Quickie Freqtrade Strategy: Backtest Results (−24.7%)

How Quickie trades

Quickie buys when ADX shows a strong trend (above 30), the 9-period TEMA is rising from below the middle Bollinger Band, and price is still below its 200-period SMA. It exits on an extreme trend reading (ADX above 70) with TEMA turning down.

An aggressive stepped take-profit (15% in the first 10 minutes, falling to 1% after 100) means most trades end on ROI rather than the exit signal.

Rules at a glance

Indicators: TEMA 9, Bollinger Bands, ADX, SMA 200
Take-profit (minimal ROI): 15% after 10m; 6% after 15m; 3% after 30m; 1% after 1h 40m
Stoploss: −25%
Trailing stop: Off
Exit signal: Used

Results by month

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

JFMAMJJASOND2023-81+48-83-13+77+134+1302024-102+80+93-164+54-65-87-156+26+26+216+232025-96-320+36-36+13

Results by coin

Pair	Trades	Win rate	Profit (USDT)	Avg. hold
ADA/USDT	106	95.3%	+120.72	2d 4h
LINK/USDT	96	96.9%	+57.86	2d 0h
BTC/USDT	86	94.2%	+24.09	5d 7h
BNB/USDT	84	96.4%	+14.02	5d 1h
AVAX/USDT	89	94.4%	−31.03	1d 19h
XRP/USDT	123	95.1%	−46.19	2d 23h
SOL/USDT	110	93.6%	−66.78	1d 23h
DOT/USDT	88	92.0%	−71.56	2d 10h
DOGE/USDT	117	93.2%	−110.59	1d 20h
ETH/USDT	111	91.9%	−137.79	3d 4h

Why trades closed

Exit reason	Trades	Win rate	Profit (USDT)
Take-profit (ROI)	952	100.0%	+2,107.98
Still open at end of test	3	0.0%	−54.05
Exit signal	13	0.0%	−186.83
Stoploss	42	0.0%	−2,114.36

The code

Quickie comes from the community freqtrade-strategies repository (GPL-3.0). These are the parts that decide when it trades; the full file is 78 lines.

Entry rules

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
                (dataframe['adx'] > 30) &
                (dataframe['tema'] < dataframe['bb_middleband']) &
                (dataframe['tema'] > dataframe['tema'].shift(1)) &
                (dataframe['sma_200'] > dataframe['close'])

        ),
        'enter_long'] = 1
    return dataframe

Exit rules

def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
                (dataframe['adx'] > 70) &
                (dataframe['tema'] > dataframe['bb_middleband']) &
                (dataframe['tema'] < dataframe['tema'].shift(1))
        ),
        'exit_long'] = 1
    return dataframe

Indicators

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    macd = ta.MACD(dataframe)
    dataframe['macd'] = macd['macd']
    dataframe['macdsignal'] = macd['macdsignal']
    dataframe['macdhist'] = macd['macdhist']

    dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
    dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200)
    dataframe['sma_50'] = ta.SMA(dataframe, timeperiod=200)

    dataframe['adx'] = ta.ADX(dataframe)

    # required for graphing
    bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
    dataframe['bb_lowerband'] = bollinger['lower']
    dataframe['bb_middleband'] = bollinger['mid']
    dataframe['bb_upperband'] = bollinger['upper']

    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 Quickie --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.