Five Machine Learning Methods Crypto Traders Should Know About

Five Machine Learning Methods Crypto Traders Should Know About

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In a recent , I discussed the relevance of the machine learning techniques powering the famous OpenAI’s GPT-3 could have for the crypto market. GPT-3 – which can answer questions, perform language analysis and generate text – might be the most famous achievements in recent years of the deep learning space. But, by no means, is it the most applicable to the crypto space. In this article, I would like to discuss some novel areas of deep learning that can have a near immediate impact in the quant models applied to crypto. 

Models such as GPT-3 or Google’s BERT are the result of a massive breakthrough in deep learning known as language pretrained and transformer models. These techniques, arguably, represent the biggest milestone in the last few years of the deep learning industry and their impact hasn’t gone unnoticed in capital markets

In the last year, there have been active research efforts in quantitative finance exploring how transformer models can be applied to different asset classes. However, the results of these efforts remain sketchy showing that transformers are far from ready to operate in financial datasets and they remain mostly applicable to textual data. But there is no reason to feel bad. While adapting transformers to financial scenarios remains relatively challenging, other new areas of the deep learning space are showing promise when applied in quant models on various asset classes including crypto. 

From many angles, crypto seems to be like the perfect asset class for deep learning-based quant models. That’s because of the  the digital DNA and the transparency of crypto assets and that the rise of crypto has coincided with a renaissance of machine learning and the emergence of deep learning.

After decades of struggle and a couple of so-called “artificial intelligence (AI) winters,” deep learning has finally become real and somewhat mainstream across different areas of the software industry. Quantitative finance has been one of the fastest adopters of new deep learning technologies and research. It is very common for some of the top quant funds in the market to experiment with the same types of ideas coming out of high tech AI research labs such as Facebook, Google or Microsoft. 

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