mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-10-28 04:52:07 +00:00
Refactored stock->TA (#664)
* Ignore nft of the day test * Refactor stock only TA functions
This commit is contained in:
jmaslek
@@ -0,0 +1,30 @@
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"""Finbrain model"""
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__docformat__ = "numpy"
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import requests
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def get_technical_summary_report(ticker: str) -> str:
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"""Get technical summary report provided by FinBrain's API
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Parameters
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----------
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ticker : str
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Ticker to get the technical summary
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Returns
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-------
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report:str
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technical summary report
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"""
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result = requests.get(f"https://api.finbrain.tech/v0/technicalSummary/{ticker}")
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report = ""
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if result.status_code == 200:
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if "technicalSummary" in result.json():
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report = result.json()["technicalSummary"]
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else:
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print("Unexpected data format from FinBrain API")
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else:
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print("Request error in retrieving sentiment from FinBrain API")
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return report
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@@ -1,38 +1,10 @@
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import argparse
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from typing import List
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import requests
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from gamestonk_terminal.helper_funcs import (
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parse_known_args_and_warn,
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)
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"""Finbrain view"""
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__docformat__ = "numpy"
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from gamestonk_terminal.stocks.technical_analysis import finbrain_model
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def get_technical_summary_report(ticker: str) -> str:
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"""Get technical summary report provided by FinBrain's API
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Parameters
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----------
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ticker : str
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Ticker to get the technical summary
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Returns
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-------
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str
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technical summary report
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"""
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result = requests.get(f"https://api.finbrain.tech/v0/technicalSummary/{ticker}")
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report = ""
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if result.status_code == 200:
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if "technicalSummary" in result.json():
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report = result.json()["technicalSummary"]
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else:
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print("Unexpected data format from FinBrain API")
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else:
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print("Request error in retrieving sentiment from FinBrain API")
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return report
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def technical_summary_report(other_args: List[str], ticker: str):
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def technical_summary_report(ticker: str):
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"""Print technical summary report provided by FinBrain's API
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Parameters
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@@ -42,28 +14,8 @@ def technical_summary_report(other_args: List[str], ticker: str):
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ticker : str
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Ticker to get the technical summary
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"""
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parser = argparse.ArgumentParser(
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add_help=False,
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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prog="summary",
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description="""
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Technical summary report provided by FinBrain's API.
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FinBrain Technologies develops deep learning algorithms for financial analysis
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and prediction, which currently serves traders from more than 150 countries
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all around the world. [Source: https://finbrain.tech]
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""",
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)
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try:
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ns_parser = parse_known_args_and_warn(parser, other_args)
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if not ns_parser:
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return
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report = get_technical_summary_report(ticker)
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if report:
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print(report.replace(". ", ".\n"))
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print("")
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except Exception as e:
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print(e)
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print("")
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report = finbrain_model.get_technical_summary_report(ticker)
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if report:
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print(report.replace(". ", ".\n"))
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print("")
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@@ -0,0 +1,40 @@
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"""Finnhub model"""
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__docformat__ = "numpy"
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import requests
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import pandas as pd
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from gamestonk_terminal import config_terminal as cfg
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def get_pattern_recognition(ticker: str, resolution: str) -> pd.DataFrame:
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"""Get pattern recognition data
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Parameters
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----------
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ticker : str
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Ticker to get pattern recognition data
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resolution : str
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Resolution of data to get pattern recognition from
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Returns
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-------
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pd.DataFrame
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Get datapoints corresponding to pattern signal data
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"""
