Created by W.Langdon from gp-bibliography.bib Revision:1.7954
we present a novel GP that uses DC-based indicators to form trading strategies, namely GP-DC. We evaluate the cumulative return, rate of return, risk, and Sharpe ratio of the GP-DC trading strategies under 33 datasets from 3 international stock markets, and we compare the GP performance to strategies derived under physical time, namely GP-PT, and also to a buy and hold trading strategy. Our results show that the GP-DC is able to outperform both GP-PT and the buy and hold strategy, making DC-based trading strategies a powerful complementary approach for algorithmic trading.",
PPSN2022",
Genetic Programming entries for Xinpeng Long Michael Kampouridis Panagiotis Kanellopoulos