612 lines
26 KiB
Python
612 lines
26 KiB
Python
# pragma pylint: disable=too-many-instance-attributes, pointless-string-statement
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"""
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This module contains the hyperopt logic
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"""
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import logging
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import random
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import sys
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import warnings
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from datetime import datetime, timezone
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from math import ceil
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Tuple
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import progressbar
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import rapidjson
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from colorama import Fore, Style
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from colorama import init as colorama_init
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from joblib import Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_objects
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from joblib.externals import cloudpickle
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from pandas import DataFrame
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from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN
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from freqtrade.data.converter import trim_dataframes
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from freqtrade.data.history import get_timerange
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from freqtrade.enums import HyperoptState
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from freqtrade.exceptions import OperationalException
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from freqtrade.misc import deep_merge_dicts, file_dump_json, plural
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from freqtrade.optimize.backtesting import Backtesting
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# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
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from freqtrade.optimize.hyperopt_auto import HyperOptAuto
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from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
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from freqtrade.optimize.hyperopt_tools import (HyperoptStateContainer, HyperoptTools,
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hyperopt_serializer)
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from freqtrade.optimize.optimize_reports import generate_strategy_stats
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from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
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# Suppress scikit-learn FutureWarnings from skopt
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", category=FutureWarning)
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from skopt import Optimizer
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from skopt.space import Dimension
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progressbar.streams.wrap_stderr()
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progressbar.streams.wrap_stdout()
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logger = logging.getLogger(__name__)
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INITIAL_POINTS = 30
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# Keep no more than SKOPT_MODEL_QUEUE_SIZE models
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# in the skopt model queue, to optimize memory consumption
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SKOPT_MODEL_QUEUE_SIZE = 10
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MAX_LOSS = 100000 # just a big enough number to be bad result in loss optimization
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class Hyperopt:
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"""
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Hyperopt class, this class contains all the logic to run a hyperopt simulation
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To run a backtest:
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hyperopt = Hyperopt(config)
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hyperopt.start()
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"""
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def __init__(self, config: Dict[str, Any]) -> None:
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self.buy_space: List[Dimension] = []
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self.sell_space: List[Dimension] = []
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self.protection_space: List[Dimension] = []
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self.roi_space: List[Dimension] = []
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self.stoploss_space: List[Dimension] = []
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self.trailing_space: List[Dimension] = []
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self.dimensions: List[Dimension] = []
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self.config = config
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self.min_date: datetime
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self.max_date: datetime
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self.backtesting = Backtesting(self.config)
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self.pairlist = self.backtesting.pairlists.whitelist
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self.custom_hyperopt: HyperOptAuto
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self.analyze_per_epoch = self.config.get('analyze_per_epoch', False)
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HyperoptStateContainer.set_state(HyperoptState.STARTUP)
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if not self.config.get('hyperopt'):
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self.custom_hyperopt = HyperOptAuto(self.config)
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else:
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raise OperationalException(
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"Using separate Hyperopt files has been removed in 2021.9. Please convert "
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"your existing Hyperopt file to the new Hyperoptable strategy interface")
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self.backtesting._set_strategy(self.backtesting.strategylist[0])
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self.custom_hyperopt.strategy = self.backtesting.strategy
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self.hyperopt_pickle_magic(self.backtesting.strategy.__class__.__bases__)
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self.custom_hyperoptloss: IHyperOptLoss = HyperOptLossResolver.load_hyperoptloss(
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self.config)
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self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
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time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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strategy = str(self.config['strategy'])
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self.results_file: Path = (self.config['user_data_dir'] / 'hyperopt_results' /
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f'strategy_{strategy}_{time_now}.fthypt')
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self.data_pickle_file = (self.config['user_data_dir'] /
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'hyperopt_results' / 'hyperopt_tickerdata.pkl')
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self.total_epochs = config.get('epochs', 0)
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self.current_best_loss = 100
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self.clean_hyperopt()
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self.num_epochs_saved = 0
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self.current_best_epoch: Optional[Dict[str, Any]] = None
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# Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
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if self.config.get('use_max_market_positions', True):
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self.max_open_trades = self.config['max_open_trades']
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else:
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logger.debug('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
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self.max_open_trades = 0
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self.position_stacking = self.config.get('position_stacking', False)
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if HyperoptTools.has_space(self.config, 'sell'):
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# Make sure use_exit_signal is enabled
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self.config['use_exit_signal'] = True
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self.print_all = self.config.get('print_all', False)
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self.hyperopt_table_header = 0
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self.print_colorized = self.config.get('print_colorized', False)
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self.print_json = self.config.get('print_json', False)
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@staticmethod
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def get_lock_filename(config: Dict[str, Any]) -> str:
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return str(config['user_data_dir'] / 'hyperopt.lock')
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def clean_hyperopt(self) -> None:
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"""
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Remove hyperopt pickle files to restart hyperopt.
