| freqai.outlier_threshold | 0.999 | float (0,1) | Quantile threshold for predictions outlier filtering. |
| _Optuna / HPO_ | | | |
| freqai.optuna_hyperopt.enabled | true | bool | Enables HPO. |
-| freqai.optuna_hyperopt.n_jobs | CPU threads / 4 | int >= 1 | Parallel HPO workers. |
+| freqai.optuna_hyperopt.sampler | `tpe` | enum {`tpe`,`auto`} | HPO sampler algorithm. `tpe` uses TPESampler with multivariate and group, `auto` uses AutoSampler. |
| freqai.optuna_hyperopt.storage | `file` | enum {`file`,`sqlite`} | HPO storage backend. |
| freqai.optuna_hyperopt.continuous | true | bool | Continuous HPO. |
| freqai.optuna_hyperopt.warm_start | true | bool | Warm start HPO with previous best value(s). |
| freqai.optuna_hyperopt.n_startup_trials | 15 | int >= 0 | HPO startup trials. |
| freqai.optuna_hyperopt.n_trials | 50 | int >= 1 | Maximum HPO trials. |
+| freqai.optuna_hyperopt.n_jobs | CPU threads / 4 | int >= 1 | Parallel HPO workers. |
| freqai.optuna_hyperopt.timeout | 7200 | int >= 0 | HPO wall-clock timeout in seconds. |
| freqai.optuna_hyperopt.label_candles_step | 1 | int >= 1 | Step for Zigzag NATR horizon search space. |
| freqai.optuna_hyperopt.train_candles_step | 10 | int >= 1 | Step for training sets size search space. |
ARG optuna_version=4.6.0
+USER root
+RUN apt-get update \
+ && apt-get install -y --no-install-recommends git \
+ && rm -rf /var/lib/apt/lists/*
+USER ftuser
RUN pip install --user --no-cache-dir \
optuna==${optuna_version} \
- optuna-dashboard
+ optuna-dashboard \
+ optunahub \
+ -r https://hub.optuna.org/samplers/auto_sampler/requirements.txt
LABEL org.opencontainers.image.source="freqai-strategies" \
org.opencontainers.image.title="freqtrade-reforcexy" \
import matplotlib.pyplot as plt
import matplotlib.transforms as mtransforms
import numpy as np
+import optunahub
import torch as th
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
from numpy.typing import NDArray
from optuna import Trial, TrialPruned, create_study, delete_study
from optuna.exceptions import ExperimentalWarning
-from optuna.pruners import HyperbandPruner
-from optuna.samplers import TPESampler
+from optuna.pruners import BasePruner, HyperbandPruner
+from optuna.samplers import BaseSampler, TPESampler
from optuna.storages import (
BaseStorage,
JournalStorage,
"timeout_hours": 0, // Maximum time in hours for hyperopt (0 = no timeout)
"continuous": false, // If true, perform continuous optimization
"warm_start": false, // If true, enqueue previous best params if exists
- "seed": 42, // RNG seed
+ "sampler": "tpe", // Optuna sampler (tpe|auto)
"storage": "sqlite", // Optuna storage backend (sqlite|file)
- }
+ "seed": 42, // RNG seed
}
}
Requirements:
except (ValueError, KeyError):
return False
+ def create_sampler(self) -> BaseSampler:
+ sampler = self.rl_config_optuna.get("sampler", "tpe")
+ if sampler == "auto":
+ return optunahub.load_module("samplers/auto_sampler").AutoSampler(
+ seed=self.rl_config_optuna.get("seed", 42)
+ )
+ elif sampler == "tpe":
+ return TPESampler(
+ n_startup_trials=self.optuna_n_startup_trials,
+ multivariate=True,
+ group=True,
+ seed=self.rl_config_optuna.get("seed", 42),
+ )
+ else:
+ raise ValueError(
+ f"Unsupported sampler: '{sampler}'. Supported samplers: 'tpe', 'auto'"
+ )
+
+ @staticmethod
+ def create_pruner(
+ min_resource: int, max_resource: int, reduction_factor: int
+ ) -> BasePruner:
+ return HyperbandPruner(
+ min_resource=min_resource,
+ max_resource=max_resource,
+ reduction_factor=reduction_factor,
+ )
+
def optimize(
self, dk: FreqaiDataKitchen, total_timesteps: int
) -> Optional[Dict[str, Any]]:
reduction_factor * 2, (total_timesteps // self.n_envs) // resource_eval_freq
)
min_resource = max(1, max_resource // reduction_factor)
+
study: Study = create_study(
study_name=study_name,
- sampler=TPESampler(
