Keep numeric domains consistent across prediction, metadata and trading.
Preserve RL overrides and forced-disable rules.
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"rule": "reportArgumentType",
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"message": "Argument of type \"VecMonitor | SubprocVecEnv | Env[Unknown, Unknown]\" cannot be assigned to parameter \"eval_env\" of type \"VecEnv | None\" in function \"get_callbacks\"\n Type \"VecMonitor | SubprocVecEnv | Env[Unknown, Unknown]\" is not assignable to type \"VecEnv | None\"\n Type \"Env[Unknown, Unknown]\" is not assignable to type \"VecEnv | None\"\n \"Env[Unknown, Unknown]\" is not assignable to \"VecEnv\"\n \"Env[Unknown, Unknown]\" is not assignable to \"None\"",
"rule": "reportArgumentType",
"severity": "error",
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"rule": "reportArgumentType",
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"message": "Argument of type \"float | list[float]\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"float | list[float]\" is not assignable to type \"ConvertibleToFloat\"\n Type \"list[float]\" is not assignable to type \"ConvertibleToFloat\"\n \"list[float]\" is not assignable to \"str\"\n \"list[float]\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"list[float]\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"list[float]\" is incompatible with protocol \"SupportsIndex\"\n ...",
"rule": "reportArgumentType",
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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",
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"message": "Argument of type \"float | list[float]\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"float | list[float]\" is not assignable to type \"ConvertibleToFloat\"\n Type \"list[float]\" is not assignable to type \"ConvertibleToFloat\"\n \"list[float]\" is not assignable to \"str\"\n \"list[float]\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"list[float]\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"list[float]\" is incompatible with protocol \"SupportsIndex\"",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"message": "Argument of type \"float | list[float]\" cannot be assigned to parameter \"x\" of type \"ConvertibleToFloat\" in function \"__new__\"\n Type \"float | list[float]\" is not assignable to type \"ConvertibleToFloat\"\n Type \"list[float]\" is not assignable to type \"ConvertibleToFloat\"\n \"list[float]\" is not assignable to \"str\"\n \"list[float]\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"list[float]\" is incompatible with protocol \"SupportsFloat\"\n \"__float__\" is not present\n \"list[float]\" is incompatible with protocol \"SupportsIndex\"\n ...",
"rule": "reportArgumentType",
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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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"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",
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"rule": "reportReturnType",
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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\"",
"rule": "reportArgumentType",
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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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"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\"",
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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 ...",
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"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"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",
"severity": "error",
"startCharacter": 35,
- "startLine": 5513
+ "startLine": 5529
},
{
"endCharacter": 67,
- "endLine": 5513,
+ "endLine": 5529,
"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"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\"\n ...",
"rule": "reportArgumentType",
"severity": "error",
"startCharacter": 35,
- "startLine": 5513
+ "startLine": 5529
},
{
"endCharacter": 87,
- "endLine": 5516,
+ "endLine": 5532,
"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"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\"",
"rule": "reportArgumentType",
"severity": "error",
"startCharacter": 46,
- "startLine": 5516
+ "startLine": 5532
},
{
"endCharacter": 87,
- "endLine": 5516,
+ "endLine": 5532,
"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"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",
"severity": "error",
"startCharacter": 46,
- "startLine": 5516
+ "startLine": 5532
},
{
"endCharacter": 93,
- "endLine": 5517,
+ "endLine": 5533,
"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"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\"",
"rule": "reportArgumentType",
"severity": "error",
"startCharacter": 49,
- "startLine": 5517
+ "startLine": 5533
},
{
"endCharacter": 93,
- "endLine": 5517,
+ "endLine": 5533,
"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"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",
"severity": "error",
"startCharacter": 49,
- "startLine": 5517
+ "startLine": 5533
},
{
"endCharacter": 89,
- "endLine": 5518,
+ "endLine": 5534,
"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"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\"",
"rule": "reportArgumentType",
"severity": "error",
"startCharacter": 47,
- "startLine": 5518
+ "startLine": 5534
},
{
"endCharacter": 89,
- "endLine": 5518,
+ "endLine": 5534,
"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"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",
"severity": "error",
"startCharacter": 47,
- "startLine": 5518
+ "startLine": 5534
},
{
"endCharacter": 89,
- "endLine": 5519,
+ "endLine": 5535,
"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"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",
"severity": "error",
"startCharacter": 46,
- "startLine": 5519
+ "startLine": 5535
},
{
"endCharacter": 89,
- "endLine": 5519,
+ "endLine": 5535,
"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"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\"\n ...",
"rule": "reportArgumentType",
"severity": "error",
"startCharacter": 46,
- "startLine": 5519
+ "startLine": 5535
},
{
"endCharacter": 75,
- "endLine": 5520,
+ "endLine": 5536,
"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"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",
