Backtesting is a key component of effective trading system development. It is accomplished by reconstructing, with historical data, trades that would have occurred in the past using rules defined by a given strategy. The result offers statistics to gauge the effectiveness of the strategy.
However, there are many factors traders pay attention to when they are backtesting trading strategies. Here is a list of some important things to remember while backtesting.
Keep in mind the broad market trends in the time frame in which a given strategy was tested. For example, if a strategy was only backtested from 1999-2000, it may not fare well in a bear market. It is often a good idea to backtest over a long time frame that includes several different types of market conditions.
Consider the universe in which backtesting occurred. For example, if a broad market system is tested with a universe consisting of tech stocks, it may fail to do well in different sectors. As a general rule, if a strategy is slated towards a specific genre of stock, limit the universe to that genre. However, in all other cases, keep a large universe for testing purposes.
Volatility measures are extremely important to consider in developing a trading system. This is especially true for leveraged accounts, which receive margin calls if their equity drops below a certain point. Traders should seek to keep volatility low in order to reduce risk and enable an easier transition in and out of a given stock.
Number of Bars
The average number of bars held is also very important to watch when developing a trading system. Although most backtesting software consists of commission costs in the final calculations, that does not mean you should ignore this statistic. If possible, raising your average number of bars held can lessen commission costs, and improve your overall return.
Exposure is a double-edged sword. Increased exposure can lead to higher profits or higher losses, while decreased exposure means lower profits or lower losses.
However, it is basically a good idea to keep exposure below 70% in order to reduce risk and enable an easier transition in and out of a given stock.
The average-gain/loss statistic, combined with the wins-to-losses ratio, can be useful for determining optimal position, sizing and money management using techniques. Traders can take larger positions and reduce commission costs by increasing their average gains and increasing their wins-to-losses ratio.
Annualized return is important because it is a tool that benchmarks a system\’s returns against other investment venues. It is important not only to look at the overall annualized return but also to take into account the increased or decreased risk. This can happen by looking at the risk-adjusted return, which accounts for various risk factors.
Backtesting customization is extremely important. Many backtesting applications have input for commission amounts, round (or fractional) lot sizes, tick sizes, margin requirements, interest rates, slippage assumptions, position-sizing rules, same-bar exit rules, (trailing) stop settings and much more.
To get the most accurate backtesting results, it is important to tune these settings to mimic the broker when the system goes live.
Backtesting can sometimes lead to something known as over-optimization. This is a condition where performance results are tuned so highly to the past that they are no longer as accurate in the future. It is generally a good idea to implement rules that apply to all stocks, or a select set of targeted stocks, and are not optimized to the extent that the rules are no longer understandable by the creator.
Backtesting is not always the most accurate way to evaluate how effective a given trading system is. Sometimes strategies that performed well in the past fail to do well in the present.
Past performance doesn’t indicate future results. Be sure to paper trade a system that has been successful before going live to be sure that the strategy still applies in practice.
Backtesting is one of the most main aspects of developing a trading system. If created and interpreted properly, it can help traders optimize and improve their strategies, find any technical or theoretical flaws, as well as gain confidence in their strategy before applying it to the real world markets.
See Also: Common Active Trading Strategies
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