mirror of
https://github.com/freqtrade/freqtrade.git
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690 lines
28 KiB
Python
690 lines
28 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, Optional
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import rapidjson
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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 rich.console import Console
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from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN, 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, 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_output import HyperoptOutput
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from freqtrade.optimize.hyperopt_tools import (
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HyperoptStateContainer,
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HyperoptTools,
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hyperopt_serializer,
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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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from freqtrade.util import get_progress_tracker
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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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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 start a hyperopt run:
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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: 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._hyper_out: HyperoptOutput = HyperoptOutput(streaming=True)
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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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)
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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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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 = (
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self.config["user_data_dir"]
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/ "hyperopt_results"
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/ f"strategy_{strategy}_{time_now}.fthypt"
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)
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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.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.market_change = 0.0
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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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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: Config) -> 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(
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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)}
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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(
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epoch,
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f,
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default=hyperopt_serializer,
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number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN,
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)
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f.write("\n")
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self.num_epochs_saved += 1
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logger.debug(
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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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)
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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)}, 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"] = {
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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 print_results(self, results: dict[str, Any]) -> 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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self._hyper_out.add_data(
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self.config,
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[results],
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self.total_epochs,
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self.print_all,
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)
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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 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.")
|
|
else:
|
|
acq_optimizer = "auto"
|
|
|
|
logger.info(f"Using estimator {estimator}.")
|
|
return Optimizer(
|
|
dimensions,
|
|
base_estimator=estimator,
|
|
acq_optimizer=acq_optimizer,
|
|
n_initial_points=INITIAL_POINTS,
|
|
acq_optimizer_kwargs={"n_jobs": cpu_count},
|
|
random_state=self.random_state,
|
|
model_queue_size=SKOPT_MODEL_QUEUE_SIZE,
|
|
)
|
|
|
|
def run_optimizer_parallel(self, parallel: Parallel, asked: list[list]) -> list[dict[str, Any]]:
|
|
"""Start optimizer in a parallel way"""
|
|
return parallel(
|
|
delayed(wrap_non_picklable_objects(self.generate_optimizer))(v) for v in asked
|
|
)
|
|
|
|
def _set_random_state(self, random_state: Optional[int]) -> int:
|
|
return random_state or random.randint(1, 2**16 - 1) # noqa: S311
|
|
|
|
def advise_and_trim(self, data: dict[str, DataFrame]) -> dict[str, DataFrame]:
|
|
preprocessed = self.backtesting.strategy.advise_all_indicators(data)
|
|
|
|
# Trim startup period from analyzed dataframe to get correct dates for output.
|
|
# This is only used to keep track of min/max date after trimming.
|
|
# The result is NOT returned from this method, actual trimming happens in backtesting.
|
|
trimmed = trim_dataframes(preprocessed, self.timerange, self.backtesting.required_startup)
|
|
self.min_date, self.max_date = get_timerange(trimmed)
|
|
if not self.market_change:
|
|
self.market_change = calculate_market_change(trimmed, "close")
|
|
|
|
# Real trimming will happen as part of backtesting.
|
|
return preprocessed
|
|
|
|
def prepare_hyperopt_data(self) -> None:
|
|
HyperoptStateContainer.set_state(HyperoptState.DATALOAD)
|
|
data, self.timerange = self.backtesting.load_bt_data()
|
|
self.backtesting.load_bt_data_detail()
|
|
logger.info("Dataload complete. Calculating indicators")
|
|
|
|
if not self.analyze_per_epoch:
|
|
HyperoptStateContainer.set_state(HyperoptState.INDICATORS)
|
|
|
|
preprocessed = self.advise_and_trim(data)
|
|
|
|
logger.info(
|
|
f"Hyperopting with data from "
|
|
f"{self.min_date.strftime(DATETIME_PRINT_FORMAT)} "
|
|
f"up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} "
|
|
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 if i > 0 else n_points))
|
|
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 evaluate_result(self, val: dict[str, Any], current: int, is_random: bool):
|
|
"""
|
|
Evaluate results returned from generate_optimizer
|
|
"""
|
|
val["current_epoch"] = current
|
|
val["is_initial_point"] = current <= INITIAL_POINTS
|
|
|
|
logger.debug("Optimizer epoch evaluated: %s", 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
|
|
self.print_results(val)
|
|
|
|
if is_best:
|
|
self.current_best_loss = val["loss"]
|
|
self.current_best_epoch = val
|
|
|
|
self._save_result(val)
|
|
|
|
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)
|
|
|
|
try:
|
|
with Parallel(n_jobs=config_jobs) as parallel:
|
|
jobs = parallel._effective_n_jobs()
|
|
logger.info(f"Effective number of parallel workers used: {jobs}")
|
|
console = Console(
|
|
color_system="auto" if self.print_colorized else None,
|
|
)
|
|
|
|
# Define progressbar
|
|
with get_progress_tracker(
|
|
console=console,
|
|
cust_callables=[self._hyper_out],
|
|
) as pbar:
|
|
task = pbar.add_task("Epochs", total=self.total_epochs)
|
|
|
|
start = 0
|
|
|
|
if self.analyze_per_epoch:
|
|
# First analysis not in parallel mode when using --analyze-per-epoch.
|
|
# This allows dataprovider to load it's informative cache.
|
|
asked, is_random = self.get_asked_points(n_points=1)
|
|
f_val0 = self.generate_optimizer(asked[0])
|
|
self.opt.tell(asked, [f_val0["loss"]])
|
|
self.evaluate_result(f_val0, 1, is_random[0])
|
|
pbar.update(task, advance=1)
|
|
start += 1
|
|
|
|
evals = ceil((self.total_epochs - start) / 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 - start)
|
|
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)
|
|
self.opt.tell(asked, [v["loss"] for v in f_val])
|
|
|
|
for j, val in enumerate(f_val):
|
|
# Use human-friendly indexes here (starting from 1)
|
|
current = i * jobs + j + 1 + start
|
|
|
|
self.evaluate_result(val, current, is_random[j])
|
|
pbar.update(task, advance=1)
|
|
|
|
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
|
|
)
|
|
elif self.num_epochs_saved > 0:
|
|
print(
|
|
f"No good result found for given optimization function in {self.num_epochs_saved} "
|
|
f"{plural(self.num_epochs_saved, 'epoch')}."
|
|
)
|
|
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.")
|