# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/ElliotV5_309.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 datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
import technical.indicators as ftt
from freqtrade.exchange import timeframe_to_prev_date, timeframe_to_seconds
import pandas_ta as pta














buy_params = {
    "base_nb_candles_buy": 11,
    "ewo_high": 2.337,
    "ewo_low": -15.87,
    "low_offset": 0.979,
    "rsi_buy": 55,
}

sell_params = {
    "base_nb_candles_sell": 17,
    "high_offset": 0.997,
}


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



    dataframe['adx'] = ta.ADX(dataframe)

    dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
    dataframe['plus_di'] = ta.PLUS_DI(dataframe)

    dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
    dataframe['minus_di'] = ta.MINUS_DI(dataframe)

    aroon = ta.AROON(dataframe)
    dataframe['aroonup'] = aroon['aroonup']
    dataframe['aroondown'] = aroon['aroondown']
    dataframe['aroonosc'] = ta.AROONOSC(dataframe)

    dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)

    keltner = qtpylib.keltner_channel(dataframe)
    dataframe["kc_upperband"] = keltner["upper"]
    dataframe["kc_lowerband"] = keltner["lower"]
    dataframe["kc_middleband"] = keltner["mid"]
    dataframe["kc_percent"] = (
        (dataframe["close"] - dataframe["kc_lowerband"]) /
        (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
    )
    dataframe["kc_width"] = (
        (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
    )

    dataframe['uo'] = ta.ULTOSC(dataframe)

    dataframe['cci'] = ta.CCI(dataframe)

    dataframe['rsi'] = ta.RSI(dataframe)

    rsi = 0.1 * (dataframe['rsi'] - 50)
    dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)

    dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)

    stoch = ta.STOCH(dataframe)
    dataframe['slowd'] = stoch['slowd']
    dataframe['slowk'] = stoch['slowk']

    stoch_fast = ta.STOCHF(dataframe)
    dataframe['fastd'] = stoch_fast['fastd']
    dataframe['fastk'] = stoch_fast['fastk']



    stoch_rsi = ta.STOCHRSI(dataframe)
    dataframe['fastd_rsi'] = stoch_rsi['fastd']
    dataframe['fastk_rsi'] = stoch_rsi['fastk']

    macd = ta.MACD(dataframe)
    dataframe['macd'] = macd['macd']
    dataframe['macdsignal'] = macd['macdsignal']
    dataframe['macdhist'] = macd['macdhist']

    dataframe['mfi'] = ta.MFI(dataframe)

    dataframe['roc'] = ta.ROC(dataframe)



    bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
    dataframe['bb_lowerband'] = bollinger['lower']
    dataframe['bb_middleband'] = bollinger['mid']
    dataframe['bb_upperband'] = bollinger['upper']
    dataframe["bb_percent"] = (
        (dataframe["close"] - dataframe["bb_lowerband"]) /
        (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
    )
    dataframe["bb_width"] = (
        (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
    )

    dataframe['sar'] = ta.SAR(dataframe)

    dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)



    hilbert = ta.HT_SINE(dataframe)
    dataframe['htsine'] = hilbert['sine']
    dataframe['htleadsine'] = hilbert['leadsine']



    dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)

    dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)

    dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)

    dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe)  # values [0, 100]

    dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe)  # values [0, 100]

    dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe)  # values [0, 100]



    dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)

    dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)

    dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)

    dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)

    dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)

    dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)



    dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)

    dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe)  # values [0, -100, 100]

    dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe)  # values [0, -100, 100]

    dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe)  # values [0, -100, 100]

    dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe)  # values [0, -100, 100]

    dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe)  # values [0, -100, 100]



    heikinashi = qtpylib.heikinashi(dataframe)
    dataframe['ha_open'] = heikinashi['open']
    dataframe['ha_close'] = heikinashi['close']
    dataframe['ha_high'] = heikinashi['high']
    dataframe['ha_low'] = heikinashi['low']

    return dataframe


class Github_remiotore_ccxt_freqtrade__ElliotV5_309__20260111_210550(IStrategy):
    INTERFACE_VERSION = 2
































