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bfc4c021e7
* Report fixes * replace wrong typing * mypy * mypy again Co-authored-by: james <jmaslek11@gmail.com>
125 lines
3.4 KiB
Python
125 lines
3.4 KiB
Python
""" Finnhub Model """
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__docformat__ = "numpy"
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import logging
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from datetime import datetime, timedelta
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from typing import List, Dict, Optional
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import finnhub
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import pandas as pd
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from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
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from openbb_terminal import config_terminal as cfg
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from openbb_terminal.decorators import check_api_key, log_start_end
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from openbb_terminal.helper_funcs import similar
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from openbb_terminal.rich_config import console
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logger = logging.getLogger(__name__)
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@log_start_end(log=logger)
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@check_api_key(["API_FINNHUB_KEY"])
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def get_company_news(
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symbol: str,
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start_date: Optional[str] = None,
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end_date: Optional[str] = None,
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) -> List[Dict]:
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"""Get news from a company. [Source: Finnhub]
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Parameters
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----------
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symbol : str
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company ticker to look for news articles
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start_date: Optional[str]
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date to start searching articles, with format YYYY-MM-DD
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end_date: Optional[str]
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date to end searching articles, with format YYYY-MM-DD
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Returns
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-------
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articles : List
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term to search on the news articles
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"""
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if start_date is None:
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start_date = (datetime.now() - timedelta(days=30)).strftime("%Y-%m-%d")
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if end_date is None:
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end_date = datetime.now().strftime("%Y-%m-%d")
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try:
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finnhub_client = finnhub.Client(api_key=cfg.API_FINNHUB_KEY)
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articles = finnhub_client.company_news(
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symbol.upper(), _from=start_date, to=end_date
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)
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return articles
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except Exception as e:
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console.print(f"[red]{e}\n[/red]")
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return [{}]
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@log_start_end(log=logger)
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def process_news_headlines_sentiment(
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articles: List[Dict],
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) -> pd.DataFrame:
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"""Process news headlines sentiment of a company using a VADER model.
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Parameters
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----------
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articles : List[Dict]
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list of articles with `headline` and `datetime` keys
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Returns
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-------
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pd.DataFrame
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Headlines sentiment using VADER model over time
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"""
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l_datetime = list()
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l_compound = list()
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if articles and len(articles) > 1:
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analyzer = SentimentIntensityAnalyzer()
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last_headline = ""
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for article in articles:
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# allows to discard news with similar headline
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if similar(last_headline, article["headline"].upper()) < 0.7:
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l_compound.append(
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analyzer.polarity_scores(article["headline"])["compound"]
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)
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l_datetime.append(datetime.fromtimestamp(article["datetime"]))
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last_headline = article["headline"].upper()
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return pd.DataFrame(l_compound, index=l_datetime).sort_index()
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@log_start_end(log=logger)
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def get_headlines_sentiment(
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symbol: str,
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) -> pd.DataFrame:
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"""Get headlines sentiment using VADER model over time. [Source: Finnhub]
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Parameters
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----------
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symbol : str
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Ticker of company
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Returns
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----------
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pd.DataFrame
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The news article information
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"""
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start = datetime.now() - timedelta(days=30)
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if not symbol:
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console.print("[red]Do not run this command without setting a ticker.[/red]\n")
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return pd.DataFrame()
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articles = get_company_news(
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symbol.upper(),
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start_date=start.strftime("%Y-%m-%d"),
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end_date=datetime.now().strftime("%Y-%m-%d"),
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)
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sentiment = process_news_headlines_sentiment(articles)
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return sentiment
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