READING · SHELF · v6.0CURRENTLY · QUEUE · REFERENCE SHELF
Reading. Re-reading. Reference shelf.
The re-reads matter more than the first reads.
A dated, opinionated list of what I'm reading and re-reading. Public-data only. no proprietary data sources, no NDA-protected materials. Inspired by Derek Sivers's/book shelf and the institutional library conventions at AQR / Jane Street.
CHRISTIAN.T.MACIONUTC+8SHELF · v6.0PUBLIC DATAOWNER-VERIFIED
currently · 2026-08-09
On the desk right now.
Advances in Financial Machine Learning
The textbook that names everything I've built a backtest around: deflated Sharpe, PBO, CSCV, fractional differentiation. Re-read twice a year.
The Deflated Sharpe Ratio
The 12-page paper that started it. Every multi-trial strategy I ship gets the DSR test before deploy.
Algorithmic Trading
The textbook that taught me how to write a backtest that doesn't lie. Re-read every time I touch a new cost model.
Trading and Exchanges
Market microstructure for the practitioner. The chapter on order types is the only correct one I have ever read.
Hamlet
Not finance. But: 'There is nothing either good or bad, but thinking makes it so.' Worth re-reading between every drawdown.
A Man for All Markets
The autobiographical proof that an academic with a card-counting edge can survive translation to a hedge fund. The chapter on Kelly sizing is the reason I cap every vol-target at 0.5× Kelly.
in queue
In the queue.
The Mathematics of Financial Derivatives
Re-deriving the Greeks by hand. Slow, deliberate. The only way the math actually sticks.
Heard on the Street
Quantitative interview prep, but the chapters on Brownian motion are a clean refresher before each new factor model.
Antifragile
Optionality, convexity, and the difference between 'robust' and 'antifragile'. Useful for sizing.
reference shelf · permanent
Permanent reference.
Numerical Recipes
Open the relevant chapter before using any optimizer. Saves hours of debugging local-minima surprises.
Information Theory, Inference, and Learning Algorithms
The KL-divergence chapter is the cleanest derivation I know. The RAG eval gate borrows the cross-entropy framing.
Reinforcement Learning
The reference. The reflect-revise agent is a degenerate policy-gradient loop over a single trajectory.
policy
What is and isn't here.
- Public data only. Every book listed is either a public-domain text, a publicly-sold textbook, or an open-access paper.
- No proprietary material. I do not list strategy-specific books from NDA-protected desks. If you saw it referenced there, that's the only category I'd ask you not to ask about.
- Updated quarterly. This page refreshes with the queue when a book finishes its turn on the desk. Last refresh: 2026-08-09.