In recent years, quantitative (quant) trading has gone from mysticism to being part of the everyday vocabulary of capital markets. The rapid proliferation of algorithmic trading together with trends such as machine learning has some experts thinking that every trading fund will eventually become a quant fund. Crypto was born in this golden era of quant financing and its digital and programmable nature makes it an ideal asset class for quant strategies. However, quant trading in crypto is both incredibly challenging and different from other asset trading.
There are a number of relatively simple factors that make quant strategies for crypto assets unique. To put those factors in perspective, we can start by understanding the history of quant trading since its inception.
A brief history of quant finance
The roots of quantitative finance/trading can be traced back to mathematicians such as Louis Bachelier and his seminal work, “Theory of Speculation,” which outlined a model to price options under normal distributions. Bachelier’s ideas were mostly forgotten for over a century before being rediscovered by economists including Paul Samuelsonand Robert Merton in their work in options pricing.
In the first half of the 20th century, most of the work around quant finance still lacked practical applications. This started to change in the 1950s when Harry Markowitz relied on computational finance methods to solve portfolio optimization problems, opening the doors to algorithmic trading across large numbers of securities. A remarkable figure in the history of quant finance has been the famous mathematician and hedge fund manager Edward Thorp, who adapted a lot of his work predicting and simulating blackjack card games to exploit pricing anomalies in securities markets. Very similar ideas to Thorp’s were formalized by economists Fischer Black and Myron Scholes when developing the Black–Scholes model, which was awarded the 1997 Nobel Prize in Economics. These ideas are still at the center of modern quant strategies, including those in crypto.
Despite its roots in academic research, the history of quant trading is tightly linked to technological developments in capital markets. From the transition from floor trading to electronic markets, the emergence of dark pools or the renaissance of movements such as machine learning, most pivotal moments have been enabled by technological breakthroughs.
The early 2000s became the golden era of quant trading, with billions of dollars flowing from traditional discretionary funds to quant alternatives. This is the universe into which crypto was born. Crypto represents not only a new asset class but a technological breakthrough in financial markets and, as a result, presented a new landscape for quant trading.
What makes quant for crypto different?
Despite the diversity in financial markets, the mechanics of quant strategies remain relatively similar across asset classes. It turns out that quant strategies that trade oil futures or traditional equities are incredibly similar in terms of datasets, techniques and infrastructure. Even more interesting, the technological evolutions in financial markets have benefited all asset classes fairly evenly. For instance, when dark pools were established, they were used by high frequency trading (HFT) funds to trade all sorts of financial instruments. From that perspective, quant trading technology has evolved at a very similar pace across all asset classes.
Crypto is the first asset class that combines new financial instruments with incremental technology improvements such as programmability or decentralization. There are several factors that make quant strategies in crypto unique, but most of them can be grouped into three fundamental categories: New Sources of Alpha, Programmable Financial Primitives and Unconventional Risk Models.
Despite the confluence of positive factors, building quant strategies in crypto is different than in traditional capital markets. Unexplored sources of alpha, a new generation of financial primitives and unconventional risk models are some of the factors pushing the boundaries of quant trading in crypto.
Crypto might not have been designed for quant strategies but it could end up being the asset class that catalyzes a new wave of innovation in the quant space.
X FORCE Foundation believes that it should seize this opportunity to do more in-depth research in this field, thereby promoting the development of the entire encryption industry. Therefore, X FORCE cooperated with GrandBit, which has many years of experience as a cryptocurrency market maker, to set up a laboratory.
GrandBit is an experienced cryptocurrency market maker, which focuses on providing strategic consultants, market-making robot for CEX and DEX, as well as docking services on the tokens for the project party. And built a good reputation in the industry for a long time.
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