Execution Intelligence API

A quant-grade execution system that minimizes slippage, market impact, and adverse selection when executing large trades. Built to help hedge funds and quant shops preserve alpha through adaptive execution strategies.

🎯 Overview

The Execution Intelligence API provides an adaptive execution engine that reacts to real-time market conditions and produces measurable cost savings compared to naive execution strategies (TWAP/VWAP). The system combines sophisticated market microstructure models with machine learning to optimize trade execution.

Key Features

• Adaptive Execution Strategies: Market Order, TWAP, VWAP, POV with dynamic optimization
• Real-time Market Impact Modeling: Order book imbalance, spread analysis, volatility prediction
• Cost Optimization: Minimizes implementation shortfall through intelligent order slicing
• Backtesting Framework: Comprehensive simulation with slippage models and commission handling
• Production-Ready API: FastAPI endpoints for seamless integration

🚀 Quick Start

Installation

# Clone the repository
git clone <repository-url>
cd Quantinent

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Running the API

# Start the FastAPI server
uvicorn src.api.main:app --reload --host 0.0.0.0 --port 8000

# API will be available at http://localhost:8000
# Interactive docs at http://localhost:8000/docs

Basic Usage

import requests

# Optimize execution for a large order
response = requests.post("http://localhost:8000/optimize_execution", json={
    "symbol": "AAPL",
    "order_size": 100000,
    "urgency": "medium",
    "side": "buy",
    "market_data": {...}
})

execution_schedule = response.json()
print(f"Expected cost savings: {execution_schedule['cost_savings']}%")

🏗️ Architecture

The system is built with a modular architecture:

• Core Engine (src/core/): Execution strategies and optimization algorithms
• Market Models (src/models/): Price impact and slippage prediction
• Backtester (src/backtester/): Historical simulation framework
• API Layer (src/api/): FastAPI endpoints and request handling
• Utilities (src/utils/): Data processing and analysis tools

📊 Performance Metrics

The system provides comprehensive execution quality metrics:

• Implementation Shortfall: Actual vs. expected execution cost
• Cost Savings: Percentage improvement over baseline strategies
• Fill Quality: Market impact and slippage analysis
• Execution Alpha: Risk-adjusted performance metrics

🔬 Research & Methodology

This system implements state-of-the-art execution algorithms based on:

• Market microstructure theory
• Order book dynamics and liquidity modeling
• Machine learning for short-term price prediction
• Adaptive optimization under uncertainty

📚 Documentation

• Architecture Guide - System design and module flow
• Methodology Guide - Mathematical foundations and finance theory
• API Reference - Complete endpoint documentation
• Demo Script - Video walkthrough outline

🧪 Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=src

# Code quality checks
black src/
flake8 src/
mypy src/

📈 Examples

See the notebooks/ directory for Jupyter notebooks demonstrating:

• Strategy backtesting and comparison
• Market impact analysis
• Parameter optimization
• Performance benchmarking

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes with proper tests
4. Ensure code quality checks pass
5. Submit a pull request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🔗 References

• Almgren, R., Thum, C., Hauptmann, E., & Li, H. (2005). Direct estimation of equity market impact
• Cartea, Á., & Jaimungal, S. (2012). Optimal execution with limit and market orders
• Obizhaeva, A. A., & Wang, J. (2013). Optimal trading strategy and supply/demand dynamics