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 available. The problem: publishers demand a minimum number of pages, often far more than the author has to tell. This forces him 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 about 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.

**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 and you have all
you need for understanding financial math.

**Ruey S. Tsay, Analysis of Financial Time Series.** If you read the Schaum's Outline book and 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. No math required.

**Johann C. Lotter, Black Book of Financial Hacking /
Das Börsenhackerbuch. **
Developing algorithmic trading systems for forex, stocks, options with Zorro.
Contains the basic algorithms behind the Z systems.
With source code.

**Philip Z Maymin, Financial Hacking.** Introduction to
options and derivatives.

**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.**
Recommended for creating deep learning strategies with Zorro and
Keras / Tensorflow.

**Urban Jaekle / Emilio Tomasini, Trading Systems.** Developing
and evaluating algorithmic trading systems for forex.

**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.
With source code for all trading algorithms.

**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 funny 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.** A must-read for trading system developers.

Investopedia - huge online glossary about trading.

Zorro User Forum - if you need help coding.

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 with many methods and strategies. 20% discount
for Zorro S users.