# source: https://raw.githubusercontent.com/kristenmo20/freqtrade-strategies/09b72453243a6c46c89690ef8316152dd8b97cb4/AverageStrategy.py
# --- Do not remove these libs ---
from functools import reduce
from freqtrade.strategy import IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
from pandas import DataFrame
# --------------------------------

import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class github_kristenmo20_freqtrade_strategies__AverageStrategy__20230209_175427(IStrategy):

    # def leverage(self, pair: str, current_rate: float,
    #              proposed_leverage: 3.0, max_leverage: 5.0, side: str, **kwargs) -> float:
    #     return 3.0

    INTERFACE_VERSION: int = 3
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {
        "0": 0.341,
        "1648": 0.162,
        "4193": 0.091,
        "4979": 0
    }

     # Trailing stop-loss
    trailing_stop = True
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.013
    trailing_stop_positive_offset = 0.107  # Disabled / not configured

    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.264
    # Optimal timeframe for the strategy
    timeframe = '4h'

    buy_range_short = IntParameter(5, 20, default=7)
    buy_range_long = IntParameter(20, 120, default=75)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        # Combine all ranges ... to avoid duplicate calculation
        for val in list(set(list(self.buy_range_short.range) + list(self.buy_range_long.range))):
            dataframe[f'ema{val}'] = ta.EMA(dataframe, timeperiod=val)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                qtpylib.crossed_above(
                    dataframe[f'ema{self.buy_range_short.value}'],
                    dataframe[f'ema{self.buy_range_long.value}']
                ) &
                (dataframe['volume'] > 0)
            ),
            'enter_long'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                qtpylib.crossed_above(
                    dataframe[f'ema{self.buy_range_long.value}'],
                    dataframe[f'ema{self.buy_range_short.value}']
                    ) &
                (dataframe['volume'] > 0)
            ),
            'exit_long'] = 1
        return dataframe