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response = requests.get(
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f"https://finnhub.io/api/v1/scan/pattern?symbol={ticker}&resolution={resolution}&token={cfg.API_FINNHUB_KEY}"
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)
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# pylint:disable=no-else-return
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if response.status_code == 200:
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d_data = response.json()
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if "points" in d_data:
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return pd.DataFrame(d_data["points"]).T
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else:
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print("Response is empty")
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return pd.DataFrame()
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else:
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print(f"Error in requests with code: {response.status_code}")
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return pd.DataFrame()
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@@ -1,54 +1,39 @@
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import argparse
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from typing import List
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"""Finnhub view"""
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__docformat__ = "numpy"
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import os
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import math
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from datetime import datetime
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import requests
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import yfinance as yf
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import mplfinance as mpf
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import pandas as pd
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from gamestonk_terminal import config_terminal as cfg
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from gamestonk_terminal.helper_funcs import (
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parse_known_args_and_warn,
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plot_autoscale,
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)
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from gamestonk_terminal.helper_funcs import plot_autoscale, export_data
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from gamestonk_terminal.stocks.technical_analysis import finnhub_model
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def get_pattern_recognition(ticker: str, resolution: str) -> pd.DataFrame:
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"""Get pattern recognition data
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Parameters
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----------
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ticker : str
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Ticker to get pattern recognition data
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resolution : str
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Resolution of data to get pattern recognition from
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Returns
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-------
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pd.DataFrame
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Get datapoints corresponding to pattern signal data
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"""
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response = requests.get(
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f"https://finnhub.io/api/v1/scan/pattern?symbol={ticker}&resolution={resolution}&token={cfg.API_FINNHUB_KEY}"
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)
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if response.status_code == 200:
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d_data = response.json()
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if "points" in d_data:
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return pd.DataFrame(d_data["points"]).T
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return pd.DataFrame()
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def plot_pattern_recognition(ticker: str, pattern: pd.DataFrame):
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def plot_pattern_recognition(ticker: str, resolution: str, export: str):
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"""Plot pattern recognition signal
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Parameters
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----------
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ticker : str
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Ticker to display pattern recognition on top of the data
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pattern : pd.DataFrame
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Pattern recognition signal data
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resolution : str
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Resolution of data to get pattern recognition from
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export: str
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Format of export file
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"""
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pattern = finnhub_model.get_pattern_recognition(ticker, resolution)
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export_data(
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export,
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os.path.dirname(os.path.abspath(__file__)),
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"pr",
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pattern,
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)
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l_segments = list()
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for i in pattern:
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a_part = ("", "")
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@@ -102,7 +87,6 @@ def plot_pattern_recognition(ticker: str, pattern: pd.DataFrame):
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start=datetime.utcfromtimestamp(start_time).strftime("%Y-%m-%d"),
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progress=False,
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)
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df_stock["date_id"] = (df_stock.index.date - df_stock.index.date.min()).astype(
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"timedelta64[D]"
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)
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@@ -138,49 +122,3 @@ def plot_pattern_recognition(ticker: str, pattern: pd.DataFrame):
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print(
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f"Pattern: {pattern[0]['patternname']} ({pattern[0]['patterntype']})", "\n"
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)
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def pattern_recognition_view(other_args: List[str], ticker: str):
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"""Display pattern recognition signals on the data
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Parameters
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----------
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other_args : List[str]
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Command line arguments to be processed with argparse
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ticker : str
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Ticker to display pattern recognition on top of the data
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"""
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parser = argparse.ArgumentParser(
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add_help=False,
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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prog="pr",
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description="""
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Display pattern recognition signals on the data. [Source: https://finnhub.io]
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""",
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)
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parser.add_argument(
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"-r",
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"--resolution",
