AI-Powered Stock Prediction System

This project is a comprehensive stock market prediction application that combines Deep Learning (LSTM) for technical analysis with Large Language Models (GPT via OpenRouter) for fundamental news sentiment analysis. It provides multi-timeframe forecasts (7-day, 30-day, 6-month) and actionable investment recommendations.

🚀 Features

• Dual-Engine Analysis:
  • Technical: LSTM (Long Short-Term Memory) neural networks trained on historical price data.
  • Fundamental: AI-powered sentiment analysis of real-time news articles using GPT-3.5/4.
• Multi-Timeframe Predictions: Short-term (7 days), medium-term (30 days), and long-term (6 months).
• Consolidated Insights: Combines technical and fundamental signals into a weighted final prediction with confidence scores.
• Interactive Dashboards:
  • Streamlit UI: A data-science focused dashboard with detailed charts and metrics.
  • Flask UI: A modern web interface for easy access.
• Comprehensive Reporting: Detailed breakdown of market trends, sector performance, volatility events, and buy/sell signals.

🛠️ Installation

1. Clone the repository (if applicable) or navigate to the project directory.

2. Install dependencies:

   pip install -r requirements.txt

3. Set up Environment Variables: Create a .env file in the root directory with your API keys:

   OPENROUTER_API_KEY=your_openrouter_api_key
   NEWSORG_API_KEY=your_newsapi_key
   STOCK_API_BASE_URL=http://127.0.0.1:8000

   Note: You can get a free NewsAPI key from newsapi.org.

🖥️ Usage

You can run the application in three ways depending on your preference:

Option 1: Streamlit Dashboard (Recommended for Analytics)

This provides the most detailed visualization and control.

1. Start the integrated API server:

   python Models/integrated_api.py

2. In a separate terminal, launch the Streamlit app:

   streamlit run streamlit_ui.py

3. Open your browser at http://localhost:8501.

Option 2: Flask Web Application

A simplified web interface.

1. Start the Flask server:

   python flask_app.py

2. Open your browser at http://localhost:5000.

Option 3: Command Line Interface (CLI)

Run a quick analysis directly in your terminal.

1. Run the master predictor:

   python master_predictor.py

2. Enter the stock symbol (e.g., RELIANCE.NS, TCS.NS) when prompted.

🧠 Workflow

1. Data Ingestion:

   • Fetches historical stock candle data via the internal API.
   • Retrieves the latest news articles for the target company via NewsAPI.

2. Technical Analysis (LSTM Pipeline):

   • Preprocesses data with dynamic lookback windows.
   • Trains a custom LSTM neural network on multiple timeframes (Daily, Weekly, Monthly).
   • Generates future price sequences for 7, 30, and 180 days.

3. Fundamental Analysis (News Pipeline):

   • Sentiment Analysis: Evaluates news articles for positive/negative sentiment, weighted by recency.
   • Market Context: Analyzes broader sector trends and market conditions.
   • Event Detection: Identifies catalysts like earnings, mergers, or regulatory changes.
   • LLM Processing: Uses OpenRouter (GPT) to synthesize this information into quantitative impact scores.

4. Ensemble Prediction:

   • Combines the LSTM price targets with the News Sentiment impact.
   • Applies weighting (default: 50% Technical / 50% Fundamental) to calculate the final target price.
   • Calculates a Confidence Score based on the agreement between the two models.

5. Output:

   • Generates a final recommendation: STRONG BUY, BUY, HOLD, SELL, or STRONG SELL.
   • Visualizes the predicted price path and key metrics.

📂 Project Structure

• flask_app.py: Main entry point for the Flask web application.
• streamlit_ui.py: Main entry point for the Streamlit dashboard.
• master_predictor.py: CLI orchestration script.
• Models/: Contains the core logic.
  • stock_prediction.py: LSTM model implementation.
  • news.py: News fetching and LLM analysis logic.
  • integrated_api.py: FastAPI backend for the Streamlit app.
• Api/: Internal data API (likely for raw stock data).

⚠️ Disclaimer

This tool is for educational purposes only. Always conduct your own research before making investment decisions.