A Multi-step Approach for Minimizing Risk in Decentralized Exchanges

TL;DR

This paper presents a three-step method combining machine learning and optimization techniques to minimize risk in Automated Market Makers within decentralized exchanges, reducing computational load and improving accuracy.

Contribution

The authors introduce a novel multi-step approach that integrates Kernel Ridge Regression with direct optimization to efficiently minimize risk in decentralized exchange models.

Findings

Reduced computational complexity in risk minimization

Enhanced accuracy of the risk minimization process

Effective application to Automated Market Maker functions

Abstract

Decentralized Exchanges are becoming even more predominant in today's finance. Driven by the need to study this phenomenon from an academic perspective, the SIAG/FME Code Quest 2023 was announced. Specifically, participating teams were asked to implement, in Python, the basic functions of an Automated Market Maker and a liquidity provision strategy in an Automated Market Maker to minimize the Conditional Value at Risk, a critical measure of investment risk. As the competition's winning team, we highlight our approach in this work. In particular, as the dependence of the final return on the initial wealth distribution is highly non-linear, we cannot use standard ad-hoc approaches. Additionally, classical minimization techniques would require a significant computational load due to the cost of the target function. For these reasons, we propose a three-step approach. In the first step, the target function is approximated by a Kernel Ridge Regression. Then, the approximating function is minimized. In the final step, the previously discovered minimum is utilized as the starting point for directly optimizing the desired target function. By using this procedure, we can both reduce the computational complexity and increase the accuracy of the solution. Finally, the overall computational load is further reduced thanks to an algorithmic trick concerning the returns simulation and the usage of Cython.