import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy import merge_informative_pair
from freqtrade.strategy.interface import IStrategy
from freqtrade.persistence import Trade
from pandas import DataFrame
from datetime import datetime, timedelta



























def SSLChannels(dataframe, length=7):
    df = dataframe.copy()
    df['ATR'] = ta.ATR(df, timeperiod=14)
    df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR']
    df['smaLow'] = df['low'].rolling(length).mean() - df['ATR']
    df['hlv'] = np.where(df['close'] > df['smaHigh'], 1,
                         np.where(df['close'] < df['smaLow'], -1, np.NAN))
    df['hlv'] = df['hlv'].ffill()
    df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
    df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])
    return df['sslDown'], df['sslUp']


class CombinedBinHAndClucV6_2(IStrategy):
    INTERFACE_VERSION = 2

    minimal_roi = {
        "0": 0.0181
    }

    stoploss = -0.99  # effectively disabled.

    timeframe = '5m'
    inf_1h = '1h'  # informative tf

    use_sell_signal = True
    sell_profit_only = True

    sell_profit_offset = 0.001
    ignore_roi_if_buy_signal = True

    trailing_stop = True
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.025

    use_custom_stoploss = True

    process_only_new_candles = False

    startup_candle_count: int = 200

    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:

        if (current_profit < 0) & (current_time - timedelta(minutes=300) > trade.open_date_utc):
            return 0.01
        return 0.99

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
                           rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool:
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        try:
            last_candle = dataframe.iloc[-1].squeeze()
        except IndexError:
            return True

        if (last_candle['rsi'] > 50):
            return False

        return True

    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."

        informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)

        informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
        informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)

        informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)

        ssl_down_1h, ssl_up_1h = SSLChannels(informative_1h, 20)
        informative_1h['ssl_down'] = ssl_down_1h
        informative_1h['ssl_up'] = ssl_up_1h

        return informative_1h

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

        bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2)
        dataframe['lower'] = bb_40['lower']
        dataframe['mid'] = bb_40['mid']
        dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs()
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()

        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['ema_slow'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()

        dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)

        dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5)

        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)

        return dataframe

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

        informative_1h = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(
            dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)

        dataframe = self.normal_tf_indicators(dataframe, metadata)

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (  # strategy BinHV45
                (dataframe['close'] > dataframe['ema_200_1h']) &
                (dataframe['ema_50'] > dataframe['ema_200']) &
                (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &

                dataframe['lower'].shift().gt(0) &
                dataframe['bbdelta'].gt(dataframe['close'] * 0.031) &
                dataframe['closedelta'].gt(dataframe['close'] * 0.018) &
                dataframe['tail'].lt(dataframe['bbdelta'] * 0.233) &
                dataframe['close'].lt(dataframe['lower'].shift()) &
                dataframe['close'].le(dataframe['close'].shift()) &
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            )
            |
            (  # strategy ClucMay72018
                (dataframe['close'] > dataframe['ema_200']) &
                (dataframe['close'] > dataframe['ema_200_1h']) &

                (dataframe['close'] < dataframe['ema_slow']) &
                (dataframe['close'] < 0.993 * dataframe['bb_lowerband']) &
                (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 21)) &
                (dataframe['volume'] > 0)
            )
            |
            (
                (dataframe['close'] < dataframe['sma_5']) &
                (dataframe['ssl_up_1h'] > dataframe['ssl_down_1h']) &
                (dataframe['ema_50'] > dataframe['ema_200']) &
                (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &

                (dataframe['rsi'] < dataframe['rsi_1h'] - 43.276) &
                (dataframe['volume'] > 0)
            ),
            'buy'
        ] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (  # Improves the profit slightly.
                (dataframe['close'] > dataframe['bb_upperband']) &
                (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) &
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'sell'
        ] = 1
        return dataframe