gogo2/TESTCASES

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

TESTCASES/
├── 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

# 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:

# 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.