from freqtrade.strategy import IStrategy from pandas import DataFrame import pandas_ta as ta from freqtrade.exchange import timeframe_to_minutes class VolBreak_RSI_Adaptive(IStrategy): INTERFACE_VERSION = 3 timeframe = "1h" minimal_roi = {"0": 0.10, "60": 0.05, "120": 0.03} stoploss = -0.05 trailing_stop = True trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True startup_candle_count: int = 20 def custom_stake_amount(self, pair: str, current_time, current_rate: float, proposed_stake: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if dataframe.empty: return proposed_stake last_candle = dataframe.iloc[-1] atr = last_candle["atr"] wallet = self.wallets.get_available_stake_amount() stake = (wallet * 0.01) / (atr / current_rate) return min(stake, proposed_stake) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["atr"] = ta.atr(dataframe["high"], dataframe["low"], dataframe["close"], length=14) dataframe["rsi"] = ta.rsi(dataframe["close"], length=14) dataframe["breakout_upper"] = dataframe["close"].shift(1) + (dataframe["atr"] * 1.5) dataframe["breakout_lower"] = dataframe["close"].shift(1) - (dataframe["atr"] * 1.5) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ (dataframe["close"] > dataframe["breakout_upper"]) & (dataframe["rsi"] > 50) & (dataframe["volume"] > 0), "enter_long"] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ (dataframe["close"] < dataframe["breakout_lower"]) | (dataframe["rsi"] < 30), "exit_long"] = 1 return dataframe