Algorithmic Trading with Python: Quantitative Methods and Strategy Development

Great addition to the library in terms of perspectives and approaches to algo trading. Code actually works and serves as a nice introduction to Python through a trading lens.I was concerned after reading another Amazon review that said that this book required a premium Python package. However, this turned out NOT to be the case as everything was in the GitHub. A++++

Algorithmic Trading is a fantastic book where the author lays out the motivation for the code. The book accomplishes what it claims to accomplish. I've had no trouble using the code and the Github repo. I've found this book to be a wonderful resource to consult on my trading journey.

Author has clear expository style demonstrating good knowledge of the subject matter, esp. statistical methods, along with a strong point of view based upon experience. This book looks quite presentable, likely having been prepared using a technical publishing package, so the equations are well formatted and python code readily distinguishable from the text. Usage of full sized pages provides plenty of space for comfortable layout of code and graphs. Could benefit from an Index and Glossary. GitHub repository available for code samples, where author encourages discussion.

This book is extremely detailed and provides tons of information regarding trading with Python. Many reviews claim that there are additional purchases, but that is not true. At the beginning of the book, there is a link to a GitHub repository that has tons of test data as well as already functional programs that are extremely helpful in further understanding the information in the book.

Hi,The book contains valuable material, and the code implemented is of high quality.However, there is no need to purchase the actual physical copy, all of the material is on a GitHub page.Save your money!

I bought this book with a high hope that there would be some high quality backtest results demonstrating how effective various indicators and strategies implemented with the Python frameworks taught in the book have been, but found NONE. Instead, I found some good backtest results from Forecasting and Timing Markets: A Quantitative Approach, which demonstrated most models performed very well with 29 symbols and 15 years of historic data. Finally, Python is extremely slow compared with other languages such as C/C++ and Java, so even if one builds an auto trading system with Python packages introduced in the book, the latency would be a huge problem.

Excellent presentation – the algorithms are concisely and clearly explained, and the code snippets are invaluable. The chapters build on each other in an integrated way that is easy and enjoyable to follow. Highly recommend this book to anyone wanting an overview of the field or someone who just wants to brush up and/or have a handy reference or anyone for that matter working in Python. Great resource!

The back testing code (the main reason you buy this book) requires you sign up for ActiveState for the python pypl package. ActiveState then tries to get you to install Komodo IDE, then it tries to sell you a corporate package. Without this, you can't get past the 5th line in the back test code.Save your money on this book. You can easily get the python codes for the SMA, MACD, and Bollinger bands off the internet. You can also learn to calculate performance like log graphs, equity curves, volatility, CAGR, SHARP ratio, Sortino ratio, drawdowns, and pure profit score.The entire github repository code is /chrisconlan/algorithmic-trading-with-pythonAs an alternative, check out the PyAlgo lib to backtest any strategy with real stock market data.