stock-market/stock-sentiments

github link

#!/usr/bin/env python
# coding: utf-8

# This script will analyze sentiments about stocks and will export to .csv


# Multiple Stocks Sentiment from Yahoo finance, exported to cvs

import requests
from bs4 import BeautifulSoup
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer
import pandas as pd


def get_news(ticker):
    # Get the latest news articles for the specified stock from Yahoo Finance
    url = f"https://finance.yahoo.com/quote/{ticker}/"
    page = requests.get(url)
    soup = BeautifulSoup(page.content, "html.parser")
    results = soup.find_all("li", class_="js-stream-content")
    news = []
    for result in results:
        headline = result.find("h3").text
        news.append(headline)
    return news


def sentiment_analysis(news):
    # Perform sentiment analysis on the news articles using the nltk library
    nltk.download("vader_lexicon")
    analyzer = SentimentIntensityAnalyzer()
    sentiments = []
    for article in news:
        sentiment = analyzer.polarity_scores(article)["compound"]
        sentiments.append(sentiment)
    return sentiments


def analyze_stock_sentiments(ticker):
    # Get the news articles and perform sentiment analysis
    news = get_news(ticker)
    sentiments = sentiment_analysis(news)
    if not sentiments:
        return None
    # Calculate the average sentiment of the news articles
    avg_sentiment = sum(sentiments) / len(sentiments)
    return avg_sentiment


# Example usage for multiple stocks
tickers = ["AAPL", "DVA", "KO", "META", "MSFT", "PATH", "PYPL"]
sentiments = {}
for ticker in tickers:
    sentiment = analyze_stock_sentiments(ticker)
    sentiments[ticker] = sentiment


# Export the sentiment analysis results to a pandas DataFrame
df = pd.DataFrame.from_dict(sentiments, orient="index", columns=["Sentiment"])
df.index.name = "Ticker"


# Write the DataFrame to a CSV file
df.to_csv("stock_sentiments.csv")