When traders can't make money with trading, they write trading books. Even some who do make money write books. So there are tons of books about trading methods and systems around. The problem: publishers demand a minimum number of pages, usually far more than the authors have to tell. This forces them to fill the rest with platitudes, large lists, tables, charts, example trades, and other filler material. But fortunately, some trading books have real content inside.
Here's a non-complete list of useful books and links to algorithmic trading and its mathematical backgrounds. If you can't read them all, get the Black Book and the books by Aronson and Chan. They give a good insight without requiring a strong mathematical background. If you don't want to read any, take the Algo Bootcamp. It is probably the best and most thorough algorithmic trading course available today.
Thomas Piketty, Capital in the Twenty-First Century. To get the whole picture.
Murray R. Spiegel, Larry J. Stephens: Schaum's Outline of Theory and Problems of Statistics. Beginner's course into probability and statistics with lots of examples. Work through this book to get all you need for understanding financial math.
Ruey S. Tsay, Analysis of Financial Time Series. If you hadn't had enough of mathematics, this is the hard stuff that introduces the mathematical models of price series.
Ian Goodfellow, Yoshua Bengio, Aaron Courville: Deep Learning. Comprehensive introduction in modern concepts of machine learning, covering anything from linear algebra and statistics up to the structures and implementation of modern artificial neural networks.
David Aronson, Evidence-based Technical Analysis. Excellent, maybe a little elaborate book about the pitfalls of backtesting trading strategies. A classic. Must read for algo trading. Almost no math required.
Johann Christian Lotter,
Black Book of Financial
Hacking / Das
Algo trading systems for forex, stocks, and options. Introduction to algorithmic
trading with C, and to the algorithms behind the Z systems.
With source code. =>
For a 35% book discount with a permanent Zorro S license, contact firstname.lastname@example.org.
Ernest P. Chan, Quantitative Trading. Insight in strategy testing and portfolio optimization with many practical advices.
John F. Ehlers, Rocket Science for Traders. Trading with signal processing methods. Source code in EasyLanguage.
Marcos Lopez de Prado, Advances in Financial Machine Learning. What to consider when applying ML to finance.
Francois Chollet / J. J. Allaire, Deep Learning with R. All you need for creating deep learning strategies with Zorro and Keras / Tensorflow.
Philip Z Maymin, Financial Hacking. Excellent title and good intro to options and derivatives.
Ralph Vince, Handbook of Portfolio Mathematics. How to allocate your capital in an optimal way among different assets and strategies.
Gary Antonacci, Dual Momentum Investing. Portfolio rotation based on an empirical approach, used for the Z9 system.
William R. Gallacher, Winner Take All. This book (from 1994) is a good read and an intelligent insight into the trading scene and its gurus.
Scott Patterson, The Quants. History of algorithmic funds.
Robert Harris, The Fear Index. What happens when an algo trading system goes wrong - really wrong.
Investopedia - Huge online glossary about trading.
Zorro User Forum - If you need help, but don't want to pay for it.
Financial Hacker, Robot Wealth - Scripts, strategies, and experiments with Zorro and R.
Steve Hopwood's - Trader forum with focus on algorithmic trading.
Algo Bootcamp - Learn serious algo trading. 20% discount with some Zorro S licenses.
► latest version online