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"""
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for f in [self.data_pickle_file, self.results_file]:
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p = Path(f)
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if p.is_file():
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logger.info(f"Removing `{p}`.")
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p.unlink()
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def hyperopt_pickle_magic(self, bases) -> None:
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"""
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Hyperopt magic to allow strategy inheritance across files.
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For this to properly work, we need to register the module of the imported class
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to pickle as value.
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"""
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for modules in bases:
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if modules.__name__ != 'IStrategy':
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cloudpickle.register_pickle_by_value(sys.modules[modules.__module__])
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self.hyperopt_pickle_magic(modules.__bases__)
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def _get_params_dict(self, dimensions: List[Dimension], raw_params: List[Any]) -> Dict:
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# Ensure the number of dimensions match
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# the number of parameters in the list.
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if len(raw_params) != len(dimensions):
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raise ValueError('Mismatch in number of search-space dimensions.')
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# Return a dict where the keys are the names of the dimensions
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# and the values are taken from the list of parameters.
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return {d.name: v for d, v in zip(dimensions, raw_params)}
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def _save_result(self, epoch: Dict) -> None:
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"""
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Save hyperopt results to file
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Store one line per epoch.
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While not a valid json object - this allows appending easily.
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:param epoch: result dictionary for this epoch.
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"""
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epoch[FTHYPT_FILEVERSION] = 2
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with self.results_file.open('a') as f:
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rapidjson.dump(epoch, f, default=hyperopt_serializer,
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number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN)
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f.write("\n")
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self.num_epochs_saved += 1
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logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
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f"saved to '{self.results_file}'.")
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# Store hyperopt filename
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latest_filename = Path.joinpath(self.results_file.parent, LAST_BT_RESULT_FN)
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file_dump_json(latest_filename, {'latest_hyperopt': str(self.results_file.name)},
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log=False)
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def _get_params_details(self, params: Dict) -> Dict:
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"""
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Return the params for each space
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"""
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result: Dict = {}
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if HyperoptTools.has_space(self.config, 'buy'):
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result['buy'] = {p.name: params.get(p.name) for p in self.buy_space}
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if HyperoptTools.has_space(self.config, 'sell'):
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result['sell'] = {p.name: params.get(p.name) for p in self.sell_space}
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if HyperoptTools.has_space(self.config, 'protection'):
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result['protection'] = {p.name: params.get(p.name) for p in self.protection_space}
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if HyperoptTools.has_space(self.config, 'roi'):
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result['roi'] = {str(k): v for k, v in
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self.custom_hyperopt.generate_roi_table(params).items()}
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if HyperoptTools.has_space(self.config, 'stoploss'):
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result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space}
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if HyperoptTools.has_space(self.config, 'trailing'):
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result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
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return result
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def _get_no_optimize_details(self) -> Dict[str, Any]:
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"""
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Get non-optimized parameters
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"""
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result: Dict[str, Any] = {}
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strategy = self.backtesting.strategy
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if not HyperoptTools.has_space(self.config, 'roi'):
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result['roi'] = {str(k): v for k, v in strategy.minimal_roi.items()}
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if not HyperoptTools.has_space(self.config, 'stoploss'):
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result['stoploss'] = {'stoploss': strategy.stoploss}
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if not HyperoptTools.has_space(self.config, 'trailing'):
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result['trailing'] = {
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'trailing_stop': strategy.trailing_stop,
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'trailing_stop_positive': strategy.trailing_stop_positive,
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'trailing_stop_positive_offset': strategy.trailing_stop_positive_offset,
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'trailing_only_offset_is_reached': strategy.trailing_only_offset_is_reached,
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}
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return result
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def print_results(self, results) -> None:
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"""
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Log results if it is better than any previous evaluation
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TODO: this should be moved to HyperoptTools too
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"""
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is_best = results['is_best']
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if self.print_all or is_best:
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print(
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HyperoptTools.get_result_table(
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self.config, results, self.total_epochs,
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self.print_all, self.print_colorized,
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self.hyperopt_table_header
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)
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)
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self.hyperopt_table_header = 2
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def init_spaces(self):
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"""
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Assign the dimensions in the hyperoptimization space.
