Build a Stock-News Correlator

// 1. Fetch stock data
const stockRes = await fetch(
  'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=AAPL&apikey=YOUR_API_KEY'
);
const stockData = await stockRes.json();
const prices = Object.entries(stockData['Time Series (Daily)']).slice(0, 7);

// 2. Fetch company news
const newsRes = await fetch(
  'https://gnews.io/api/v4/search?q=Apple+AAPL&token=YOUR_API_KEY&max=10'
);
const { articles } = await newsRes.json();

// 3. Analyze sentiment of each article
const sentiments = await Promise.all(articles.map(async (a) => {
  const res = await fetch('https://api.cloudmersive.com/nlp-v2/analytics/sentiment', {
    method: 'POST',
    headers: { 'Apikey': 'YOUR_KEY', 'Content-Type': 'application/json' },
    body: JSON.stringify({ TextToAnalyze: a.title + ' ' + a.description })
  });
  return res.json();
}));

// 4. Correlate sentiment with price changes
const avgSentiment = sentiments.reduce((sum, s) => sum + (s.SentimentScoreResult || 0), 0) / sentiments.length;
const priceChange = ((prices[0][1]['4. close'] - prices[6][1]['4. close']) / prices[6][1]['4. close'] * 100).toFixed(2);

console.log(`AAPL 7-day change: ${priceChange}%`);
console.log(`Avg news sentiment: ${avgSentiment.toFixed(3)}`);
console.log(`Correlation: ${avgSentiment > 0 && priceChange > 0 ? 'Aligned' : 'Divergent'}`);