Algorithmic Trading: Winning Strategies and Their Rationale

CHAPTER 1

Backtesting and Automated Execution

While the focus of this book is on specific categories of strategies and not on general techniques of backtesting, there are a number of important considerations and common pitfalls to all strategies that need to be addressed first. If one blithely goes ahead and backtests a strategy without taking care to avoid these pitfalls, the backtesting will be useless. Or worse—it will be misleading and may cause significant financial losses.

Since backtesting typically involves the computation of an expected return and other statistical measures of the performance of a strategy, it is reasonable to question the statistical significance of these numbers. We will discuss various ways of estimating statistical significance using the methodologies of hypothesis testing and Monte Carlo simulations. In general, the more round trip trades there are in the backtest, the higher will be the statistical significance. But even if a backtest is done correctly without pitfalls and with high statistical significance, it doesn't necessarily mean that it is predictive of future returns. Regime shifts can spoil everything, and a few important historical examples will be highlighted.

The choice of a software platform for backtesting is also an important consideration and needs to be tackled early on. A good choice not only will vastly increase your productivity, it will also allow you to backtest the broadest possible spectrum of strategies in the broadest variety ...