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"rule": "reportArgumentType",
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"message": "Argument of type \"Any | None\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"Any | None\" is not assignable to type \"ConvertibleToFloat\"\n Type \"None\" is not assignable to type \"ConvertibleToFloat\"\n \"None\" is not assignable to \"str\"\n \"None\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"None\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"None\" is incompatible with protocol \"SupportsIndex\"\n ...",
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"rule": "reportArgumentType",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"Any | None\" cannot be assigned to parameter \"timeframe\" of type \"str\" in function \"steps_to_days\"\n Type \"Any | None\" is not assignable to type \"str\"\n \"None\" is not assignable to \"str\"",
"rule": "reportArgumentType",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"Any | None\" cannot be assigned to parameter \"timeframe\" of type \"str\" in function \"steps_to_days\"\n Type \"Any | None\" is not assignable to type \"str\"\n \"None\" is not assignable to \"str\"",
"rule": "reportArgumentType",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"Any | None\" cannot be assigned to parameter \"timeframe\" of type \"str\" in function \"steps_to_days\"\n Type \"Any | None\" is not assignable to type \"str\"\n \"None\" is not assignable to \"str\"",
"rule": "reportArgumentType",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"Any | None\" cannot be assigned to parameter \"timeframe\" of type \"str\" in function \"steps_to_days\"\n Type \"Any | None\" is not assignable to type \"str\"\n \"None\" is not assignable to \"str\"",
"rule": "reportArgumentType",
"severity": "error",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"VecMonitor | SubprocVecEnv | Env[Unknown, Unknown]\" cannot be assigned to parameter \"eval_env\" of type \"BaseEnvironment\" in function \"get_callbacks\"\n Type \"VecMonitor | SubprocVecEnv | Env[Unknown, Unknown]\" is not assignable to type \"BaseEnvironment\"\n \"SubprocVecEnv\" is not assignable to \"BaseEnvironment\"",
"rule": "reportArgumentType",
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},
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"NDArray[float32]\" cannot be assigned to parameter \"x\" of type \"float32\" in function \"append\"\n \"ndarray[_AnyShape, dtype[float32]]\" is not assignable to \"floating[_32Bit]\"",
"rule": "reportArgumentType",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
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"rule": "reportPossiblyUnboundVariable",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Parameter declaration \"seed\" is obscured by a declaration of the same name",
"rule": "reportRedeclaration",
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"rule": "reportRedeclaration",
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"message": "Argument of type \"list[() -> BaseEnvironment]\" cannot be assigned to parameter \"env_fns\" of type \"list[() -> Env[Unknown, Unknown]]\" in function \"__init__\"\n \"list[() -> BaseEnvironment]\" is not assignable to \"list[() -> Env[Unknown, Unknown]]\"\n Type parameter \"_T@list\" is invariant, but \"() -> BaseEnvironment\" is not the same as \"() -> Env[Unknown, Unknown]\"\n Consider switching from \"list\" to \"Sequence\" which is covariant",
"rule": "reportArgumentType",
"severity": "error",
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},
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"list[() -> BaseEnvironment]\" cannot be assigned to parameter \"env_fns\" of type \"list[() -> Env[Unknown, Unknown]]\" in function \"__init__\"\n \"list[() -> BaseEnvironment]\" is not assignable to \"list[() -> Env[Unknown, Unknown]]\"\n Type parameter \"_T@list\" is invariant, but \"() -> BaseEnvironment\" is not the same as \"() -> Env[Unknown, Unknown]\"\n Consider switching from \"list\" to \"Sequence\" which is covariant",
"rule": "reportArgumentType",
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},
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"list[() -> BaseEnvironment]\" cannot be assigned to parameter \"env_fns\" of type \"list[() -> Env[Unknown, Unknown]]\" in function \"__init__\"\n \"list[() -> BaseEnvironment]\" is not assignable to \"list[() -> Env[Unknown, Unknown]]\"\n Type parameter \"_T@list\" is invariant, but \"() -> BaseEnvironment\" is not the same as \"() -> Env[Unknown, Unknown]\"\n Consider switching from \"list\" to \"Sequence\" which is covariant",
"rule": "reportArgumentType",
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},
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"list[() -> BaseEnvironment]\" cannot be assigned to parameter \"env_fns\" of type \"list[() -> Env[Unknown, Unknown]]\" in function \"__init__\"\n \"list[() -> BaseEnvironment]\" is not assignable to \"list[() -> Env[Unknown, Unknown]]\"\n Type parameter \"_T@list\" is invariant, but \"() -> BaseEnvironment\" is not the same as \"() -> Env[Unknown, Unknown]\"\n Consider switching from \"list\" to \"Sequence\" which is covariant",
"rule": "reportArgumentType",
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},
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"rule": "reportOptionalOperand",
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"rule": "reportOptionalOperand",
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"message": "Operator \"*\" not supported for \"None\"",
"rule": "reportOptionalOperand",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Operator \"*\" not supported for \"None\"",
"rule": "reportOptionalOperand",
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{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Operator \"*\" not supported for types \"Any | None\" and \"Any | int | None\"\n Operator \"*\" not supported for types \"None\" and \"None\"\n Operator \"*\" not supported for types \"None\" and \"int\"",
"rule": "reportOperatorIssue",
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{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Operator \"*\" not supported for types \"Any | None\" and \"Any | int | None\"\n Operator \"*\" not supported for types \"None\" and \"None\"\n Operator \"*\" not supported for types \"None\" and \"int\"",
"rule": "reportOperatorIssue",
"severity": "error",