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action="store",
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dest="resolution",
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type=str,
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default="D",
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choices=["1", "5", "15", "30", "60", "D", "W", "M"],
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help="Plot resolution to look for pattern signals",
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)
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try:
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ns_parser = parse_known_args_and_warn(parser, other_args)
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if not ns_parser:
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return
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df_pattern = get_pattern_recognition(ticker, ns_parser.resolution)
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if df_pattern.empty:
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print("No pattern identified in this data", "\n")
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return
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plot_pattern_recognition(ticker, df_pattern)
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except Exception as e:
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print(e, "\n")
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28
gamestonk_terminal/stocks/technical_analysis/finviz_model.py
Normal file
28
gamestonk_terminal/stocks/technical_analysis/finviz_model.py
Normal file
@@ -0,0 +1,28 @@
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"""Finviz model"""
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__docformat__ = "numpy"
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import requests
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from finvizfinance.quote import finvizfinance
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from finvizfinance.util import headers
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def get_finviz_image(ticker: str) -> bytes:
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"""Get finviz image for given ticker
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Parameters
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----------
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ticker : str
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ticker
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Returns
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-------
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bytes
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Image in byted format
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"""
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stock = finvizfinance(ticker)
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image_url = stock.TickerCharts(urlonly=True)
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r = requests.get(image_url, stream=True, headers=headers, timeout=5)
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r.raise_for_status()
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r.raw.decode_content = True
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return r.content
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@@ -1,67 +1,39 @@
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""" Finviz View """
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__docformat__ = "numpy"
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import argparse
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import io
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from typing import List
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import requests
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import matplotlib.pyplot as plt
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from finvizfinance.quote import finvizfinance
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from finvizfinance.util import headers
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from PIL import Image
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from gamestonk_terminal.helper_funcs import (
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parse_known_args_and_warn,
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plot_autoscale,
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)
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from gamestonk_terminal.stocks.technical_analysis import finviz_model
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from gamestonk_terminal.config_plot import PLOT_DPI
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from gamestonk_terminal import feature_flags as gtff
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def view(other_args: List[str], ticker: str):
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"""View historical price with trendlines. [Source: Finviz]
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def view(ticker: str):
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"""View finviz image for ticker
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Parameters
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----------
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other_args : List[str]
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argparse other args
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ticker: str
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stock ticker
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ticker : str
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Stock ticker
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"""
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parser = argparse.ArgumentParser(
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add_help=False,
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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prog="view",
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description="""
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View historical price with trendlines. [Source: Finviz]
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""",
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)
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image_data = finviz_model.get_finviz_image(ticker)
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dataBytesIO = io.BytesIO(image_data)
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im = Image.open(dataBytesIO)
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try:
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ns_parser = parse_known_args_and_warn(parser, other_args)
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if not ns_parser:
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return
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fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
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ax.set_axis_off()
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fig.subplots_adjust(left=0, right=1, top=1, bottom=0)
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stock = finvizfinance(ticker)
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image_url = stock.TickerCharts(urlonly=True)
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if gtff.USE_ION:
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plt.ion()
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r = requests.get(image_url, stream=True, headers=headers, timeout=5)
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r.raise_for_status()
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r.raw.decode_content = True
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dataBytesIO = io.BytesIO(r.content)
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im = Image.open(dataBytesIO)
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fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
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ax.set_axis_off()
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fig.subplots_adjust(left=0, right=1, top=1, bottom=0)
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if gtff.USE_ION:
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plt.ion()
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plt.imshow(im)
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plt.show()
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print("")
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except SystemExit:
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print("")
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plt.imshow(im)
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plt.show()
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print("")