"severity": "error",
"startCharacter": 39,
- "startLine": 5520
+ "startLine": 5536
},
{
"endCharacter": 75,
- "endLine": 5520,
+ "endLine": 5536,
"file": "ReforceXY/user_data/freqaimodels/ReforceXY.py",
"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\"\n ...",
"rule": "reportArgumentType",
"severity": "error",
"startCharacter": 39,
- "startLine": 5520
+ "startLine": 5536
},
{
"endCharacter": 33,
- "endLine": 36,
+ "endLine": 37,
"file": "ReforceXY/user_data/strategies/RLAgentStrategy.py",
"message": "Argument of type \"Scalar\" cannot be assigned to parameter \"x\" of type \"ConvertibleToInt\" in function \"__new__\"\n Type \"Scalar\" is not assignable to type \"ConvertibleToInt\"\n Type \"complex\" is not assignable to type \"ConvertibleToInt\"\n \"complex\" is not assignable to \"str\"\n \"complex\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"complex\" is incompatible with protocol \"SupportsInt\"\n \"__int__\" is not present\n \"complex\" is incompatible with protocol \"SupportsIndex\"",
"rule": "reportArgumentType",
"severity": "error",
"startCharacter": 20,
- "startLine": 36
+ "startLine": 37
},
{
"endCharacter": 33,
- "endLine": 36,
+ "endLine": 37,
"file": "ReforceXY/user_data/strategies/RLAgentStrategy.py",
"message": "Argument of type \"Scalar\" cannot be assigned to parameter \"x\" of type \"ConvertibleToInt\" in function \"__new__\"\n Type \"Scalar\" is not assignable to type \"ConvertibleToInt\"\n Type \"complex\" is not assignable to type \"ConvertibleToInt\"\n \"complex\" is not assignable to \"str\"\n \"complex\" is incompatible with protocol \"Buffer\"\n \"__buffer__\" is not present\n \"complex\" is incompatible with protocol \"SupportsInt\"\n \"__int__\" is not present\n \"complex\" is incompatible with protocol \"SupportsIndex\"\n ...",
"rule": "reportArgumentType",
"severity": "error",
"startCharacter": 20,
- "startLine": 36
+ "startLine": 37
},
{
"endCharacter": 17,
- "endLine": 63,
+ "endLine": 64,
"file": "ReforceXY/user_data/strategies/RLAgentStrategy.py",
"message": "\"can_short\" overrides symbol of same name in class \"IStrategy\"\n \"property\" is not assignable to \"bool\"",
"rule": "reportIncompatibleVariableOverride",
"severity": "error",
"startCharacter": 8,
- "startLine": 63
+ "startLine": 64
},
{
"endCharacter": 19,
- "endLine": 67,
+ "endLine": 68,
"file": "ReforceXY/user_data/strategies/RLAgentStrategy.py",
"message": "\"protections\" overrides symbol of same name in class \"IStrategy\"\n \"property\" is not assignable to \"list[Unknown]\"",
"rule": "reportIncompatibleVariableOverride",
"severity": "error",
"startCharacter": 8,
- "startLine": 67
+ "startLine": 68
}
],
"filesAnalyzed": 3,
DEFAULT_BASE_FACTOR: Final[float] = 100.0
DEFAULT_DISCOUNT_GAMMA: Final[float] = 0.95
+ DEFAULT_SEED: Final[int] = 42
+ DEFAULT_OPTUNA_SEED: Final[int] = 42
+ DEFAULT_N_ENVS: Final[int] = 1
+ DEFAULT_N_EVAL_ENVS: Final[int] = 1
+ DEFAULT_FRAME_STACKING: Final[int] = 0
+ DEFAULT_N_EVAL_STEPS: Final[int] = 10_000
+ DEFAULT_N_EVAL_EPISODES: Final[int] = 5
+ DEFAULT_TENSORBOARD_THROTTLE: Final[int] = 1
+ DEFAULT_OPTUNA_PURGE_PERIOD: Final[int] = 0
DEFAULT_MAX_TRADE_DURATION_CANDLES: Final[int] = 128
DEFAULT_IDLE_DURATION_MULTIPLIER: Final[int] = 4
self.recurrent: bool = self.model_type == ReforceXY._MODEL_TYPES[1] # "RecurrentPPO"
self.lr_schedule: bool = self.rl_config.get("lr_schedule", False)
self.cr_schedule: bool = self.rl_config.get("cr_schedule", False)
- self.n_envs: int = self.rl_config.get("n_envs", 1)
- self.n_eval_envs: int = self.rl_config.get("n_eval_envs", 1)
+ self.n_envs: int = self.rl_config.get("n_envs", ReforceXY.DEFAULT_N_ENVS)
+ self.n_eval_envs: int = self.rl_config.get("n_eval_envs", ReforceXY.DEFAULT_N_EVAL_ENVS)
self.multiprocessing: bool = self.rl_config.get("multiprocessing", False)
self.eval_multiprocessing: bool = self.rl_config.get("eval_multiprocessing", False)
- self.frame_stacking: int = self.rl_config.get("frame_stacking", 0)
- self.n_eval_steps: int = self.rl_config.get("n_eval_steps", 10_000)
- self.n_eval_episodes: int = self.rl_config.get("n_eval_episodes", 5)
+ self.frame_stacking: int = self.rl_config.get(
+ "frame_stacking", ReforceXY.DEFAULT_FRAME_STACKING
+ )
+ self.n_eval_steps: int = self.rl_config.get("n_eval_steps", ReforceXY.DEFAULT_N_EVAL_STEPS)
+ self.n_eval_episodes: int = self.rl_config.get(
+ "n_eval_episodes", ReforceXY.DEFAULT_N_EVAL_EPISODES
+ )
self.max_no_improvement_evals: int = self.rl_config.get("max_no_improvement_evals", 0)
self.min_evals: int = self.rl_config.get("min_evals", 0)
- self.rl_config.setdefault("tensorboard_throttle", 1)
+ self.rl_config.setdefault("tensorboard_throttle", ReforceXY.DEFAULT_TENSORBOARD_THROTTLE)
self.plot_new_best: bool = self.rl_config.get("plot_new_best", False)
self.check_envs: bool = self.rl_config.get("check_envs", True)
self.progressbar_callback: ProgressBarCallback | None = None
self.optuna_timeout_hours: float = self.rl_config_optuna.get("timeout_hours", 0)
self.optuna_n_trials: int = self.rl_config_optuna.get("n_trials", 100)
self.optuna_n_startup_trials: int = self.rl_config_optuna.get("n_startup_trials", 15)
- self.optuna_purge_period: int = int(self.rl_config_optuna.get("purge_period", 0))
+ self.optuna_purge_period: int = int(
+ self.rl_config_optuna.get("purge_period", ReforceXY.DEFAULT_OPTUNA_PURGE_PERIOD)
+ )
self.optuna_eval_callback: MaskableTrialEvalCallback | None = None
self._model_params_cache: dict[str, Any] | None = None
self._frame_buffers: dict[str, tuple[Any, deque[NDArray[np.float32]]]] = {}
function will set them to proper values and warn them
"""
if not isinstance(self.n_envs, int) or self.n_envs < 1:
- logger.warning("Config [global]: n_envs=%r invalid; defaulting to 1", self.n_envs)
- self.n_envs = 1
+ logger.warning(
+ "Config [global]: n_envs=%r invalid; defaulting to %d",
+ self.n_envs,
+ ReforceXY.DEFAULT_N_ENVS,
+ )
+ self.n_envs = ReforceXY.DEFAULT_N_ENVS
if not isinstance(self.n_eval_envs, int) or self.n_eval_envs < 1:
logger.warning(
- "Config [global]: n_eval_envs=%r invalid; defaulting to 1",
+ "Config [global]: n_eval_envs=%r invalid; defaulting to %d",
self.n_eval_envs,
+ ReforceXY.DEFAULT_N_EVAL_ENVS,
)
- self.n_eval_envs = 1
+ self.n_eval_envs = ReforceXY.DEFAULT_N_EVAL_ENVS
if self.multiprocessing and self.n_envs <= 1:
logger.warning(
"Config [global]: multiprocessing=True requires n_envs=%d>1; defaulting to False",