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{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Operator \">\" not supported for \"None\"",
"rule": "reportOptionalOperand",
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"startCharacter": 15,
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{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"VecEnv\" cannot be assigned to parameter \"eval_env\" of type \"BaseEnvironment\" in function \"get_callbacks\"\n \"VecEnv\" is not assignable to \"BaseEnvironment\"",
"rule": "reportArgumentType",
"severity": "error",
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{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "\"is_pruned\" is not a known attribute of \"None\"",
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{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "\"best_mean_reward\" is not a known attribute of \"None\"",
"rule": "reportOptionalMemberAccess",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Cannot assign to attribute \"train_env\" for class \"ReforceXY*\"\n Type \"None\" is not assignable to type \"VecMonitor | SubprocVecEnv | Env[Unknown, Unknown]\"\n \"None\" is not assignable to \"VecMonitor\"\n \"None\" is not assignable to \"SubprocVecEnv\"\n \"None\" is not assignable to \"Env[Unknown, Unknown]\"",
"rule": "reportAttributeAccessIssue",
"severity": "error",
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},
{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Cannot assign to attribute \"eval_env\" for class \"ReforceXY*\"\n Type \"None\" is not assignable to type \"VecMonitor | SubprocVecEnv | Env[Unknown, Unknown]\"\n \"None\" is not assignable to \"VecMonitor\"\n \"None\" is not assignable to \"SubprocVecEnv\"\n \"None\" is not assignable to \"Env[Unknown, Unknown]\"",
"rule": "reportAttributeAccessIssue",
"severity": "error",
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{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Declaration \"MyRLEnv\" is obscured by a declaration of the same name",
"rule": "reportRedeclaration",
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"startCharacter": 0,
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},
{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Declaration \"_last_closed_position\" is obscured by a declaration of the same name",
"rule": "reportRedeclaration",
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{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Declaration \"_last_closed_trade_tick\" is obscured by a declaration of the same name",
"rule": "reportRedeclaration",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Method \"reset\" overrides class \"BaseEnvironment\" in an incompatible manner\n Return type mismatch: base method returns type \"tuple[DataFrame, dict[Unknown, Unknown]]\", override returns type \"tuple[NDArray[float32], dict[str, Any]]\"\n \"tuple[NDArray[float32], dict[str, Any]]\" is not assignable to \"tuple[DataFrame, dict[Unknown, Unknown]]\"\n Tuple entry 1 is incorrect type\n \"ndarray[_AnyShape, dtype[float32]]\" is not assignable to \"DataFrame\"",
"rule": "reportIncompatibleMethodOverride",
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},
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Type \"tuple[DataFrame, dict[Unknown, Unknown]]\" is not assignable to return type \"tuple[NDArray[float32], dict[str, Any]]\"\n \"DataFrame\" is not assignable to \"ndarray[_AnyShape, dtype[float32]]\"",
"rule": "reportReturnType",
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},
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"float | None\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"float | None\" is not assignable to type \"ConvertibleToFloat\"\n Type \"None\" is not assignable to type \"ConvertibleToFloat\"\n \"None\" is not assignable to \"str\"\n \"None\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"None\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"None\" is incompatible with protocol \"SupportsIndex\"",
"rule": "reportArgumentType",
"severity": "error",
"startCharacter": 44,
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},
{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"float | None\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"float | None\" is not assignable to type \"ConvertibleToFloat\"\n Type \"None\" is not assignable to type \"ConvertibleToFloat\"\n \"None\" is not assignable to \"str\"\n \"None\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"None\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"None\" is incompatible with protocol \"SupportsIndex\"\n ...",
"rule": "reportArgumentType",
"severity": "error",
"startCharacter": 44,
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},
{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"float | None\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"float | None\" is not assignable to type \"ConvertibleToFloat\"\n Type \"None\" is not assignable to type \"ConvertibleToFloat\"\n \"None\" is not assignable to \"str\"\n \"None\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"None\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"None\" is incompatible with protocol \"SupportsIndex\"",
"rule": "reportArgumentType",
"severity": "error",
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{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"float | None\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"float | None\" is not assignable to type \"ConvertibleToFloat\"\n Type \"None\" is not assignable to type \"ConvertibleToFloat\"\n \"None\" is not assignable to \"str\"\n \"None\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"None\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"None\" is incompatible with protocol \"SupportsIndex\"\n ...",
"rule": "reportArgumentType",
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{
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Method \"_get_observation\" overrides class \"BaseEnvironment\" in an incompatible manner\n Return type mismatch: base method returns type \"DataFrame\", override returns type \"NDArray[float32]\"\n \"ndarray[_AnyShape, dtype[float32]]\" is not assignable to \"DataFrame\"",
"rule": "reportIncompatibleMethodOverride",
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"message": "\"name\" is not a known attribute of \"None\"",
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"rule": "reportArgumentType",
