How to Learn Algorithmic Trading
Readers seeking to learn quantitative / algorithmic trading often ask where to begin. As with any discipline, best approach is to get mentored by an 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).
Two quick notes in advance, based upon reader comments (to which Quantivity defers, having not formally worked in the industry):
- Terminology note: “algorithmic trading” within the industry usually refers to optimal order execution (e.g. blocks at VWAP). In contrast, this post intends to capture the union of the larger universe of systematic, quantitative, and algorithmic trading from a retail perspective
- Intent: purpose of this post is to cover a diverse set of seminal literature, of which only a subset is applicable to any particular quant specialization (retail, prop, buy side, sell side, etc) and certainly should not be considered necessary for getting an entry-level job
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)
- Algorithmic Trading and DMA: An Introduction to Direct Access Trading Strategies by Barry Johnson: introduction to modern algorithmic trading
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
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.