import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair, DecimalParameter, stoploss_from_open, RealParameter
from pandas import DataFrame, Series
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)

def top_percent_change(dataframe: DataFrame, length: int) -> float:
        """
        Percentage change of the current close from the range maximum Open price

        :param dataframe: DataFrame The original OHLC dataframe
        :param length: int The length to look back
        """
        if length == 0:
            return (dataframe['open'] - dataframe['close']) / dataframe['close']
        else:
            return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close']

class SafeCluc(IStrategy):
    """
    PASTE OUTPUT FROM HYPEROPT HERE
    Can be overridden for specific sub-strategies (stake currencies) at the bottom.
    """
    
    #hypered params
    entry_params = {
        "bbdelta_close": 0.01728,
        "bbdelta_tail": 0.79169,
        "close_bblower": 0.00221,
        "closedelta_close": 0.00823,
        "rocr_1h": 0.85822,
    }

    # Sell hyperspace params:
    exit_params = {
        # custom stoploss params, come from BB_RPB_TSL
        "pHSL": -0.178,
        "pPF_1": 0.018,
        "pPF_2": 0.09,
        "pSL_1": 0.013,
        "pSL_2": 0.063,

        # exit signal params
        "exit_bbmiddle_close": 1.00282,
        "exit_fisher": 0.31055,
    }

    # ROI table:
    minimal_roi = {
        "0": 0.056,
        "28": 0.038,
        "84": 0.026,
        "201": 0.005
    }

    # Stoploss:
    stoploss = -0.99  # use custom stoploss

    """
    END HYPEROPT
    """

    timeframe = '5m'

    # Make sure these match or are not overridden in config
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # Custom stoploss
    use_custom_stoploss = True

    process_only_new_candles = True
    startup_candle_count = 168

    order_types = {
        'entry': 'market',
        'exit': 'market',
        'emergencyexit': 'market',
        'forceentry': "market",
        'forceexit': 'market',
        'stoploss': 'market',
        'stoploss_on_exchange': False,

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

    # entry params
    rocr_1h = RealParameter(0.5, 1.0, default=0.54904, space='entry', optimize=True)
    bbdelta_close = RealParameter(0.0005, 0.02, default=0.01965, space='entry', optimize=True)
    closedelta_close = RealParameter(0.0005, 0.02, default=0.00556, space='entry', optimize=True)
    bbdelta_tail = RealParameter(0.7, 1.0, default=0.95089, space='entry', optimize=True)
    close_bblower = RealParameter(0.0005, 0.02, default=0.00799, space='entry', optimize=True)

    # exit params
    exit_fisher = RealParameter(0.1, 0.5, default=0.38414, space='exit', optimize=True)
    exit_bbmiddle_close = RealParameter(0.97, 1.1, default=1.07634, space='exit', optimize=True)

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

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

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

    # come from BB_RPB_TSL
    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)

        rsi = ta.RSI(dataframe)
        dataframe["rsi"] = rsi
        rsi = 0.1 * (rsi - 50)
        dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)

        dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84)
        dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112)

        dataframe['tpct_change_1']   = top_percent_change(dataframe, 1)
        dataframe['tpct_change_2']   = top_percent_change(dataframe, 2)
        dataframe['tpct_change_4']   = top_percent_change(dataframe, 4)
        dataframe['tpct_change_9']   = top_percent_change(dataframe, 9)

        ############################################################################

        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)

        # 1h mama > fama for general trend check
        informative['hl2'] = (informative['high'] + informative['low']) / 2
        informative['mama'], informative['fama'] = ta.MAMA(informative['hl2'], 0.5, 0.05)
        informative['mama_diff'] = ( ( informative['mama'] - informative['fama'] ) / informative['hl2'] )

        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        rsi_check = (
                (dataframe['rsi_84'] < 60) &
                (dataframe['rsi_112'] < 60)
            )

        is_crash_1 = (
                (dataframe['tpct_change_1'] < 0.08) &
                (dataframe['tpct_change_2'] < 0.08) &
                (dataframe['tpct_change_4'] < 0.10)
            )

        pump_protection_loose = (
                (dataframe['close'].rolling(48).max() >= (dataframe['close'] * 1.05 )) &
                ( (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.125 )) )
            )

        dataframe.loc[
            (
                dataframe['rocr_1h'].gt(self.rocr_1h.value)
            )
            &
            (
                (
                     (dataframe['lower'].shift().gt(0)) &
                     (dataframe['bbdelta'].gt(dataframe['ha_close'] * self.bbdelta_close.value)) &
                     (dataframe['closedelta'].gt(dataframe['ha_close'] * self.closedelta_close.value)) &
                     (dataframe['tail'].lt(dataframe['bbdelta'] * self.bbdelta_tail.value)) &
                     (dataframe['ha_close'].lt(dataframe['lower'].shift())) &
                     (dataframe['ha_close'].le(dataframe['ha_close'].shift()))
                )
                |
                (
                     (dataframe['ha_close'] < dataframe['ema_slow']) &
                     (dataframe['ha_close'] < self.close_bblower.value * dataframe['bb_lowerband'])
                )
            )
            &
            (
                 # General bull trend check
                 (dataframe['mama_1h'] > dataframe['fama_1h']) &
                 (dataframe['mama_diff_1h'] > 0.02) &

                 # Protection
                 (dataframe['close'] < dataframe['fama_1h']) &
                 (rsi_check) &
                 (is_crash_1) &
                 (pump_protection_loose)
            )
            ,'entry'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe.loc[
            (dataframe['fisher'] > self.exit_fisher.value) &
            (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'] * self.exit_bbmiddle_close.value) > dataframe['bb_middleband']) &
            (dataframe['volume'] > 0),
            'exit'
        ] = 1

        return dataframe