A structured curriculum in factor investing.

Build your first systematic framework

Understand factor families, build ranking systems and complete a first operational deployment.

• Understand factor families and their empirical foundations.
• Explore factors inside Portfolio123 with practical workflows.
• Build a universe, conduct out-of-sample checks and develop a robust ranking system.
• Deploy a framework operationally and apply elementary risk controls.

Risk management and robustness

Convert a working framework into one that holds up under realistic conditions.

• Reduce concentration risk and introduce portfolio guardrails.
• Apply robustness checks and identify overfitting.
• Construct long-short structures with controlled exposures.
• Design macro indicators and antifragile portfolio sleeves.

Machine learning applications

Validation, explainability, regime stress-testing and operational monitoring.

• Bias-variance trade-offs, generalization and the nature of edge.
• Feature engineering, redundancy control and walk-forward validation.
• Regime, liquidity and noise stress-testing.
• Operational monitoring, drift detection and retraining policy.

Review, diagnostics and framework design

Single sessions on a specific problem in your own research, booked outside the level structure.

• Methodology review of a ranking system, screen or backtest you have already built.
• Code review of your research scripts, in Python or in Portfolio123 syntax.
• Framework design worked through with you, from universe to rebalance rules.
• Diagnostics on a specific problem you bring, from data handling to validation.