stable/tests/strategy/strats/strategy_test_v3.py

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement

from datetime import datetime

import talib.abstract as ta
from pandas import DataFrame

import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.persistence import Trade
from freqtrade.strategy import (BooleanParameter, DecimalParameter, IntParameter, IStrategy,
                                RealParameter)


class StrategyTestV3(IStrategy):
    """
    Strategy used by tests freqtrade bot.
    Please do not modify this strategy, it's  intended for internal use only.
    Please look at the SampleStrategy in the user_data/strategy directory
    or strategy repository https://github.com/freqtrade/freqtrade-strategies
    for samples and inspiration.
    """
    INTERFACE_VERSION = 3

    # Minimal ROI designed for the strategy
    minimal_roi = {
        "40": 0.0,
        "30": 0.01,
        "20": 0.02,
        "0": 0.04
    }

    # Optimal stoploss designed for the strategy
    stoploss = -0.10

    # Optimal timeframe for the strategy
    timeframe = '5m'

    # Optional order type mapping
    order_types = {
        'entry': 'limit',
        'exit': 'limit',
        'stoploss': 'limit',
        'stoploss_on_exchange': False
    }

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 20

    # Optional time in force for orders
    order_time_in_force = {
        'entry': 'gtc',
        'exit': 'gtc',
    }

    buy_params = {
        'buy_rsi': 35,
        # Intentionally not specified, so "default" is tested
        # 'buy_plusdi': 0.4
    }

    sell_params = {
        'sell_rsi': 74,
        'sell_minusdi': 0.4
    }

    buy_rsi = IntParameter([0, 50], default=30, space='buy')
    buy_plusdi = RealParameter(low=0, high=1, default=0.5, space='buy')
    sell_rsi = IntParameter(low=50, high=100, default=70, space='sell')
    sell_minusdi = DecimalParameter(low=0, high=1, default=0.5001, decimals=3, space='sell',
                                    load=False)
    protection_enabled = BooleanParameter(default=True)
    protection_cooldown_lookback = IntParameter([0, 50], default=30)

    # TODO: Can this work with protection tests? (replace HyperoptableStrategy implicitly ... )
    # @property
    # def protections(self):
    #     prot = []
    #     if self.protection_enabled.value:
    #         prot.append({
    #             "method": "CooldownPeriod",
    #             "stop_duration_candles": self.protection_cooldown_lookback.value
    #         })
    #     return prot

    bot_started = False

    def bot_start(self):
        self.bot_started = True

    def informative_pairs(self):

        return []

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

        # Momentum Indicator
        # ------------------------------------

        # ADX
        dataframe['adx'] = ta.ADX(dataframe)

        # MACD
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']

        # Minus Directional Indicator / Movement
        dataframe['minus_di'] = ta.MINUS_DI(dataframe)

        # Plus Directional Indicator / Movement
        dataframe['plus_di'] = ta.PLUS_DI(dataframe)

        # RSI
        dataframe['rsi'] = ta.RSI(dataframe)

        # Stoch fast
        stoch_fast = ta.STOCHF(dataframe)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']

        # Bollinger bands
        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']

        # EMA - Exponential Moving Average
        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)

        return dataframe

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

        dataframe.loc[
            (
                (dataframe['rsi'] < self.buy_rsi.value) &
                (dataframe['fastd'] < 35) &
                (dataframe['adx'] > 30) &
                (dataframe['plus_di'] > self.buy_plusdi.value)
            ) |
            (
                (dataframe['adx'] > 65) &
                (dataframe['plus_di'] > self.buy_plusdi.value)
            ),
            'enter_long'] = 1
        dataframe.loc[
            (
                qtpylib.crossed_below(dataframe['rsi'], self.sell_rsi.value)
            ),
            'enter_short'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (
                    (qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) |
                    (qtpylib.crossed_above(dataframe['fastd'], 70))
                ) &
                (dataframe['adx'] > 10) &
                (dataframe['minus_di'] > 0)
            ) |
            (
                (dataframe['adx'] > 70) &
                (dataframe['minus_di'] > self.sell_minusdi.value)
            ),
            'exit_long'] = 1

        dataframe.loc[
            (
                qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)
            ),
            'exit_short'] = 1

        return dataframe

    def leverage(self, pair: str, current_time: datetime, current_rate: float,
                 proposed_leverage: float, max_leverage: float, side: str,
                 **kwargs) -> float:
        # Return 3.0 in all cases.
        # Bot-logic must make sure it's an allowed leverage and eventually adjust accordingly.

        return 3.0

    def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float,
                              current_profit: float, min_stake: float, max_stake: float, **kwargs):

        if current_profit < -0.0075:
            orders = trade.select_filled_orders(trade.entry_side)
            return round(orders[0].cost, 0)

        return None


class StrategyTestV3Futures(StrategyTestV3):
    can_short = True