Strategy001 Freqtrade Strategy: Backtest Results (−46.3%)

How Strategy001 trades

Freqtrade's first sample strategy. It buys when the 20 EMA crosses above the 50 EMA and the latest Heikin-Ashi candle is green and above the 20 EMA. It exits when the 50 crosses above the 100 with a red Heikin-Ashi candle, but only if the trade is in profit.

Because exits only fire in profit, losing trades are closed by the 10% stoploss or wait for the ROI table. It is a learning template rather than a finished system.

Rules at a glance

Indicators: EMA 20 / 50 / 100, Heikin-Ashi candles
Take-profit (minimal ROI): 5% from entry; 4% after 20m; 3% after 30m; 1% after 1h 0m
Stoploss: −10%
Trailing stop: Off
Exit signal: Used

Results by month

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

JFMAMJJASOND2023-99-7-126-19+80+134+122024-156+94+15-225+86-131+12-99+23-17+155-1952025+2-84-0+35+49

Results by coin

Pair	Trades	Win rate	Profit (USDT)	Avg. hold
BNB/USDT	159	91.8%	+35.86	1d 21h
SOL/USDT	196	89.8%	+33.45	14h 26m
DOT/USDT	142	90.1%	+2.61	1d 1h
BTC/USDT	147	90.5%	−2.20	2d 8h
ETH/USDT	162	90.1%	−14.80	1d 10h
LINK/USDT	145	89.0%	−30.89	23h 54m
DOGE/USDT	161	87.0%	−82.03	1d 1h
AVAX/USDT	149	86.6%	−113.91	19h 23m
XRP/USDT	190	87.4%	−130.18	1d 5h
ADA/USDT	161	85.7%	−160.79	22h 43m

Why trades closed

Exit reason	Trades	Win rate	Profit (USDT)
Take-profit (ROI)	1431	100.0%	+3,201.87
Still open at end of test	2	0.0%	−19.23
Stoploss	179	0.0%	−3,645.53

The code

Strategy001 comes from the community freqtrade-strategies repository (GPL-3.0). These are the parts that decide when it trades; the full file is 122 lines.

Entry rules

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
            qtpylib.crossed_above(dataframe['ema20'], dataframe['ema50']) &
            (dataframe['ha_close'] > dataframe['ema20']) &
            (dataframe['ha_open'] < dataframe['ha_close'])  # green bar
        ),
        'enter_long'] = 1

    return dataframe

Exit rules

def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
            qtpylib.crossed_above(dataframe['ema50'], dataframe['ema100']) &
            (dataframe['ha_close'] < dataframe['ema20']) &
            (dataframe['ha_open'] > dataframe['ha_close'])  # red bar
        ),
        'exit_long'] = 1
    return dataframe

Indicators

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

    dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
    dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
    dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)

    heikinashi = qtpylib.heikinashi(dataframe)
    dataframe['ha_open'] = heikinashi['open']
    dataframe['ha_close'] = heikinashi['close']

    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 Strategy001 --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.