IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2008.09471.html
   My bibliography  Save this paper

GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading

Author

Listed:
  • Zezheng Zhang
  • Matloob Khushi

Abstract

Foreign exchange is the largest financial market in the world, and it is also one of the most volatile markets. Technical analysis plays an important role in the forex market and trading algorithms are designed utilizing machine learning techniques. Most literature used historical price information and technical indicators for training. However, the noisy nature of the market affects the consistency and profitability of the algorithms. To address this problem, we designed trading rule features that are derived from technical indicators and trading rules. The parameters of technical indicators are optimized to maximize trading performance. We also proposed a novel cost function that computes the risk-adjusted return, Sharpe and Sterling Ratio (SSR), in an effort to reduce the variance and the magnitude of drawdowns. An automatic robotic trading (RoboTrading) strategy is designed with the proposed Genetic Algorithm Maximizing Sharpe and Sterling Ratio model (GA-MSSR) model. The experiment was conducted on intraday data of 6 major currency pairs from 2018 to 2019. The results consistently showed significant positive returns and the performance of the trading system is superior using the optimized rule-based features. The highest return obtained was 320% annually using 5-minute AUDUSD currency pair. Besides, the proposed model achieves the best performance on risk factors, including maximum drawdowns and variance in return, comparing to benchmark models. The code can be accessed at https://github.com/zzzac/rule-based-forextrading-system

Suggested Citation

  • Zezheng Zhang & Matloob Khushi, 2020. "GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading," Papers 2008.09471, arXiv.org.
    • Handle: RePEc:arx:papers:2008.09471
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2008.09471
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Slimane Sefiane & Mohamed Benbouziane, 2012. "Portfolio Selection Using Genetic Algorithm," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 2(4), pages 1-9.
    2. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    3. Taylor, Mark P. & Allen, Helen, 1992. "The use of technical analysis in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 11(3), pages 304-314, June.
    4. Rosdyana Mangir Irawan Kusuma & Trang-Thi Ho & Wei-Chun Kao & Yu-Yen Ou & Kai-Lung Hua, 2019. "Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market," Papers 1903.12258, arXiv.org.
    5. Yun-Cheng Tsai & Jun-Hao Chen & Jun-Jie Wang, 2018. "Predict Forex Trend via Convolutional Neural Networks," Papers 1801.03018, arXiv.org.
    6. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jaideep Singh & Matloob Khushi, 2021. "Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating," Papers 2103.09106, arXiv.org.
    2. Srivinay & B. C. Manujakshi & Mohan Govindsa Kabadi & Nagaraj Naik, 2022. "A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network," Data, MDPI, vol. 7(5), pages 1-11, April.
    3. Akhilesh Prasad & Arumugam Seetharaman, 2021. "Importance of Machine Learning in Making Investment Decision in Stock Market," Vikalpa: The Journal for Decision Makers, , vol. 46(4), pages 209-222, December.
    4. Yunze Li & Yanan Xie & Chen Yu & Fangxing Yu & Bo Jiang & Matloob Khushi, 2021. "Feature importance recap and stacking models for forex price prediction," Papers 2107.14092, arXiv.org.
    5. Zexin Hu & Yiqi Zhao & Matloob Khushi, 2021. "A Survey of Forex and Stock Price Prediction Using Deep Learning," Papers 2103.09750, arXiv.org.
    6. Mukul Jaggi & Priyanka Mandal & Shreya Narang & Usman Naseem & Matloob Khushi, 2021. "Text Mining of Stocktwits Data for Predicting Stock Prices," Papers 2103.16388, arXiv.org.
    7. Gil Cohen, 2024. "Polynomial Moving Regression Band Stocks Trading System," Risks, MDPI, vol. 12(10), pages 1-15, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. James Wallbridge, 2020. "Transformers for Limit Order Books," Papers 2003.00130, arXiv.org.
    2. Taewook Kim & Ha Young Kim, 2019. "Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-23, February.
    3. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
    4. Zhaofeng Zhang & Banghao Chen & Shengxin Zhu & Nicolas Langren'e, 2024. "Quantformer: from attention to profit with a quantitative transformer trading strategy," Papers 2404.00424, arXiv.org, revised Oct 2024.
    5. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," LawArXiv kczj5, Center for Open Science.
    6. Ehsan Hoseinzade & Saman Haratizadeh & Arash Khoeini, 2019. "U-CNNpred: A Universal CNN-based Predictor for Stock Markets," Papers 1911.12540, arXiv.org.
    7. Sergio Consoli & Luca Tiozzo Pezzoli & Elisa Tosetti, 2022. "Neural forecasting of the Italian sovereign bond market with economic news," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 197-224, December.
    8. Firuz Kamalov, 2019. "Forecasting significant stock price changes using neural networks," Papers 1912.08791, arXiv.org.
    9. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2019. "Deep Reinforcement Learning for Trading," Papers 1911.10107, arXiv.org.
    10. Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
    11. Adamantios Ntakaris & Moncef Gabbouj & Juho Kanniainen, 2023. "Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting," Papers 2304.09840, arXiv.org, revised May 2023.
    12. Jungsik Hwang, 2020. "Modeling Financial Time Series using LSTM with Trainable Initial Hidden States," Papers 2007.06848, arXiv.org.
    13. Ehsan Hoseinzade & Saman Haratizadeh, 2018. "CNNPred: CNN-based stock market prediction using several data sources," Papers 1810.08923, arXiv.org.
    14. Sangyeon Kim & Myungjoo Kang, 2019. "Financial series prediction using Attention LSTM," Papers 1902.10877, arXiv.org.
    15. Saeed Nosratabadi & Amir Mosavi & Puhong Duan & Pedram Ghamisi, 2020. "Data Science in Economics," Papers 2003.13422, arXiv.org.
    16. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
    17. Iwao Maeda & David deGraw & Michiharu Kitano & Hiroyasu Matsushima & Hiroki Sakaji & Kiyoshi Izumi & Atsuo Kato, 2020. "Deep Reinforcement Learning in Agent Based Financial Market Simulation," JRFM, MDPI, vol. 13(4), pages 1-17, April.
    18. Qi Zhao, 2020. "A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data," Papers 2010.07404, arXiv.org.
    19. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.
    20. Hanyao Gao & Gang Kou & Haiming Liang & Hengjie Zhang & Xiangrui Chao & Cong-Cong Li & Yucheng Dong, 2024. "Machine learning in business and finance: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2008.09471. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.