# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/ElliotV9_exit.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

buy_params = {
      "base_nb_candles_buy": 17,
      "ewo_high": 3.34,
      "ewo_low": -17.457,
      "low_offset": 0.978,
      "rsi_buy": 65
    }

sell_params = {
      "base_nb_candles_sell": 49,
      "high_offset": 1.012  #was 1.1019   # 1.015 was good !
    }

def EWO(dataframe, ema_length=5, ema2_length=35):
    df = dataframe.copy()
    ema1 = ta.SMA(df, timeperiod=ema_length)
    ema2 = ta.SMA(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_freqtrade__ElliotV9_exit__20260111_210550(IStrategy):
    INTERFACE_VERSION = 2

    minimal_roi = {
        "0": 0.215,
        "40": 0.132,
        "87": 0.086,
        "201": 0.03
    }

    stoploss = -0.10

    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 = 0.03
    #trailing_only_offset_is_reached = True

    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = True

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

    process_only_new_candles = True
    startup_candle_count = 450 # was 2000

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

    use_custom_stoploss = False

    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['atr'] = ta.ATR(dataframe, timeperiod=14)  # Calculate ATR


        return dataframe

    def populate_entry_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_exit_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['rsi'] > 63) &
                #(dataframe['rsi'] > 62) & (dataframe['rsi'] < dataframe['rsi'].rolling(10).max()) &

                #(dataframe['atr'] < dataframe['atr'].rolling(2).mean()) & # ATR must be below a short-term average
                 #(dataframe['atr'] < dataframe['atr'].shift(1)) & # ATR must be below a short-term average
                (dataframe['volume'] > 0)
            )
        )

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

        return dataframe