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

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 Cluc4werk(IStrategy):
    INTERFACE_VERSION = 3
    # Used for "informative pairs"
    stake = 'BTC'
    fiat = 'USD'
    '\n    PASTE OUTPUT FROM HYPEROPT HERE\n    '
    # Buy hyperspace params:
    entry_params = {'bbdelta-close': 0.00793, 'bbdelta-tail': 0.83802, 'close-bblower': 0.0034, 'closedelta-close': 0.00613, 'rocr-1h': 0.64081, 'volume': 21}
    # Sell hyperspace params:
    exit_params = {'exit-bbmiddle-close': 0.97703}
    # ROI table:
    minimal_roi = {'0': 0.0155, '109': 0.01075, '393': 0.00771, '587': 0.00643, '711': 0.00377, '770': 0.00114, '1039': 0}
    # Stoploss:
    stoploss = -0.31742
    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.31289
    trailing_stop_positive_offset = 0.33275
    trailing_only_offset_is_reached = True
    '\n    END HYPEROPT\n    '
    timeframe = '1m'
    # Make sure these match or are not overridden in config
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.0
    ignore_roi_if_entry_signal = True

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, '1h') for pair in pairs]
        '\n        Idea is to have "STAKE/USD" and "COIN/USD" as informative pairs as they move inverse of COIN/STAKE.\n        For example, stake currency is BTC, whitelist is */BTC\n        Current pair being examined (metadata[\'pair\']) is XLM/BTC\n        Be able to have informative pairs BTC/USD and XLM/USD available for use with some indicators for all pairs in the whitelist.\n        Ideally have this work gracefully with a change to the stake/whitelist in the config file.\n        If a desired informative pair does not exist (e.g. if exchange doesnt trade XLM/USD in this example), simply ignore those indicators without errors.\n        '
        coin, stake = metadata['pair'].split('/')
        informative_pairs += [('ETH/USD', timeframe), ('BTC/USD', timeframe)]
        return informative_pairs

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Set Up Bollinger Bands
        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()
        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()
        dataframe['rocr'] = ta.ROCR(dataframe, timeperiod=28)
        inf_tf = '1h'
        '\n        informative = self.dp.get_pair_dataframe(pair="ETH/USDT", timeframe="5m")\n        # ETH/USDT RSI based on 5m candles\n        informative[\'rsi\'] = ta.RSI(informative, timeperiod=14)\n        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, \'5m\', ffill=True)\n        '
        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
        informative['rocr'] = ta.ROCR(informative, timeperiod=168)
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        params = self.entry_params
        dataframe.loc[dataframe['rocr_1h'].gt(params['rocr-1h']) & (dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * params['bbdelta-close']) & dataframe['closedelta'].gt(dataframe['close'] * params['closedelta-close']) & dataframe['tail'].lt(dataframe['bbdelta'] * params['bbdelta-tail']) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) | (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < params['close-bblower'] * dataframe['bb_lowerband']) & (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['high'].le(dataframe['high'].shift(1))) &
        #(dataframe['close'] > dataframe['bb_middleband']) &
        #(qtpylib.crossed_above(dataframe['close'],dataframe['bb_middleband'])) &
        dataframe.loc[qtpylib.crossed_above(dataframe['close'] * params['exit-bbmiddle-close'], dataframe['bb_middleband']) & (dataframe['volume'] > 0), 'exit'] = 1
        return dataframe