Supertrend Freqtrade Strategy: Backtest Results (+68.3%)

How Supertrend trades

Supertrend enters on the 1-hour chart only when three Supertrend indicators with different settings all point up, and exits only when three separate sell-side Supertrends all point down. A trailing stop locks in gains after a 14.4% move.

Requiring three indicators to agree reduces false signals but delays entries and exits. The author notes the Supertrend implementation has not been independently validated.

Rules at a glance

Indicators: 3 × Supertrend (buy), 3 × Supertrend (sell)
Take-profit (minimal ROI): 8.7% from entry; 5.8% after 6h 12m; 2.9% after 14h 21m; 0% after 1d 13h
Stoploss: −26.5%
Trailing stop: Trails 5% behind the high once profit passes 14.4%
Exit signal: Used

Results by month

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

JFMAMJJASOND2023-90+35-120+18+234+33+2032024-87+252-38-107+66-133+39-66+110-22+601-1202025+62-155-157+43+81

Results by coin

Pair	Trades	Win rate	Profit (USDT)	Avg. hold
DOGE/USDT	149	67.1%	+343.13	1d 4h
SOL/USDT	217	68.2%	+194.83	1d 6h
AVAX/USDT	136	64.7%	+116.98	1d 2h
BTC/USDT	200	58.5%	+84.46	1d 12h
LINK/USDT	134	61.9%	+63.77	1d 6h
ETH/USDT	180	58.9%	+41.25	1d 9h
XRP/USDT	144	56.2%	−4.89	1d 12h
ADA/USDT	146	58.9%	−33.56	1d 7h
BNB/USDT	169	53.8%	−51.68	1d 13h
DOT/USDT	88	59.1%	−71.17	1d 7h

Why trades closed

Exit reason	Trades	Win rate	Profit (USDT)
Take-profit (ROI)	1100	86.5%	+5,873.78
Still open at end of test	3	0.0%	−21.53
Trailing stop	1	0.0%	−50.05
Exit signal	459	0.0%	−5,119.08

The code

Supertrend comes from the community freqtrade-strategies repository (GPL-3.0). These are the parts that decide when it trades; the full file is 156 lines.

Entry rules

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
           (dataframe[f'supertrend_1_buy_{self.buy_m1.value}_{self.buy_p1.value}'] == 'up') &
           (dataframe[f'supertrend_2_buy_{self.buy_m2.value}_{self.buy_p2.value}'] == 'up') &
           (dataframe[f'supertrend_3_buy_{self.buy_m3.value}_{self.buy_p3.value}'] == 'up') & # The three indicators are 'up' for the current candle
           (dataframe['volume'] > 0) # There is at least some trading volume
    ),
        'enter_long'] = 1

    return dataframe

Exit rules

def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
           (dataframe[f'supertrend_1_sell_{self.sell_m1.value}_{self.sell_p1.value}'] == 'down') &
           (dataframe[f'supertrend_2_sell_{self.sell_m2.value}_{self.sell_p2.value}'] == 'down') &
           (dataframe[f'supertrend_3_sell_{self.sell_m3.value}_{self.sell_p3.value}'] == 'down') & # The three indicators are 'down' for the current candle
           (dataframe['volume'] > 0) # There is at least some trading volume
        ),
        'exit_long'] = 1

    return dataframe

Indicators

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    new_cols = []

    for multiplier in self.buy_m1.range:
        for period in self.buy_p1.range:
            new_cols.append(self.supertrend_direction(
                dataframe, multiplier, period, f'supertrend_1_buy_{multiplier}_{period}'))

    for multiplier in self.buy_m2.range:
        for period in self.buy_p2.range:
            new_cols.append(self.supertrend_direction(
                dataframe, multiplier, period, f'supertrend_2_buy_{multiplier}_{period}'))

    for multiplier in self.buy_m3.range:
        for period in self.buy_p3.range:
            new_cols.append(self.supertrend_direction(
                dataframe, multiplier, period, f'supertrend_3_buy_{multiplier}_{period}'))

    for multiplier in self.sell_m1.range:
        for period in self.sell_p1.range:
            new_cols.append(self.supertrend_direction(
                dataframe, multiplier, period, f'supertrend_1_sell_{multiplier}_{period}'))

    for multiplier in self.sell_m2.range:
        for period in self.sell_p2.range:
            new_cols.append(self.supertrend_direction(
                dataframe, multiplier, period, f'supertrend_2_sell_{multiplier}_{period}'))

    for multiplier in self.sell_m3.range:
        for period in self.sell_p3.range:
            new_cols.append(self.supertrend_direction(
                dataframe, multiplier, period, f'supertrend_3_sell_{multiplier}_{period}'))

    if new_cols:
        dataframe = pd.concat([dataframe] + new_cols, axis=1)

    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 1h --timerange 20230501-20250530
freqtrade backtesting --strategy Supertrend --timeframe 1h \
  --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.