Fine-Tuning Language Models to Match a Trading Strategy

Summary

This article outlines a workflow for adapting a pretrained language model to produce outputs aligned with a trading strategy, with foreign exchange pairs as the intended application. It explains why fine-tuning can be useful after general pretraining and surveys supervised, continued unsupervised, and reinforcement learning approaches. It also describes full-parameter tuning and parameter-efficient alternatives such as adapters, prompt tuning, and prefix tuning.

The practical example formulates a strategy, creates a dataset of input-output pairs, and fine-tunes GPT-2, with the broader series expected to cover inference, integration into an expert advisor, and historical testing. The article is methodological rather than empirical: it supplies no trading performance results, and it notes that the dataset example is not suitable for every tuning method. It also presents several techniques conceptually without giving complete code examples for all of them.

Key ideas

• Fine-tuning adapts a broadly pretrained language model to a specific downstream task such as expressing a defined trading strategy.
• The article distinguishes supervised, unsupervised continued training, and reinforcement learning approaches to fine-tuning.
• Full-parameter tuning updates the whole model, while adapters, prompt tuning, and prefix tuning aim to reduce training costs.
• The described workflow pairs strategy-aligned inputs and outputs in a dataset, then fine-tunes GPT-2.
• The article does not report trading results, and its example dataset is not presented as generally suitable for all methods.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.