# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/BinHV45_343.py

from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
import numpy as np


import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib

def bollinger_bands(stock_price, window_size, num_of_std):
    rolling_mean = stock_price.rolling(window=window_size).mean()
    rolling_std  = stock_price.rolling(window=window_size).std()
    lower_band = rolling_mean - (rolling_std * num_of_std)

    return rolling_mean, lower_band

class Github_remiotore_freqtrade__BinHV45_343__20260111_210550(IStrategy):
    minimal_roi = {
        "0": 0.0125
    }

    stoploss = -0.05
    ticker_interval = '1m'

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

        mid, lower = bollinger_bands(dataframe['close'], window_size=40, num_of_std=2)
        dataframe['mid'] = np.nan_to_num(mid) #replace nan with zero
        dataframe['lower'] = np.nan_to_num(lower) #replace nan with zero
        dataframe['bbdelta'] = (dataframe['mid'] - dataframe['lower']).abs() #absolute delta between mid and lower bb bands
        dataframe['pricedelta'] = (dataframe['open'] - dataframe['close']).abs() #absolute delta between 
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                dataframe['lower'].shift().gt(0) &
                dataframe['bbdelta'].gt(dataframe['close'] * 0.008) &
                dataframe['closedelta'].gt(dataframe['close'] * 0.0175) &
                dataframe['tail'].lt(dataframe['bbdelta'] * 0.25) &
                dataframe['close'].lt(dataframe['lower'].shift()) &
                dataframe['close'].le(dataframe['close'].shift())
            ),
            'buy'] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        no sell signal
        """
        dataframe['sell'] = 0
        return dataframe