AverageStrategy Freqtrade Strategy: Backtest Results (+123.3%)

How AverageStrategy trades

A classic two-line crossover on the 4-hour chart. AverageStrategy buys when the 8-period EMA crosses above the 21-period EMA and sells on the opposite cross. A 50% take-profit and a 20% stoploss act as outer limits.

Crossover systems lose often but let winners run, which is why the win rate is low while the total return can still be positive. Expect long flat or losing stretches in sideways markets.

Rules at a glance

Indicators: EMA 8, EMA 21
Take-profit (minimal ROI): 50% from entry
Stoploss: −20%
Trailing stop: Off
Exit signal: Used

Results by month

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

JFMAMJJASOND2023-14+74-77-28+110+163+1852024-99+223+259-22+32-80+21-39+43+21+405+1052025-1-119+33-33+71

Results by coin

Pair	Trades	Win rate	Profit (USDT)	Avg. hold
SOL/USDT	75	36.0%	+298.16	3d 17h
BNB/USDT	76	36.8%	+199.06	4d 3h
AVAX/USDT	33	39.4%	+181.46	3d 18h
BTC/USDT	77	33.8%	+145.50	4d 8h
ETH/USDT	74	29.7%	+127.70	3d 14h
DOGE/USDT	51	29.4%	+126.66	2d 20h
DOT/USDT	43	32.6%	+119.67	3d 14h
XRP/USDT	54	25.9%	+109.86	3d 0h
ADA/USDT	51	25.5%	+33.48	2d 17h
LINK/USDT	58	20.7%	−108.14	2d 16h

Why trades closed

Exit reason	Trades	Win rate	Profit (USDT)
Take-profit (ROI)	15	100.0%	+1,501.55
Still open at end of test	2	50.0%	+6.12
Stoploss	2	0.0%	−80.71
Exit signal	573	29.3%	−193.54

The code

AverageStrategy comes from the community freqtrade-strategies repository (GPL-3.0). These are the parts that decide when it trades; the full file is 79 lines.

Entry rules

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
            qtpylib.crossed_above(
                dataframe[f'ema{self.buy_range_short.value}'],
                dataframe[f'ema{self.buy_range_long.value}']
            ) &
            (dataframe['volume'] > 0)
        ),
        'enter_long'] = 1

    return dataframe

Exit rules

def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
            qtpylib.crossed_above(
                dataframe[f'ema{self.buy_range_long.value}'],
                dataframe[f'ema{self.buy_range_short.value}']
                ) &
            (dataframe['volume'] > 0)
        ),
        'exit_long'] = 1
    return dataframe

Indicators

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

    # Combine all ranges ... to avoid duplicate calculation
    for val in list(set(list(self.buy_range_short.range) + list(self.buy_range_long.range))):
        dataframe[f'ema{val}'] = ta.EMA(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 4h --timerange 20230501-20250530
freqtrade backtesting --strategy AverageStrategy --timeframe 4h \
  --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.