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"rule": "reportArgumentType",
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"message": "Argument of type \"float | None\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"float | None\" is not assignable to type \"ConvertibleToFloat\"\n Type \"None\" is not assignable to type \"ConvertibleToFloat\"\n \"None\" is not assignable to \"str\"\n \"None\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"None\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"None\" is incompatible with protocol \"SupportsIndex\"\n ...",
"rule": "reportArgumentType",
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"rule": "reportArgumentType",
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"message": "Argument of type \"Any | None\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"Any | None\" is not assignable to type \"ConvertibleToFloat\"\n Type \"None\" is not assignable to type \"ConvertibleToFloat\"\n \"None\" is not assignable to \"str\"\n \"None\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"None\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"None\" is incompatible with protocol \"SupportsIndex\"\n ...",
"rule": "reportArgumentType",
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"message": "Argument of type \"Mapping[Unknown, Unknown]\" cannot be assigned to parameter \"src\" of type \"dict[str, Any]\" in function \"deepmerge\"\n \"Mapping[Unknown, Unknown]\" is not assignable to \"dict[str, Any]\"",
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"message": "Argument of type \"Any | None\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"Any | None\" is not assignable to type \"ConvertibleToFloat\"\n Type \"None\" is not assignable to type \"ConvertibleToFloat\"\n \"None\" is not assignable to \"str\"\n \"None\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"None\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"None\" is incompatible with protocol \"SupportsIndex\"\n ...",
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"message": "Argument of type \"Any | None\" cannot be assigned to parameter \"x\" of type \"ConvertibleToInt\" in function \"__new__\"\n Type \"Any | None\" is not assignable to type \"ConvertibleToInt\"\n Type \"None\" is not assignable to type \"ConvertibleToInt\"\n \"None\" is not assignable to \"str\"\n \"None\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"None\" is incompatible with protocol \"SupportsInt\"\n \"__int__\" is not present\n \"None\" is incompatible with protocol \"SupportsIndex\"",
"rule": "reportArgumentType",
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"message": "Argument of type \"Any | None\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"Any | None\" is not assignable to type \"ConvertibleToFloat\"\n Type \"None\" is not assignable to type \"ConvertibleToFloat\"\n \"None\" is not assignable to \"str\"\n \"None\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"None\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"None\" is incompatible with protocol \"SupportsIndex\"\n ...",
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"message": "Argument of type \"Any | None\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"Any | None\" is not assignable to type \"ConvertibleToFloat\"\n Type \"None\" is not assignable to type \"ConvertibleToFloat\"\n \"None\" is not assignable to \"str\"\n \"None\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"None\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"None\" is incompatible with protocol \"SupportsIndex\"\n ...",
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import numpy as np
import optunahub
import pandas as pd
+import rapidjson
import torch as th
-from freqtrade.freqai.data_drawer import FreqaiDataDrawer
+from datasieve.pipeline import Pipeline
+from freqtrade.exceptions import DependencyException
+from freqtrade.freqai.data_drawer import (
+ FEATURE_PIPELINE,
+ METADATA,
+ METADATA_NUMBER_MODE,
+ FreqaiDataDrawer,
+)
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment
from freqtrade.freqai.tensorboard.TensorboardCallback import TensorboardCallback
from freqtrade.strategy import timeframe_to_minutes
from gymnasium.spaces import Box
+from joblib.externals import cloudpickle
from matplotlib.lines import Line2D
from numpy.typing import NDArray
from optuna import Trial, TrialPruned, create_study, delete_study
_DATE_PRED_DEDUP_SENTINEL = "_freqai_strategies_date_pred_repair_patched"
+def _recorded_prediction_mask(frame: pd.DataFrame) -> NDArray[np.bool_]:
+ """Identify recorded rows from metadata, never from prediction magnitudes."""
+ recorded = np.zeros(len(frame), dtype=bool)
+ if "close_price" in frame:
+ close = pd.to_numeric(frame["close_price"], errors="coerce")
+ recorded |= (close.gt(0) & close.lt(np.inf)).fillna(False).to_numpy(dtype=bool)
+ if "do_predict" in frame:
+ status = pd.to_numeric(frame["do_predict"], errors="coerce")
+ recorded |= (
+ (status.gt(-np.inf) & status.lt(np.inf) & status.ne(0))
+ .fillna(False)
+ .to_numpy(dtype=bool)
+ )
+ return recorded
+
+
+def _produced_prediction_mask(frame: pd.DataFrame) -> NDArray[np.bool_]:
+ """Exclude expired-model placeholders from recorded model observations."""
+ produced = _recorded_prediction_mask(frame)
+ if "do_predict" in frame:
+ status = pd.to_numeric(frame["do_predict"], errors="coerce")
+ produced &= status.ne(2).fillna(True).to_numpy(dtype=bool)
+ return produced
+
+
def _dedupe_historic_predictions_on_date_pred(frame: pd.DataFrame) -> pd.DataFrame:
"""Normalize dates and retain the latest recorded row per candle, in date order.
result["date_pred"] = date_pred
return result
- recorded = np.zeros(len(frame), dtype=bool)
- if "close_price" in frame:
- close = pd.to_numeric(frame["close_price"], errors="coerce")
- recorded |= (close.gt(0) & close.lt(np.inf)).fillna(False).to_numpy(dtype=bool)
- if "do_predict" in frame:
- status = pd.to_numeric(frame["do_predict"], errors="coerce")
- recorded |= (status.notna() & status.ne(0)).to_numpy(dtype=bool)
+ recorded = _recorded_prediction_mask(frame)
# Rank only metadata, without copying or coercing all prediction columns.
order = pd.DataFrame(
{"date_pred": date_pred.array, "recorded": recorded, "position": np.arange(len(frame))}
return result.reset_index(drop=True)
+def _align_historic_predictions(history: pd.DataFrame, dataframe: pd.DataFrame) -> pd.DataFrame:
+ """Align unique, normalized history to requested candle dates and exact index."""
