Mean Deviation
Mean deviation is a measure of variability defined as the average absolute deviation from the mean. It is used to describe dispersion in a data series.
Formula
Mean Deviation = (1/n) * sum(|x_i - mean|)
Interpretation
A larger mean deviation indicates a wider spread of values around the mean. It is less sensitive to extreme outliers than variance because it uses absolute values instead of squares.
Example
For values 10, 12, 14 with mean 12, the absolute deviations are 2, 0, 2. The mean deviation is (2 + 0 + 2) / 3 = 1.33.
Use in trading
Mean deviation appears in indicators such as the Commodity Channel Index, where it helps normalize price deviations.
Practical checklist
- Define the time horizon for Mean Deviation and the market context.
- Identify the data inputs you trust, such as price, volume, or schedule dates.
- Write a clear entry and exit rule before committing capital.
- Size the position so a single error does not damage the account.
- Document the result to improve repeatability.
Common pitfalls
- Treating Mean Deviation as a standalone signal instead of context.
- Ignoring liquidity, spreads, and execution friction.
- Using a rule on a different timeframe than it was designed for.
- Overfitting a small sample of past examples.
- Assuming the same behavior in abnormal volatility.
Data and measurement
Good analysis starts with consistent data. For Mean Deviation, confirm the data source, the time zone, and the sampling frequency. If the concept depends on settlement or schedule dates, align the calendar with the exchange rules. If it depends on price action, consider using adjusted data to handle corporate actions.
Risk management notes
Risk control is essential when applying Mean Deviation. Define the maximum loss per trade, the total exposure across related positions, and the conditions that invalidate the idea. A plan for fast exits is useful when markets move sharply.
Variations and related terms
Many traders use Mean Deviation alongside broader concepts such as trend analysis, volatility regimes, and liquidity conditions. Similar tools may exist with different names or slightly different definitions, so clear documentation prevents confusion.
Practical checklist
- Define the time horizon for Mean Deviation and the market context.
- Identify the data inputs you trust, such as price, volume, or schedule dates.
- Write a clear entry and exit rule before committing capital.
- Size the position so a single error does not damage the account.
- Document the result to improve repeatability.
Common pitfalls
- Treating Mean Deviation as a standalone signal instead of context.
- Ignoring liquidity, spreads, and execution friction.
- Using a rule on a different timeframe than it was designed for.
- Overfitting a small sample of past examples.
- Assuming the same behavior in abnormal volatility.
Data and measurement
Good analysis starts with consistent data. For Mean Deviation, confirm the data source, the time zone, and the sampling frequency. If the concept depends on settlement or schedule dates, align the calendar with the exchange rules. If it depends on price action, consider using adjusted data to handle corporate actions.
Risk management notes
Risk control is essential when applying Mean Deviation. Define the maximum loss per trade, the total exposure across related positions, and the conditions that invalidate the idea. A plan for fast exits is useful when markets move sharply.
Variations and related terms
Many traders use Mean Deviation alongside broader concepts such as trend analysis, volatility regimes, and liquidity conditions. Similar tools may exist with different names or slightly different definitions, so clear documentation prevents confusion.
Practical checklist
- Define the time horizon for Mean Deviation and the market context.
- Identify the data inputs you trust, such as price, volume, or schedule dates.
- Write a clear entry and exit rule before committing capital.
- Size the position so a single error does not damage the account.
- Document the result to improve repeatability.
Common pitfalls
- Treating Mean Deviation as a standalone signal instead of context.
- Ignoring liquidity, spreads, and execution friction.
- Using a rule on a different timeframe than it was designed for.
- Overfitting a small sample of past examples.
- Assuming the same behavior in abnormal volatility.
Data and measurement
Good analysis starts with consistent data. For Mean Deviation, confirm the data source, the time zone, and the sampling frequency. If the concept depends on settlement or schedule dates, align the calendar with the exchange rules. If it depends on price action, consider using adjusted data to handle corporate actions.
Risk management notes
Risk control is essential when applying Mean Deviation. Define the maximum loss per trade, the total exposure across related positions, and the conditions that invalidate the idea. A plan for fast exits is useful when markets move sharply.