Backtesting is the general method for seeing how well a strategy or model would have done ex-post. Backtesting assesses the viability of a trading strategy by discovering how it would play out using historical data. If backtesting works, traders and analysts may have the confidence to employ it going forward.

Understanding Backtesting

Backtesting allows a trader to simulate a trading strategy using historical data to generate results and analyze risk and profitability before risking any actual capital.

A well-conducted backtest that yields positive results assures traders that the strategy is fundamentally sound and is likely to yield profits when implemented in reality. In contrast, a well-conducted backtest that yields suboptimal results will prompt traders to alter or reject the strategy.

As long as a trading idea can be quantified, it can be backtested. Some traders and investors may seek the expertise of a qualified programmer to develop the idea into a testable form. Typically, this involves a programmer coding the idea into the proprietary language hosted by the trading platform.

The programmer can incorporate user-defined input variables that allow the trader to “tweak” the system. An example of this would be in the simple moving average (SMA) crossover system. The trader would be able to input (or change) the lengths of the two moving averages used in the system. The trader could then backtest to determine which lengths of moving averages would have performed the best on the historical data.

The ideal backtest chooses sample data from a relevant time period of a duration that reflects a variety of market conditions. In this way, one can better judge whether the results of the backtest represent a fluke or sound trading.

The historical data set must include a truly representative sample of stocks, including those of companies that eventually went bankrupt or were sold or liquidated. The alternative, including only data from historical stocks that are still around today, will produce artificially high returns in backtesting.

A backtest should consider all trading costs, however insignificant, as these can add up over the course of the backtesting period and drastically affect the appearance of a strategy’s profitability. Traders should ensure that their backtesting software accounts for these costs.

Out-of-sample testing and forward performance testing provide further confirmation regarding a system’s effectiveness and can show a system’s true colors before real cash is on the line. A strong correlation between backtesting, out-of-sample, and forward performance testing results is vital for determining the viability of a trading system.

Some Pitfalls of Backtesting

For backtesting to provide meaningful results, traders must develop their strategies and test them in good faith, avoiding bias as much as possible. That means the strategy should be developed without relying on the data used in backtesting.

That’s harder than it seems. Traders generally build strategies based on historical data. They must be strict about testing with different data sets from those they train their models on. Otherwise, the backtest will produce glowing results that mean nothing.

Similarly, traders must avoid data dredging, in which they test a wide range of hypothetical strategies against the same set of data, which will also produce successes that fail in real-time markets because there are many invalid strategies that would beat the market over a specific time period by chance.

One way to compensate for the tendency to data dredge or cherry-pick is to use a strategy that succeeds in the relevant, or in-sample, time period and backtest it with data from a different, or out-of-sample, time period. If in-sample and out-of-sample backtests yield similar results, then they are more likely to be proved valid.