# source: https://raw.githubusercontent.com/Industrial/ft_userdata/cddef4af28d3ee2cb0cf841a410708dfa1d37c49/user_data/strategies/SMAOG_273.py
import talib.abstract as ta
from freqtrade.strategy import CategoricalParameter
from freqtrade.strategy import DecimalParameter, IntParameter
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame


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


class Github_Industrial_ft_userdata__SMAOG_273__20250216_195752(IStrategy):
    INTERFACE_VERSION = 2
    buy_params = {
        "base_nb_candles_buy": 26,
        "buy_trigger": "SMA",
        "low_offset": 0.968,
        "pair_is_bad_0_threshold": 0.555,
        "pair_is_bad_1_threshold": 0.172,
        "pair_is_bad_2_threshold": 0.198,
    }
    sell_params = {
        "base_nb_candles_sell": 28,
        "high_offset": 0.985,
        "sell_trigger": "EMA",
    }
    base_nb_candles_buy = IntParameter(
        16,
        45,
        default=buy_params["base_nb_candles_buy"],
        space="buy",
        optimize=False,
        load=True,
    )
    base_nb_candles_sell = IntParameter(
        16,
        45,
        default=sell_params["base_nb_candles_sell"],
        space="sell",
        optimize=False,
        load=True,
    )
    low_offset = DecimalParameter(
        0.8,
        0.99,
        default=buy_params["low_offset"],
        space="buy",
        optimize=False,
        load=True,
    )
    high_offset = DecimalParameter(
        0.8,
        1.1,
        default=sell_params["high_offset"],
        space="sell",
        optimize=False,
        load=True,
    )
    buy_trigger = CategoricalParameter(
        ma_types.keys(),
        default=buy_params["buy_trigger"],
        space="buy",
        optimize=False,
        load=True,
    )
    sell_trigger = CategoricalParameter(
        ma_types.keys(),
        default=sell_params["sell_trigger"],
        space="sell",
        optimize=False,
        load=True,
    )
    pair_is_bad_0_threshold = DecimalParameter(
        0.0, 0.600, default=0.220, space="buy", optimize=True, load=True
    )
    pair_is_bad_1_threshold = DecimalParameter(
        0.0, 0.350, default=0.090, space="buy", optimize=True, load=True
    )
    pair_is_bad_2_threshold = DecimalParameter(
        0.0, 0.200, default=0.060, space="buy", optimize=True, load=True
    )

    timeframe = "5m"
    stoploss = -0.09

    minimal_roi = {
        "0": 0.6,
    }

    trailing_stop = True
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.005
    trailing_stop_positive_offset = 0.02
    use_sell_signal = False
    sell_profit_only = False
    ignore_roi_if_buy_signal = False
    process_only_new_candles = True
    startup_candle_count = 400

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        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"].rolling(144).min() - dataframe["close"])
                        / dataframe["close"]
                    )
                    >= self.pair_is_bad_0_threshold.value
                )
                | (
                    (
                        (dataframe["open"].rolling(12).min() - dataframe["close"])
                        / dataframe["close"]
                    )
                    >= self.pair_is_bad_1_threshold.value
                )
                | (
                    (
                        (dataframe["open"].rolling(2).min() - dataframe["close"])
                        / dataframe["close"]
                    )
                    >= self.pair_is_bad_2_threshold.value
                )
            ).astype("int")
            dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50)
            dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)
            dataframe["rsi_exit"] = ta.RSI(dataframe, timeperiod=2)
        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"].rolling(144).min() - dataframe["close"])
                        / dataframe["close"]
                    )
                    >= self.pair_is_bad_0_threshold.value
                )
                | (
                    (
                        (dataframe["open"].rolling(12).min() - dataframe["close"])
                        / dataframe["close"]
                    )
                    >= self.pair_is_bad_1_threshold.value
                )
                | (
                    (
                        (dataframe["open"].rolling(2).min() - dataframe["close"])
                        / dataframe["close"]
                    )
                    >= self.pair_is_bad_2_threshold.value
                )
            ).astype("int")
            dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50)
            dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)
        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"] = (
                ta.EMA(dataframe, int(self.base_nb_candles_sell.value))
                * self.high_offset.value
            )
        dataframe.loc[
            (
                (dataframe["close"] > dataframe["ma_offset_sell"])
                & (
                    (dataframe["open"] < dataframe["open"].shift(1))
                    | (dataframe["rsi_exit"] < 50)
                    | (dataframe["rsi_exit"] < dataframe["rsi_exit"].shift(1))
                )
                & (dataframe["volume"] > 0)
            ),
            "sell",
        ] = 1
        return dataframe