Optimizing Parameters Without Fooling Yourself
Posted: Sun Oct 04, 2026 11:45 am
Optimizing Parameters Without Fooling Yourself
Optimization means testing different settings to find the best-performing ones. Done carefully, it improves a strategy. Done carelessly, it creates fantasies.
The key is to look for plateaus, not peaks. If a moving average period of 20 gives the best result, check 15, 18, 22, and 25. If they all perform reasonably, the idea is robust, and the best number matters little. If only 20 works, and neighbors fail, you have found noise.
Limit the number of parameters you optimize. Each additional parameter multiplies the combinations and the chance of finding something that fits by luck. Optimizing two settings is manageable. Optimizing eight is a recipe for overfitting.
Use a sensible performance measure. Maximizing net profit alone often selects risky settings. Consider metrics that include drawdown, such as profit divided by maximum drawdown, or expectancy in R with a minimum number of trades.
Choose the middle of a stable region instead of the single best point. It tends to hold up better in the future.
Keep part of the data untouched for validation, and run the chosen settings on it once.
Finally, accept that optimal settings drift over time, and periodic review is better than constant retuning.
Practical step: pick one parameter in your strategy and produce a table of results for five values on each side of your current choice.
Optimization means testing different settings to find the best-performing ones. Done carefully, it improves a strategy. Done carelessly, it creates fantasies.
The key is to look for plateaus, not peaks. If a moving average period of 20 gives the best result, check 15, 18, 22, and 25. If they all perform reasonably, the idea is robust, and the best number matters little. If only 20 works, and neighbors fail, you have found noise.
Limit the number of parameters you optimize. Each additional parameter multiplies the combinations and the chance of finding something that fits by luck. Optimizing two settings is manageable. Optimizing eight is a recipe for overfitting.
Use a sensible performance measure. Maximizing net profit alone often selects risky settings. Consider metrics that include drawdown, such as profit divided by maximum drawdown, or expectancy in R with a minimum number of trades.
Choose the middle of a stable region instead of the single best point. It tends to hold up better in the future.
Keep part of the data untouched for validation, and run the chosen settings on it once.
Finally, accept that optimal settings drift over time, and periodic review is better than constant retuning.
Practical step: pick one parameter in your strategy and produce a table of results for five values on each side of your current choice.