Readers seeking to learn algorithmic trading often ask where to begin. As with any discipline, best approach is to get mentored by an algo trading expert. Short of that, read all the seminal works; ideally several times each.

The following is a reading list intended for retail traders introducing standard terminology and introductory topics, with bias to equity, exchange-traded derivatives, and FX. A caveat of this list is acknowledgment of the old adage that “there are no good books” and certainly none capture anywhere near the current state-of-the-art; that said, some books are better than none. This list focuses on those texts which build intuition, in preference to mathematical rigor. Each of the following is recommended, and a valued member of the Quantivity library.

Readers are encouraged to comment on their favorite omissions.

This post is the first in a series on learning algorithmic trading. Part 2 covers foundations of mathematical finance. Part 3 covers modern financial modeling and analysis. Readers familiar with systematic trading are encouraged to proceed to Part 2. Readers new to systematic trading (usually coming from either fundamental or discretionary technical analysis worlds), the following nicely motivate further study. Due to focus on retail quant / algo, the following is knowingly weak in structuring and modern asset pricing (with complete absence of exotics).

Without further ado…

Flaunt academic finance and begin with behavioral finance (thanks to quant.this, for this concise list from a large literature):

* Reminiscences of a Stock Operator, by Lefèvre: classic speculator introduction via Livermore
* When Genius Failed, by Lowenstein: popular recantation of the LTCM fiasco
* Predictably Irrational, by Ariely: popular introduction to behavioral economics
* Behavioral Investing, by Montier: snippets of common wisdom

Dive into systematic trading systems (entry, exit, holding, Kelly betting, money management, etc), with a focus on the concepts (rather than mechanics, which are dated):

* Trade Your Way to Financial Freedom, by Tharp: standard retail overview, ignoring the ridiculous title
* Mathematics of Money Management, by Vince: standard retail introduction to money management
* Intermarket Trading Strategies, by Katsanos: random mix of trading strategies
* Advanced Trading Rules, by Acar and Satchell: survey of trading strategies
* Applied Quantitative Methods for Trading and Investment, by Dunis et al: survey of trading strategies (including a hint of Burgess statarb)

Finally, for completeness, review a bit of technical analysis (TA) with a skeptical eye—while recognizing it is the predecessor of modern algo. Although Quantivity does not recommend TA in general (as much is subject to lookback bias), several concepts are seminal; for example, modern mathematical finance and microstructure formalizes and expands ideas such as moving averages, convolution / filtering, behavioral (e.g. overbought / oversold indicators), and moment derivatives (e.g. momentum and acceleration). Given that caveat, the following single reference is offered:

* Technical Analysis from A to Z, by Achelis: standard reference text on TA

With this introduction, readers are encouraged to dive into Part 2: foundations of financial mathematics, followed by Part 3: modern financial modeling and analysis.

Note: this post has been updated from its original draft, incorporating numerous insightful comments and recommendations (as retained below). Thanks to awwthor, quant.this, Josh Ulrich, Gappy, and Bjørn for their comments and recommendations.

Excellent readership and thoughtful comments on the original How to Learn Algorithmic Trading have motivated two follow-up posts on learning quantitative / algorithmic trading (while retrospectively revising the original to improve consistency). This Part focuses on the cross-discipline foundations of financial mathematics, whose knowledge is generally assumed by practitioners and financial modeling literature. The subsequent, Part 3, focuses on modern financial modeling and analysis.

Depending on reader interest, this topic may warrant a future series of posts to delve into seminal literature in selected trading disciplines, such as suggested by etrading on the Penn-Lehman Automated Trading Project.

Thanks to awwthor, quant.this, Josh Ulrich, Gappy, and Bjørn for their comments and recommendations. As with the original post, the following is intended to inform retail quantitative trading with a bias to equity, exchange-traded derivatives, and FX.

