BigZ04

import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from datetime import datetime, timedelta
from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter
from functools import reduce
###########################################################################################################
##                                  BigZ04 by ilya                                                       ##
##                                                                                                       ##
##    https://github.com/i1ya/freqtrade-strategies                                                       ##
##    The stratagy most inspired by iterativ (authors of the CombinedBinHAndClucV6)                      ##
##                                                                                                       ##                                                                                                       ##
###########################################################################################################
##     The main point of this strat is:                                                                  ##
##        -  make drawdown as low as possible                                                            ##
##        -  entry at dip                                                                                  ##
##        -  exit quick as fast as you can (release money for the next entry)                              ##
##        -  soft check if market if rising                                                              ##
##        -  hard check is market if fallen                                                              ##
##        -  11 entry signals                                                                              ##
##        -  stoploss function preventing from big fall                                                  ##
##        -  no exit signal. Whether ROI or stoploss =)                                                  ##
##                                                                                                       ##
###########################################################################################################
##                 GENERAL RECOMMENDATIONS                                                               ##
##                                                                                                       ##
##   For optimal performance, suggested to use between 2 and 4 open trades, with unlimited stake.        ##
##                                                                                                       ##
##   As a pairlist you can use VolumePairlist.                                                           ##
##                                                                                                       ##
##   Ensure that you don't override any variables in your config.json. Especially                        ##
##   the timeframe (must be 5m).                                                                         ##
##                                                                                                       ##
##   exit_profit_only:                                                                                   ##
##       True - risk more (gives you higher profit and higher Drawdown)                                  ##
##       False (default) - risk less (gives you less ~10-15% profit and much lower Drawdown)             ##
##                                                                                                       ##
###########################################################################################################
##               DONATIONS 2 @iterativ (author of the original strategy)                                 ##
##                                                                                                       ##
##   Absolutely not required. However, will be accepted as a token of appreciation.                      ##
##                                                                                                       ##
##   BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk                                                     ##
##   ETH: 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91                                                     ##
##                                                                                                       ##
###########################################################################################################

class BigZ04(IStrategy):
    INTERFACE_VERSION = 3  # I feel lucky!
    # We're going up?
    minimal_roi = {'0': 0.028, '10': 0.018, '40': 0.005, '180': 0.018}
    stoploss = -0.99  # effectively disabled.
    timeframe = '5m'
    inf_1h = '1h'
    # Sell signal
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.001  # it doesn't meant anything, just to guarantee there is a minimal profit.
    ignore_roi_if_entry_signal = False
    # Trailing stoploss
    trailing_stop = False
    trailing_only_offset_is_reached = False
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.025
    # Custom stoploss
    use_custom_stoploss = True
    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 200
    # Optional order type mapping.
    order_types = {'entry': 'market', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False}
    #############
    # Enable/Disable conditions
    entry_params = {'entry_condition_0_enable': True, 'entry_condition_1_enable': True, 'entry_condition_2_enable': True, 'entry_condition_3_enable': True, 'entry_condition_4_enable': True, 'entry_condition_5_enable': True, 'entry_condition_6_enable': True, 'entry_condition_7_enable': True, 'entry_condition_8_enable': True, 'entry_condition_9_enable': True, 'entry_condition_10_enable': True, 'entry_condition_11_enable': True, 'entry_condition_12_enable': True, 'entry_condition_13_enable': False}
    ############################################################################
    # Buy
    entry_condition_0_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    entry_condition_1_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    entry_condition_2_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    entry_condition_3_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    entry_condition_4_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    entry_condition_5_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    entry_condition_6_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    entry_condition_7_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    entry_condition_8_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    entry_condition_9_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    entry_condition_10_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    entry_condition_11_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    entry_condition_12_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    entry_condition_13_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    entry_bb20_close_bblowerband_safe_1 = DecimalParameter(0.7, 1.1, default=0.989, space='entry', optimize=False, load=True)
    entry_bb20_close_bblowerband_safe_2 = DecimalParameter(0.7, 1.1, default=0.982, space='entry', optimize=False, load=True)
    entry_volume_pump_1 = DecimalParameter(0.1, 0.9, default=0.4, space='entry', decimals=1, optimize=False, load=True)
    entry_volume_drop_1 = DecimalParameter(1, 10, default=3.8, space='entry', decimals=1, optimize=False, load=True)
    entry_volume_drop_2 = DecimalParameter(1, 10, default=3, space='entry', decimals=1, optimize=False, load=True)
    entry_volume_drop_3 = DecimalParameter(1, 10, default=2.7, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1h_1 = DecimalParameter(10.0, 40.0, default=16.5, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1h_2 = DecimalParameter(10.0, 40.0, default=15.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1h_3 = DecimalParameter(10.0, 40.0, default=20.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1h_4 = DecimalParameter(10.0, 40.0, default=35.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1h_5 = DecimalParameter(10.0, 60.0, default=39.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1 = DecimalParameter(10.0, 40.0, default=28.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_2 = DecimalParameter(7.0, 40.0, default=10.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_3 = DecimalParameter(7.0, 40.0, default=14.2, space='entry', decimals=1, optimize=False, load=True)
    entry_macd_1 = DecimalParameter(0.01, 0.09, default=0.02, space='entry', decimals=2, optimize=False, load=True)
    entry_macd_2 = DecimalParameter(0.01, 0.09, default=0.03, space='entry', decimals=2, optimize=False, load=True)

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, **kwargs) -> bool:
        return True

