# source: https://raw.githubusercontent.com/secondlinger/WinnieBari/73bedc96c87439cd3ca5ae33aca9cb01ed125468/user_data/strategies/NASOSv7.py
from datetime import datetime
from functools import reduce

import talib.abstract as ta
from pandas import DataFrame

import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter
from freqtrade.strategy.interface import IStrategy

buy_params = {
    "base_nb_candles_buy": 8,
    "ewo_high": 2.403,
    "ewo_high_2": -5.585,
    "ewo_low": -14.378,
    "lookback_candles": 3,
    "low_offset": 0.984,
    "low_offset_2": 0.942,
    "profit_threshold": 1.008,
    "rsi_buy": 72
}

sell_params = {
    "base_nb_candles_sell": 16,
    "high_offset": 1.084,
    "high_offset_2": 1.401,
    "pHSL": -0.15,
    "pPF_1": 0.016,
    "pPF_2": 0.024,
    "pSL_1": 0.014,
    "pSL_2": 0.022
}


def EWO(dataframe, ema_length=5, ema2_length=35):
    df = dataframe.copy()
    ema1 = ta.EMA(df, timeperiod=ema_length)
    ema2 = ta.EMA(df, timeperiod=ema2_length)
    emadif = (ema1 - ema2) / df['low'] * 100
    return emadif


class Github_secondlinger_WinnieBari__NASOSv7__20250919_011108(IStrategy):
    INTERFACE_VERSION = 3

    minimal_roi = {
        "0": 100
    }

    stoploss = -0.35

    base_nb_candles_buy = IntParameter(
        2, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True)
    base_nb_candles_sell = IntParameter(
        2, 25, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True)
    low_offset = DecimalParameter(
        0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=False)
    low_offset_2 = DecimalParameter(
        0.9, 0.99, default=buy_params['low_offset_2'], space='buy', optimize=False)
    high_offset = DecimalParameter(
        0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=True)
    high_offset_2 = DecimalParameter(
        0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=True)

    fast_ewo = 50
    slow_ewo = 200

    lookback_candles = IntParameter(
        1, 24, default=buy_params['lookback_candles'], space='buy', optimize=True)

    profit_threshold = DecimalParameter(1.0, 1.03,
                                        default=buy_params['profit_threshold'], space='buy', optimize=True)

    ewo_low = DecimalParameter(-20.0, -8.0,
                               default=buy_params['ewo_low'], space='buy', optimize=True)
    ewo_high = DecimalParameter(
        2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=True)

    ewo_high_2 = DecimalParameter(
        -6.0, 12.0, default=buy_params['ewo_high_2'], space='buy', optimize=True)

    rsi_buy = IntParameter(50, 100, default=buy_params['rsi_buy'], space='buy', optimize=True)

    pHSL = DecimalParameter(-0.200, -0.040, default=-0.15, decimals=3,
                            space='sell', optimize=False, load=True)

    pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3,
                             space='sell', optimize=False, load=True)
    pSL_1 = DecimalParameter(0.008, 0.020, default=0.014, decimals=3,
                             space='sell', optimize=False, load=True)

    pPF_2 = DecimalParameter(0.040, 0.100, default=0.024, decimals=3,
                             space='sell', optimize=False, load=True)
    pSL_2 = DecimalParameter(0.020, 0.070, default=0.022, decimals=3,
                             space='sell', optimize=False, load=True)

    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.016
    trailing_only_offset_is_reached = True

    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = False

    timeframe = '5m'
    inf_1h = '1h'

    process_only_new_candles = True
    startup_candle_count = 200
    use_custom_stoploss = True

    plot_config = {
        'main_plot': {
            'ma_buy': {'color': 'orange'},
            'ma_sell': {'color': 'orange'},
        },
    }

    slippage_protection = {
        'retries': 3,
        'max_slippage': -0.02
    }

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:

        # 値を事前に取得（プロパティアクセスを減らす）
        HSL, PF_1, SL_1, PF_2, SL_2 = (
            self.pHSL.value, self.pPF_1.value, self.pSL_1.value,
            self.pPF_2.value, self.pSL_2.value
        )

