Strategy002 Freqtrade Strategy: Backtest Results (−25.9%)

How Strategy002 trades

Strategy002 buys an oversold hammer candle: RSI under 30, slow stochastic under 20 and price below the lower Bollinger Band. It exits when the Parabolic SAR flips above price and the Fisher-transformed RSI is above 0.3.

Requiring a hammer pattern on top of three oversold readings makes entries rare. Few trades means results can swing a lot on a different time window.

Rules at a glance

Indicators: RSI, Stochastic, Bollinger Bands, Hammer candle, Parabolic SAR
Take-profit (minimal ROI): 5% from entry; 4% after 20m; 3% after 30m; 1% after 1h 0m
Stoploss: −10%
Trailing stop: Off
Exit signal: Used

Results by month

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

JFMAMJJASOND2023-19+12-51+4-8+22+172024+1+15+16+9-0+29-62-53-13-31+19+492025-42-169-39+16+19

Results by coin

Pair	Trades	Win rate	Profit (USDT)	Avg. hold
XRP/USDT	52	94.2%	+15.34	23h 25m
DOT/USDT	51	94.1%	+10.31	14h 6m
DOGE/USDT	49	93.9%	+10.29	7h 1m
AVAX/USDT	43	90.7%	−7.83	8h 29m
BTC/USDT	43	93.0%	−30.06	23h 29m
LINK/USDT	45	88.9%	−31.75	13h 50m
BNB/USDT	43	90.7%	−44.29	1d 11h
SOL/USDT	49	87.8%	−48.79	14h 8m
ETH/USDT	39	87.2%	−53.57	20h 53m
ADA/USDT	48	85.4%	−78.54	16h 22m

Why trades closed

Exit reason	Trades	Win rate	Profit (USDT)
Take-profit (ROI)	207	100.0%	+495.42
Exit signal	212	100.0%	+112.96
Still open at end of test	1	0.0%	−11.99
Stoploss	42	0.0%	−855.27

The code

Strategy002 comes from the community freqtrade-strategies repository (GPL-3.0). These are the parts that decide when it trades; the full file is 136 lines.

Entry rules

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
            (dataframe['rsi'] < 30) &
            (dataframe['slowk'] < 20) &
            (dataframe['bb_lowerband'] > dataframe['close']) &
            (dataframe['CDLHAMMER'] == 100)
        ),
        'enter_long'] = 1

    return dataframe

Exit rules

def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
            (dataframe['sar'] > dataframe['close']) &
            (dataframe['fisher_rsi'] > 0.3)
        ),
        'exit_long'] = 1
    return dataframe

Indicators

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

    # Stoch
    stoch = ta.STOCH(dataframe)
    dataframe['slowk'] = stoch['slowk']

    # RSI
    dataframe['rsi'] = ta.RSI(dataframe)

    # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
    rsi = 0.1 * (dataframe['rsi'] - 50)
    dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)

    # Bollinger bands
    bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
    dataframe['bb_lowerband'] = bollinger['lower']

    # SAR Parabol
    dataframe['sar'] = ta.SAR(dataframe)

    # Hammer: values [0, 100]
    dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)

    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 Strategy002 --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.