Development of Trading Systems using Genetic Programming with a Case Study

Table of Contents

1 Introduction

1.1 Motivation

1.2 Objective and structure

2 Basic principles and state of the art

2.1 Genetic Programming

2.1.1 Program Structure

2.1.2 Initialization of the GP Population

2.1.3 The Genetic Operators

2.1.4 Fitness Function

2.1.5 Selection

2.1.6 Process of the algorithm

2.1.7 Crossover, building blocks and schemata

2.1.8 Approaches against macromutation

2.1.9 Modularization

2.1.10 Further approaches for improvement

2.2 Artificial Neural Networks

2.2.1 Components of neural networks

2.2.2 Network topologies

2.2.3 Learning methods

2.3 Trading Systems

2.3.1 Tape Reader

2.3.2 Market timing

2.3.3 Position sizing

2.3.4 Comparison of trading systems

2.3.5 Fundamental versus technical analysis

2.3.6 The Currency Market

2.3.7 Approaches for the development of trading systems

3 Draft

3.1 Overview

3.2 Requirements on the software

3.3 Conception of software

3.3.1 The Evolutionary Algorithm

3.3.2 The fitness function

4 Implementation

4.1 Components of the developed software

4.2 Classes of the exchange rate data server

4.3 Classes of the Evolutionary Algorithm

4.4 Overview over the framework ECJ

4.5 Problems during experiments

5 Experiment results

5.1 Results with node weights

5.1.1 Results of the training time period

5.1.2 Results of the validation time period

5.1.3 Results of the test time period

5.1.4 Results as monthly turnovers

5.1.5 Created trading rules

5.2 Results without node weights

5.2.1 Results of the training period

5.2.2 Results of the validation time periods

5.2.3 Results of the test period

5.2.4 Results as monthly returns

5.2.5 Created trading rules

5.3 Identification and application of optimal f

6 Discussion and evaluation

6.1 Outlook

7 Summary

Objective and Research Scope

This thesis aims to apply Genetic Programming (GP) to the automated development of trading systems for the financial market, specifically the currency market (EUR/USD), by analyzing their profitability through historical simulations. The core research question addresses whether GP can generate effective, interpretable trading rules that adapt to changing market conditions, while maintaining a balance between system complexity and practical applicability.

• Application of Genetic Programming for automated trading system generation.
• Use of historical exchange rate data and technical indicators for backtesting.
• Implementation of a modular, software-based framework ("EVAM") for evolutionary development.
• Evaluation of "node weights" to improve interpretability and control macromutation in GP.
• Integration of risk-adjusted performance metrics, including the optimization of "optimal f" for position sizing.

Excerpt from the Book

Summary of Chapters

1 Introduction: Discusses the motivation behind using evolutionary algorithms for automated trading and defines the objectives and structure of the thesis.

2 Basic principles and state of the art: Provides a comprehensive overview of Genetic Programming, Artificial Neural Networks, and technical trading systems as the theoretical foundation.

3 Draft: Outlines the conceptual design and software requirements for the evolutionary asset management system (EVAM).

4 Implementation: Details the technical realization of the software components and the framework integration.

5 Experiment results: Presents the findings from simulations with and without node weights, including performance data and created trading rules.

6 Discussion and evaluation: Analyzes the experimental results and provides an outlook on future potential developments.

7 Summary: Concludes the thesis by revisiting the main findings regarding the applicability of GP in currency trading.

Keywords

Genetic Programming, Trading Systems, Currency Market, Artificial Neural Networks, Backtesting, Evolutionary Algorithms, Node Weights, Technical Analysis, Financial Forecasting, Position Sizing, Optimal f, EUR/USD, Automated Trading, Machine Learning, Optimization

Frequently Asked Questions

What is the primary focus of this thesis?

The thesis focuses on the automated development of trading systems for the currency market using Genetic Programming (GP) to generate profitable and interpretable trading rules based on historical exchange rate data.

Which financial market is analyzed in this work?

The study focuses specifically on the EUR/USD currency pair, utilizing high-frequency historical data to develop and test trading strategies.

What is the main objective of the research?

The primary objective is to design a framework that can generate trading systems capable of adapting to changing market conditions, while balancing the trade-off between rule complexity and the user's ability to interpret those rules.

Which machine learning methodology is utilized?

The work employs Genetic Programming (GP) as a heuristic search algorithm to evolve sets of trading rules. Additionally, it integrates "node weights" to control the evolution process and improve rule interpretability.

What is covered in the main body of the work?

The main body covers the theoretical principles of GP and neural networks, the design and software architecture of the "EVAM" framework, the technical implementation details, and an empirical analysis of experiment results comparing different settings.

Which keywords best describe this research?

Key terms include Genetic Programming, Trading Systems, Currency Market, Backtesting, Evolutionary Algorithms, Position Sizing, and Optimal f.

How does the author handle the problem of overfitting in trading strategies?

The author employs a strategy of rolling time periods (training, validation, and test) to ensure that models are evaluated on unseen data, thus avoiding over-optimization and ensuring better generalization.

What are "node weights" in the context of this GP implementation?

Node weights are numerical properties assigned to individual nodes in the rule tree. They are used to influence the likelihood of mutation or crossover occurring at specific points, thereby helping to preserve successful subtrees and enhance interpretability.

Why does the author prefer GP over Artificial Neural Networks for this task?

While both methods perform well, the author argues that the rules generated by GP are more transparent and interpretable than the "black box" nature of neural networks, which improves user confidence in the generated systems.