To begin, start with solid theoretical econometrics, with emphasis on time series, and meet regression:

* Time Series Analysis, by Hamilton: classic text on time series econometrics
* Econometric Analysis, by Greene: classic text on theoretical econometrics

Next, dive into filtering and wavelets and meet Fourier:

* Wavelet Methods for Time Series Analysis, by Percival and Walden: standard theoretical text on wavelets
* An Introduction to Wavelets and Other Filtering Methods in Finance and Economics, by Gençay, Selçuk, and Whitcher: applied filtering and wavelets for finance and economics

Explore modern statistical / machine learning and meet reinforcement and (un)supervision, descendant of original Turing / von Neumann “AI” tradition:

* Artificial Intelligence: A Modern Approach, by Russell and Norvig: standard introduction to classic AI
* The Elements of Statistical Learning, by Hastie, Tibshirani, and Friedman: standard intermediate statistical learning
* Pattern Recognition and Machine Learning, by Bishop: intermediate classification and learning
* Pattern Classification, by Duda: standard introductory classification

Review operations research and meet duality, with focus on mathematical optimization (not to be confused with computer science “programming”); thanks to Gappy, since my references pre-date many of these:

* Linear and Nonlinear Programming, by Luenberger: standard introduction to optimization
* Nonlinear Programming, by Bazaraa et al.: standard non-linear optimization
* Convex Optimization, by Boyd and Vandenberghe: standard context optimization, including approximation, fitting, and estimation

Finally, for those interested in options and vol, review modern stochastic calculus and meet Ito- (presuming working knowledge of measure theory and stochastic processes):

* Financial Calculus, by Baxter and Rennie: pleasant intuitive introduction
* Stochastic Calculus for Finance I, by Shreve: gentle introduction via binomial
* Stochastic Calculus for Finance II, by Shreve: gentle continuous-time introduction

Continue on to Part 3 to dive into modern financial modeling and analysis.

Third in a series on learning quantitative / algorithmic trading, this post focuses on financial modeling and analysis, assuming understanding of financial mathematics from Part 2 and overview of quantitative trading from Part 1. After digesting these, readers should be capable of both building interesting systematic trading systems and understanding microstructure dynamics that drive modern market making (sell side) and large block trading (buy side).

Thanks to awwthor, quant.this, Josh Ulrich, Gappy, and Bjørn for their comments and recommendations on the original post. As with the preceding two posts, the following is intended to inform retail quantitative trading with a bias to equity, exchange-traded derivatives, and FX.

Begin with standard introductory financial time series asset dynamics, volatility, and forecast modeling:

* Analysis of Financial Time Series, by Tsay: standard applied time series text for financial econometrics
* Market Models: A Guide to Financial Data Analysis, by Alexander: excellent introduction to financial modeling and forecast
* Asset Price Dynamics, Volatility, and Prediction, by Taylor: classic text on financial modeling and forecast

Proceed to modern portfolio theory and financial engineering:

* Modern Portfolio Theory and Investment Analysis, by Elton et al.: standard text on modern portfolio theory
* Options, Futures and Other Derivatives, by Hull: standard reference for introductory financial engineering
* Active Portfolio Management, by Grinold & Kahn: standard introduction to quantitative portfolio management by the BGI guys who invented it
* Principles of Financial Engineering, by Neftci: intermediate financial engineering

Continue on to volatility for options and correlation / dispersion for arb:

* Volatility and Correlation, by Rebonato: excellent coverage of volatility and correlation
* Volatility Trading, by Sinclair: volatility arbitrage by a retail practitioner
* Volatility Surface, by Gatheral: theoretical coverage of vol models, by well-known researcher
* Options as a Strategic Investment, by McMillan: classic introductory text on derivative hedging and volatility trading
* Option Volatility & Pricing, by Natenberg: dated practitioner introduction to volatility trading

Finally, delve into high-frequency & market microstructure to enjoy foundations of modern buy and sell sides:

* Trading and Exchanges: Market Microstructure for Practitioners, by Harris: practitioner introduction to stylized financial microstructure effects
* An Introduction to High-Frequency Finance, by Dacorogna et al.: theoretical and dated practitioner introduction to HF, with emphasis on FX
* Empirical Market Microstructure, by Hasbrouck: intermediate equity market microstructure, with coverage of standard theoretical models
* Microstructure Approach to Exchange Rates, by Lyons: intermediate FX market microstructure
* Market Microstructure Theory, by O’Hara: classic introduction to microstructure theory; now dated
* Optimal Trading Strategies, by Kissell and Glantz: practitioner introduction to market impact and optimal execution

From here, readers can happily delve into the journal literature.