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22d • General discussion
My book recommendations!
Each book covers a different failure mode.
Carver gives you the operational discipline to actually run a system instead of just theorizing about one.
Masters stops you from fooling yourself with overfit indicators before you even get to ML.
López de Prado stops you from fooling yourself with overfit ML.
Ilmanen stops you from expecting returns that don't exist in the data.
And Wilmott sits underneath all of it as the theory backstop — reminding you that every model is an approximation, and the moment you forget that is the moment you blow up.
The Reading List
1. Advances in Financial Machine Learning — Marcos López de Prado
The book that put "your backtest is probably lying to you" into a formal, citable framework. Covers meta-labeling, purged cross-validation, fractional differentiation, and why most published trading strategies don't survive out of sample. Dense, math-heavy, and worth every hour.
2. Expected Returns — Antti Ilmanen
Not a trading book — an asset-return book. A brutally thorough survey of what actually drives long-run returns across equities, bonds, credit, and alternatives, and why most investor intuition about "risk premia" is wrong or overstated. This is the book that keeps you honest about what edge is even possible.
3. Systematic Trading — Robert Carver
The most practical entry on this list. Carver takes what he ran at AHL and turns it into an actual playbook: position sizing, instrument diversification, forecast scaling, and how to build a rules-based system you can run without flinching. Less theory, more "here's how you actually do it."
4. Statistically Sound Indicators for Financial Market Prediction — Timothy Masters
The book most people skip and shouldn't. Before you build any model, you need indicators that carry real information — not noise dressed up as signal. Masters (ex-spy-photo analyst turned quant statistician) gives you the tools to test whether an indicator's edge is real or just overfit luck.
5. Frequently Asked Questions in Quantitative Finance — Paul Wilmott
The reference you keep on the desk. Short, sharp answers to the theory questions that trip people up — options pricing, model risk, the assumptions embedded in every quant formula. Wilmott built the CQF and then spent a career warning people not to trust their own models blindly; this book is that skepticism in FAQ form.
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My book recommendations!
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