Backtesting
Backtesting is the process of testing how a particular strategy would have performed if it had been applied in the past. It can be applied in situations where the application of the strategy does not affect the outcome of the process being studied, such as when studying strategies applying to capital markets, or when the process being studied is a model of real-world behavior where feedback can be modeled, such as when studying modeling strategies for weather patterns.
In the application of backtesting techniques to capital markets, backtesting is a specific type of historical testing that determines the performance of the strategy if it had actually been employed during past periods and market conditions. Since backtesting uses real-world data, it has advantages over testing with synthesized data sets. While backtesting does not allow one to predict how a strategy will perform under future conditions, its primary benefit lies in understanding the vulnerabilities of a strategy as it encountered real-world conditions of the past. This enables the designer of a strategy to "learn from their mistakes" without actually having to make them with actual money.
A key element of backtesting that differentiates it from other forms of historical testing is that backtesting calculates how a strategy would have performed if it had actually been applied in the past. This requires the backtest to replicate the market conditions of the time in question in order to get an accurate result. Examples of these market conditions include screening/buying/selling stocks that no longer exist, or using market index compositions as they were in the past, rather than current compositions. Due to the expense of obtaining these data sets, most backtesting is usually performed by institutions and professional money managers.
Various types of capital market strategies can be backtested, such as asset allocation strategies, stock screening strategies, and trading strategies. Other types of strategies are less amenable to backtesting, such as programmed trading strategies for buying or selling large quantities of a stock at the best prices by spreading the trade over a period of hours, days or weeks. This is because the act of selling large quantities of an individual issue affect the trading price for that issue, resulting in a feedback loop. Since the feedback loop is the effect being studied, backtesting is inappropriate for such strategies.