+ dates = pd.to_datetime(dataframe["date"], utc=True, errors="coerce", format="mixed")
+ indexed = history.set_index("date_pred", drop=False)
+ aligned = indexed.reindex(pd.DatetimeIndex(dates))
+ missing = ~pd.DatetimeIndex(dates).isin(indexed.index)
+ aligned.index = dataframe.index
+ aligned["date_pred"] = dates.array
+ if "do_predict" in aligned:
+ aligned["do_predict"] = aligned["do_predict"].where(~missing, 0)
+ else:
+ aligned["do_predict"] = 0
+ return aligned
+
+
def _install_date_pred_dedup_patch() -> None:
"""Repair persisted history and duplicates produced by older Freqtrade writers.
self.historic_predictions[pair] = _dedupe_historic_predictions_on_date_pred(
self.historic_predictions[pair]
)
- original_set_initial(self, pair, pred_df, dataframe)
- history = self.historic_predictions[pair]
- repaired = _dedupe_historic_predictions_on_date_pred(history)
- if repaired is not history:
- self.historic_predictions[pair] = repaired
- self.model_return_values[pair] = repaired.tail(len(dataframe.index)).reset_index(
- drop=True
- )
+ original_set_initial(
+ self, pair, pred_df.reset_index(drop=True), dataframe.reset_index(drop=True)
+ )
+ repaired = _dedupe_historic_predictions_on_date_pred(self.historic_predictions[pair])
+ self.historic_predictions[pair] = repaired
+ self.model_return_values[pair] = _align_historic_predictions(repaired, dataframe)
@wraps(original_append)
def append_model_predictions(
)
if self.historic_predictions[pair].empty and not strat_df.empty:
# Legacy append requires an initialized row; let upstream construct it.
- original_set_initial(self, pair, pd.DataFrame(index=pd.RangeIndex(1)), strat_df.tail(1))
- original_append(self, pair, predictions, do_preds, dk, strat_df)
- history = self.historic_predictions[pair]
- repaired = _dedupe_historic_predictions_on_date_pred(history)
- if repaired is not history:
- self.historic_predictions[pair] = repaired
- self.model_return_values[pair] = repaired.tail(len(strat_df.index)).reset_index(
- drop=True
+ original_set_initial(
+ self,
+ pair,
+ pd.DataFrame(index=pd.RangeIndex(1)),
+ strat_df.tail(1).reset_index(drop=True),
)
+ original_append(self, pair, predictions, do_preds, dk, strat_df)
+ repaired = _dedupe_historic_predictions_on_date_pred(self.historic_predictions[pair])
+ self.historic_predictions[pair] = repaired
+ self.model_return_values[pair] = _align_historic_predictions(repaired, strat_df)
@wraps(original_attach)
def attach_return_values_to_return_dataframe(
self, pair: str, dataframe: pd.DataFrame
) -> pd.DataFrame:
- self.model_return_values[pair] = _dedupe_historic_predictions_on_date_pred(
- self.model_return_values[pair]
- )
+ repaired = _dedupe_historic_predictions_on_date_pred(self.historic_predictions[pair])
+ self.historic_predictions[pair] = repaired
+ self.model_return_values[pair] = _align_historic_predictions(repaired, dataframe)
return original_attach(self, pair, dataframe)
replacements = (
...
"freqai": {
...
- "fit_live_predictions_candles": 0, // Optional non-negative integer; omitted or 0 disables prediction statistics
+ "fit_live_predictions_candles": 0, // Optional non-negative integer; omitted or 0 disables action statistics
+ // Live/dry-run: latest N produced observations per pair after session startup.
+ // Restart resets warmup; numeric mean/std are zero until N observations exist.
+ // Downtime and expired status 2 do not count; neutral/exit actions and recorded
+ // rejected predictions do. Backtests use the previous N rows, not the current row.
+ // Numeric objects are coerced; non-finite samples are ignored. Empty finite
+ // samples yield zeros; constants have zero spread; nonnumeric objects are skipped.
+ // Statistics do not gate RL actions. Population standard deviation is used.
"model_training_parameters": {
"device": "auto", // PyTorch device (auto|cpu|cuda|cuda:0)
"gpu_memory_fraction": null, // GPU VRAM fraction limit per process (0.0, 1.0], null disables
}
}
}
+
+ Continual learning keeps the deployed policy's fitted feature coordinates frozen,
+ including feature selection and scaling. Reset trained models or use a new
+ freqai.identifier to change those coordinates or migrate legacy artifacts.
+ First training and continual_learning=false use fresh pipelines and policies.
+ Hyperopt always uses fresh current-window pipelines and cold policies; final
+ continuation uses the original raw split in the deployed coordinates. Selected
+ constructor/network parameters do not rebuild an existing policy's architecture;
+ a resumed policy keeps its own discount gamma for environment reward shaping.
+ Raw OHLC prices are preserved separately for every environment. Noise augments
+ training features only, never evaluation or prediction features.
+ Prediction uses the best checkpoint selected by the current fit's evaluation,
+ falling back to the final policy if no usable current checkpoint was selected.
