Financial Forecasting with the Combination of Physical and Event-based Time using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8355

@PhdThesis{Xinpeng_Long:thesis,

• author = "Xinpeng Long",
• title = "Financial Forecasting with the Combination of Physical and Event-based Time using Genetic Programming",
• school = "School of Computer Science and Electronic Engineering, University of Essex",
• year = "2024",
• address = "UK",
• month = oct,
• keywords = "genetic algorithms, genetic programming, GP-DC-PT, alpha, apple stock price",
• URL = "https://repository.essex.ac.uk/40709/1/PhD_Xinpeng__Edited_%20%281%29.pdf",
• size = "182 pages",
• abstract = "This thesis explores the application of genetic programming (GP) within the directional changes (DC) framework for algorithmic trading. Traditional algorithmic trading methods rely on datasets with fixed time intervals, such as hourly or daily data, leading to a discontinuous representation of time. DC provides an alternative by transforming these datasets into event-driven sequences, allowing for a unique price analysis approach. The first part of the thesis compares GP with machine learning (ML) algorithms in algorithmic trading, focusing on factors like market data, time periods, forecasting windows, and transaction costs-variables often neglected in previous studies. A comprehensive evaluation of a GP-based financial approach is conducted, comparing it to nine popular ML algorithms and the buy-and-hold strategy, using daily data from 220 datasets across 10 international markets. Results show that GP not only yields profitable results but also outperforms ML algorithms in terms of risk and Sharpe ratio. The second part investigates GP within the DC framework, introducing two novel algorithms: GP-DC, which uses only DC-based indicators, and GP-DC-PT, which combines DC-based and physical-time indicators from technical analysis. Both approaches out- perform non-DC-based GP strategies, technical analysis, and buy-and-hold benchmarks, with GP-DC-PT achieving an average return of over 18 percent, highlighting the advantage of incorporating DC into trading strategies. Finally, the thesis introduces two multi-objective optimization algorithms, MOO2 and MOO3, based on the NSGA-II framework, which optimize two and three fitness functions, respectively, using DC and physical-time indicators. Both MOO2 and MOO3 outperform single-objective methods, with MOO3 showing consistent improvements across all metrics. These findings suggest that incorporating directional changes significantly enhances trading strategies return and risk performance",

}

Genetic Programming entries for Xinpeng Long

Citations