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"""
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if HyperoptTools.has_space(self.config, 'protection'):
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# Protections can only be optimized when using the Parameter interface
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logger.debug("Hyperopt has 'protection' space")
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# Enable Protections if protection space is selected.
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self.config['enable_protections'] = True
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self.protection_space = self.custom_hyperopt.protection_space()
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if HyperoptTools.has_space(self.config, 'buy'):
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logger.debug("Hyperopt has 'buy' space")
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self.buy_space = self.custom_hyperopt.buy_indicator_space()
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if HyperoptTools.has_space(self.config, 'sell'):
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logger.debug("Hyperopt has 'sell' space")
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self.sell_space = self.custom_hyperopt.sell_indicator_space()
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if HyperoptTools.has_space(self.config, 'roi'):
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logger.debug("Hyperopt has 'roi' space")
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self.roi_space = self.custom_hyperopt.roi_space()
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if HyperoptTools.has_space(self.config, 'stoploss'):
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logger.debug("Hyperopt has 'stoploss' space")
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self.stoploss_space = self.custom_hyperopt.stoploss_space()
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if HyperoptTools.has_space(self.config, 'trailing'):
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logger.debug("Hyperopt has 'trailing' space")
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self.trailing_space = self.custom_hyperopt.trailing_space()
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self.dimensions = (self.buy_space + self.sell_space + self.protection_space
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+ self.roi_space + self.stoploss_space + self.trailing_space)
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def assign_params(self, params_dict: Dict, category: str) -> None:
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"""
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Assign hyperoptable parameters
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"""
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for attr_name, attr in self.backtesting.strategy.enumerate_parameters(category):
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if attr.optimize:
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# noinspection PyProtectedMember
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attr.value = params_dict[attr_name]
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def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
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"""
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Used Optimize function.
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Called once per epoch to optimize whatever is configured.
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Keep this function as optimized as possible!
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"""
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HyperoptStateContainer.set_state(HyperoptState.OPTIMIZE)
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backtest_start_time = datetime.now(timezone.utc)
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params_dict = self._get_params_dict(self.dimensions, raw_params)
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# Apply parameters
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if HyperoptTools.has_space(self.config, 'buy'):
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self.assign_params(params_dict, 'buy')
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if HyperoptTools.has_space(self.config, 'sell'):
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self.assign_params(params_dict, 'sell')
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if HyperoptTools.has_space(self.config, 'protection'):
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self.assign_params(params_dict, 'protection')
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if HyperoptTools.has_space(self.config, 'roi'):
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self.backtesting.strategy.minimal_roi = (
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self.custom_hyperopt.generate_roi_table(params_dict))
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if HyperoptTools.has_space(self.config, 'stoploss'):
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self.backtesting.strategy.stoploss = params_dict['stoploss']
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if HyperoptTools.has_space(self.config, 'trailing'):
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d = self.custom_hyperopt.generate_trailing_params(params_dict)
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self.backtesting.strategy.trailing_stop = d['trailing_stop']
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self.backtesting.strategy.trailing_stop_positive = d['trailing_stop_positive']
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self.backtesting.strategy.trailing_stop_positive_offset = \
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d['trailing_stop_positive_offset']
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self.backtesting.strategy.trailing_only_offset_is_reached = \
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d['trailing_only_offset_is_reached']
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with self.data_pickle_file.open('rb') as f:
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processed = load(f, mmap_mode='r')
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if self.analyze_per_epoch:
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# Data is not yet analyzed, rerun populate_indicators.
