# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-michael-k8s-namespace/Quickie.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

class Github_DerSalvador_freqtrade_helm_chart__Quickie__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    '\n\n    author@: Gert Wohlgemuth\n\n    idea:\n        momentum based strategie. The main idea is that it closes trades very quickly, while avoiding excessive losses. Hence a rather moderate stop loss in this case\n    '
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {'100': 0.01, '30': 0.03, '15': 0.06, '10': 0.15}
    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.25
    # Optimal timeframe for the strategy
    timeframe = '5m'

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']
        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
        dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200)
        dataframe['sma_50'] = ta.SMA(dataframe, timeperiod=200)
        dataframe['adx'] = ta.ADX(dataframe)
        # required for graphing
        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['adx'] > 30) & (dataframe['tema'] < dataframe['bb_middleband']) & (dataframe['tema'] > dataframe['tema'].shift(1)) & (dataframe['sma_200'] > dataframe['close']), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['adx'] > 70) & (dataframe['tema'] > dataframe['bb_middleband']) & (dataframe['tema'] < dataframe['tema'].shift(1)), 'exit_long'] = 1
        return dataframe