Learning Algorithmic Trading: A Comprehensive Guide

In the rapidly evolving world of finance, algorithmic trading has emerged as a powerful tool for investors seeking to maximize returns. For those eager to delve into this field, understanding where to begin can be daunting. This post offers a reading list that lays the foundation for learning quantitative and algorithmic trading, focusing on retail traders dealing with equities, exchange-traded derivatives, and forex.

Getting Mentored by an Expert: A Great Starting Point

The best way to learn algorithmic trading is to get mentored by a seasoned professional. However, for those without immediate access to such guidance, reading the seminal works in the field provides an excellent alternative. This post aims to present a list of recommended books that build intuition and introduce standard terminology and introductory topics.

The Essential Reading List for Retail Algorithmic Traders

1. Reminiscences of a Stock Operator, by Lefèvre - A classic introduction to speculation via the insights of Jesse Livermore, one of the most successful traders in history.

2. When Genius Failed, by Lowenstein - This book offers a popular recounting of the Long-Term Capital Management (LTCM) fiasco, which provides valuable lessons about risk management and systemic failures.

3. Predictably Irrational, by Ariely - A popular introduction to behavioral economics that sheds light on the psychological biases that affect trading decisions.

4. Behavioral Investing, by Montier - This book offers snippets of common wisdom in behavioral finance.

5. Trade Your Way to Financial Freedom, by Tharp - A standard retail overview of systematic trading systems, despite the somewhat misleading title.

6. Mathematics of Money Management, by Vince - An introduction to money management concepts that are essential for managing risk in algorithmic trading.

7. Intermarket Trading Strategies, by Katsanos - A random mix of trading strategies covering entry, exit, holding, and other aspects.

8. Advanced Trading Rules, by Acar and Satchell - This book surveys a wide range of trading strategies, offering valuable insights for algorithmic traders.

9. Applied Quantitative Methods for Trading and Investment, by Dunis et al - A survey of trading strategies that includes a hint of Burgess statarb, a popular algorithm in quantitative finance.

10. Algorithmic Trading and DMA: An Introduction to Direct Access Trading Strategies, by Barry Johnson - An introduction to modern algorithmic trading, covering topics such as optimal order execution and direct market access.

Technical Analysis: A Precursor to Modern Algorithmic Trading

While Quantivity does not recommend technical analysis in general due to the potential for lookback bias, several concepts are seminal and worth understanding. These include moving averages, convolution/filtering, behavioral indicators, and moment derivatives like momentum and acceleration. Achelis' Technical Analysis from A to Z serves as a standard reference text on these topics.

Diving Deeper: Foundations of Mathematical Finance and Modern Financial Modeling

This post is the first in a series that covers the foundations of mathematical finance, modern financial modeling, and algorithmic trading. Readers familiar with systematic trading are encouraged to proceed to Part 2, which delves into the mathematical underpinnings of these concepts. For those new to systematic trading, this series provides an excellent starting point for further study.