- author = "Xinpeng Long and Michael Kampouridis and Panagiotis Kanellopoulos",
- title = "Genetic Programming for Combining Directional Changes Indicators in International Stock Markets",
- booktitle = "Parallel Problem Solving from Nature - PPSN XVII - 17th International Conference, PPSN 2022, Proceedings, Part II",
- year = "2022",
- editor = "Guenter Rudolph and Anna V. Kononova and Hernan E. Aguirre and Pascal Kerschke and Gabriela Ochoa and Tea Tusar",
- volume = "13399",
- series = "Lecture Notes in Computer Science",
- pages = "33--47",
- address = "Dortmund, Germany",
- month = sep # " 10-14",
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, Directional changes, Algorithmic trading",
- timestamp = "Tue, 16 Aug 2022 16:15:42 +0200",
-
biburl = "
https://dblp.org/rec/conf/ppsn/LongKK22.bib",
- bibsource = "dblp computer science bibliography, https://dblp.org",
- isbn13 = "978-3-031-14720-3",
-
DOI = "
doi:10.1007/978-3-031-14721-0_3",
-
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.
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.",
- notes = "PPSN2022",
