The Bespoke Options Reading list contains books and academic papers cited in the Bespoke Options Blog.
Building Winning Algorithmic Trading Systems: A Trader's Journey From Data Mining to Monte Carlo Simulation to Live by Kevin Davey This book presents interesting insight into the journey of an independent trader from "data mining to monte carlo simulation to live trading". The book presents very practical insight into many aspects of algorithmic trading as well as backtesting. In addition to the frank and honest testimony from his own failures and successes, the discussion around walk forward optimization and Monte Carlo simulation was detailed enough to implement.
Mastering Python for Finance by James Ma Weiming The author presents a a broad range of material in quant finance from a discussion of time series models and linear algebra, numerical procedures and their use in pricing derivatives, through interactive data analysis and big data technologies. All of it is light enough in theory that you don't have to be a quant to follow along but presents enough to get you started. The code is not overly sophisticated rather straight forward object-oriented programming focussing on solving the problem at hand.
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney From the guy who invented one of the most successful Python modules in a while, Pandas has become ubiquitous in data analysis of all kinds. Python for Data Analysis provides a summary of the package and provides some examples across a array of applications. A good example for trading is an example of using Pandas to explore a grid of backtesting model parameterizations. He also presents an example of rolling futures contracts. A lot of what is in the book can probably be pieced together through countless searches on Google, but it is nice to have it all in once place.
Quantitative Trading: How to Build Your Own Algorithmic Trading Business by Ernie Chan Motivation. That's what I felt after reading Ernie's examination of systematic trading at the retail level. As is common in books aimed at those beginning the journey of systematic trading, he discusses the the setup, pros, and cons of algorithmic trading. What is a bit less common is his discussion around actually setting up a business. He presents a few strategies that might be viable with some modification but also presents rich source code (in MATLAB) that can used for all strategies. A must read for those beginning the journey in systematic and algorithmic trading.
Algorithmic Trading: Winning Strategies and Their Rationale by Ernie Chan In Ernie's follow up book, he focuses more on trading strategies than the basics of setting up and running an algorithmic trading operation. While the strategies may seem basic (mean reversion and momentum), they represent very powerful strategies that may be adapted easily. He presents them very clearly and offers ample source code (again in MATLAB) that can be used immediately. Another thing I like about Ernie's approach, is the statistical basis in which he analyzes the strategies he presents. This is an extremely important piece of building trading strategies.