Mean Reversion Algo Trading with Python

Unlike trend-following strategies, mean reversion bets that prices will return to their historical average. It is one of the oldest and most academically studied strategies, forming the backbone of many algorithmic trading python systems.

Statistical Foundation

Mean reversion requires assets that are statistically stationary or cointegrated. Use the Augmented Dickey-Fuller (ADF) test to verify stationarity before deploying any mean reversion algo trading strategy.

from statsmodels.tsa.stattools import adfuller\nresult = adfuller(price_series)\nprint("p-value:", result[1])  # p < 0.05 suggests stationarity

Bollinger Band Entry

A common entry trigger is a price crossing two standard deviations below the moving average. This is a classic signal in python crypto trading strategies for range-bound markets.

Risks

Mean reversion fails catastrophically in trending markets. Always use a stop-loss and monitor your strategy's regime exposure. A market that was mean-reverting for six months can start trending violently on a single macro event.