from functools import reduce
from typing import Optional

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 freqtrade.strategy import merge_informative_pair, DecimalParameter, stoploss_from_open, IntParameter
from pandas import DataFrame, Series, DatetimeIndex, merge
from datetime import datetime


def bollinger_bands(stock_price, window_size, num_of_std):
    rolling_mean = stock_price.rolling(window=window_size).mean()
    rolling_std = stock_price.rolling(window=window_size).std()
    lower_band = rolling_mean - (rolling_std * num_of_std)
    return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band)


def ha_typical_price(bars):
    res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3.
    return Series(index=bars.index, data=res)


class ClucHAnixV1(IStrategy):

    """
    PASTE OUTPUT FROM HYPEROPT HERE
    Can be overridden for specific sub-strategies (stake currencies) at the bottom.
    """

    buy_params = {
        'bbdelta-close': 0.01965,
        'bbdelta-tail': 0.95089,
        'close-bblower': 0.00799,
        'closedelta-close': 0.00556,
        'rocr-1h': 0.54904,

        # lambo2_
        "lambo2_ema_14_factor": 0.981,
        "lambo2_rsi_14_limit": 39,
        "lambo2_rsi_4_limit": 44,

        # yolo
        'adx': 34,
        'aroon-down': 33,
        'aroon-up': 98
    }

    # Sell hyperspace params:
    sell_params = {
        # custom stoploss params, come from BB_RPB_TSL
        "pHSL": -0.99,
        "pPF_1": 0.02,
        "pPF_2": 0.05,
        "pSL_1": 0.02,
        "pSL_2": 0.04,

        'sell-fisher': 0.38414, 
        'sell-bbmiddle-close': 1.07634
    }

    # ROI table:
    minimal_roi = {
        "0": 100
    }

    # Stoploss:
    stoploss = -0.99   # use custom stoploss

    # Trailing stop:
    trailing_stop = False
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.012
    trailing_only_offset_is_reached = False

    """
    END HYPEROPT
    """
    
    timeframe = '1m'

    # Make sure these match or are not overridden in config
    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

    # Custom stoploss
    use_custom_stoploss = True

    process_only_new_candles = True
    startup_candle_count = 168

    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'emergencysell': 'limit',
        'forcebuy': "limit",
        'forcesell': 'limit',
        'stoploss': 'limit',
        'stoploss_on_exchange': False,

        'stoploss_on_exchange_interval': 60,
        'stoploss_on_exchange_limit_ratio': 0.99
    }

    # hard stoploss profit
    pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', load=True)
    # profit threshold 1, trigger point, SL_1 is used
    pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True)
    pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True)

    # profit threshold 2, SL_2 is used
    pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True)
    pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True)

    # lambo2_
    lambo2_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3,  default=buy_params['lambo2_ema_14_factor'], space='buy', optimize=True)
    lambo2_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_4_limit'], space='buy', optimize=True)
    lambo2_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_14_limit'], space='buy', optimize=True)

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

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:
    
        # hard stoploss profit
        HSL = self.pHSL.value
        PF_1 = self.pPF_1.value
        SL_1 = self.pSL_1.value
        PF_2 = self.pPF_2.value
        SL_2 = self.pSL_2.value

        # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
        # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value
        # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.

        if current_profit > PF_2:
            sl_profit = SL_2 + (current_profit - PF_2)
        elif current_profit > PF_1:
            sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1))
        else:
            sl_profit = HSL

        # Only for hyperopt invalid return
        if sl_profit >= current_profit:
            return -0.99
    
        return stoploss_from_open(sl_profit, current_profit)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # # Heikin Ashi Candles
        heikinashi = qtpylib.heikinashi(dataframe)
        dataframe['ha_open'] = heikinashi['open']
        dataframe['ha_close'] = heikinashi['close']
        dataframe['ha_high'] = heikinashi['high']
        dataframe['ha_low'] = heikinashi['low']

        # Set Up Bollinger Bands
        mid, lower = bollinger_bands(ha_typical_price(dataframe), window_size=40, num_of_std=2)
        dataframe['lower'] = lower
        dataframe['mid'] = mid
        
        dataframe['bbdelta'] = (mid - dataframe['lower']).abs()
        dataframe['closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs()
        dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs()

        dataframe['bb_lowerband'] = dataframe['lower']
        dataframe['bb_middleband'] = dataframe['mid']

        dataframe['ema_fast'] = ta.EMA(dataframe['ha_close'], timeperiod=3)
        dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50)
        dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()
        dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28)

        # lambo2_
        dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14)
        dataframe['rsi'] = ta.RSI(dataframe)
        dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)

        # YOLO
        dataframe['adx'] = ta.ADX(dataframe, timeperiod=90)
        aroon = ta.AROON(dataframe, timeperiod=60)
        dataframe['aroon-down'] = aroon['aroondown']
        dataframe['aroon-up'] = aroon['aroonup']

        # cci
        dataframe = self.resample(dataframe, self.timeframe, 5)
        dataframe['cci_one'] = ta.CCI(dataframe, timeperiod=170)
        dataframe['cci_two'] = ta.CCI(dataframe, timeperiod=34)
        dataframe['mfi'] = ta.MFI(dataframe)
        dataframe['cmf'] = self.chaikin_mf(dataframe)

        rsi = 0.1 * (dataframe["rsi"] - 50)
        dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
        
        inf_tf = '1h'
        
        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
        
        inf_heikinashi = qtpylib.heikinashi(informative)

        informative['ha_close'] = inf_heikinashi['close']
        informative['rocr'] = ta.ROCR(informative['ha_close'], timeperiod=168)
     
