# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/Momentumv2.py
from pandas import DataFrame
from functools import reduce
from datetime import datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import IStrategy
from freqtrade.strategy import IntParameter, DecimalParameter
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib

class Github_DerSalvador_freqtrade_helm_chart__Momentumv2__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    minimal_roi = {'0': 0.22, '1260': 0.17, '1944': 0.09, '7200': 0}
    stoploss = -0.08
    use_custom_stoploss = True
    trailing_stop = False
    timeframe = '4h'
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    startup_candle_count: int = 100
    order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': True}
    # Buy Parameters
    entry_ema = IntParameter(10, 100, default=30, space='entry', optimize=True, load=True)
    # Sell Parameters
    exit_rsi = DecimalParameter(70, 99, default=80, space='exit', optimize=True, load=True)
    # Stoploss Parameters
    atr_timeperiod = IntParameter(5, 21, default=7, space='exit')
    atr_multiplier = DecimalParameter(2.5, 3.5, default=2.5, space='exit')
    entry_params = {'entry_ema': 80}
    exit_params = {'exit_rsi': 90, 'atr_multiplier': 2.6, 'atr_timeperiod': 12}

    @property
    def protections(self):
        return [{'method': 'CooldownPeriod', 'stop_duration_candles': 6}]

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # MACD
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        # EMA
        dataframe['ema'] = ta.EMA(dataframe, timeperiod=self.entry_ema.value)
        # Average True Index Trailing Stoploss
        dataframe['atr'] = ta.ATR(dataframe, timeperiod=self.atr_timeperiod.value)
        dataframe['atr_trailing'] = dataframe['close'] - dataframe['atr'] * self.atr_multiplier.value
        return dataframe

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        stoploss_price = last_candle['atr_trailing']
        if stoploss_price < current_rate:
            return stoploss_price / current_rate - 1
        return 1

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']))
        conditions.append(dataframe['close'] > dataframe['ema'])
        conditions.append(dataframe['volume'] > 0)
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append(qtpylib.crossed_below(dataframe['macd'], dataframe['macdsignal']) | qtpylib.crossed_below(dataframe['rsi'], self.exit_rsi.value))
        conditions.append(dataframe['volume'] > 0)
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit'] = 1
        return dataframe