# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/Low_BB.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 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 as np # noqa

class Github_DerSalvador_freqtrade_helm_chart__Low_BB__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    '\n\n    author@: Thorsten\n\n    works on new objectify branch!\n\n    idea:\n        entry after crossing .98 * lower_bb and exit if trailing stop loss is hit\n    '
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {'0': 0.9, '1': 0.05, '10': 0.04, '15': 0.5}
    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.015
    # Optimal timeframe for the strategy
    timeframe = '1m'

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        ##################################################################################
        # entry and exit indicators
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']
        # dataframe['cci'] = ta.CCI(dataframe)
        # dataframe['mfi'] = ta.MFI(dataframe)
        # dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)
        # dataframe['canentry'] = np.NaN
        # dataframe['canentry2'] = np.NaN
        # dataframe.loc[dataframe.close.rolling(49).min() <= 1.1 * dataframe.close, 'canentry'] == 1
        # dataframe.loc[dataframe.close.rolling(600).max() < 1.2 * dataframe.close, 'canentry'] = 1
        # dataframe.loc[dataframe.close.rolling(600).max() * 0.8 >  dataframe.close, 'canentry2'] = 1
        ##################################################################################
        # required for graphing
        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_middleband'] = bollinger['mid']
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the entry signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with entry column
        """
        dataframe.loc[dataframe['close'] <= 0.98 * dataframe['bb_lowerband'], 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the exit signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with entry column
        """
        dataframe.loc[(), 'exit_long'] = 1
        return dataframe