A modern, intuitive web application for predicting stock prices using LSTM neural networks. Built with cutting-edge technology to make stock prediction accessible, beautiful, and surprisingly accurate.
Because seeing is believing (and reading is for chumps).
Click Below👇
- 🧠 Deep Learning Power: Advanced LSTM neural networks that actually learn from market patterns
- 🌐 Modern Web Interface: Sleek Streamlit-based UI that puts old desktop apps to shame
- 📊 Interactive Charts: Beautiful Plotly visualizations that respond to your every hover
- 📈 Real-time Data: Live stock data from Yahoo Finance, because fresh data makes better predictions
- 🎯 Smart Forecasting: Predict multiple days ahead with configurable parameters
- 📱 Responsive Design: Works perfectly on desktop, tablet, and mobile
- ⚡ Lightning Fast: Optimized processing with progress tracking
- 🔮 Future Vision: See tomorrow's prices today (with scientific disclaimers, of course)
- Double-click
run_app.bat - Wait for your browser to open automatically
- Start predicting!
# Install dependencies
pip install -r requirements.txt
# Launch the app
streamlit run main.pyUse the sidebar controls to set up your prediction:
- 📈 Stock Ticker: Enter any stock symbol (AAPL, GOOGL, TSLA, etc.)
- 📅 Date Range: Choose your historical data timeframe
- 🔢 Window Size: Days to look back (10-100, default: 60)
- 🔮 Forecast Days: Days to predict ahead (1-30, default: 7)
- 🧠 Training Epochs: Model training intensity (5-50, default: 20)
- Click the "🚀 Run Prediction" button
- Watch the real-time progress bar
- See your results unfold beautifully
- 📊 Metrics Dashboard: RMSE, MAE, and accuracy scores
- 📈 Interactive Charts: Hover, zoom, and explore your predictions
- 🔮 Future Forecast: See predicted prices with confidence intervals
- 📉 Training History: Monitor how well your model learned
- 📥 Data Acquisition: Fetches real-time stock data from Yahoo Finance API
- 🧹 Data Preprocessing: Cleans and normalizes data using MinMaxScaler
- 📊 Sequence Generation: Creates time-series windows for pattern recognition
- ✅ Data Validation: Ensures data quality and completeness
- 🧠 Neural Network: 2-layer LSTM with 50 units each
- 🎯 Dropout Layers: 20% dropout to prevent overfitting
- ⚙️ Optimization: Adam optimizer with mean squared error loss
- 📈 Training: Configurable epochs with validation split
- 🔮 Forecasting: Multi-step ahead predictions using recursive approach
- 📊 Metrics: RMSE, MAE, and custom accuracy calculations
- 📈 Visualization: Interactive Plotly charts with hover details
- 💾 State Management: Efficient memory usage with Streamlit caching
- 🌐 Streamlit: Modern web app framework
- 🧠 TensorFlow/Keras: Deep learning powerhouse
- 📊 Plotly: Interactive visualization magic
- 📈 yfinance: Real-time stock data
- 🔢 NumPy/Pandas: Data manipulation masters
- 🎯 scikit-learn: ML utilities and metrics
streamlit>=1.28.0
plotly>=5.15.0
tensorflow>=2.13.0
yfinance>=0.2.0
pandas>=2.0.0
numpy>=1.24.0
scikit-learn>=1.3.0
matplotlib>=3.7.0
- 📅 Use 2+ years of historical data for better training
- 🎯 Popular stocks (AAPL, MSFT, GOOGL) have cleaner data
- ⏰ Avoid recent IPOs - they lack historical patterns
- 📈 Window Size: 60 days works well for most stocks
- 🔄 Epochs: Start with 20, increase for better accuracy
- 📊 Forecast Days: 7-14 days for reliable predictions
- ⚡ Faster Training: Reduce epochs for quick experiments
- 🎯 Better Accuracy: Increase epochs and window size
- 💾 Memory: Close other apps for large datasets
- 🚨 Not Financial Advice: This is an educational tool, not investment guidance
- 📊 Past Performance ≠ Future Results: Markets are unpredictable
- 💰 Risk Management: Never invest more than you can afford to lose
- 🤝 Consult Professionals: Always seek qualified financial advice
- 🎲 Market Randomness: No model can predict black swan events
- 📈 Short-term Volatility: Daily predictions are inherently noisy
- 🌍 External Factors: News, politics, and sentiment affect prices
- 🔄 Model Retraining: Markets evolve, models need updates
| Problem | Solution |
|---|---|
| "No data found" | ✅ Check ticker symbol spelling |
| Slow performance | ✅ Reduce epochs or date range |
| Import errors | ✅ Run pip install -r requirements.txt |
| Memory issues | ✅ Close other applications |
| Browser won't open | ✅ Manually go to http://localhost:8501 |
- 📖 Check the logs in the terminal for error details
- 🔄 Restart the app if it becomes unresponsive
- 🧹 Clear browser cache if charts don't load
- 💻 Update packages with
pip install --upgrade -r requirements.txt
We welcome contributions! Here's how you can help:
- 🐛 Bug Reports: Found an issue? Let us know!
- 💡 Feature Requests: Have ideas? We'd love to hear them!
- 🔧 Code Improvements: PRs are always welcome
- 📚 Documentation: Help make the docs even better
- 🎨 UI/UX: Make the interface more beautiful
# Fork the repository
git clone https://github.com/yourusername/stock-predictor.git
cd stock-predictor
# Create virtual environment
python -m venv .venv
source .venv/bin/activate # or .venv\Scripts\activate on Windows
# Install development dependencies
pip install -r requirements.txt
# Make your changes and test
streamlit run main.pyMIT License - Feel free to use, modify, and distribute this code. Just remember to give credit where it's due!
- 📊 Yahoo Finance for providing free stock data
- 🧠 TensorFlow team for the amazing ML framework
- 🌐 Streamlit for making web apps ridiculously easy
- 📈 Plotly for beautiful, interactive charts
- 🎯 The open-source community for endless inspiration
Launch the app and start exploring the fascinating world of stock prediction!
Built with ❤️ by developers who believe that making complex AI accessible is the future of technology.
May your predictions be accurate and your portfolios ever-growing! 📈✨