+
Requirements:
- pip install optuna optunahub -r https://hub.optuna.org/samplers/auto_sampler/requirements.txt
_LOG_2: Final[float] = math.log(2.0)
+ _DEPLOYMENT_COORDINATE_MARKER_KEY: Final[str] = "reforcexy_deployment_coordinates"
+ _DEPLOYMENT_COORDINATE_GENERATION: Final[str] = "frozen-pipelines-v1"
+
DEFAULT_BASE_FACTOR: Final[float] = 100.0
DEFAULT_MAX_TRADE_DURATION_CANDLES: Final[int] = 128
_QUARANTINE_TAG: Final[str] = "corrupt"
_QUARANTINE_TIE_BREAK_LIMIT: Final[int] = 99
_BEST_PARAMS_LOCK_FILENAME: Final[str] = ".hyperopt-best-params.lock"
+ # Bump on objective changes that make persisted trials or best params incompatible.
+ _OPTUNA_OBJECTIVE_IDENTITY: Final[str] = "raw-market-prices-v1"
_PPO_N_STEPS: Final[tuple[int, ...]] = (512, 1024, 2048, 4096)
_PPO_N_STEPS_MIN: Final[int] = min(_PPO_N_STEPS)
_PPO_N_STEPS_MAX: Final[int] = max(_PPO_N_STEPS)
gamma: float | None = None
if model_params and isinstance(model_params.get("gamma"), (int, float)):
- gamma = float(model_params.get("gamma"))
+ gamma = float(model_params["gamma"])
elif self.hyperopt:
best_trial_params = self.load_best_trial_params(pair)
if best_trial_params and isinstance(best_trial_params.get("gamma"), (int, float)):
prices_train: DataFrame,
prices_test: DataFrame,
dk: FreqaiDataKitchen,
+ model_params: dict[str, Any] | None = None,
) -> None:
"""
Set training and evaluation environments
logger.info("Env [%s]: closing environments", dk.pair)
self.close_envs()
- train_df = data_dictionary.get("train_features")
- test_df = data_dictionary.get("test_features")
- env_dict = self.pack_env_dict(dk.pair)
+ train_df = data_dictionary["train_features"]
+ test_df = data_dictionary["test_features"]
+ env_dict = self.pack_env_dict(dk.pair, model_params=model_params)
seed = self.get_model_params().get("seed", 42)
if self.check_envs:
callbacks.append(self.optuna_eval_callback)
return callbacks
- def fit(self, data_dictionary: dict[str, Any], dk: FreqaiDataKitchen, **kwargs) -> Any:
+ def _resolve_deployment_state(
+ self, dk: FreqaiDataKitchen, pair: str
+ ) -> tuple[Any, Pipeline] | None:
+ """Restore the deployed policy and an independent fitted feature pipeline."""
+ if not self.continual_learning:
+ return None
+ model = self.dd.model_dictionary.get(pair)
+ previous = self.dd.pair_dict.get(pair, {})
+ if model is None and not previous.get("model_filename"):
+ return None
+ try:
+ cached = self.dd.meta_data_dictionary.get(pair, {})
+ metadata = cached.get(METADATA)
+ feature_pipeline = cached.get(FEATURE_PIPELINE)
+ if model is None or metadata is None or feature_pipeline is None:
+ # The current kitchen points to the new window, not deployed files.
+ previous_dk = copy.copy(dk)
+ previous_dk.data_path = Path(previous["data_path"])
+ previous_dk.model_filename = previous["model_filename"]
+ prefix = previous_dk.data_path / previous_dk.model_filename
+ if metadata is None:
+ with Path(f"{prefix}_{METADATA}.json").open("r") as fp:
+ metadata = rapidjson.load(fp, number_mode=METADATA_NUMBER_MODE)
+ if feature_pipeline is None:
+ with Path(f"{prefix}_{FEATURE_PIPELINE}.pkl").open("rb") as fp:
+ feature_pipeline = cloudpickle.load(fp)
+ if model is None:
+ model = self.MODELCLASS.load(Path(f"{prefix}_model"))
+ if (
+ metadata.get(self._DEPLOYMENT_COORDINATE_MARKER_KEY)
+ != self._DEPLOYMENT_COORDINATE_GENERATION
+ ):
+ raise ValueError(
+ "persisted deployment coordinate generation is missing or incompatible"
+ )
+ if not isinstance(model, self.MODELCLASS):
+ raise ValueError("persisted policy type differs from the configured model")
+ for name, expected, actual in (
+ ("features", dk.training_features_list, metadata["training_features_list"]),
+ ("labels", dk.label_list, metadata["label_list"]),
+ (
+ "feature pipeline inputs",
+ dk.training_features_list,
+ feature_pipeline.features_in,
+ ),
+ ):
+ if list(expected) != list(actual):
+ raise ValueError(f"persisted {name} differ in names or order")
+ state = model, copy.deepcopy(feature_pipeline)
+ except Exception as exc:
+ raise DependencyException(
+ f"[{pair}] Cannot continue training with matching persisted pipelines: {exc}. "
+ "Reset trained models or use a new freqai.identifier."
+ ) from exc
+ logger.info(
+ f"[{pair}] Continuing deployment in the persisted feature coordinate system; "
+ "reset trained models to change pipeline configuration"
+ )
+ return state
+
+ def _apply_training_pipeline(
+ self,
+ data_dictionary: dict[str, Any],
+ dk: FreqaiDataKitchen,
+ deployment_state: tuple[Any, Pipeline] | None = None,
+ ) -> dict[str, Any]:
+ """Preserve chronological raw splits; never transform RL action labels."""
