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adaptive roi_space
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hroff-1902
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c0784b7c33
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5b9711c002
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@ -23,7 +23,8 @@ from skopt.space import Dimension
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from freqtrade.configuration import Arguments
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from freqtrade.data.history import load_data, get_timeframe
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from freqtrade.optimize.backtesting import Backtesting
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# Import IHyperOptLoss to allow users import from this file
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# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
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from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F4
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from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F4
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from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver, HyperOptLossResolver
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@ -2,6 +2,8 @@
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IHyperOpt interface
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This module defines the interface to apply for hyperopts
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"""
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import logging
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import math
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from abc import ABC, abstractmethod
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from typing import Dict, Any, Callable, List
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@ -9,15 +11,18 @@ from typing import Dict, Any, Callable, List
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from pandas import DataFrame
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from skopt.space import Dimension, Integer, Real
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from freqtrade.exchange import timeframe_to_minutes
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logger = logging.getLogger(__name__)
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class IHyperOpt(ABC):
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"""
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Interface for freqtrade hyperopts
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Defines the mandatory structure must follow any custom strategies
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Defines the mandatory structure must follow any custom hyperopts
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Attributes you can use:
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minimal_roi -> Dict: Minimal ROI designed for the strategy
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stoploss -> float: optimal stoploss designed for the strategy
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Class attributes you can use:
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ticker_interval -> int: value of the ticker interval to use for the strategy
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"""
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ticker_interval: str
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@ -75,6 +80,83 @@ class IHyperOpt(ABC):
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return roi_table
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@staticmethod
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def roi_space() -> List[Dimension]:
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"""
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Create a ROI space.
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Defines values to search for each ROI steps.
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This method implements adaptive roi hyperspace with varied
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ranges for parameters which automatically adapts to the
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ticker interval used.
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It's used by Freqtrade by default, if no custom roi_space method is defined.
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"""
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# Default scaling coefficients for the roi hyperspace. Can be changed
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# to adjust resulting ranges of the ROI tables.
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# Increase if you need wider ranges in the roi hyperspace, decrease if shorter
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# ranges are needed.
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roi_t_alpha = 1.0
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roi_p_alpha = 1.0
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ticker_interval_mins = timeframe_to_minutes(IHyperOpt.ticker_interval)
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# We define here limits for the ROI space parameters automagically adapted to the
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# ticker_interval used by the bot:
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#
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# * 'roi_t' (limits for the time intervals in the ROI tables) components
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# are scaled linearly.
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# * 'roi_p' (limits for the ROI value steps) components are scaled logarithmically.
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#
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# The scaling is designed so that it maps exactly to the legacy Freqtrade roi_space()
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# method for the 5m ticker interval.
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roi_t_scale = ticker_interval_mins / 5
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roi_p_scale = math.log1p(ticker_interval_mins) / math.log1p(5)
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roi_limits = {
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'roi_t1_min': int(10 * roi_t_scale * roi_t_alpha),
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'roi_t1_max': int(120 * roi_t_scale * roi_t_alpha),
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'roi_t2_min': int(10 * roi_t_scale * roi_t_alpha),
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'roi_t2_max': int(60 * roi_t_scale * roi_t_alpha),
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'roi_t3_min': int(10 * roi_t_scale * roi_t_alpha),
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'roi_t3_max': int(40 * roi_t_scale * roi_t_alpha),
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'roi_p1_min': 0.01 * roi_p_scale * roi_p_alpha,
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'roi_p1_max': 0.04 * roi_p_scale * roi_p_alpha,
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'roi_p2_min': 0.01 * roi_p_scale * roi_p_alpha,
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'roi_p2_max': 0.07 * roi_p_scale * roi_p_alpha,
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'roi_p3_min': 0.01 * roi_p_scale * roi_p_alpha,
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'roi_p3_max': 0.20 * roi_p_scale * roi_p_alpha,
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}
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logger.debug(f"Using roi space limits: {roi_limits}")
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p = {
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'roi_t1': roi_limits['roi_t1_min'],
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'roi_t2': roi_limits['roi_t2_min'],
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'roi_t3': roi_limits['roi_t3_min'],
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'roi_p1': roi_limits['roi_p1_min'],
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'roi_p2': roi_limits['roi_p2_min'],
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'roi_p3': roi_limits['roi_p3_min'],
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}
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logger.info(f"Min roi table: {IHyperOpt.generate_roi_table(p)}")
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p = {
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'roi_t1': roi_limits['roi_t1_max'],
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'roi_t2': roi_limits['roi_t2_max'],
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'roi_t3': roi_limits['roi_t3_max'],
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'roi_p1': roi_limits['roi_p1_max'],
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'roi_p2': roi_limits['roi_p2_max'],
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'roi_p3': roi_limits['roi_p3_max'],
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}
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logger.info(f"Max roi table: {IHyperOpt.generate_roi_table(p)}")
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return [
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Integer(roi_limits['roi_t1_min'], roi_limits['roi_t1_max'], name='roi_t1'),
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Integer(roi_limits['roi_t2_min'], roi_limits['roi_t2_max'], name='roi_t2'),
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Integer(roi_limits['roi_t3_min'], roi_limits['roi_t3_max'], name='roi_t3'),
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Real(roi_limits['roi_p1_min'], roi_limits['roi_p1_max'], name='roi_p1'),
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Real(roi_limits['roi_p2_min'], roi_limits['roi_p2_max'], name='roi_p2'),
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Real(roi_limits['roi_p3_min'], roi_limits['roi_p3_max'], name='roi_p3'),
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]
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@staticmethod
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def stoploss_space() -> List[Dimension]:
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"""
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@ -87,19 +169,14 @@ class IHyperOpt(ABC):
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Real(-0.5, -0.02, name='stoploss'),
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]
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@staticmethod
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def roi_space() -> List[Dimension]:
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"""
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Create a ROI space.
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# This is needed for proper unpickling the class attribute ticker_interval
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# which is set to the actual value by the resolver.
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# Why do I still need such shamanic mantras in modern python?
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def __getstate__(self):
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state = self.__dict__.copy()
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state['ticker_interval'] = self.ticker_interval
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return state
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Defines values to search for each ROI steps.
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You may override it in your custom Hyperopt class.
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"""
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return [
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Integer(10, 120, name='roi_t1'),
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Integer(10, 60, name='roi_t2'),
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Integer(10, 40, name='roi_t3'),
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Real(0.01, 0.04, name='roi_p1'),
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Real(0.01, 0.07, name='roi_p2'),
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Real(0.01, 0.20, name='roi_p3'),
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]
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def __setstate__(self, state):
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self.__dict__.update(state)
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IHyperOpt.ticker_interval = state['ticker_interval']
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@ -34,7 +34,7 @@ class HyperOptResolver(IResolver):
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self.hyperopt = self._load_hyperopt(hyperopt_name, extra_dir=config.get('hyperopt_path'))
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# Assign ticker_interval to be used in hyperopt
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self.hyperopt.__class__.ticker_interval = str(config['ticker_interval'])
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IHyperOpt.ticker_interval = str(config['ticker_interval'])
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if not hasattr(self.hyperopt, 'populate_buy_trend'):
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logger.warning("Custom Hyperopt does not provide populate_buy_trend. "
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