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On Data-Driven Drawdown Control with Restart Mechanism in Trading

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  • Chung-Han Hsieh

Abstract

This paper extends the existing drawdown modulation control policy to include a novel restart mechanism for trading. It is known that the drawdown modulation policy guarantees the maximum percentage drawdown no larger than a prespecified drawdown limit for all time with probability one. However, when the prespecified limit is approaching in practice, such a modulation policy becomes a stop-loss order, which may miss the profitable follow-up opportunities if any. Motivated by this, we add a data-driven restart mechanism into the drawdown modulation trading system to auto-tune the performance. We find that with the restart mechanism, our policy may achieve a superior trading performance to that without the restart, even with a nonzero transaction costs setting. To support our findings, some empirical studies using equity ETF and cryptocurrency with historical price data are provided.

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  • Chung-Han Hsieh, 2023. "On Data-Driven Drawdown Control with Restart Mechanism in Trading," Papers 2303.02613, arXiv.org.
    • Handle: RePEc:arx:papers:2303.02613
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    References listed on IDEAS

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    1. Alexei Chekhlov & Stanislav Uryasev & Michael Zabarankin, 2005. "Drawdown Measure In Portfolio Optimization," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 13-58.
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    3. Leonard Maclean & Edward Thorp & William Ziemba, 2010. "Long-term capital growth: the good and bad properties of the Kelly and fractional Kelly capital growth criteria," Quantitative Finance, Taylor & Francis Journals, vol. 10(7), pages 681-687.
    4. Chung-Han Hsieh, 2022. "On Robustness of Double Linear Trading with Transaction Costs," Papers 2209.12383, arXiv.org.
    5. Sanford J. Grossman & Zhongquan Zhou, 1993. "Optimal Investment Strategies For Controlling Drawdowns," Mathematical Finance, Wiley Blackwell, vol. 3(3), pages 241-276, July.
    6. Pei-Ting Wang & Chung-Han Hsieh, 2022. "On Data-Driven Log-Optimal Portfolio: A Sliding Window Approach," Papers 2206.12148, arXiv.org.
    7. Stephen Boyd & Enzo Busseti & Steven Diamond & Ronald N. Kahn & Kwangmoo Koh & Peter Nystrup & Jan Speth, 2017. "Multi-Period Trading via Convex Optimization," Papers 1705.00109, arXiv.org.
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