What is Backtesting? Its Meaning, Definition, and Benefits

Last Updated: 21 Jan 2025

Backtesting is an essential part of the trading and investment process as it reveals how a strategy would perform under real-market conditions. It enables traders and analysts to assess, through historical trades, whether their approaches would yield potential success. It’s a great way to see where the strengths and weaknesses are so that the strategy can be optimised before real money is involved.

The process provides a transparent view of the risk, profitability, and effectiveness of a trading system based on past performance. Learning backtesting could be the secret sauce to enhance your trading strategy and increase your confidence in the capital markets. Keep reading this article to understand the definition, methodology, and key benefits of backtesting.

Meaning and Definition of Backtesting

Backtesting means the process of testing a trading strategy on historical data to assess its accuracy. Technical traders often use this to test the trading strategies to find how it is likely to perform in the real market. Though, no funds are invested in reality. Backtesting is based on the phenomenon that, the strategy which performed well in the past, is expected to work well in the future and vice versa.

Before backtesting, investors look into some essential elements. A clear picture of trading strategy, expected risk and profit of the asset, historical data of the financial assets, among other parameters. The trader must be aware of what they want to find out when backtesting a strategy and its expected outcomes.

Additionally, traders decide on the level of risk and expected return. Data and time-frame are of utmost importance when backtesting. Moreover, the trader should select the period which reflects the current market situation. Misleading data or inappropriate time selection may lead to inaccurate results.

Backtesting can be done either manually or by software. To manually backtest, traders first define the financial asset and sample time frame to be tested. Then, they can start observing and analyzing trades based on the strategy in the time frame selected. A trader can observe price charts and gross and net returns from the recorded trades.

Example of backtesting

For example, Chirag wants to backtest the strategy of going short when short-term MA falls below the long-term MA, as he thinks this strategy leads to 1.5x more profit. First, he will take a sample time. Then, he will get the price data from the sample time and calculate moving averages. Next, he will sell the stock whenever short-term MA falls below the long-term MA. Then, he can plot the returns, draw a curve and analyze the result. From the result he gets, he can decide whether to go ahead or reject the strategy.

Backtesting using software differs among various software options. Though, these are the common steps. First, traders feed the historic data including relevant financial assets and periods. Next, he needs to set parameters of trading strategy which can be initial capital, size of the portfolio, benchmark, profit level, stop loss instructions, and so on. Then, he can run a backtest. Most of the software provides strategy optimization features, too.

Backtesting vs. Forward Performance Testing

Forward performance testing is another important method that plays a crucial role in developing a trading strategy. A similarity between backtesting and forward performance testing is that traders do not have to risk their capital while performing them. Check out the differences between the two methods below:

Feature	Backtesting	Forward Performance Testing
Definition	Simulating trades using historical data	Simulated live trades, also called paper trading or out-of-sample trading
Data Used	Historical data (past data)	Real-time or out-of-sample data (live market conditions)
Trade Execution	No live trades executed; simulated trades	No actual trades executed; only recorded trades based on system’s logic
Risk	No risk to capital (paper trades)	No risk to capital (paper trades)
Purpose	To evaluate how the system would have performed in the past	To evaluate how the system would perform under current market conditions
Type of Data	Historical data, often with a fixed timeframe	Live market conditions, real-time data
Outcome	Shows potential historical profitability	Shows how the strategy performs in real-time without actual investments
Follow System Logic	Follows system logic but no live market influence	Must strictly follow the system’s logic to reflect real-time decisions
Simulated Trades Documented	Trades based on historical data with results	Trades based on current system logic, recorded but not executed
Evaluation of Strategy	Helps interpret past performance	Helps inform traders about how strategy would perform now

How to Do Backtesting in Trading

Now that you know what backtesting is, it’s time to learn how to do it right. To backtest a strategy, follow these steps:

• Set Parameters: You should start by defining your strategy’s key parameters. Since you’re not using real money, you can play around with different settings that align with your strategy.
• Identify Trades: Examine trades that have been executed to gain insight into the market conditions that prevailed at the time. The farther you look back, the better insights you will get.
• Price Chart Analysis: Backtesting depends on analysing price charts to a huge extent. Look for patterns in long-term charts that will assist you in finding the correct entry and exit points in the strategy.
• Analyse Results: Once the backtest has been executed, analyse key performance metrics such as profitability, risk-adjusted returns, win rate, and drawdowns to assess the strategy’s effectiveness.
• Refine the Strategy: Review the backtest results and identify areas that need improvement. Modify parameters, trading rules, or risk management techniques to optimize the strategy’s performance.
• Test the Strategy: After making the adjustments, test and validate it on different datasets or periods. This ensures that it is reliable and consistently performs well across various market conditions.

