Sentiment Stocker 📈

Sentiment Stocker is a next-generation financial intelligence terminal that combines technical analysis with AI-driven sentiment tracking. It aggregates news from multiple sources, analyzes market narratives, and provides a holistic view of a stock's health.

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🚀 Key Features

🧠 Sentinel Intelligence (Sentiment Lab)

• Hybrid Fear & Greed Gauge: A unified "Market Emotion Index" (0-100) combining Technical RSI (Price Momentum) and News Sentiment (Narrative).
  • Calculation: (RSI + Normalized Sentiment) / 2
  • Visuals: Custom gauge with color-coded zones (Extreme Fear to Extreme Greed).
• Narrative Analysis: Breaks down news into actionable topics (e.g., "Earnings", "Regulatory", "Macro", "M&A").
  • ✨ Personalizable: Add your own custom topics to track specific storylines.
• Supply Chain Ripple Effects: Monitors upstream sectors (e.g., Raw Materials, Shipping) to predict impact on the target stock.

📋 Smart Watchlist (Sidebar)

• Live Sentiment Tracking: Pin your favorite stocks to the sidebar.
• Real-Time Scores: View the sentiment score alongside the ticker symbol for an instant portfolio health check.
• Performance Optimized: Uses a lightweight "Quick Scan" mode to prevent dashboard lag.

📰 Advanced News Engine

• Strict Quality Filter: Toggle on "🛡️ Only Verified Sources" to filter out noise.
  • Tier 1 (Verified): Bloomberg, Reuters, WSJ, CNBC.
  • Tier 2 (Standard): MarketWatch, Yahoo Finance, Benzinga.
  • Tier 3 (Unverified): Reddit, Social Media, Forums.
• Competitor Analysis: Automatically identifies and analyzes sentiment for top 3 peers.
• URL Scanner: Paste any article link to analyze its specific sentiment score instantly.

📊 Professional Visualization

• Market Heatmap: Real-time view of global sector performance (Tech, Finance, Energy, etc.).
• Price + Sentiment Overlay: Interactive charts overlaying news sentiment trends on top of price action.

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🛠️ Installation

1. Clone the repository:

   git clone https://github.com/Manan1511/Sentiment_Stocker.git
   cd Sentiment_Stocker

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   python -m streamlit run main.py

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⚙️ Configuration

• API Keys: The app uses finnhub-python for some data. A demo key is included, but for heavy usage, set your own:
  • Set environment variable FINNHUB_API_KEY or replace it in main.py.
• Data Sources: Uses yfinance for market data (free, no key required).

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📸 Screenshots

Dashboard	Sentiment Lab
Real-time price & sentiment	Hybrid Fear & Greed Gauge

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📝 License

MIT License. Free for educational and personal use.