352 lines
13 KiB
Python
352 lines
13 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 os
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import sys
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from operator import itemgetter
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from pathlib import Path
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from pprint import pprint
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from typing import Any, Dict, List
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from joblib import Parallel, delayed, dump, load, wrap_non_picklable_objects, cpu_count
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from pandas import DataFrame
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from skopt import Optimizer
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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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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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logger = logging.getLogger(__name__)
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INITIAL_POINTS = 30
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MAX_LOSS = 100000 # just a big enough number to be bad result in loss optimization
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TICKERDATA_PICKLE = os.path.join('user_data', 'hyperopt_tickerdata.pkl')
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TRIALSDATA_PICKLE = os.path.join('user_data', 'hyperopt_results.pickle')
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HYPEROPT_LOCKFILE = os.path.join('user_data', 'hyperopt.lock')
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class Hyperopt(Backtesting):
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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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super().__init__(config)
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self.custom_hyperopt = HyperOptResolver(self.config).hyperopt
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self.custom_hyperoptloss = HyperOptLossResolver(self.config).hyperoptloss
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self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
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self.total_epochs = config.get('epochs', 0)
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self.current_best_loss = 100
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if not self.config.get('hyperopt_continue'):
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self.clean_hyperopt()
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else:
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logger.info("Continuing on previous hyperopt results.")
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# Previous evaluations
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self.trials_file = TRIALSDATA_PICKLE
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self.trials: List = []
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# Populate functions here (hasattr is slow so should not be run during "regular" operations)
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if hasattr(self.custom_hyperopt, 'populate_buy_trend'):
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self.advise_buy = self.custom_hyperopt.populate_buy_trend # type: ignore
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if hasattr(self.custom_hyperopt, 'populate_sell_trend'):
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self.advise_sell = self.custom_hyperopt.populate_sell_trend # type: ignore
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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 self.has_space('sell'):
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# Make sure experimental is enabled
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if 'experimental' not in self.config:
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self.config['experimental'] = {}
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self.config['experimental']['use_sell_signal'] = True
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def clean_hyperopt(self):
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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 [TICKERDATA_PICKLE, TRIALSDATA_PICKLE]:
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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 get_args(self, params):
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dimensions = self.hyperopt_space()
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# Ensure the number of dimensions match
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# the number of parameters in the list x.
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if len(params) != len(dimensions):
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raise ValueError('Mismatch in number of search-space dimensions. '
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f'len(dimensions)=={len(dimensions)} and len(x)=={len(params)}')
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# Create 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 x.
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arg_dict = {dim.name: value for dim, value in zip(dimensions, params)}
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return arg_dict
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def save_trials(self) -> None:
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"""
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Save hyperopt trials to file
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"""
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if self.trials:
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logger.info('Saving %d evaluations to \'%s\'', len(self.trials), self.trials_file)
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dump(self.trials, self.trials_file)
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def read_trials(self) -> List:
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"""
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Read hyperopt trials file
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"""
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logger.info('Reading Trials from \'%s\'', self.trials_file)
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trials = load(self.trials_file)
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os.remove(self.trials_file)
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return trials
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def log_trials_result(self) -> None:
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"""
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Display Best hyperopt result
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"""
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results = sorted(self.trials, key=itemgetter('loss'))
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best_result = results[0]
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params = best_result['params']
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log_str = self.format_results_logstring(best_result)
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print(f"\nBest result:\n{log_str}\nwith values:")
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pprint(params, indent=4)
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if self.has_space('roi'):
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print("ROI table:")
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pprint(self.custom_hyperopt.generate_roi_table(params), indent=4)
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def log_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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"""
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print_all = self.config.get('print_all', False)
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if print_all or results['loss'] < self.current_best_loss:
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log_str = self.format_results_logstring(results)
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if print_all:
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print(log_str)
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else:
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print('\n' + log_str)
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else:
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print('.', end='')
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sys.stdout.flush()
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def format_results_logstring(self, results) -> str:
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# Output human-friendly index here (starting from 1)
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current = results['current_epoch'] + 1
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total = self.total_epochs
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res = results['results_explanation']
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loss = results['loss']
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self.current_best_loss = results['loss']
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log_str = f'{current:5d}/{total}: {res} Objective: {loss:.5f}'
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log_str = f'*{log_str}' if results['is_initial_point'] else f' {log_str}'
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return log_str
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def has_space(self, space: str) -> bool:
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"""
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Tell if a space value is contained in the configuration
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"""
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return any(s in self.config['spaces'] for s in [space, 'all'])
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def hyperopt_space(self) -> List[Dimension]:
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"""
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Return the space to use during Hyperopt
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"""
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spaces: List[Dimension] = []
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if self.has_space('buy'):
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logger.debug("Hyperopt has 'buy' space")
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spaces += self.custom_hyperopt.indicator_space()
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if self.has_space('sell'):
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logger.debug("Hyperopt has 'sell' space")
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spaces += self.custom_hyperopt.sell_indicator_space()
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if self.has_space('roi'):
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logger.debug("Hyperopt has 'roi' space")
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spaces += self.custom_hyperopt.roi_space()
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if self.has_space('stoploss'):
