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https://github.com/freqtrade/freqtrade.git
synced 2025-11-29 08:33:07 +00:00
change SKDecimal low/high to be rounded by decimals
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@@ -330,7 +330,6 @@ class Hyperopt:
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self.config,
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self.hyperopter.get_strategy_name(),
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self.current_best_epoch,
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self.hyperopter.o_dimensions,
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)
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HyperoptTools.show_epoch_details(
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@@ -7,7 +7,6 @@ from typing import Any
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import numpy as np
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import rapidjson
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from optuna.distributions import BaseDistribution
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from pandas import isna, json_normalize
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from freqtrade.constants import FTHYPT_FILEVERSION, Config
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@@ -15,7 +14,6 @@ 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, round_dict, safe_value_fallback2
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from freqtrade.optimize.hyperopt_epoch_filters import hyperopt_filter_epochs
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from freqtrade.optimize.space import SKDecimal, _adjust_discrete_uniform
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logger = logging.getLogger(__name__)
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@@ -67,22 +65,11 @@ class HyperoptTools:
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params,
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strategy_name: str,
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filename: Path,
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o_dimensions: dict[str, BaseDistribution] | None = None,
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):
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"""
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Generate files
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"""
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final_params = deepcopy(params["params_not_optimized"])
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if o_dimensions:
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for key, val in params["params_details"].items():
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if isinstance(val, dict):
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for key1, val1 in val.items():
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if isinstance(o_dimensions.get(key1), SKDecimal):
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step = getattr(o_dimensions.get(key1), "step", None)
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if step:
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params["params_details"][key][key1] = _adjust_discrete_uniform(
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val1, step
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)
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final_params = deep_merge_dicts(params["params_details"], final_params)
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final_params = {
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"strategy_name": strategy_name,
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@@ -115,15 +102,12 @@ class HyperoptTools:
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config: Config,
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strategy_name: str,
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params: dict,
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o_dimensions: dict[str, BaseDistribution] | None = None,
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):
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if params.get(FTHYPT_FILEVERSION, 1) >= 2 and not config.get("disableparamexport", False):
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# Export parameters ...
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fn = HyperoptTools.get_strategy_filename(config, strategy_name)
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if fn:
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HyperoptTools.export_params(
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params, strategy_name, fn.with_suffix(".json"), o_dimensions
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)
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HyperoptTools.export_params(params, strategy_name, fn.with_suffix(".json"))
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else:
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logger.warning("Strategy not found, not exporting parameter file.")
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@@ -1,4 +1,4 @@
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from .decimalspace import SKDecimal, _adjust_discrete_uniform
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from .decimalspace import SKDecimal
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from .optunaspaces import (
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DimensionProtocol,
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ft_CategoricalDistribution,
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@@ -13,4 +13,4 @@ Categorical = ft_CategoricalDistribution
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Integer = ft_IntDistribution
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Real = ft_FloatDistribution
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__all__ = ["Categorical", "Dimension", "Integer", "Real", "SKDecimal", "_adjust_discrete_uniform"]
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__all__ = ["Categorical", "Dimension", "Integer", "Real", "SKDecimal"]
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@@ -1,4 +1,4 @@
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import decimal
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from math import log10
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from optuna.distributions import FloatDistribution
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@@ -22,32 +22,7 @@ class SKDecimal(FloatDistribution):
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self.name = name
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super().__init__(
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low=_adjust_discrete_uniform(low, self.step),
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high=_adjust_discrete_uniform_high(low, high, self.step),
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low=round(low, int(log10(1 / self.step))) if self.step < 1 else low,
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high=round(high, int(log10(1 / self.step))) if self.step < 1 else high,
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step=self.step,
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)
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def _adjust_discrete_uniform_high(low: float, high: float, step: float | None) -> float:
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if step:
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d_high = decimal.Decimal(str(high))
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d_low = decimal.Decimal(str(low))
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d_step = decimal.Decimal(str(step))
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d_r = d_high - d_low
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if d_r % d_step != decimal.Decimal("0"):
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high = float((d_r // d_step) * d_step + d_low)
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return high
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def _adjust_discrete_uniform(val: float, step: float | None) -> float:
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if step:
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d_val = decimal.Decimal(str(val))
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d_step = decimal.Decimal(str(step))
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if d_val % d_step != decimal.Decimal("0"):
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val = float((d_val // d_step) * d_step)
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return val
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