Five Machine Learning Methods Crypto Traders Should Know About

Friday 16 October 2020, 11:37 PM AEST - 1 week ago


In a recent article, I discussed the relevance of the machine learning techniques powering the famous OpenAIs 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.

Jesus Rodriguez is the CEO of IntoTheBlock, a market intelligence platform for crypto assets. He has held leadership roles at major technology companies and hedge funds. He is an active investor, speaker, author and guest lecturer at Columbia University in New York.

Models such as GPT-3 or Googles 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 hasnt 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. Thats 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. Quantita ...

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