MetaPS: Adaptive Programmatic Strategy Selection for Market Agents

2026-06-21

AI Summary

This work addresses the instability and lack of interpretability in directly using large language models (LLMs) for trading decisions, as well as the inadequacy of single fixed strategies in dynamic markets. The authors propose an executable decision-making paradigm that adaptively selects optimal modules from a programmable strategy library and generates actions conditioned on market states, rather than emitting raw trading signals. Their approach leverages simulation-based supervision, converting simulated performance into state–strategy paired data to enable efficient and interpretable strategy selection. Experiments across multi-asset and commodity trading environments demonstrate that fine-tuned models ranging from 0.8B to 9B parameters significantly outperform fixed strategies, end-to-end LLMs, and API-based agents—with even smaller models surpassing more powerful API-driven counterparts.

Abstract

No single market strategy always wins: momentum, mean reversion, risk control,and event-driven rules can each succeed or fail as market conditions change.Rather than asking large language models to directly generate market actions,we study an executable decision paradigm where an agent selects from a library of programmatic strategies, each implemented as a code module mapping market observations to actions.We propose \textbf{MetaPS}, a simulation-guided framework for adaptive programmatic strategy selection. MetaPS rolls out candidate strategies in simulated or backtested markets, identifies states where particular strategies lead to better future outcomes, and converts these state--strategy pairs into supervised fine-tuning data. During inference, the simulator is no longer queried: MetaPS observes only the current market state and candidate strategy context, selects a suitable strategy program, and the selected program produces the final action. Experiments on multi-stock trading and a controlled goods-exchange sandbox show that MetaPS consistently improves across model scales from 0.8B to 9B parameters. It outperforms fixed-strategy baselines, direct decision-making agents, and prompted API-based LLM agents; in several settings, compact fine-tuned models even surpass stronger API models. These results demonstrate that market simulations can provide scalable and targeted supervision for learning adaptive, interpretable, and executable strategy selection.

Problem

• market strategy selection
• adaptive decision-making
• programmatic trading
• market conditions
• strategy adaptation

Innovation

• adaptive strategy selection
• programmatic trading
• simulation-guided learning
• market simulation
• executable decision paradigm