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
from pandas import DataFrame, Series

class Cluc7werk(IStrategy):
    INTERFACE_VERSION = 3
    '\n    PASTE OUTPUT FROM HYPEROPT HERE\n    '
    # Buy hyperspace params:
    entry_params = {'bbdelta-close': 0.00732, 'bbdelta-tail': 0.94138, 'close-bblower': 0.0199, 'closedelta-close': 0.01825, 'fisher': -0.22987, 'volume': 16}
    # Sell hyperspace params:
    exit_params = {'exit-bbmiddle-close': 0.99184, 'exit-fisher': 0.26832}
    # ROI table:
    minimal_roi = {'0': 0.15373, '14': 0.1105, '57': 0.08376, '147': 0.03427, '201': 0.01352, '366': 0.00667, '469': 0}
    # Stoploss:
    stoploss = -0.02
    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.01007
    trailing_stop_positive_offset = 0.01258
    trailing_only_offset_is_reached = False
    '\n    END HYPEROPT\n    '
    timeframe = '1m'
    startup_candle_count: int = 72
    # Make sure these match or are not overridden in config
    use_exit_signal = True
    exit_profit_only = True
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = True

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Set Up Bollinger Bands
        upper_bb1, mid_bb1, lower_bb1 = ta.BBANDS(dataframe['close'], timeperiod=40)
        upper_bb2, mid_bb2, lower_bb2 = ta.BBANDS(qtpylib.typical_price(dataframe), timeperiod=20)
        # only putting some bands into dataframe as the others are not used elsewhere in the strategy
        dataframe['lower-bb1'] = lower_bb1
        dataframe['lower-bb2'] = lower_bb2
        dataframe['mid-bb2'] = mid_bb2
        dataframe['bb1-delta'] = (mid_bb1 - dataframe['lower-bb1']).abs()
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
        dataframe['ema_fast'] = ta.EMA(dataframe['close'], timeperiod=6)
        dataframe['ema_slow'] = ta.EMA(dataframe['close'], timeperiod=48)
        dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=24).mean()
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=9)
        # # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
        rsi = 0.1 * (dataframe['rsi'] - 50)
        dataframe['fisher-rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        params = self.entry_params
        dataframe.loc[dataframe['fisher-rsi'].lt(params['fisher']) & (dataframe['bb1-delta'].gt(dataframe['close'] * params['bbdelta-close']) & dataframe['closedelta'].gt(dataframe['close'] * params['closedelta-close']) & dataframe['tail'].lt(dataframe['bb1-delta'] * params['bbdelta-tail']) & dataframe['close'].lt(dataframe['lower-bb1'].shift()) & dataframe['close'].le(dataframe['close'].shift()) | (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < params['close-bblower'] * dataframe['lower-bb2']) & (dataframe['volume'] < dataframe['volume_mean_slow'].shift(1) * params['volume'])), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        params = self.exit_params
        dataframe.loc[(dataframe['close'] * params['exit-bbmiddle-close'] > dataframe['mid-bb2']) & dataframe['ema_fast'].gt(dataframe['close']) & dataframe['fisher-rsi'].gt(params['exit-fisher']) & dataframe['volume'].gt(0), 'exit'] = 1
        return dataframe