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

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 typing import Dict, List
from functools import reduce
from pandas import DataFrame, DatetimeIndex, merge

import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy  # noqa
from technical.util import resample_to_interval, resampled_merge
import pandas as pd


class Github_remiotore_freqtrade__ScalpingCCI__20260111_210550(IStrategy):
    """
        this strategy is based around the idea of generating a lot of potentatils buys and make tiny profits on each trade

        we recommend to have at least 60 parallel trades at any time to cover non avoidable losses.

        Recommended is to only sell based on ROI for this strategy
    """


    minimal_roi = {
        "0": 0.02,
        "10": 0.05,
        "20": 0.04,
        "60": 0.3,
        "120": 0.2
    }




    stoploss = -0.04


    ticker_interval = '15'

    def get_ticker_indicator(self):
        if 'm' in self.ticker_interval:
            return [int(s) for s in self.ticker_interval.split('m') if s.isdigit()][0]
        elif 'h' in self.ticker_interval:
            return [int(s) for s in self.ticker_interval.split('h') if s.isdigit()][0]*60
        return int(self.ticker_interval)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['sma'] = ta.EMA(dataframe, timeperiod=20, price='close')

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

        dataframe_daily = resample_to_interval(dataframe, self.get_ticker_indicator() * 96)



        dataframe_daily['high_daily'] = dataframe_daily['high']
        dataframe_daily['low_daily'] = dataframe_daily['low'] 
        dataframe_daily['pivot'] = (dataframe_daily['high'] + dataframe_daily['low'] + dataframe_daily['close']) / 3
        dataframe_daily['bc'] = (dataframe_daily['high'] + dataframe_daily['low'] ) / 2
        dataframe_daily['tc'] = (dataframe_daily['pivot'] - dataframe_daily['bc']) / 2

        dataframe = resampled_merge(dataframe, dataframe_daily)

        dataframe.fillna(method='ffill', inplace=True)

        pd.set_option('display.max_rows', 500)
        pd.set_option('display.max_columns', 500)
        pd.set_option('display.width', 1000)




        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe['close'] > dataframe['sma']) &
                (qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) & 
                (dataframe['close'] > dataframe['pivot']) &
                (dataframe['close'] > dataframe['bc']) &
                (dataframe['close'] > dataframe['tc']) & 
                (
                   (dataframe['pivot'] > dataframe['pivot'].shift(97)) &
                    (dataframe['bc'] > dataframe['bc'].shift(97)) &
                    (dataframe['close'] > dataframe['tc'].shift(97)) 
                )
            ),
            'buy'] = 1
        return dataframe
    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe['open'] >= (0.98 * dataframe['high_daily']))
            ) |
            (
                (qtpylib.crossed_below(dataframe['macd'], dataframe['macdsignal']))
            ),
            'sell'] = 1
        return dataframe