mirror of
https://github.com/OpenBB-finance/OpenBB.git
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6426b76771
* decorated functions and removed assertion tests that are now caught by decorator logs * modified functions with @property * fixed bc of coingecko * fixed outedated tests
86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
"""Tradingview model"""
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__docformat__ = "numpy"
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import logging
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import pandas as pd
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import requests
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from tradingview_ta import TA_Handler
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from gamestonk_terminal import config_terminal as cfg
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from gamestonk_terminal.decorators import log_start_end
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logger = logging.getLogger(__name__)
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INTERVALS = {
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"1m": "1 min",
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"5m": "5 min",
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"15m": "15 min",
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"1h": "1 hour",
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"4h": "4 hours",
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"1d": "1 day",
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"1W": "1 week",
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"1M": "1 month",
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}
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SCREENERS = ["crypto", "forex", "cfd"]
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@log_start_end(log=logger)
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def get_tradingview_recommendation(
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ticker: str, screener: str, exchange: str, interval: str
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) -> pd.DataFrame:
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"""Get tradingview recommendation based on technical indicators
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Parameters
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----------
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ticker : str
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Ticker to get the recommendation from tradingview based on technical indicators
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screener : str
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Screener based on tradingview docs https://python-tradingview-ta.readthedocs.io/en/latest/usage.html
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exchange: str
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Exchange based on tradingview docs https://python-tradingview-ta.readthedocs.io/en/latest/usage.html
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interval: str
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Interval time to check technical indicators and correspondent recommendation
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Returns
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-------
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df_recommendation: pd.DataFrame
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Dataframe of tradingview recommendations based on technical indicators
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"""
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if not exchange:
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s_req = f"https://www.alphavantage.co/query?function=OVERVIEW&symbol={ticker}&apikey={cfg.API_KEY_ALPHAVANTAGE}"
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result = requests.get(s_req, stream=True)
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exchange = result.json()["Exchange"]
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if interval:
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intervals = [interval]
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else:
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intervals = ["1M", "1W", "1d", "4h", "1h", "15m", "5m", "1m"]
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df_recommendation = pd.DataFrame()
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index_recommendation = []
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for an_interval in intervals:
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# If the returned data was successful
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if result.status_code == 200:
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stock_recommendation = TA_Handler(
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symbol=ticker,
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screener=screener,
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exchange=exchange,
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interval=an_interval,
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)
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d_recommendation = stock_recommendation.get_analysis().summary
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df_recommendation = df_recommendation.append(
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d_recommendation, ignore_index=True
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)
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index_recommendation.append(INTERVALS[an_interval])
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df_recommendation.index = index_recommendation
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df_recommendation[["BUY", "NEUTRAL", "SELL"]] = df_recommendation[
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["BUY", "NEUTRAL", "SELL"]
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].astype(int)
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df_recommendation.index.name = "Interval"
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return df_recommendation
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