Best Time Frame for Backtesting a Trading Strategy

Two key factors determine the ideal backtesting period for a trading strategy. They are as follows:

Average Holding Period

The holding period can be classified into three categories, and each will need its own backtesting period:

• Long-Term: Strategies that hold positions for over a month require a 15-year backtest period. This longer time duration is great for evaluating the strategy through different market cycles.
• Short-Term: If systems have held their positions for less than one week, the best length to backtest would be 10 years. This provides enough data to determine performance in shorter-term market conditions.
• Intraday: For strategies that hold positions for less than a day, a backtesting period of 3 to 4 years is sufficient. This lets traders determine how the strategy plays out under different intraday market conditions.

Strategy Type

The type of strategy you want to implement will also determine the suitable period to backtest a certain strategy. Here are the appropriate durations for all of them:

• Trend-following: Traders need a lot of historical data to test a trading strategy during different market cycles. That’s because trends can last a long time. In those cases, a decade is more appropriate.
• Mean Reversion: Backtesting duration will be dependent on the time frame. Some short timeframes may need a couple of years of data. But a long-time frame might need a longer historical period to analyse the mean-reversal behaviour accurately.
• Volatility-Based Strategies: Strategies like volatility breakouts or volatility-based position sizing demand a diverse approach. The backtesting period should be shorter according to the level of volatility. When the market changes a lot, you want as many years behind you as possible.

Advantages & Disadvantages of Backtesting

Advantages	Disadvantages
Risk-Free Evaluation: Backtesting allows you to evaluate strategies without risking real capital.	Overfitting: There’s a risk of optimizing a strategy too much for historical data, which may not perform well in live markets.
Objective Insights: Provides quantifiable results and performance metrics for evaluating strategies.	Past Performance Doesn’t Guarantee Future Results: Market conditions change over time, and past performance may not reflect future outcomes.
Identifies Strengths and Weaknesses: Highlights areas for improvement in a trading strategy before implementing it live.	Data Limitations: Historical data may be incomplete, biased, or inaccurate, leading to unreliable results.
Improves Strategy Confidence: Enhances confidence in a strategy before committing real funds to it.	Doesn’t Account for Slippage/Transaction Costs: Backtests often don’t account for factors like slippage or real-time transaction fees.
Helps with Risk Management: Allows you to assess risk factors, such as drawdowns and volatility, to adjust the strategy for better risk control.	Limited by Historical Data: Backtesting results are confined to the available historical data, which might not encompass all market conditions.
Customization: Offers flexibility to experiment with different parameters and settings for the strategy.	Time-Consuming: The backtesting process can be time-intensive, especially with large datasets or complex strategies.
Informs Strategy Refinement: Provides valuable feedback that can be used to fine-tune trading rules and risk management.	Doesn’t Simulate Real Market Conditions: Backtesting doesn’t account for human emotions or unforeseen market events that affect live trading.

Conclusion

While there isn’t a distinctive test that can predict future performance, backtesting proves to be an efficient way to evaluate trading strategies before executing them in the real market. However, backtesting can be misleading if conducted with bias, and even if conducted properly, using it in isolation may not give efficient results. Thus, backtesting is better used with other parameters to assess the viability of trading strategies, such as using a stock trading app to track the performance of the strategy in real time.

Frequently Asked Questions

How do you perform a backtest?

To perform a backtest, first, you need to collect required historical data and feed it into the software. After that, you need to set parameters of trading strategy which can be initial capital, size of portfolio, benchmark, profit level, stop loss instructions, and so on. Then you can backtest the data set using above mentioned data.

Why is backtesting used for time series problems?

Backtesting is used for time series because it is sequence-based and provides close results to real-life conditions. Another reason is random validation does not work for time series and backtest does not use cross-validation.

Does backtesting really work?

Though backtesting is an efficient way to test the trading strategy, there is no guarantee that it will work. Past performance does not guarantee future results. There does not exist any test that exactly tells how the trading system will behave.

What mistakes should you avoid when backtesting a strategy?

Common mistakes include overfitting the strategy to historical data, ignoring transaction costs and slippage, using insufficient data, relying on unrealistic assumptions, and failing to account for market conditions that could change. Avoiding these errors ensures more reliable backtesting results that better reflect real-world performance.

How many stocks should traders use to backtest a strategy?

The number of stocks to use depends on the strategy’s goals. A diverse sample of 20-30 stocks is typically sufficient for testing, providing a range of market conditions. However, using more stocks (50-100) can offer more robust insights into the strategy’s adaptability and consistency.