Optimization of the trading rule in foreign exchange using genetic algorithm

Hirabayashi, Akinori; Aranha, Claus; Iba, Hitoshi

doi:10.1145/1569901.1570106

Cited by 53 publications

(31 citation statements)

References 11 publications

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“…The authors' algorithm outperformed randomly initialized strategies and typically produced average profits from -5 to 15%. Hirabayashi et al [8] analyzed the hourly closing price of USD/JPY and EUR/JPY with data from [2005][2006][2007][2008]. Their GA system outperformed a neural network, with a GA using leveraged trades performing better than a non-leveraged version.…”

Section: Genetic Programming For Forex Tradingmentioning

confidence: 99%

Interday foreign exchange trading using linear genetic programming

Wilson

Banzhaf

2010

Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation

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Foreign exchange (forex) market trading using evolutionary algorithms is an active and controversial area of research. We investigate the use of a linear genetic programming (LGP) system for automated forex trading of four major currency pairs. Fitness functions with varying degrees of conservatism through the incorporation of maximum drawdown are considered. The use of the fitness types in the LGP system for different currency value trends are examined in terms of performance over time, underlying trading strategies, and overall profitability. An analysis of trade profitability shows that the LGP system is very accurate at both buying to achieve profit and selling to prevent loss, with moderate levels of trading activity.

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Section: Genetic Programming For Forex Tradingmentioning

confidence: 99%

Interday foreign exchange trading using linear genetic programming

Wilson

Banzhaf

2010

Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation

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“…On the other hand, some methods to search the optimal parameters to calculate technical indicators using genetic algorithms were proposed [5], [6]. These methods searched only the parameters.…”

Section: A Related Workmentioning

confidence: 99%

“…Some works have been done for acquiring trading strategy using evolutionary computation [3], [4], [5], [6]. Their methods are based on technical analysis, which is one of the two basic types of approaches in stock trading.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Genotype Representations to Acquire Stock Trading Strategy Using Genetic Algorithms

Matsui

Sato

2010

Transactions on Computational Science VIII

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Abstract-Automatic trading methods are important issues in recent financial markets. In this paper, we compare some genotype coding methods of technical indicators and their parameters to acquire stock trading strategy using genetic algorithms (GAs). In previous works, the locus-based representation is widely used for encoding technical indicators on chromosomes in GAs, and the direct coding is also widely adopted for encoding the parameters of the indicators. However, these conventional methods are not so effective for the GA search. Therefore, we propose a new genotype coding methods, namely the allele-based indirect coding. We examine the performance of the proposed and conventional coding methods in stock trading of twenty companies in the first section of the Tokyo Stock Exchange for recent ten years. In our empirical results, the allele-based indirect coding is superior to the other ones both on the cumulative profits and the computational costs.

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“…In the case of a trading algorithm, GAs have been used to determine the optimum trading model parameters, such as the connection weights of the ANN, feature selection, and the settlement width (difference between the closing price of the position and the price at the time of settlement) [5] [6]. In this context, a GA can provide a variety of agents with different trading strategies.…”

Section: Introductionmentioning

confidence: 99%

Predicting Optimal Trading Actions Using a Genetic Algorithm and Ensemble Method

Kuroda¹

2017

IIM

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Machine learning has been applied to the foreign exchange market for algorithmic trading. However, the selection of trading algorithms is a difficult problem. In this work, an approach that combines trading agents is designed. In the proposed approach, an artificial neural network is used to predict the optimum actions of each agent for USD/JPY currency pairs. The agents are trained using a genetic algorithm and are then combined using an ensemble method. We compare the performance of the combined agent to the average performance of many agents. Simulation results show that the total return is better when the combined agent is used.

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Optimization of the trading rule in foreign exchange using genetic algorithm

Cited by 53 publications

References 11 publications

Interday foreign exchange trading using linear genetic programming

Interday foreign exchange trading using linear genetic programming

A Comparison of Genotype Representations to Acquire Stock Trading Strategy Using Genetic Algorithms

Predicting Optimal Trading Actions Using a Genetic Algorithm and Ensemble Method

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