In recent discourse, the integration of fine-tuned GPT-2 models into quantitative trading strategies has been detailed. The primary method involves converting the GPT-2 model to ONNX format to allow interoperability within the MQL5 environment. Despite challenges like complexity in conversion and performance issues, ONNX enhances model integration efficiency in EAs.

Alternatively, directly leveraging Python scripts through WinAPI or socket communication offers simplicity and flexibility. However, these methods introduce performance and dependency concerns. Current consensus advocates ONNX conversion for cross-platform support, despite the file size and tokenizer issues in MQL5, necessitating further optimization efforts.
#MQL5 #MT5 #EA #ONNX

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