EMASkipPump Freqtrade Strategy: Backtest Results (−80.1%)

How EMASkipPump trades

EMASkipPump buys when price makes a 12-candle low below both short EMAs and the lower Bollinger Band, but skips candles with a volume spike (a likely pump or dump). It sells on the opposite setup: a 12-candle high above the upper band.

The volume filter is the idea in the name. A 5% stoploss and a 10% take-profit frame each trade.

Rules at a glance

Indicators: EMA 5 / 12 / 21, Bollinger Bands, Rolling min / max
Take-profit (minimal ROI): 10% from entry
Stoploss: −5%
Trailing stop: Off
Exit signal: Used

Results by month

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

JFMAMJJASOND2023-340-137-205-57-62

Results by coin

Pair	Trades	Win rate	Profit (USDT)	Avg. hold
DOGE/USDT	133	59.4%	−44.85	2h 51m
BTC/USDT	187	40.6%	−50.00	3h 11m
ETH/USDT	157	45.9%	−52.87	3h 4m
DOT/USDT	104	48.1%	−56.93	3h 1m
AVAX/USDT	116	45.7%	−77.17	3h 1m
XRP/USDT	210	49.5%	−95.15	3h 2m
BNB/USDT	181	38.1%	−99.19	3h 3m
LINK/USDT	140	49.3%	−102.04	3h 9m
ADA/USDT	152	45.4%	−110.37	2h 55m
SOL/USDT	182	55.5%	−112.36	3h 4m

Why trades closed

Exit reason	Trades	Win rate	Profit (USDT)
Exit signal	1506	49.3%	−221.03
Stoploss	56	0.0%	−579.90

The code

EMASkipPump comes from the community freqtrade-strategies repository (GPL-3.0). These are the parts that decide when it trades; the full file is 86 lines.

Entry rules

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

    dataframe.loc[
        (dataframe['volume'] < (dataframe['volume'].rolling(window=30).mean().shift(1) * 20)) &
        (dataframe['close'] < dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
        (dataframe['close'] < dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &
        (dataframe['close'] == dataframe['min']) &
        (dataframe['close'] <= dataframe['bb_lowerband']),
        'enter_long'
    ] = 1

    return dataframe

Exit rules

def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

    dataframe.loc[
        (dataframe['close'] > dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
        (dataframe['close'] > dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &
        (dataframe['close'] >= dataframe['max']) &
        (dataframe['close'] >= dataframe['bb_upperband']),
        'exit_long'
    ] = 1

    return dataframe

Indicators

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

    dataframe['ema_{}'.format(self.EMA_SHORT_TERM)] = ta.EMA(
        dataframe, timeperiod=self.EMA_SHORT_TERM
    )
    dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)] = ta.EMA(
        dataframe, timeperiod=self.EMA_MEDIUM_TERM
    )
    dataframe['ema_{}'.format(self.EMA_LONG_TERM)] = ta.EMA(
        dataframe, timeperiod=self.EMA_LONG_TERM
    )

    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['min'] = ta.MIN(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
    dataframe['max'] = ta.MAX(dataframe, timeperiod=self.EMA_MEDIUM_TERM)

    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 EMASkipPump --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.