Backtesting

When researching an automated trading strategy for expected behavior, a key component in a good algorithmic trading research system is a good backtester. A backtester is used to simulate automated trading strategy behavior and retrieve statistics on expected PnLs, expected risk exposure, and other metrics based on historically recorded market data. The basic idea is to answer the question: given historical data, what kind of performance would a specific trading strategy have? This is built by recording historical market data accurately, having a framework to replay it, having a framework that can accept simulated order flow from potential trading strategies, and mimicking how a trading exchange would match this strategy's order flow in the presence of other market participants as specified in the historical market data. It is also a good place to try out different trading strategies to see what ideas work before deploying them to market.

Building and maintaining a highly accurate backtester is one of the most complicated tasks involved in setting up an algorithmic trading research system. It has to accurately simulate things such as software latencies, network latencies, accurate FIFO priorities for orders, slippage, fees, and, in some cases, also the market impact caused by the order flow for the strategy under consideration (that is, how the other market participants may react in the presence of this strategy's order flow and trading activity). We will revisit backtesting at the end of this chapter and then again in later chapters in this book. Finally, we explain practical issues faced in setting up and calibrating a backtester, their impact on an algorithmic trading strategy, and what approaches best minimize damage caused due to inaccurate backtesting.

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