System for foreign exchange trading using genetic algorithms and reinforcement learning

Hryshko, Andrei; Downs, T.

doi:10.1080/00207720412331303697

Cited by 14 publications

(14 citation statements)

References 12 publications

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“…Comparable studies of other foreign exchange trading systems in the literature (using interday, non-leveraged rates over periods ranging from a partial year up to two years) yielded final profits of 1 to 6% [11], 0.1 to 5% [1], and 6.5% [9]. Our results are thus promising; however, it should be noted that final profit depends heavily on the trading period chosen (especially the arbitrary stopping point) and is not completely comparable across studies.…”

Section: Profitability Analysismentioning

confidence: 99%

“…Profits of 89% for GBP/USD and 80% for JPY/USD were achieved for the 10 year test period, but the authors state that these returns are low for a such a long test period. Hryshko and Downs [9] applied a GA to the evolution of entry and exit trading rules, along with reinforcment learning, to the EUR/USD rate from June 2 to December 31, 2002 with a five minute frequency. The authors found that the system achieved profitability of 7% for the first 2 months out of sample, and 6.5% in the 3.5 months out of sample.…”

Section: Genetic Programming For Forex Tradingmentioning

confidence: 99%

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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: Profitability Analysismentioning

confidence: 99%

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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“…The GBLM was trained using extreme learning machine similar to standard artificial neural networks [5]. Reinforcement learning could also be used as a subsequent step to genetic algorithm for forecasting of foreign exchange rates [27]. Hryshko et al in 2004 initially used a genetic algorithm for in-sample trading strategy search to select the optimal trading strategy of entry and exit rules [27].…”

Section: Advanced Learning Strategies In Reinforcement Learningmentioning

confidence: 99%

“…Reinforcement learning could also be used as a subsequent step to genetic algorithm for forecasting of foreign exchange rates [27]. Hryshko et al in 2004 initially used a genetic algorithm for in-sample trading strategy search to select the optimal trading strategy of entry and exit rules [27]. The data, namely the financial indicators making up the rules were then passed on to the reinforcement learning, Q-learning algorithm engine [27].…”

Section: Advanced Learning Strategies In Reinforcement Learningmentioning

confidence: 99%

“…Hryshko et al in 2004 initially used a genetic algorithm for in-sample trading strategy search to select the optimal trading strategy of entry and exit rules [27]. The data, namely the financial indicators making up the rules were then passed on to the reinforcement learning, Q-learning algorithm engine [27]. The algorithm could then be used online to continually search for the optimal strategy through repetitive experience [27].…”

Section: Advanced Learning Strategies In Reinforcement Learningmentioning

confidence: 99%

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Reinforcement Learning in Financial Markets

Meng

Khushi

2019

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Recently there has been an exponential increase in the use of artificial intelligence for trading in financial markets such as stock and forex. Reinforcement learning has become of particular interest to financial traders ever since the program AlphaGo defeated the strongest human contemporary Go board game player Lee Sedol in 2016. We systematically reviewed all recent stock/forex prediction or trading articles that used reinforcement learning as their primary machine learning method. All reviewed articles had some unrealistic assumptions such as no transaction costs, no liquidity issues and no bid or ask spread issues. Transaction costs had significant impacts on the profitability of the reinforcement learning algorithms compared with the baseline algorithms tested. Despite showing statistically significant profitability when reinforcement learning was used in comparison with baseline models in many studies, some showed no meaningful level of profitability, in particular with large changes in the price pattern between the system training and testing data. Furthermore, few performance comparisons between reinforcement learning and other sophisticated machine/deep learning models were provided. The impact of transaction costs, including the bid/ask spread on profitability has also been assessed. In conclusion, reinforcement learning in stock/forex trading is still in its early development and further research is needed to make it a reliable method in this domain.Google Scholar was used to search for the reinforcement learning articles for this systematic review. By typing into Google Scholar the key phrases "reinforcement learning forex" and "reinforcement learning stock trading" and then all the results were filtered according to the selection process given below in Figure 1. The search results returned from Google Scholar was automatically sorted by relevance, therefore only the first few pages are selected for manual inspection for eligibility. Afterwards, the results of the searched phrases were manually inspected without opening the article links to determine the most relevant articles for this systematic review. The selected articles from this step were then opened and their content read through to determine the final list of articles to be reviewed. Of 27 articles reviewed, 20 articles implemented or simulated trades to maximise profit and 7 articles were only interested in forecasting future financial asset prices. Of 20 trading articles 11 articles provided comparison with other models and 10 did not.

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Portfolio Investments in the Forex Market

Juszczuk

Kozák

2022

Intelligent Information and Database Systems

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System for foreign exchange trading using genetic algorithms and reinforcement learning

Cited by 14 publications

References 12 publications

Interday foreign exchange trading using linear genetic programming

Interday foreign exchange trading using linear genetic programming

Reinforcement Learning in Financial Markets

Portfolio Investments in the Forex Market

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