# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/ml_strategy.py


import talib.abstract as ta
from pandas import DataFrame, to_datetime

import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.indicator_helpers import fishers_inverse
from freqtrade.strategy.interface import IStrategy
from freqtrade.optimize import ml_utils

class Github_remiotore_ccxt_freqtrade__ml_strategy__20260111_210550(IStrategy):
    """
    Default Strategy provided by freqtrade bot.
    You can override it with your own strategy
    """

    minimal_roi = {
        "40":  0.0,         #in 40min
        "30":  0.01,        #in 30min
        "20":  0.02,        #in 20min
        "0":  0.04
    }

    stoploss = -0.10

    ticker_interval = 5

    slippage = 0.01
    
    def ML_parse_ticker_dataframe(self, dataframe: DataFrame) -> DataFrame:
        """
        Analyses the trend for the given ticker history
        :param ticker: See exchange.get_ticker_history
        :return: DataFrame
        """
        frame = dataframe.set_index('date')
        frame.index.names = [None]
        
        frame.index = to_datetime(frame.index, utc=True, infer_datetime_format=True)

        return frame
        
    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
        """
        Adds several different TA indicators to the given DataFrame

        Performance Note: For the best performance be frugal on the number of indicators
        you are using. Let uncomment only the indicator you are using in your strategies
        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
        """
        
        preprocessed  = self.ML_parse_ticker_dataframe(dataframe)

        test_ratio = 0.2

        data = ml_utils.run_pipeline(preprocessed, test_ratio)
        data['date'] = data.index



        heikinashi = qtpylib.heikinashi(data)
        data['ha_open'] = heikinashi['open']
        data['ha_close'] = heikinashi['close']
        data['ha_high'] = heikinashi['high']
        data['ha_low'] = heikinashi['low']
        
        return data

    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[ (dataframe['Prediction'] == 1), 'buy'] = 1
        
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        
        dataframe.loc[ (dataframe['Prediction'] == 0), 'sell'] = 1
    
        return dataframe