    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
        return False

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        # Manage losing trades and open room for better ones.
        if current_profit > 0:
            return 0.99
        else:
            trade_time_50 = trade.open_date_utc + timedelta(minutes=50)
            # Trade open more then 60 minutes. For this strategy it's means -> loss
            # Let's try to minimize the loss
            if current_time > trade_time_50:
                try:
                    number_of_candle_shift = int((current_time - trade_time_50).total_seconds() / 300)
                    dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
                    candle = dataframe.iloc[-number_of_candle_shift].squeeze()
                    # We are at bottom. Wait...
                    if candle['rsi_1h'] < 30:
                        return 0.99
                    # Are we still sinking? 
                    if candle['close'] > candle['ema_200']:
                        if current_rate * 1.025 < candle['open']:
                            return 0.01
                    if current_rate * 1.015 < candle['open']:
                        return 0.01
                except IndexError as error:
                    # Whoops, set stoploss at 10%
                    return 0.1
        return 0.99

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, '1h') for pair in pairs]
        return informative_pairs

    def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        assert self.dp, 'DataProvider is required for multiple timeframes.'
        # Get the informative pair
        informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
        # EMA
        informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
        informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
        # RSI
        informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        informative_1h['bb_lowerband'] = bollinger['lower']
        informative_1h['bb_middleband'] = bollinger['mid']
        informative_1h['bb_upperband'] = bollinger['upper']
        return informative_1h

    def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> 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['volume_mean_slow'] = dataframe['volume'].rolling(window=48).mean()
        # EMA
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
        dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
        dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
        # MACD 
        dataframe['macd'], dataframe['signal'], dataframe['hist'] = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
        # SMA
        dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5)
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # The indicators for the 1h informative timeframe
        informative_1h = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)
        # The indicators for the normal (5m) timeframe
        dataframe = self.normal_tf_indicators(dataframe, metadata)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append(self.entry_condition_12_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband'] * 0.993) & (dataframe['low'] < dataframe['bb_lowerband'] * 0.985) & (dataframe['close'].shift() > dataframe['bb_lowerband']) & (dataframe['rsi_1h'] < 72.8) & (dataframe['open'] > dataframe['close']) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0))  # Make sure Volume is not 0
        conditions.append(self.entry_condition_11_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['hist'] > 0) & (dataframe['hist'].shift() > 0) & (dataframe['hist'].shift(2) > 0) & (dataframe['hist'].shift(3) > 0) & (dataframe['hist'].shift(5) > 0) & (dataframe['bb_middleband'] - dataframe['bb_middleband'].shift(5) > dataframe['close'] / 200) & (dataframe['bb_middleband'] - dataframe['bb_middleband'].shift(10) > dataframe['close'] / 100) & (dataframe['bb_upperband'] - dataframe['bb_lowerband'] < dataframe['close'] * 0.1) & (dataframe['open'].shift() - dataframe['close'].shift() < dataframe['close'] * 0.018) & (dataframe['rsi'] > 51) & (dataframe['open'] < dataframe['close']) & (dataframe['open'].shift() > dataframe['close'].shift()) & (dataframe['close'] > dataframe['bb_middleband']) & (dataframe['close'].shift() < dataframe['bb_middleband'].shift()) & (dataframe['low'].shift(2) > dataframe['bb_middleband'].shift(2)) & (dataframe['volume'] > 0))  # Make sure Volume is not 0
        conditions.append(self.entry_condition_0_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['rsi'] < 30) & (dataframe['close'] * 1.024 < dataframe['open'].shift(3)) & (dataframe['rsi_1h'] < 71) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0))
        conditions.append(self.entry_condition_1_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband'] * self.entry_bb20_close_bblowerband_safe_1.value) & (dataframe['rsi_1h'] < 69) & (dataframe['open'] > dataframe['close']) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0))
        conditions.append(self.entry_condition_2_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] < dataframe['bb_lowerband'] * self.entry_bb20_close_bblowerband_safe_2.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0))
        conditions.append(self.entry_condition_3_enable.value & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['rsi'] < self.entry_rsi_3.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_3.value) & (dataframe['volume'] > 0))
        conditions.append(self.entry_condition_4_enable.value & (dataframe['rsi_1h'] < self.entry_rsi_1h_1.value) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (dataframe['volume'] > 0))  # Make sure Volume is not 0
        conditions.append(self.entry_condition_5_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_macd_1.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0))
        conditions.append(self.entry_condition_6_enable.value & (dataframe['rsi_1h'] < self.entry_rsi_1h_5.value) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_macd_2.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (dataframe['volume'] > 0))
        conditions.append(self.entry_condition_7_enable.value & (dataframe['rsi_1h'] < self.entry_rsi_1h_2.value) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_macd_1.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0))
        conditions.append(self.entry_condition_8_enable.value & (dataframe['rsi_1h'] < self.entry_rsi_1h_3.value) & (dataframe['rsi'] < self.entry_rsi_1.value) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (dataframe['volume'] > 0))
        conditions.append(self.entry_condition_9_enable.value & (dataframe['rsi_1h'] < self.entry_rsi_1h_4.value) & (dataframe['rsi'] < self.entry_rsi_2.value) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0))
        conditions.append(self.entry_condition_10_enable.value & (dataframe['rsi_1h'] < self.entry_rsi_1h_4.value) & (dataframe['close_1h'] < dataframe['bb_lowerband_1h']) & (dataframe['hist'] > 0) & (dataframe['hist'].shift(2) < 0) & (dataframe['rsi'] < 40.5) & (dataframe['hist'] > dataframe['close'] * 0.0012) & (dataframe['open'] < dataframe['close']) & (dataframe['volume'] > 0))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:  # Don't be gready, exit fast
        # Make sure Volume is not 0
        dataframe.loc[(dataframe['close'] > dataframe['bb_middleband'] * 1.01) & (dataframe['volume'] > 0), 'exit_long'] = 0
        return dataframe