ClucMay72018 Freqtrade Strategy: Backtest Results (−12.6%)

How ClucMay72018 trades

ClucMay72018 buys when price closes more than 1.5% below the lower Bollinger Band while trading under its 50-period EMA, and volume is not abnormally high. It sells as soon as price recovers above the middle band, or at a 1% take-profit.

One of the best-known community strategies. Small, frequent targets with a 5% stoploss make it a high-win-rate, low-payoff system: a handful of stopouts can erase many small wins.

Rules at a glance

Indicators: Bollinger Bands 20/2, EMA 50, Volume filter
Take-profit (minimal ROI): 1% from entry
Stoploss: −5%
Trailing stop: Off
Exit signal: Used

Results by month

Profit or loss in USDT for trades closed in each month.

JFMAMJJASOND2023-6+14+26+0+22+30+342024-94-2-61-113+0+8+24-22+2+10+18+802025+19-103-8+7-11

Results by coin

Pair	Trades	Win rate	Profit (USDT)	Avg. hold
SOL/USDT	53	84.9%	+20.28	9m
BTC/USDT	11	81.8%	+6.85	10m
XRP/USDT	73	83.6%	+3.91	13m
ADA/USDT	65	83.1%	−5.96	5m
BNB/USDT	26	80.8%	−9.88	7m
AVAX/USDT	61	77.0%	−11.18	11m
DOGE/USDT	78	76.9%	−21.46	15m
ETH/USDT	30	73.3%	−31.00	12m
DOT/USDT	59	74.6%	−33.77	9m
LINK/USDT	61	73.8%	−43.89	14m

Why trades closed

Exit reason	Trades	Win rate	Profit (USDT)
Take-profit (ROI)	399	100.0%	+798.56
Exit signal	33	27.3%	−42.96
Stoploss	85	0.0%	−881.71

The code

ClucMay72018 comes from the community freqtrade-strategies repository (GPL-3.0). These are the parts that decide when it trades; the full file is 84 lines.

Entry rules

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
                (dataframe['close'] < dataframe['ema100']) &
                (dataframe['close'] < 0.985 * dataframe['bb_lowerband']) &
                (dataframe['volume'] < (dataframe['volume'].rolling(window=30).mean().shift(1) * 20))
        ),
        'enter_long'] = 1

    return dataframe

Exit rules

def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
            (dataframe['close'] > dataframe['bb_middleband'])
        ),
        'exit_long'] = 1
    return dataframe

Indicators

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe['rsi'] = ta.RSI(dataframe, timeperiod=5)
    rsiframe = DataFrame(dataframe['rsi']).rename(columns={'rsi': 'close'})
    dataframe['emarsi'] = ta.EMA(rsiframe, timeperiod=5)
    macd = ta.MACD(dataframe)
    dataframe['macd'] = macd['macd']
    dataframe['adx'] = ta.ADX(dataframe)
    bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
    dataframe['bb_lowerband'] = bollinger['lower']
    dataframe['bb_middleband'] = bollinger['mid']
    dataframe['bb_upperband'] = bollinger['upper']
    dataframe['ema100'] = ta.EMA(dataframe, timeperiod=50)
    return dataframe

Reproduce this test

With freqtrade installed (see our Docker guide), download the data and run the same backtest:

freqtrade download-data --exchange binance --pairs ADA/USDT AVAX/USDT BNB/USDT BTC/USDT DOGE/USDT DOT/USDT ETH/USDT LINK/USDT SOL/USDT XRP/USDT --timeframes 5m --timerange 20230501-20250530
freqtrade backtesting --strategy ClucMay72018 --timeframe 5m \
  --timerange 20230601-20250530 --dry-run-wallet 1000 --stake-amount 200 --max-open-trades 5 --fee 0.001

Results can differ slightly with a different data source or freqtrade version. See how we test.