- n_startup_trials=self.optuna_n_startup_trials,
- multivariate=True,
- group=True,
- seed=self.rl_config_optuna.get("seed", 42),
- ),
- pruner=HyperbandPruner(
- min_resource=min_resource,
- max_resource=max_resource,
- reduction_factor=reduction_factor,
+ sampler=self.create_sampler(),
+ pruner=ReforceXY.create_pruner(
+ min_resource, max_resource, reduction_factor
),
direction=StudyDirection.MAXIMIZE,
storage=storage,
USER root
RUN apt-get update \
- && apt-get install -y --no-install-recommends build-essential \
+ && apt-get install -y --no-install-recommends build-essential git \
&& rm -rf /var/lib/apt/lists/*
USER ftuser
RUN pip install --user --no-cache-dir \
optuna==${optuna_version} \
optuna-integration==${optuna_version} \
optuna-dashboard \
+ optunahub \
+ -r https://hub.optuna.org/samplers/auto_sampler/requirements.txt \
scikit-learn-extra==${scikit_learn_extra_version} \
scikit-image==${scikit_image_version}
import numpy as np
import optuna
+import optunahub
import pandas as pd
import scipy as sp
import skimage
.get("n_jobs", 1),
max(int(self.max_system_threads / 4), 1),
),
+ "sampler": "tpe",
"storage": "file",
"continuous": True,
"warm_start": True,
)
return storage
+ def optuna_create_pruner(
+ self, is_single_objective: bool
+ ) -> optuna.pruners.BasePruner:
+ if is_single_objective:
+ return optuna.pruners.HyperbandPruner(
+ min_resource=self._optuna_config.get("min_resource")
+ )
+ else:
+ return optuna.pruners.NopPruner()
+
+ def optuna_create_sampler(self) -> optuna.samplers.BaseSampler:
+ sampler = self._optuna_config.get("sampler", "tpe")
+ if sampler == "auto":
+ return optunahub.load_module("samplers/auto_sampler").AutoSampler(
+ seed=self._optuna_config.get("seed")
+ )
+ elif sampler == "tpe":
+ return optuna.samplers.TPESampler(
+ n_startup_trials=self._optuna_config.get("n_startup_trials"),
+ multivariate=True,
+ group=True,
+ seed=self._optuna_config.get("seed"),
+ )
+ else:
+ raise ValueError(
+ f"Unsupported sampler: '{sampler}'. Supported samplers: 'tpe', 'auto'"
+ )
+
def optuna_create_study(
self,
pair: str,
if continuous:
QuickAdapterRegressorV3.optuna_study_delete(study_name, storage)
- if is_study_single_objective:
- pruner = optuna.pruners.HyperbandPruner(
- min_resource=self._optuna_config.get("min_resource")
- )
- else:
- pruner = optuna.pruners.NopPruner()
-
try:
return optuna.create_study(
study_name=study_name,
- sampler=optuna.samplers.TPESampler(
- n_startup_trials=self._optuna_config.get("n_startup_trials"),
- multivariate=True,
- group=True,
- seed=self._optuna_config.get("seed"),
- ),
- pruner=pruner,
+ sampler=self.optuna_create_sampler(),
+ pruner=self.optuna_create_pruner(is_study_single_objective),
direction=direction,
directions=directions,
storage=storage,
INTERFACE_VERSION = 3
def version(self) -> str:
- return "3.3.169"
+ return "3.3.170"
timeframe = "5m"
self._candle_threshold_cache: dict[CandleThresholdCacheKey, float] = {}
self._cached_df_signature: dict[str, DfSignature] = {}
- def _df_signature(self, df: DataFrame) -> DfSignature:
+ @staticmethod
+ def _df_signature(df: DataFrame) -> DfSignature:
n = len(df)
if n == 0:
return (0, None)
interpolation_direction: Literal["direct", "inverse"] = "direct",
quantile_exponent: float = 1.5,
) -> float:
- df_signature = self._df_signature(df)
+ df_signature = QuickAdapterV3._df_signature(df)
prev_df_signature = self._cached_df_signature.get(pair)
if prev_df_signature != df_signature:
self._candle_deviation_cache = {
max_natr_ratio_percent: float,
candle_idx: int = -1,
) -> float:
- df_signature = self._df_signature(df)
+ df_signature = QuickAdapterV3._df_signature(df)
prev_df_signature = self._cached_df_signature.get(pair)
if prev_df_signature != df_signature:
self._candle_threshold_cache = {
repositories / freqai-strategies.git / commitdiff