self.plot_new_best = False
if not isinstance(self.frame_stacking, int) or self.frame_stacking < 0:
logger.warning(
- "Config [global]: frame_stacking=%r invalid; defaulting to 0",
+ "Config [global]: frame_stacking=%r invalid; defaulting to %d",
self.frame_stacking,
+ ReforceXY.DEFAULT_FRAME_STACKING,
)
- self.frame_stacking = 0
+ self.frame_stacking = ReforceXY.DEFAULT_FRAME_STACKING
if self.frame_stacking == 1:
logger.warning(
"Config [global]: frame_stacking=1 equivalent to no stacking; defaulting to 0"
self.frame_stacking = 0
if not isinstance(self.n_eval_steps, int) or self.n_eval_steps <= 0:
logger.warning(
- "Config [global]: n_eval_steps=%r invalid; defaulting to 10000",
+ "Config [global]: n_eval_steps=%r invalid; defaulting to %d",
self.n_eval_steps,
+ ReforceXY.DEFAULT_N_EVAL_STEPS,
)
- self.n_eval_steps = 10_000
+ self.n_eval_steps = ReforceXY.DEFAULT_N_EVAL_STEPS
if not isinstance(self.n_eval_episodes, int) or self.n_eval_episodes <= 0:
logger.warning(
- "Config [global]: n_eval_episodes=%r invalid; defaulting to 5",
+ "Config [global]: n_eval_episodes=%r invalid; defaulting to %d",
self.n_eval_episodes,
+ ReforceXY.DEFAULT_N_EVAL_EPISODES,
)
- self.n_eval_episodes = 5
+ self.n_eval_episodes = ReforceXY.DEFAULT_N_EVAL_EPISODES
if not isinstance(self.optuna_purge_period, int) or self.optuna_purge_period < 0:
logger.warning(
- "Config [global]: purge_period=%r invalid; defaulting to 0",
+ "Config [global]: purge_period=%r invalid; defaulting to %d",
self.optuna_purge_period,
+ ReforceXY.DEFAULT_OPTUNA_PURGE_PERIOD,
)
- self.optuna_purge_period = 0
+ self.optuna_purge_period = ReforceXY.DEFAULT_OPTUNA_PURGE_PERIOD
if self.rl_config_optuna.get("continuous", False) and self.optuna_purge_period > 0:
logger.warning(
"Config [global]: purge_period has no effect when continuous=True; defaulting to 0"
"enabling add_state_info"
)
self.rl_config["add_state_info"] = True
- tensorboard_throttle = self.rl_config.get("tensorboard_throttle", 1)
+ tensorboard_throttle = self.rl_config.get(
+ "tensorboard_throttle", ReforceXY.DEFAULT_TENSORBOARD_THROTTLE
+ )
if not isinstance(tensorboard_throttle, int) or tensorboard_throttle < 1:
logger.warning(
- "Config [global]: tensorboard_throttle=%r invalid; defaulting to 1",
+ "Config [global]: tensorboard_throttle=%r invalid; defaulting to %d",
tensorboard_throttle,
+ ReforceXY.DEFAULT_TENSORBOARD_THROTTLE,
)
- self.rl_config["tensorboard_throttle"] = 1
+ self.rl_config["tensorboard_throttle"] = ReforceXY.DEFAULT_TENSORBOARD_THROTTLE
if self.continual_learning and bool(self.frame_stacking):
logger.warning(
"Config [global]: continual_learning=True incompatible with frame_stacking=%d; defaulting to False",
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
effective_params = self.get_model_params() if model_params is None else model_params
- seed = effective_params.get("seed", 42)
+ seed = effective_params.get("seed", ReforceXY.DEFAULT_SEED)
if self.check_envs:
logger.info("Env [%s]: checking environments", dk.pair)
model_params: dict[str, Any] = copy.deepcopy(self.model_training_parameters)
- model_params.setdefault("seed", 42)
+ model_params.setdefault("seed", ReforceXY.DEFAULT_SEED)
model_params.setdefault("gamma", ReforceXY.DEFAULT_DISCOUNT_GAMMA)
if not self.hyperopt and self.lr_schedule:
if self.activate_tensorboard:
info_callback = InfoMetricsCallback(
actions=Actions,
- throttle=self.rl_config.get("tensorboard_throttle", 1),
+ throttle=self.rl_config.get(
+ "tensorboard_throttle", ReforceXY.DEFAULT_TENSORBOARD_THROTTLE
+ ),
verbose=verbose,
)
callbacks.append(info_callback)
f"Valid: {', '.join(ReforceXY._SAMPLERS)}"
)
sampler = cast("SamplerType", sampler_value)
- seed = self.rl_config_optuna.get("seed", 42)
+ seed = self.rl_config_optuna.get("seed", ReforceXY.DEFAULT_OPTUNA_SEED)
match sampler:
case ReforceXY._SAMPLERS.tpe:
logger.info(
trial: Trial | None = None,
model_params: dict[str, Any] | None = None,
) -> tuple[VecEnv, VecEnv | None]:
- seed: int = self.get_model_params().get("seed", 42) if seed is None else seed
+ seed: int = (
+ self.get_model_params().get("seed", ReforceXY.DEFAULT_SEED) if seed is None else seed
+ )
if trial is not None:
seed += trial.number
set_random_seed(seed)
# Ensure that the sampled parameters take precedence
params = deepmerge(self.get_model_params(), params)
params["gamma"] = ReforceXY._resolve_discount_gamma(params)
- params["seed"] = params.get("seed", 42) + trial.number
+ params["seed"] = params.get("seed", ReforceXY.DEFAULT_SEED) + trial.number
logger.info("Hyperopt [%s]: trial #%d params: %s", study_name, trial.number, params)
nan_encountered = False
Tensorboard callback
"""
- def __init__(self, *args, throttle: int = 1, **kwargs):
+ def __init__(self, *args, throttle: int = ReforceXY.DEFAULT_TENSORBOARD_THROTTLE, **kwargs):
super().__init__(*args, **kwargs)
- self.throttle = 1 if throttle < 1 else throttle
+ self.throttle = ReforceXY.DEFAULT_TENSORBOARD_THROTTLE if throttle < 1 else throttle
def _safe_logger_record(
self, key: str, value: Any, exclude: tuple[str, ...] | None = None
)
pass
- @staticmethod
- def _build_train_freq(
- train_freq: TrainFreq | int | tuple[int, ...] | list[int] | None,
- ) -> int | None:
- train_freq_val: int | None = None
- if isinstance(train_freq, TrainFreq) and hasattr(train_freq, "frequency"):
- if isinstance(train_freq.frequency, int):
- train_freq_val = train_freq.frequency
- elif isinstance(train_freq, (tuple, list)) and train_freq:
- if isinstance(train_freq[0], int):
- train_freq_val = train_freq[0]
- elif isinstance(train_freq, int):
- train_freq_val = train_freq
-
- return train_freq_val
-
def _on_training_start(self) -> None:
lr = getattr(self.model, "learning_rate", None)
lr_schedule, lr_iv, lr_fv = get_schedule_type(lr)
"exploration_rate": float(self.model.exploration_rate),
}
)
- train_freq = InfoMetricsCallback._build_train_freq(
- getattr(self.model, "train_freq", None)
- )
+ train_freq = _build_train_freq(getattr(self.model, "train_freq", None))
if train_freq is not None:
hparam_dict.update({"train_freq": train_freq})
if ReforceXY._MODEL_TYPES[4] in self.model.__class__.__name__: # "QRDQN"
return -1
-def compute_gradient_steps(train_freq: Any, subsample_steps: Any) -> int:
- tf: int | None = None
+def _build_train_freq(train_freq: Any) -> int | None:
+ """Extract the collection frequency from an SB3 object or constructor value."""
