# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------

import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib
import datetime
from technical.util import resample_to_interval, resampled_merge
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter

# author @tirail

ma_types = {
	'SMA': ta.SMA,
	'EMA': ta.EMA,
}

class SMAOffset(IStrategy):
	INTERFACE_VERSION = 2

	# hyperopt and paste results here
	# Buy hyperspace params:
	buy_params = {
		"base_nb_candles_buy": 30,
		"buy_trigger": 'SMA',
		"low_offset": 0.958,
	}

	# Sell hyperspace params:
	sell_params = {
		"base_nb_candles_sell": 30,
		"high_offset": 1.012,
		"sell_trigger": 'EMA',
	}

	# Stoploss:
	stoploss = -0.5

	# ROI table:
	minimal_roi = {
		"0": 1,
	}

	base_nb_candles_buy = IntParameter(5, 80, default=buy_params['base_nb_candles_buy'], space='buy')
	base_nb_candles_sell = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell')
	low_offset = DecimalParameter(0.8, 0.99, default=buy_params['low_offset'], space='buy')
	high_offset = DecimalParameter(0.8, 1.1, default=sell_params['high_offset'], space='sell')
	buy_trigger = CategoricalParameter(ma_types.keys(), default=buy_params['buy_trigger'], space='buy')
	sell_trigger = CategoricalParameter(ma_types.keys(), default=sell_params['sell_trigger'], space='sell')

	# Trailing stop:
	trailing_stop = False
	trailing_stop_positive = 0.0001
	trailing_stop_positive_offset = 0
	trailing_only_offset_is_reached = False

	# Optimal timeframe for the strategy
	timeframe = '5m'

	use_sell_signal = True
	sell_profit_only = False

	process_only_new_candles = True
	startup_candle_count = 30

	plot_config = {
		'main_plot': {
			'ma_offset_buy': {'color': 'orange'},
			'ma_offset_sell': {'color': 'orange'},
		},
	}

	use_custom_stoploss = False

	def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
						current_rate: float, current_profit: float, **kwargs) -> float:
		return 1

	def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		if not self.config['runmode'].value == 'hyperopt':
			dataframe['ma_offset_buy'] = ma_types[self.buy_trigger.value](dataframe, int(self.base_nb_candles_buy.value)) * self.low_offset.value
			dataframe['ma_offset_sell'] = ma_types[self.sell_trigger.value](dataframe, int(self.base_nb_candles_sell.value)) * self.high_offset.value
		return dataframe

	def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		if self.config['runmode'].value == 'hyperopt':
			dataframe['ma_offset_buy'] = ma_types[self.buy_trigger.value](dataframe, int(self.base_nb_candles_buy.value)) * self.low_offset.value

		dataframe.loc[
			(
					(dataframe['close'] < dataframe['ma_offset_buy']) &
					(dataframe['volume'] > 0)
			),
			'buy'] = 1
		return dataframe

	def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		if self.config['runmode'].value == 'hyperopt':
			dataframe['ma_offset_sell'] = ma_types[self.sell_trigger.value](dataframe, int(self.base_nb_candles_sell.value)) * self.high_offset.value

		dataframe.loc[
			(
					(dataframe['close'] > dataframe['ma_offset_sell']) &
					(dataframe['volume'] > 0)
			),
			'sell'] = 1
		return dataframe