AdxSmasS_v7_long_and_short

from freqtrade.strategy import IStrategy
from pandas import DataFrame
from freqtrade.persistence import Trade
from datetime import datetime, timedelta

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



class AdxSmasS_v7_long_and_short(IStrategy):
    """

    author@: Gert Wohlgemuth

    converted from:

    https://github.com/sthewissen/Mynt/blob/master/src/Mynt.Core/Strategies/AdxSmas.cs

    """

    INTERFACE_VERSION: int = 3

    can_short: bool = True

    can_long: bool = True



    minimal_roi = {
        "2160": 0.025,
        "1440": 0.05,
        "720": 0.075,
        "0": 0.1
    }

    stoploss = -0.25

    timeframe = '1h'

    use_custom_stoploss = True

    @property
    def protections(self):
        return [
            {
                "method": "CooldownPeriod",
                "stop_duration_candles": 3
            },
            {
                "method": "MaxDrawdown",
                "lookback_period_candles": 12,
                "trade_limit": 20,
                "stop_duration_candles": 3,
                "max_allowed_drawdown": 0.075
            },
            {
                "method": "LowProfitPairs",
                "lookback_period_candles": 6,
                "trade_limit": 2,
                "stop_duration_candles": 60,
                "required_profit": 0.03
            },
            {
                "method": "LowProfitPairs",
                "lookback_period_candles": 24,
                "trade_limit": 4,
                "stop_duration_candles": 2,
                "required_profit": 0.01
            }
        ]

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                            current_rate: float, current_profit: float, **kwargs) -> float:

        if current_profit < 0.03:
            return -1 # return a value bigger than the initial stoploss to keep using the initial stoploss

        desired_stoploss = current_profit / 2

        return max(min(desired_stoploss, 0.075), 0.02)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
        dataframe['short'] = ta.SMA(dataframe, timeperiod=3)
        dataframe['long'] = ta.SMA(dataframe, timeperiod=6)

        return dataframe

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

        dataframe.loc[
            (
                (dataframe['adx'] < 25) &
                (qtpylib.crossed_above(dataframe['short'], dataframe['long']))
            ),
            'enter_long'] = 1

        dataframe.loc[
            (
                (dataframe['adx'] < 25) &
                (qtpylib.crossed_above(dataframe['long'], dataframe['short']))
            ),
            'enter_short'] = 1

        return dataframe

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

        dataframe.loc[
            (
                (dataframe['adx'] > 25) &
                (qtpylib.crossed_above(dataframe['long'], dataframe['short']))
            ),
            'exit_long'] = 1

        dataframe.loc[
            (
                (dataframe['adx'] > 25) &
                (qtpylib.crossed_above(dataframe['short'], dataframe['long']))
            ),
            'exit_short'] = 1

        return dataframe