10 Reasons Quant Strategies for Crypto Fail

10 Reasons Quant Strategies for Crypto Fail

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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. 

The terms “crypto” and “quant” seem to go perfectly together. Bitcoin and crypto assets were born during one of the most exciting times in capital markets coinciding with the golden era of quantitative finance. The technological acceleration caused by movements such as cloud computing and big data together with the renaissance of machine learning have collided to cause the perfect storm in favor of the quant revolution. Billions of dollars are shifting hands every year from discretionary funds into quant vehicles, and Wall Street cannot hire mathematicians and machine learning experts fast enough. 

Being a completely digital asset class, crypto seems like the perfect target for quant models. And yet, quant strategies remain constrained to relatively simple techniques such as statistical arbitrage (a pair trade strategy that looks to exploit market inefficiencies in a pair of securities) and we still haven’t seen the emergence of large dominant quant desks in the market. Despite the attractive characteristics of crypto assets for quant strategies, crypto poses unique challenges for quant models and the reality is that most quant strategies in crypto fail. In this article, I would like to explore some of the fundamental but not obvious reasons that can cause the failure of most quant strategies in the crypto space.

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