import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame

from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
								IStrategy, IntParameter)


import talib
import talib.abstract as ta
import pandas_ta as pta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.util import resample_to_interval, resampled_merge


class PatternRecognition(IStrategy):

	INTERFACE_VERSION: int = 3
	# Buy hyperspace params:
	buy_params = {
		"buy_pr1": "CDLHIGHWAVE",
		"buy_vol1": -100,
	}

	# ROI table:
	minimal_roi = {
		"0": 0.936,
		"5271": 0.332,
		"18147": 0.086,
		"48152": 0
	}

	# Stoploss:
	stoploss = -0.288

	# Trailing stop:
	trailing_stop = True
	trailing_stop_positive = 0.032
	trailing_stop_positive_offset = 0.084
	trailing_only_offset_is_reached = True

	# Optimal timeframe for the strategy.
	timeframe = '1d'
	prs = talib.get_function_groups()['Pattern Recognition']

	# # Strategy parameters
	buy_pr1 = CategoricalParameter(prs, default=prs[0], space="buy")
	buy_vol1 = CategoricalParameter([-100,100], default=0, space="buy")



	def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		for pr in self.prs:
			dataframe[pr] = getattr(ta, pr)(dataframe)

		return dataframe

	def get_name(self) -> str:
		return "pattern_strategy"

	def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		dataframe.loc[
			(
				(dataframe[self.buy_pr1.value]==self.buy_vol1.value)
				# |(dataframe[self.buy_pr2.value]==self.buy_vol2.value)
			),
			'enter_long'] = 1

		return dataframe

	def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		dataframe.loc[
			(
			#	 (dataframe[self.sell_pr1.value]==self.sell_vol1.value)|
			#	 (dataframe[self.sell_pr2.value]==self.sell_vol2.value)
			),
			'exit_long'] = 1

		return dataframe