Jesus Rodriguez: 10 Reasons Why Quant Strategies for Crypto Fail

Quant strategies remain constrained to relatively simple techniques such as statistical arbitrage 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.

Most of the quant strategies proven effective in traditional capital markets are likely to not work as well when applied to crypto assets.

Based on some of our recent experience at IntoTheBlock working on predictive models and quant strategies, I've listed some of the factors that I believe can cause the failure of quant models for crypto assets.

A side effect of the small market datasets in crypto assets is the propensity of most machine learning quant models to overfit or to "Optimize for the training dataset." We constantly see quant models that perform incredibly well during backtesting just to fail when applied to real market conditions.

Blockchain datasets remain one of the richest sources of alpha for quant strategies in the crypto space.

This causes many crypto quant desks to spend numerous hours trying to recreate factor-based strategies that are highly unlikely to perform in the crypto space.

In the crypto space, the quant infrastructure of most hedge funds remains relatively simple which makes it difficult to operate certain types of strategies.

Crypto is an ideal asset class for quant strategies and, in the long run, quant funds should be the dominant investment vehicle in crypto.