Average Range
Average range measures the typical price movement within a period by averaging the high-low range over a chosen window. It is a simple volatility proxy that focuses on intraday movement.
Calculation
Average Range over N periods: Average Range = (1/N) * sum(High - Low)
Use in Trading
Average range is used to size stops, estimate daily volatility, and gauge whether a session is unusually active or quiet. It can also help identify breakout conditions when the current range expands beyond the average.
Difference from ATR
Average range does not include gaps between sessions because it ignores the previous close. Average True Range (ATR) accounts for gaps and is more comprehensive for gap-prone markets.
Limitations
Because it ignores closing gaps, average range can underestimate volatility in markets with frequent overnight moves. It is best used alongside other volatility measures.
Computation Details
Compute the metric on consistent sampling intervals. If the input uses prices, decide whether to use close, typical, or average prices. If the input uses returns, document the return type and whether log or simple returns are used.
When the metric is annualized, use a consistent period count, such as 252 for trading days. The result should be comparable across instruments only if the same conventions are used.
Interpretation
Metrics provide a summary of behavior rather than a full distribution. High values can indicate opportunity or risk depending on the context. It is helpful to compare the current value to its own history rather than rely on a fixed threshold.
Applications
Metrics support position sizing, risk limits, and performance evaluation. They also help identify regime changes when the metric shifts outside its normal range.
Combining the metric with trend or liquidity indicators can reduce false signals.
Pitfalls and Data Issues
Common pitfalls include using data with gaps, mixing adjusted and unadjusted prices, or using too short a window. For thinly traded assets, the metric can be distorted by outliers.
Example Scenario
Consider a liquid instrument with stable spreads and average volatility. A rule based implementation can be tested on a multi year sample and then on an out of sample period. The goal is to verify that the behavior of Average Range is consistent across regimes and that the edge does not depend on a narrow set of conditions.
Implementation Checklist
- Confirm data quality and consistent timestamps
- Define entry and exit rules in plain language
- Validate position sizing and risk limits
- Track execution costs and slippage
- Review performance by regime and by instrument
Example Scenario
Consider a liquid instrument with stable spreads and average volatility. A rule based implementation can be tested on a multi year sample and then on an out of sample period. The goal is to verify that the behavior of Average Range is consistent across regimes and that the edge does not depend on a narrow set of conditions.
Implementation Checklist
- Confirm data quality and consistent timestamps
- Define entry and exit rules in plain language
- Validate position sizing and risk limits
- Track execution costs and slippage
- Review performance by regime and by instrument
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