# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement

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

from freqtrade.strategy import IStrategy

import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class TheForceV7(IStrategy):
  
    INTERFACE_VERSION = 2
    
        
    ##############################################################################
    ##                       THE FORCE ver 7 by Ofesad                          ##
    ##                                                                          ##
    ##  “The force is an energy field created by all living things.             ##
    ##  It surrounds us and penetrates us; it binds the galaxy together.”       ##
    ##--------------------------------------------------------------------------##
    ##  Thanks to everyone who helped in this strategy development and testing. ## 
    ##  Special thanks to xmatthias for his hard work on freqtrade.             ##
    ##############################################################################
    ##                        MAY THE FORCE BE WITH YOU                         ##
    ##############################################################################
    #   Disclaimer: This strategy is under development.                          #
    #   I do not recommend running it live until further development.            #
    #                          TEST IT BEFORE USING IT                           #
    ##############################################################################
    ##                               DONATIONS                                  ##
    #   Some of you ask me for my address to sent donations.                     #
    #   I never ask'd for nothing in return when sharing this,                   #
    #   but I would accept'em as a token of appreciation.                        #
    #----------------------------------------------------------------------------#
    #   BTC: 1Pmx5Sc8n2cY2wf5BQ3W7TTSDangphnzUW                                  #
    #   ETH / USDT / USDC: 0x253d7384785603444767da64c9a1b650b41638c5            #                 
    ##############################################################################
    
    #	V4 added roi_multiplier if you want to change the roi to n times the original value.
    #	Based on Kage Raken idea and testing.
    
    #roi_multiplier = 3
    
    minimal_roi = {
        #   Original roi
        #"30": 0.005 * roi_multiplier,
        #"15": 0.01 * roi_multiplier,
        #"0": 0.012 * roi_multiplier
        
        #   V7 roi disabled to improve profit thanks to Geroge Z idea
		"0": 10 
    }

    
    #	V2 changed stoploss to 10%
    #	stoploss = -0.03 #  V1 original stoploss
    stoploss = -0.1

    # Trailing stoploss
    trailing_stop = False
    # trailing_only_offset_is_reached = False
    # trailing_stop_positive = 0.01
    # trailing_stop_positive_offset = 0.0  # Disabled / not configured

    # Optimal timeframe for the strategy.
    timeframe = '5m'

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

    # 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

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30

    # Optional order type mapping.
    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    # Optional order time in force.
    order_time_in_force = {
        'buy': 'gtc',
        'sell': 'gtc'
    }
    
    plot_config = {
        # Main plot indicators (Moving averages, ...)
        'main_plot': {
            'tema': {},
            'sar': {'color': 'white'},
        },
        'subplots': {
            # Subplots - each dict defines one additional plot
            "MACD": {
                'macd': {'color': 'blue'},
                'macdsignal': {'color': 'orange'},
            },
            "RSI": {
                'rsi': {'color': 'red'},
            }
        }
    }
    def informative_pairs(self):
        """
        Define additional, informative pair/interval combinations to be cached from the exchange.
        These pair/interval combinations are non-tradeable, unless they are part
        of the whitelist as well.
        For more information, please consult the documentation
        :return: List of tuples in the format (pair, interval)
            Sample: return [("ETH/USDT", "5m"),
                            ("BTC/USDT", "15m"),
                            ]
        """
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Adds several different TA indicators to the given DataFrame

        Performance Note: For the best performance be frugal on the number of indicators
        you are using. Let uncomment only the indicator you are using in your strategies
        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
        :param dataframe: Dataframe with data from the exchange
        :param metadata: Additional information, like the currently traded pair
        :return: a Dataframe with all mandatory indicators for the strategies
        """
        
        # Momentum Indicators
        # ------------------------------------

        # Stochastic Slow added in V3 thanks to the idea of JoeSchr
        stoch = ta.STOCH(dataframe)
        dataframe['slowd'] = stoch['slowd']
        dataframe['slowk'] = stoch['slowk']
        
        #RSI
        dataframe['rsi7'] = ta.RSI(dataframe, timeperiod=7)