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        params = self.buy_params

        conditions = []
        dataframe.loc[:, 'buy_tag'] = ''

        lambo2 = (
            (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) &
            (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) &
            (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value))
        )

        clucha = (
                (dataframe['rocr_1h'].gt(params['rocr-1h'])) &
                ((
                         (dataframe['lower'].shift().gt(0)) &
                         (dataframe['bbdelta'].gt(dataframe['ha_close'] * params['bbdelta-close'])) &
                         (dataframe['closedelta'].gt(dataframe['ha_close'] * params['closedelta-close'])) &
                         (dataframe['tail'].lt(dataframe['bbdelta'] * params['bbdelta-tail'])) &
                         (dataframe['ha_close'].lt(dataframe['lower'].shift())) &
                         (dataframe['ha_close'].le(dataframe['ha_close'].shift()))
                 ) |
                 (
                         (dataframe['ha_close'] < dataframe['ema_slow']) &
                         (dataframe['ha_close'] < params['close-bblower'] * dataframe['bb_lowerband'])
                 ))
        )

        yolo = (
                (dataframe['adx'] > params['adx']) &
                (dataframe['aroon-up'] > params['aroon-up']) &
                (dataframe['aroon-down'] < params['aroon-down']) &
                (dataframe['volume'] > 0)
        )

        cci = (
                (dataframe['cci_one'] < -100)
                & (dataframe['cci_two'] < -100)
                & (dataframe['cmf'] < -0.1)
                & (dataframe['mfi'] < 25)
                # insurance
                & (dataframe['resample_medium'] > dataframe['resample_short'])
                & (dataframe['resample_long'] < dataframe['close'])
        )

        conditions.append(lambo2)
        dataframe.loc[lambo2, 'buy_tag'] += 'lambo2 '

        conditions.append(clucha)
        dataframe.loc[clucha, 'buy_tag'] += 'clucHA '

        conditions.append(yolo)
        dataframe.loc[yolo, 'buy_tag'] += 'yolo '

        conditions.append(cci)
        dataframe.loc[cci, 'buy_tag'] += 'cci '

        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:
        params = self.sell_params

        dataframe.loc[
            (dataframe['fisher'] > params['sell-fisher']) &
            (dataframe['ha_high'].le(dataframe['ha_high'].shift(1))) &
            (dataframe['ha_high'].shift(1).le(dataframe['ha_high'].shift(2))) &
            (dataframe['ha_close'].le(dataframe['ha_close'].shift(1))) &
            (dataframe['ema_fast'] > dataframe['ha_close']) &
            ((dataframe['ha_close'] * params['sell-bbmiddle-close']) > dataframe['bb_middleband']) &
            (dataframe['volume'] > 0)
            ,
            'sell'
        ] = 1

        return dataframe

    # cci method
    @staticmethod
    def chaikin_mf(df, periods=20):
        close = df['close']
        low = df['low']
        high = df['high']
        volume = df['volume']

        mfv = ((close - low) - (high - close)) / (high - low)
        mfv = mfv.fillna(0.0)  # float division by zero
        mfv *= volume
        cmf = mfv.rolling(periods).sum() / volume.rolling(periods).sum()

        return Series(cmf, name='cmf')

    @staticmethod
    def resample(dataframe, interval, factor):
        # defines the reinforcement logic
        # resampled dataframe to establish if we are in an uptrend, downtrend or sideways trend
        df = dataframe.copy()
        df = df.set_index(DatetimeIndex(df['date']))
        ohlc_dict = {
            'open': 'first',
            'high': 'max',
            'low': 'min',
            'close': 'last'
        }
        df = df.resample(str(int(interval[:-1]) * factor) + 'min', label="right").agg(ohlc_dict)
        df['resample_sma'] = ta.SMA(df, timeperiod=100, price='close')
        df['resample_medium'] = ta.SMA(df, timeperiod=50, price='close')
        df['resample_short'] = ta.SMA(df, timeperiod=25, price='close')
        df['resample_long'] = ta.SMA(df, timeperiod=200, price='close')
        df = df.drop(columns=['open', 'high', 'low', 'close'])
        df = df.resample(interval[:-1] + 'min')
        df = df.interpolate(method='time')
        df['date'] = df.index
        df.index = range(len(df))
        dataframe = merge(dataframe, df, on='date', how='left')
        return dataframe


class ClucV1DCA(ClucHAnixV1):
    position_adjustment_enable = True

    max_rebuy_orders = 1
    max_rebuy_multiplier = 2

    # This is called when placing the initial order (opening trade)
    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: float, max_stake: float,
                            entry_tag: Optional[str], **kwargs) -> float:

        if (self.config['position_adjustment_enable'] is True) and (self.config['stake_amount'] == 'unlimited'):
            return proposed_stake / self.max_rebuy_multiplier
        else:
            return proposed_stake

    def adjust_trade_position(self, trade: Trade, current_time: datetime,
                              current_rate: float, current_profit: float, min_stake: float,
                              max_stake: float, **kwargs):

        if (self.config['position_adjustment_enable'] is False) or (current_profit > -0.08):
            return None

        filled_buys = trade.select_filled_orders('buy')
        count_of_buys = len(filled_buys)

        # Maximum 2 rebuys, equal stake as the original
        if 0 < count_of_buys <= self.max_rebuy_orders:
            try:
                # This returns first order stake size
                stake_amount = filled_buys[0].cost
                # This then calculates current safety order size
                stake_amount = stake_amount
                return stake_amount
            except Exception as exception:
                return None

        return None