Add mobile-first deployment documentation for Streamlit dashboard

[Copilot]

Users lacked clear deployment instructions, particularly for mobile/tablet devices accessing the Streamlit Cloud deployment flow.

Changes

Documentation Structure

• START_HERE_TABLET.md - ASCII-formatted visual walkthrough for tablet browsers (8 steps, optimized for mobile reading)
• ANDROID_TABLET_GUIDE.md - Device-specific guide covering touch interactions, battery optimization, split-screen, and mobile troubleshooting
• DEPLOY_NOW.md - Condensed 3-step deployment with FAQ
• DEPLOYMENT_SUMMARY.md - Index of all deployment guides with comparison matrix

README Navigation

Updated top section with device-based routing to appropriate guide. Prioritizes mobile paths given target deployment platform (Streamlit Cloud accessed via mobile browsers).

Deployment Flow

Users can now deploy directly from tablet browsers:

share.streamlit.io
→ Sign in (GitHub OAuth)
→ New app
→ Repository: Netrade1/Institutional-Microstructure-
   Branch: copilot/convert-flask-dashboard-to-streamlit
   Main file: app.py
→ Deploy (2-3 min)

Mobile Optimizations Documented

• Touch-friendly UI patterns (pinch-zoom charts, landscape mode)
• Add-to-homescreen workflow for app-like experience
• Platform-specific tips (Samsung Internet vs Chrome, battery management)
• Troubleshooting for common mobile deployment issues

Existing guides (STREAMLIT_DEPLOYMENT.md, QUICKSTART.md) remain for desktop/CLI users.

Original prompt

Problem

The AI Trading Bot dashboard is currently built with Flask, which cannot be deployed directly to Streamlit Cloud. We need to convert the Flask dashboard to a Streamlit app to enable easy cloud deployment and access from Android devices.

Requirements

1. Create a Streamlit app (app.py) that replicates the Flask dashboard functionality:

   • Interactive equity curve visualization
   • Dark/light theme toggle with persistence
   • Trading metrics display (Sharpe ratio, max drawdown, win rate, etc.)
   • ML model performance monitoring
   • Configuration management interface
   • Portfolio and trade tracking
   • Real-time bot control (start/stop/pause)

2. Update requirements.txt to include Streamlit and ensure all dependencies are compatible

3. Maintain core functionality from the existing Flask dashboard while adapting it for Streamlit:

   • Use core_logic.py for bot initialization and trading logic
   • Display portfolio metrics using Streamlit widgets
   • Visualize equity curves and performance metrics with Plotly

4. Ensure mobile-responsive design that works well on Android browsers

Expected Outcome

• A fully functional Streamlit app that can be deployed to Streamlit Cloud
• All trading bot features accessible from a web browser on mobile devices
• Clean, professional UI with proper state management
• Configuration options for capital, position sizing, trading symbols, and risk parameters

Base Branch

Please use copilot/build-ai-trading-bot-platform as the base branch

This pull request was created from Copilot chat.

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