FrayLIVEBTC15m

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame

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


# This class is a sample. Feel free to customize it.
class FrayLIVEBTC15m(IStrategy):
    """
    This is a sample strategy to inspire you.
    More information in https://www.freqtrade.io/en/latest/strategy-customization/

    You can:
        :return: a Dataframe with all mandatory indicators for the strategies
    - Rename the class name (Do not forget to update class_name)
    - Add any methods you want to build your strategy
    - Add any lib you need to build your strategy

    You must keep:
    - the lib in the section "Do not remove these libs"
    - the methods: populate_indicators, populate_buy_trend, populate_sell_trend
    You should keep:
    - timeframe, minimal_roi, stoploss, trailing_*
    """
    
    
    # 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

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
       "0":0.0136,
       "114":0.06,
       "192":0.021,
       "510":0.01
    }

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.346
    # Trailing stoploss
    trailing_stop = True
    trailing_only_offset_is_reached = False
    trailing_stop_positive = 0.039
    trailing_stop_positive_offset = 0.048  # Disabled / not configured
    

  # Hyperoptable parameters
  #Buy Space
    #RSI Fisher for rising track
    buy_frsi = DecimalParameter(-0.5, 1, decimals = 3, default = 0.25, space="buy")
    
    #RSI Fisher for Dip Reversal
    buy_dip_frsi = DecimalParameter(-0.9, 0.2, decimals = 2, default = -0.7, space="buy")
    frsi_pct = DecimalParameter(0, 1, decimals = 4, default = 0.6, space="buy") #use pct rate to calc percentage of rsi rising against previous candles
    ema_pct = DecimalParameter(0, 0.1, decimals = 4, default = 0.08, space="buy")  #percentages of Difference between EMA7 against EMA7-TEMA
    macdn_buy = DecimalParameter(0, 0.8, decimals = 2, default = 0.09, space="buy")
   #Sell Position
    sell_frsi = DecimalParameter(-1, 1, decimals=2, default=-0.84, space="sell") #Main F-RSI
    macd_diff = DecimalParameter(0, 0.01, decimals=4, default=0.0047, space='sell') #Distance between MACD and MACD SIGNAL
    macdn_sell = DecimalParameter(0.2, 0.99, decimals=2, default= 0.81, space="sell") #MACD signal position near middle line
  #Protection Space  
    cooldown_lookback = IntParameter(2, 90, default=3, space="protection", optimize=True)
    stop_duration = IntParameter(12, 100, default=3, space="protection", optimize=True)
    use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True)

    #ProtectionGuard
    @property
    def protections(self):
            return [
                {
                    "method": "CooldownPeriod",
                    "stop_duration_candles": self.cooldown_lookback.value
                },
                {
                    "method": "MaxDrawdown",
                    "lookback_period_candles": self.cooldown_lookback.value,
                    "trade_limit": 5,
                    "stop_duration_candles": self.stop_duration.value,
                    "max_allowed_drawdown": 0.9
                },
                {
                    "method": "StoplossGuard",
                    "lookback_period_candles": 20,
                    "trade_limit": 3,
                    "stop_duration_candles": 4,
                    "only_per_pair": False
                },
                {
                    "method": "LowProfitPairs",
                    "lookback_period_candles": 24,
                    "trade_limit": 2,
                    "stop_duration_candles": 4,
                    "required_profit": 0.01
                }
                
            ]


    # Optimal timeframe for the strategy.
    timeframe = '15m'

    # 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 = 40

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

    # Optional order time in force.
    order_time_in_force = {
        'buy': 'gtc',
        'sell': 'gtc'
    }

    plot_config = {
        'main_plot': {
            'tema': {},
            'sar': {'color': 'blue'},
            'ema7':{'color': 'red'},
            'ema12':{'color': 'yellow'}
        },
        'subplots': {
            "MACD": {
                'macdn': {'color': 'blue'},
                'macdnsig': {'color': 'orange'}
                #'macdhist':{}
            },
            
            "FISHERS RSI":{
                'frsi':{'color':'green'},
            }
        }
    }

    #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)
        #return [("ETH/USDT", "15m"),
        #        ("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
        # ------------------------------------

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

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

        # # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
        rsi = 0.1 * (dataframe['rsi'] - 50)
        dataframe['frsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)

        # # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
        # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)

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

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

        # # Stochastic RSI
        # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
        # STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
        # stoch_rsi = ta.STOCHRSI(dataframe)
        # dataframe['fastd_rsi'] = stoch_rsi['fastd']
        # dataframe['fastk_rsi'] = stoch_rsi['fastk']

        # MACD
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        #dataframe['macdhist'] = macd['macdhist']
        
	#Normalize MACD
        macdn = dataframe['macd']
        macdmin = (dataframe['macd'].min())
        macdmax = (dataframe['macd'].max())
        
        dataframe['macdn'] = (macdn - macdmin) / (macdmax - macdmin)
        
        macdnsig = dataframe['macdsignal']
        macdnsigmax = (dataframe['macdsignal'].max())
        macdnsigmin = (dataframe['macdsignal'].min())
        dataframe['macdnsig'] = (macdnsig - macdnsigmin) / (macdnsigmax - macdnsigmin)

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

        # Overlap Studies
        # ------------------------------------

        # Bollinger Bands
        #bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=18, 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"]
        #)

        # Bollinger Bands - Weighted (EMA based instead of SMA)
        # weighted_bollinger = qtpylib.weighted_bollinger_bands(
        #     qtpylib.typical_price(dataframe), window=20, stds=2
        # )
        # dataframe["wbb_upperband"] = weighted_bollinger["upper"]
        # dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
        # dataframe["wbb_middleband"] = weighted_bollinger["mid"]
        # dataframe["wbb_percent"] = (
        #     (dataframe["close"] - dataframe["wbb_lowerband"]) /
        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
        # )
        # dataframe["wbb_width"] = (
        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) /
        #     dataframe["wbb_middleband"]
        # )

