StockPredictor

A comprehensive stock prediction application built with React.js frontend, Flask backend, and LSTM machine learning models for accurate 30-day stock price predictions.

Features

• Real-time Stock Charts: Interactive charts using Chart.js and Recharts for visualizing stock price data
• LSTM Predictions: Advanced machine learning models achieving ~87% accuracy on 30-day predictions
• User Authentication: Secure JWT-based authentication system
• Portfolio Management: Track your stock holdings and performance
• Price Alerts: Set custom alerts for price changes (above/below thresholds)
• Watchlist: Add and manage stocks you're interested in
• Reports: Generate PDF reports for portfolio performance, predictions, and transaction history
• Responsive Design: Works seamlessly on desktop, tablet, and mobile devices

Tech Stack

Frontend

• React.js with Hooks
• Axios for API calls
• Chart.js and Recharts for data visualization
• CSS3 with responsive design
• Context API for state management

Backend

• Flask with SQLAlchemy
• PostgreSQL database
• JWT for authentication
• Flask-CORS for cross-origin requests
• RESTful API design

Machine Learning

• TensorFlow/Keras LSTM models
• Pandas and NumPy for data processing
• Scikit-learn for preprocessing
• Yahoo Finance API for historical data

Installation

Prerequisites

• Python 3.8+
• Node.js 14+
• PostgreSQL
• Git

Backend Setup

1. Navigate to the backend directory:

   cd backend

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up the database:

   # Create PostgreSQL database named 'stockpredictor'
   # Update .env file with your database credentials

5. Run the Flask app:

   python app.py

Frontend Setup

1. Navigate to the frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Start the development server:

   npm start

ML Model Setup

1. Navigate to the ml_model directory:

   cd ml_model

2. Train the models:

   python train.py

Environment Variables

Create a .env file in the root directory with the following variables:

FLASK_ENV=development
SECRET_KEY=your-secret-key-here
JWT_SECRET_KEY=your-jwt-secret-key-here
DATABASE_URL=postgresql://username:password@localhost/stockpredictor
CORS_ORIGINS=http://localhost:3000
DEBUG=True

API Documentation

Authentication

• POST /api/auth/register - Register a new user
• POST /api/auth/login - Login user

Stocks

• GET /api/stocks/<symbol> - Get historical stock data

Predictions

• GET /api/predictions/<symbol> - Get 30-day price predictions

Watchlist

• GET /api/watchlist - Get user's watchlist
• POST /api/watchlist - Add stock to watchlist
• DELETE /api/watchlist/<item_id> - Remove from watchlist

Alerts

• GET /api/alerts - Get user's alerts
• POST /api/alerts - Create new alert
• DELETE /api/alerts/<alert_id> - Delete alert

Reports

• GET /api/reports/<report_type> - Generate PDF report

Usage

1. Register/Login to access the application
2. Search for stocks using the search bar on the dashboard
3. View real-time charts and predictions
4. Set up price alerts for stocks you're interested in
5. Add stocks to your watchlist
6. Generate reports for your portfolio and predictions

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

License

This project is licensed under the MIT License.