On Data-Driven Drawdown Control with Restart Mechanism in Trading
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866910309053104128 |
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| author | Hsieh, Chung-Han |
| author_facet | Hsieh, Chung-Han |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_02613 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | On Data-Driven Drawdown Control with Restart Mechanism in Trading Hsieh, Chung-Han Optimization and Control Computational Finance Risk Management 91G10, 91G70 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. |
| title | On Data-Driven Drawdown Control with Restart Mechanism in Trading |
| topic | Optimization and Control Computational Finance Risk Management 91G10, 91G70 |
| url | https://arxiv.org/abs/2303.02613 |