The development of quantitative analytics has always been heavily correlated with the pace and extent of broader technological development. The efforts of early pioneers, such as Harry Markowitz’s work on portfolio selection in the 1950s, and Fischer Black and Myron Scholes' development of pricing models in the 1970s sowed the seeds of a discipline that would flourish in earnest once the financial industry began to transition into the digital era during the latter years of the 20th century.
Over recent decades, the increasing availability of data and the necessary computing power to crunch it has led to more sophisticated trading strategies, with a growing availability of tools and infrastructure to support them. Moreover, the ability to automate trading has enabled transactions at speeds now faster than humans could physically process – limited only by the capacity of the servers running the algorithms. Quant traders and analysts with the skills and experience to generate alpha above and beyond the competition have become highly sought-after talents for financial organizations.
However, even quants used to working at the cutting edge of TradFi will find opportunities and risks within cryptocurrency and decentralized finance that simply don't exist in the established financial markets. For this reason, digital currencies and DeFi represent the next technological leap forward for quantitative analytics. As such, quants need to be equipped with the right knowledge and, importantly, the right tools to tackle this new frontier of finance. This necessity is what drove the development of the Nuant Quantitative System, a set of comprehensive solutions for use in DeFi.
Quants in TradFi mine data from a wide variety of sources to improve the accuracy of their models in predicting the market. Along with direct sources of financial data such as brokers or exchanges, quants are also increasingly seeking out more diverse sources and types of data (such as sentiment analysis) or indirect data (such as consumer credit card spending) to predict sales. These developments effectively blur the boundaries between traditional quant and fundamental investing – a phenomenon known as "quantamental" investing.
With cryptocurrencies and DeFi, everything happens on-chain, providing an unprecedented map of data flows that can be translated into market insights. On-chain data reveals information about the activities of different groups of participants and how they interact with the markets, including long- and short-term holders, miners, whales, exchanges, and more.
Quants can harness this data to develop strategies based on on-chain signals such as exchange flows or long-term holder activity, or using a combination of on- and off-chain sources.
A quantitative approach to risk management in TradFi invariably links risk to price and the development of strategies such as hedging to account for different price-related events.
Crypto's notorious volatility means that price risk is inherent in any digital asset risk management strategy, but it's only one component. In a blockchain environment, risk management involves a multi-faceted approach.
For example, smart contracts can be vulnerable to bugs and manipulation by fraudulent actors. In March 2023, Euler Finance became the victim of a $200 million hack, although the thief later returned the funds. Code security audits by reputable independent firms have become a necessary precaution, although they are far from failsafe.
Governance risk is another factor to consider. DeFi protocols are often subject to decentralized governance by token holders, which is laid down in code. Governance rules must be well-structured and efficient enough not to compromise the protocol's operation. In May 2023, an attacker successfully executed a governance attack on Tornado Cash, a privacy protocol, by exploiting a bug that allowed them to create fraudulent voting capabilities – enough to outweigh legitimate token holders in a poll.
Digital asset volatility also creates economic risks in a DeFi environment. Providing liquidity to lending or token swap pools can result in impermanent loss, where changes in the value of a token pair relative to one another can result in losses that outweigh any gains from yield-generating mechanisms.
TradFi relies on vastly complex systems of intermediaries and ancillary services such as brokers, market makers, or insurers that are often relatively opaque when developing a quant strategy. In contrast, DeFi automates and disintermediates many of these services using smart contracts.
For example, Uniswap's automated market maker (AMM) smart contracts underpin liquidity pools that enable users to swap tokens for a nominal fee paid to token holders who contribute token liquidity to the pool. In launching this model, Uniswap popularized the concept of the decentralized exchange based on a new paradigm that doesn't require market makers. Now, there is also infrastructure in place supporting services such as insurance, thanks to protocols such as Nexus Mutual and Etherisc.
These smart contracts are transparently available on the blockchain, meaning that quants can develop algorithms that interact with them directly, as well as use them as a source of data to drive the development of other quant strategies.
The Nuant Quantitative System (NQS) is a brand-new state-of-the-art, integrated solution specifically designed to address the growing needs of the decentralized finance (DeFi) industry. The NQS provides a robust suite of tools for quantitative analytics, risk management, and research, enabling quant strategists to confidently navigate the complex DeFi ecosystem.
Built on a layered architecture, NQS seamlessly integrates three components: the Data Stream (DS), the Quantitative Layer (QL), and the Integrated Environment (IE). Together, they offer an end-to-end quantitative system tailored to the unique demands of DeFi.
At the heart of the NQS is the Data Stream (DS), a powerful blockchain data infrastructure that combines data fetching, aggregation, efficient storage, and reliable delivery. The Data Stream encompasses a wide range of data types, such as raw data, event-level granularity, balances, transactions, market data, and more. It also includes data from leading DeFi protocols like Uniswap, Aave, and Compound, providing users with a comprehensive and up-to-date view of the DeFi landscape.
This robust data infrastructure ensures users have access to accurate and reliable information crucial for making informed decisions in the fast-paced DeFi market.
The Quantitative Framework (QF) is a state-of-the-art framework that empowers users to model, evaluate, and optimize DeFi protocols and strategies. Leveraging the Data Stream's solid foundation, the QF features cross-protocol simulation and advanced simulations, enabling users to assess the risks of various protocols, analyze performance, and compare DeFi strategies in a unified environment.
These functionalities helps users identify potential synergies, optimize strategies, and effectively manage risks.
Based on the popular Visual Studio Code platform, the Nuant Integrated Environment (IE) is a web-based IDE that enables developers to rapidly build, test, and deploy data-intensive applications and analytics leveraging blockchain data. The IE supports popular programming languages like Python, TypeScript, and Solidity, and offers seamless integration with the NQS's Data Stream and Quantitative Layer.
Additionally, the IE provides a suite of powerful tools for code editing, debugging, and version control, facilitating efficient development and deployment of DeFi applications and strategies.
The evolution of quantitative trading and analytics has been closely linked to technological advancements. As the financial industry transitions into the digital era, cryptocurrencies and DeFi represent the next frontier for quants. The unique characteristics of on-chain data, novel risk factors, and the automation and disintermediation offered by smart contracts present new challenges and opportunities for quantitative analysis and strategies.
The Nuant Quantitative System has been specifically designed to address the needs of DeFi, providing a comprehensive suite of tools for quantitative analytics, risk management, and research. With its robust data infrastructure, advanced modeling capabilities, and integrated development environment, NQS empowers quants to navigate the complex DeFi ecosystem with confidence. The Nuant Portfolio Management System (PMS) already fills a critical gap in the digital asset infrastructure landscape with a unified dashboard and analytics. NQS helps to further bridge the gap between traditional quant approaches and DeFi. To uncover opportunities in DeFi and experience the power of NQS, get in touch with us and book a demo today.
Attacker hijacks Tornado Cash governance via malicious proposal. (2023, May 21). Cointelegraph.
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Euler Says All “Recoverable Funds” Stolen in $200M Hack Have Been Returned. (2023, April 4). Retrieved May 25, 2023. Coindesk.
How Uniswap works | Uniswap. (n.d.). Retrieved May 25, 2023. Uniswap.
Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77–91.
Morgan Stanley. (2018, May). Quantamental investing: The future is now | by Morgan Stanley Investment Management | Harvest. Retrieved May 25, 2023.
Singh, O. (2022, April 25). What is impermanent loss and how to avoid it? Cointelegraph.
https://www.journals.uchicago.edu/doi/10.1086/260062 | Journal of Political Economy: Vol 81, No 3. (1973, May-Jun). Retrieved May 25, 2023.