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@@ -10,7 +10,7 @@ import matplotlib.pyplot as plt
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from prompt_toolkit.completion import NestedCompleter
|
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|
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from gamestonk_terminal import feature_flags as gtff
|
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from gamestonk_terminal.helper_funcs import get_flair
|
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from gamestonk_terminal.helper_funcs import get_flair, parse_known_args_and_warn
|
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from gamestonk_terminal.menu import session
|
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from gamestonk_terminal.stocks.technical_analysis import (
|
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finviz_view,
|
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@@ -62,13 +62,13 @@ class TechnicalAnalysisController:
|
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|
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def __init__(
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self,
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ticker: str,
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s_stock: str,
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start: datetime,
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interval: str,
|
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stock: pd.DataFrame,
|
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):
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"""Constructor"""
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self.ticker = ticker
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self.s_stock = s_stock
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self.start = start
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self.interval = interval
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self.stock = stock
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@@ -81,55 +81,50 @@ class TechnicalAnalysisController:
|
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|
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def print_help(self):
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"""Print help"""
|
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print(
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"https://github.com/GamestonkTerminal/GamestonkTerminal/tree/main/gamestonk_terminal"
|
||||
"/stocks/technical_analysis"
|
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)
|
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s_intraday = (f"Intraday {self.interval }", "Daily")[self.interval == "1440min"]
|
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|
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s_intraday = (f"Intraday {self.interval}", "Daily")[self.interval == "1440min"]
|
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if self.start:
|
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print(
|
||||
f"\n{s_intraday} Stock: {self.ticker} (from {self.start.strftime('%Y-%m-%d')})"
|
||||
)
|
||||
str1 = f"\n{s_intraday} Stock: {self.s_stock} (from {self.start.strftime('%Y-%m-%d')})"
|
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else:
|
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print(f"\n{s_intraday} Stock: {self.ticker}")
|
||||
str1 = f"\n{s_intraday} Stock: {self.s_stock}"
|
||||
|
||||
print("\nTechnical Analysis:")
|
||||
print(" cls clear screen")
|
||||
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(" view view historical data and trendlines [Finviz]")
|
||||
print(" summary technical summary report [FinBrain API]")
|
||||
print(
|
||||
" recom recommendation based on Technical Indicators [Tradingview API]"
|
||||
)
|
||||
print(" pr pattern recognition [Finnhub]")
|
||||
print("")
|
||||
print("overlap:")
|
||||
print(" ema exponential moving average")
|
||||
print(" sma simple moving average")
|
||||
print(" vwap volume weighted average price")
|
||||
print(" zlma zero lag moving average")
|
||||
print("momentum:")
|
||||
print(" cci commodity channel index")
|
||||
print(" macd moving average convergence/divergence")
|
||||
print(" rsi relative strength index")
|
||||
print(" stoch stochastic oscillator")
|
||||
print(" fisher fisher transform")
|
||||
print(" cg centre of gravity")
|
||||
print("trend:")
|
||||
print(" adx average directional movement index")
|
||||
print(" aroon aroon indicator")
|
||||
print("volatility:")
|
||||
print(" bbands bollinger bands")
|
||||
print("volume:")
|
||||
print(" ad accumulation/distribution line values")
|
||||
print(" obv on balance volume")
|
||||
print("custom:")
|
||||
print(" fib fibonacci retracement")
|
||||
print("")
|
||||
help_str = f"""https://github.com/GamestonkTerminal/GamestonkTerminal/tree/main/gamestonk_terminal/stocks/technical_analysis
|
||||
{str1}
|
||||
|
||||
Technical Analysis:
|
||||
cls clear screen
|
||||
help show this menu again
|
||||
q quit this menu, and shows back to main menu
|
||||
quit quit to abandon program
|
||||
|
||||
view view historical data and trendlines [Finviz]
|
||||
summary technical summary report [FinBrain API]
|
||||
recom recommendation based on Technical Indicators [Tradingview API]
|
||||
pr pattern recognition [Finnhub]
|
||||
|
||||
overlap:
|
||||
ema exponential moving average
|
||||
sma simple moving average
|
||||
zlma zero lag moving average"
|
||||
vwap volume weighted average price
|
||||
momentum:
|
||||
cci commodity channel index
|
||||
macd moving average convergence/divergence
|
||||
rsi relative strength index
|
||||
stoch stochastic oscillator
|
||||
fisher fisher transform
|
||||
cg centre of gravity
|
||||
trend:
|
||||
adx average directional movement index
|
||||
aroon aroon indicator
|
||||
volatility:
|
||||
bbands bollinger bands
|
||||
volume:
|
||||
ad accumulation/distribution line values
|
||||
obv on balance volume
|
||||
custom:
|
||||
fib fibonacci retracement
|
||||
"""
|
||||
print(help_str)
|
||||
|
||||
def switch(self, an_input: str):
|
||||
"""Process and dispatch input
|
||||
@@ -178,92 +173,234 @@ class TechnicalAnalysisController:
|
||||
# SPECIFIC
|
||||
def call_view(self, other_args: List[str]):
|
||||
"""Process view command"""
|
||||
finviz_view.view(other_args, self.ticker)
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
add_help=False,
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||||
prog="view",
|
||||
description="""
|
||||
View historical price with trendlines. [Source: Finviz]
|
||||
""",
|
||||
)
|
||||
try:
|
||||
ns_parser = parse_known_args_and_warn(parser, other_args)
|
||||
if not ns_parser:
|
||||
return
|
||||
|
||||
finviz_view.view(self.s_stock)
|
||||
|
||||
except Exception as e:
|
||||
print(e, "\n")
|
||||
|
||||
def call_summary(self, other_args: List[str]):
|
||||
"""Process summary command"""
|
||||
finbrain_view.technical_summary_report(other_args, self.ticker)
|
||||
parser = argparse.ArgumentParser(
|
||||
add_help=False,
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||||
prog="summary",
|
||||
description="""
|
||||
Technical summary report provided by FinBrain's API.
|
||||
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
|
||||
|
||||
finbrain_view.technical_summary_report(self.s_stock)
|
||||
|
||||
except Exception as e:
|
||||
print(e, "\n")
|
||||
|
||||
def call_recom(self, other_args: List[str]):
|
||||
"""Process recom command"""
|
||||
tradingview_view.print_recommendation(other_args, self.ticker)
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
add_help=False,
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||||
prog="recom",
|
||||
description="""
|
||||
Print tradingview recommendation based on technical indicators.
|
||||
[Source: https://pypi.org/project/tradingview-ta/]
|
||||
""",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-s",
|
||||
"--screener",
|
||||
action="store",
|
||||
dest="screener",
|
||||
type=str,
|
||||
default="america",
|
||||
choices=["crypto", "forex", "cfd"],
|
||||
help="Screener. See https://python-tradingview-ta.readthedocs.io/en/latest/usage.html",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-e",
|
||||
"--exchange",
|
||||
action="store",
|
||||
dest="exchange",
|
||||
type=str,
|
||||
default="",
|
||||
help="""Set exchange. For Forex use: 'FX_IDC', and for crypto use 'TVC'.
|
||||
See https://python-tradingview-ta.readthedocs.io/en/latest/usage.html.
|
||||
By default Alpha Vantage tries to get this data from the ticker. """,
|
||||
)
|
||||
parser.add_argument(
|
||||
"-i",
|
||||
"--interval",
|
||||
action="store",
|
||||
dest="interval",
|
||||
type=str,
|
||||
default="",
|
||||
choices=["1M", "1W", "1d", "4h", "1h", "15m", "5m", "1m"],
|
||||
help="""Interval, that corresponds to the recommendation given by tradingview based on technical indicators.
