKTESTING
if ns_parser.s_end_date:
plt.figure()
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.subplot(211)
plt.plot(
df_future.index,

192
gamestonk_terminal/sentiment/sen_menu.py
View File

@@ -1,192 +0,0 @@
import argparse
from gamestonk_terminal import feature_flags as gtff
from gamestonk_terminal.helper_funcs import get_flair
from gamestonk_terminal.menu import session
from gamestonk_terminal.sentiment import google_api, reddit_api, stocktwits_api
from prompt_toolkit.completion import NestedCompleter
def print_sentiment():
""" Print help """
print("\nSentiment:")
print(" help show this sentiment menu again")
print(" q quit this menu, and shows back to main menu")
print(" quit quit to abandon program")
print("")
print("Reddit:")
print(" wsb show what WSB gang is up to in subreddit wallstreetbets")
print(" watchlist show other users watchlist")
print(" popular show popular tickers")
print(
" spac_c show other users spacs announcements from subreddit SPACs community"
)
print(" spac show other users spacs announcements from other subs")
print("")
print("Stocktwits:")
print(" bullbear estimate quick sentiment from last 30 messages on board")
print(" messages output up to the 30 last messages on the board")
print(" trending trending stocks")
print(" stalker stalk stocktwits user's last messages")
print("")
print("Twitter:")
print(" infer infer about stock's sentiment from latest tweets")
print(" sentiment in-depth sentiment prediction from tweets over time")
print("")
print("Google:")
print(" mentions interest over time based on stock's mentions")
print(" regions regions that show highest interest in stock")
print(" queries top related queries with this stock")
print(" rise top rising related queries with stock")
print("")
return
def sen_menu(s_ticker, s_start):
# Add list of arguments that the discovery parser accepts
sen_parser = argparse.ArgumentParser(prog="sen", add_help=False)
choices = [
"help",
"q",
"quit",
"watchlist",
"spac",
"spac_c",
"wsb",
"popular",
"bullbear",
"messages",
"trending",
"stalker",
"infer",
"sentiment",
"mentions",
"regions",
"queries",
"rise",
]
sen_parser.add_argument("cmd", choices=choices)
completer = NestedCompleter.from_nested_dict({c: None for c in choices})
print_sentiment()
# Loop forever and ever
while True:
# Get input command from user
if session and gtff.USE_PROMPT_TOOLKIT:
as_input = session.prompt(
f"{get_flair()} (sen)> ",
completer=completer,
)
else:
as_input = input(f"{get_flair()} (sen)> ")
# Parse sentiment command of the list of possible commands
try:
(ns_known_args, l_args) = sen_parser.parse_known_args(as_input.split())
except SystemExit:
print("The command selected doesn't exist\n")
continue
if ns_known_args.cmd == "help":
print_sentiment()
elif ns_known_args.cmd == "q":
# Just leave the DISC menu
return False
elif ns_known_args.cmd == "quit":
# Abandon the program
return True
elif ns_known_args.cmd == "watchlist":
reddit_api.watchlist(l_args)
elif ns_known_args.cmd == "spac":
reddit_api.spac(l_args)
elif ns_known_args.cmd == "spac_c":
reddit_api.spac_community(l_args)
elif ns_known_args.cmd == "wsb":
reddit_api.wsb_community(l_args)
elif ns_known_args.cmd == "popular":
reddit_api.popular_tickers(l_args)
elif ns_known_args.cmd == "bullbear":
stocktwits_api.bullbear(l_args, s_ticker)
elif ns_known_args.cmd == "messages":
stocktwits_api.messages(l_args, s_ticker)
elif ns_known_args.cmd == "trending":
stocktwits_api.trending(l_args)
elif ns_known_args.cmd == "stalker":
stocktwits_api.stalker(l_args)
elif ns_known_args.cmd == "infer":
if not gtff.ENABLE_PREDICT:
print("Predict is not enabled in feature_flags.py")
print("Twitter inference menu is disabled")
print("")
continue
try:
# pylint: disable=import-outside-toplevel
from gamestonk_terminal.sentiment import twitter_api
except ModuleNotFoundError as e:
print("One of the optional packages seems to be missing")
print("Optional packages need to be installed")
print(e)
print("")
continue
except Exception as e:
print(e)
print("")
continue
twitter_api.inference(l_args, s_ticker)
elif ns_known_args.cmd == "sentiment":
if not gtff.ENABLE_PREDICT:
print("Predict is not enabled in config_terminal.py")
print("Twitter sentiment menu is disabled")
print("")
continue
try:
# pylint: disable=import-outside-toplevel
from gamestonk_terminal.sentiment import twitter_api
except ModuleNotFoundError as e:
print("One of the optional packages seems to be missing")
print("Optional packages need to be installed")
print(e)
print("")
continue
except Exception as e:
print(e)
print("")
continue
twitter_api.sentiment(l_args, s_ticker)
elif ns_known_args.cmd == "mentions":
google_api.mentions(l_args, s_ticker, s_start)
elif ns_known_args.cmd == "regions":
google_api.regions(l_args, s_ticker)
elif ns_known_args.cmd == "queries":
google_api.queries(l_args, s_ticker)
elif ns_known_args.cmd == "rise":
google_api.rise(l_args, s_ticker)
else:
print("Command not recognized!")

