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Drawdown Control with a Data-Driven Restart Mechanism

Article arXiv papers · Author: Chung-Han Hsieh
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Summary

The paper modifies a drawdown modulation trading policy by adding a data-driven restart mechanism. The underlying policy is described as keeping maximum percentage drawdown within a prespecified limit with probability one. In practice, however, nearing that limit can make the policy behave like a stop-loss, potentially causing the trader to miss profitable opportunities if prices subsequently recover or continue favorably.

The proposed restart mechanism is intended to retune the system after drawdown pressure, allowing trading to resume rather than remaining effectively stopped. The authors report that the mechanism may improve trading performance relative to the original policy, including when transaction costs are nonzero. They support the proposal with historical-price empirical studies involving equity ETFs and cryptocurrency. The excerpt gives no specific performance measures, restart rules, asset details, or cost assumptions, so the strength and portability of the reported advantage cannot be evaluated from this summary.

Key ideas

  • Drawdown modulation is described as enforcing a prespecified maximum percentage drawdown with probability one.
  • As the drawdown limit approaches, the policy can function like a stop-loss and miss later opportunities.
  • A data-driven restart mechanism is added to allow the trading system to resume and retune its performance.
  • The authors report potentially better performance with restarting, including under nonzero transaction costs.
  • Historical price studies cover equity ETFs and cryptocurrency, but the excerpt gives no detailed results or restart parameters.

Tags

Risk managementEquitiesCryptoBacktesting
Full text
# On Data-Driven Drawdown Control with Restart Mechanism in Trading


# On Data-Driven Drawdown Control with Restart Mechanism in Trading









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.