s_sniperhyper

import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame

from functools import reduce
import technical.indicators as technicali
import technical.pivots_points as technicalp
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
                                IStrategy, IntParameter)


# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class s_sniperhyper(IStrategy):
     # Strategy interface version - allow new iterations of the strategy interface.
    # Check the documentation or the Sample strategy to get the latest version.
    INTERFACE_VERSION = 2

    # # ROI table:
    minimal_roi = {
        "0": 0.166,
        "109": 0.115,
        "180": 0.054,
        "538": 0
    }

    stoploss = -0.252
    
    #hyperobtalbe
     # Buy hyperspace params:
    buy_params = {
        "buy_aroondown": 11,
        "buy_aroonup": 69,
        "buy_arrondown_cat": False,
        "buy_arronup_cat": True,
        "buy_emas_cat": False,
        "buy_td": 5,
        "buy_td_cat": True,
        "buy_tke_cat": True,
        "buy_tke_val": 39,
        "buy_vfi_cat": True,
        "buy_vfi_cat2": False,
        "buy_vfi_cat3": True,
        "buy_vwmacd_cat": False,
        "buyema": 112,
    }

    # Sell hyperspace params:
    sell_params = {
        "sell_aroondown": 96,
        "sell_aroonup": 16,
        "sell_arrondown_cat": False,
        "sell_arronup_cat": True,
        "sell_td_cat": False,
        "sell_tkcros_cat": True,
        "sell_tke_cat": True,
        "sell_tke_val": 64,
    }
   
    
    
    
    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.164
    trailing_stop_positive_offset = 0.187
    trailing_only_offset_is_reached = True

    use_custom_stoploss = False

    # Optimal timeframe for the strategy.
    timeframe = '15m'
    #inf_1h = '1h'

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True

    # These values can be overridden in the "ask_strategy" section in the config.
    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = True

    startup_candle_count: int = 30

    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False

    }
    plot_config = {
        'main_plot': {
            'tema': {},
            'sar': {'color': 'white'},
        },
        'subplots': {
            "MACD": {
                'macd': {'color': 'blue'},
                'macdsignal': {'color': 'orange'},
            },
            "RSI": {
                'rsi': {'color': 'red'},
            }
        }
    }
    
    #buy ints


    buy_aroonup = IntParameter(40, 100, default = 77, space = 'buy')
    buy_aroondown = IntParameter(0, 40, default = 24, space = 'buy' )
    buyema = IntParameter(45,120, default= 70, space ='buy')

    buy_tke_val = IntParameter(1, 40, default = 37, space = 'buy')
    
    #buy_mma_shift1 = IntParameter(1, 10, default = 2, space = 'buy')
    #buy_mma_shift2 = IntParameter(3, 20, default = 6, space = 'buy')
    buy_td = IntParameter(3, 9, default = 9, space = 'buy')

    #buy_vidya = IntParameter(3, 25, default = 11, space = 'buy')

    #sell ints

    sell_aroonup = IntParameter(0, 75, default = 24, space = 'sell' )
    sell_aroondown = IntParameter(40, 100, default = 68, space = 'sell')
    sell_tke_val = IntParameter(60, 90, default = 78, space = 'sell')


    #buy cat

    buy_arrondown_cat = CategoricalParameter([True, False], default = True, space = 'buy' )
    buy_arronup_cat = CategoricalParameter([True, False], default = True, space = 'buy' )
    
    #buy_tkcros_cat = CategoricalParameter([True, False], default = True, space = 'buy' )
    
    buy_emas_cat = CategoricalParameter([True, False], default = True, space = 'buy' )

    #buy_adx_cat = CategoricalParameter([True, False], default = True, space = 'buy' )
    buy_tke_cat = CategoricalParameter([True, False], default = True, space = 'buy' )
   # buy_pivot_cat = CategoricalParameter([True, False], default = True, space = 'buy' )
    buy_td_cat = CategoricalParameter([True, False], default = True, space = 'buy' )
    buy_vwmacd_cat = CategoricalParameter([True, False], default = True, space = 'buy' )
    buy_vfi_cat = CategoricalParameter([True, False], default = True, space = 'buy' )
    buy_vfi_cat2 = CategoricalParameter([True, False], default = True, space = 'buy' )
    buy_vfi_cat3 = CategoricalParameter([True, False], default = True, space = 'buy' )
    #buy_madrid_cat = CategoricalParameter([True, False], default = True, space = 'buy' )
   # buy_ssl_cat = CategoricalParameter([True, False], default = True, space = 'buy' )
    #sell cat
    sell_arronup_cat = CategoricalParameter([True, False], default = True, space = 'sell' )
    sell_arrondown_cat = CategoricalParameter([True, False], default = True, space = 'sell' )
    sell_tkcros_cat = CategoricalParameter([True, False], default = True, space = 'sell' )
    sell_tke_cat = CategoricalParameter([True, False], default = True, space = 'sell' )
    #sell_pivot_cat = CategoricalParameter([True, False], default = True, space = 'sell' )
    sell_td_cat = CategoricalParameter([True, False], default = True, space = 'sell' )



