Multi-objective optimization case study for algorithmic trading strategies in foreign exchange markets
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Multi‑objective optimization case study for algorithmic trading strategies in foreign exchange markets JeongHoe Lee1 · Navid Sabbaghi2 Received: 29 November 2018 / Accepted: 8 October 2019 © Springer Nature Switzerland AG 2019
Abstract This research focuses on a case study of two approaches for producing algorithmic trading rules in foreign exchange markets using genetic algorithms: multi-objective optimization and spontaneous optimization of design variables. First, while conventional trading systems explore a single-objective function such as the Sharpe ratio or only profit, multi-objective optimization allows us to manage the essential trade-off among profit, standard deviation, and maximum-drop. Our approach improves present trading systems, thus avoiding the possibility of substantial losses and, in addition, it can increase investment profits. Second, design parameters such as trading volume, the amount of historical data, and trading gateways of technical indicators are continuously optimized in real time, in contrast, to traditional trading algorithms that have mostly relied on a few prefixed values for the design variables in an optimization problem. Incorporating these research approaches into a genetic algorithm methodology will improve the robustness of results. Keywords Multi-objective optimization · Trading strategies · Foreign exchange markets · Genetic algorithm JEL classification G110 · C600 · C610 · C630
1 Introduction High-frequency foreign exchange trading strategies have been the focus of much effort in recent years by both traders and researchers. Fundamental analysis, as it relates to foreign exchange trading, requires that many global and macroeconomic factors be taken into account. To incorporate this wide array of influences * JeongHoe Lee 1
Standard & Poor’s (S&P Global Ratings), Model Validation Group, 55 Water Street, 40th Floor, New York, NY 10041, USA
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Saint Mary’s College of California, 1928 St Marys Rd, Moraga, CA 94575, USA
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Digital Finance
into algorithmic trading schemes would be a difficult task. Furthermore, because fundamental factors do not vary significantly over the short time horizons used in algorithmic trading, the task of developing algorithmic trading rules seems to lend itself naturally to technical analysis approaches. On the other hand, technical analysis is the practice by traders of using statistical indicators based on historical price and volume data to identify current price trends, price trend reversals, and other market conditions, and thereby predict future price movements. In this paper, we perform algorithmic searches for the optimum indicator values and for optimum operator values in a multi-objective optimization framework, focusing on the trading market’s needs. Our approach goes beyond what previous studies have done by incorporating multiple objective functions, thus recognizing the trade-offs that market traders must make daily. By incorporating the three objective functions of profit, deviation, and maximum dr
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