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logger.debug("Hyperopt has 'stoploss' space")
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spaces += self.custom_hyperopt.stoploss_space()
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return spaces
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def generate_optimizer(self, _params: Dict) -> Dict:
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"""
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Used Optimize function. 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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params = self.get_args(_params)
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if self.has_space('roi'):
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self.strategy.minimal_roi = self.custom_hyperopt.generate_roi_table(params)
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if self.has_space('buy'):
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self.advise_buy = self.custom_hyperopt.buy_strategy_generator(params)
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if self.has_space('sell'):
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self.advise_sell = self.custom_hyperopt.sell_strategy_generator(params)
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if self.has_space('stoploss'):
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self.strategy.stoploss = params['stoploss']
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processed = load(TICKERDATA_PICKLE)
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min_date, max_date = get_timeframe(processed)
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results = self.backtest(
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{
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'stake_amount': self.config['stake_amount'],
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'processed': processed,
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'max_open_trades': self.max_open_trades,
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'position_stacking': self.position_stacking,
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'start_date': min_date,
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'end_date': max_date,
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}
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)
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results_explanation = self.format_results(results)
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trade_count = len(results.index)
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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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if trade_count < self.config['hyperopt_min_trades']:
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return {
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'loss': MAX_LOSS,
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'params': params,
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'results_explanation': results_explanation,
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}
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loss = self.calculate_loss(results=results, trade_count=trade_count,
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min_date=min_date.datetime, max_date=max_date.datetime)
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return {
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'loss': loss,
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'params': params,
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'results_explanation': results_explanation,
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}
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def format_results(self, results: DataFrame) -> str:
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"""
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Return the formatted results explanation in a string
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"""
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trades = len(results.index)
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avg_profit = results.profit_percent.mean() * 100.0
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total_profit = results.profit_abs.sum()
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stake_cur = self.config['stake_currency']
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profit = results.profit_percent.sum() * 100.0
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duration = results.trade_duration.mean()
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return (f'{trades:6d} trades. Avg profit {avg_profit: 5.2f}%. '
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f'Total profit {total_profit: 11.8f} {stake_cur} '
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f'({profit: 7.2f}Σ%). Avg duration {duration:5.1f} mins.')
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def get_optimizer(self, cpu_count) -> Optimizer:
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return Optimizer(
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self.hyperopt_space(),
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base_estimator="ET",
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acq_optimizer="auto",
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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.config.get('hyperopt_random_state', None)
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)
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def run_optimizer_parallel(self, parallel, asked) -> List:
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return parallel(delayed(
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wrap_non_picklable_objects(self.generate_optimizer))(v) for v in asked)
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def load_previous_results(self):
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""" read trials file if we have one """
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if os.path.exists(self.trials_file) and os.path.getsize(self.trials_file) > 0:
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self.trials = self.read_trials()
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logger.info(
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'Loaded %d previous evaluations from disk.',
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len(self.trials)
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)
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def start(self) -> None:
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timerange = Arguments.parse_timerange(None if self.config.get(
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'timerange') is None else str(self.config.get('timerange')))
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data = load_data(
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datadir=Path(self.config['datadir']) if self.config.get('datadir') else None,
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pairs=self.config['exchange']['pair_whitelist'],
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ticker_interval=self.ticker_interval,
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refresh_pairs=self.config.get('refresh_pairs', False),
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exchange=self.exchange,
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timerange=timerange
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)
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if not data:
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logger.critical("No data found. Terminating.")
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return
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min_date, max_date = get_timeframe(data)
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logger.info(
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'Hyperopting with data from %s up to %s (%s days)..',
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min_date.isoformat(),
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max_date.isoformat(),
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(max_date - min_date).days
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)
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self.strategy.advise_indicators = \
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self.custom_hyperopt.populate_indicators # type: ignore
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preprocessed = self.strategy.tickerdata_to_dataframe(data)
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dump(preprocessed, TICKERDATA_PICKLE)
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# We don't need exchange instance anymore while running hyperopt
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self.exchange = None # type: ignore
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self.load_previous_results()
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cpus = cpu_count()
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logger.info(f'Found {cpus} CPU cores. Let\'s make them scream!')
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config_jobs = self.config.get('hyperopt_jobs', -1)
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logger.info(f'Number of parallel jobs set as: {config_jobs}')
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opt = self.get_optimizer(config_jobs)
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try:
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with Parallel(n_jobs=config_jobs) as parallel:
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jobs = parallel._effective_n_jobs()
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logger.info(f'Effective number of parallel workers used: {jobs}')
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EVALS = max(self.total_epochs // jobs, 1)
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for i in range(EVALS):
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asked = opt.ask(n_points=jobs)
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f_val = self.run_optimizer_parallel(parallel, asked)
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opt.tell(asked, [v['loss'] for v in f_val])
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for j in range(jobs):
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current = i * jobs + j
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val = f_val[j]
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val['current_epoch'] = current
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val['is_initial_point'] = current < INITIAL_POINTS
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self.log_results(val)
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self.trials.append(val)
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logger.debug(f"Optimizer epoch evaluated: {val}")
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except KeyboardInterrupt:
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print('User interrupted..')
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self.save_trials()
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self.log_trials_result()
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