STREAMLIT_DEPLOYMENT.md

Streamlit Cloud Deployment Guide

Overview

This guide will help you deploy your algorithmic trading system to Streamlit Cloud.

Files Created for Deployment

1. streamlit_app.py (Root Entry Point)

• Main entry point for Streamlit Cloud
• Imports and runs the UI from the ui/ directory

2. .streamlit/config.toml

• Streamlit configuration for deployment
• Optimized for cloud deployment

3. packages.txt

• System dependencies for the deployment environment

4. Updated requirements.txt

• Fixed dependencies for cloud deployment
• Removed problematic packages

Deployment Steps

1. Push to GitHub

git add .
git commit -m "feat: prepare for Streamlit Cloud deployment"
git push origin main

2. Deploy to Streamlit Cloud

1. Go to share.streamlit.io
2. Sign in with your GitHub account
3. Click "New app"
4. Configure your app:
   • Repository: your-username/algorithmic_trading
   • Branch: main
   • Main file path: streamlit_app.py
   • App URL: Choose your preferred URL

3. Environment Variables (Optional)

If you want to use real API keys, add these in Streamlit Cloud:

• ALPACA_API_KEY
• ALPACA_SECRET_KEY
• ALPACA_BASE_URL

Deployment Features

✅ Deployment Mode

• The app automatically detects deployment environment
• Falls back to demo mode if modules aren't available
• Shows sample data and mock trading functionality

✅ Error Handling

• Graceful handling of missing dependencies
• Informative error messages
• Fallback functionality

✅ Performance Optimized

• Minimal dependencies
• Efficient imports
• Cloud-optimized configuration

Troubleshooting

Common Issues

1. Import Errors

   • The app now handles missing modules gracefully
   • Check the browser console for specific error messages

2. Dependency Issues

   • All dependencies are now properly specified in requirements.txt
   • System packages are included in packages.txt

3. Configuration Issues

   • The app uses a default configuration in deployment mode
   • No external files are required

Debug Mode

To debug deployment issues:

1. Check the Streamlit Cloud logs
2. Look for error messages in the browser console
3. Verify all files are properly committed to GitHub

Local Testing

Test the deployment version locally:

streamlit run streamlit_app.py

Features Available in Deployment

✅ Working Features

• Configuration loading (demo mode)
• Data visualization with sample data
• Chart generation
• UI navigation
• Mock trading interface

⚠️ Limited Features

• Real API connections (requires environment variables)
• Live trading (demo mode only)
• Model training (simulated)

Next Steps

1. Deploy to Streamlit Cloud using the steps above
2. Test the deployment to ensure everything works
3. Add environment variables for real API access
4. Monitor the deployment for any issues

Support

If you encounter issues:

1. Check the Streamlit Cloud logs
2. Verify all files are committed to GitHub
3. Test locally with streamlit run streamlit_app.py
4. Review the error handling in the code

The deployment is now ready! 🚀