TradePulse

A comprehensive desktop application for stock market analysis, trading, and portfolio management designed for day traders and financial analysts.

Features

• Data Grid: Historical data from Redline utility and live feed data
• Models Grid: ML models (ADM, CIPO, BICIPO) with performance metrics
• AI Module: AI-driven trading strategies and risk management
• Message Bus: ZeroMQ-based inter-module communication
• Multi-asset Support: Stocks, currencies, cryptocurrencies, and commodities

Architecture

• Modular, multi-process architecture
• Each module operates as standalone application
• Centralized Message Bus for communication
• Grid-based UI with interactive visualizations
• M3 Silicon (ARM64) optimized

Quick Start

Prerequisites

• Python 3.12+
• Conda or Mamba
• PostgreSQL (optional, for remote storage)

Installation

1. Clone the repository:

git clone <repository-url>
cd TradePulse

2. Create conda environment:

mamba env create -f environment.yml
conda activate tradepulse

3. Install dependencies:

pip install -r requirements.txt

4. Configure the application:

# Edit config.json with your settings

5. Run the application:

python main.py

Module Structure

TradePulse/
├── main.py                 # Main application entry point
├── module_manager.py       # Module management and orchestration
├── config.json            # Configuration file
├── data_grid/            # Data collection and visualization
├── models_grid/          # ML models and predictions
├── ai_module/            # AI trading strategies
├── utils/                # Shared utilities
├── tests/                # Test suite
└── docker-compose.yml    # Docker orchestration

Running Modules

🚀 Full Application (Recommended)

python main.py

This launches the complete TradePulse system with all modules running.

📊 Individual Modules

Data Grid (Data Fetching & Visualization)

python -m data_grid

What it does:

• Fetches historical and live market data
• Creates interactive charts (candlestick, line, volume)
• Exports data to various formats (CSV, Excel, JSON)
• Manages data sources and caching

Models Grid (Machine Learning Models)

python -m models_grid

What it does:

• Trains and evaluates ML models (ADM, CIPO, BICIPO)
• Generates predictions and performance metrics
• Manages model selection and feature importance
• Provides model comparison and analysis

AI Module (Trading Strategies)

python -m ai_module

What it does:

• Generates AI-driven trading strategies
• Manages risk assessment and position sizing
• Optimizes portfolio allocation
• Analyzes market conditions and confidence scores

🧪 Testing

Run All Tests

python -m pytest tests/

Run Specific Test Files

python -m pytest tests/test_integration.py
python -m pytest tests/test_components.py
python -m pytest tests/test_utilities.py

Run Tests with Coverage

python -m pytest tests/ --cov=. --cov-report=html

🐳 Docker (Alternative)

Start All Services

docker-compose up

Start Specific Services

docker-compose up data_grid models_grid ai_module

Run in Background

docker-compose up -d

View Logs

docker-compose logs -f

Configuration

Edit config.json to configure:

• Database connections (SQLite, PostgreSQL, DuckDB)
• API keys and data sources
• ML model parameters
• Trading parameters
• Visualization preferences
• Alert thresholds

Module Communication

All modules communicate through the Message Bus:

• Publish/Subscribe pattern using ZeroMQ
• Heartbeat monitoring for health checks
• Automatic reconnection on failures
• Message queuing for reliability

Development

Code Structure

• Maximum 200 lines per file (enforced)
• Shared utilities in utils/ directory
• Consolidated performance metrics
• No duplicate code across modules

Adding New Modules

1. Create module directory with __init__.py
2. Implement __main__.py entry point
3. Add to module_manager.py
4. Update config.json if needed

Running in Development Mode

# Set environment variable for development
export TRADEPULSE_DEV=1

# Run with debug logging
python -m data_grid --debug

Troubleshooting

Common Issues

1. Module Connection Failed

   • Check Message Bus is running
   • Verify network ports (default: 5555-5560)
   • Check firewall settings

2. Database Connection Error

   • Verify database credentials in config.json
   • Check database service is running
   • Test connection manually

3. ML Model Training Fails

   • Check available memory
   • Verify data quality and format
   • Check model parameters in config

Logs and Debugging

# View application logs
tail -f logs/tradepulse.log

# Run with verbose output
python main.py --verbose

# Check module health
python module_manager.py --health-check

Performance

System Requirements

• Minimum: 8GB RAM, 4 CPU cores
• Recommended: 16GB RAM, 8 CPU cores
• Storage: 10GB+ for data and models
• Network: Stable internet for live data

Optimization Tips

• Use SSD storage for database
• Enable data caching in config
• Adjust ML model complexity
• Monitor memory usage

License

[License information]

Support

For issues and questions, please refer to the documentation or create an issue in the repository.