freqtrade_origin/freqtrade/optimize/hyperopt_interface.py

"""
IHyperOpt interface
This module defines the interface to apply for hyperopts
"""
import logging
import math

from abc import ABC, abstractmethod
from typing import Dict, Any, Callable, List

from pandas import DataFrame
from skopt.space import Dimension, Integer, Real

from freqtrade import OperationalException
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.misc import round_dict


logger = logging.getLogger(__name__)


def _format_exception_message(method: str, space: str) -> str:
    return (f"The '{space}' space is included into the hyperoptimization "
            f"but {method}() method is not found in your "
            f"custom Hyperopt class. You should either implement this "
            f"method or remove the '{space}' space from hyperoptimization.")


class IHyperOpt(ABC):
    """
    Interface for freqtrade hyperopts
    Defines the mandatory structure must follow any custom hyperopts

    Class attributes you can use:
        ticker_interval -> int: value of the ticker interval to use for the strategy
    """
    ticker_interval: str

    @staticmethod
    @abstractmethod
    def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Populate indicators that will be used in the Buy and Sell strategy.
        :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe().
        :return: A Dataframe with all mandatory indicators for the strategies.
        """

    @staticmethod
    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
        """
        Create a buy strategy generator.
        """
        raise OperationalException(_format_exception_message('buy_strategy_generator', 'buy'))

    @staticmethod
    def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
        """
        Create a sell strategy generator.
        """
        raise OperationalException(_format_exception_message('sell_strategy_generator', 'sell'))

    @staticmethod
    def indicator_space() -> List[Dimension]:
        """
        Create an indicator space.
        """
        raise OperationalException(_format_exception_message('indicator_space', 'buy'))

    @staticmethod
    def sell_indicator_space() -> List[Dimension]:
        """
        Create a sell indicator space.
        """
        raise OperationalException(_format_exception_message('sell_indicator_space', 'sell'))

    @staticmethod
    def generate_roi_table(params: Dict) -> Dict[int, float]:
        """
        Create a ROI table.

        Generates the ROI table that will be used by Hyperopt.
        You may override it in your custom Hyperopt class.
        """
        roi_table = {}
        roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
        roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
        roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
        roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0

        return roi_table

    @staticmethod
    def roi_space() -> List[Dimension]:
        """
        Create a ROI space.

        Defines values to search for each ROI steps.

        This method implements adaptive roi hyperspace with varied
        ranges for parameters which automatically adapts to the
        ticker interval used.

        It's used by Freqtrade by default, if no custom roi_space method is defined.
        """

        # Default scaling coefficients for the roi hyperspace. Can be changed
        # to adjust resulting ranges of the ROI tables.
        # Increase if you need wider ranges in the roi hyperspace, decrease if shorter
        # ranges are needed.
        roi_t_alpha = 1.0
        roi_p_alpha = 1.0

        ticker_interval_mins = timeframe_to_minutes(IHyperOpt.ticker_interval)

        # We define here limits for the ROI space parameters automagically adapted to the
        # ticker_interval used by the bot:
        #
        # * 'roi_t' (limits for the time intervals in the ROI tables) components
        #   are scaled linearly.
        # * 'roi_p' (limits for the ROI value steps) components are scaled logarithmically.
        #
        # The scaling is designed so that it maps exactly to the legacy Freqtrade roi_space()
        # method for the 5m ticker interval.
        roi_t_scale = ticker_interval_mins / 5
        roi_p_scale = math.log1p(ticker_interval_mins) / math.log1p(5)
        roi_limits = {
            'roi_t1_min': int(10 * roi_t_scale * roi_t_alpha),
            'roi_t1_max': int(120 * roi_t_scale * roi_t_alpha),
            'roi_t2_min': int(10 * roi_t_scale * roi_t_alpha),
            'roi_t2_max': int(60 * roi_t_scale * roi_t_alpha),
            'roi_t3_min': int(10 * roi_t_scale * roi_t_alpha),
            'roi_t3_max': int(40 * roi_t_scale * roi_t_alpha),
            'roi_p1_min': 0.01 * roi_p_scale * roi_p_alpha,
            'roi_p1_max': 0.04 * roi_p_scale * roi_p_alpha,
            'roi_p2_min': 0.01 * roi_p_scale * roi_p_alpha,
            'roi_p2_max': 0.07 * roi_p_scale * roi_p_alpha,
            'roi_p3_min': 0.01 * roi_p_scale * roi_p_alpha,
            'roi_p3_max': 0.20 * roi_p_scale * roi_p_alpha,
        }
        logger.debug(f"Using roi space limits: {roi_limits}")
        p = {
            'roi_t1': roi_limits['roi_t1_min'],
            'roi_t2': roi_limits['roi_t2_min'],
            'roi_t3': roi_limits['roi_t3_min'],
            'roi_p1': roi_limits['roi_p1_min'],
            'roi_p2': roi_limits['roi_p2_min'],
            'roi_p3': roi_limits['roi_p3_min'],
        }
        logger.info(f"Min roi table: {round_dict(IHyperOpt.generate_roi_table(p), 5)}")
        p = {
            'roi_t1': roi_limits['roi_t1_max'],
            'roi_t2': roi_limits['roi_t2_max'],
            'roi_t3': roi_limits['roi_t3_max'],
            'roi_p1': roi_limits['roi_p1_max'],
            'roi_p2': roi_limits['roi_p2_max'],
            'roi_p3': roi_limits['roi_p3_max'],
        }
        logger.info(f"Max roi table: {round_dict(IHyperOpt.generate_roi_table(p), 5)}")

        return [
            Integer(roi_limits['roi_t1_min'], roi_limits['roi_t1_max'], name='roi_t1'),
            Integer(roi_limits['roi_t2_min'], roi_limits['roi_t2_max'], name='roi_t2'),
            Integer(roi_limits['roi_t3_min'], roi_limits['roi_t3_max'], name='roi_t3'),
            Real(roi_limits['roi_p1_min'], roi_limits['roi_p1_max'], name='roi_p1'),
            Real(roi_limits['roi_p2_min'], roi_limits['roi_p2_max'], name='roi_p2'),
            Real(roi_limits['roi_p3_min'], roi_limits['roi_p3_max'], name='roi_p3'),
        ]

    @staticmethod
    def stoploss_space() -> List[Dimension]:
        """
        Create a stoploss space.

        Defines range of stoploss values to search.
        You may override it in your custom Hyperopt class.
        """
        return [
            Real(-0.35, -0.02, name='stoploss'),
        ]

    # This is needed for proper unpickling the class attribute ticker_interval
    # which is set to the actual value by the resolver.
    # Why do I still need such shamanic mantras in modern python?
    def __getstate__(self):
        state = self.__dict__.copy()
        state['ticker_interval'] = self.ticker_interval
        return state

    def __setstate__(self, state):
        self.__dict__.update(state)
        IHyperOpt.ticker_interval = state['ticker_interval']