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Quant strategies now in DeFi

Quant DeFi Strategies

Before delving into viable DeFi strategies, we must first define the quant and explain how it works.

What is Quant or Algorithmic Trading?

A significant shift in trading that has taken place in the last couple of decades is the rise of quant or algorithmic trading. Quant is responsible for changing the entire landscape of trading, in cryptocurrencies, in all markets. Period!

The decision-making capability of human beings is discussed in seconds, minutes, and hours. For instance, a decent chess player can take a few seconds or minutes to decide to come up with their best moves in a game. 

When it comes to cognitive functioning, the limit of human decision-making capability ends at around 1,000ms. On the other hand, anything faster than milliseconds becomes increasingly complex, especially when several parameters are thrown into the mix. Once that happens, the decision-making process gets bogged down. 

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The advent of algorithmic trading has made it a unanimous choice and continues to grow unabatedly, especially in the crypto investment markets, where more and more institutional investors are heading. Institutional investors often rely on algorithmic trading and volume to enter any trading area.

Many investors forget when dealing with quant that a bot doing the trading isn’t magic, and in particular, to have a computer make lightning-fast decisions, it has to have data. No data, no directions on making decisions based on that data, no algorithmic trading, period!

This creativity is called the data-first perspective. What makes the DeFi ecosystem particularly good for Quant trading is the fact that it is data-rich. Even though DeFi quant is still in its nascent stages, many exciting developments are beginning to influence this market area. Let us discuss the basics of designing quant strategies in the DeFi space.

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What Characteristics Make Quant Ideal for DeFi Markets?

One of the reasons DeFi is suited for algorithmic trading is its financial dynamics.

From a purely computer-based algorithmic standpoint, three unique qualities make DeFi an ideal arena to try out quant trading strategies:

  1. All Data In Hand:  Unlike the traditional financial market that is centrally controlled and humanly managed, DeFi, and by extension, blockchain network, is decentralized. All transactions recorded are immutable and recorded in public ledgers that anyone can download and inspect. This, in turn, creates an implicit trust that no other centralized trading platform can rival. Additionally, this phenomenon also produces a unique collection of data points that can provide superior signals for quant models. This level of transparency, along with a trustless and decentralized system and the absence of intermediaries, is uniquely ideal for creating accurate predictive and simulation models.
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  1. Customizable: The introduction of Smart Contracts as part of the Blockchain 2.0 of the Ethereum networks has been, undoubtedly, the most significant influencer for the explosive growth of DeFi. From an algorithmic prediction perspective, to fulfill contracts automatically without intervention from a third party is a godsend. Based on the idea that a trader can simultaneously deploy all building blocks of a trading strategy, starting from borrowing to execution of strategy to insurance can intuitively be included through smart contracts, enabling an unfathomable automation level just a decade ago. This allows quant strategy and financial modeling sophistication that is simply impossible in traditional capital markets.
  2. Volatile and Price Inefficient:  The purpose of Quant models and algorithmic trading is to attempt to exploit systemic inefficiencies in financial markets. To some degree, the modern quant financial modeling has its roots in the Black-Sholes formula meant for identifying inefficiencies in options markets. Consequently, the DeFi, systemic inefficiencies should not be seen as a bug but a feature. Like the automated market makers(AMMs), adopted automatic features utilize inefficiencies as their primary motive, which will not change anytime soon. If seen through that lens, the DeFi market is “regularly inefficient” by nature, not by error, which translates to the fact that money-making opportunities for algorithmic trading will be ever-present as an everyday occurrence.
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Crypto is an entirely new investment vehicle that is completely digital and thus makes for a perfect candidate for testing quant models. However, trading bots and algorithmic trading systems have remained relatively simple, for instance, statistical arbitrage with a pair of securities. Another important fact is that institutional quant desks are absent in the crypto (especially DeFi) market.

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Although crypto assets are highly suitable for quant strategies, let’s briefly discuss some fundamental reasons for the causes of failure of most quant strategies in the crypto space:

  1. Limited Data Set: In traditional money markets, AI-based machine learning-based quant strategies implemented are trained on decades of data from conventional capital markets. The entire history of crypto started 9n 2009, and the amount of time various assets have been around being in months, even for Bitcoin and Ethereum, the data available is relatively small. Implementing any machine learning model fails to generalize to learn any knowledge from such small datasets. For example, If you were to attempt to build a quant prediction model for crypto like ChainLink (LINK), which has been performing spectacularly recently. You’ll be disappointed to learn that LINK’s trading history data can fit in months worth of traditional market models, which is no way near what is needed to train most machine learning models in quant finance.
  2. Outliers and Extremes: The price volatility of crypto assets with massive price crashes or sudden spikes in the span of just a few hours primarily produces data that can’t be normalized. Such events are mainstays in the trading history of many crypto assets. Most models will error out in the machine learning world with massive prices because the training data is often more constant. In practice, many machine learning quant models fail miserably to predict such massive movements over minor events like a tweet from Elon Musk. It is challenging to train a quant model with those types of events during the model’s training.
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In conclusion, Quant DeFi strategies are sure to be very useful due to the factors we discussed. Still, it will require time to collect enough quality data without the market volatility crypto markets exhibit regularly.

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