# MultiMa Strategy
# Author: @Mablue (Masoud Azizi)
# github: https://github.com/mablue/
# (First Hyperopt it.A hyperopt file is available)
#
# --- Do not remove these libs ---
from freqtrade.strategy.parameters import  IntParameter
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame

# --------------------------------

# Add your lib to import here
import ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from functools import reduce


class MultiMa(IStrategy):

    buy_ma_count = IntParameter(0, 10, default=10, space="buy")
    buy_ma_gap = IntParameter(2, 10, default=2, space="buy")
    buy_ma_shift = IntParameter(0, 10, default=0, space="buy")
    # buy_ma_rolling = IntParameter(0, 10, default=0, space='buy')

    sell_ma_count = IntParameter(0, 10, default=10, space="sell")
    sell_ma_gap = IntParameter(2, 10, default=2, space="sell")
    sell_ma_shift = IntParameter(0, 10, default=0, space="sell")
    # sell_ma_rolling = IntParameter(0, 10, default=0, space='sell')

    # ROI table:
    minimal_roi = {"0": 0.30873, "569": 0.16689, "3211": 0.06473, "7617": 0}

    # Stoploss:
    stoploss = -0.1

    # Buy hypers
    timeframe = "4h"

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

        # We will dinamicly generate the indicators
        # cuz this method just run one time in hyperopts
        # if you have static timeframes you can move first loop of buy and sell trends populators inside this method

        return dataframe

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

        for i in self.buy_ma_count.range:
            dataframe[f"buy-ma-{i+1}"] = ta.SMA(
                dataframe, timeperiod=int((i + 1) * self.buy_ma_gap.value)
            )

        conditions = []

        for i in self.buy_ma_count.range:
            if i > 1:
                shift = self.buy_ma_shift.value
                for shift in self.buy_ma_shift.range:
                    conditions.append(
                        dataframe[f"buy-ma-{i}"].shift(shift)
                        > dataframe[f"buy-ma-{i-1}"].shift(shift)
                    )
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), "buy"] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        for i in self.sell_ma_count.range:
            dataframe[f"sell-ma-{i+1}"] = ta.SMA(
                dataframe, timeperiod=int((i + 1) * self.sell_ma_gap.value)
            )

        conditions = []

        for i in self.sell_ma_count.range:
            if i > 1:
                shift = self.sell_ma_shift.value
                for shift in self.sell_ma_shift.range:
                    conditions.append(
                        dataframe[f"sell-ma-{i}"].shift(shift)
                        < dataframe[f"sell-ma-{i-1}"].shift(shift)
                    )
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), "sell"] = 1
        return dataframe