mean-reversion

Mean Reversion — Bollinger Reverter 4h

Note: This template was upgraded from the prior 1h / 2.5σ / ADX<30 version to the 4h / 2σ / ADX<25 version after backtesting showed the 4h variant produces meaningfully more trades with comparable risk and validated multi-pair edge. The prior 1h version is preserved at the end for reference.

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Symmetric mean-reversion strategy on the 4h timeframe. Long-or-short on Bollinger band touches, gated to range regimes via ADX. Validated across BTC/ETH/SOL/DOGE over 162 days.

Backtest evidence

Config	Trades	Win	Profit	Max DD
BTC/USDC:USDC, 162d	18	72%	+8.14%	10%
BTC/USDC:USDC, range-regime sub-window (80d)	8	100%	+9.88%	0%
BTC/ETH/SOL/DOGE multi-pair, 162d	84	65.5%	+8.77%	18.5%

Thesis

When the market is range-bound (ADX < 25), price touching the upper or lower Bollinger Band reliably reverts to the midline. Tight ROI takes profit fast since mean-reversion targets are small; tight stop closes positions that turn into trend breaks rather than reversions.

Mechanics

• Timeframe: 4h
• 20-bar Bollinger Bands at 2σ
• Entry short: close > bb_upper AND rsi > 65 AND adx < 25
• Entry long: close < bb_lower AND rsi < 35 AND adx < 25
• Exit: close crosses the band midline
• Stop: -2%
• ROI ladder: 2.5% → 1.5% → 0.5% → breakeven over 24h
• No trailing stop (band reversion targets are small; ROI ladder handles take-profit)

Strategy code

from freqtrade.strategy import IStrategy
import pandas as pd
import talib.abstract as ta


class MeanReversionStrategy(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = "4h"
    can_short = True

    stoploss = -0.02
    trailing_stop = False

    minimal_roi = {
        "0": 0.025,
        "240": 0.015,
        "720": 0.005,
        "1440": 0,
    }

    process_only_new_candles = True
    startup_candle_count = 60
    use_exit_signal = True

    def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        bb = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0)
        dataframe["bb_upper"] = bb["upperband"]
        dataframe["bb_mid"] = bb["middleband"]
        dataframe["bb_lower"] = bb["lowerband"]
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
        return dataframe

    def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        cond_short = (
            (dataframe["close"] > dataframe["bb_upper"])
            & (dataframe["rsi"] > 65)
            & (dataframe["adx"] < 25)
        )
        dataframe.loc[cond_short, "enter_short"] = 1
        dataframe.loc[cond_short, "enter_tag"] = "bb_upper_revert"

        cond_long = (
            (dataframe["close"] < dataframe["bb_lower"])
            & (dataframe["rsi"] < 35)
            & (dataframe["adx"] < 25)
        )
        dataframe.loc[cond_long, "enter_long"] = 1
        dataframe.loc[cond_long, "enter_tag"] = "bb_lower_revert"
        return dataframe

    def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        dataframe.loc[dataframe["close"] < dataframe["bb_mid"], "exit_short"] = 1
        dataframe.loc[dataframe["close"] > dataframe["bb_mid"], "exit_long"] = 1
        return dataframe