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processed = self.advise_and_trim(processed)
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bt_results = self.backtesting.backtest(
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processed=processed,
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start_date=self.min_date,
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end_date=self.max_date,
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max_open_trades=self.max_open_trades,
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position_stacking=self.position_stacking,
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enable_protections=self.config.get('enable_protections', False),
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)
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backtest_end_time = datetime.now(timezone.utc)
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bt_results.update({
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'backtest_start_time': int(backtest_start_time.timestamp()),
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'backtest_end_time': int(backtest_end_time.timestamp()),
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})
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return self._get_results_dict(bt_results, self.min_date, self.max_date,
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params_dict,
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processed=processed)
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def _get_results_dict(self, backtesting_results, min_date, max_date,
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params_dict, processed: Dict[str, DataFrame]
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) -> Dict[str, Any]:
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params_details = self._get_params_details(params_dict)
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strat_stats = generate_strategy_stats(
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self.pairlist, self.backtesting.strategy.get_strategy_name(),
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backtesting_results, min_date, max_date, market_change=0
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)
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results_explanation = HyperoptTools.format_results_explanation_string(
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strat_stats, self.config['stake_currency'])
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not_optimized = self.backtesting.strategy.get_no_optimize_params()
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not_optimized = deep_merge_dicts(not_optimized, self._get_no_optimize_details())
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trade_count = strat_stats['total_trades']
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total_profit = strat_stats['profit_total']
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# If this evaluation contains too short amount of trades to be
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# interesting -- consider it as 'bad' (assigned max. loss value)
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# in order to cast this hyperspace point away from optimization
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# path. We do not want to optimize 'hodl' strategies.
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loss: float = MAX_LOSS
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if trade_count >= self.config['hyperopt_min_trades']:
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loss = self.calculate_loss(results=backtesting_results['results'],
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trade_count=trade_count,
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min_date=min_date, max_date=max_date,
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config=self.config, processed=processed,
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backtest_stats=strat_stats)
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return {
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'loss': loss,
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'params_dict': params_dict,
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'params_details': params_details,
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'params_not_optimized': not_optimized,
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'results_metrics': strat_stats,
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'results_explanation': results_explanation,
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'total_profit': total_profit,
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}
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def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
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estimator = self.custom_hyperopt.generate_estimator(dimensions=dimensions)
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acq_optimizer = "sampling"
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if isinstance(estimator, str):
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if estimator not in ("GP", "RF", "ET", "GBRT"):
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raise OperationalException(f"Estimator {estimator} not supported.")
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else:
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acq_optimizer = "auto"
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logger.info(f"Using estimator {estimator}.")
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return Optimizer(
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dimensions,
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base_estimator=estimator,
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acq_optimizer=acq_optimizer,
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n_initial_points=INITIAL_POINTS,
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acq_optimizer_kwargs={'n_jobs': cpu_count},
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random_state=self.random_state,
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model_queue_size=SKOPT_MODEL_QUEUE_SIZE,
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)
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def run_optimizer_parallel(self, parallel: Parallel, asked: List[List], i: int) -> List:
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return parallel(delayed(
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wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
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def _set_random_state(self, random_state: Optional[int]) -> int:
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return random_state or random.randint(1, 2**16 - 1)
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def advise_and_trim(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
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preprocessed = self.backtesting.strategy.advise_all_indicators(data)
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# Trim startup period from analyzed dataframe to get correct dates for output.
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trimmed = trim_dataframes(preprocessed, self.timerange, self.backtesting.required_startup)
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self.min_date, self.max_date = get_timerange(trimmed)
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# Real trimming will happen as part of backtesting.
