Drawdown Duration

Drawdown duration measures the length of time it takes for a strategy or portfolio to recover from a peak to the prior high. It complements drawdown depth by focusing on time rather than magnitude.

Calculation

• Identify a peak value
• Track the time until the equity curve exceeds that peak
• The number of periods between these points is the drawdown duration

Use in Risk Analysis

Long drawdown durations can stress investor patience and operational risk. Strategies with short durations are often more manageable, even if drawdowns are similar in size.

Reporting

Drawdown duration is often reported alongside maximum drawdown. Some investors care more about time under water than the size of the loss.

Practical Considerations

Drawdown duration depends on sampling frequency and portfolio valuation rules. It should be measured consistently across strategies for comparison.

Operational Notes

Definitions and conventions should be consistent across datasets and venues. A small difference in data fields or session boundaries can change outcomes, especially for short term strategies. Document inputs and assumptions so results can be reproduced.

If the concept depends on exchange rules or broker behavior, confirm those rules for the specific venue. Operational details often explain why a trade behaved differently than expected.

Stress Scenarios

During volatility spikes, liquidity can evaporate and price gaps can appear. Under these conditions, indicators can lag, order types can misfire, and spreads can widen sharply.

Stress testing the concept against fast markets, thin liquidity, and sudden news helps reveal hidden risks. If a strategy only works in calm conditions, size and timing should reflect that.

Documentation Tips

Keep a short checklist of the rules, parameters, and decision points. Record how the concept is used in live trading and compare it to backtest assumptions. This makes future refinement easier and reduces drift in execution.

Common Questions

Traders often ask how sensitive results are to parameter choices, how the concept behaves in different regimes, and whether it scales with size. Answering these questions early improves reliability and prevents overfitting.

Checklist

• Define the exact rule in plain language
• Validate data quality and timing
• Quantify execution costs
• Set risk limits and stop logic
• Review performance by regime

Checklist

• Define the exact rule in plain language
• Validate data quality and timing
• Quantify execution costs
• Set risk limits and stop logic
• Review performance by regime

Checklist

• Define the exact rule in plain language
• Validate data quality and timing
• Quantify execution costs
• Set risk limits and stop logic
• Review performance by regime

Checklist

• Define the exact rule in plain language
• Validate data quality and timing
• Quantify execution costs
• Set risk limits and stop logic
• Review performance by regime

Checklist

• Define the exact rule in plain language
• Validate data quality and timing
• Quantify execution costs
• Set risk limits and stop logic
• Review performance by regime

Checklist

• Define the exact rule in plain language
• Validate data quality and timing
• Quantify execution costs
• Set risk limits and stop logic
• Review performance by regime

Checklist

• Define the exact rule in plain language
• Validate data quality and timing
• Quantify execution costs
• Set risk limits and stop logic
• Review performance by regime

Checklist

• Define the exact rule in plain language
• Validate data quality and timing
• Quantify execution costs
• Set risk limits and stop logic
• Review performance by regime

Checklist

• Define the exact rule in plain language
• Validate data quality and timing
• Quantify execution costs
• Set risk limits and stop logic
• Review performance by regime

Checklist

• Define the exact rule in plain language
• Validate data quality and timing
• Quantify execution costs
• Set risk limits and stop logic
• Review performance by regime

Checklist

• Define the exact rule in plain language
• Validate data quality and timing
• Quantify execution costs
• Set risk limits and stop logic
• Review performance by regime

Checklist

• Define the exact rule in plain language
• Validate data quality and timing
• Quantify execution costs
• Set risk limits and stop logic
• Review performance by regime

Checklist

• Define the exact rule in plain language
• Validate data quality and timing
• Quantify execution costs
• Set risk limits and stop logic
• Review performance by regime

Checklist

• Define the exact rule in plain language
• Validate data quality and timing
• Quantify execution costs
• Set risk limits and stop logic
• Review performance by regime

Checklist

• Define the exact rule in plain language
• Validate data quality and timing
• Quantify execution costs
• Set risk limits and stop logic
• Review performance by regime