mean-reversionlisted

# Mean Reversion Mean reversion is the statistical tendency for prices, spreads, or other financial variables to return toward a long-run average after deviating from it. A mean-reverting series overshoots its mean, then corrects back -- creating predictable oscillations that can be traded. ## When Mean Reversion Works - **Ranging markets**: Sideways price action with clear support/resistance - **Pairs spreads**: Spread between cointegrated assets reverts to equilibrium - **Oversold/overbought extremes**: RSI, Bollinger Band, or z-score extremes in stationary series - **Funding rate arbitrage**: Perpetual funding rates revert to baseline - **Stablecoin depegs**: Classic mean-reversion opportunity (peg = known mean) - **Post-dump recovery**: Brief mean-reversion windows after initial PumpFun dumps ## When Mean Reversion Fails - Strong trending markets (most crypto most of the time) - Regime changes: what was stationary becomes non-stationary - Structural breaks: token migration, protocol upgrade, delistings - Low liquidity: wide spreads consume mean-reversion profits --- ## Testing for Mean Reversion Before trading mean reversion, you must statistically confirm the series is mean-reverting. Three complementary tests: ### 1. Augmented Dickey-Fuller (ADF) Test Tests the null hypothesis that a series has a unit root (non-stationary). ```python from scipy import stats import numpy as np def adf_test(series: np.ndarray, max_lag: int = 0) -> dict: """Run ADF test. Re