# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/MAC_0.py



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.abstract as ta
import pandas_ta as pta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class Github_remiotore_freqtrade__MAC_0__20260111_210550(IStrategy):
    """
    This is a strategy template to get you started.
    More information in https://www.freqtrade.io/en/latest/strategy-customization/

    You can:
        :return: a Dataframe with all mandatory indicators for the strategies
    - Rename the class name (Do not forget to update class_name)
    - Add any methods you want to build your strategy
    - Add any lib you need to build your strategy

    You must keep:
    - the lib in the section "Do not remove these libs"
    - the methods: populate_indicators, populate_buy_trend, populate_sell_trend
    You should keep:
    - timeframe, minimal_roi, stoploss, trailing_*
    """


    INTERFACE_VERSION = 2

    timeframe = '1d'


    minimal_roi = {
        "0": 10
    }


    stoploss = -0.15

    trailing_stop = False




    process_only_new_candles = False

    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

    startup_candle_count: int = 50

    buy_rsi = IntParameter(10, 40, default=30, space="buy")
    sell_rsi = IntParameter(60, 90, default=70, space="sell")

    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    order_time_in_force = {
        'buy': 'gtc',
        'sell': 'gtc'
    }
    
    @property
    def plot_config(self):
        return {

            'main_plot': {
                'ema50': {'color': 'blue'},
                'ema200': {'color': 'orange'},
            },
            'subplots': {

            }
        }

    def informative_pairs(self):
        """
        Define additional, informative pair/interval combinations to be cached from the exchange.
        These pair/interval combinations are non-tradeable, unless they are part
        of the whitelist as well.
        For more information, please consult the documentation
        :return: List of tuples in the format (pair, interval)
            Sample: return [("ETH/USDT", "5m"),
                            ("BTC/USDT", "15m"),
                            ]
        """
        return []

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

        dataframe['rsi'] = ta.RSI(dataframe)

        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']





        dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)

        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)








        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (

                (dataframe['ema20'] > dataframe['ema50']) &  
                (dataframe['ema20'].shift() < dataframe['ema50'].shift()) &  
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (

                (dataframe['ema20'] < dataframe['ema50']) &  
                (dataframe['ema20'].shift() > dataframe['ema50'].shift()) &  
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'sell'] = 1
        return dataframe