Book Recommendations

AFML

Advanced

Advances in Financial Machine Learning

Marcos López de Prado — 2018, Wiley

The most rigorous treatment of machine learning applied to finance. De Prado exposes the many ways standard ML workflows fail in financial settings — from feature engineering with financial time series to the combinatorial purged cross-validation method that avoids leakage. Essential chapters cover meta-labelling, fractional differentiation, and the triple-barrier method for labelling. A must-read if you're building data-driven strategies.

Machine Learning Backtesting Feature Engineering Portfolio Construction

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AT

Intermediate

Algorithmic Trading

Ernest P. Chan — 2013, Wiley

Ernest Chan's second book dives deep into mean-reversion and momentum strategies with real, implementable code examples in MATLAB and Python. Topics include Kalman filters for pairs trading, regime detection, and intraday momentum. Chan writes from experience as a practitioner, making this one of the most practically useful books in the genre. A great complement to his first book, "Quantitative Trading".

Mean Reversion Momentum Pairs Trading MATLAB

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PFF

Intermediate

Python for Finance

Yves Hilpisch — 2nd ed. 2019, O'Reilly

A comprehensive guide to financial applications in Python, covering everything from data management with pandas and NumPy to Monte Carlo simulation, derivatives pricing with Black-Scholes, and building algorithmic trading bots. Hilpisch's writing bridges the gap between finance theory and Python implementation cleanly. The second edition adds chapters on AI-based trading strategies and backtesting infrastructure.

Python Data Analysis Derivatives Pricing Backtesting

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MSM

Beginner

The Man Who Solved the Market

Gregory Zuckerman — 2019, Portfolio/Penguin

The definitive account of Jim Simons and Renaissance Technologies — the most successful quant fund in history. Zuckerman traces Simons' journey from Cold War codebreaker and mathematician to founder of the Medallion Fund, which has returned ~66% annually before fees since 1988. Beyond the finance, the book is a gripping story of brilliant, eccentric minds working together to solve one of the world's hardest problems: predicting markets.

Biography Quant Funds Renaissance Technologies

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OFD

Advanced

Options, Futures & Other Derivatives

John C. Hull — 11th ed. 2022, Pearson

Known simply as "Hull", this is the standard textbook for derivatives markets worldwide — used in MBA programmes, CFA prep, and by practitioners. It covers the mechanics of futures, swaps, and options markets in exhaustive detail, then moves into pricing with Black-Scholes, Greeks, volatility smiles, interest rate models, and credit risk. Dense but rewarding — the kind of reference you return to repeatedly.

Options Futures Derivatives Pricing Risk Management

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IBB

Beginner

Inside the Black Box

Rishi K. Narang — 2nd ed. 2013, Wiley

A rare non-technical look inside quantitative hedge funds. Narang demystifies the black box by walking through every layer — alpha models, risk models, transaction cost models, portfolio construction, and execution. Written for anyone who wants to understand how systematic funds actually work without needing a PhD in mathematics. An excellent starting point before diving into more technical books.

Quant Funds Alpha Models Risk Models Execution

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MLAT

Advanced

Machine Learning for Algorithmic Trading

Stefan Jansen — 2nd ed. 2020, Packt

One of the most comprehensive hands-on books covering the full ML pipeline for trading — from data sourcing and alternative data to NLP on earnings calls, deep learning for price prediction, and reinforcement learning for execution. All code examples in Python. At 800+ pages, it is essentially a reference textbook for the modern quant developer. Pairs excellently with De Prado's more theoretical approach.

Machine Learning NLP Deep Learning Python

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WQ

Beginner

The Quants

Scott Patterson — 2010, Crown Business

A gripping narrative of the rise and near-fall of the quant world during the 2007–2008 financial crisis. Patterson profiles the key players — Ed Thorp, Peter Muller, Ken Griffin, Cliff Asness — and traces how their models, built on similar assumptions, all broke down simultaneously in the "Quant Quake" of August 2007. Essential context for understanding systemic risk in quantitative strategies.

Biography Financial Crisis Quant Funds Systemic Risk

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