bollinger-reverter-4h

Bollinger Reverter 4h

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

Searchable under: mean reversion, bollinger band, range trader, chop strategy, ADX filter.

Backtest evidence

Config	Trades	Win rate	Profit	Max DD
BTC/USDC:USDC, 162d	18	72.2%	+8.14%	10%
BTC/USDC:USDC, second-half / chop (80d)	8	100%	+9.88%	0%
BTC/USDC:USDC, first-half / strong bear (82d)	10	50%	-1.75%	10%
Multi-pair (BTC/ETH/SOL/DOGE), 162d	84	65.5%	+8.77%	18.5%

Per-pair breakdown (multi-pair 162d):

Pair	Trades	Win	Profit
BTC/USDC:USDC	29	72%	+3.76%
ETH/USDC:USDC	19	74%	+4.39%
SOL/USDC:USDC	15	60%	+1.27%
DOGE/USDC:USDC	21	52%	-0.65%

3 of 4 majors profitable, DOGE marginally negative. Generalizes well; not BTC-specific.

Thesis

When the market is range-bound (ADX < 25), price touching the upper or lower Bollinger Band is statistically likely to revert to the midline. Tight ROI ladder takes profit fast since mean-reversion targets are small; tight stop prevents the position from holding if the band touch turns into a trend break.

Mechanics

• Pair: validated on majors; extend to any pair with sustained 24h volume > $50M
• Timeframe: 4h
• Indicators: 20-bar Bollinger Bands (2σ), RSI(14), ADX(14)
• Entry short: close > upper_band AND RSI > 65 AND ADX < 25
• Entry long: close < lower_band AND RSI < 35 AND ADX < 25
• Exit short: close < bb_mid
• Exit long: close > bb_mid
• Stops: -2% hard stop
• ROI ladder: 2.5% immediate, 1.5% after 4h, 0.5% after 12h, breakeven after 24h
• No trailing stop (mean reversion targets are short — let ROI or signal-exit fire)

Full strategy code

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


class BollingerReverter4hStrategy(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = "4h"
    can_short = True

    stoploss = -0.02
    trailing_stop = False

    minimal_roi = {
        "0": 0.025,    # take 2.5% immediately
        "240": 0.015,  # 1.5% after 4 hours (1 bar)
        "720": 0.005,  # 0.5% after 12 hours (3 bars)
        "1440": 0,     # breakeven after 24 hours
    }

    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