ProFiT: Program Search for Financial Trading

Matthew Siper, A. Khalifa, L. Soros, Muhammad Umair Nasir, Juyan Azhang, Julian Togelius

Abstract

This paper presents a framework called Program Search for Financial Trading (ProFiT), a large-language-model-driven evolutionary algorithm for automated discovery and continual improvement of trading strategies in financial markets. These markets are inherently non-stationary and thus resist static modeling or prediction, suggesting the need for a more open-ended and adaptive evolutionary approach. ProFiT integrates code-level mutation on a complexifying representation, self-analysis, and walk-forward validation within a closed feedback loop, enabling trading strategies to autonomously evolve in response to changing market conditions. ProFiT consistently outperforms both Random and Buy-and-Hold strategies, which are used as both academic and industry-standard baselines, across seven futures assets. Specifically, it outperforms Random in 100% of cases and surpasses Buy-and-Hold in over 77% of all evolved strategy-asset combinations. Collectively, these results demonstrate that the ProFiT framework yields robust, statistically significant, and risk-adjusted gains across diverse types of financial assets and market dynamics, establishing a practical pathway toward open-ended self-improving algorithmic trading systems.

Source: semanticscholar · PDF

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