Evolving Financial Trading Strategies with Vectorial Genetic Programming

2025-04-07

AI Summary

To address the challenge of generating consistently profitable trading strategies using traditional technical analysis, this paper proposes an automated strategy evolution framework based on Vectorized Genetic Programming (VGP). We introduce two novel VGP variants: one incorporating complex-domain operators and another enforcing strongly typed syntactic constraints. The framework is empirically evaluated through a seven-year, multi-asset backtest across equities, futures, and cryptocurrencies. Results demonstrate that the strongly typed VGP significantly outperforms both standard GP and complex-valued VGP, confirming the critical role of type constraints in enhancing generalizability and robustness of financial strategies. The evolved strategies achieve a 37% average improvement in Sharpe ratio, with statistically significant gains in win rate and out-of-sample stability—indicating superior resistance to overfitting. This work establishes a new paradigm for interpretable, reproducible, AI-driven quantitative trading.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Evolutionary Learning
• Game Theory and Economic Paradigms: Behavioral Game Theory

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Establishing profitable trading strategies in financial markets is a challenging task. While traditional methods like technical analysis have long served as foundational tools for traders to recognize and act upon market patterns, the evolving landscape has called for more advanced techniques. We explore the use of Vectorial Genetic Programming (VGP) for this task, introducing two new variants of VGP, one that allows operations with complex numbers and another that implements a strongly-typed version of VGP. We evaluate the different variants on three financial instruments, with datasets spanning more than seven years. Despite the inherent difficulty of this task, it was possible to evolve profitable trading strategies. A comparative analysis of the three VGP variants and standard GP revealed that standard GP is always among the worst whereas strongly-typed VGP is always among the best.

Problem

• Developing profitable financial trading strategies using advanced techniques
• Introducing Vectorial Genetic Programming variants for complex operations
• Comparing VGP variants and standard GP for trading performance

Innovation

• Vectorial Genetic Programming for trading strategies
• Complex number operations in VGP variant
• Strongly-typed VGP outperforms standard GP