Mean Reversion

Mean Reversion — Bollinger Reverter 4h

Note: This template was upgraded from the prior 1h / 2.5σ / ADX bbupper AND rsi > 65 AND adx pd.DataFrame: bb = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0) dataframe["bbupper"] = bb["upperband"] dataframe["bbmid"] = bb["middleband"] dataframe["bblower"] = bb["lowerband"] dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) return dataframe

def populateentrytrend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: condshort = ( (dataframe["close"] > dataframe["bbupper"]) & (dataframe["rsi"] > 65) & (dataframe["adx"] pd.DataFrame: dataframe.loc[dataframe["close"] dataframe["bbmid"], "exitlong"] = 1 return dataframe

## Reference config (multi-pair)

```json
{
  "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"}]
}

Honest framing

In strong-trend windows the strategy loses small (-1.75% on BTC during the first-half strong bear). In rangy windows it shines (+9.88% on BTC second-half). The mixed-regime full-period multi-pair number (+8.77% in 162d on $350 wallet) is the credible expectation.

DOGE was the negative pair (-0.65%) — meme volatility breaks more bands than reverts to them. Use this strategy on majors.

Pair with donchian-strong-regime for full-regime coverage.

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Prior version (1h, 2.5σ, archived)

The previous version was tighter (2.5σ bands, ADX<30) on a 1h timeframe. Its own honest framing noted "5 trades in 4 months" — too rare to be useful. The 4h version produces ~3× the signal density with the same risk profile. The 1h version is preserved here for users who want a deeper-fade variant:

# Archived 1h variant — fewer, deeper signals
timeframe = "1h"
# bb = ta.BBANDS(dataframe, timeperiod=100, nbdevup=2.5, nbdevdn=2.5)
# rsi gates same; adx < 30 (looser)

If you prefer the rarer-but-deeper setup, restore the 1h timeframe and 2.5σ. The exit logic is unchanged.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Superior-Trade
• Source: Superior-Trade/superior-skills
• License: MIT

Install and usage instructions live in the source repository linked above.