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Multi-objective optimization case study for algorithmic trading strategies in foreign exchange markets

Author

Listed:
  • JeongHoe Lee

    (Standard & Poor’s (S&P Global Ratings), Model Validation Group)

  • Navid Sabbaghi

    (Saint Mary’s College of California)

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.

Suggested Citation

  • JeongHoe Lee & Navid Sabbaghi, 2020. "Multi-objective optimization case study for algorithmic trading strategies in foreign exchange markets," Digital Finance, Springer, vol. 2(1), pages 15-37, September.
    • Handle: RePEc:spr:digfin:v:2:y:2020:i:1:d:10.1007_s42521-019-00016-9
    DOI: 10.1007/s42521-019-00016-9
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    References listed on IDEAS

    as
    1. Babaei, Sadra & Sepehri, Mohammad Mehdi & Babaei, Edris, 2015. "Multi-objective portfolio optimization considering the dependence structure of asset returns," European Journal of Operational Research, Elsevier, vol. 244(2), pages 525-539.
    2. Lwin, Khin T. & Qu, Rong & MacCarthy, Bart L., 2017. "Mean-VaR portfolio optimization: A nonparametric approach," European Journal of Operational Research, Elsevier, vol. 260(2), pages 751-766.
    3. M. A. H. dempster & C. M. Jones, 2001. "A real-time adaptive trading system using genetic programming," Quantitative Finance, Taylor & Francis Journals, vol. 1(4), pages 397-413.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Multi-objective optimization; Trading strategies; Foreign exchange markets; Genetic algorithm;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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