Mean Reversion

Mean Reversion

Mean reversion is a hypothesis that a specified price/spread process returns toward a modeled center fast enough to overcome costs. Verify stationarity or stable conditional behavior before trading; an individual price level may trend indefinitely.

Detection Methods

Z-Score

Z-Score = (Series - rolling mean) / rolling standard deviation. Select the series, lookback, entry/exit bands, and time stop in training data. A z-score is descriptive and is not normally distributed by assumption.

Bollinger Band Method

• Band touch/pierce + reversal candle → trade toward middle band
• Test band, RSI, and reversal features for incremental value; they are correlated price transforms

RSI Extreme Method

Calibrate RSI percentiles/bands to the instrument and regime; no fixed threshold creates a buy or sell.

Regime and Stationarity Gate

Use an objective, calibrated regime/stationarity gate. ADX can be one feature but does not establish mean reversion or set size.

Workflow

1. Check regime (must pass first):

   get_indicators(indicator_code="dmi", symbol=<symbol>, exchange=<exchange>, interval=<interval>)
   

   Apply the frozen regime and stationarity gates; return no trade when either fails.

2. Get BB and RSI:

   get_indicators(indicator_code="bbands", symbol=<symbol>, exchange=<exchange>, interval=<interval>)
   get_indicators(indicator_code="rsi", symbol=<symbol>, exchange=<exchange>, interval=<interval>)
   get_indicators(indicator_code="ema", symbol=<symbol>, exchange=<exchange>, interval=<interval>)
   

3. Get candles for confirmation:

   get_candles_around_date(symbol=<symbol>, exchange=<exchange>, interval=<interval>, date=<date>)
   

4. Mark setup:

   draw_chart_analysis(action="create", drawing={
       "type": "demand",
       "points": [
           {"time": <extreme_time>, "price": <lower_bb>},
           {"time": <current_time>, "price": <entry_zone>}
       ],
       "options": {"text": "Mean Reversion Buy (RSI: 22, Z: -2.3)"}
   })
   

5. Exits: predeclare center, partial-center, opposite-band, stop, and maximum-holding-time variants; retain only net-positive held-out logic without universal win rates.

Evidence and Validation

• Treat the setup as a testable hypothesis, not a prediction. Define thresholds, entry, invalidation, and exit before evaluating outcomes.
• Calibrate on the same instrument, venue, session, and timeframe. Use closed candles and a held-out or walk-forward sample; record every variant tried.
• Include spread, fees, slippage, borrow or funding, partial fills, and latency. Reject the setup when net expectancy is not positive or depends on one narrow parameter.
• Return observed inputs, missing data, cost assumptions, entry, invalidation, exit, and a valid, watch, or no-trade status.
• Research basis: Mean reversion is a model assumption, not a universal property. Optimal mean-reversion research derives thresholds under an Ornstein–Uhlenbeck process; verify that the spread is stationary before applying them.

Key Rules

• Never assume a series mean reverts; verify the process and monitor parameter/stationarity breaks.
• NEVER enter on BB touch alone; require confirmation candle (engulfing, hammer, doji)
• Compare center and opposite-band exits rather than assigning realism beforehand.
• A squeeze is compression, not proof that a breakout is coming.
• Do not count BB, RSI, and z-score as independent confirmation.
• Use a time stop tied to estimated half-life and exit on a stationarity/regime break.
• If price is at lower BB due to fundamental repricing (earnings, news), it is not "oversold"

Related Skills

• bollinger-bands — BB touches are the primary visual mean reversion signal
• market-regime-detection — supply a calibrated regime feature and uncertainty state