Mean Reversion Trading
Mean reversion trading is the broader practice of trading around the idea that prices, spreads, or ratios revert toward a typical level. It can be applied intraday or over longer horizons.
Common approaches
- Single asset reversion using moving averages or bands.
- Pairs trading where a spread reverts to a historical mean.
- Statistical arbitrage across baskets of related assets.
Example
Two highly correlated stocks diverge. A trader buys the underperformer and sells the outperformer, expecting the spread to narrow.
Key risks
- Regime shifts can change the mean itself.
- Correlations can break during stress.
- Overfitting can create false signals.
Practical notes
Successful mean reversion trading depends on strong risk limits, robust data, and a clear definition of what constitutes normal behavior.
Practical checklist
- Define the time horizon for Mean Reversion Trading 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 Trading 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 Trading, 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 Trading. 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 Trading 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 Trading 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 Trading 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 Trading, 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 Trading. 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 Trading 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 Trading 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 Trading 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 Trading, 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.