Recommended books

Reading that pairs well with building AI-driven strategies—covering foundations, quant history, and modern ML (educational references, not financial advice).

• Dark Pools

  Scott Patterson

  Follow-up to The Quants on market structure, dark liquidity, and how execution venues evolved—useful context next to microstructure-heavy blog topics.

• Machine Learning for Algorithmic Trading

  Stefan Jansen

  Hands-on Python workflows from data ingestion to strategy evaluation—aligned with the ML + backtesting stack discussed across our articles.

• The Quants

  Scott Patterson

  A narrative history of how mathematical models reshaped Wall Street—from early stat arb to the rise of systematic hedge funds.

• Advances in Financial Machine Learning

  Marcos López de Prado

  Practical methods for labeling, cross-validation, and feature importance tailored to financial time series.

• Algorithmic Trading

  Ernest Chan

  Hands-on quantitative strategy workflow: backtesting, execution, and risk—useful for practitioners moving from research to production.

• Trading and Exchanges

  Larry Harris

  Market microstructure and how orders interact—helpful background for execution and market making.

• Options, Futures, and Other Derivatives

  John Hull

  Classic reference for derivatives pricing models that underpin many systematic approaches.

• Statistical Arbitrage

  Andrew Pole

  Comprehensive guide to the stat-arb workflow—from instrument selection and spread modelling to execution; complements the pairs-trading and cointegration themes across our blog.

• Pairs Trading

  Ganapathy Vidyamurthy

  Rigorous quantitative treatment of mean-reversion pair strategies: cointegration tests, signal construction, and risk control—foundational reading for market-neutral quant desks.

Educational references only. MarketMaker.cc does not endorse any publisher; verify editions and applicability to your jurisdiction.