        # # EMA - Exponential Moving Average
        dataframe['ema7'] = ta.EMA(dataframe, timeperiod=7)
        #dataframe['ema30'] = ta.EMA(dataframe, timeperiod=30)
        dataframe['ema12'] = ta.EMA(dataframe, timeperiod=14)
        #dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)


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

        # TEMA - Triple Exponential Moving Average
        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=7)

        
        # # Inverted Hammer: values [0, 100]
        # dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
        # # Dragonfly Doji: values [0, 100]
        # dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
        # # Piercing Line: values [0, 100]
        # dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
        # # Morningstar: values [0, 100]
        # dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
        # # Three White Soldiers: values [0, 100]
        # dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]

        # Pattern Recognition - Bearish candlestick patterns
        # ------------------------------------
        # # Hanging Man: values [0, 100]
        # dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
        # # Shooting Star: values [0, 100]
        # dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
        # # Gravestone Doji: values [0, 100]
        # dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
        # # Dark Cloud Cover: values [0, 100]
        # dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
        # # Evening Doji Star: values [0, 100]
        # dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
        # # Evening Star: values [0, 100]
        # dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)

        # Pattern Recognition - Bullish/Bearish candlestick patterns
        # ------------------------------------
        # # Three Line Strike: values [0, -100, 100]
        # dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
        # # Spinning Top: values [0, -100, 100]
        # dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
        # # Engulfing: values [0, -100, 100]
        # dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
        # # Harami: values [0, -100, 100]
        # dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
        # # Three Outside Up/Down: values [0, -100, 100]
        # dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
        # # Three Inside Up/Down: values [0, -100, 100]
        # dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]

        # # Chart type
        # # ------------------------------------
        # # Heikin Ashi Strategy
        # heikinashi = qtpylib.heikinashi(dataframe)
        # dataframe['ha_open'] = heikinashi['open']
        # dataframe['ha_close'] = heikinashi['close']
        # dataframe['ha_high'] = heikinashi['high']
        # dataframe['ha_low'] = heikinashi['low']

        # Retrieve best bid and best ask from the orderbook
        # ------------------------------------
        
        #first check if dataprovider is available
        if self.dp:
            if self.dp.runmode.value in ('live', 'dry_run'):
                ob = self.dp.orderbook(metadata['pair'], 1)
                dataframe['best_bid'] = ob['bids'][0][1]
                dataframe['best_ask'] = ob['asks'][0][0]
        

        return dataframe
    #def informative_pairs(self):

        # get access to all pairs available in whitelist.
      #  pairs = self.dp.current_whitelist()
        # Assign tf to each pair so they can be downloaded and cached for strategy.
     #   informative_pairs = [(pair, '1d') for pair in pairs]
    #return informative_pairs

    # fetch current dataframe
       #if self.dp:
        #   if self.dp.runmode.value in ('live', 'dry_run'):
         #      dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
          #                                                            timeframe=self.timeframe)
      # 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
        """
        frsi_last3 = dataframe['frsi'].tail(3)
        frsi3rdlast, frsi2ndlast, frsilast = frsi_last3
        last_ema7 = dataframe['ema7']
        last_tema = dataframe['tema']
        macdn_buy_low = float(self.macdn_buy.value)       
        dataframe.loc[
            #New FrayStrategy
            #Buy Rising Track Signal
            (
                (qtpylib.crossed_above(dataframe['frsi'], self.buy_frsi.value)) &
               #(dataframe['ema7'] > dataframe['ema12']) &
                (dataframe['macdnsig'] > dataframe['macdnsig'].shift(1)) &
                (dataframe['macdnsig'] < dataframe['macdn']) &
                #(dataframe['sar'] > dataframe['sar'].shift(1)) &
                (dataframe['sar'] <= dataframe['tema']) &
                (dataframe['volume'] > 0)
             ),
             'buy'] = 1
        return dataframe

        dataframe.loc[
           #Buy Dip as best as you can
            (
                (last_tema < last_ema7 ) &
                (last_ema7 < dataframe['ema12']) &
                (qtpylib.crossed_below(dataframe['frsi'], self.buy_dip_frsi.value)) &
                (abs(frsilast - frsi2ndlast) < abs(frsi2ndlast - frsi3rdlast) < self.frsi_pct.value ) &
                (((last_ema7 - last_tema) / last_tema ) >= self.ema_pct.value ) & #Guard against False Bottom
                (dataframe['macdn'] <= macdn_buy_low ) &
		(dataframe['volume'] > 0)
            ),
            'buy'] = 1
        return dataframe


    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        HUEHUEHUE
        """
        last_macd = dataframe['macd']
        last_macdsig = dataframe['macdsignal']
        dataframe.loc[
             (
                (dataframe['tema'] > dataframe['ema7']) &
                (dataframe['tema'] < dataframe['tema'].shift(1)) &
                #(dataframe['frsi'] < dataframe['frsi'].shift(1)) &
                (qtpylib.crossed_above(dataframe['frsi'], self.sell_frsi.value )) &
                #(dataframe['macd'] < dataframe['macdsignal']) &
                ((( last_macd - last_macdsig ) / last_macdsig ) <= self.macd_diff.value ) &
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            
             ),
             'sell'] = 1
        return dataframe
        dataframe.loc[
             (
                (((last_ema7 - last_tema) / last_tema ) >= self.ema_pct.value ) &
                (dataframe['macdn'] <= self.macdn_sell.value) &
                (dataframe['volume'] > 0 )
             ),
             'sell'] = 1
        return dataframe