mirror of
https://github.com/OpenBB-finance/OpenBB.git
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b71abcfbf4
* quick econ fix * black * keys and feature flags * terminal name 👀 * some more replacements * some more replacements * edit pyproject * gst -> openbb * add example portfolios back to git * Update api from gst * sorry. skipping some tests * another round of names * another round of test edits * Missed some .gst refs and update timezone * water mark stuff * Fixing Names in terminal.spec and name of GTFF_DEFAULTS to OBBFF_DEFAULTS * fix more GST to OpenBB Terminal * Logging : merge conflicts with main * Revert wrong files Co-authored-by: Andrew <andrew.kenreich@gmail.com> Co-authored-by: DidierRLopes <dro.lopes@campus.fct.unl.pt> Co-authored-by: Chavithra PARANA <chavithra@gmail.com>
63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
"""Finnhub View"""
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__docformat__ = "numpy"
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import logging
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import os
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import pandas as pd
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from openbb_terminal.common.behavioural_analysis import finnhub_model
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from openbb_terminal.decorators import log_start_end
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from openbb_terminal.helper_funcs import export_data
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from openbb_terminal.rich_config import console
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from openbb_terminal.decorators import check_api_key
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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 display_sentiment_stats(ticker: str, export: str = ""):
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"""
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Sentiment stats which displays buzz, news score, articles last week, articles weekly average,
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bullish vs bearish percentages, sector average bullish percentage, and sector average news score
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Parameters
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----------
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ticker : str
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Ticker to get sentiment stats
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export : str
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Format to export data
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"""
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d_stats = finnhub_model.get_sentiment_stats(ticker)
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print(d_stats)
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if d_stats.empty:
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return
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if d_stats:
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console.print(
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f"""
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Buzz: {round(100*d_stats['buzz']['buzz'],2)} %
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News Score: {round(100*d_stats['companyNewsScore'],2)} %
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Articles Last Week: {d_stats['buzz']['articlesInLastWeek']}
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Articles Weekly Average: {d_stats['buzz']['weeklyAverage']}
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Bullish: {round(100*d_stats['sentiment']['bullishPercent'],2)} %
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Bearish: {round(100*d_stats['sentiment']['bearishPercent'],2)} %
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Sector Average Bullish: {round(100*d_stats['sectorAverageBullishPercent'],2)}
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Sector Average News Score: {round(100*d_stats['sectorAverageNewsScore'],2)} %"""
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)
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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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"stats",
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pd.DataFrame(d_stats),
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)
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else:
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console.print("No sentiment stats found.")
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console.print("")
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