|
||||
See https://python-tradingview-ta.readthedocs.io/en/latest/usage.html""",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--export",
|
||||
choices=["csv", "json", "xlsx"],
|
||||
default="",
|
||||
type=str,
|
||||
dest="export",
|
||||
help="Export dataframe data to csv,json,xlsx file",
|
||||
)
|
||||
|
||||
try:
|
||||
ns_parser = parse_known_args_and_warn(parser, other_args)
|
||||
if not ns_parser:
|
||||
return
|
||||
tradingview_view.print_recommendation(
|
||||
ticker=self.s_stock,
|
||||
screener=ns_parser.screener,
|
||||
exchange=ns_parser.exchange,
|
||||
interval=ns_parser.interval,
|
||||
export=ns_parser.export,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
print(e, "\n")
|
||||
|
||||
def call_pr(self, other_args: List[str]):
|
||||
"""Process pr command"""
|
||||
finnhub_view.pattern_recognition_view(other_args, self.ticker)
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
add_help=False,
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||||
prog="pr",
|
||||
description="""
|
||||
Display pattern recognition signals on the data. [Source: https://finnhub.io]
|
||||
""",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-r",
|
||||
"--resolution",
|
||||
action="store",
|
||||
dest="resolution",
|
||||
type=str,
|
||||
default="D",
|
||||
choices=["1", "5", "15", "30", "60", "D", "W", "M"],
|
||||
help="Plot resolution to look for pattern signals",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--export",
|
||||
choices=["csv", "json", "xlsx"],
|
||||
default="",
|
||||
type=str,
|
||||
dest="export",
|
||||
help="Export dataframe data to csv,json,xlsx file",
|
||||
)
|
||||
|
||||
try:
|
||||
ns_parser = parse_known_args_and_warn(parser, other_args)
|
||||
if not ns_parser:
|
||||
return
|
||||
|
||||
finnhub_view.plot_pattern_recognition(
|
||||
ticker=self.s_stock,
|
||||
resolution=ns_parser.resolution,
|
||||
export=ns_parser.export,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
print(e, "\n")
|
||||
|
||||
# COMMON
|
||||
def call_ema(self, other_args: List[str]):
|
||||
"""Process ema command"""
|
||||
overlap.ema(other_args, self.ticker, self.interval, self.stock)
|
||||
overlap.ema(other_args, self.s_stock, self.interval, self.stock)
|
||||
|
||||
def call_sma(self, other_args: List[str]):
|
||||
"""Process sma command"""
|
||||
overlap.sma(other_args, self.ticker, self.interval, self.stock)
|
||||
overlap.sma(other_args, self.s_stock, self.interval, self.stock)
|
||||
|
||||
def call_vwap(self, other_args: List[str]):
|
||||
"""Process vwap command"""
|
||||
overlap.vwap(other_args, self.ticker, self.interval, self.stock)
|
||||
overlap.vwap(other_args, self.s_stock, self.interval, self.stock)
|
||||
|
||||
def call_zlma(self, other_args: List[str]):
|
||||
"""Process zlma command"""
|
||||
overlap.zlma(other_args, self.ticker, self.interval, self.stock)
|
||||
overlap.zlma(other_args, self.s_stock, self.interval, self.stock)
|
||||
|
||||
def call_cci(self, other_args: List[str]):
|
||||
"""Process cci command"""
|
||||
momentum.cci(other_args, self.ticker, self.interval, self.stock)
|
||||
momentum.cci(other_args, self.s_stock, self.interval, self.stock)
|
||||
|
||||
def call_macd(self, other_args: List[str]):
|
||||
"""Process macd command"""
|
||||
momentum.macd(other_args, self.ticker, self.interval, self.stock)
|
||||
momentum.macd(other_args, self.s_stock, self.interval, self.stock)
|
||||
|
||||
def call_rsi(self, other_args: List[str]):
|
||||
"""Process rsi command"""
|
||||
momentum.rsi(other_args, self.ticker, self.interval, self.stock)
|
||||
momentum.rsi(other_args, self.s_stock, self.interval, self.stock)
|
||||
|
||||
def call_stoch(self, other_args: List[str]):
|
||||
"""Process stoch command"""
|
||||
momentum.stoch(other_args, self.ticker, self.interval, self.stock)
|
||||
momentum.stoch(other_args, self.s_stock, self.interval, self.stock)
|
||||
|
||||
