Collaborative Multiobjective Evolutionary Algorithms in search of better Pareto Fronts. An application to trading systems

TL;DR

This paper introduces a collaborative multi-objective evolutionary algorithm approach to optimize technical indicator parameters in financial and other time series data, improving investment returns over traditional strategies.

Contribution

It presents a novel combination of multiple MOEAs working together to construct a comprehensive Pareto front for real-time financial data optimization.

Findings

Enhanced investment returns compared to Buy & Hold strategy

Effective multi-objective optimization for financial time series

Applicable to other time series like glucose data

Abstract

Technical indicators use graphic representations of data sets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not necessarily known and depend on many factors: the market in which it operates, the size of the time window, and others. This paper focuses on the real-time optimization of the parameters applied for analyzing time series of data. In particular, we optimize the parameters of technical and financial indicators and propose other applications, such as glucose time series. We propose the combination of several Multi-objective Evolutionary Algorithms (MOEAs). Unlike other approaches, this paper applies a set of different MOEAs, collaborating to construct a global Pareto Set of solutions. Solutions for financial problems seek high returns with minimal risk. The optimization process is continuous and occurs at the same frequency as the investment time interval. This technique permits the application of non-dominated solutions obtained with different MOEAs simultaneously. Experimental results show that this technique increases the returns of the commonly used Buy \& Hold strategy and other multi-objective strategies, even for daily operations.