On the design of searching algorithm for parameter plateau in quantitative trading strategies using particle swarm optimization
Jimmy Ming‐Tai Wu, Wen-Yu Lin, Ko-Wei Huang, Mu‐En Wu · Knowledge-Based Systems · 2024
Quantitative trading, relying on diverse parameter combinations, is becoming increasingly the norm for trading strategies in financial investments. The performance of these strategies is intricately linked to these parameters. However, the performance on the training set after backtesting does not ensure success on a test set and may lead to overfitting. This study emphasizes enhancing stability and robustness in trading-strategy parameters by introducing a ’parameter plateau.’ Traditional brute-force methods for exploring high-dimensional parameter spaces can be intricate and time-consuming. To address this challenge, we present an efficient alternative that identifies stable and robust parameters by configuring parameter plateaus to mitigate overfitting risks. A step-by-step search algorithm is proposed to determine the optimal parameters, leveraging the power of particle-swarm optimization. In continuous, multi-dimensional solution spaces, particle-swarm optimization is invaluable for the swift and effective discovery of the desired solutions. Experiments underscore the substantial influence of the parameter plateau concept on parameter selection, highlighting the pivotal role of particle-swarm optimization in efficiently navigating complex solution spaces and thereby enabling the discovery of stable and profitable trading strategies.