+ dd = data_dictionary.copy()
+ dk.feature_pipeline = (
+ self.define_data_pipeline(threads=dk.thread_count)
+ if deployment_state is None
+ else deployment_state[1]
+ )
+ for split in ("train", "test"):
+ features = data_dictionary[f"{split}_features"]
+ labels = data_dictionary[f"{split}_labels"]
+ weights = data_dictionary[f"{split}_weights"]
+ if split == "test" and features.empty:
+ continue
+ transform = (
+ dk.feature_pipeline.fit_transform
+ if split == "train" and deployment_state is None
+ else dk.feature_pipeline.transform
+ )
+ transformed, transformed_labels, transformed_weights = transform(
+ features.copy(), labels.copy(), weights.copy()
+ )
+ transformed = cast("pd.DataFrame", transformed)
+ transformed_labels = cast("pd.DataFrame", transformed_labels)
+ transformed_weights = cast("NDArray[np.float64]", np.asarray(transformed_weights))
+ if len(transformed) != len(features) or len(transformed_weights) != len(features):
+ raise DependencyException(
+ f"[{dk.pair}] RL preprocessing must preserve chronological rows "
+ "and their alignment with raw prices."
+ )
+ # DataSieve reconstructs frames with RangeIndex while retaining row order.
+ transformed.index = features.index
+ transformed_labels.index = labels.index
+ if split == "train" and deployment_state is not None:
+ # Noise.transform is a no-op; only augmentation may be fitted.
+ for name, transformer in dk.feature_pipeline.steps:
+ if name == "noise":
+ noisy, _, _, _ = transformer.fit_transform(transformed.to_numpy(copy=True))
+ transformed = pd.DataFrame(
+ noisy, columns=transformed.columns, index=transformed.index
+ )
+ dd[f"{split}_features"] = transformed
+ dd[f"{split}_labels"] = transformed_labels
+ dd[f"{split}_weights"] = transformed_weights
+ dk.data_dictionary = dd
+ return dd
+
+ def train(self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs) -> Any:
+ """Train cold candidates and continue only in persisted deployment coordinates."""
+ # Drawer metadata can alias this kitchen; never invalidate the deployed marker.
+ dk.data = dk.data.copy()
+ dk.data.pop(self._DEPLOYMENT_COORDINATE_MARKER_KEY, None)
+ logger.info(f"--------------------Starting training {pair} --------------------")
+ features_filtered, labels_filtered = dk.filter_features(
+ unfiltered_df, dk.training_features_list, dk.label_list, training_filter=True
+ )
+ raw_data = dk.make_train_test_datasets(features_filtered, labels_filtered)
+ self.df_raw = copy.deepcopy(raw_data["train_features"])
+ dk.fit_labels()
+ # Capture prices once, before normalization and optional raw-OHLC removal.
+ prices_train, prices_test = self.build_ohlc_price_dataframes(raw_data, pair, dk)
+ deployment_state = self._resolve_deployment_state(dk, pair)
+ dd = self._apply_training_pipeline(
+ raw_data, dk, deployment_state=None if self.hyperopt else deployment_state
+ )
+ logger.info(
+ f"Training model on {len(dd['train_features'].columns)}"
+ f" features and {len(dd['train_features'])} data points"
+ )
+ model = self.fit(
+ dd,
+ dk,
+ prices_train=prices_train,
+ prices_test=prices_test,
+ deployment_state=deployment_state,
+ raw_data_dictionary=raw_data if self.hyperopt and deployment_state else None,
+ **kwargs,
+ )
+ if model is not None:
+ dk.data[self._DEPLOYMENT_COORDINATE_MARKER_KEY] = self._DEPLOYMENT_COORDINATE_GENERATION
+ logger.info(f"--------------------done training {pair}--------------------")
+ return model
+
+ def fit(
+ self,
+ data_dictionary: dict[str, Any],
+ dk: FreqaiDataKitchen,
+ *,
+ prices_train: DataFrame,
+ prices_test: DataFrame,
+ deployment_state: tuple[Any, Pipeline] | None = None,
+ raw_data_dictionary: dict[str, Any] | None = None,
+ **kwargs,
+ ) -> Any:
"""
Model fitting method
:param data_dictionary: dict = common data dictionary containing all train/test features/labels/weights.
start_time = time.time()
if self.hyperopt:
- best_params = self.optimize(dk, total_timesteps)
+ best_params = self.optimize(dk, total_timesteps, prices_train, prices_test)
if best_params is None:
logger.error(
"Hyperopt [%s]: optimization failed, using default model params",
model_params = best_params
else:
model_params = self.get_model_params()
+ if raw_data_dictionary is not None:
+ data_dictionary = self._apply_training_pipeline(
+ raw_data_dictionary, dk, deployment_state=deployment_state
+ )
logger.info("Model [%s]: %s params: %s", dk.pair, self.model_type, model_params)
# "PPO"
else:
tensorboard_log_path = None
- # Rebuild train and eval environments before training to sync model parameters
- prices_train, prices_test = self.build_ohlc_price_dataframes(
- dk.data_dictionary, dk.pair, dk
+ # Reward shaping must use the resumed policy's discount gamma,
+ # not the configured or HPO-selected value.
