# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/SMAIP3v2.py



from datetime import datetime, timedelta

import talib.abstract as ta
from pandas import DataFrame

from freqtrade.persistence import Trade
from freqtrade.strategy import CategoricalParameter
from freqtrade.strategy import DecimalParameter, IntParameter
from freqtrade.strategy.interface import IStrategy


ma_types = {
    'SMA': ta.SMA,
    'EMA': ta.EMA,
}


class Github_remiotore_ccxt_freqtrade__SMAIP3v2__20260111_210550(IStrategy):
    INTERFACE_VERSION = 2


    buy_params = {
        "base_nb_candles_buy": 18,
        "buy_trigger": "SMA",
        "low_offset": 0.968,
        "pair_is_bad_1_threshold": 0.130,
        "pair_is_bad_2_threshold": 0.075,
    }

    sell_params = {
        "base_nb_candles_sell": 26,
        "high_offset": 0.985,
        "sell_trigger": "EMA",
    }

    stoploss = -0.23


    minimal_roi = {
        "0": 0.026
    }

    base_nb_candles_buy = IntParameter(16, 60, default=buy_params['base_nb_candles_buy'], space='buy')
    base_nb_candles_sell = IntParameter(16, 60, default=sell_params['base_nb_candles_sell'], space='sell')
    low_offset = DecimalParameter(0.8, 0.99, default=buy_params['low_offset'], space='buy')
    high_offset = DecimalParameter(0.8, 1.1, default=sell_params['high_offset'], space='sell')
    buy_trigger = CategoricalParameter(ma_types.keys(), default=buy_params['buy_trigger'], space='buy')
    sell_trigger = CategoricalParameter(ma_types.keys(), default=sell_params['sell_trigger'], space='sell')

    pair_is_bad_1_threshold = DecimalParameter(0.00, 0.30, default=0.200, space='buy')
    pair_is_bad_2_threshold = DecimalParameter(0.00, 0.25, default=0.072, space='buy')

    trailing_stop = True
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.003
    trailing_stop_positive_offset = 0.018

    timeframe = '5m'

    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

    process_only_new_candles = True
    startup_candle_count = 200

    plot_config = {
        'main_plot': {
            'ma_offset_buy': {'color': 'orange'},
            'ma_offset_sell': {'color': 'orange'},
        },
    }


    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]
        previous_candle_1 = dataframe.iloc[-2]

        if (last_candle is not None):
            if (sell_reason in ['roi','sell_signal','trailing_stop_loss']):
                if (last_candle['open'] > previous_candle_1['open']) and (last_candle['rsi'] > 50) and (last_candle['rsi'] > previous_candle_1['rsi']):
                    return False
        return True

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)

        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=2)

        if not self.config['runmode'].value == 'hyperopt':
            dataframe['ma_offset_buy'] = ma_types[self.buy_trigger.value](dataframe,
                                                                          int(self.base_nb_candles_buy.value)) * self.low_offset.value
            dataframe['ma_offset_sell'] = ma_types[self.sell_trigger.value](dataframe,
                                                                            int(self.base_nb_candles_sell.value)) * self.high_offset.value

            dataframe['pair_is_bad'] = (
                    (((dataframe['open'].shift(12) - dataframe['close']) / dataframe[
                        'close']) >= self.pair_is_bad_1_threshold.value) |
                    (((dataframe['open'].shift(6) - dataframe['close']) / dataframe[
                        'close']) >= self.pair_is_bad_2_threshold.value)).astype('int')

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if self.config['runmode'].value == 'hyperopt':
            dataframe['ma_offset_buy'] = ma_types[self.buy_trigger.value](dataframe,
                                                                          int(self.base_nb_candles_buy.value)) * self.low_offset.value
            dataframe['pair_is_bad'] = (
                    (((dataframe['open'].shift(12) - dataframe['close']) / dataframe[
                        'close']) >= self.pair_is_bad_1_threshold.value) |
                    (((dataframe['open'].shift(6) - dataframe['close']) / dataframe[
                        'close']) >= self.pair_is_bad_2_threshold.value)).astype('int')

        dataframe.loc[
            (
                    (dataframe['ema_50'] > dataframe['ema_200']) &
                    (dataframe['close'] > dataframe['ema_200']) &
                    (dataframe['pair_is_bad'] < 1) &
                    (dataframe['close'] < dataframe['ma_offset_buy']) &
                    (dataframe['volume'] > 0)

            ),
            'buy'] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if self.config['runmode'].value == 'hyperopt':
            dataframe['ma_offset_sell'] = ma_types[self.sell_trigger.value](dataframe,
                                                                            int(self.base_nb_candles_sell.value)) * self.high_offset.value

        dataframe.loc[
            (
                    (dataframe['close'] > dataframe['ma_offset_sell']) &
                    (dataframe['volume'] > 0)
            ),
            'sell'] = 1
        return dataframe