SwingHighToSky Freqtrade Strategy: Backtest Results (+8.7%)

How SwingHighToSky trades

SwingHighToSky buys when a 72-period CCI is below -175 and a 36-period RSI is below 90, and sells when a 66-period CCI is above -106 and a 45-period RSI is above 88. All of these numbers come from hyperopt.

An RSI threshold of 90 for buying is almost always true, so the CCI condition decides entries on its own. This is another strategy whose parameters were fitted rather than designed.

Rules at a glance

Indicators: CCI 72, RSI 36, CCI 66, RSI 45
Take-profit (minimal ROI): 27.06% from entry; 8.53% after 33m; 4.093% after 1h 4m; 0% after 4h 4m
Stoploss: −34.34%
Trailing stop: Off
Exit signal: Used

Results by month

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

JFMAMJJASOND2023-92+17+6+0+32+68+942024+46+66+119-172+32+8-110-232+16+24+173+582025+38-147+2+11+32

Results by coin

Pair	Trades	Win rate	Profit (USDT)	Avg. hold
LINK/USDT	117	74.4%	+123.55	1d 5h
XRP/USDT	149	74.5%	+102.76	1d 18h
BTC/USDT	141	68.1%	+57.06	2d 19h
DOGE/USDT	145	71.7%	+44.49	1d 4h
BNB/USDT	148	78.4%	+42.65	2d 8h
ADA/USDT	153	75.2%	+7.72	1d 14h
DOT/USDT	116	65.5%	−36.79	1d 17h
AVAX/USDT	127	70.9%	−42.11	1d 9h
ETH/USDT	138	66.7%	−57.56	2d 14h
SOL/USDT	127	65.4%	−154.62	1d 18h

Why trades closed

Exit reason	Trades	Win rate	Profit (USDT)
Take-profit (ROI)	1339	72.4%	+1,298.29
Still open at end of test	5	0.0%	−38.29
Stoploss	17	0.0%	−1,172.83

The code

SwingHighToSky comes from the community freqtrade-strategies repository (GPL-3.0). These are the parts that decide when it trades; the full file is 110 lines.

Entry rules

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

    dataframe.loc[
        (
            (dataframe[f'cci-{self.buy_cciTime.value}'] < self.buy_cci.value) &
            (dataframe[f'rsi-{self.buy_rsiTime.value}'] < self.buy_rsi.value)
        ),
        'enter_long'] = 1

    return dataframe

Exit rules

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

    dataframe.loc[
        (
            (dataframe[f'cci-sell-{self.sell_cciTime.value}'] > self.sell_cci.value) &
            (dataframe[f'rsi-sell-{self.sell_rsiTime.value}'] > self.sell_rsi.value)
        ),
        'exit_long'] = 1

    return dataframe

Indicators

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

    for val in self.buy_cciTime.range:
        dataframe[f'cci-{val}'] = ta.CCI(dataframe, timeperiod=val)

    for val in self.sell_cciTime.range:
        dataframe[f'cci-sell-{val}'] = ta.CCI(dataframe, timeperiod=val)

    for val in self.buy_rsiTime.range:
        dataframe[f'rsi-{val}'] = ta.RSI(dataframe, timeperiod=val)

    for val in self.sell_rsiTime.range:
        dataframe[f'rsi-sell-{val}'] = ta.RSI(dataframe, timeperiod=val)

    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 15m --timerange 20230501-20250530
freqtrade backtesting --strategy SwingHighToSky --timeframe 15m \
  --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.