    minimal_roi = {
        "0": 0.032,
        "25": 0.02,
        "77": 0.01,
        "153": 0
    }

    stoploss = -0.30






    base_nb_candles_buy = IntParameter(
        5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True)
    base_nb_candles_sell = IntParameter(
        5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True)
    low_offset = DecimalParameter(
        0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True)
    high_offset = DecimalParameter(
        0.99, 1.1, default=sell_params['high_offset'], space='sell', optimize=True)

    fast_ewo = 50
    slow_ewo = 200
    ewo_low = DecimalParameter(-20.0, -8.0,
                               default=buy_params['ewo_low'], space='buy', optimize=True)
    ewo_high = DecimalParameter(
        2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=True)
    rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True)

    trailing_stop = True
    trailing_stop_positive = 0.005
    trailing_stop_positive_offset = 0.05
    trailing_only_offset_is_reached = True

    use_sell_signal = True
    sell_profit_only = False
    sell_profit_offset = 0.01
    ignore_roi_if_buy_signal = True

    order_time_in_force = {
        'buy': 'gtc',
        'sell': 'ioc'
    }

    timeframe = '5m'
    informative_timeframe = '1h'

    process_only_new_candles = True
    startup_candle_count = 200

    plot_config = {
        'main_plot': {
            'ma_buy': {'color': 'orange'},
            'ma_sell': {'color': 'orange'},
        },
    }

    use_custom_stoploss = False

    slippage_protection = {
        'retries': 3,
        'max_slippage': -0.02
    }

    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]

        if (last_candle is not None):
            if (sell_reason in ['sell_signal']):
                if (last_candle['hma_50']*1.149 > last_candle['ema_100']) and (last_candle['close'] < last_candle['ema_100']*0.951):  # *1.2
                    return False

        try:
            state = self.slippage_protection['__pair_retries']
        except KeyError:
            state = self.slippage_protection['__pair_retries'] = {}

        candle = dataframe.iloc[-1].squeeze()

        slippage = (rate / candle['close']) - 1
        if slippage < self.slippage_protection['max_slippage']:
            pair_retries = state.get(pair, 0)
            if pair_retries < self.slippage_protection['retries']:
                state[pair] = pair_retries + 1
                return False

        state[pair] = 0

        return True

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:

        stoploss = self.stoploss
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        if last_candle is None:
            return stoploss

        trade_date = timeframe_to_prev_date(
            self.timeframe, trade.open_date_utc - timedelta(seconds=timeframe_to_seconds(self.timeframe)))
        trade_candle = dataframe.loc[dataframe['date'] == trade_date]
        if trade_candle.empty:
            return stoploss

        trade_candle = trade_candle.squeeze()

        dur_minutes = (current_time - trade.open_date_utc).seconds // 60

        slippage_ratio = trade.open_rate / trade_candle['close'] - 1
        slippage_ratio = slippage_ratio if slippage_ratio > 0 else 0
        current_profit_comp = current_profit + slippage_ratio

        if current_profit_comp >= self.trailing_stop_positive_offset:
            return self.trailing_stop_positive

        for x in self.minimal_roi:
            dur = int(x)
            roi = self.minimal_roi[x]
            if dur_minutes >= dur and current_profit_comp >= roi:
                return 0.001

        return stoploss

    def informative_pairs(self):

        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]

        return informative_pairs

    def get_informative_indicators(self, metadata: dict):

        dataframe = self.dp.get_pair_dataframe(
            pair=metadata['pair'], timeframe=self.informative_timeframe)

        return dataframe

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

        for val in self.base_nb_candles_buy.range:
            dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)

        for val in self.base_nb_candles_sell.range:
            dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)

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

        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)

        dataframe['hma_50'] = pta.hma(dataframe['close'], 50)
        dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)

        return dataframe

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

        conditions.append(
            (
                (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
                (dataframe['EWO'] > self.ewo_high.value) &
                (dataframe['rsi'] < self.rsi_buy.value) &
                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
                (dataframe['EWO'] < self.ewo_low.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 = []

        conditions.append(
            (
                (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
                (dataframe['volume'] > 0)
            )
        )

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

        return dataframe