Building Quantitative Trading Systems with Machine Learning
A structured program for developing algorithmic strategies using statistical models and data analysis
This course walks through the technical framework needed to design, test, and implement machine learning models for quantitative trading. You'll work with real market data, backtest strategies, and understand the practical challenges of applying ML to financial markets.
What the program covers
The curriculum is divided into three focused areas, each building on technical concepts and practical application. You'll move from foundational statistics through model development to deployment considerations.
Data Processing and Feature Engineering
Learn to clean financial data, handle missing values, normalize price series, and extract meaningful features from raw market information. You'll work with time series analysis, technical indicators, and alternative data sources to create predictor variables.
Model Development and Validation
Build regression models, classification systems, and ensemble methods for price prediction and signal generation. Focus on cross-validation techniques specific to time series, avoiding look-ahead bias, and understanding overfitting in financial contexts.
Strategy Implementation and Risk Management
Take models from research to production, implementing position sizing, stop-loss logic, and portfolio constraints. Learn backtesting frameworks, transaction cost modeling, and slippage estimation to evaluate real-world performance accurately.
Technical skills and practical application
The program emphasizes both the statistical foundations and the implementation details. You'll gain hands-on experience with Python libraries, data APIs, and backtesting platforms commonly used in quantitative finance.
Core Technical Skills
- Python for financial analysis (pandas, numpy, scikit-learn)
- Statistical modeling and hypothesis testing for market data
- Time series forecasting and autoregressive models
- Feature selection and dimensionality reduction techniques
- Cross-validation strategies for sequential data
- Performance metrics specific to trading systems
- Data visualization for model interpretation
Implementation and Testing
- Backtesting frameworks and historical simulation
- Transaction cost modeling and execution assumptions
- Portfolio construction and position sizing algorithms
- Risk-adjusted performance measurement (Sharpe, Sortino)
- Walk-forward analysis and out-of-sample testing
- API integration for market data feeds
- Code organization for research-to-production workflow
Ready to start building trading systems?
The program runs continuously with new cohorts beginning regularly. Each session includes live code reviews, Q&A sessions with practitioners, and access to a community of quantitative traders working on similar problems.
Weeks of structured content
Practical coding projects
Hours of video instruction