Bollinger reverter 4h

Skill Superior-Trade/superior-skills/skills/v2/strategies/bollinger-reverter-4h

Open agent skills and tool schemas for Superior Trade — build, backtest, and deploy trading strategies on Hyperliquid

Install

npx -y skills add Superior-Trade/superior-skills --skill bollinger-reverter-4h

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What its author says it does

Copied from the file, not written here

Use when writing a symmetric Bollinger-band mean-reversion strategy on the 4h timeframe — anything described as BB reverter, range trader, chop strategy, ADX-gated mean reversion, band-fade with ROI ladder. Long-or-short on 2σ band touches with RSI confirmation, gated to ADX<25 range regimes. Validated +8.77%/65.5% win across BTC/ETH/SOL/DOGE over 162d; depends entirely on its minimal_roi ladder (2.5% → 1.5% → 0.5% → breakeven). Pairs with donchian-strong-regime for full-regime coverage.

SKILL.md

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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

Reference config (multi-pair)

{
  "exchange": {
    "name": "hyperliquid",
    "pair_whitelist": ["BTC/USDC:USDC", "ETH/USDC:USDC", "SOL/USDC:USDC", "DOGE/USDC:USDC"]
  },
  "stake_currency": "USDC",
  "stake_amount": 75,
  "dry_run_wallet": {"USDC": 350},
  "timeframe": "4h",
  "max_open_trades": 4,
  "minimal_roi": {"0": 100.0},
  "stoploss": -0.02,
  "trading_mode": "futures",
  "margin_mode": "isolated",
  "entry_pricing": {"price_side": "same", "price_last_balance": 0.0},
  "exit_pricing": {"price_side": "same", "price_last_balance": 0.0},
  "pairlists": [{"method": "StaticPairList"}]
}

Strategy-level minimal_roi overrides config-level — the ROI ladder is what makes this work.

Honest framing

The 100% second-half BTC win rate is partly small sample (8 trades). The full-period multi-pair result (+8.77%, 84 trades, 65.5% win) is the more credible expectation. Range-bound regimes are when this prints; in strong trends it modestly loses (-1.75% on BTC during the first-half strong bear) because band touches keep continuing rather than reverting.

In any window with mixed regimes, the strategy should be net positive because the chop periods dominate by count.

The DOGE result (-0.65%) is the failure case — meme-coin volatility breaks more bands than reverts to them. Use this strategy on majors, not meme pairs.

Tunables

Parameter	Range	Effect
BB period	18 - 24	Length of mean-reversion window
BB σ	1.8 - 2.5	Wider = rarer signals, deeper reversion
RSI confirmation	60-70 / 30-40	Confirms exhaustion at band edge
ADX cutoff	20 - 30	Below = range regime; above = trend (skip)
ROI tier 0	0.020 - 0.030	Initial take-profit
Stop	-0.015 to -0.025	Tight enough that one trend break doesn't erase the lifetime edge

Known failure modes

Regime transition: when chop turns into trend mid-trade, the band-touch-revert signal becomes a band-break-continuation. Stops should fire fast; this is what the -2% stop is for
Meme/low-cap pairs: bands break more than they revert. Restrict to majors
News spikes: a sudden 5%+ move blows through multiple bands; the stop will fire but execution slippage hurts. Consider pausing during scheduled macro events

Pairing

Designed to coexist with donchian-strong-regime — they fire on mutually exclusive regimes (ADX < 25 here, regime-strong gate there)
Supersedes the prior 1h variant of mean-reversion

Deployment recommendation

Run as its own sub-account with stake_amount sized so 4× max_open_trades fits within the wallet plus 1.5× buffer. Multi-pair allocation across BTC/ETH/SOL is the validated default.