if isinstance(train_freq, TrainFreq):
- tf = train_freq.frequency if isinstance(train_freq.frequency, int) else None
+ return train_freq.frequency if isinstance(train_freq.frequency, int) else None
if isinstance(train_freq, (tuple, list)) and train_freq:
- tf = train_freq[0] if isinstance(train_freq[0], int) else None
- elif isinstance(train_freq, int):
- tf = train_freq
+ return train_freq[0] if isinstance(train_freq[0], int) else None
+ return train_freq if isinstance(train_freq, int) else None
+
+
+def compute_gradient_steps(train_freq: Any, subsample_steps: Any) -> int:
+ tf = _build_train_freq(train_freq)
ss: int | None = subsample_steps if isinstance(subsample_steps, int) else None
import numpy as np
import pandas as pd
+from freqtrade.constants import LongShort
+from freqtrade.freqai.RL.Base5ActionRLEnv import Actions
# import talib.abstract as ta
from freqtrade.persistence import Trade
from pandas import DataFrame
TradingMode = Literal["margin", "futures", "spot"]
-TradeDirection = Literal["long", "short"]
logger = logging.getLogger(__name__)
INTERFACE_VERSION = 3
_TRADING_MODES: Final[tuple[TradingMode, ...]] = ("margin", "futures", "spot")
- _TRADE_DIRECTIONS: Final[tuple[TradeDirection, ...]] = ("long", "short")
- _ACTION_ENTER_LONG: Final[int] = 1
- _ACTION_EXIT_LONG: Final[int] = 2
- _ACTION_ENTER_SHORT: Final[int] = 3
- _ACTION_EXIT_SHORT: Final[int] = 4
+ _TRADE_DIRECTIONS: Final[tuple[LongShort, ...]] = ("long", "short")
+ _ACTION_ENTER_LONG: Final[int] = Actions.Long_enter.value
+ _ACTION_EXIT_LONG: Final[int] = Actions.Long_exit.value
+ _ACTION_ENTER_SHORT: Final[int] = Actions.Short_enter.value
+ _ACTION_EXIT_SHORT: Final[int] = Actions.Short_exit.value
_leverage_warning_state: tuple[str, str] | None = None
@property
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"rule": "reportArgumentType",
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
"message": "Argument of type \"Unknown | NDArray[Unknown] | Any | list[Unknown] | ndarray[_AnyShape, dtype[floating[Any]]]\" cannot be assigned to parameter \"base_weights\" of type \"NDArray[floating[Any]]\" in function \"_shuffle_split_rows\"\n Type \"Unknown | NDArray[Unknown] | Any | list[Unknown] | ndarray[_AnyShape, dtype[floating[Any]]]\" is not assignable to type \"NDArray[floating[Any]]\"\n \"list[Unknown]\" is not assignable to \"ndarray[_AnyShape, dtype[floating[Any]]]\"",
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"message": "Argument of type \"Unknown | NDArray[Unknown] | Any | list[Unknown] | ndarray[_AnyShape, dtype[floating[Any]]] | None\" cannot be assigned to parameter \"label_weights\" of type \"NDArray[floating[Any]] | None\" in function \"_shuffle_split_rows\"\n Type \"Unknown | NDArray[Unknown] | Any | list[Unknown] | ndarray[_AnyShape, dtype[floating[Any]]] | None\" is not assignable to type \"NDArray[floating[Any]] | None\"\n Type \"list[Unknown]\" is not assignable to type \"NDArray[floating[Any]] | None\"\n \"list[Unknown]\" is not assignable to \"ndarray[_AnyShape, dtype[floating[Any]]]\"\n \"list[Unknown]\" is not assignable to \"None\"",
"rule": "reportArgumentType",
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
"message": "Argument of type \"NDArray[floating[Any]] | Unknown | NDArray[Unknown] | Any | list[Unknown]\" cannot be assigned to parameter \"base_weights\" of type \"NDArray[floating[Any]]\" in function \"_compose_train_weights_with_support\"\n Type \"NDArray[floating[Any]] | Unknown | NDArray[Unknown] | Any | list[Unknown]\" is not assignable to type \"NDArray[floating[Any]]\"\n \"list[Unknown]\" is not assignable to \"ndarray[_AnyShape, dtype[floating[Any]]]\"",
"rule": "reportArgumentType",
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},
{
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
"message": "Argument of type \"NDArray[floating[Any]] | Unknown | NDArray[Unknown] | Any | list[Unknown] | None\" cannot be assigned to parameter \"label_weights\" of type \"NDArray[floating[Any]] | None\" in function \"_compose_train_weights_with_support\"\n Type \"NDArray[floating[Any]] | Unknown | NDArray[Unknown] | Any | list[Unknown] | None\" is not assignable to type \"NDArray[floating[Any]] | None\"\n Type \"list[Unknown]\" is not assignable to type \"NDArray[floating[Any]] | None\"\n \"list[Unknown]\" is not assignable to \"ndarray[_AnyShape, dtype[floating[Any]]]\"\n \"list[Unknown]\" is not assignable to \"None\"",
"rule": "reportArgumentType",
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},
{
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
"message": "Argument of type \"NDArray[floating[Any]] | Unknown | NDArray[Unknown] | Any | list[Unknown]\" cannot be assigned to parameter \"base_weights\" of type \"NDArray[floating[Any]]\" in function \"_compose_eval_weights\"\n Type \"NDArray[floating[Any]] | Unknown | NDArray[Unknown] | Any | list[Unknown]\" is not assignable to type \"NDArray[floating[Any]]\"\n \"list[Unknown]\" is not assignable to \"ndarray[_AnyShape, dtype[floating[Any]]]\"",
"rule": "reportArgumentType",
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{
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
"message": "Argument of type \"DataFrame | Unknown | NDArray[Unknown] | Any | list[Unknown]\" cannot be assigned to parameter \"train_df\" of type \"DataFrame\" in function \"build_data_dictionary\"\n Type \"DataFrame | Unknown | NDArray[Unknown] | Any | list[Unknown]\" is not assignable to type \"DataFrame\"\n \"list[Unknown]\" is not assignable to \"DataFrame\"",
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"message": "Argument of type \"DataFrame | Unknown | NDArray[Unknown] | Any | list[Unknown]\" cannot be assigned to parameter \"test_df\" of type \"DataFrame\" in function \"build_data_dictionary\"\n Type \"DataFrame | Unknown | NDArray[Unknown] | Any | list[Unknown]\" is not assignable to type \"DataFrame\"\n \"list[Unknown]\" is not assignable to \"DataFrame\"",
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},
{
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
"message": "Argument of type \"DataFrame | Unknown | NDArray[Unknown] | Any | list[Unknown]\" cannot be assigned to parameter \"train_labels\" of type \"DataFrame\" in function \"build_data_dictionary\"\n Type \"DataFrame | Unknown | NDArray[Unknown] | Any | list[Unknown]\" is not assignable to type \"DataFrame\"\n \"list[Unknown]\" is not assignable to \"DataFrame\"",
