Backtesting in Trading: Full Overview

How Backtesting Works in Trading Strategies

Backtesting lets a trader test a trading strategy with past data. This helps them see results and analyze risk and profit before using real money.

A good backtest that shows positive results gives traders confidence. It suggests that the strategy is solid and may make money in real life. In contrast, a well-conducted backtest that yields suboptimal results will prompt traders to alter or reject the strategy.

IMPORTANT

Complex trading strategies, like those used by automated systems, depend on backtesting to show their value. They cannot be easily evaluated in other ways.

As long as a trading idea can be quantified, it can be backtested. Some traders and investors may look for a skilled programmer. They want to turn their idea into a testable form. A programmer usually codes the idea into the proprietary language of 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 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 test different moving average lengths. This would show which ones worked best with past data.

Creating an Effective Backtesting Environment

The best backtests use sample data that spans various market conditions. In this way, one can better judge whether the results of the backtest represent a fluke or sound trading.

The dataset should represent a variety of stocks, including those from companies that went bankrupt or were sold. The alternative, which uses data from historical stocks still available today, will show unrealistically high returns in backtesting.

A backtest should include all trading costs, no matter how small. These costs can add up over time and greatly change how profitable a strategy looks. Traders should ensure that their backtesting software accounts for these costs.

Out-of-sample and forward performance testing help confirm a system's effectiveness before using real money. A strong link between backtesting, out-of-sample testing, and forward performance testing is important. This helps to decide if a trading system is effective. 

Backtesting vs. Forward Performance Testing: Key Differences

Forward performance testing, or paper trading, offers another set of out-of-sample data to evaluate a system. Forward performance testing simulates actual trading by following the system's logic in a live market. It is also known as paper trading.

In this method, all trades are done on paper. Trade entries and exits are recorded, along with any profit or loss. However, no real trades take place.

It's vital to stick to the system's logic during forward testing for accurate evaluation. Traders should be honest about their trade entries and exits.

They should avoid cherry-picking trades. They should not leave out a trade and say, "I would have never taken that trade." If the trade would have occurred following the system's logic, it should be documented and evaluated.

Backtesting Versus Scenario Analysis: Understand the Differences

While backtesting uses actual historical data to test for fit or success, scenario analysis makes use of hypothetical data that simulates various possible outcomes. For example, scenario analysis simulates changes in portfolio values or key factors, like interest rate shifts.

Scenario analysis is often used to estimate how a portfolio's value changes after a bad event. It can also look at a worst-case scenario.

Avoiding Common Backtesting Mistakes and Pitfalls

To get useful results from backtesting, traders need to create their strategies. They should test them honestly and avoid bias. 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. Traders should strictly test with data sets different from those used to train their models. Otherwise, the backtest may show positive results that are meaningless.

Traders should avoid data dredging. This is when they test many hypothetical strategies using the same data set. This can lead to successes that do not work in real markets. Many invalid strategies may seem to beat the market by chance over a specific time.

To avoid data dredging, use a successful in-sample strategy and backtest it with out-of-sample data. If in-sample and out-of-sample backtests yield similar results, then they are more likely to be proved valid.

The Bottom Line

Backtesting is an essential tool for evaluating the viability and effectiveness of trading strategies without risking real capital. By simulating trades with past data, traders can learn about risks and profits. This helps them make smart choices before using strategies in real markets. Successful backtesting depends on using diverse data sets, incorporating all trading costs, and avoiding biases to ensure accurate results.

Additionally, forward performance testing complements backtesting by simulating trades in a live environment to further validate strategy effectiveness. Together, these processes empower traders to refine their strategies and increase their chances of success in real-time markets.