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Modelling - Backtesting allows us to (safely!) test new models of certain market phenomena, such as transaction costs, order routing, latency, liquidity or other market microstructure issues.Backtesting provides us with another filtration mechanism, as we can eliminate strategies that do not meet our performance needs. Filtration - If you recall from the article on Strategy Identification, our goal at the initial research stage was to set up a strategy pipeline and then filter out any strategy that did not meet certain criteria.What are key reasons for backtesting an algorithmic strategy? That is the essence of the idea, although of course the 'devil is always in the details'! The accumulation of this profit/loss over the duration of your strategy backtest will lead to the total profit and loss (also known as the 'P&L' or 'PnL'). Each trade (which we will mean here to be a 'round-trip' of two signals) will have an associated profit or loss. In simple terms, backtesting is carried out by exposing your particular strategy algorithm to a stream of historical financial data, which leads to a set of trading signals. The process by which this is carried out is known as backtesting.

What is Backtesting?Īlgorithmic trading stands apart from other types of investment classes because we can more reliably provide expectations about future performance from past performance, as a consequence of abundant data availability. Let's begin by discussing what backtesting is and why we should carry it out in our algorithmic trading. We will end with a discussion on the performance of our backtests and finally provide an example of a common quant strategy, known as a mean-reverting pairs trade. Then we will discuss transaction costs and how to correctly model them in a backtest setting. We will also consider how to make the backtesting process more realistic by including the idiosyncrasies of a trading exchange. In subsequent articles we will look at the details of strategy implementations that are often barely mentioned or ignored. Next I will present a comparison of the various available backtesting software options. Then I will elucidate upon the biases we touched upon in the Beginner's Guide to Quantitative Trading. What will we discuss in this section? I'll begin by defining backtesting and then I will describe the basics of how it is carried out. I couldn't hope to cover all of those topics in one article, so I'm going to split them into two or three smaller pieces. Both of these longer, more involved articles have been very popular so I'll continue in this vein and provide detail on the topic of strategy backtesting.Īlgorithmic backtesting requires knowledge of many areas, including psychology, mathematics, statistics, software development and market/exchange microstructure. This article continues the series on quantitative trading, which started with the Beginner's Guide and Strategy Identification.

Historical market drawdown duration graphic