18
poetry.lock generated
View File

@@ -476,6 +476,17 @@ category = "main"
optional = true
python-versions = "*"
[[package]]
name = "fredapi"
version = "0.4.3"
description = "Python API for Federal Reserve Economic Data (FRED) from St. Louis Fed"
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
pandas = "*"
[[package]]
name = "ftfy"
version = "5.9"
@@ -2506,7 +2517,8 @@ prediction = ["fbprophet", "tensorflow", "pmdarima", "transformers", "flair", "t
[metadata]
lock-version = "1.1"
python-versions = "^3.6.8"
content-hash = "c4a363cd8824c8ccab409ccfc74956db82f9cf9800cd46539b14c7ea927f36de"
content-hash = "184502c6b8d4b7db5baa4323e7f70b2585c6140c304583f0678e24681b8f9c9b"
[metadata.files]
absl-py = [
@@ -2810,6 +2822,10 @@ flatbuffers = [
{file = "flatbuffers-1.12-py2.py3-none-any.whl", hash = "sha256:9e9ef47fa92625c4721036e7c4124182668dc6021d9e7c73704edd395648deb9"},
{file = "flatbuffers-1.12.tar.gz", hash = "sha256:63bb9a722d5e373701913e226135b28a6f6ac200d5cc7b4d919fa38d73b44610"},
]
fredapi = [
{file = "fredapi-0.4.3-py3-none-any.whl", hash = "sha256:e06075592eabddadfe0635f3c59e9072c9f48647430328b4582171ea10f7df2f"},
{file = "fredapi-0.4.3.tar.gz", hash = "sha256:d9b3194fb60541991bd33f019c710d4a9580ecfb5e47efbf2d2571888a2aac02"},
]
ftfy = [
{file = "ftfy-5.9.tar.gz", hash = "sha256:8c4fb2863c0b82eae2ab3cf353d9ade268dfbde863d322f78d6a9fd5cefb31e9"},
]

1
pyproject.toml
View File

@@ -38,6 +38,7 @@ jupyter = "^1.0.0"
mplfinance = "^0.12.7-alpha.7"
seaborn = "^0.11.0"
scipy = "1.5.4"
fredapi = "^0.4.3"
screeninfo = "^0.6.7"
[tool.poetry.dev-dependencies]

1
requirements.txt
View File

@@ -25,6 +25,7 @@ decorator==4.4.2; python_full_version >= "3.6.1" and python_version >= "3.6" and
defusedxml==0.7.1; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
entrypoints==0.3; python_version >= "3.6"
finviz==1.3.4
fredapi==0.4.3
fundamentalanalysis==0.2.6
hijri-converter==2.1.1; python_version >= "3.6"
holidays==0.10.5.2

19
terminal.py
View File

@@ -19,9 +19,11 @@ from gamestonk_terminal.helper_funcs import b_is_stock_market_open, get_flair
from gamestonk_terminal.main_helper import clear, export, load, print_help, view, candle
from gamestonk_terminal.menu import session
from gamestonk_terminal.papermill import papermill_controller as mill
from gamestonk_terminal.sentiment import sen_menu as sm
from gamestonk_terminal.behavioural_analysis import ba_controller
from gamestonk_terminal.technical_analysis import ta_menu as tam
from gamestonk_terminal.comparison_analysis import ca_menu as cam
from gamestonk_terminal.options import op_menu as opm
from gamestonk_terminal.fred import fred_menu as fm
# import warnings
# warnings.simplefilter("always")
@@ -64,13 +66,15 @@ def main():
"export",
"disc",
"mill",
"sen",
"ba",
"res",
"fa",
"ta",
"dd",
"pred",
"ca",
"op",
"fred",
]
menu_parser.add_argument("opt", choices=choices)
completer = NestedCompleter.from_nested_dict({c: None for c in choices})
@@ -160,8 +164,8 @@ def main():
elif ns_known_args.opt == "mill":
b_quit = mill.papermill_menu()
elif ns_known_args.opt == "sen":
b_quit = sm.sen_menu(s_ticker, s_start)
elif ns_known_args.opt == "ba":
b_quit = ba_controller.menu(s_ticker, s_start)
elif ns_known_args.opt == "res":
b_quit = rm.res_menu(s_ticker, s_start, s_interval)
@@ -178,6 +182,12 @@ def main():
elif ns_known_args.opt == "dd":
b_quit = ddm.dd_menu(df_stock, s_ticker, s_start, s_interval)
elif ns_known_args.opt == "op":
b_quit = opm.opt_menu(s_ticker)
elif ns_known_args.opt == "fred":
b_quit = fm.fred_menu()
elif ns_known_args.opt == "pred":
if not gtff.ENABLE_PREDICT:
@@ -224,7 +234,6 @@ def main():
except Exception as e:
print(e)
print("Either the ticker or the API_KEY are invalids. Try again!")
b_quit = False
return
else:

10
tests/test_ba_finbrain_api.py Normal file
View File

@@ -0,0 +1,10 @@
""" behavioural_analysis/finbrain_view.py tests """
import unittest
# from gamestonk_terminal.behavioural_analysis.finbrain_view import sentiment_analysis
class TestBaFinbrainApi(unittest.TestCase):
def test_sentiment_analysis(self):
# sentiment_analysis([], "TSLA")
print("")

5
tests/test_sentiment_twitter_api.py
View File

@@ -4,7 +4,10 @@ import unittest
# pylint: disable=unused-import
try:
from gamestonk_terminal.sentiment.twitter_api import inference, sentiment
from gamestonk_terminal.behavioural_analysis.twitter_view import (
inference,
sentiment,
)
except ModuleNotFoundError as e:
print("One of the optional packages seems to be missing")
print(e)