Mean Reversion Strategy
A mean reversion strategy assumes that prices tend to return to a typical value after extreme moves. The strategy seeks to buy when price is far below its mean and sell when price is far above it.
Core components
- A definition of the mean, such as a moving average or VWAP.
- A measure of deviation, such as standard deviation or a z score.
- Entry and exit rules based on deviation thresholds.
- Risk controls for trend days when reversion fails.
Example
A trader uses a 20 period moving average and enters when price is 2 standard deviations below the mean. The trade exits when price returns to the mean.
Strengths and weaknesses
Mean reversion can work in range bound markets, but it can suffer during strong trends or structural breaks. Filters for regime and volatility help reduce risk.
Practical notes
Transaction costs and slippage can erode small edge strategies. Backtesting with realistic costs is essential.
Practical checklist
- Define the time horizon for Mean Reversion Strategy 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 Reversion Strategy 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 Reversion Strategy, 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 Reversion Strategy. 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 Reversion Strategy 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 Reversion Strategy 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 Reversion Strategy 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 Reversion Strategy, 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 Reversion Strategy. 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 Reversion Strategy 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 Reversion Strategy 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 Reversion Strategy 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 Reversion Strategy, 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.