def call_fisher(self, other_args: List[str]):
|
||||
"""Process fisher command"""
|
||||
momentum.fisher(other_args, self.ticker, self.interval, self.stock)
|
||||
momentum.fisher(other_args, self.s_stock, self.interval, self.stock)
|
||||
|
||||
def call_cg(self, other_args: List[str]):
|
||||
"""Process cg command"""
|
||||
momentum.cg(other_args, self.ticker, self.interval, self.stock)
|
||||
momentum.cg(other_args, self.s_stock, self.interval, self.stock)
|
||||
|
||||
def call_adx(self, other_args: List[str]):
|
||||
"""Process adx command"""
|
||||
trend_indicators.adx(other_args, self.ticker, self.interval, self.stock)
|
||||
trend_indicators.adx(other_args, self.s_stock, self.interval, self.stock)
|
||||
|
||||
def call_aroon(self, other_args: List[str]):
|
||||
"""Process aroon command"""
|
||||
trend_indicators.aroon(other_args, self.ticker, self.interval, self.stock)
|
||||
trend_indicators.aroon(other_args, self.s_stock, self.interval, self.stock)
|
||||
|
||||
def call_bbands(self, other_args: List[str]):
|
||||
"""Process bbands command"""
|
||||
volatility.bbands(other_args, self.ticker, self.interval, self.stock)
|
||||
volatility.bbands(other_args, self.s_stock, self.interval, self.stock)
|
||||
|
||||
def call_ad(self, other_args: List[str]):
|
||||
"""Process ad command"""
|
||||
volume.ad(other_args, self.ticker, self.interval, self.stock)
|
||||
volume.ad(other_args, self.s_stock, self.interval, self.stock)
|
||||
|
||||
def call_obv(self, other_args: List[str]):
|
||||
"""Process obv command"""
|
||||
volume.obv(other_args, self.ticker, self.interval, self.stock)
|
||||
volume.obv(other_args, self.s_stock, self.interval, self.stock)
|
||||
|
||||
def call_fib(self, other_args: List[str]):
|
||||
"""Process fib command"""
|
||||
custom_indicators_view.fibinocci_retracement(
|
||||
other_args, self.stock, self.ticker
|
||||
other_args, self.stock, self.s_stock
|
||||
)
|
||||
|
||||
|
||||
def menu(ticker: str, start: datetime, interval: str, stock: pd.DataFrame):
|
||||
def menu(s_stock: str, start: datetime, interval: str, stock: pd.DataFrame):
|
||||
"""Technical Analysis Menu"""
|
||||
|
||||
ta_controller = TechnicalAnalysisController(ticker, start, interval, stock)
|
||||
ta_controller = TechnicalAnalysisController(s_stock, start, interval, stock)
|
||||
ta_controller.call_help(None)
|
||||
|
||||
while True:
|
||||
|
||||
@@ -0,0 +1,81 @@
|
||||
"""Tradingview model"""
|
||||
__docformat__ = "numpy"
|
||||
|
||||
import requests
|
||||
from tradingview_ta import TA_Handler
|
||||
import pandas as pd
|
||||
|
||||
from gamestonk_terminal import config_terminal as cfg
|
||||
|
||||
INTERVALS = {
|
||||
"1m": "1 min",
|
||||
"5m": "5 min",
|
||||
"15m": "15 min",
|
||||
"1h": "1 hour",
|
||||
"4h": "4 hours",
|
||||
"1d": "1 day",
|
||||
"1W": "1 week",
|
||||
"1M": "1 month",
|
||||
}
|
||||
|
||||
|
||||
def get_tradingview_recommendation(
|
||||
ticker: str, screener: str, exchange: str, interval: str
|
||||
) -> pd.DataFrame:
|
||||
"""Get tradingview recommendation based on technical indicators
|
||||
|
||||
Parameters
|
||||
----------
|
||||
ticker : str
|
||||
Ticker to get the recommendation from tradingview based on technical indicators
|
||||
screener : str
|
||||
Screener based on tradingview docs https://python-tradingview-ta.readthedocs.io/en/latest/usage.html
|
||||
exchange: str
|
||||
Exchange based on tradingview docs https://python-tradingview-ta.readthedocs.io/en/latest/usage.html
|
||||
interval: str
|
||||
Interval time to check technical indicators and correspondent recommendation
|
||||
|
||||
Returns
|
||||
-------
|
||||
df_recommendation: pd.DataFrame
|
||||
Dataframe of tradingview recommendations based on technical indicators
|
||||
"""
|
||||
|
||||
if not exchange:
|
||||
s_req = f"https://www.alphavantage.co/query?function=OVERVIEW&symbol={ticker}&apikey={cfg.API_KEY_ALPHAVANTAGE}"
|
