Mean Reversion

Mean Reversion is the hypothesis that prices deviating significantly from a statistical average tend to return to it, and the family of strategies built on that premise. Implementation requires two components: a measure of "normal" (typically a moving average, VWAP, or Bollinger middle line) and a measure of deviation extremeness (standard deviations, Bollinger Band width, Keltner Channel distance, or Z-score).

Mean-reversion strategies perform well in ranging markets and underperform in trending ones. The opposite of breakout strategies. The core risk is misidentifying trend onset as an "extreme deviation" to fade, then catching a falling knife. Mitigations include: requiring confluent signals (volume climax, bearish divergence, support/resistance confluence) before entering, using tight stops beyond structural levels, filtering by higher-timeframe regime (only take reversion setups when the higher-timeframe market is ranging), and combining with mean-reversion-friendly instruments (ETFs on developed-market equity indices tend to mean-revert more reliably than single stocks or crypto).