# 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.