||||
result = requests.get(s_req, stream=True)
|
||||
exchange = result.json()["Exchange"]
|
||||
|
||||
if not interval:
|
||||
df_recommendation = pd.DataFrame()
|
||||
index_recommendation = list()
|
||||
for interv in ["1M", "1W", "1d", "4h", "1h", "15m", "5m", "1m"]:
|
||||
# If the returned data was successful
|
||||
if result.status_code == 200:
|
||||
stock_recommendation = TA_Handler(
|
||||
symbol=ticker,
|
||||
screener=screener,
|
||||
exchange=exchange,
|
||||
interval=interv,
|
||||
)
|
||||
d_recommendation = stock_recommendation.get_analysis().summary
|
||||
df_recommendation = df_recommendation.append(
|
||||
d_recommendation, ignore_index=True
|
||||
)
|
||||
index_recommendation.append(INTERVALS[interv])
|
||||
df_recommendation.index = index_recommendation
|
||||
df_recommendation[["BUY", "NEUTRAL", "SELL"]] = df_recommendation[
|
||||
["BUY", "NEUTRAL", "SELL"]
|
||||
].astype(int)
|
||||
|
||||
df_recommendation.index.name = "Interval"
|
||||
|
||||
else:
|
||||
stock_recommendation = TA_Handler(
|
||||
symbol=ticker, screener=screener, exchange=exchange, interval=interval
|
||||
)
|
||||
d_recommendation = stock_recommendation.get_analysis().summary
|
||||
df_recommendation = pd.DataFrame.from_dict(d_recommendation, orient="index").T
|
||||
df_recommendation.index = INTERVALS[interv]
|
||||
|
||||
return df_recommendation
|
||||
@@ -1,155 +1,47 @@
|
||||
import argparse
|
||||
from typing import List
|
||||
import requests
|
||||
import pandas as pd
|
||||
from tradingview_ta import TA_Handler
|
||||
"""Tradingview view"""
|
||||
__docformat__ = "numpy"
|
||||
|
||||
from gamestonk_terminal import config_terminal as cfg
|
||||
from gamestonk_terminal.helper_funcs import (
|
||||
parse_known_args_and_warn,
|
||||
)
|
||||
import os
|
||||
|
||||
INTERVALS = {
|
||||
"1m": "1 min",
|
||||
"5m": "5 min",
|
||||
"15m": "15 min",
|
||||
"1h": "1 hour",
|
||||
"4h": "4 hours",
|
||||
"1d": "1 day",
|
||||
"1W": "1 week",
|
||||
"1M": "1 month",
|
||||
}
|
||||
from tabulate import tabulate
|
||||
|
||||
from gamestonk_terminal.stocks.technical_analysis import tradingview_model
|
||||
from gamestonk_terminal.helper_funcs import export_data
|
||||
|
||||
|
||||
def get_tradingview_recommendation(
|
||||
ticker: str, screener: str, exchange: str, interval: str
|
||||
) -> str:
|
||||
"""Get tradingview recommendation based on technical indicators
|
||||
def print_recommendation(
|
||||
ticker: str, screener: str, exchange: str, interval: str, export: str
|
||||
):
|
||||
"""Print tradingview recommendation based on technical indicators
|
||||
|
||||
Parameters
|
||||
----------
|
||||
ticker : str
|
||||
Ticker to get the recommendation from tradingview based on technical indicators
|
||||
Ticker to get tradingview recommendation based on technical indicators
|
||||
screener : str
|
||||
Screener based on tradingview docs https://python-tradingview-ta.readthedocs.io/en/latest/usage.html
|
||||
exchange: str
|
||||
Exchange based on tradingview docs https://python-tradingview-ta.readthedocs.io/en/latest/usage.html
|
||||
interval: str
|
||||
Interval time to check technical indicators and correspondent recommendation
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
tradingview recommendation based on technical indicators
|
||||
export: str
|
||||
Format of export file
|
||||
"""
|
||||
|
||||
if not exchange:
|
||||
s_req = f"https://www.alphavantage.co/query?function=OVERVIEW&symbol={ticker}&apikey={cfg.API_KEY_ALPHAVANTAGE}"
|
||||
result = requests.get(s_req, stream=True)
|
||||
exchange = result.json()["Exchange"]
|
||||
|
||||
if not interval:
|
||||
df_recommendation = pd.DataFrame()
|
||||
index_recommendation = list()
|
||||
for interv in ["1M", "1W", "1d", "4h", "1h", "15m", "5m", "1m"]:
|
||||
# If the returned data was successful
|
||||
if result.status_code == 200:
|
||||
stock_recommendation = TA_Handler(
|
||||
