# Source: generated via dynamic_strategy_generator
from freqtrade.strategy import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib

class ACO_64_16(IStrategy):
    timeframe = '1h'
    
    # Standard ROI and Stoploss
    minimal_roi = {"0": 0.1, "60": 0.05, "120": 0.0}
    stoploss = -0.05
    
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['aroonosc'] = ta.AROONOSC(dataframe, timeperiod=14)
        dataframe['dema'] = ta.DEMA(dataframe, timeperiod=10)
        dataframe['kama'] = ta.KAMA(dataframe, timeperiod=30)
        dataframe['natr'] = ta.NATR(dataframe, timeperiod=14)
        dataframe['wma'] = ta.WMA(dataframe, timeperiod=20)
        bbands = ta.BBANDS(dataframe, timeperiod=14, nbdevup=2.0, nbdevdn=2.0)
        dataframe['upperband'] = bbands['upperband']
        dataframe['middleband'] = bbands['middleband']
        dataframe['lowerband'] = bbands['lowerband']
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
        (
            qtpylib.crossed_above(dataframe['aroonosc'], 0)
        ) & (
            qtpylib.crossed_above(dataframe['close'], dataframe['dema'])
        ) & (
            qtpylib.crossed_above(dataframe['close'], dataframe['kama'])
        ) & (
            (dataframe['natr'] > 2.0)
        ),
        'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
        (
            qtpylib.crossed_below(dataframe['close'], dataframe['wma'])
        ) & (
            (dataframe['close'] > dataframe['upperband'] * 0.98)
        ),
        'exit_long'] = 1
        return dataframe