How Does Backtesting Work?
Backtesting is essential for traders to evaluate a strategy's effectiveness using historical data, identifying strengths and weaknesses before risking real capital. Integrity in the process is crucial for ensuring real-world applicability.
What Is Backtesting?
Backtesting is the process of evaluating how well a trading strategy or model would have performed in the past. By analyzing historical data, traders can assess the viability of their strategies and see how they would have fared under various market conditions.
If the backtesting results are positive, traders and analysts can try applying the strategy with their live trading account.
Understanding Backtesting
Backtesting enables traders to simulate a trading strategy using historical data, generating results that help analyze risk and profitability before committing real capital.
A thorough backtest that yields positive results provides assurance that the strategy is fundamentally sound and likely to generate profits in live trading. Conversely, a backtest with suboptimal results encourages traders to modify or abandon the strategy.
As long as a trading idea can be quantified, it can be backtested. Some traders may enlist the help of a qualified programmer to turn their ideas into testable formats. This often involves coding the strategy into the proprietary language of the trading platform.
The programmer can also incorporate user-defined input variables, allowing traders to adjust the system parameters. For example, in a simple moving average (SMA) crossover system, traders could input or modify the lengths of the two moving averages.
They can then backtest different lengths to determine which combinations would have performed best based on historical data.
Backtesting Scenario Example
An effective backtest utilizes sample data from a relevant time period that reflects a range of market conditions. This approach allows for a more accurate assessment of whether the results indicate a reliable trading strategy or merely a fluke.
The historical dataset should include a representative sample of cryptocurrencies, including those from that ultimately disappeared from the market such as FTT. Limiting the dataset to only include cryptocurrencies that are still active today can lead to artificially inflated returns during backtesting.
It's essential for a backtest to account for all trading costs, no matter how minor they may seem. Over time, these costs can accumulate and significantly impact the perceived profitability of a strategy. Ensure that your backtesting software incorporates these expenses.
Additionally, out-of-sample testing and forward performance testing provide further validation of a trading system’s effectiveness. These methods can reveal how a system performs under real market conditions before actual capital is at risk.
A strong correlation between backtesting results, out-of-sample testing, and forward performance testing is crucial for determining the viability of any trading strategy.
Forward Performance Testing
Forward performance testing, often referred to as paper trading, offers traders an additional layer of out-of-sample data to assess a trading system. This method simulates actual trading by following the system's logic in a live market environment.
In this process, all trades are recorded on paper—documenting trade entries, exits, and any resulting profits or losses—without executing real trades.
A crucial aspect of forward performance testing is to adhere strictly to the system's logic. Deviating from this can compromise the accuracy of the evaluation.
It's essential to be honest about every trade entry and exit, avoiding practices like cherry-picking trades or excluding a trade by rationalizing that "I would have never taken that trade." If a trade aligns with the system's logic, it should be documented and evaluated accordingly.
Backtesting vs. Scenario Analysis
Backtesting employs actual historical data to evaluate the effectiveness of a trading strategy, while scenario analysis utilizes hypothetical data to simulate various potential outcomes.
For example, scenario analysis can model specific changes in the values of a portfolio's securities or key factors, such as shifts in interest rates.
This method is particularly useful for estimating how a portfolio's value might change in response to adverse events and can help assess theoretical worst-case scenarios. By exploring these potential outcomes, traders can better prepare for unexpected market conditions and make more informed decisions.
Backtesting Drawback
For backtesting to yield meaningful results, traders need to develop their strategies and test them with integrity, minimizing bias as much as possible. This means creating a strategy without relying on the data that will be used in the backtesting process.
Achieving this can be more challenging than it appears. Traders often develop strategies based on historical data, but they must be disciplined about testing them with different datasets than those used for training. Failing to do so can lead to backtests that show impressive results but lack real-world applicability.
Traders should also avoid data dredging, which involves testing numerous hypothetical strategies against the same dataset. This practice can produce seemingly successful strategies that ultimately fail in real-time markets, as many invalid strategies can randomly appear profitable over a specific time period.
To mitigate the risks of data dredging or cherry-picking, it's beneficial to use a strategy that performs well in the relevant in-sample period and then backtest it with out-of-sample data. If both in-sample and out-of-sample backtests yield consistent results, it increases the likelihood that the strategy is valid.
Bottom Line
Backtesting is a crucial tool for traders seeking to evaluate the viability of a strategy before deploying real capital. By simulating how a strategy would have performed in the past using historical data, traders can identify strengths, weaknesses, and areas for adjustment.
However, the integrity of the process is paramount—traders must avoid bias, data dredging, and overfitting to ensure the strategy's real-world applicability. When combined with out-of-sample and forward performance testing, backtesting provides valuable insights, helping traders make more informed decisions and better manage risk.
