# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
import pandas as pd
# inspired by @tirail SMAOffset
entry_params = {'ma_lower_length': 15, 'ma_lower_offset': 0.96, 'informative_fast_length': 20, 'informative_slow_length': 25, 'rsi_fast_length': 4, 'rsi_fast_threshold': 35, 'rsi_slow_length': 20, 'rsi_slow_confirmation': 1}
exit_params = {'ma_middle_1_length': 30, 'ma_middle_1_offset': 0.995, 'ma_upper_length': 20, 'ma_upper_offset': 1.01}

class MADisplaceV3(IStrategy):
    INTERFACE_VERSION = 3
    ma_lower_length = IntParameter(15, 25, default=entry_params['ma_lower_length'], space='entry')
    ma_lower_offset = DecimalParameter(0.95, 0.97, default=entry_params['ma_lower_offset'], space='entry')
    informative_fast_length = IntParameter(15, 35, default=entry_params['informative_fast_length'], space='disable')
    informative_slow_length = IntParameter(20, 40, default=entry_params['informative_slow_length'], space='disable')
    rsi_fast_length = IntParameter(2, 8, default=entry_params['rsi_fast_length'], space='disable')
    rsi_fast_threshold = IntParameter(5, 35, default=entry_params['rsi_fast_threshold'], space='disable')
    rsi_slow_length = IntParameter(10, 45, default=entry_params['rsi_slow_length'], space='disable')
    rsi_slow_confirmation = IntParameter(1, 5, default=entry_params['rsi_slow_confirmation'], space='disable')
    ma_middle_1_length = IntParameter(15, 35, default=exit_params['ma_middle_1_length'], space='exit')
    ma_middle_1_offset = DecimalParameter(0.93, 1.005, default=exit_params['ma_middle_1_offset'], space='exit')
    ma_upper_length = IntParameter(15, 25, default=exit_params['ma_upper_length'], space='exit')
    ma_upper_offset = DecimalParameter(1.005, 1.025, default=exit_params['ma_upper_offset'], space='exit')
    minimal_roi = {'0': 1}
    stoploss = -0.2
    trailing_stop = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.025
    trailing_only_offset_is_reached = True
    timeframe = '5m'
    use_exit_signal = True
    exit_profit_only = False
    process_only_new_candles = True
    plot_config = {'main_plot': {'ma_lower': {'color': 'red'}, 'ma_middle_1': {'color': 'green'}, 'ma_upper': {'color': 'pink'}}}
    use_custom_stoploss = True
    startup_candle_count = 200
    informative_timeframe = '1h'

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
        return informative_pairs

    def get_informative_indicators(self, metadata: dict):
        if self.config['runmode'].value == 'hyperopt':
            dataframe = self.informative_dataframe.copy()
        else:
            dataframe = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)
        dataframe['ema_fast'] = ta.EMA(dataframe, timeperiod=int(self.informative_fast_length.value))
        dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=int(self.informative_slow_length.value))
        dataframe['uptrend'] = (dataframe['ema_fast'] > dataframe['ema_slow']).astype('int')
        return dataframe

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        if current_profit < -0.04 and current_time - timedelta(minutes=35) > trade.open_date_utc:
            return -0.01
        return -0.99

    def get_main_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=int(self.rsi_fast_length.value))
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=int(self.rsi_slow_length.value))
        dataframe['rsi_slow_descending'] = (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift()).astype('int')
        dataframe['ma_lower'] = ta.SMA(dataframe, timeperiod=int(self.ma_lower_length.value)) * self.ma_lower_offset.value
        dataframe['ma_middle_1'] = ta.SMA(dataframe, timeperiod=int(self.ma_middle_1_length.value)) * self.ma_middle_1_offset.value
        dataframe['ma_upper'] = ta.SMA(dataframe, timeperiod=int(self.ma_upper_length.value)) * self.ma_upper_offset.value
        # drop NAN in hyperopt to fix "'<' not supported between instances of 'str' and 'int' error
        if self.config['runmode'].value == 'hyperopt':
            dataframe = dataframe.dropna()
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if self.config['runmode'].value == 'hyperopt':
            self.informative_dataframe = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)
        if self.config['runmode'].value != 'hyperopt':
            informative = self.get_informative_indicators(metadata)
            dataframe = self.merge_informative(informative, dataframe)
            dataframe = self.get_main_indicators(dataframe, metadata)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # calculate indicators with adjustable params for hyperopt
        # it's calling multiple times and dataframe overrides same columns
        # so check if any calculated column already exist
        if self.config['runmode'].value == 'hyperopt' and 'uptrend' not in dataframe:
            informative = self.get_informative_indicators(metadata)
            dataframe = self.merge_informative(informative, dataframe)
            dataframe = self.get_main_indicators(dataframe, metadata)
            pd.options.mode.chained_assignment = None
        dataframe.loc[(dataframe['rsi_slow_descending'].rolling(self.rsi_slow_confirmation.value).sum() == self.rsi_slow_confirmation.value) & (dataframe['rsi_fast'] < self.rsi_fast_threshold.value) & (dataframe['uptrend'] > 0) & (dataframe['close'] < dataframe['ma_lower']) & (dataframe['volume'] > 0), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if self.config['runmode'].value == 'hyperopt' and 'uptrend' not in dataframe:
            informative = self.get_informative_indicators(metadata)
            dataframe = self.merge_informative(informative, dataframe)
            dataframe = self.get_main_indicators(dataframe, metadata)
            pd.options.mode.chained_assignment = None
        dataframe.loc[((dataframe['uptrend'] == 0) | (dataframe['close'] > dataframe['ma_upper']) | qtpylib.crossed_below(dataframe['close'], dataframe['ma_middle_1'])) & (dataframe['volume'] > 0), 'exit_long'] = 1
        return dataframe

    def merge_informative(self, informative: DataFrame, dataframe: DataFrame) -> DataFrame:
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True)
        # don't overwrite the base dataframe's HLCV information
        skip_columns = [s + '_' + self.informative_timeframe for s in ['date', 'open', 'high', 'low', 'close', 'volume']]
        dataframe.rename(columns=lambda s: s.replace('_{}'.format(self.informative_timeframe), '') if not s in skip_columns else s, inplace=True)
        return dataframe