# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/RSIDivergence_238.py

from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame


import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib
import datetime
from technical.util import resample_to_interval, resampled_merge
from freqtrade.strategy import DecimalParameter, IntParameter, BooleanParameter

rangeUpper = 60
rangeLower = 5

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['close'] * 100
    return emadif

def valuewhen(dataframe, condition, source, occurrence):
    copy = dataframe.copy()
    copy['colFromIndex'] = copy.index
    copy = copy.sort_values(by=[condition, 'colFromIndex'], ascending=False).reset_index(drop=True)
    copy['valuewhen'] = np.where(copy[condition] > 0, copy[source].shift(-occurrence), copy[source])
    copy['barrsince'] = copy['colFromIndex'] - copy['colFromIndex'].shift(-occurrence)
    copy.loc[
        (
            (rangeLower <= copy['barrsince']) &
            (copy['barrsince']  <= rangeUpper)
        )
    , "in_range"] = 1
    copy['in_range'] = copy['in_range'].fillna(0)
    copy = copy.sort_values(by=['colFromIndex'], ascending=True).reset_index(drop=True)
    return copy['valuewhen'], copy['in_range']


class Github_remiotore_freqtrade__RSIDivergence_238__20260111_210550(IStrategy):
    INTERFACE_VERSION = 2

    buy_params = {
        'use_bull': True,
        'use_hidden_bull': False,
        "ewo_high": 5.835,
        "low_rsi_buy": 30,
        "high_rsi_buy": 60,
        "low_adx_buy": 30,
        "high_adx_buy": 30,
        "low_stoch_buy": 20,
        "high_stoch_buy": 80,
        "low_osc_buy": 80,
        "high_osc_buy": 80,
    }

    sell_params = {
        'use_bear': True,
        'use_hidden_bear': True
    }

    minimal_roi = {
        "0": 0.05,
    }

    stoploss = -0.08

    trailing_stop = True
    trailing_stop_positive = 0.005
    trailing_stop_positive_offset = 0.02
    trailing_only_offset_is_reached = True

    timeframe = '5m'

    use_custom_stoploss = False

    use_bull = BooleanParameter(default=buy_params['use_bull'], space='buy', optimize=False)
    use_hidden_bull = BooleanParameter(default=buy_params['use_hidden_bull'], space='buy', optimize=False)
    use_bear = BooleanParameter(default=sell_params['use_bear'], space='sell', optimize=True)
    use_hidden_bear = BooleanParameter(default=sell_params['use_hidden_bear'], space='sell', optimize=True)

    fast_ewo = 50
    slow_ewo = 200
    ewo_high = DecimalParameter(0, 7.0, default=buy_params['ewo_high'], space='buy', optimize=False)
    low_rsi_buy = IntParameter(0, 100, default=buy_params['low_rsi_buy'], space='buy', optimize=True)
    high_rsi_buy = IntParameter(0, 100, default=buy_params['high_rsi_buy'], space='buy', optimize=True)
    low_adx_buy = IntParameter(0, 100, default=buy_params['low_adx_buy'], space='buy', optimize=True)
    high_adx_buy = IntParameter(0, 100, default=buy_params['high_adx_buy'], space='buy', optimize=True)
    low_stoch_buy = IntParameter(0, 100, default=buy_params['low_stoch_buy'], space='buy', optimize=True)
    high_stoch_buy = IntParameter(0, 100, default=buy_params['high_stoch_buy'], space='buy', optimize=True)
    low_osc_buy = IntParameter(0, 100, default=buy_params['low_osc_buy'], space='buy', optimize=True)
    high_osc_buy = IntParameter(0, 100, default=buy_params['high_osc_buy'], space='buy', optimize=True)

    startup_candle_count: int = 30

    osc = 'slowd'
    len = 14
    src = 'close'
    lbL = 5
    lbR = 5

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        study(title="Divergence Indicator", format=format.price, resolution="")
        len = input(title="RSI Period", minval=1, defval=14)
        src = input(title="RSI Source", defval=close)
        lbR = input(title="Pivot Lookback Right", defval=5) # lookahead
        lbL = input(title="Pivot Lookback Left", defval=5)
        rangeUpper = input(title="Max of Lookback Range", defval=60)
        rangeLower = input(title="Min of Lookback Range", defval=5)
        plotBull = input(title="Plot Bullish", defval=true)
        plotHiddenBull = input(title="Plot Hidden Bullish", defval=false)
        plotBear = input(title="Plot Bearish", defval=true)
        plotHiddenBear = input(title="Plot Hidden Bearish", defval=false)
        bearColor = color.red
        bullColor = color.green
        hiddenBullColor = color.new(color.green, 80)
        hiddenBearColor = color.new(color.red, 80)
        textColor = color.white
        noneColor = color.new(color.white, 100)
        osc = rsi(src, len)
        """
        dataframe['RSI'] = ta.RSI(dataframe[self.src], self.len)
        dataframe['RSI'] = dataframe['RSI'].fillna(0)
        stoch = ta.STOCH(dataframe, fastk_period=10, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
        dataframe['slowk'] = stoch['slowk']
        dataframe['slowd'] = stoch['slowd']
        dataframe['osc'] = dataframe[self.osc]

