Dark Pools
Scott PattersonFollow-up to The Quants on market structure, dark liquidity, and how execution venues evolved - useful context next to microstructure-heavy blog topics.
Reading that pairs well with building AI-driven strategies - covering foundations, quant history, and modern ML (educational references, not financial advice).
Machine Learning for Algorithmic Trading
Stefan JansenHands-on Python workflows from data ingestion to strategy evaluation - aligned with the ML + backtesting stack discussed across our articles.
The Quants
Scott PattersonA 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 PradoPractical methods for labeling, cross-validation, and feature importance tailored to financial time series.
Algorithmic Trading
Ernest ChanHands-on quantitative strategy workflow: backtesting, execution, and risk - useful for practitioners moving from research to production.
Trading and Exchanges
Larry HarrisMarket microstructure and how orders interact - helpful background for execution and market making.
Options, Futures, and Other Derivatives
John HullClassic reference for derivatives pricing models that underpin many systematic approaches.
Statistical Arbitrage
Andrew PoleComprehensive 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 VidyamurthyRigorous 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.