# source: https://raw.githubusercontent.com/enricogolfieri/yuccatrader/b4877c2592d67333df5c99662e962190c636f3b4/user_data/strategies/TheSimpleStrategy.py
# --- 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 freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,IStrategy, IntParameter)


class github_enricogolfieri_yuccatrader__TheSimpleStrategy__20220818_151950(IStrategy):


    # Minimal ROI designed for the strategy.
    # adjust based on market conditions. We would recommend to keep it low for quick turn arounds
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {
        "0": 0.01
    }

    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.25

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

    # --- Define spaces for the indicators ---
    macd_fast_period = IntParameter(low=10, high=20, default=12, space='buy', optimize=True)
    macd_slow_period= IntParameter(low=20, high=35, default=26, space='buy', optimize=True)
    macd_signal_period = IntParameter(low=5, high=15, default=9, space='sell', optimize=True)

    bbwindow = IntParameter(low=8, high=20, default=12, space='sell', optimize=True)
    bbdeviation = DecimalParameter(low=1, high=3, default=2, space='sell', optimize=True)

    sell_rsi = IntParameter(75, 95, default=85, space="sell")

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # MACD
        macd = ta.MACD(dataframe, fastperiod=self.macd_fast_period.value, slowperiod=self.macd_slow_period.value, signalperiod=self.macd_signal_period.value)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']

        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)

        # required for graphing
        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=self.bbwindow.value, stds=self.bbdeviation.value)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_middleband'] = bollinger['mid']

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (
                        (dataframe['macd'] > 0)  # over 0
                        & (dataframe['macd'] > dataframe['macdsignal'])  # over signal
                        & (dataframe['b'])
                )
            ),
            'buy'] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # different strategy used for sell points, due to be able to duplicate it to 100%
        dataframe.loc[
            (
                (dataframe['rsi'] > self.sell_rsi.value)  # over sell_rsi
            ),
            'sell'] = 1
        return dataframe