# source: https://raw.githubusercontent.com/cycle-luca/freqtrade-stragies/09dafacb5d0cc8b76876919dc12345a346b644f7/EMASkipPump.py
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
from typing import Optional
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy  # noqa
import datetime

class Github_cycle_luca_freqtrade_stragies__EMASkipPump__20230905_043222(IStrategy):

    """
        basic strategy, which trys to avoid pump and dump market conditions. Shared from the tradingview
        slack
    """
    EMA_SHORT_TERM = 21
    EMA_MEDIUM_TERM = 55
    EMA_LONG_TERM = 120

    # Minimal ROI designed for the strategy.
    # we only sell after 100%, unless our sell points are found before
    minimal_roi = {
        "0": 3
    }

    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    # should be converted to a trailing stop loss
    stoploss = -0.15

    # Optimal timeframe for the strategy
    timeframe = '15m'



    def leverage(self, pair: str, current_time: datetime, current_rate: float,
                 proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
                 **kwargs) -> float:
        """
        Customize leverage for each new trade. This method is only called in futures mode.

        :param pair: Pair that's currently analyzed
        :param current_time: datetime object, containing the current datetime
        :param current_rate: Rate, calculated based on pricing settings in exit_pricing.
        :param proposed_leverage: A leverage proposed by the bot.
        :param max_leverage: Max leverage allowed on this pair
        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
        :param side: 'long' or 'short' - indicating the direction of the proposed trade
        :return: A leverage amount, which is between 1.0 and max_leverage.
        """
        return 3.0


    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """ Adds several different TA indicators to the given DataFrame
        """

        dataframe['ema_{}'.format(self.EMA_SHORT_TERM)] = ta.EMA(
            dataframe, timeperiod=self.EMA_SHORT_TERM
        )
        dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)] = ta.EMA(
            dataframe, timeperiod=self.EMA_MEDIUM_TERM
        )
        dataframe['ema_{}'.format(self.EMA_LONG_TERM)] = ta.EMA(
            dataframe, timeperiod=self.EMA_LONG_TERM
        )

        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']

        dataframe['min'] = ta.MIN(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
        dataframe['max'] = ta.MAX(dataframe, timeperiod=self.EMA_MEDIUM_TERM)

        return dataframe

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

        dataframe.loc[
            (dataframe['close'] < dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
            (dataframe['close'] < dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &
            (dataframe['close'] == dataframe['min']) &
            (dataframe['close'] <= 0.92 * dataframe['bb_lowerband']),
            'enter_long'
        ] = 1

        return dataframe

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

        dataframe.loc[
            (dataframe['close'] > dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
            (dataframe['close'] > dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &
            (dataframe['close'] >= 1.03 * dataframe['bb_upperband']),
            'exit_long'
        ] = 1

        return dataframe