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"message": "Argument of type \"DataFrame | Unknown | NDArray[Unknown] | Any | list[Unknown]\" cannot be assigned to parameter \"test_labels\" of type \"DataFrame\" in function \"build_data_dictionary\"\n Type \"DataFrame | Unknown | NDArray[Unknown] | Any | list[Unknown]\" is not assignable to type \"DataFrame\"\n \"list[Unknown]\" is not assignable to \"DataFrame\"",
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
"message": "Cannot access attribute \"empty\" for class \"list[Unknown]\"\n Attribute \"empty\" is unknown",
"rule": "reportAttributeAccessIssue",
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{
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
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"rule": "reportAttributeAccessIssue",
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},
{
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
"message": "Argument of type \"Unknown | Any | ((value: Unknown, start: SupportsIndex = 0, stop: SupportsIndex = sys.maxsize, /) -> int)\" cannot be assigned to parameter \"target\" of type \"Index[Any]\" in function \"get_indexer\"\n Type \"Unknown | Any | ((value: Unknown, start: SupportsIndex = 0, stop: SupportsIndex = sys.maxsize, /) -> int)\" is not assignable to type \"Index[Any]\"\n \"MethodType\" is not assignable to \"Index[Any]\"",
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"rule": "reportAttributeAccessIssue",
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"rule": "reportArgumentType",
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"rule": "reportReturnType",
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def test_knn_density_aggregation_and_neighbour_bounds_are_fatal(self):
cases = {
- "label_density_aggregation": ("median", "supported values are power_mean"),
- "label_density_n_neighbors": (0, "must be int >= 1"),
- "label_density_aggregation_param": (1.5, "must be in \\[0, 1\\]"),
+ "label_density_aggregation": ("median",),
+ "label_density_n_neighbors": (0, True, np.bool_(True), 1.5, "5"),
+ "label_density_aggregation_param": (
+ 1.5,
+ True,
+ np.bool_(False),
+ "0.5",
+ 1 + 0j,
+ [0.5],
+ 10**500,
+ np.longdouble("1e400"),
+ ),
}
- for key, (value, fragment) in cases.items():
- with self.subTest(key=key):
- ft_params = {"label_density_aggregation": "quantile"}
- ft_params[key] = value
- model = label_regressor(ft_params=ft_params)
- with self.assertRaisesRegex(ValueError, fragment):
- model._resolve_label_method_config("knn")
+ for key, values in cases.items():
+ for value in values:
+ with self.subTest(key=key, value=value):
+ ft_params = {"label_density_aggregation": "quantile"}
+ ft_params[key] = value
+ model = label_regressor(ft_params=ft_params)
+ with self.assertRaises(ValueError):
+ model._resolve_label_method_config("knn")
+
+ def test_invalid_scalar_orders_cannot_enter_persisted_selection_metadata(self):
+ for value in (True, np.bool_(False), "2.0", 2 + 0j, [2.0], 10**500):
+ with self.subTest(value=value):
+ model = label_regressor(ft_params={"label_p_order": value})
+ with self.assertRaises(ValueError):
+ model._optuna_label_selection_metadata()
def test_knn_aggregation_param_defaults_follow_the_aggregation(self):
expected = {
self.assertEqual(metadata["method_config"]["category"], "cluster")
self.assertIsInstance(json_roundtrip(metadata), dict)
+ def test_selection_metadata_serializes_a_numpy_aggregation_parameter(self):
+ model = label_regressor(
+ ft_params={
+ "label_method": "knn",
+ "label_density_aggregation": "quantile",
+ "label_density_aggregation_param": np.float32(0.5),
+ }
+ )
+ metadata = model._optuna_label_selection_metadata()
+ self.assertEqual(json_roundtrip(metadata)["method_config"]["aggregation_param"], 0.5)
+
def test_selection_metadata_refuses_non_finite_weights_and_p_order(self):
cases = {
- "label_weights": (
- {"label_weights": [0.5, np.inf]},
- "label_weights contains non-finite",
- ),
- "label_p_order": (
- {"label_p_order": float("nan")},
- "label_p_order is non-finite",
- ),
+ "label_weights": {"label_weights": [0.5, np.inf]},
+ "label_p_order": {"label_p_order": float("nan")},
}
- for name, (ft_params, fragment) in cases.items():
- with self.subTest(field=name), self.assertRaisesRegex(ValueError, fragment):
+ for name, ft_params in cases.items():
+ with self.subTest(field=name), self.assertRaises(ValueError):
label_regressor(ft_params=ft_params)._optuna_label_selection_metadata()
def test_optuna_create_sampler_maps_every_enum_to_its_sampler(self):
None,
"0.5",
[1.0],
+ True,
+ np.bool_(True),
+ 1 + 0j,
+ 10**500,
+ np.longdouble("1e400"),
):
with self.subTest(value=value):
self.assertIsNone(QuickAdapterRegressorV3.optuna_validate_value(value))
- def test_validate_value_passes_finite_numbers_through_unchanged(self):
- self.assertEqual(QuickAdapterRegressorV3.optuna_validate_value(3), 3)
- self.assertEqual(QuickAdapterRegressorV3.optuna_validate_value(-1.25), -1.25)
- self.assertEqual(QuickAdapterRegressorV3.optuna_validate_value(0.0), 0.0)
+ def test_validate_value_preserves_finite_python_and_numpy_scores(self):
+ for value, expected in (
+ (3, 3.0),
+ (-1.25, -1.25),
+ (0.0, 0.0),
+ (np.int64(2), 2.0),
+ (np.float32(0.5), 0.5),
+ ):
+ with self.subTest(value=value):
+ self.assertEqual(QuickAdapterRegressorV3.optuna_validate_value(value), expected)
+
+ def test_loaded_holdout_rmse_preserves_valid_scores_and_rejects_invalid_metadata(self):
+ for value, expected in (
+ (0, 0.0),
+ (0.25, 0.25),
+ (np.int64(2), 2.0),
+ (np.float32(0.5), 0.5),
+ (-1, np.inf),
+ (None, np.inf),
+ ("0.25", np.inf),
+ (True, np.inf),
+ (np.bool_(True), np.inf),
+ (np.nan, np.inf),
+ (np.inf, np.inf),
+ (-np.inf, np.inf),
+ (10**500, np.inf),
+ (np.longdouble("1e400"), np.inf),
+ ):
+ with self.subTest(value=value):
+ model, dk, _ = calibrated()
+ model._session_fitted_pairs.clear()
+ dk.data["extra_returns_per_train"]["holdout_rmse"] = value
+
+ model.fit_live_predictions(dk, PAIR)
+
+ self.assertEqual(dk.data["extra_returns_per_train"]["holdout_rmse"], expected)
def test_holdout_rmse_is_infinite_rather_than_a_fabricated_score(self):
# (in-memory score, persisted replay column, pair already fitted this session)
ABOVE_IDEAL = np.array([[2.0, 2.0]])
#: A row whose two objectives are unequal, so its power mean genuinely depends on p.