        # elif チェーンを最適化
        if current_profit > PF_2:
            sl_profit = SL_2 + (current_profit - PF_2)
        elif current_profit > PF_1:
            # 計算を簡素化
            ratio = (current_profit - PF_1) / (PF_2 - PF_1)
            sl_profit = SL_1 + (ratio * (SL_2 - SL_1))
        else:
            sl_profit = HSL

        return stoploss_from_open(sl_profit, current_profit)


    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
                           rate: float, time_in_force: str, sell_reason: str,
                           current_time: datetime, **kwargs) -> bool:

        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1]

        if (last_candle is not None):
            if (sell_reason in ['sell_signal']):
                if (last_candle['hma_50'] * 1.149 > last_candle['ema_100']) and (
                        last_candle['close'] < last_candle['ema_100'] * 0.951):  # *1.2
                    return False

        try:
            state = self.slippage_protection['__pair_retries']
        except KeyError:
            state = self.slippage_protection['__pair_retries'] = {}

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

        slippage = (rate / candle['close']) - 1
        if slippage < self.slippage_protection['max_slippage']:
            pair_retries = state.get(pair, 0)
            if pair_retries < self.slippage_protection['retries']:
                state[pair] = pair_retries + 1
                return False

        state[pair] = 0

        return True

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, '1h') for pair in pairs]
        return informative_pairs

    def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        assert self.dp, "DataProvider is required for multiple timeframes."

        informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)

        return informative_1h

    def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        for val in self.base_nb_candles_buy.range:
            dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)

        for val in self.base_nb_candles_sell.range:
            dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)

        dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
        dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)

        dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)

        dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)

        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)

        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        informative_1h = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(
            dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)

        dataframe = self.normal_tf_indicators(dataframe, metadata)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dont_buy_conditions = []

        dont_buy_conditions.append(
            (

                (dataframe['close_1h'].rolling(self.lookback_candles.value).max()
                 < (dataframe['close'] * self.profit_threshold.value))
            )
        )

        dataframe.loc[
            (
                    (dataframe['rsi_fast'] < 35) &
                    (dataframe['close'] < (
                            dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
                    (dataframe['EWO'] > self.ewo_high.value) &
                    (dataframe['rsi'] < self.rsi_buy.value) &
                    (dataframe['volume'] > 0) &
                    (dataframe['close'] < (
                            dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))
            ),
            ['enter_long', 'buy_tag']] = (1, 'ewo1')

        dataframe.loc[
            (
                    (dataframe['rsi_fast'] < 35) &
                    (dataframe['close'] < (
                            dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value)) &
                    (dataframe['EWO'] > self.ewo_high_2.value) &
                    (dataframe['rsi'] < self.rsi_buy.value) &
                    (dataframe['volume'] > 0) &
                    (dataframe['close'] < (
                            dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
                    (dataframe['rsi'] < 25)
            ),
            ['enter_long', 'buy_tag']] = (1, 'ewo2')

        dataframe.loc[
            (
                    (dataframe['rsi_fast'] < 35) &
                    (dataframe['close'] < (
                            dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
                    (dataframe['EWO'] < self.ewo_low.value) &
                    (dataframe['volume'] > 0) &
                    (dataframe['close'] < (
                            dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))
            ),
            ['enter_long', 'buy_tag']] = (1, 'ewolow')

        if dont_buy_conditions:
            for condition in dont_buy_conditions:
                dataframe.loc[condition, 'enter_long'] = 0

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []

        conditions.append(
            ((dataframe['close'] > dataframe['sma_9']) &
             (dataframe['close'] > (
                     dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) &
             (dataframe['rsi'] > 50) &
             (dataframe['volume'] > 0) &
             (dataframe['rsi_fast'] > dataframe['rsi_slow'])
             )
            |
            (
                    (dataframe['close'] < dataframe['hma_50']) &
                    (dataframe['close'] > (
                            dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
                    (dataframe['volume'] > 0) &
                    (dataframe['rsi_fast'] > dataframe['rsi_slow'])
            )

        )

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x | y, conditions),
                'exit_long'
            ] = 1

        return dataframe