Optimal Parameter Selection and Indicator Design for Technical Analysis Strategies by Computer Software: An Empirical Analysis of the Taiwan Futures Market

In algorithmic trading, “overfitting” often arises during parameter optimization. To avoid scenarios where in-sample performance is excellent but out-of-sample performance fails to meet expectations, appropriate parameter combinations are crucial for enhancing the robustness of a strategy. To find the appropriate parameter combinations, we identified “parameter plateaus” in the three-dimensional space generated by strategy performance metrics. These plateaus represent parameter combinations where the surrounding performance metrics are relatively similar, reducing the risk of drastic performance drops. Utilizing the four parameter selection methods designed in this study (weighted selection, Standard Deviation Selection, Island Area Selection, and Island Volume Selection), we selected parameter combinations in-sample and validated them out-of-sample, complemented by “rolling window analysis” for long-term profitability stability. We used historical backtesting data from the Taiwan Stock Index Futures, covering the period from 1 January 2000 to 31 December 2022. The data were paired with trading strategies developed based on the moving average technical indicator. Through the four parameter selection methods and the system backtesting approach using rolling windows, we identified parameter combinations in-sample and then validated them out-of-sample. The results showed that the performance metrics improved by more than 50% over those generated using traditional optimal point selection methods, demonstrating the superiority of the parameter selection methods proposed in this study.