+ resumed_gamma: dict[str, Any] | None = None
+ if deployment_state is not None:
+ learner_gamma = getattr(deployment_state[0], "gamma", None)
+ if isinstance(learner_gamma, (int, float)) and np.isfinite(learner_gamma):
+ resumed_gamma = {"gamma": float(learner_gamma)}
+ self.set_train_and_eval_environments(
+ data_dictionary,
+ prices_train,
+ prices_test,
+ dk,
+ model_params=resumed_gamma,
)
- self.set_train_and_eval_environments(dk.data_dictionary, prices_train, prices_test, dk)
- model = self.get_init_model(dk.pair)
+ model = deployment_state[0] if deployment_state is not None else None
if model is not None:
logger.info(
"Training [%s]: continual training activated, starting from previously trained model state",
dk.pair,
)
+ model.tb_logger = getattr(self, "tb_logger", None)
model.set_env(self.train_env)
else:
model = self.MODELCLASS(
time_spent = time.time() - start_time
self.dd.update_metric_tracker("fit_time", time_spent, dk.pair)
- model_filename = dk.model_filename if dk.model_filename else "best"
- model_filepath = Path(dk.data_path / f"{model_filename}_model.zip")
- if model_filepath.is_file():
+ model_filepath = Path(dk.data_path) / "best_model.zip"
+ current_best = np.isfinite(getattr(self.eval_callback, "best_mean_reward", np.nan))
+ if current_best and model_filepath.is_file():
logger.info("Model [%s]: found best model at %s", dk.pair, model_filepath)
try:
- best_model = self.MODELCLASS.load(dk.data_path / f"{model_filename}_model")
+ best_model = self.MODELCLASS.load(model_filepath)
+ # SB3 archives exclude replay buffers; restore the final buffer so
+ # the next cached retrain keeps the experience accumulated this fit.
+ if (
+ hasattr(model, "replay_buffer")
+ and getattr(best_model, "replay_buffer", None) is not None
+ ):
+ best_model.replay_buffer = model.replay_buffer
return best_model
except Exception as e:
logger.error(
)
logger.warning(
- "Model [%s]: best model not found at %s, using final model",
+ "Model [%s]: no usable current best checkpoint at %s, using final model",
dk.pair,
model_filepath,
)
return model
+ def fit_live_predictions(self, dk: FreqaiDataKitchen, pair: str) -> None:
+ """Compute optional action statistics from prior prediction observations."""
+ fit_live_predictions_candles = self.freqai_info.get("fit_live_predictions_candles", 0)
+ if not fit_live_predictions_candles:
+ return
+
+ warmed_up = True
+ history = self.dd.historic_predictions[pair]
+ if self.live:
+ history = _dedupe_historic_predictions_on_date_pred(history)
+ if not hasattr(self, "_prediction_session_cutoffs"):
+ self._prediction_session_cutoffs: dict[str, pd.Timestamp] = {}
+ if pair not in self._prediction_session_cutoffs:
+ initial_dates = pd.to_datetime(
+ self.dd.model_return_values[pair]["date_pred"],
+ utc=True,
+ errors="coerce",
+ format="mixed",
+ )
+ self._prediction_session_cutoffs[pair] = initial_dates.max()
+ cutoff = self._prediction_session_cutoffs[pair]
+ eligible = (
+ _produced_prediction_mask(history) & history["date_pred"].gt(cutoff).to_numpy()
+ )
+ history = history.loc[eligible]
+ remaining = fit_live_predictions_candles - len(history)
+ warmed_up = remaining <= 0
+ if not warmed_up:
+ logger.warning(
+ f"[{pair}] Fit live predictions not warmed up: {remaining} produced observations until warmup completion"
+ )
+ pred_df = history.tail(fit_live_predictions_candles).reset_index(drop=True)
+
+ dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
+ for label_col in dk.label_list + dk.unique_class_list:
+ raw_label = pred_df.get(label_col)
+ if raw_label is None:
+ continue
+ # Downtime filling can leave numeric predictions in object-typed columns.
+ pred_label = pd.to_numeric(raw_label, errors="coerce")
+ if raw_label.dtype == object and pred_label.isna().all():
+ continue
+ if not warmed_up:
+ f = [0.0, 0.0]
+ else:
+ values = pred_label.to_numpy(dtype=float, na_value=np.nan)
+ values = values[np.isfinite(values)]
+ if values.size == 0:
+ f = (0.0, 0.0)
+ else:
+ sample_mean = float(np.mean(values))
+ sample_std = float(np.std(values, ddof=0))
+ f = (
+ sample_mean if np.isfinite(sample_mean) else 0.0,
+ sample_std if np.isfinite(sample_std) else 0.0,
+ )
+ dk.data["labels_mean"][label_col], dk.data["labels_std"][label_col] = (
+ f[0],
+ f[1],
+ )
+
def rl_model_predict(
self, dataframe: DataFrame, dk: FreqaiDataKitchen, model: Any
) -> DataFrame:
max(min_resource, ReforceXY._ceil_to_multiple(total_timesteps, rollout)),
)
- def optimize(self, dk: FreqaiDataKitchen, total_timesteps: int) -> dict[str, Any] | None:
+ def optimize(
+ self,
+ dk: FreqaiDataKitchen,
+ total_timesteps: int,
+ prices_train: DataFrame,
+ prices_test: DataFrame,
+ ) -> dict[str, Any] | None:
"""
Runs hyperparameter optimization using Optuna and returns the best hyperparameters found merged with the user defined parameters
+ Only studies and best params with the current objective identity are reused.
