# source: https://raw.githubusercontent.com/ak4noir/freqtrade-strategies/2f02b6510af8dddbdfeabb09bdc8c5609de8ff53/user_data/strategies/fast_supertrend.py
import numpy as np
import pandas as pd
import talib.abstract as ta
from freqtrade.strategy import IntParameter
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame


class Github_ak4noir_freqtrade_strategies__fast_supertrend__20240407_180126(IStrategy):
    """
    HYPEROPT
    """

    # Buy hyperspace params:
    buy_params = {
        "buy_m1": 4,
        "buy_m2": 7,
        "buy_m3": 1,
        "buy_p1": 8,
        "buy_p2": 9,
        "buy_p3": 8,
    }

    # Sell hyperspace params:
    sell_params = {
        "sell_m1": 1,
        "sell_m2": 3,
        "sell_m3": 6,
        "sell_p1": 16,
        "sell_p2": 18,
        "sell_p3": 18,
    }

    # ROI table:
    minimal_roi = {"0": 0.087, "372": 0.058, "861": 0.029, "2221": 0}

    # Stoploss:
    stoploss = -0.265

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.05
    trailing_stop_positive_offset = 0.144
    trailing_only_offset_is_reached = False

    """
    END HYPEROPT
    """

    timeframe = "1h"

    startup_candle_count = 18

    buy_m1 = IntParameter(1, 7, default=4)
    buy_m2 = IntParameter(1, 7, default=4)
    buy_m3 = IntParameter(1, 7, default=4)
    buy_p1 = IntParameter(7, 21, default=14)
    buy_p2 = IntParameter(7, 21, default=14)
    buy_p3 = IntParameter(7, 21, default=14)

    sell_m1 = IntParameter(1, 7, default=4)
    sell_m2 = IntParameter(1, 7, default=4)
    sell_m3 = IntParameter(1, 7, default=4)
    sell_p1 = IntParameter(7, 21, default=14)
    sell_p2 = IntParameter(7, 21, default=14)
    sell_p3 = IntParameter(7, 21, default=14)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        for multiplier in self.buy_m1.range:
            for period in self.buy_p1.range:
                dataframe[f"supertrend_1_buy_{multiplier}_{period}"] = self.supertrend(
                    dataframe, multiplier, period
                )["STX"]

        for multiplier in self.buy_m2.range:
            for period in self.buy_p2.range:
                dataframe[f"supertrend_2_buy_{multiplier}_{period}"] = self.supertrend(
                    dataframe, multiplier, period
                )["STX"]

        for multiplier in self.buy_m3.range:
            for period in self.buy_p3.range:
                dataframe[f"supertrend_3_buy_{multiplier}_{period}"] = self.supertrend(
                    dataframe, multiplier, period
                )["STX"]

        for multiplier in self.sell_m1.range:
            for period in self.sell_p1.range:
                dataframe[f"supertrend_1_sell_{multiplier}_{period}"] = self.supertrend(
                    dataframe, multiplier, period
                )["STX"]

        for multiplier in self.sell_m2.range:
            for period in self.sell_p2.range:
                dataframe[f"supertrend_2_sell_{multiplier}_{period}"] = self.supertrend(
                    dataframe, multiplier, period
                )["STX"]

        for multiplier in self.sell_m3.range:
            for period in self.sell_p3.range:
                dataframe[f"supertrend_3_sell_{multiplier}_{period}"] = self.supertrend(
                    dataframe, multiplier, period
                )["STX"]

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (  # The three indicators are 'up' for the current candle
                (
                    dataframe[
                        f"supertrend_1_buy_{self.buy_m1.value}_{self.buy_p1.value}"
                    ]
                    == "up"
                )
                & (
                    dataframe[
                        f"supertrend_2_buy_{self.buy_m2.value}_{self.buy_p2.value}"
                    ]
                    == "up"
                )
                & (
                    dataframe[
                        f"supertrend_3_buy_{self.buy_m3.value}_{self.buy_p3.value}"
                    ]
                    == "up"
                )
                & (dataframe["volume"] > 0)  # There is at least some trading volume
            ),
            "buy",
        ] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (  # The three indicators are 'down' for the current candle
                (
                    dataframe[
                        f"supertrend_1_sell_{self.sell_m1.value}_{self.sell_p1.value}"
                    ]
                    == "down"
                )
                & (
                    dataframe[
                        f"supertrend_2_sell_{self.sell_m2.value}_{self.sell_p2.value}"
                    ]
                    == "down"
                )
                & (
                    dataframe[
                        f"supertrend_3_sell_{self.sell_m3.value}_{self.sell_p3.value}"
                    ]
                    == "down"
                )
                & (dataframe["volume"] > 0)  # There is at least some trading volume
            ),
            "sell",
        ] = 1

        return dataframe

    """
        Supertrend Indicator; adapted for freqtrade
        from: https://github.com/freqtrade/freqtrade-strategies/issues/30
    """

    def supertrend(self, dataframe: DataFrame, multiplier, period):
        df = dataframe.copy()
        last_row = dataframe.tail(1).index.item()

        df["TR"] = ta.TRANGE(df)
        df["ATR"] = ta.SMA(df["TR"], period)

        st = "ST_" + str(period) + "_" + str(multiplier)
        stx = "STX_" + str(period) + "_" + str(multiplier)

        # Compute basic upper and lower bands
        BASIC_UB = ((df["high"] + df["low"]) / 2 + multiplier * df["ATR"]).values
        BASIC_LB = ((df["high"] + df["low"]) / 2 - multiplier * df["ATR"]).values
        FINAL_UB = np.zeros(last_row + 1)
        FINAL_LB = np.zeros(last_row + 1)
        ST = np.zeros(last_row + 1)
        CLOSE = df["close"].values

        # Compute final upper and lower bands
        for i in range(period, last_row):
            FINAL_UB[i] = (
                BASIC_UB[i]
                if BASIC_UB[i] < FINAL_UB[i - 1] or CLOSE[i - 1] > FINAL_UB[i - 1]
                else FINAL_UB[i - 1]
            )
            FINAL_LB[i] = (
                BASIC_LB[i]
                if BASIC_LB[i] > FINAL_LB[i - 1] or CLOSE[i - 1] < FINAL_LB[i - 1]
                else FINAL_LB[i - 1]
            )

        # Set the Supertrend value
        for i in range(period, last_row):
            ST[i] = (
                FINAL_UB[i]
                if ST[i - 1] == FINAL_UB[i - 1] and CLOSE[i] <= FINAL_UB[i]
                else (
                    FINAL_LB[i]
                    if ST[i - 1] == FINAL_UB[i - 1] and CLOSE[i] > FINAL_UB[i]
                    else (
                        FINAL_LB[i]
                        if ST[i - 1] == FINAL_LB[i - 1] and CLOSE[i] >= FINAL_LB[i]
                        else (
                            FINAL_UB[i]
                            if ST[i - 1] == FINAL_LB[i - 1] and CLOSE[i] < FINAL_LB[i]
                            else 0.00
                        )
                    )
                )
            )
        df_ST = pd.DataFrame(ST, columns=[st])
        df = pd.concat([df, df_ST], axis=1)

        # Mark the trend direction up/down
        df[stx] = np.where(
            (df[st] > 0.00), np.where((df["close"] < df[st]), "down", "up"), np.NaN
        )

        df.fillna(0, inplace=True)

        return DataFrame(index=df.index, data={"ST": df[st], "STX": df[stx]})