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return preprocessed
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def prepare_hyperopt_data(self) -> None:
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HyperoptStateContainer.set_state(HyperoptState.DATALOAD)
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data, self.timerange = self.backtesting.load_bt_data()
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self.backtesting.load_bt_data_detail()
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logger.info("Dataload complete. Calculating indicators")
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if not self.analyze_per_epoch:
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HyperoptStateContainer.set_state(HyperoptState.INDICATORS)
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preprocessed = self.advise_and_trim(data)
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logger.info(f'Hyperopting with data from '
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f'{self.min_date.strftime(DATETIME_PRINT_FORMAT)} '
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f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
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f'({(self.max_date - self.min_date).days} days)..')
|
|
# Store non-trimmed data - will be trimmed after signal generation.
|
|
dump(preprocessed, self.data_pickle_file)
|
|
else:
|
|
dump(data, self.data_pickle_file)
|
|
|
|
def get_asked_points(self, n_points: int) -> Tuple[List[List[Any]], List[bool]]:
|
|
"""
|
|
Enforce points returned from `self.opt.ask` have not been already evaluated
|
|
|
|
Steps:
|
|
1. Try to get points using `self.opt.ask` first
|
|
2. Discard the points that have already been evaluated
|
|
3. Retry using `self.opt.ask` up to 3 times
|
|
4. If still some points are missing in respect to `n_points`, random sample some points
|
|
5. Repeat until at least `n_points` points in the `asked_non_tried` list
|
|
6. Return a list with length truncated at `n_points`
|
|
"""
|
|
def unique_list(a_list):
|
|
new_list = []
|
|
for item in a_list:
|
|
if item not in new_list:
|
|
new_list.append(item)
|
|
return new_list
|
|
i = 0
|
|
asked_non_tried: List[List[Any]] = []
|
|
is_random_non_tried: List[bool] = []
|
|
while i < 5 and len(asked_non_tried) < n_points:
|
|
if i < 3:
|
|
self.opt.cache_ = {}
|
|
asked = unique_list(self.opt.ask(n_points=n_points * 5))
|
|
is_random = [False for _ in range(len(asked))]
|
|
else:
|
|
asked = unique_list(self.opt.space.rvs(n_samples=n_points * 5))
|
|
is_random = [True for _ in range(len(asked))]
|
|
is_random_non_tried += [rand for x, rand in zip(asked, is_random)
|
|
if x not in self.opt.Xi
|
|
and x not in asked_non_tried]
|
|
asked_non_tried += [x for x in asked
|
|
if x not in self.opt.Xi
|
|
and x not in asked_non_tried]
|
|
i += 1
|
|
|
|
if asked_non_tried:
|
|
return (
|
|
asked_non_tried[:min(len(asked_non_tried), n_points)],
|
|
is_random_non_tried[:min(len(asked_non_tried), n_points)]
|
|
)
|
|
else:
|
|
return self.opt.ask(n_points=n_points), [False for _ in range(n_points)]
|
|
|
|
def get_progressbar_widgets(self):
|
|
if self.print_colorized:
|
|
widgets = [
|
|
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
|
|
' (', progressbar.Percentage(), ')] ',
|
|
progressbar.Bar(marker=progressbar.AnimatedMarker(
|
|
fill='\N{FULL BLOCK}',
|
|
fill_wrap=Fore.GREEN + '{}' + Fore.RESET,
|
|
marker_wrap=Style.BRIGHT + '{}' + Style.RESET_ALL,
|
|
)),
|
|
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
|
|
]
|
|
else:
|
|
widgets = [
|
|
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
|
|
' (', progressbar.Percentage(), ')] ',
|
|
progressbar.Bar(marker=progressbar.AnimatedMarker(
|
|
fill='\N{FULL BLOCK}',
|
|
)),
|
|
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
|
|
]
|
|
return widgets
|
|
|
|
def start(self) -> None:
|
|
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state'))
|
|
logger.info(f"Using optimizer random state: {self.random_state}")
