9394559170

Committed 26 Apr 2024 06:36AM UTC coverage: 94.656% (-0.02%) from 94.674%

Build # 9394559170

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xmatthias

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Loader should be passed as kwarg for clarity

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/freqtrade/optimize/hyperopt_interface.py

"""
IHyperOpt interface
This module defines the interface to apply for hyperopt
"""
import logging
import math
from abc import ABC
from typing import Dict, List, Union

from sklearn.base import RegressorMixin
from skopt.space import Categorical, Dimension, Integer

from freqtrade.constants import Config
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.misc import round_dict
from freqtrade.optimize.space import SKDecimal
from freqtrade.strategy import IStrategy


logger = logging.getLogger(__name__)

EstimatorType = Union[RegressorMixin, str]


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

    Class attributes you can use:
        timeframe -> int: value of the timeframe to use for the strategy
    """
    timeframe: str
    strategy: IStrategy

    def __init__(self, config: Config) -> None:
        self.config = config

        # Assign timeframe to be used in hyperopt
        IHyperOpt.timeframe = str(config['timeframe'])

    def generate_estimator(self, dimensions: List[Dimension], **kwargs) -> EstimatorType:
        """
        Return base_estimator.
        Can be any of "GP", "RF", "ET", "GBRT" or an instance of a class
        inheriting from RegressorMixin (from sklearn).
        """
        return 'ET'

    def generate_roi_table(self, 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

    def roi_space(self) -> 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
        timeframe 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

        timeframe_min = timeframe_to_minutes(self.timeframe)

        # We define here limits for the ROI space parameters automagically adapted to the
        # timeframe 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 timeframe.
        roi_t_scale = timeframe_min / 5
        roi_p_scale = math.log1p(timeframe_min) / 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(self.generate_roi_table(p), 3)}")
        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(self.generate_roi_table(p), 3)}")

        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'),
            SKDecimal(roi_limits['roi_p1_min'], roi_limits['roi_p1_max'], decimals=3,
                      name='roi_p1'),
            SKDecimal(roi_limits['roi_p2_min'], roi_limits['roi_p2_max'], decimals=3,
                      name='roi_p2'),
            SKDecimal(roi_limits['roi_p3_min'], roi_limits['roi_p3_max'], decimals=3,
                      name='roi_p3'),
        ]

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

        Defines range of stoploss values to search.
        You may override it in your custom Hyperopt class.
        """
        return [
            SKDecimal(-0.35, -0.02, decimals=3, name='stoploss'),
        ]

    def generate_trailing_params(self, params: Dict) -> Dict:
        """
        Create dict with trailing stop parameters.
        """
        return {
            'trailing_stop': params['trailing_stop'],
            'trailing_stop_positive': params['trailing_stop_positive'],
            'trailing_stop_positive_offset': (params['trailing_stop_positive'] +
                                              params['trailing_stop_positive_offset_p1']),
            'trailing_only_offset_is_reached': params['trailing_only_offset_is_reached'],
        }

    def trailing_space(self) -> List[Dimension]:
        """
        Create a trailing stoploss space.

        You may override it in your custom Hyperopt class.
        """
        return [
            # It was decided to always set trailing_stop is to True if the 'trailing' hyperspace
            # is used. Otherwise hyperopt will vary other parameters that won't have effect if
            # trailing_stop is set False.
            # This parameter is included into the hyperspace dimensions rather than assigning
            # it explicitly in the code in order to have it printed in the results along with
            # other 'trailing' hyperspace parameters.
            Categorical([True], name='trailing_stop'),

            SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'),

            # 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
            # so this intermediate parameter is used as the value of the difference between
            # them. The value of the 'trailing_stop_positive_offset' is constructed in the
            # generate_trailing_params() method.
            # This is similar to the hyperspace dimensions used for constructing the ROI tables.
            SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),

            Categorical([True, False], name='trailing_only_offset_is_reached'),
        ]

    def max_open_trades_space(self) -> List[Dimension]:
        """
        Create a max open trades space.