ASYMMETRIC_ROW = np.array([[0.25, 0.75]])
+INVALID_REAL_SCALARS = (
+ np.inf,
+ -np.inf,
+ np.nan,
+ True,
+ np.bool_(False),
+ "2.0",
+ 2 + 0j,
+ [],
+ np.array([2.0]),
+ 10**500,
+ np.longdouble("1e400"),
+)
def front() -> np.ndarray:
# The knn route hard-codes the raise mode, so a bad aggregation param aborts
# the selection instead of being quietly downgraded to a different aggregate.
line = np.array([[0.0, 0.0], [1.0, 0.0], [4.0, 0.0]])
- for aggregation, param, constraint in (
- ("quantile", 1.5, r"must be in \[0, 1\]"),
- ("quantile", -0.1, r"must be in \[0, 1\]"),
- ("power_mean", np.inf, "must be finite"),
+ for aggregation, param in (
+ ("quantile", 1.5),
+ ("quantile", -0.1),
+ ("power_mean", np.inf),
):
with self.subTest(aggregation=aggregation, param=param):
- with self.assertRaisesRegex(ValueError, constraint) as caught:
+ with self.assertRaises(ValueError) as caught:
QuickAdapterRegressorV3._knn_based_selection(
line,
aggregation,
pd.Series(dtype=float),
pd.Series([np.nan, np.nan]),
pd.Series([np.nan, np.inf]),
+ pd.Series([1 + 2j]),
+ pd.Series([10**500], dtype=object),
+ pd.Series([np.longdouble("1e400")], dtype=object),
):
with self.subTest(series=series.tolist()):
self.assertEqual(QuickAdapterRegressorV3.safe_min_pred(series), -2.0)
# ------------------------------------------------------------------ scalar validators
- def test_a_non_finite_scalar_is_refused_in_raise_mode(self):
- for value in (np.inf, -np.inf, np.nan):
+ def test_an_invalid_scalar_is_refused_in_raise_mode(self):
+ for value in INVALID_REAL_SCALARS:
with (
self.subTest(value=value, mode="raise"),
- self.assertRaisesRegex(ValueError, "must be finite"),
+ self.assertRaises(ValueError),
):
QuickAdapterRegressorV3._validate_scalar(value, ctx="label_p_order", mode=RAISE)
- def test_a_non_finite_scalar_is_warned_and_dropped_in_warn_mode(self):
- for value in (np.inf, -np.inf, np.nan):
+ def test_an_invalid_scalar_is_warned_and_dropped_in_warn_mode(self):
+ for value in INVALID_REAL_SCALARS:
with self.subTest(value=value, mode="warn"):
with self.assertLogs(LOGGER_NAME, "WARNING") as captured:
warned = QuickAdapterRegressorV3._validate_scalar(
value, ctx="label_p_order", mode=WARN
)
self.assertIsNone(warned)
- self.assertIn("must be finite", captured.output[0])
+ self.assertIn("label_p_order", captured.output[0])
- def test_a_non_finite_scalar_is_silently_dropped_in_none_mode(self):
- for value in (np.inf, -np.inf, np.nan):
+ def test_an_invalid_scalar_is_silently_dropped_in_none_mode(self):
+ for value in INVALID_REAL_SCALARS:
with self.subTest(value=value, mode="none"), self.assertNoLogs(LOGGER_NAME, "WARNING"):
self.assertIsNone(
QuickAdapterRegressorV3._validate_scalar(value, ctx="label_p_order", mode=NONE)
)
- # These three used to share one `with` block behind the raising call, so the warn and
- # none branches never executed at all: the raise on the first line unwound the block.
- # The suite was green and the test name claimed all three modes. Removing the non-finite
- # warning in `_validate_scalar`, or turning its `return None` into `return 0.0`, both
- # survived — the first because the warning was never asserted, the second because the
- # fallback case in the file used a degenerate row below the ideal count.
-
- def test_a_predicate_violation_is_reported_with_the_supplied_constraint(self):
- with self.assertRaisesRegex(ValueError, "must be > 0"):
+ def test_a_predicate_violation_preserves_raise_and_warn_modes(self):
+ with self.assertRaises(ValueError):
QuickAdapterRegressorV3._validate_scalar(
-1.0,
ctx="label_p_order",
constraint="must be > 0",
)
)
- self.assertIn("must be > 0", captured.output[0])
+ self.assertIn("label_p_order", captured.output[0])
def test_a_valid_scalar_is_returned_as_a_float_in_every_mode(self):
for mode in (RAISE, WARN, NONE):
- with self.subTest(mode=mode), self.assertNoLogs(LOGGER_NAME, "WARNING"):
- result = QuickAdapterRegressorV3._validate_scalar(3, ctx="label_p_order", mode=mode)
- self.assertIsInstance(result, float)
- self.assertAlmostEqual(result, 3.0, places=12)
+ for value, expected in ((3, 3.0), (np.int64(2), 2.0), (np.float32(0.5), 0.5)):
+ with (
+ self.subTest(mode=mode, value=value),
+ self.assertNoLogs(LOGGER_NAME, "WARNING"),
+ ):
+ result = QuickAdapterRegressorV3._validate_scalar(
+ value, ctx="label_p_order", mode=mode
+ )
+ self.assertIsInstance(result, float)
+ self.assertEqual(result, expected)
def test_a_missing_scalar_is_never_validated(self):
for mode in (RAISE, WARN, NONE):
)
def test_the_minkowski_p_must_be_strictly_positive(self):
- with self.assertRaisesRegex(ValueError, "must be > 0"):
+ with self.assertRaises(ValueError):
QuickAdapterRegressorV3._validate_minkowski_p(0.0, ctx="label_p_order")
for valid in (1e-9, 1.0, 2.0, np.inf - 0.0):
with self.subTest(valid=valid):
for invalid in (-1e-9, 1.0 + 1e-9):
with (
self.subTest(invalid=invalid),
- self.assertRaisesRegex(ValueError, r"must be in \[0, 1\]"),
+ self.assertRaises(ValueError),
):
QuickAdapterRegressorV3._validate_quantile_q(invalid, ctx="label_quantile")
+ def test_float_normalization_does_not_hide_quantile_or_positive_order_violations(self):
+ above_one = np.nextafter(np.longdouble(1), np.longdouble(2))
+ with self.assertRaises(ValueError):
+ QuickAdapterRegressorV3._validate_quantile_q(above_one, ctx="label_quantile")
+ below_float_range = np.nextafter(np.longdouble(0), np.longdouble(1))
+ if float(below_float_range) == 0.0:
+ with self.assertRaises(ValueError):
+ QuickAdapterRegressorV3._validate_minkowski_p(
+ below_float_range, ctx="label_p_order"
+ )
+
def test_the_power_mean_p_is_only_required_to_be_finite(self):
