The best books for algorithmic trading, in the order that helps

By John, builder of Wise Apple · Updated August 27, 2026

You've seen the thread with 47 books in it. You bought three, read half of one, and you still don't know whether your backtest means anything. The problem isn't that you need more titles. It's that nobody told you which problem each book solves, or which one to open first.

A recurring request on r/algotrading is a reading list, and the answers are always the same shape: dozens of titles, no order, no explanation of what any of them will fix. This page is the opposite. Eleven books, grouped by the thing that's actually going wrong at your desk — you can't tell edge from luck, your backtests don't survive contact with a live account, your machine-learning model looks perfect and then doesn't, you don't know how big to trade, or you don't understand what happens between your order and your fill.

Every book here is real, well known, and something I'd hand to a friend. No affiliate links, no prices. Where a famous title is missing, it's on purpose, and the reasons are near the bottom.

Start here if your problem is: I can't tell edge from luck

Evidence-Based Technical Analysis — David Aronson (2007)

The single most useful book for someone who has been burned by their own backtests. Aronson's subject is not any particular strategy. It's the question of what would count as evidence that a rule works, and why most of what passes for evidence in trading — chart patterns, anecdote, a good year — isn't. The middle of the book is a plain-language course in statistical inference for traders, including the data-mining problem: if you test enough rules, the best one looks good by construction. His case study tests thousands of rules on the S&P 500 and finds that none survive a data-snooping adjustment, which is a sobering thing to read before you post your equity curve. Skip it if you already think in terms of null hypotheses and multiple testing; read the case study anyway.

A Non-Random Walk Down Wall Street — Andrew Lo and A. Craig MacKinlay (1999)

The academic counterweight. Lo and MacKinlay collect their papers showing that prices are not a pure random walk — there is structure, it's measurable, and it's small. That is the correct prior for everything you do next: edges exist, they're modest, and they take real statistical work to find. Lo's later "Adaptive Markets Hypothesis" (2004) is the short version of the same idea: edges appear, get competed away, and reappear elsewhere. Skip it if you dislike econometrics. Read the introduction and the first chapter regardless.

Read next if your problem is: my backtests don't survive live trading

The Evaluation and Optimization of Trading Strategies — Robert Pardo (2008)

Pardo popularised walk-forward analysis, and this is the book where he explains it properly: optimise on one window, test on the next, roll forward, and judge the strategy on the stitched-together out-of-sample results only. It is older than the machine-learning literature and better than most of it on the one topic that matters most — how to optimise without curve-fitting. The walk-forward guide on this site is a compressed version of his argument. Skip it if you already run rolling out-of-sample tests and know why the window size should be set by trade count rather than months. Most people who think they do, don't.

Quantitative Trading — Ernest Chan (2008)

Chan's first book is the most honest account of what a one-person systematic trading operation actually looks like: which strategies are realistic for retail capital, how to backtest them without the usual leaks, how to think about capacity and costs. It's short, practical and slightly dated in its tooling. His two follow-ups, Algorithmic Trading (2013) and Machine Trading (2017), go deeper into mean reversion, momentum and, eventually, machine learning. Read the first one; pick up the second if you're building mean-reversion or momentum systems; treat the third as optional. Skip the series if you already run a live system with realistic costs and a holdout. The chapter on transaction costs is worth the price of the first book by itself.

Read next if your problem is: my ML model looks perfect and then doesn't

Advances in Financial Machine Learning — Marcos López de Prado (2018)

The book everyone on the forum has heard of and fewer have finished. It deserves the reputation, with a warning. López de Prado's contribution is a set of methods that fix the ways standard machine learning fails on market data: the triple-barrier method for labels, meta-labeling for deciding whether to take a trade, purged and embargoed cross-validation so the model can't peek at the future, and the deflated Sharpe ratio (with Bailey, 2014) for discounting a result by the number of trials that produced it. Those chapters — labels, cross-validation, backtest statistics — are essential and are the source for this site's meta-labeling and out-of-sample guides. The warning: it's written for people with a graduate background and a research team, and large stretches (fractional differentiation, microstructural features, HPC) are not where a retail trader's problems are. Skip the second half. Read chapters 3, 4, 7, 11 and 14 twice.

A recurring question is what to read after AFML for feature engineering. The honest answer is that no book covers it well for trading specifically, because good features are instrument-specific and the general advice fits on one page: make features stationary, scale them using only the training window, and prefer a few that have a reason over many that don't. The feature engineering guide covers that page.

