Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative Trading

TL;DR

This paper introduces a novel offline reinforcement learning approach for quantitative trading by fine-tuning a pre-trained GPT-2 model with LoRA within a Decision Transformer framework, achieving competitive results with existing methods.

Contribution

It combines pre-trained language models and LoRA for efficient offline RL in trading, addressing temporal dependencies and overfitting issues.

Findings

Model learns effectively from expert trajectories.

Achieves superior rewards in certain trading scenarios.

Performs competitively with established offline RL algorithms.

Abstract

Developing effective quantitative trading strategies using reinforcement learning (RL) is challenging due to the high risks associated with online interaction with live financial markets. Consequently, offline RL, which leverages historical market data without additional exploration, becomes essential. However, existing offline RL methods often struggle to capture the complex temporal dependencies inherent in financial time series and may overfit to historical patterns. To address these challenges, we introduce a Decision Transformer (DT) initialized with pre-trained GPT-2 weights and fine-tuned using Low-Rank Adaptation (LoRA). This architecture leverages the generalization capabilities of pre-trained language models and the efficiency of LoRA to learn effective trading policies from expert trajectories solely from historical data. Our model performs competitively with established offline RL algorithms, including Conservative Q-Learning (CQL), Implicit Q-Learning (IQL), and Behavior Cloning (BC), as well as a baseline Decision Transformer with randomly initialized GPT-2 weights and LoRA. Empirical results demonstrate that our approach effectively learns from expert trajectories and secures superior rewards in certain trading scenarios, highlighting the effectiveness of integrating pre-trained language models and parameter-efficient fine-tuning in offline RL for quantitative trading. Replication code for our experiments is publicly available at https://github.com/syyunn/finrl-dt