        # MACD
        macd = ta.MACD(dataframe,12,26,1)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']

        # EMA - Exponential Moving Average
        dataframe['ema5h'] = ta.EMA(dataframe['high'], timeperiod=5)
        dataframe['ema5l'] = ta.EMA(dataframe['low'], timeperiod=5)
        dataframe['ema5c'] = ta.EMA(dataframe['close'], timeperiod=5)
        dataframe['ema5o'] = ta.EMA(dataframe['open'], timeperiod=5)
        dataframe['ema200c'] = ta.MA(dataframe['close'], 200)
        
        # VOLUME variation added in V7 thanks to the idea of revolover
        dataframe['volvar'] = (dataframe['volume'].rolling(100).mean() * 1.5)
        
        # BOLLINGER
        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=21, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_middleband'] = bollinger['mid']
        
        
        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                ( 
                    (
                        ( #Original buy condition
                            (dataframe['slowk'] >= 20) & (dataframe['slowk'] <= 80)
                            &
                            (dataframe['slowd'] >= 20) & (dataframe['slowd'] <= 80)
                        )
                        |
                        (  #V3 added based on SmoothScalp
                            (dataframe['slowk'] < 30) & (dataframe['slowd'] < 30) &
                            (qtpylib.crossed_above(dataframe['slowk'], dataframe['slowd']))
                        )
                    )
                    &
                    ( #Original buy condition #Might need improvement to have better signals
                        (dataframe['macd'] > dataframe['macd'].shift(1))
                        &
                        (dataframe['macdsignal'] > dataframe['macdsignal'].shift(1))
                    )
                    &
                    ( #Original buy condition
                        (dataframe['close'] > dataframe['close'].shift(1))
                        & #V6 added condition to improve buy's
                        (dataframe['open'] > dataframe['open'].shift(1)) 
                    )
                    &
                    ( #Original buy condition
                        (dataframe['ema5c'] >= dataframe['ema5o'])
                        |
                        (dataframe['open'] < dataframe['ema5l'])
                    )
                    &
                    (
                       #V7 added volume condition
                       (dataframe['volume'] > dataframe['volvar'])
                    )
                )
                |
                ( # V2 Added buy condition w/ Bollingers bands
                    (dataframe['slowk'] >= 20) & (dataframe['slowk'] <= 80)
                    &
                    (dataframe['slowd'] >= 20) & (dataframe['slowd'] <= 80)
                    &
                    (
                        (dataframe['close'] <= dataframe['bb_lowerband'])
                        |
                        (dataframe['open'] <= dataframe['bb_lowerband'])
                    )
                )
                |
                (  # V5 added Pullback RSI thanks to simoelmou
                    (dataframe['close'] > dataframe['ema200c']) 
					&
                    (dataframe['rsi7'] < 35)
                )
            ),
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        dataframe.loc[
            ( 
                (
                    (
                        ( #Original sell condition
                            (dataframe['slowk'] <= 80)  & (dataframe['slowd'] <= 80)
                        )
                        |
                        ( #V3 added based on SmoothScalp
                            (qtpylib.crossed_above(dataframe['slowk'], 70))
							|
                            (qtpylib.crossed_above(dataframe['slowd'], 70))
                        )
                    )
                    &
                    ( #Original sell condition
                        (dataframe['macd'] < dataframe['macd'].shift(1))
                        &
                        (dataframe['macdsignal'] < dataframe['macdsignal'].shift(1))
                    )
                    &
                    ( #Original sell condition
                        (dataframe['ema5c'] < dataframe['ema5o'])
                        |
                        (dataframe['open'] >= dataframe['ema5h']) # V3 added based on SmoothScalp
                    )
                )
                |
                ( # V2 Added sell condition w/ Bollingers bands
                    (dataframe['slowk'] <= 80)
                    &
                    (dataframe['slowd'] <= 80)
                    &
                    ( 
                        (dataframe['close'] >= dataframe['bb_upperband'])
                        |
                        (dataframe['open'] >= dataframe['bb_upperband'])
                    )
                )
                |
                (# V6 Added sell condition for extra high values
                    (dataframe['high'] > dataframe['bb_upperband'])
                    &
                    (((dataframe['high'] - dataframe['bb_upperband']) * 100 / dataframe['bb_upperband']) > 1)
                )
                
            ),
            'sell'] = 1
        return dataframe