On Data-Driven Drawdown Control with Restart Mechanism in Trading

Paper: arXiv 2303.02613

Authors: 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.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a strong theoretical extension to probabilistic drawdown modulation by introducing a practical data-driven restart mechanism. The empirical validation across ETFs and cryptocurrencies demonstrates clear real-world applicability even under non-zero transaction costs.

Research Flowchart

  flowchart TD
    A["Research Goal:<br>Prevent premature stop-loss in drawdown-controlled trading"] --> B["Data Inputs:<br>Historical Equity ETFs & Cryptocurrency Price Data"]
    B --> C["Methodology:<br>Drawdown Modulation Policy + Data-Driven Restart Mechanism"]
    C --> D["Simulation Process:<br>Backtesting with Transaction Costs & Auto-Tuning"]
    D --> E["Key Outcome:<br>Guarantees Max Drawdown Limit with Probability One"]
    D --> F["Key Outcome:<br>Superior Performance & Profit Capture vs. Non-Restart"]