# p=0 is the geometric mean and p<0 a harmonic-style mean, so unlike the
# Minkowski order there is no sign constraint here.
for invalid in (np.inf, np.nan):
with (
self.subTest(invalid=invalid),
- self.assertRaisesRegex(ValueError, "must be finite"),
+ self.assertRaises(ValueError),
):
QuickAdapterRegressorV3._validate_power_mean_p(invalid, ctx="label_p_order")
import numpy as np
import pandas as pd
-from qa_support import QaTestCase
+from qa_support import QaTestCase, model_config, temporary_directory
from QuickAdapterV3 import QuickAdapterV3
LONG = QuickAdapterV3._TRADE_LONG
class ReversalConfirmationTest(QaTestCase):
+ def test_numpy_candle_precision_preserves_real_reversal_decisions(self):
+ for dtype in (np.float32, np.float64):
+ for side, closes, opens, passing_rate, failing_rate in (
+ (LONG, [100, 102, 104], [99, 101, 103], 110, 104),
+ (SHORT, [100, 98, 96], [101, 99, 97], 90, 96),
+ ):
+ with self.subTest(dtype=dtype, side=side), temporary_directory() as temp:
+ model = QuickAdapterV3(config=model_config(temp))
+ model.freqai_info = model.config["freqai"]
+ model.bot_start()
+ df = pd.DataFrame(
+ {
+ "date": pd.date_range("2026-01-01", periods=3, freq="5min", tz="UTC"),
+ "open": np.array(opens, dtype=dtype),
+ "close": np.array(closes, dtype=dtype),
+ "natr_label_period_candles": np.ones(3, dtype=dtype),
+ "label_period_candles": np.full(3, 2, dtype=dtype),
+ "label_natr_multiplier": np.ones(3, dtype=dtype),
+ }
+ )
+ for rate, expected in ((passing_rate, True), (failing_rate, False)):
+ self.assertEqual(
+ bool(
+ self._confirm(
+ model,
+ df,
+ side=side,
+ order=ENTRY,
+ rate=dtype(rate),
+ lookback=1,
+ decay=dtype(1),
+ lo=dtype(0.5),
+ hi=dtype(1),
+ )
+ ),
+ expected,
+ )
+
def _confirm(self, model, df, *, side, order, rate, lookback=0, decay=1.0, lo=0.5, hi=1.0):
return model.reversal_confirmed(
df,
LabelWeightSupportError,
OptunaNamespace,
Regressor,
+ _NumericValidator,
_optuna_quarantine_path,
+ coerce_finite_float,
compose_sample_weights,
ensure_datetime_series,
enum_error_message,
@staticmethod
def _coerce_int(value: Any, name: str, *, minimum: int) -> int:
- if isinstance(value, bool) or not isinstance(value, int) or value < minimum:
+ validator = _NumericValidator(min_value=minimum, require_int=True)
+ if not validator(value):
raise ValueError(
- f"Invalid data_split_parameters.{name} value {value!r}: must be int >= {minimum}"
+ f"Invalid data_split_parameters.{name} value {value!r}: {validator.message(name)}"
)
return value
@staticmethod
def _validate_scalar(
- value: float | None,
+ value: Any,
*,
ctx: str,
mode: ValidationMode,
) -> float | None:
if value is None:
return None
- if mode == _VALIDATION_MODES[2]: # "none"
- return (
- float(value)
- if (np.isfinite(value) and (predicate is None or predicate(value)))
- else None
- )
-
- if not np.isfinite(value):
- msg = f"Invalid {ctx} value {value!r}: must be finite"
- if mode == _VALIDATION_MODES[1]: # "raise"
- raise ValueError(msg)
- logger.warning(f"{msg}, using default")
- return None
-
- if predicate is not None and not predicate(value):
+ scalar = coerce_finite_float(value)
+ if scalar is None:
+ msg = f"Invalid {ctx} value {value!r}: must be a finite real number representable as a float"
+ elif predicate is not None and (not predicate(value) or not predicate(scalar)):
msg = f"Invalid {ctx} value {value!r}: {constraint}"
- if mode == _VALIDATION_MODES[1]: # "raise"
- raise ValueError(msg)
- logger.warning(f"{msg}, using default")
+ else:
+ return scalar
+ if mode == _VALIDATION_MODES[2]: # "none"
return None
-
- return float(value)
+ if mode == _VALIDATION_MODES[1]: # "raise"
+ raise ValueError(msg)
+ logger.warning(f"{msg}, using default")
+ return None
@staticmethod
def _validate_minkowski_p(
"label_density_n_neighbors",
QuickAdapterRegressorV3.LABEL_DENSITY_N_NEIGHBORS_DEFAULT,
)
- if not isinstance(n_neighbors, int) or n_neighbors < 1:
+ validator = _NumericValidator(min_value=1, require_int=True)
+ if not validator(n_neighbors):
raise ValueError(
- f"Invalid label_density_n_neighbors value {n_neighbors!r}: must be int >= 1"
+ f"Invalid label_density_n_neighbors value {n_neighbors!r}: "
+ f"{validator.message('label_density_n_neighbors')}"
)
config["n_neighbors"] = n_neighbors
if aggregation_param is not None:
if aggregation == QuickAdapterRegressorV3._DENSITY_AGG_QUANTILE:
- QuickAdapterRegressorV3._validate_quantile_q(
+ aggregation_param = QuickAdapterRegressorV3._validate_quantile_q(
aggregation_param,
ctx="label_density_aggregation_param",
)
elif aggregation == QuickAdapterRegressorV3._DENSITY_AGG_POWER_MEAN:
- QuickAdapterRegressorV3._validate_power_mean_p(
+ aggregation_param = QuickAdapterRegressorV3._validate_power_mean_p(
aggregation_param,
ctx="label_density_aggregation_param",
)
label_p_order = self.ft_params.get("label_p_order")
if label_weights is not None and not all(np.isfinite(float(w)) for w in label_weights):
raise ValueError(f"label_weights contains non-finite values: {label_weights!r}")
- if label_p_order is not None and not np.isfinite(float(label_p_order)):
- raise ValueError(f"label_p_order is non-finite: {label_p_order!r}")
+ label_p_order = QuickAdapterRegressorV3._validate_scalar(
+ label_p_order,
+ ctx="label_p_order",
+ mode=_VALIDATION_MODES[1], # "raise"
+ )
return {
"schema_version": _OPTUNA_LABEL_SELECTION_SCHEMA_VERSION,
"method_config": self._resolve_label_method_config(label_method),
"label_weights": (
[float(w) for w in label_weights] if label_weights is not None else None
),
- "label_p_order": (float(label_p_order) if label_p_order is not None else None),
+ "label_p_order": label_p_order,
}
@cached_property
@staticmethod
def _coerce_holdout_rmse(value: Any) -> float:
"""Keep genuine zero error; negative, missing and non-finite scores are unavailable."""
- if (
- isinstance(value, (int, float, np.integer, np.floating))
- and not isinstance(value, (bool, np.bool_))
- and np.isfinite(value)
- and value >= 0
- ):
- return float(value)
- return np.inf
+ score = coerce_finite_float(value)
+ return score if score is not None and value >= 0 else np.inf
@staticmethod
def _kitchen_model_identity(dk: FreqaiDataKitchen) -> tuple[str, str]:
@staticmethod
def optuna_validate_value(value: Any) -> float | None:
- return value if isinstance(value, (int, float)) and np.isfinite(value) else None
+ return coerce_finite_float(value)
def min_max_pred(
self,
fallback: float,
) -> float:
try:
- reduced = reducer(pred_label)
+ reduced = coerce_finite_float(reducer(pred_label))
except Exception:
- reduced = None
- if (
- reduced is not None
- and isinstance(reduced, (int, float, np.number))
- and np.isfinite(reduced)
- ):
- return float(reduced)
