# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/BinHV27_2.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 freqtrade.vendor.qtpylib.indicators as qtpylib
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, DatetimeIndex, merge


import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy  # noqa


class Github_remiotore_ccxt_freqtrade__BinHV27_2__20260111_210550(IStrategy):
    """

        strategy sponsored by user BinH from slack

    """

    minimal_roi = {
        "0": 1
    }

    stoploss = -0.50
    timeframe = '15m'

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = numpy.nan_to_num(ta.RSI(dataframe, timeperiod=5))
        rsiframe = DataFrame(dataframe['rsi']).rename(columns={'rsi': 'close'})
        dataframe['emarsi'] = numpy.nan_to_num(ta.EMA(rsiframe, timeperiod=5))
        dataframe['adx'] = numpy.nan_to_num(ta.ADX(dataframe))
        dataframe['minusdi'] = numpy.nan_to_num(ta.MINUS_DI(dataframe))
        minusdiframe = DataFrame(dataframe['minusdi']).rename(columns={'minusdi': 'close'})
        dataframe['minusdiema'] = numpy.nan_to_num(ta.EMA(minusdiframe, timeperiod=25))
        dataframe['plusdi'] = numpy.nan_to_num(ta.PLUS_DI(dataframe))
        plusdiframe = DataFrame(dataframe['plusdi']).rename(columns={'plusdi': 'close'})
        dataframe['plusdiema'] = numpy.nan_to_num(ta.EMA(plusdiframe, timeperiod=5))
        dataframe['lowsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=60))
        dataframe['highsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=120))
        dataframe['fastsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=120))
        dataframe['slowsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=240))
        dataframe['bigup'] = dataframe['fastsma'].gt(dataframe['slowsma']) & ((dataframe['fastsma'] - dataframe['slowsma']) > dataframe['close'] / 300)
        dataframe['bigdown'] = ~dataframe['bigup']
        dataframe['trend'] = dataframe['fastsma'] - dataframe['slowsma']
        dataframe['preparechangetrend'] = dataframe['trend'].gt(dataframe['trend'].shift())
        dataframe['preparechangetrendconfirm'] = dataframe['preparechangetrend'] & dataframe['trend'].shift().gt(dataframe['trend'].shift(2))
        dataframe['continueup'] = dataframe['slowsma'].gt(dataframe['slowsma'].shift()) & dataframe['slowsma'].shift().gt(dataframe['slowsma'].shift(2))
        dataframe['delta'] = dataframe['fastsma'] - dataframe['fastsma'].shift()
        dataframe['slowingdown'] = dataframe['delta'].lt(dataframe['delta'].shift())
        return dataframe
    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            dataframe['slowsma'].gt(0) &
            dataframe['close'].lt(dataframe['highsma']) &
            dataframe['close'].lt(dataframe['lowsma']) &
            dataframe['minusdi'].gt(dataframe['minusdiema']) &
            dataframe['rsi'].ge(dataframe['rsi'].shift()) &
            (
              (
                ~dataframe['preparechangetrend'] &
                ~dataframe['continueup'] &
                dataframe['adx'].gt(25) &
                dataframe['bigdown'] &
                dataframe['emarsi'].le(20)
              ) |
              (
                ~dataframe['preparechangetrend'] &
                dataframe['continueup'] &
                dataframe['adx'].gt(30) &
                dataframe['bigdown'] &
                dataframe['emarsi'].le(20)
              ) |
              (
                ~dataframe['continueup'] &
                dataframe['adx'].gt(35) &
                dataframe['bigup'] &
                dataframe['emarsi'].le(20)
              ) |
              (
                dataframe['continueup'] &
                dataframe['adx'].gt(30) &
                dataframe['bigup'] &
                dataframe['emarsi'].le(25)
              )
            ),
            'buy'] = 1
        return dataframe
    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
              (
                ~dataframe['preparechangetrendconfirm'] &
                ~dataframe['continueup'] &
                (dataframe['close'].gt(dataframe['lowsma']) | dataframe['close'].gt(dataframe['highsma'])) &
                dataframe['highsma'].gt(0) &
                dataframe['bigdown']
              ) |
              (
                ~dataframe['preparechangetrendconfirm'] &
                ~dataframe['continueup'] &
                dataframe['close'].gt(dataframe['highsma']) &
                dataframe['highsma'].gt(0) &
                (dataframe['emarsi'].ge(75) | dataframe['close'].gt(dataframe['slowsma'])) &
                dataframe['bigdown']
              ) |
              (
                ~dataframe['preparechangetrendconfirm'] &
                dataframe['close'].gt(dataframe['highsma']) &
                dataframe['highsma'].gt(0) &
                dataframe['adx'].gt(30) &
                dataframe['emarsi'].ge(80) &
                dataframe['bigup']
              ) |
              (
                dataframe['preparechangetrendconfirm'] &
                ~dataframe['continueup'] &
                dataframe['slowingdown'] &
                dataframe['emarsi'].ge(75) &
                dataframe['slowsma'].gt(0)
              ) |
              (
                dataframe['preparechangetrendconfirm'] &
                dataframe['minusdi'].lt(dataframe['plusdi']) &
                dataframe['close'].gt(dataframe['lowsma']) &
                dataframe['slowsma'].gt(0)
              )
            ),
            'sell'] = 1
        return dataframe