With the rapid development of advanced intelligence systems, traders with historically-driven trade ideas will thrive. Algorithms can now analyze historical trends to influence traders’ strategies.
The reason these algorithms and mathematical models are accurate and consistent is due to backtesting. In this blog post, we will discuss what exactly backtesting is and how and why you need to implement it immediately.
While most people have heard of “backtesting” before, very few understand the importance of it and how to implement it. Backtesting before making long-term trade decisions or strategy formations can not only save you from unexpected losses, but it can optimize your gains.
However, there are also downsides to this. Many who don’t fully understand backtesting try implementing it without fully developing strategies; immense loss results from this failure. The rest of the post will go into more detail about the advantages and disadvantages of backtesting.
What is backtesting?
Backtesting is the process of reconstructing trades with historical data that would have occurred in the past using rules defined by your individual strategy. The result of this reconstruction will provide statistics that give feedback on the effectiveness of the strategy.
Backtesting is based on the idea that a strategy in the past, with similar data, will work again in the future. It is based on large amounts of data and takes user input to see results.
Backtesting provides valuable statistical feedback about a strategy. Some of these statistics include volatility metrics, win/loss ratios, annualized return, profit/loss, etc. Many trading softwares include a backtesting service, in which you as the trader customize the settings for the backtesting. Some softwares even include added functionality, such as position sizing and optimization.
What are some tips when using backtesting trading strategies?
When implementing backtesting trade strategies, there are a few caveats and tricks to ensure your inputs will optimize your trades and produce high-quality results. For example, it is essential to consider broad market trends in the time frame a strategy was tested. If a given strategy only was tested from 2007-2011, it will have very different results than if that strategy was executed from 2018-2020 (two different markets and financial positions of the economy).
Another rule to follow is that you must consider volatility measures, one of the key functions in backtesting. When developing a trading system, volatility has the greatest impact on your short-term profit/loss performance. Seek to keep volatility low to reduce risk and increase the ability to transition in and out of a security.
What are some downsides of backtesting?
No strategy or model is perfect or will lead to profit 100% of the time. Backtesting is no exception. For example, it can sometimes lead to something called “over-optimization.” This occurs when results are tuned in so highly to the past that the expectation is unrealistic for the future.
To combat this, implement rules that apply to all stocks and all sectors and are not over-optimized. Also, backtesting is not always the most accurate in gauging a trade system’s effectiveness. Sometimes, external factors that were not present in the past may negatively impact a trading strategy.
Overall, backtesting has the potential to positively impact your trading strategies. By customizing backtesting software, you can test and analyze a trading strategy based on historical data.
While it is not effective or accurate 100% of the time, in similar economic situations, backtesting serves as evidence to follow through a certain strategy. It is essential to know what you need to customize and how you will transition between trades via backtesting.
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