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+# Manual Trade Annotation UI
+
+A web-based interface for manually marking profitable buy/sell signals on historical market data to generate training test cases for machine learning models.
+
+## Overview
+
+This tool allows traders to:
+- View multi-timeframe candlestick charts
+- Navigate through historical data
+- Mark entry and exit points for trades
+- Generate test cases in realtime format
+- Train models with annotated data
+- Simulate inference to measure model performance
+
+## Project Structure
+
+```
+ANNOTATE/
+├── web/                      # Web application
+│   ├── app.py               # Main Flask/Dash application
+│   ├── templates/           # Jinja2 HTML templates
+│   │   ├── base_layout.html
+│   │   ├── annotation_dashboard.html
+│   │   └── components/
+│   └── static/              # Static assets
+│       ├── css/
+│       ├── js/
+│       └── images/
+├── core/                    # Core business logic
+│   ├── annotation_manager.py
+│   ├── training_simulator.py
+│   └── data_loader.py
+├── data/                    # Data storage
+│   ├── annotations/
+│   ├── test_cases/
+│   └── training_results/
+└── tests/                   # Test files
+```
+
+## Installation
+
+```bash
+# Install dependencies (if not already installed)
+pip install dash plotly pandas numpy
+
+# Run the application
+python TESTCASES/web/app.py
+```
+
+## Usage
+
+1. **Start the application**: Run `python TESTCASES/web/app.py`
+2. **Open browser**: Navigate to `http://localhost:8051`
+3. **Select symbol and timeframe**: Choose trading pair and timeframes to display
+4. **Navigate to time period**: Use date picker or scroll to find market conditions
+5. **Mark trades**: Click on chart to mark entry point, click again for exit
+6. **Generate test cases**: Click "Generate Test Case" to create training data
+7. **Train models**: Select model and click "Train" to run training session
+8. **Simulate inference**: Click "Simulate" to test model performance
+
+## Features
+
+- Multi-timeframe synchronized charts (1s, 1m, 1h, 1d)
+- Interactive trade marking with P&L calculation
+- Test case generation in realtime format
+- Model training integration
+- Inference simulation with performance metrics
+- Session persistence and auto-save
+- Dark theme UI
+
+## Integration with Main System
+
+This sub-project is designed to be self-contained but can be integrated with the main trading system:
+
+```python
+# Import annotation manager in main system
+from TESTCASES.core.annotation_manager import AnnotationManager
+
+# Import training simulator
+from TESTCASES.core.training_simulator import TrainingSimulator
+
+# Use generated test cases in training
+test_cases = annotation_manager.get_test_cases()
+```
+
+## Configuration
+
+Configuration is loaded from the main `config.yaml` file. The application uses:
+- Data provider settings for historical data access
+- Model paths for training integration
+- Symbol and timeframe configurations
+
+## Development
+
+To add new features:
+1. Update requirements in `.kiro/specs/manual-trade-annotation-ui/requirements.md`
+2. Update design in `.kiro/specs/manual-trade-annotation-ui/design.md`
+3. Add tasks to `.kiro/specs/manual-trade-annotation-ui/tasks.md`
+4. Implement changes following the task list
+
+## License
+
+Part of the AI Trading System project.