    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
      vfi = technicali.vfi(dataframe)
      
      aroon = ta.AROON(dataframe)
      #ichi = technicali.ichimoku(dataframe)
      #mma = technicali.mmar(dataframe)
      tke = technicali.TKE(dataframe)
      #pivot = technicalp.pivots_points(dataframe)
      td = technicali.td_sequential(dataframe)
      #VIDYA = technicali.VIDYA(dataframe, length = self.buy_vidya.value)
      vwmacd = technicali.vwmacd(dataframe)
      #ssl = technicali.SSLChannels(dataframe)
      #aroon
      dataframe['aroonup'] = aroon['aroonup']
      dataframe['aroondown'] = aroon['aroondown']
      # pivots
      #dataframe['p'] = pivot['pivot']

      #ADx
      #dataframe['adx'] = ta.ADX(dataframe)
      #td
      dataframe['td'] = td['TD_count']

     
      #ichi
      #dataframe['tenkan'] = ichi['tenkan_sen']
      #dataframe['kijun'] = ichi['kijun_sen']
      #dataframe['span_a'] = ichi['senkou_span_a']
      #dataframe['span_b'] = ichi['senkou_span_b']
      #dataframe['cloud_green']=ichi['cloud_green']
      #dataframe['cloud_red']=ichi['cloud_red']
      #vidya
      #dataframe['VIDYA'] = VIDYA
      #ema
      dataframe['ema200'] = ta.EMA(dataframe, timeperiod = self.buyema.value)
      #vfi
      dataframe['vfi'] = vfi[0]
      dataframe['vfima'] = vfi[1]
      #vwmacd
      dataframe['vwmacd'] = vwmacd['vwmacd']
      dataframe['vwmacds'] = vwmacd['signal']
      #madrid usage
      #dataframe ['mmalead'] = mma[9]
      
      
      #dataframe['sslup'] = ssl[1]
      #dataframe['ssldown'] = ssl[0]

      #tke
      dataframe['TKE'] = tke[0]

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

        

        if self.buy_vfi_cat == True:
            conditions.append(qtpylib.crossed_above(dataframe['vfi'], dataframe['vfima']))

        if self.buy_vfi_cat2 == True:
            conditions.append(qtpylib.crossed_above(dataframe['vfi'], 0))
        if self.buy_vfi_cat3 == True:
            conditions.append(qtpylib.crossed_above(dataframe['vfima'], 0))

        if self.buy_vwmacd_cat.value == True:
            conditions.append(qtpylib.crossed_above(dataframe['vwmacd'], dataframe['vwmacds']))

        #if self.buy_ssl_cat.value == True:
         #   conditions.append(qtpylib.crossed_above(dataframe['ssl_up'], dataframe['ssl_down']))

        if self.buy_tke_cat.value == True:
            conditions.append(qtpylib.crossed_above(dataframe['TKE'], self.buy_tke_val.value))

        #if self.buy_vidya_cat == True:
       # conditions.append(qtpylib.crossed_above(dataframe["close"], dataframe['VIDYA']))

        #if self.buy_adx_cat.value == True:
         #   conditions.append(dataframe['adx'] > self.buy_adx.value)

        if self.buy_arronup_cat.value == True:
            conditions.append(qtpylib.crossed_above(dataframe['aroonup'], self.buy_aroonup.value))

        if self.buy_arrondown_cat.value == True:
            conditions.append(qtpylib.crossed_above(dataframe['aroondown'], self.buy_aroondown.value))
        
        #if self.buy_tkcros_cat.value == True:
         #   conditions.append(qtpylib.crossed_above(dataframe['tenkan'], dataframe['kijun']))
        if self.buy_emas_cat.value == True:
            conditions.append(qtpylib.crossed_above(dataframe["close"], dataframe['ema200'])) 
        #if self.buy_clud_cat.value == True:
         #   conditions.append(dataframe['cloud_red']== self.buy_cloud.value)
        
        #if self.buy_pivot_cat.value == True:
         #   conditions.append(qtpylib.crossed_above(dataframe["close"], dataframe['p']))
        conditions.append(dataframe['volume'] > 0)
        if self.buy_td_cat == True:
            conditions.append(dataframe['td'] == self.buy_td.value)


        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.sell_tke_cat.value == True:
            conditions.append(qtpylib.crossed_below(dataframe['TKE'], self.sell_tke_val.value))

        #if self.sell_ssl_cat.value == True:
         #   conditions.append(qtpylib.crossed_below(dataframe['sslup'], dataframe['ssldown']))
        if self.sell_arrondown_cat.value == True:
            conditions.append(qtpylib.crossed_above(dataframe['aroondown'], self.sell_aroondown.value))
        if self.sell_arronup_cat.value == True:
            conditions.append(qtpylib.crossed_below(dataframe['aroonup'], self.sell_aroonup.value))
        #if self.sell_tkcros_cat.value == True:
         #   conditions.append(qtpylib.crossed_below(dataframe['tenkan'], dataframe['kijun']))
        #if self.sell_pivot_cat.value == True:
         #   conditions.append(qtpylib.crossed_below(dataframe["close"], dataframe['p']))

        conditions.append(dataframe['volume'] > 0)
        
        
        
        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x & y, conditions),
                'sell'] = 1


        return dataframe