        dataframe['min'] = dataframe['osc'].rolling(self.lbL).min()
        dataframe['prevMin'] = np.where(dataframe['min'] > dataframe['min'].shift(), dataframe['min'].shift(), dataframe['min'])
        dataframe.loc[
            (
                (dataframe['osc'].shift(1) == dataframe['prevMin'].shift(1)) &
                (dataframe['osc'] != dataframe['prevMin'])
            )
        , 'plFound'] = 1

        dataframe['max'] = dataframe['osc'].rolling(self.lbL).max()
        dataframe['prevMax'] = np.where(dataframe['max'] < dataframe['max'].shift(), dataframe['max'].shift(), dataframe['max'])
        dataframe.loc[
            (
            (dataframe['osc'].shift(1) == dataframe['prevMax'].shift(1)) &
            (dataframe['osc'] != dataframe['prevMax'])
            )
        , 'phFound'] = 1




        dataframe['valuewhen_plFound_osc'], dataframe['inrange_plFound_osc'] = valuewhen(dataframe, 'plFound', 'osc', 1)
        dataframe.loc[
            (
                (dataframe['osc'] > dataframe['valuewhen_plFound_osc']) &
                (dataframe['inrange_plFound_osc'] == 1)
             )
        , 'oscHL'] = 1


        dataframe['valuewhen_plFound_low'], dataframe['inrange_plFound_low'] = valuewhen(dataframe, 'plFound', 'low', 1)
        dataframe.loc[
            (dataframe['low'] < dataframe['valuewhen_plFound_low'])
            , 'priceLL'] = 1

        dataframe.loc[
            (
                (dataframe['priceLL'] == 1) &
                (dataframe['oscHL'] == 1) &
                (dataframe['plFound'] == 1)
            )
            , 'bullCond'] = 1























        dataframe['valuewhen_plFound_osc'], dataframe['inrange_plFound_osc'] = valuewhen(dataframe, 'plFound', 'osc', 1)
        dataframe.loc[
            (
                (dataframe['osc'] < dataframe['valuewhen_plFound_osc']) &
                (dataframe['inrange_plFound_osc'] == 1)
             )
        , 'oscLL'] = 1




        dataframe['valuewhen_plFound_low'], dataframe['inrange_plFound_low'] = valuewhen(dataframe,'plFound', 'low', 1)
        dataframe.loc[
            (dataframe['low'] > dataframe['valuewhen_plFound_low'])
            , 'priceHL'] = 1

        dataframe.loc[
            (
                (dataframe['priceHL'] == 1) &
                (dataframe['oscLL'] == 1) &
                (dataframe['plFound'] == 1)
            )
            , 'hiddenBullCond'] = 1

























        dataframe['valuewhen_phFound_osc'], dataframe['inrange_phFound_osc'] = valuewhen(dataframe, 'phFound', 'osc', 1)
        dataframe.loc[
            (
                (dataframe['osc'] < dataframe['valuewhen_phFound_osc']) &
                (dataframe['inrange_phFound_osc'] == 1)
             )
        , 'oscLH'] = 1




        dataframe['valuewhen_phFound_high'], dataframe['inrange_phFound_high'] = valuewhen(dataframe, 'phFound', 'high', 1)
        dataframe.loc[
            (dataframe['high'] > dataframe['valuewhen_phFound_high'])
            , 'priceHH'] = 1


        dataframe.loc[
            (
                (dataframe['priceHH'] == 1) &
                (dataframe['oscLH'] == 1) &
                (dataframe['phFound'] == 1)
            )
            , 'bearCond'] = 1

























        dataframe['valuewhen_phFound_osc'], dataframe['inrange_phFound_osc'] = valuewhen(dataframe, 'phFound', 'osc', 1)
        dataframe.loc[
            (
                (dataframe['osc'] > dataframe['valuewhen_phFound_osc']) &
                (dataframe['inrange_phFound_osc'] == 1)
             )
        , 'oscHH'] = 1




        dataframe['valuewhen_phFound_high'], dataframe['inrange_phFound_high'] = valuewhen(dataframe, 'phFound', 'high', 1)
        dataframe.loc[
            (dataframe['high'] < dataframe['valuewhen_phFound_high'])
            , 'priceLH'] = 1


        dataframe.loc[
            (
                (dataframe['priceLH'] == 1) &
                (dataframe['oscHH'] == 1) &
                (dataframe['phFound'] == 1)
            )
            , 'hiddenBearCond'] = 1




















