ADXMomentum Freqtrade Strategy: Backtest Results (+32.5%)

How ADXMomentum trades

ADXMomentum waits for a trend that is both strong and pointing up. It enters when ADX is above 25, 14-period momentum is positive and the +DI line sits above both 25 and the -DI line. The mirror image of that condition (momentum turning negative while -DI takes over) closes the trade.

The 1% take-profit at minute zero does most of the work: about 86% of trades close through ROI long before the trend-based exit fires, which explains the very high win rate. Losing trades are mostly closed by the exit signal; the wide 25% stoploss was rarely reached.

Rules at a glance

Indicators: ADX, +DI / -DI, Momentum
Take-profit (minimal ROI): 1% from entry
Stoploss: −25%
Trailing stop: Off
Exit signal: Used

Results by month

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

JFMAMJJASOND2023-43-55-161+38+220-39+892024-138+192+43-153+63-116-6-13+80-32+505-1302025+88-177-95+20+141

Results by coin

Pair	Trades	Win rate	Profit (USDT)	Avg. hold
LINK/USDT	321	91.3%	+152.38	9h 5m
SOL/USDT	455	89.7%	+103.03	12h 33m
ADA/USDT	383	88.0%	+76.03	9h 50m
XRP/USDT	352	86.1%	+57.56	10h 30m
AVAX/USDT	361	89.8%	+56.68	9h 21m
DOGE/USDT	406	88.2%	+29.65	9h 6m
BTC/USDT	338	76.6%	−2.67	20h 6m
BNB/USDT	261	80.5%	−10.15	19h 6m
ETH/USDT	333	79.6%	−55.89	17h 57m
DOT/USDT	215	83.7%	−81.95	13h 25m

Why trades closed

Exit reason	Trades	Win rate	Profit (USDT)
Take-profit (ROI)	2937	100.0%	+5,879.17
Still open at end of test	1	0.0%	−4.40
Stoploss	2	0.0%	−100.69
Exit signal	485	0.0%	−5,449.42

The code

ADXMomentum comes from the community freqtrade-strategies repository (GPL-3.0). These are the parts that decide when it trades; the full file is 69 lines.

Entry rules

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
                (dataframe['adx'] > 25) &
                (dataframe['mom'] > 0) &
                (dataframe['plus_di'] > 25) &
                (dataframe['plus_di'] > dataframe['minus_di'])

        ),
        'enter_long'] = 1
    return dataframe

Exit rules

def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
                (dataframe['adx'] > 25) &
                (dataframe['mom'] < 0) &
                (dataframe['minus_di'] > 25) &
                (dataframe['plus_di'] < dataframe['minus_di'])

        ),
        'exit_long'] = 1
    return dataframe

Indicators

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
    dataframe['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=25)
    dataframe['minus_di'] = ta.MINUS_DI(dataframe, timeperiod=25)
    dataframe['sar'] = ta.SAR(dataframe)
    dataframe['mom'] = ta.MOM(dataframe, timeperiod=14)

    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 ADXMomentum --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.