Portfolio Optimization Through Machine Learning
What You'll Actually Do
This workshop gets you working directly with real portfolio data and machine learning models. You'll start by understanding how traditional optimization works, then move into implementing neural networks and ensemble methods to predict asset returns and manage risk.
The focus is on practical application. You'll build models that handle market volatility, test different rebalancing strategies, and learn to interpret results in a way that makes sense for actual investment decisions. No theory for theory's sake—just techniques you can use.
By the end, you'll have working code for portfolio optimization, understand how to validate model performance against real market conditions, and know when machine learning helps versus when simpler methods work better. The assignments walk you through feature engineering, backtesting frameworks, and risk-adjusted performance metrics.
1 Foundation Setup
Set up your environment and work through the basics of portfolio theory. You'll implement mean-variance optimization from scratch to understand what machine learning is trying to improve.
- Covariance matrix estimation
- Sharpe ratio calculation
- Constraint handling
2 Predictive Models
Build and train models that forecast returns and volatility. You'll work with LSTM networks for time series, random forests for feature importance, and gradient boosting for robust predictions.
- Time series feature engineering
- Model validation techniques
- Ensemble prediction strategies
3 Risk Management
Integrate risk models into your optimization framework. Learn to handle tail risk, drawdown constraints, and portfolio rebalancing under transaction costs and market impact.
- Value-at-Risk estimation
- Conditional VaR modeling
- Dynamic allocation strategies
4 Backtesting Framework
Build a complete backtesting system that accounts for real-world constraints. You'll test your strategies across different market regimes and learn to spot overfitting before it costs you.
- Walk-forward validation
- Performance attribution
- Regime detection methods
Flexible Schedule
Work through assignments at your own pace with open enrollment
Real Datasets
Practice with actual market data and institutional-grade tools
Code Reviews
Get feedback on your implementations and optimization approaches
Project Portfolio
Build complete models you can show in interviews or client meetings
How It Works
Each week covers a specific aspect of machine learning for portfolio management. You'll receive step-by-step assignments that build on previous work, starting with data preprocessing and ending with a fully functional optimization system.
The assignments use Python with common libraries like pandas, scikit-learn, TensorFlow, and PyPortfolioOpt. You'll work in Jupyter notebooks initially, then transition to proper module structures as complexity increases. All code is yours to keep and modify.
Support happens through code review sessions where we examine your implementations, discuss alternatives, and troubleshoot issues. You'll also get access to reference implementations and detailed documentation explaining the mathematical foundations behind each technique.
The final project involves building a complete backtesting framework that runs your optimized portfolios through historical data, generating performance reports with metrics like information ratio, maximum drawdown, and turnover analysis. This becomes a tangible demonstration of your skills.