A Comprehensive Guide to Backtesting and How to Perform One
By Indeed Editorial Team
Published May 2, 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
Many money management professionals consider backtesting an essential tool for financial success. Without backtesting, these professionals might not think of risking their funds in a financial market. Understanding how to use this method of testing the market can help you succeed as a trader. In this article, we discuss what backtesting is, outline why traders use it, describe how they use backtests, review the requirements to perform a backtest, discover the best testing method, learn how to backtest a strategy, and examine how managers evaluate the results.
What is backtesting?
Backtesting is a method that shows how well an investment model or trading strategy might perform in the past. The theory of this method is that a trading strategy that performs well in the past can do so again in the future. Investors can also identify and avoid strategies that don't perform well. This testing usually involves translating a strategy into logic with clear rules for entry and exit. The investor can then process these rules using historical data to simulate the strategy's results.
Due to its complexity, only a few organizations traditionally practice this method. Specifically, those who may have access to developer resources and data sets of sufficient details and scope. With the recent creation of online testing tools, some can offer simple user interfaces, introducing this method to a broader audience.
Why use testing in a trading strategy
Two of the primary functions for building an investment or trading strategy are risk and return and their relationship. Testing can help professionals quantify these factors and display the strategy's overall profitability and risk. Testing the strategy is vital to know how it can perform during real market scenarios. It may allow traders to simulate a trading idea using historical data and test the trader's risk management. Historical data, predictive analysis models, and forward-looking indicators can all help build a great trading strategy.
Testing a trading strategy can help investors find weak spots, test the strategy's resilience, and highlight areas to adjust without taking more risk. When incorporating real market data, traders can accurately show their strategy's future performance and whether it's viable under actual market conditions. The trader can clear all issues, strengthen their risk management, and build their confidence in the trading strategy, which can help ensure they achieve an excellent result when implementing their strategy in a real-world market.
How traders use backtests
The typical use of a backtest by finance professionals is that it can allow traders to predict the future success of a trading strategy that applies to a specific security or across a market. Backtests can also have relevance in finance areas other than strategy development. For example, a portfolio manager can leverage backtests to a portfolio to determine the ideal portfolio allocation and optimize their rebalancing strategies. This backtest category may fit investment approaches that are less active and feature a longer time period.
At the institutional level, backtests have a crucial role in protecting the health of the entire banking industry. The process can help provide industry authorities and regulators with an essential evaluation technique to validate the competence of internal value at risk models. These models are often a measure of the risk of loss for investments. The backtest processes can also identify banks using models that can underestimate risk.
It's vital you try to find unbiased data to prevent distorting the strategy's performance. Staying away from biases entirely may not be possible, but investors can mitigate their effects to get as much reliable and transparent results as possible. The following are the biases that can affect a traders' data and their strategy's performance:
Optimization bias describes situations where a trader can introduce additional parameters and win trades until the performance of their strategies matches their expectations. This can result in an artificial inflation of results. Traders can avoid this by letting their strategy run on real-world data, and if they don't see the results they're looking for, they can stop testing it and begin a new plan.
The look-ahead bias is when a trader accidentally includes future dates within the testing simulation. This error can happen because of technical bugs or improper parameter calculations. Traders can avoid these situations by ensuring they double-check their data and testing strategy before going live.
Other types of biases traders can experience when performing a backtest include:
Survivorship bias: This happens when a trader only considers still active stocks and ignores stocks that aren't active anymore. They can avoid this by using recent data in their backtests and monitor delisted stocks as a part of their strategy.
Psychological tolerance bias: This refers to a trader performing a backtest for a long-term period to improve the strategy's performance, only to trade on a short-term basis. The trader can avoid this by focusing on a plan that matches their timeframe.
What is the best backtest strategy?
There isn't a definite best strategy for trading in the financial markets. The best strategy often depends on the trader's personality, goals, and experience. The following are two methods that a trader might consider using as a part of their backtest strategy:
A trader with an interest in day trading, which involves buying and selling positions by the end of the trading day, can manually backtest intraday charts. The easiest backtest can include looking at a chart's one-minute or five-minute timeframes of the asset. These can provide the trader with the details of the asset's profit that week. The trader can find previous trades using their strategy and then add the profits and losses.
Backtest vs. forward test
Forward testing simulates a live trading condition with real-world stock data, but the trader can trade with funds with no real monetary value. This often requires a trader to watch the market live and use their plan to watch for an entry or exit signal as they happen. Forward testing is a method that can confirm or refute whether a trader's strategy has profit potential after performing a backtest. This form of live-testing can be slower because traders execute their strategy as the information comes instead of using previous historical data that's already available.
A trader can use forward tests and backtests together to provide them with a complete understanding of how their strategy may perform, both historically and in real-time.
How to backtest a strategy
Traders can use the following steps to perform a backtest on their trading strategy:
1. Locate historical data
Traders can feed their backtest system with accurate historical data. When testing a strategy using historical data, it's usually necessary for the trader to specify a period for their test. They can then use another data set for an alternative period. The reason traders often test their strategy over different periods is to validate the strategy's reliability and mitigate any changes they didn't plan for during the process.
2. Establish parameters
During this step, the trader sets up some parameters according to the complexity of the backtest. These parameters can include the capital at risk, initial capital, commission fees, portfolio size, benchmark, and average bid-ask spread. The trader can also establish specific parameters of their trading strategy. These may include instructions for stop loss and trailing stop loss, when to close a position, take profit level, or type of positions they prefer.
3. Test data sets
The trader can now use the information they collect to simulate various trades over a specific period. They can also run the process again using another data set when they conclude the backtest. This may help ensure they cut off potential biases and events that the trader didn't plan. Traders can also use backtest software that supports automated strategy optimization. These features allow a computer to determine what input might work well with their strategy. It may also provide them with ideas on how to optimize their model.
How traders evaluate backtest results
When the backtest is complete, traders can interpret the results and see how the strategy performs. There isn't a standard to define the success of a trading strategy, but the analysis the trader performs typically focuses on some key components, such as the assets of the portfolio, the unique characteristics of the strategy, and the market environment. Some trading strategies are riskier than others, but they can also achieve greater results, while others can be more conservative and might lead to a smaller increase in value.
A trader can often evaluate their results by defining their risk tolerance and goals. After running the backtest, they can check whether the strategy generates the results they want. The backtest method the trader uses can generate results for different measures. These measures typically include total return over time, net profit and loss, market exposure, and volatility.
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