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TrendGuardStrategy

🏆 League #1016 / 1939
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calvinbtc/crypto-quant-mvp/freqtrade/user_data/strategies/TrendGuardStrategy.py · first seen 2026-07-16 · repo updated 2026-04-29 · ⬇ 1 download

Basics mode: futures timeframe: 5m interface version: 3
Settings stoploss: -0.1 has minimal roi process only new candles startup candle count: 120
Indicators ATR EMA RSI talib
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from freqtrade.strategy import IStrategy
from pandas import DataFrame
import talib.abstract as ta


class TrendGuardStrategy(IStrategy):
    INTERFACE_VERSION = 3

    timeframe = "5m"
    can_short = True
    process_only_new_candles = True
    startup_candle_count = 120
    use_exit_signal = True
    position_adjustment_enable = False

    minimal_roi = {"0": 0.035, "60": 0.018, "180": 0.0}
    stoploss = -0.1000

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=12)
        dataframe["ema_slow"] = ta.EMA(dataframe, timeperiod=48)
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
        dataframe["volume_mean"] = dataframe["volume"].rolling(30).mean()
        dataframe["trend_strength"] = (
            dataframe["ema_fast"] - dataframe["ema_slow"]
        ) / dataframe["close"]
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        long_condition = (
            (dataframe["ema_fast"] > dataframe["ema_slow"])
            & (dataframe["rsi"] > 52)
            & (dataframe["rsi"] < 72)
            & (dataframe["volume"] > dataframe["volume_mean"])
        )
        dataframe.loc[long_condition, "enter_long"] = 1

        if self.can_short:
            short_condition = (
                (dataframe["ema_fast"] < dataframe["ema_slow"])
                & (dataframe["rsi"] < 48)
                & (dataframe["rsi"] > 28)
                & (dataframe["volume"] > dataframe["volume_mean"])
            )
            dataframe.loc[short_condition, "enter_short"] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        exit_long = (
            (dataframe["ema_fast"] < dataframe["ema_slow"])
            | (dataframe["rsi"] > 78)
        )
        dataframe.loc[exit_long, "exit_long"] = 1

        if self.can_short:
            exit_short = (
                (dataframe["ema_fast"] > dataframe["ema_slow"])
                | (dataframe["rsi"] < 22)
            )
            dataframe.loc[exit_short, "exit_short"] = 1
        return dataframe