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

class ClucFiatROI(IStrategy):
    INTERFACE_VERSION = 3
    # Buy hyperspace params:
    entry_params = {'bbdelta-close': 0.00642, 'bbdelta-tail': 0.75559, 'close-bblower': 0.01415, 'closedelta-close': 0.00883, 'fisher': -0.97101, 'volume': 18}
    # Sell hyperspace params:
    exit_params = {'exit-bbmiddle-close': 0.95153, 'exit-fisher': 0.60924}
    # ROI table:
    minimal_roi = {'0': 0.04354, '5': 0.03734, '8': 0.02569, '10': 0.019, '76': 0.01283, '235': 0.007, '415': 0}
    # Stoploss:
    stoploss = -0.34299
    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.01057
    trailing_stop_positive_offset = 0.03668
    trailing_only_offset_is_reached = True
    '\n    END HYPEROPT\n    '
    timeframe = '5m'
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = True
    startup_candle_count: int = 48

    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)
        # These indicators are used to persist a entry signal in live trading only
        # They dramatically slow backtesting down
        if self.config['runmode'].value in ('live', 'dry_run'):
            dataframe['sar'] = ta.SAR(dataframe)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        params = self.entry_params
        active_trade = False
        if self.config['runmode'].value in ('live', 'dry_run'):
            active_trade = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True)]).all()
        conditions = []
        '\n        If this is a fresh entry, apple additional conditions.\n        Idea is to leverage "ignore_roi_if_entry_signal = True" functionality by using certain\n        indicators for active trades while applying additional protections to new trades.\n        '
        if not active_trade:
            conditions.append(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'])))
        else:
            conditions.append(dataframe['close'] > dataframe['close'].shift())
            conditions.append(dataframe['close'] > dataframe['sar'])
        conditions.append(dataframe['volume'].gt(0))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), '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
    '\n    https://www.freqtrade.io/en/latest/strategy-advanced/\n\n    Custom Order Timeouts\n    '

    def check_entry_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
        ob = self.dp.orderbook(pair, 1)
        current_price = ob['bids'][0][0]
        # Cancel entry order if price is more than 1% above the order.
        if current_price > order['price'] * 1.01:
            return True
        return False

    def check_exit_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
        ob = self.dp.orderbook(pair, 1)
        current_price = ob['asks'][0][0]
        # Cancel exit order if price is more than 1% below the order.
        if current_price < order['price'] * 0.99:
            return True
        return False