Discussing the article: "Bivariate Copulae in MQL5 (Part 1): Implementing Gaussian and Student's t-Copulae for Dependency Modeling"

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Check out the new article: Bivariate Copulae in MQL5 (Part 1): Implementing Gaussian and Student's t-Copulae for Dependency Modeling.

This is the first part of an article series presenting the implementation of bivariate copulae in MQL5. This article presents code implementing Gaussian and Student's t-copulae. It also delves into the fundamentals of statistical copulae and related topics. The code is based on the Arbitragelab Python package by Hudson and Thames.

Copula-based trading strategies offer an interesting alternative approach to statistical arbitrage by modeling the dependence between two assets using a copula function. Traditional pairs trading relies on temporary divergences from an expected long-term relationship, and a copula can be used to model this relationship to identify trading opportunities. The theoretical advantage of using copulae lies in their ability to capture non-linear and asymmetric dependencies between assets.

With that in mind, this article marks the beginning of a series on the implementation of tools for copula-based trading strategies. In this first installment, we explore the fundamentals of statistical copulae in the context of pairs trading and dependency modeling. We also present MQL5 code for preprocessing datasets before fitting copula models. The broader focus of the series will be a library that implements commonly used copula models. To begin, this article introduces implementations of the Gaussian and Student's t copulae.

Author: Francis Dube