        You may override it in your custom Hyperopt class.
        """
        return [
            Integer(-1, 10, name='max_open_trades'),
        ]

    # This is needed for proper unpickling the class attribute timeframe
    # 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['timeframe'] = self.timeframe
        return state

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

1	"""
2	IHyperOpt interface
3	This module defines the interface to apply for hyperopt
4	"""
5	import logging	1✔
6	import math	1✔
7	from abc import ABC	1✔
8	from typing import Dict, List, Union	1✔
9
10	from sklearn.base import RegressorMixin	1✔
11	from skopt.space import Categorical, Dimension, Integer	1✔
12
13	from freqtrade.constants import Config	1✔
14	from freqtrade.exchange import timeframe_to_minutes	1✔
15	from freqtrade.misc import round_dict	1✔
16	from freqtrade.optimize.space import SKDecimal	1✔
17	from freqtrade.strategy import IStrategy	1✔
18
19
20	logger = logging.getLogger(__name__)	1✔
21
22	EstimatorType = Union[RegressorMixin, str]	1✔
23
24
25	class IHyperOpt(ABC):	1✔
26	"""
27	Interface for freqtrade hyperopt
28	Defines the mandatory structure must follow any custom hyperopt
29
30	Class attributes you can use:
31	timeframe -> int: value of the timeframe to use for the strategy
32	"""
33	timeframe: str	1✔
34	strategy: IStrategy	1✔
35
36	def __init__(self, config: Config) -> None:	1✔
37	self.config = config	1✔
38
39	# Assign timeframe to be used in hyperopt
40	IHyperOpt.timeframe = str(config['timeframe'])	1✔
41
42	def generate_estimator(self, dimensions: List[Dimension], **kwargs) -> EstimatorType:	1✔
43	"""
44	Return base_estimator.
45	Can be any of "GP", "RF", "ET", "GBRT" or an instance of a class
46	inheriting from RegressorMixin (from sklearn).
47	"""
48	return 'ET'	1✔
49
50	def generate_roi_table(self, params: Dict) -> Dict[int, float]:	1✔
51	"""
52	Create a ROI table.
53
54	Generates the ROI table that will be used by Hyperopt.
55	You may override it in your custom Hyperopt class.
56	"""
57	roi_table = {}	1✔
58	roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']	1✔
59	roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']	1✔
60	roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']	1✔
61	roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0	1✔
62
63	return roi_table	1✔
64
65	def roi_space(self) -> List[Dimension]:	1✔
66	"""
67	Create a ROI space.
68
69	Defines values to search for each ROI steps.
70
71	This method implements adaptive roi hyperspace with varied
72	ranges for parameters which automatically adapts to the
73	timeframe used.
74
75	It's used by Freqtrade by default, if no custom roi_space method is defined.
76	"""
77
78	# Default scaling coefficients for the roi hyperspace. Can be changed
79	# to adjust resulting ranges of the ROI tables.
80	# Increase if you need wider ranges in the roi hyperspace, decrease if shorter
81	# ranges are needed.
82	roi_t_alpha = 1.0	1✔
83	roi_p_alpha = 1.0	1✔
84
85	timeframe_min = timeframe_to_minutes(self.timeframe)	1✔
86
87	# We define here limits for the ROI space parameters automagically adapted to the
88	# timeframe used by the bot:
89	#
90	# * 'roi_t' (limits for the time intervals in the ROI tables) components
91	# are scaled linearly.
92	# * 'roi_p' (limits for the ROI value steps) components are scaled logarithmically.
93	#
94	# The scaling is designed so that it maps exactly to the legacy Freqtrade roi_space()
95	# method for the 5m timeframe.
96	roi_t_scale = timeframe_min / 5	1✔
97	roi_p_scale = math.log1p(timeframe_min) / math.log1p(5)	1✔
98	roi_limits = {	1✔
99	'roi_t1_min': int(10 * roi_t_scale * roi_t_alpha),
100	'roi_t1_max': int(120 * roi_t_scale * roi_t_alpha),
101	'roi_t2_min': int(10 * roi_t_scale * roi_t_alpha),
102	'roi_t2_max': int(60 * roi_t_scale * roi_t_alpha),
103	'roi_t3_min': int(10 * roi_t_scale * roi_t_alpha),
104	'roi_t3_max': int(40 * roi_t_scale * roi_t_alpha),
105	'roi_p1_min': 0.01 * roi_p_scale * roi_p_alpha,
106	'roi_p1_max': 0.04 * roi_p_scale * roi_p_alpha,
107	'roi_p2_min': 0.01 * roi_p_scale * roi_p_alpha,
108	'roi_p2_max': 0.07 * roi_p_scale * roi_p_alpha,