- return fallback
+ return fallback
+ return fallback if reduced is None else reduced
# ±2.0 fallbacks are out-of-[-1, 1] normalized-range sentinels.
@staticmethod
import numpy as np
import pandas_ta as pta
import talib.abstract as ta
+from freqtrade.constants import EntryExit, LongShort
from freqtrade.enums import TRADE_MODES
from freqtrade.exchange import (
timeframe_to_minutes,
zlema,
)
-TradeDirection = Literal["long", "short"]
InterpolationDirection = Literal["direct", "inverse"]
-OrderType = Literal["entry", "exit"]
TradingMode = Literal["spot", "margin", "futures"]
DfSignature = tuple[int, datetime.datetime | None]
class _FinalTakeProfitState(TypedDict):
version: int
exit_stage: int
- trade_direction: TradeDirection
+ trade_direction: LongShort
best_rate: float
retracement_distance: float
boundary_candle_date: str
INTERFACE_VERSION = 3
- _TRADE_DIRECTIONS: Final[tuple[TradeDirection, ...]] = ("long", "short")
- _TRADE_LONG: Final[str] = _TRADE_DIRECTIONS[0] # "long"
- _TRADE_SHORT: Final[str] = _TRADE_DIRECTIONS[1] # "short"
- _TRADE_DIRECTIONS_SET: Final[frozenset[TradeDirection]] = frozenset(_TRADE_DIRECTIONS)
+ _TRADE_DIRECTIONS: Final[tuple[LongShort, ...]] = ("long", "short")
+ _TRADE_LONG: Final[LongShort] = _TRADE_DIRECTIONS[0] # "long"
+ _TRADE_SHORT: Final[LongShort] = _TRADE_DIRECTIONS[1] # "short"
+ _TRADE_DIRECTIONS_SET: Final[frozenset[LongShort]] = frozenset(_TRADE_DIRECTIONS)
_INTERPOLATION_DIRECTIONS: Final[tuple[InterpolationDirection, ...]] = (
"direct",
"inverse",
)
_INTERPOLATION_DIRECT: Final[str] = _INTERPOLATION_DIRECTIONS[0] # "direct"
_INTERPOLATION_INVERSE: Final[str] = _INTERPOLATION_DIRECTIONS[1] # "inverse"
- _ORDER_TYPES: Final[tuple[OrderType, ...]] = ("entry", "exit")
- _ORDER_ENTRY: Final[str] = _ORDER_TYPES[0] # "entry"
- _ORDER_EXIT: Final[str] = _ORDER_TYPES[1] # "exit"
- _ORDER_TYPES_SET: Final[frozenset[OrderType]] = frozenset(_ORDER_TYPES)
+ _ORDER_TYPES: Final[tuple[EntryExit, ...]] = ("entry", "exit")
+ _ORDER_ENTRY: Final[EntryExit] = _ORDER_TYPES[0] # "entry"
+ _ORDER_EXIT: Final[EntryExit] = _ORDER_TYPES[1] # "exit"
+ _ORDER_TYPES_SET: Final[frozenset[EntryExit]] = frozenset(_ORDER_TYPES)
_TRADING_MODES: Final[tuple[TradingMode, ...]] = ("spot", "margin", "futures")
_TRADING_MODE_SPOT: Final[str] = _TRADING_MODES[0] # "spot"
_TRADING_MODE_MARGIN: Final[str] = _TRADING_MODES[1] # "margin"
*,
best_rate: float,
retracement_distance: float,
- trade_direction: TradeDirection,
+ trade_direction: LongShort,
) -> float | None:
if (
not np.isfinite(best_rate)
def _build_final_take_profit_state(
*,
exit_stage: int,
- trade_direction: TradeDirection,
+ trade_direction: LongShort,
current_rate: float,
take_profit_distance: float,
retracement_fraction: float,
state: Any,
*,
exit_stage: int,
- trade_direction: TradeDirection,
+ trade_direction: LongShort,
open_rate: float,
timeframe: str,
minimum_candle_date: datetime.datetime,
self,
df: DataFrame,
pair: str,
- side: TradeDirection,
+ side: LongShort,
min_natr_multiplier_fraction: float,
max_natr_multiplier_fraction: float,
candle_idx: int = -1,
self,
df: DataFrame,
pair: str,
- side: TradeDirection,
- order: OrderType,
+ side: LongShort,
+ order: EntryExit,
rate: float,
lookback_period_candles: int,
decay_fraction: float,
return False
if order not in QuickAdapterV3._ORDER_TYPES_SET:
return False
- if not isinstance(rate, (int, float)) or not np.isfinite(rate):
+ if not is_finite_number(rate):
return False
if (
- not isinstance(min_natr_multiplier_fraction, (int, float))
- or not isinstance(max_natr_multiplier_fraction, (int, float))
- or not np.isfinite(min_natr_multiplier_fraction)
- or not np.isfinite(max_natr_multiplier_fraction)
+ not is_finite_number(min_natr_multiplier_fraction)
+ or not is_finite_number(max_natr_multiplier_fraction)
or min_natr_multiplier_fraction < 0
or max_natr_multiplier_fraction < 0
or min_natr_multiplier_fraction > max_natr_multiplier_fraction
):
return False
lookback_period_candles = min(lookback_period_candles, max_lookback_period_candles)
- if not isinstance(decay_fraction, (int, float)):
+ if not is_finite_number(decay_fraction):
logger.debug(f"[{pair}] Denied {trade_direction} {order}: invalid decay_fraction type")
return False
if not (0.0 < decay_fraction <= 1.0):
max_natr_multiplier_fraction=max_natr_multiplier_fraction,
candle_idx=-1,
)
- current_ok = np.isfinite(current_threshold) and (
+ current_ok = is_finite_number(current_threshold) and (
(side == QuickAdapterV3._TRADE_LONG and rate > current_threshold)
or (side == QuickAdapterV3._TRADE_SHORT and rate < current_threshold)
)
unmeasurable_history_ok = order == QuickAdapterV3._ORDER_EXIT and current_ok
for k in range(1, lookback_period_candles + 1):
close_k = df.iloc[-k].get("close")
- if not isinstance(close_k, (int, float)) or not np.isfinite(close_k):
+ if close_k is None or not is_finite_number(close_k):
return unmeasurable_history_ok
decay_factor = decay_fraction**k
max_natr_multiplier_fraction=decayed_max_natr_multiplier_fraction,
candle_idx=-(k + 1),
)
- if not isinstance(threshold_k, (int, float)) or not np.isfinite(threshold_k):
+ if not is_finite_number(threshold_k):
return unmeasurable_history_ok
if (side == QuickAdapterV3._TRADE_LONG and not (close_k > threshold_k)) or (
)
+def coerce_finite_float(value: Any) -> float | None:
+ """Return a finite Python float for a real numeric scalar, otherwise None."""
+ if not is_finite_number(value):
+ return None
+ try:
+ result = float(value)
+ except (TypeError, ValueError, OverflowError):
+ return None
+ return result if math.isfinite(result) else None
+
+
@dataclass(frozen=True, slots=True)
class _EnumValidator:
valid_values: tuple[str, ...]
repositories / freqai-strategies.git / commitdiff
