Algorithmic Trading
Financial Engineering
Data Science
Cont & Kukanov SOR Backtest
A quantitative finance research project implementing advanced algorithmic trading strategies. Focuses on order execution optimization across multiple trading venues using mathematical modeling.
Highlights
- Smart Order Router implementation
- Multi-venue execution optimization
- Cost minimization algorithm suite
- Comprehensive backtesting framework
Overview
Implementation of the Cont-Kukanov Smart Order Router (SOR) model for optimal order execution in fragmented equity markets. This research project applies academic finance theory to practical algorithmic trading scenarios.
Key Features
- Smart Order Routing: Algorithm to split orders across multiple trading venues
- Cost Minimization: Optimizes execution to minimize market impact and transaction costs
- Backtesting Framework: Historical data analysis to validate strategy performance
- Visualization Suite: Interactive Plotly charts for execution analysis
Technical Implementation
Built with Python's scientific computing stack: NumPy for numerical operations, Pandas for time-series data handling, SciPy for optimization, and Matplotlib/Plotly for visualization. Jupyter notebooks provide interactive analysis environment.
Research Applications
- Optimal execution in fragmented markets
- Transaction cost analysis (TCA)
- Market microstructure research
- Algorithmic trading strategy development