symbol=ticker,
|
||||
screener=screener,
|
||||
exchange=exchange,
|
||||
interval=interv,
|
||||
)
|
||||
d_recommendation = stock_recommendation.get_analysis().summary
|
||||
df_recommendation = df_recommendation.append(
|
||||
d_recommendation, ignore_index=True
|
||||
)
|
||||
index_recommendation.append(INTERVALS[interv])
|
||||
df_recommendation.index = index_recommendation
|
||||
df_recommendation[["BUY", "NEUTRAL", "SELL"]] = df_recommendation[
|
||||
["BUY", "NEUTRAL", "SELL"]
|
||||
].astype(int)
|
||||
df_recommendation["-----"] = "-----"
|
||||
df_recommendation.index.name = "Interval"
|
||||
recommendation = df_recommendation[
|
||||
["RECOMMENDATION", "-----", "BUY", "NEUTRAL", "SELL"]
|
||||
].to_string()
|
||||
|
||||
else:
|
||||
stock_recommendation = TA_Handler(
|
||||
symbol=ticker, screener=screener, exchange=exchange, interval=interval
|
||||
)
|
||||
d_recommendation = stock_recommendation.get_analysis().summary
|
||||
recommendation = f"Interval: {INTERVALS[interval]}\n"
|
||||
recommendation += f"Recommendation: {d_recommendation['RECOMMENDATION']}\n"
|
||||
recommendation += f"{int(d_recommendation['BUY'])} BUY, {int(d_recommendation['NEUTRAL'])} NEUTRAL"
|
||||
recommendation += f", {int(d_recommendation['SELL'])} SELL"
|
||||
|
||||
return recommendation
|
||||
|
||||
|
||||
def print_recommendation(other_args: List[str], ticker: str):
|
||||
"""Print tradingview recommendation based on technical indicators
|
||||
|
||||
Parameters
|
||||
----------
|
||||
other_args : List[str]
|
||||
Command line arguments to be processed with argparse
|
||||
ticker : str
|
||||
Ticker to get tradingview recommendation based on technical indicators
|
||||
"""
|
||||
parser = argparse.ArgumentParser(
|
||||
add_help=False,
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||||
prog="recom",
|
||||
description="""
|
||||
Print tradingview recommendation based on technical indicators.
|
||||
[Source: https://pypi.org/project/tradingview-ta/]
|
||||
""",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-s",
|
||||
"--screener",
|
||||
action="store",
|
||||
dest="screener",
|
||||
type=str,
|
||||
default="america",
|
||||
choices=["crypto", "forex", "cfd"],
|
||||
help="Screener. See https://python-tradingview-ta.readthedocs.io/en/latest/usage.html",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-e",
|
||||
"--exchange",
|
||||
action="store",
|
||||
dest="exchange",
|
||||
type=str,
|
||||
default="",
|
||||
help="""Set exchange. For Forex use: 'FX_IDC', and for crypto use 'TVC'.
|
||||
See https://python-tradingview-ta.readthedocs.io/en/latest/usage.html.
|
||||
By default Alpha Vantage tries to get this data from the ticker. """,
|
||||
)
|
||||
parser.add_argument(
|
||||
"-i",
|
||||
"--interval",
|
||||
action="store",
|
||||
dest="interval",
|
||||
type=str,
|
||||
default="",
|
||||
choices=["1M", "1W", "1d", "4h", "1h", "15m", "5m", "1m"],
|
||||
help="""Interval, that corresponds to the recommendation given by tradingview based on technical indicators.
|
||||
See https://python-tradingview-ta.readthedocs.io/en/latest/usage.html""",
|
||||
recom = tradingview_model.get_tradingview_recommendation(
|
||||
ticker, screener, exchange, interval
|
||||
)
|
||||
|
||||
try:
|
||||
ns_parser = parse_known_args_and_warn(parser, other_args)
|
||||
if not ns_parser:
|
||||
return
|
||||
export_data(
|
||||
export,
|
||||
os.path.dirname(os.path.abspath(__file__)),
|
||||
"recom",
|
||||
recom,
|
||||
)
|
||||
|
||||
recom = get_tradingview_recommendation(
|
||||
ticker, ns_parser.screener, ns_parser.exchange, ns_parser.interval
|
||||
)
|
||||
print(recom)
|
||||
print("")
|
||||
|
||||
except Exception as e:
|
||||
print(e, "\n")
|
||||
print(
|
||||
tabulate(
|
||||
recom, headers=["INTERVAL"] + list(recom.columns), tablefmt="fancy_grid"
|
||||
),
|
||||
"\n",
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user