# --- Do not remove these libs ---
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
# --------------------------------
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame

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))

class CombinedBinHAndCluc(IStrategy):
    INTERFACE_VERSION = 3
    # Based on a backtesting:
    # - the best perfomance is reached with "max_open_trades" = 2 (in average for any market),
    #   so it is better to increase "stake_amount" value rather then "max_open_trades" to get more profit
    # - if the market is constantly green(like in JAN 2018) the best performance is reached with
    #   "max_open_trades" = 2 and minimal_roi = 0.01
    minimal_roi = {'0': 0.05}
    stoploss = -0.05
    timeframe = '5m'
    use_exit_signal = True
    exit_profit_only = True
    ignore_roi_if_entry_signal = False

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # strategy BinHV45
        mid, lower = bollinger_bands(dataframe['close'], window_size=40, num_of_std=2)
        dataframe['lower'] = lower
        dataframe['bbdelta'] = (mid - dataframe['lower']).abs()
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
        # strategy ClucMay72018
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:  # strategy BinHV45
        # strategy ClucMay72018
        dataframe.loc[dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * 0.008) & dataframe['closedelta'].gt(dataframe['close'] * 0.0175) & dataframe['tail'].lt(dataframe['bbdelta'] * 0.25) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) | (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < 0.985 * dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume_mean_slow'].shift(1) * 20), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        """
        dataframe.loc[dataframe['close'] > dataframe['bb_middleband'], 'exit_long'] = 1
        return dataframe