mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-15 12:43:56 +00:00
444 lines
18 KiB
Python
444 lines
18 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 sys
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import warnings
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from datetime import datetime, timezone
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from typing import Any
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from joblib import dump, load
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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, Config
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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.data.metrics import calculate_market_change
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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
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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.hyperopt_auto import HyperOptAuto
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from freqtrade.optimize.hyperopt_loss.hyperopt_loss_interface import IHyperOptLoss
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from freqtrade.optimize.hyperopt_tools import (
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HyperoptStateContainer,
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HyperoptTools,
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)
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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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logger = logging.getLogger(__name__)
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MAX_LOSS = 100000 # just a big enough number to be bad result in loss optimization
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class HyperOptimizer:
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"""
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HyperoptOptimizer class
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This class is sent to the hyperopt worker processes.
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"""
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def __init__(self, config: Config) -> 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.max_open_trades_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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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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)
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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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)
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self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
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self.data_pickle_file = (
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self.config["user_data_dir"] / "hyperopt_results" / "hyperopt_tickerdata.pkl"
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)
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self.market_change = 0.0
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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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def prepare_hyperopt(self) -> None:
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# Initialize spaces ...
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self.init_spaces()
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self.prepare_hyperopt_data()
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# We don't need exchange instance anymore while running hyperopt
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self.backtesting.exchange.close()
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self.backtesting.exchange._api = None
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self.backtesting.exchange._api_async = None
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self.backtesting.exchange.loop = None # type: ignore
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self.backtesting.exchange._loop_lock = None # type: ignore
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self.backtesting.exchange._cache_lock = None # type: ignore
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# self.backtesting.exchange = None # type: ignore
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self.backtesting.pairlists = None # type: ignore
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def get_strategy_name(self) -> str:
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return self.backtesting.strategy.get_strategy_name()
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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(
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self, dimensions: list[Dimension], raw_params: list[Any]
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) -> dict[str, Any]:
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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, strict=False)}
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def get_optimizer(
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self,
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cpu_count: int,
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random_state: int,
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initial_points: int,
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model_queue_size: int,
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) -> Optimizer:
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dimensions = self.dimensions
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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=random_state,
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model_queue_size=model_queue_size,
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)
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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"] = {
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str(k): v for k, v in self.custom_hyperopt.generate_roi_table(params).items()
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}
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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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if HyperoptTools.has_space(self.config, "trades"):
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result["max_open_trades"] = {
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"max_open_trades": (
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self.backtesting.strategy.max_open_trades
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if self.backtesting.strategy.max_open_trades != float("inf")
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else -1
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)
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}
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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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if not HyperoptTools.has_space(self.config, "trades"):
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result["max_open_trades"] = {"max_open_trades": strategy.max_open_trades}
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return result
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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.backtesting.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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if HyperoptTools.has_space(self.config, "trades"):
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logger.debug("Hyperopt has 'trades' space")
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self.max_open_trades_space = self.custom_hyperopt.max_open_trades_space()
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self.dimensions = (
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self.buy_space
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+ self.sell_space
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+ self.protection_space
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+ self.roi_space
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+ self.stoploss_space
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+ self.trailing_space
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+ self.max_open_trades_space
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)
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def assign_params(self, params_dict: dict[str, Any], 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]) -> dict[str, Any]:
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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 = self.custom_hyperopt.generate_roi_table(
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params_dict
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)
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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 = d[
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"trailing_stop_positive_offset"
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]
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self.backtesting.strategy.trailing_only_offset_is_reached = d[
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"trailing_only_offset_is_reached"
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]
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if HyperoptTools.has_space(self.config, "trades"):
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if self.config["stake_amount"] == "unlimited" and (
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params_dict["max_open_trades"] == -1 or params_dict["max_open_trades"] == 0
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):
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# Ignore unlimited max open trades if stake amount is unlimited
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params_dict.update({"max_open_trades": self.config["max_open_trades"]})
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updated_max_open_trades = (
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int(params_dict["max_open_trades"])
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if (params_dict["max_open_trades"] != -1 and params_dict["max_open_trades"] != 0)
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else float("inf")
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)
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self.config.update({"max_open_trades": updated_max_open_trades})
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self.backtesting.strategy.max_open_trades = updated_max_open_trades
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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, start_date=self.min_date, end_date=self.max_date
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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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{
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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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)
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return self._get_results_dict(
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bt_results, self.min_date, self.max_date, params_dict, processed=processed
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)
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def _get_results_dict(
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self,
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backtesting_results: dict[str, Any],
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min_date: datetime,
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max_date: datetime,
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params_dict: dict[str, Any],
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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,
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self.backtesting.strategy.get_strategy_name(),
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backtesting_results,
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min_date,
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max_date,
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market_change=self.market_change,
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is_hyperopt=True,
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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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)
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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(
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results=backtesting_results["results"],
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trade_count=trade_count,
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min_date=min_date,
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max_date=max_date,
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config=self.config,
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processed=processed,
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backtest_stats=strat_stats,
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)
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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 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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# This is only used to keep track of min/max date after trimming.
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# The result is NOT returned from this method, actual trimming happens in backtesting.
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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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if not self.market_change:
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self.market_change = calculate_market_change(trimmed, "close")
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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(
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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).."
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)
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# Store non-trimmed data - will be trimmed after signal generation.
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dump(preprocessed, self.data_pickle_file)
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else:
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dump(data, self.data_pickle_file)
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