Research Repository

Genetic programming for combining directional changes indicators in international stock markets

Long, Xinpeng and Kampouridis, Michail and Kanellopoulos, Panagiotis (2022) Genetic programming for combining directional changes indicators in international stock markets. In: Parallel Problem Solving from Nature (PPSN), 2022-09-10 - 2022-09-14, Dortmund, Germany. (In Press)

[img] Text
PPSN_2022.pdf - Accepted Version
Restricted to Repository staff only until 1 January 2100.

Download (371kB) | Request a copy

Abstract

The majority of algorithmic trading studies use data under fixed physical time intervals, such as daily closing prices, which makes the flow of time discontinuous. An alternative approach, namely directional changes (DC), is able to convert physical time interval series into event-based series and allows traders to analyse price movement in a novel way. Previous work on DC has focused on proposing new DC-based indicators, similar to indicators derived from technical analysis. However, very little work has been done in combining these indicators under a trading strategy. Meanwhile, genetic programming (GP) has also demonstrated competitiveness in algorithmic trading, but the performance of GP under the DC framework remains largely unexplored. In this paper, 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's 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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Genetic Programming; Directional Changes; Algorithmic Trading
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 08 Aug 2022 09:59
Last Modified: 24 Sep 2022 03:31
URI: http://repository.essex.ac.uk/id/eprint/32960

Actions (login required)

View Item View Item