HighFreqMAPullback

from freqtrade.strategy import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import pandas as pd
from datetime import datetime
from freqtrade.persistence import Trade

class HighFreqMAPullback(IStrategy):
    INTERFACE_VERSION = 3

    timeframe = '1m'

    minimal_roi = {
        "0": 0.01,  # 1% target
        "10": 0.005  # 0.5% after 10 minutes
    }

    stoploss = -0.005  # Fixed 0.5% stoploss initially

    trailing_stop = True
    trailing_stop_positive = 0.002
    trailing_stop_positive_offset = 0.005
    trailing_only_offset_is_reached = True

    cooldown_lookback = 5  # Candles to wait after exit before new entry

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['ema9'] = ta.EMA(dataframe, timeperiod=9)
        dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
        dataframe['engulfing'] = ta.CDLENGULFING(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])  # Keep calculation but not using in entry
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['volume_sma'] = dataframe['volume'].rolling(20).mean()
        # Removed ADX
        dataframe['recent_low'] = dataframe['low'].rolling(window=5).min()
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Removed recent_exit calculation as exit_long not available here
        dataframe.loc[
            (dataframe['ema9'] > dataframe['ema21']) &  # Uptrend
            qtpylib.crossed_above(dataframe['close'], dataframe['ema9']) &  # Bounce above EMA9
            (dataframe['close'] > dataframe['open']) &  # Bullish candle for reversal
            (dataframe['rsi'] < 50) &  # Oversold filter (relaxed)
            (dataframe['volume'] > dataframe['volume_sma'] * 1.2) &  # Volume confirmation (relaxed)
            (dataframe['volume'] > 0),  # Guard
            'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe['close'] < dataframe['ema21']),  # Exit if drops below EMA21
            'exit_long'] = 1

        return dataframe

    use_custom_stoploss = True

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:
        """
        Custom stoploss - set to recent pullback low
        """
        # Get current dataframe
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if dataframe is None or len(dataframe) == 0:
            return -1  # Don't change stoploss

        last_candle = dataframe.iloc[-1].squeeze()

        # Calculate relative stoploss based on recent low
        if 'recent_low' in last_candle and not pd.isna(last_candle['recent_low']):
            sl_price = last_candle['recent_low']
            sl_offset = (sl_price / current_rate) - 1
            return sl_offset
        return -1  # Don't change if no data