Python for Finance, 2nd Edition

Chapter 15. Trading Strategies

[T]hey were silly enough to think you can look at the past to predict the future.

The Economist¹

This chapter is about the vectorized backtesting of algorithmic trading strategies. The term algorithmic trading strategy is used to describe any type of financial trading strategy that is based on an algorithm designed to take long, short, or neutral positions in financial instruments on its own without human interference. A simple algorithm, such as “altering every five minutes between a long and a neutral position in the stock of Apple, Inc.,” satisfies this definition. For the purposes of this chapter and a bit more technically, an algorithmic trading strategy is represented by some Python code that, given the availability of new data, decides whether to buy or sell a financial instrument in order to take long, short, or neutral positions in it.

The chapter does not provide an overview of algorithmic trading strategies (see “Further Resources” for references that cover algorithmic trading strategies in more detail). It rather focuses on the technical aspects of the vectorized backtesting approach for a select few such strategies. With this approach the financial data on which the strategy is tested is manipulated in general as a whole, applying vectorized operations on NumPy ndarray and pandas DataFrame objects that store the financial data.²

Another focus of the chapter is the application of machine and deep learning algorithms to formulate algorithmic ...