UniversalMACD Freqtrade Strategy: Backtest Results (+74.7%)

How UniversalMACD trades

UniversalMACD replaces the usual MACD difference with a ratio (EMA12 / EMA26 − 1) so the signal means the same thing on every coin regardless of price. It buys when that ratio sits in a narrow tuned band just below zero. Its tuned sell band has a lower bound above its upper bound, so the exit signal can never fire; trades close through the stepped take-profit or the stoploss.

Normalising MACD is a sound idea, and the result here is one of the smoothest equity curves in the test. But the entry band is very narrow and the exit is effectively the ROI table alone, so small parameter changes can produce very different results.

Rules at a glance

Indicators: EMA 12 / 26 ratio
Take-profit (minimal ROI): 21.3% from entry; 9.9% after 27m; 3% after 1h 0m; 0% after 2h 44m
Stoploss: −31.8%
Trailing stop: Off
Exit signal: Used

Results by month

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

JFMAMJJASOND2023+45+10+39+1+14+57+582024+54+11+76+0-64-17+8+27+2+16+176+1242025+58+9-20+18+46

Results by coin

Pair	Trades	Win rate	Profit (USDT)	Avg. hold
XRP/USDT	49	83.7%	+156.90	4h 5m
DOT/USDT	36	88.9%	+130.63	1d 16h
SOL/USDT	48	72.9%	+125.13	13h 42m
LINK/USDT	48	70.8%	+100.23	1d 7h
AVAX/USDT	67	77.6%	+89.74	1d 16h
DOGE/USDT	67	74.6%	+81.45	21h 7m
ETH/USDT	18	88.9%	+44.53	7h 53m
BNB/USDT	7	85.7%	+17.17	29d 5h
BTC/USDT	4	75.0%	+12.53	1h 55m
ADA/USDT	53	71.7%	−10.95	1d 15h

Why trades closed

Exit reason	Trades	Win rate	Profit (USDT)
Take-profit (ROI)	393	78.1%	+1,003.02
Stoploss	4	0.0%	−255.65

The code

UniversalMACD comes from the community freqtrade-strategies repository (GPL-3.0). These are the parts that decide when it trades; the full file is 113 lines.

Entry rules

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
            (dataframe['umacd'].between(self.buy_umacd_min.value, self.buy_umacd_max.value))

        ),
        'enter_long'] = 1

    return dataframe

Exit rules

def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
            (dataframe['umacd'].between(self.sell_umacd_min.value, self.sell_umacd_max.value))
        ),
        'exit_long'] = 1

    return dataframe

Indicators

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe['ma12'] = ta.EMA(dataframe, timeperiod=12)
    dataframe['ma26'] = ta.EMA(dataframe, timeperiod=26)
    dataframe['umacd'] = (dataframe['ma12'] / dataframe['ma26']) - 1

    # Just for show user the min and max of indicator in different coins to set inside hyperoptable variables.cuz
    # in different timeframes should change the min and max in hyperoptable variables.
    # print(dataframe['umacd'].min(), dataframe['umacd'].max())

    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 UniversalMACD --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.