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

class BcmbigzV1(IStrategy):
    INTERFACE_VERSION = 3  # I feel lucky!
    # We're going up?
    minimal_roi = {'0': 0.038, '10': 0.028, '40': 0.015, '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 = {'bzv7_entry_condition_0_enable': True, 'bzv7_entry_condition_1_enable': True, 'bzv7_entry_condition_2_enable': True, 'bzv7_entry_condition_3_enable': True, 'bzv7_entry_condition_4_enable': True, 'bzv7_entry_condition_5_enable': True, 'bzv7_entry_condition_6_enable': True, 'bzv7_entry_condition_7_enable': True, 'bzv7_entry_condition_8_enable': True, 'bzv7_entry_condition_9_enable': True, 'bzv7_entry_condition_10_enable': True, 'bzv7_entry_condition_11_enable': True, 'bzv7_entry_condition_12_enable': True, 'bzv7_entry_condition_13_enable': True}
    ############################################################################
    # BigZ07 Strategy Buy HyperParam
    bzv7_entry_condition_0_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_1_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_2_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_3_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_4_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_5_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_6_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_7_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_8_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_9_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_10_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_11_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_12_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_13_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_bb20_close_bblowerband_safe_1 = DecimalParameter(0.7, 1.1, default=0.989, space='entry', optimize=False, load=True)
    bzv7_entry_bb20_close_bblowerband_safe_2 = DecimalParameter(0.7, 1.1, default=0.982, space='entry', optimize=False, load=True)
    bzv7_entry_volume_pump_1 = DecimalParameter(0.1, 0.9, default=0.4, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_volume_drop_1 = DecimalParameter(1, 10, default=3.8, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_volume_drop_2 = DecimalParameter(1, 10, default=3, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_volume_drop_3 = DecimalParameter(1, 10, default=2.7, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_rsi_1h_1 = DecimalParameter(10.0, 40.0, default=16.5, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_rsi_1h_2 = DecimalParameter(10.0, 40.0, default=15.0, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_rsi_1h_3 = DecimalParameter(10.0, 40.0, default=20.0, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_rsi_1h_4 = DecimalParameter(10.0, 40.0, default=35.0, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_rsi_1h_5 = DecimalParameter(10.0, 60.0, default=39.0, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_rsi_1 = DecimalParameter(10.0, 40.0, default=28.0, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_rsi_2 = DecimalParameter(7.0, 40.0, default=10.0, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_rsi_3 = DecimalParameter(7.0, 40.0, default=14.2, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_macd_1 = DecimalParameter(0.01, 0.09, default=0.02, space='entry', decimals=2, optimize=False, load=True)
    bzv7_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
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        last_candle_1 = dataframe.iloc[-2].squeeze()
        if exit_reason == 'roi':
            # Looks like we can get a little have more
            if (last_candle['cmf'] < -0.1) & (last_candle['close'] > last_candle['ema_200_1h']):
                return False
        return True

    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
        return False
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        last_candle_2 = dataframe.iloc[-2].squeeze()
        if last_candle is not None:
            if (last_candle['high'] > last_candle['bb_upperband']) & (last_candle['volume'] > last_candle_2['volume'] * 1.5):
                return 'exit_signal_1'
        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'] < 40:
                        return 0.99
                    if candle['open_1h'] > candle['ema_200_1h']:
                        return 0.1
                    # Are we still sinking?
                    if current_rate * 1.025 < 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)
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        # Chaikin A/D Oscillator
        dataframe['mfv'] = MFV(dataframe)
        dataframe['cmf'] = dataframe['mfv'].rolling(20).sum() / dataframe['volume'].rolling(20).sum()
        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.bzv7_entry_condition_13_enable.value & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['cmf'] < -0.435) & (dataframe['rsi'] < 22) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0))
        conditions.append(self.bzv7_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.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < dataframe['volume'].shift() * self.bzv7_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.bzv7_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.bzv7_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.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0))
        conditions.append(self.bzv7_entry_condition_1_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband'] * self.bzv7_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.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < dataframe['volume'].shift() * self.bzv7_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.bzv7_entry_condition_2_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] < dataframe['bb_lowerband'] * self.bzv7_entry_bb20_close_bblowerband_safe_2.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < dataframe['volume'].shift() * self.bzv7_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.bzv7_entry_condition_3_enable.value & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['rsi'] < self.bzv7_entry_rsi_3.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < dataframe['volume'].shift() * self.bzv7_entry_volume_drop_3.value) & (dataframe['volume'] > 0))
        conditions.append(self.bzv7_entry_condition_4_enable.value & (dataframe['rsi_1h'] < self.bzv7_entry_rsi_1h_1.value) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < dataframe['volume'].shift() * self.bzv7_entry_volume_drop_1.value) & (dataframe['volume'] > 0))  # Make sure Volume is not 0
        conditions.append(self.bzv7_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.bzv7_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.bzv7_entry_volume_drop_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0))
        conditions.append(self.bzv7_entry_condition_6_enable.value & (dataframe['rsi_1h'] < self.bzv7_entry_rsi_1h_5.value) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.bzv7_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.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < dataframe['volume'].shift() * self.bzv7_entry_volume_drop_1.value) & (dataframe['volume'] > 0))
        conditions.append(self.bzv7_entry_condition_7_enable.value & (dataframe['rsi_1h'] < self.bzv7_entry_rsi_1h_2.value) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.bzv7_entry_macd_1.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['volume'] < dataframe['volume'].shift() * self.bzv7_entry_volume_drop_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0))
        conditions.append(self.bzv7_entry_condition_8_enable.value & (dataframe['rsi_1h'] < self.bzv7_entry_rsi_1h_3.value) & (dataframe['rsi'] < self.bzv7_entry_rsi_1.value) & (dataframe['volume'] < dataframe['volume'].shift() * self.bzv7_entry_volume_drop_1.value) & (dataframe['volume'] > 0))
        conditions.append(self.bzv7_entry_condition_9_enable.value & (dataframe['rsi_1h'] < self.bzv7_entry_rsi_1h_4.value) & (dataframe['rsi'] < self.bzv7_entry_rsi_2.value) & (dataframe['volume'] < dataframe['volume'].shift() * self.bzv7_entry_volume_drop_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0))
        conditions.append(self.bzv7_entry_condition_10_enable.value & (dataframe['rsi_1h'] < self.bzv7_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), 'entry'] = 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'] = 0
        return dataframe
# Chaikin Money Flow Volume

def MFV(dataframe):
    df = dataframe.copy()
    N = (df['close'] - df['low'] - (df['high'] - df['close'])) / (df['high'] - df['low'])
    M = N * df['volume']
    return M