Read next if your problem is: I don't know how big to trade

Systematic Trading — Robert Carver (2015)

Carver ran a systematic book at a large fund and then wrote down, for individuals, the part that funds get right and retail traders skip: position sizing, volatility targeting, and combining rules into a framework where no single idea can hurt you much. His treatment of how to scale a position to the instrument's volatility rather than to a fixed share count is the most practically useful chapter on sizing I know of, and it's the reasoning behind the position-sizing guide. Leveraged Trading (2019) is the shorter, more beginner-facing version. Skip it if you only ever trade one instrument at fixed size — but that's the thing to fix, and the book is how.

Trade Your Way to Financial Freedom — Van Tharp (1998)

The title is a problem; the content mostly isn't. Tharp is the source of the expectancy framework — average profit per trade in units of initial risk — that this site's win-rate guide is built on, and of the observation that exits and sizing matter more than entries. The psychology chapters have aged less well. Read the chapters on expectancy, R-multiples and position sizing; skip the rest.

Read next if your problem is: I don't understand what happens to my order

Trading and Exchanges — Larry Harris (2003)

A textbook on market microstructure: who the participants are, why they trade, how orders interact, where the bid-ask spread comes from, what a market maker is actually paid for. It's long and it isn't about strategy at all, which is exactly why it belongs here. Most of what makes a backtest fail live — fills you couldn't have had, costs you didn't charge — is a microstructure problem, and this is where you learn what those problems are. Skip it if you trade daily bars on liquid ETFs with modest size. Read the chapters on order types, spreads and liquidity regardless.

A Man for All Markets — Edward Thorp (2017)

A memoir, not a manual. Thorp beat blackjack with card counting, then beat markets with warrant and convertible pricing years before the academics caught up, sized his bets with the Kelly criterion (Kelly, 1956), and ran a fund for decades without a losing year. What the book teaches isn't a method; it's how a careful person thinks about edge, risk and sizing when the money is real. Read it between the harder books. Skip it if you only want equations.

One book to test, not to trust

Short Term Trading Strategies That Work — Larry Connors and Cesar Alvarez (2008)

Included for a specific reason. It's a book of concrete, fully-specified mean-reversion rules — RSI(2) pullbacks, the "Double 7" and others — with their historical statistics. That makes it perfect raw material for learning to backtest honestly: every rule is written down exactly, so you can test it yourself on current data, with realistic costs, out of sample, and see what survived. People on r/swingtrading and r/algotrading periodically do exactly this and post the results, and the pattern is what you'd expect from the simple strategies guide: some hold up in a weaker form, some don't. Do not trade anything from it on the book's numbers. Treat it as a set of exercises.

If your problem is X, read Y first

Famous books this list leaves out, and why

This is my own judgement and reasonable people disagree, so treat each of these as "read later" rather than "never".

• Grinold and Kahn, Active Portfolio Management. A genuinely great book about running a multi-asset institutional portfolio — information ratio, the fundamental law of active management, transfer coefficients. Almost none of it is the retail systematic trader's problem. Read it when you're combining dozens of signals across hundreds of instruments; until then it's a heavy way to learn one useful equation.
• Most Market Wizards-style interview books. Enjoyable, occasionally insightful, and a survivorship-bias machine: you're reading the people who won. The survivorship guide explains why that matters for strategies; it matters for careers too.
• Books that are mostly a Python tutorial. Several well-selling "algorithmic trading with Python" titles are eighty percent pandas and twenty percent trading, and the trading part usually fills at the signal bar's close and ignores costs. If you want to learn pandas, learn pandas. If you want to learn to backtest honestly, the backtesting guide is shorter and stricter.
• Anything promising a specific return. No exceptions.

How to actually read these

1. One problem at a time. Pick the row in the table that matches what's going wrong now. Read that book. Don't buy the next one until you've changed something in your process because of this one.
2. Test as you read. Every method in Pardo, Chan, López de Prado and Connors can be run on data you already have. A chapter you've tested is worth five you've highlighted.
3. Keep a list of claims. When a book states something as fact — a win rate, a Sharpe, "this always works in trending markets" — write it down as a hypothesis and check it. That's the whole of Aronson in one habit.
4. Expect to disagree with the authors. Chan and Carver disagree with each other about plenty. You're building your own process, not adopting theirs.

Where Wise Apple fits

Most of what these books describe — walk-forward windows, an embargo between training and test, triple-barrier labels, meta-labeling, volatility-scaled sizing, honest costs — is a named setting in Wise Apple's PowerCore Studio, and the backtests run on your own machine in the browser without writing the code. It won't read the books for you, and it tests one instrument at a time and places no trades. But it's a place to run the exercises: write a Connors rule in WiseApple Script, turn on Walk-Forward Window and the cost models, and see for yourself which chapters were right.