MultiMa Freqtrade Strategy: Backtest Results (+97.8%)

How MultiMa trades

MultiMa builds a ladder of triple-exponential moving averages on the 4-hour chart and enters when the averages are stacked in a particular order (four averages, 15 periods apart). It exits when a longer ladder of twelve averages flips the other way.

Slow by design: few trades, long holding times and a 34.5% stoploss. Results depend heavily on a small number of large trends.

Rules at a glance

Indicators: Stacked TEMAs
Take-profit (minimal ROI): 52.3% from entry; 12.3% after 1d 1h; 7.6% after 1d 14h; 0% after 2d 4h
Stoploss: −34.5%
Trailing stop: Off
Exit signal: Used

Results by month

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

JFMAMJJASOND2023+24+180-74-43+149+123+1302024-12+43+117+0+0+0+0-23+37-48+275+1062025+0+0+0+0-4

Results by coin

Pair	Trades	Win rate	Profit (USDT)	Avg. hold
XRP/USDT	22	68.2%	+215.81	2d 7h
DOGE/USDT	29	62.1%	+178.22	2d 15h
SOL/USDT	29	72.4%	+136.78	2d 6h
ADA/USDT	28	71.4%	+136.25	2d 21h
AVAX/USDT	26	61.5%	+97.50	3d 4h
LINK/USDT	20	80.0%	+87.82	3d 12h
ETH/USDT	20	75.0%	+53.91	4d 20h
DOT/USDT	27	70.4%	+44.79	3d 18h
BNB/USDT	26	53.8%	+21.89	3d 23h
BTC/USDT	18	61.1%	+5.07	5d 6h

Why trades closed

Exit reason	Trades	Win rate	Profit (USDT)
Take-profit (ROI)	198	83.3%	+1,662.86
Exit signal	47	0.0%	−684.82

The code

MultiMa comes from the community freqtrade-strategies repository (GPL-3.0). These are the parts that decide when it trades; the full file is 99 lines.

Entry rules

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    conditions = []
    # I used range(self.buy_ma_count.value) instade of self.buy_ma_count.range
    # Cuz it returns range(7,8) but we need range(8) for all modes hyperopt, backtest and etc

    for ma_count in range(self.buy_ma_count.value):
        key = ma_count*self.buy_ma_gap.value
        past_key = (ma_count-1)*self.buy_ma_gap.value
        if past_key > 1 and key in dataframe.keys() and past_key in dataframe.keys():
            conditions.append(dataframe[key] < dataframe[past_key])

    if conditions:
        dataframe.loc[reduce(lambda x, y: x & y, conditions), "enter_long"] = 1
    return dataframe

Exit rules

def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    conditions = []

    for ma_count in range(self.sell_ma_count.value):
        key = ma_count*self.sell_ma_gap.value
        past_key = (ma_count-1)*self.sell_ma_gap.value
        if past_key > 1 and key in dataframe.keys() and past_key in dataframe.keys():
            conditions.append(dataframe[key] > dataframe[past_key])

    if conditions:
        dataframe.loc[reduce(lambda x, y: x | y, conditions), "exit_long"] = 1
    return dataframe

Indicators

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    for count in range(self.count_max):
        for gap in range(self.gap_max):
            if count*gap > 1 and count*gap not in dataframe.keys():
                dataframe[count*gap] = ta.TEMA(
                    dataframe, timeperiod=int(count*gap)
                )
    print(" ", metadata['pair'], end="\t\r")

    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 MultiMa --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.