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X1Strategy

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win-boom/BTCquant/user_data/strategies/x1_strategy.py · first seen 2026-07-16 · repo updated 2026-06-20

Basics mode: futures timeframe: 15m interface version: 3
Settings stoploss: -0.025 has minimal roi trailing process only new candles startup candle count: 200
Indicators ADX EMA MACD ROC SMA talib
Concepts trailing
15 related strategies (⧉ identical code, ≈ similar name)

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from pandas import DataFrame
import talib.abstract as ta
from freqtrade.strategy import IStrategy


class X1Strategy(IStrategy):
    """
    X1 — 15m Long/Short | Relaxed filters for live trading
    ADX>15 (was 18), momentum>0.05 (was 0.1)
    """
    INTERFACE_VERSION = 3
    timeframe = '15m'
    can_short = True

    stoploss = -0.025
    trailing_stop = True
    trailing_stop_positive = 0.005
    trailing_stop_positive_offset = 0.018
    trailing_only_offset_is_reached = True

    minimal_roi = {"0": 0.08, "480": 0.05, "1440": 0.03, "4320": 0}

    max_open_trades = 12
    stake_amount = 5000
    startup_candle_count = 200
    process_only_new_candles = True
    use_exit_signal = False

    def populate_indicators(self, dataframe, metadata):
        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
        dataframe['ema30'] = ta.EMA(dataframe, timeperiod=30)
        macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
        dataframe['macd'] = macd['macd']
        dataframe['macd_signal'] = macd['macdsignal']
        dataframe['momentum'] = ta.ROC(dataframe, timeperiod=3)
        dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
        dataframe['volume_ratio'] = dataframe['volume'] / ta.SMA(dataframe['volume'], timeperiod=20)
        return dataframe

    def populate_entry_trend(self, dataframe, metadata):
        dataframe.loc[
            ((dataframe['ema10'] > dataframe['ema30']) &
             (dataframe['macd'] > dataframe['macd_signal']) &
             (dataframe['momentum'] > 0.05) &
             (dataframe['adx'] > 15) &
             (dataframe['volume_ratio'] > 1.0) &
             (dataframe['volume'] > 0)),
            ['enter_long', 'enter_tag']
        ] = (1, 'long_signal')

        dataframe.loc[
            ((dataframe['ema10'] < dataframe['ema30']) &
             (dataframe['macd'] < dataframe['macd_signal']) &
             (dataframe['momentum'] < -0.05) &
             (dataframe['adx'] > 15) &
             (dataframe['volume_ratio'] > 1.0) &
             (dataframe['volume'] > 0)),
            ['enter_short', 'enter_tag']
        ] = (1, 'short_signal')

        return dataframe

    def populate_exit_trend(self, dataframe, metadata):
        return dataframe