α-dominance two-objective Optimization Genetic Programming for Algorithmic Trading under a Directional Changes Environment

We present a novel genetic programming (GP) algorithm that combines physical time and event-based time indicators to trade on the stock market. Rather than only using data in fixed intervals (e.g. daily closing prices), we use directional changes to transform physical time into events and allow the GP to make trading decisions based on when significant price movements have occurred. We use a two-objective fitness function, which simultaneously optimizes return and risk. To overcome challenges with the convergence ability of the multi-objective GP, we apply an α-dominance strategy, which is able to relax the strict Pareto dominance criteria. We run experiments on 110 stocks from 10 international markets and compare results against a single-objective GP, as well as strategies based on technical analysis indicators and buy-and-hold. Results show that the proposed GP algorithm offers statistically significant improvements when compared to the above benchmarks.