# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/f_ott_strategy.py
import logging
from numpy.lib import math
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.hyper import IntParameter
from pandas import DataFrame
import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib



class Github_remiotore_freqtrade__f_ott_strategy__20260111_210550(IStrategy):



    minimal_roi = {"0": 0.1, "30": 0.75, "60": 0.05, "120": 0.025}


    stoploss = -0.265

    trailing_stop = True
    trailing_stop_positive = 0.05
    trailing_stop_positive_offset = 0.1
    trailing_only_offset_is_reached = False

    timeframe = "1h"

    startup_candle_count = 18

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

        dataframe["ott"] = self.ott(dataframe)["OTT"]
        dataframe["var"] = self.ott(dataframe)["VAR"]
        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)

        return dataframe

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

        dataframe.loc[
            (qtpylib.crossed_above(dataframe["var"], dataframe["ott"])),
            "enter_long",
        ] = 1

        dataframe.loc[
            (qtpylib.crossed_below(dataframe["var"], dataframe["ott"])),
            "enter_short",
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                dataframe["adx"]>60
            ),
            "exit_long",
        ] = 1

        dataframe.loc[
            (
                dataframe["adx"]>60
            ),
            "exit_short",
        ] = 1

        return dataframe

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

    def ott(self, dataframe: DataFrame):
        df = dataframe.copy()

        pds = 2
        percent = 1.4
        alpha = 2 / (pds + 1)

        df["ud1"] = np.where(
            df["close"] > df["close"].shift(1), (df["close"] - df["close"].shift()), 0
        )
        df["dd1"] = np.where(
            df["close"] < df["close"].shift(1), (df["close"].shift() - df["close"]), 0
        )
        df["UD"] = df["ud1"].rolling(9).sum()
        df["DD"] = df["dd1"].rolling(9).sum()
        df["CMO"] = ((df["UD"] - df["DD"]) / (df["UD"] + df["DD"])).fillna(0).abs()

        df["Var"] = 0.0
        for i in range(pds, len(df)):
            df["Var"].iat[i] = (alpha * df["CMO"].iat[i] * df["close"].iat[i]) + (
                1 - alpha * df["CMO"].iat[i]
            ) * df["Var"].iat[i - 1]

        df["fark"] = df["Var"] * percent * 0.01
        df["newlongstop"] = df["Var"] - df["fark"]
        df["newshortstop"] = df["Var"] + df["fark"]
        df["longstop"] = 0.0
        df["shortstop"] = 999999999999999999

        for i in df["UD"]:

            def maxlongstop():
                df.loc[(df["newlongstop"] > df["longstop"].shift(1)), "longstop"] = df[
                    "newlongstop"
                ]
                df.loc[(df["longstop"].shift(1) > df["newlongstop"]), "longstop"] = df[
                    "longstop"
                ].shift(1)

                return df["longstop"]

            def minshortstop():
                df.loc[
                    (df["newshortstop"] < df["shortstop"].shift(1)), "shortstop"
                ] = df["newshortstop"]
                df.loc[
                    (df["shortstop"].shift(1) < df["newshortstop"]), "shortstop"
                ] = df["shortstop"].shift(1)

                return df["shortstop"]

            df["longstop"] = np.where(
                ((df["Var"] > df["longstop"].shift(1))),
                maxlongstop(),
                df["newlongstop"],
            )

            df["shortstop"] = np.where(
                ((df["Var"] < df["shortstop"].shift(1))),
                minshortstop(),
                df["newshortstop"],
            )


        df["xlongstop"] = np.where(
            (
                (df["Var"].shift(1) > df["longstop"].shift(1))
                & (df["Var"] < df["longstop"].shift(1))
            ),
            1,
            0,
        )

        df["xshortstop"] = np.where(
            (
                (df["Var"].shift(1) < df["shortstop"].shift(1))
                & (df["Var"] > df["shortstop"].shift(1))
            ),
            1,
            0,
        )

        df["trend"] = 0
        df["dir"] = 0
        for i in df["UD"]:
            df["trend"] = np.where(
                ((df["xshortstop"] == 1)),
                1,
                (np.where((df["xlongstop"] == 1), -1, df["trend"].shift(1))),
            )

            df["dir"] = np.where(
                ((df["xshortstop"] == 1)),
                1,
                (np.where((df["xlongstop"] == 1), -1, df["dir"].shift(1).fillna(1))),
            )


        df["MT"] = np.where(df["dir"] == 1, df["longstop"], df["shortstop"])
        df["OTT"] = np.where(
            df["Var"] > df["MT"],
            (df["MT"] * (200 + percent) / 200),
            (df["MT"] * (200 - percent) / 200),
        )
        df["OTT"] = df["OTT"].shift(2)

        return DataFrame(index=df.index, data={"OTT": df["OTT"], "VAR": df["Var"]})