"""
identifier = self.freqai_info.get("identifier", "no_id_provided")
study_name = f"{identifier}-{dk.pair}"
direction = StudyDirection.MAXIMIZE
load_if_exists = not continuous and not pair_purge_triggered
+ if load_if_exists:
+ try:
+ existing_study_id = storage.get_study_id_from_name(study_name)
+ except KeyError:
+ pass
+ else:
+ existing_identity = storage.get_study_user_attrs(existing_study_id).get(
+ "objective_identity"
+ )
+ if existing_identity != ReforceXY._OPTUNA_OBJECTIVE_IDENTITY:
+ logger.warning(
+ "Hyperopt [%s]: objective identity %r incompatible with %r; resetting study",
+ study_name,
+ existing_identity,
+ ReforceXY._OPTUNA_OBJECTIVE_IDENTITY,
+ )
+ ReforceXY.delete_study(study_name, storage)
+ # Fail closed if deletion failed rather than reload incompatible trials.
+ load_if_exists = False
study: Study = create_study(
study_name=study_name,
sampler=self.create_sampler(),
storage=storage,
load_if_exists=load_if_exists,
)
+ if study.user_attrs.get("objective_identity") != ReforceXY._OPTUNA_OBJECTIVE_IDENTITY:
+ study.set_user_attr("objective_identity", ReforceXY._OPTUNA_OBJECTIVE_IDENTITY)
logger.info(
"Hyperopt [%s]: study created (direction=%s, n_trials=%s, timeout=%s, continuous=%s, load_if_exists=%s)",
study_name,
start_time = time.time()
try:
study.optimize(
- lambda trial: self.objective(trial, dk, total_timesteps),
+ lambda trial: self.objective(trial, dk, total_timesteps, prices_train, prices_test),
n_trials=self.optuna_n_trials,
timeout=(
hours_to_seconds(self.optuna_timeout_hours)
def save_best_trial_params(self, best_trial_params: dict[str, Any], pair: str) -> None:
"""
- Save the best trial hyperparameters found during hyperparameter optimization
+ Save the best trial hyperparameters with the current objective identity.
"""
best_trial_params_path = self._best_trial_params_path(pair)
logger.info("Hyperopt [%s]: saving best params to %s", pair, best_trial_params_path)
write_file.fileno(),
stat.S_IMODE(existing_metadata.st_mode),
)
- json.dump(best_trial_params, write_file, indent=4)
+ json.dump(
+ {
+ "objective_identity": ReforceXY._OPTUNA_OBJECTIVE_IDENTITY,
+ "params": best_trial_params,
+ },
+ write_file,
+ indent=4,
+ )
write_file.flush()
os.fsync(write_file.fileno())
temporary_path.replace(best_trial_params_path)
def load_best_trial_params(self, pair: str) -> dict[str, Any] | None:
"""
- Load the best trial hyperparameters found and saved during hyperparameter optimization
+ Load saved best trial hyperparameters only for the current objective identity.
"""
best_trial_params_path = self._best_trial_params_path(pair)
if not best_trial_params_path.parent.is_dir():
if quarantined is None:
raise
return None
- return best_trial_params
+ if (
+ not isinstance(best_trial_params, dict)
+ or best_trial_params.get("objective_identity") != ReforceXY._OPTUNA_OBJECTIVE_IDENTITY
+ or not isinstance(best_trial_params.get("params"), dict)
+ ):
+ logger.warning(
+ "Hyperopt [%s]: ignoring best params with missing or incompatible objective identity "
+ "(expected %r) or invalid params payload",
+ pair,
+ ReforceXY._OPTUNA_OBJECTIVE_IDENTITY,
+ )
+ return None
+ return best_trial_params["params"]
def _get_train_and_eval_environments(
self,
dk: FreqaiDataKitchen,
- train_df: DataFrame | None = None,
- test_df: DataFrame | None = None,
- prices_train: DataFrame | None = None,
- prices_test: DataFrame | None = None,
+ train_df: DataFrame,
+ test_df: DataFrame,
+ prices_train: DataFrame,
+ prices_test: DataFrame,
seed: int | None = None,
env_info: dict[str, Any] | None = None,
trial: Trial | None = None,
model_params: dict[str, Any] | None = None,
) -> tuple[VecEnv, VecEnv]:
- if train_df is None or test_df is None or prices_train is None or prices_test is None:
- train_df = dk.data_dictionary["train_features"]
- test_df = dk.data_dictionary["test_features"]
- prices_train, prices_test = self.build_ohlc_price_dataframes(
- dk.data_dictionary, dk.pair, dk
- )
seed: int = self.get_model_params().get("seed", 42) if seed is None else seed
if trial is not None:
seed += trial.number
f"Hyperopt [{trial.study.study_name}]: model type '{self.model_type}' not supported"
)
- def objective(self, trial: Trial, dk: FreqaiDataKitchen, total_timesteps: int) -> float:
+ def objective(
+ self,
+ trial: Trial,
+ dk: FreqaiDataKitchen,
+ total_timesteps: int,
+ prices_train: DataFrame,
+ prices_test: DataFrame,
+ ) -> float:
"""
Objective function for Optuna trials hyperparameter optimization
"""
tensorboard_log_path = None
train_env, eval_env = self._get_train_and_eval_environments(
- dk, trial=trial, model_params=params
+ dk,
+ train_df=dk.data_dictionary["train_features"],
+ test_df=dk.data_dictionary["test_features"],
+ prices_train=prices_train,
+ prices_test=prices_test,
+ trial=trial,
+ model_params=params,
)
model = self.MODELCLASS(
repositories / freqai-strategies.git / commitdiff