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

        dataframe['ADX'] = ta.ADX(dataframe, timeperiod=14)
        return dataframe

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

        if self.use_bull.value:
            conditions.append(
                    (
                        (dataframe['bullCond'] > 0) &
                        (dataframe['valuewhen_plFound_osc'] > self.low_osc_buy.value) &
                        (dataframe['valuewhen_plFound_osc'] < self.high_osc_buy.value) &

                        (dataframe['RSI'] < self.high_rsi_buy.value) &
                        (dataframe['RSI'] > self.low_rsi_buy.value) &
                        (dataframe['ADX'] > self.low_adx_buy.value) &
                        (dataframe['ADX'] < self.high_adx_buy.value) &
                        (dataframe['slowk'] < self.high_stoch_buy.value) &
                        (dataframe['slowk'] > self.low_stoch_buy.value) &
                        (dataframe['volume'] > 0)
                    )
                )

        if self.use_hidden_bull.value:
            conditions.append(
                (
                    (dataframe['hiddenBullCond'] > 0) &
                    (dataframe['valuewhen_plFound_osc'] > self.low_osc_buy.value) &
                    (dataframe['valuewhen_plFound_osc'] < self.high_osc_buy.value) &
                    (dataframe['RSI'] < self.high_rsi_buy.value) &
                    (dataframe['RSI'] > self.low_rsi_buy.value) &
                    (dataframe['slowk'] < self.high_stoch_buy.value) &
                    (dataframe['slowk'] > self.low_stoch_buy.value) &
                    (dataframe['ADX'] > self.low_adx_buy.value) &
                    (dataframe['ADX'] < self.high_adx_buy.value) &
                    (dataframe['volume'] > 0)
                )
            )

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

        return dataframe

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

        if self.use_bear.value:
            conditions.append(
                (
                    (dataframe['bearCond'] > 0) &
                    (dataframe['volume'] > 0)
                )
            )

        if self.use_hidden_bear.value:
            conditions.append(
                (
                    (dataframe['hiddenBearCond'] > 0) &
                    (dataframe['volume'] > 0)
                )
            )

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

        dataframe.to_csv('user_data/csvs/%s_%s.csv' % (self.__class__.__name__, metadata["pair"].replace("/", "_")))

        return dataframe

class RSIDivergence(Github_remiotore_freqtrade__RSIDivergence_238__20260111_210550):

    buy_params = {
        "high_adx_buy": 68,
        "high_osc_buy": 74,
        "high_rsi_buy": 53,
        "high_stoch_buy": 64,
        "low_adx_buy": 41,
        "low_osc_buy": 15,
        "low_rsi_buy": 9,
        "low_stoch_buy": 13,
        "ewo_high": 5.835,  # value loaded from strategy
        "use_bull": True,  # value loaded from strategy
        "use_hidden_bull": False,  # value loaded from strategy
    }

    sell_params = {
        "use_bear": False,  # value loaded from strategy
        "use_hidden_bear": False,  # value loaded from strategy
    }

    minimal_roi = {
        "0": 0.131,
        "13": 0.073,
        "56": 0.022,
        "133": 0
    }

    stoploss = -0.2  # value loaded from strategy

    trailing_stop = True  # value loaded from strategy
    trailing_stop_positive = 0.005  # value loaded from strategy
    trailing_stop_positive_offset = 0.02  # value loaded from strategy
    trailing_only_offset_is_reached = True  # value loaded from strategy


    osc = 'RSI'
    len = 14
    src = 'close'
    lbL = 40

class RSIDivergenceNPOriginal(RSIDivergence):

    buy_params = {
        "adx_buy": 36,
        "high_rsi_buy": 60,
        "high_stoch_buy": 92,
        "low_rsi_buy": 21,
        "low_stoch_buy": 6,
        "use_bull": True,
        "use_hidden_bull": False,
        "ewo_high": 5.835,  # value loaded from strategy
    }

    sell_params = {
        "use_bear": False,
        "use_hidden_bear": False,
    }

    minimal_roi = {
        "0": 0.05
    }

    stoploss = -0.2  # value loaded from strategy

    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.02
    trailing_only_offset_is_reached = True

    osc = 'RSI'
    len = 14
    src = 'close'
    lbL = 40

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

        if self.use_bull.value:
            conditions.append(
                    (
                        (dataframe['bullCond'] > 0) &
                        (dataframe['volume'] > 0)
                    )
                )

        if self.use_hidden_bull.value:
            conditions.append(
                (
                    (dataframe['hiddenBullCond'] > 0) &
                    (dataframe['volume'] > 0)
                )
            )

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

        return dataframe

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

        if self.use_bear.value:
            conditions.append(
                (
                    (dataframe['bearCond'] > 0) &
                    (dataframe['volume'] > 0)
                )
            )

        if self.use_hidden_bear.value:
            conditions.append(
                (
                    (dataframe['hiddenBearCond'] > 0) &
                    (dataframe['volume'] > 0)
                )
            )

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

        dataframe.to_csv('user_data/csvs/%s_%s.csv' % (self.__class__.__name__, metadata["pair"].replace("/", "_")))

        return dataframe