Mean Reversion Trading with Indicators and Cointegrated Pairs
Summary
The document introduces mean reversion as the tendency for prices or returns to move back toward a historical average after deviating from it. It outlines ways to define that average, detect deviations, and form signals using moving averages, RSI, and MACD. It also describes cointegrated pairs trading: identify related instruments whose spread is stationary, then buy the underperformer and sell the outperformer when the spread diverges. The Augmented Dickey-Fuller test is mentioned for assessing cointegration, and the half-life of a reverting series can help estimate a holding period.
The guide gives conceptual explanations and strategy examples, including thresholds based on standard deviations and possible stop-loss rules, but it does not provide complete, independently assessable performance evidence. Its central assumption—that a deviation will revert—can fail, especially when market conditions or relationships change. The text also discusses position sizing, stops, diversification, and volatility assessment as risk controls, while offering no quantified evaluation of their effectiveness.
Key ideas
- Mean reversion strategies look for prices or spreads that have moved unusually far from an estimated average.
- RSI, moving averages, and MACD are presented as tools for identifying possible entry conditions.
- Pairs trading uses a stationary relationship between instruments to trade temporary spread divergence.
- Cointegration differs from correlation because it concerns whether a combination of series remains stationary.
- Standard deviation thresholds, stop losses, position sizing, and holding periods are discussed as risk controls.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.