Discussing the article: "Integrate Your Own LLM into EA (Part 5): Develop and Test Trading Strategy with LLMs(IV) — Test Trading Strategy"
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Check out the new article: Integrate Your Own LLM into EA (Part 5): Develop and Test Trading Strategy with LLMs(IV) — Test Trading Strategy.
Author: Yuqiang Pan
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Check out the new article: Integrate Your Own LLM into EA (Part 5): Develop and Test Trading Strategy with LLMs(IV) — Test Trading Strategy.
With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.
In previous articles, we introduced how to fine-tune pre-trained GPT-2 models using different methods to make GPT-2 perform tasks according to our wishes, and we compared these methods across several dimensions. Of course, we only introduced several commonly used methods, which does not mean that only these methods can be used to fine-tune GPT-2 models. You can try to fine-tune GPT-2 using other methods based on our example implementation process, compare them, and choose a better model. If you encounter any issues during this process, you can leave a comment at the end of the article.
Now, our fine-tuned GPT-2 model has the initial capability to execute simple quantitative trading strategies. Therefore, this article will introduce how to integrate our fine-tuned model into our quantitative trading strategy. The model used in the example is the GPT-2 model fine-tuned with Adapter-tuning (specific article link: Integrate Your Own LLM into EA (Part 5): Develop and Test Trading Strategy with LLMs (III) – Adapter-Tuning). So, unless otherwise specified, all references to GPT-2 in this article refer to this model.
However, it should be noted that the model we fine-tuned is based on limited data for demonstration purposes and cannot handle real trading environments. Without testing and optimization, do not use them directly in real trading, which is of utmost importance. Our previous prediction code was completed in the Python environment, but MQL5, as a highly integrated programming language for the MetaTrader 5 platform, provides powerful tools to develop Expert Advisors (EAs). Therefore, to implement automated quantitative trading strategies, we need to return to the MQL5 environment. This article will step-by-step achieve this process.
Let's see how to migrate this trained model from the Python environment to the MQL5 EA, making it run directly on the MetaTrader 5 platform to support real-time trading decisions.
Author: Yuqiang Pan