Genetic Algorithm Based Quantitative Factors Construction

Abstract

Genetic Algorithm(GA) jumps out of the traditional quantitative factor construction methods. It is a "formula first and logic later" method and makes drastic improvement for existing factors with the help of biological evolution. In this research, a large number of commonly used quantitative factors are introduced as the GA operators, and we use the factors’ Sharpe Ratio and correlation with existing factors to modify the fitness function, so as to construct the GA more suitable for financial investment system. This research has carried out a large number of variations on 206 transaction-data factors that have been used for investment. Multiple rounds of evolutionary iterations show that our research can make existing quantitative factors jump out of local optimal, find more excellent and different factors, reduce the correlation among factors, approach the truth of market data distribution constantly.