109	'roi_p3_min': 0.01 * roi_p_scale * roi_p_alpha,
110	'roi_p3_max': 0.20 * roi_p_scale * roi_p_alpha,
111	}
112	logger.debug(f"Using roi space limits: {roi_limits}")	1✔
113	p = {	1✔
114	'roi_t1': roi_limits['roi_t1_min'],
115	'roi_t2': roi_limits['roi_t2_min'],
116	'roi_t3': roi_limits['roi_t3_min'],
117	'roi_p1': roi_limits['roi_p1_min'],
118	'roi_p2': roi_limits['roi_p2_min'],
119	'roi_p3': roi_limits['roi_p3_min'],
120	}
121	logger.info(f"Min roi table: {round_dict(self.generate_roi_table(p), 3)}")	1✔
122	p = {	1✔
123	'roi_t1': roi_limits['roi_t1_max'],
124	'roi_t2': roi_limits['roi_t2_max'],
125	'roi_t3': roi_limits['roi_t3_max'],
126	'roi_p1': roi_limits['roi_p1_max'],
127	'roi_p2': roi_limits['roi_p2_max'],
128	'roi_p3': roi_limits['roi_p3_max'],
129	}
130	logger.info(f"Max roi table: {round_dict(self.generate_roi_table(p), 3)}")	1✔
131
132	return [	1✔
133	Integer(roi_limits['roi_t1_min'], roi_limits['roi_t1_max'], name='roi_t1'),
134	Integer(roi_limits['roi_t2_min'], roi_limits['roi_t2_max'], name='roi_t2'),
135	Integer(roi_limits['roi_t3_min'], roi_limits['roi_t3_max'], name='roi_t3'),
136	SKDecimal(roi_limits['roi_p1_min'], roi_limits['roi_p1_max'], decimals=3,
137	name='roi_p1'),
138	SKDecimal(roi_limits['roi_p2_min'], roi_limits['roi_p2_max'], decimals=3,
139	name='roi_p2'),
140	SKDecimal(roi_limits['roi_p3_min'], roi_limits['roi_p3_max'], decimals=3,
141	name='roi_p3'),
142	]
143
144	def stoploss_space(self) -> List[Dimension]:	1✔
145	"""
146	Create a stoploss space.
147
148	Defines range of stoploss values to search.
149	You may override it in your custom Hyperopt class.
150	"""
151	return [	1✔
152	SKDecimal(-0.35, -0.02, decimals=3, name='stoploss'),
153	]
154
155	def generate_trailing_params(self, params: Dict) -> Dict:	1✔
156	"""
157	Create dict with trailing stop parameters.
158	"""
159	return {	1✔
160	'trailing_stop': params['trailing_stop'],
161	'trailing_stop_positive': params['trailing_stop_positive'],
162	'trailing_stop_positive_offset': (params['trailing_stop_positive'] +
163	params['trailing_stop_positive_offset_p1']),
164	'trailing_only_offset_is_reached': params['trailing_only_offset_is_reached'],
165	}
166
167	def trailing_space(self) -> List[Dimension]:	1✔
168	"""
169	Create a trailing stoploss space.
170
171	You may override it in your custom Hyperopt class.
172	"""
173	return [	1✔
174	# It was decided to always set trailing_stop is to True if the 'trailing' hyperspace
175	# is used. Otherwise hyperopt will vary other parameters that won't have effect if
176	# trailing_stop is set False.
177	# This parameter is included into the hyperspace dimensions rather than assigning
178	# it explicitly in the code in order to have it printed in the results along with
179	# other 'trailing' hyperspace parameters.
180	Categorical([True], name='trailing_stop'),
181
182	SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'),
183
184	# 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
185	# so this intermediate parameter is used as the value of the difference between
186	# them. The value of the 'trailing_stop_positive_offset' is constructed in the
187	# generate_trailing_params() method.
188	# This is similar to the hyperspace dimensions used for constructing the ROI tables.
189	SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),
190
191	Categorical([True, False], name='trailing_only_offset_is_reached'),
192	]
193
194	def max_open_trades_space(self) -> List[Dimension]:	1✔
195	"""
196	Create a max open trades space.
197
198	You may override it in your custom Hyperopt class.
199	"""
200	return [	1✔
201	Integer(-1, 10, name='max_open_trades'),
202	]
203
204	# This is needed for proper unpickling the class attribute timeframe
205	# which is set to the actual value by the resolver.
206	# Why do I still need such shamanic mantras in modern python?
207	def __getstate__(self):	1✔
208	state = self.__dict__.copy()	1✔
209	state['timeframe'] = self.timeframe	1✔
210	return state	1✔
211
212	def __setstate__(self, state):	1✔
213	self.__dict__.update(state)	×
214	IHyperOpt.timeframe = state['timeframe']	×

freqtrade / freqtrade / 9394559170

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