Why is it imperative to test all trading strategies before putting them into action? Whether the program you use to choose, execute and close positions is bot-based or simply a written list of rules, it’s imperative to know whether it can deliver real-world results. No one wants to lose their capital, so testing is the only way to double-check the correctness of all the assumptions, programming integrity, and other components of a trading strategy before risking funds.
Besides reviewing the logical soundness of your assumptions, be sure to do plenty of backtesting on actual data. After that, it will be time to do one or more dry runs and establish a workable feedback loop. Evaluating feedback is the heart and soul of the entire testing process. Another crucial step is making the necessary formula and code adjustments. If something looks wrong and you don’t know how to fix it, be willing to get outside help from a tech pro who can sniff out coding or programming errors. Finally, spend at least one full business week watching the system while it’s in action. Here are the pertinent details about how to proceed.
Checking the Assumptions
What’s the most efficient way to see whether trading program assumptions make logical sense? It’s a two-step cycle. First, mentally review the original logic behind each component of the plan. Then, come up with a specific example, based on current marketplace data, on which you can test the theory in its shortest form. It’s critical to measure whether each concept meets the parameters of logic.
If one of the rules is never purchase a security whose 50-day moving average has fallen below the 200-day average within the past week and remains below it, it would be helpful to find several examples of similar situations in the recent past and see what happened afterward. Then, run through the logic in your mind to verify that a falling moving average (50 going below 200) is a typical sign of weakness in price action. Attempt to find objective support from an authoritative source that confirms your basic concepts and beliefs.
Backtesting on Real Data
Backtesting on actual numbers is the most involved element of testing because it includes large amounts of data and thousands of transactions. To learn more on backtesting trading strategies, review a comprehensive guide on the subject. Decide how far in the past you want to go, which data sets make sense to include, and how to apply your new program to the data in a way that delivers readable and relevant results.
Don’t assume that a glowing outcome from backtesting is a guarantee of future success. Some bots and rules-based systems only work on similar markets that are closely related to the current and near-term future situations. What looks good on paper in the past, in other words, might not pan out in the coming months. Likewise, a negative backtesting outcome doesn’t mean your ideas are worthless. But it’s best if backtesting does show at least a glint of promise for the method you have in mind.
Establishing a Feedback Loop
In science, there’s a crucial step in creating a logically sound experimentation environment. The feedback loop is also a central component of the development of a worthwhile trade strategy. Without the ability to funnel fresh data through a screening process, it’s nearly impossible to measure, verify, correct, or otherwise evaluate early results. Jot down the proposed steps of your feedback arrangement so you’ll know how to visualize the entire process. A common initial screen is a simple check on the mechanics of the coding or order entry. If the program or method you’re using can’t enter a position successfully, then there’s a basic problem with the original plan. Arrange the loop so you can keep an eye on all the core tasks, from order placement to exit.
The final stage is long-term observation. Note that it’s a relative term because it only lasts for one week. But during that time, traders should be doing daily checks and tweaks to their programs if necessary. It’s customary to rewrite programmed bots and redo written trading rules whenever general market dynamics change. What kinds of changes warrant a redo? One of the clearest examples is when bear markets turn bullish, or vice versa. Likewise, it’s common for the best systems to run out of steam after a year or so, regardless of economic or financial conditions. The simple act of observing your theories in real-world action is the relevant, rubber meets the road phase of the entire feedback loop evaluation system.
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