|
|
self.hyperopt_table_header = -1
|
|
# Initialize spaces ...
|
|
self.init_spaces()
|
|
|
|
self.prepare_hyperopt_data()
|
|
|
|
# We don't need exchange instance anymore while running hyperopt
|
|
self.backtesting.exchange.close()
|
|
self.backtesting.exchange._api = None
|
|
self.backtesting.exchange._api_async = None
|
|
self.backtesting.exchange.loop = None # type: ignore
|
|
self.backtesting.exchange._loop_lock = None # type: ignore
|
|
self.backtesting.exchange._cache_lock = None # type: ignore
|
|
# self.backtesting.exchange = None # type: ignore
|
|
self.backtesting.pairlists = None # type: ignore
|
|
|
|
cpus = cpu_count()
|
|
logger.info(f"Found {cpus} CPU cores. Let's make them scream!")
|
|
config_jobs = self.config.get('hyperopt_jobs', -1)
|
|
logger.info(f'Number of parallel jobs set as: {config_jobs}')
|
|
|
|
self.opt = self.get_optimizer(self.dimensions, config_jobs)
|
|
|
|
if self.print_colorized:
|
|
colorama_init(autoreset=True)
|
|
|
|
try:
|
|
with Parallel(n_jobs=config_jobs) as parallel:
|
|
jobs = parallel._effective_n_jobs()
|
|
logger.info(f'Effective number of parallel workers used: {jobs}')
|
|
|
|
# Define progressbar
|
|
widgets = self.get_progressbar_widgets()
|
|
with progressbar.ProgressBar(
|
|
max_value=self.total_epochs, redirect_stdout=False, redirect_stderr=False,
|
|
widgets=widgets
|
|
) as pbar:
|
|
EVALS = ceil(self.total_epochs / jobs)
|
|
for i in range(EVALS):
|
|
# Correct the number of epochs to be processed for the last
|
|
# iteration (should not exceed self.total_epochs in total)
|
|
n_rest = (i + 1) * jobs - self.total_epochs
|
|
current_jobs = jobs - n_rest if n_rest > 0 else jobs
|
|
|
|
asked, is_random = self.get_asked_points(n_points=current_jobs)
|
|
f_val = self.run_optimizer_parallel(parallel, asked, i)
|
|
self.opt.tell(asked, [v['loss'] for v in f_val])
|
|
|
|
# Calculate progressbar outputs
|
|
for j, val in enumerate(f_val):
|
|
# Use human-friendly indexes here (starting from 1)
|
|
current = i * jobs + j + 1
|
|
val['current_epoch'] = current
|
|
val['is_initial_point'] = current <= INITIAL_POINTS
|
|
|
|
logger.debug(f"Optimizer epoch evaluated: {val}")
|
|
|
|
is_best = HyperoptTools.is_best_loss(val, self.current_best_loss)
|
|
# This value is assigned here and not in the optimization method
|
|
# to keep proper order in the list of results. That's because
|
|
# evaluations can take different time. Here they are aligned in the
|
|
# order they will be shown to the user.
|
|
val['is_best'] = is_best
|
|
val['is_random'] = is_random[j]
|
|
self.print_results(val)
|
|
|
|
if is_best:
|
|
self.current_best_loss = val['loss']
|
|
self.current_best_epoch = val
|
|
|
|
self._save_result(val)
|
|
|
|
pbar.update(current)
|
|
|
|
except KeyboardInterrupt:
|
|
print('User interrupted..')
|
|
|
|
logger.info(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
|
|
f"saved to '{self.results_file}'.")
|
|
|
|
if self.current_best_epoch:
|
|
HyperoptTools.try_export_params(
|
|
self.config,
|
|
self.backtesting.strategy.get_strategy_name(),
|
|
self.current_best_epoch)
|
|
|
|
HyperoptTools.show_epoch_details(self.current_best_epoch, self.total_epochs,
|
|
self.print_json)
|
|
else:
|
|
# This is printed when Ctrl+C is pressed quickly, before first epochs have
|
|
# a chance to be evaluated.
|
|
print("No epochs evaluated yet, no best result.")
|