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{
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
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"startCharacter": 57,
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
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{
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"startCharacter": 58,
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
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"startCharacter": 58,
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{
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
"message": "Cannot access attribute \"pop\" for class \"_NestedSequence[complex | bytes | str]\"\n Attribute \"pop\" is unknown",
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"severity": "error",
"startCharacter": 58,
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{
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
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"startCharacter": 58,
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{
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
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"startCharacter": 58,
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"file": "quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py",
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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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"message": "Cannot access attribute \"columns\" for class \"bytes\"\n Attribute \"columns\" is unknown",
"rule": "reportAttributeAccessIssue",
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"startCharacter": 61,
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"message": "Cannot access attribute \"columns\" for class \"complex\"\n Attribute \"columns\" is unknown",
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{
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"rule": "reportArgumentType",
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"rule": "reportArgumentType",
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"rule": "reportArgumentType",
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"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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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
"message": "Argument of type \"Any | None\" cannot be assigned to parameter \"timeframe\" of type \"str\" in function \"timeframe_to_prev_date\"\n Type \"Any | None\" is not assignable to type \"str\"\n \"None\" is not assignable to \"str\"",
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"message": "Argument of type \"int | None\" cannot be assigned to parameter \"trade_duration_candles\" of type \"int\" in function \"get_trade_natr\"\n Type \"int | None\" is not assignable to type \"int\"\n \"None\" is not assignable to \"int\"",
"rule": "reportArgumentType",
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.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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"file": "quickadapter/user_data/strategies/QuickAdapterV3.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": "quickadapter/user_data/strategies/QuickAdapterV3.py",
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
"message": "Argument of type \"Any | None\" cannot be assigned to parameter \"timeframe\" of type \"str\" in function \"get_analyzed_dataframe\"\n Type \"Any | None\" is not assignable to type \"str\"\n \"None\" is not assignable to \"str\"",
"rule": "reportArgumentType",
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{
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
"message": "Operator \"+\" not supported for \"None\"",
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"startCharacter": 21,
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{
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
"message": "Operator \"*\" not supported for types \"float\" and \"Any | None\"\n Operator \"*\" not supported for types \"float\" and \"None\"",
"rule": "reportOperatorIssue",
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"rule": "reportArgumentType",
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
"message": "Argument of type \"str\" cannot be assigned to parameter \"side\" of type \"TradeDirection\" in function \"reversal_confirmed\"\n Type \"str\" is not assignable to type \"TradeDirection\"\n \"str\" is not assignable to type \"Literal['long']\"\n \"str\" is not assignable to type \"Literal['short']\"",
"rule": "reportArgumentType",
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{
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
"message": "Argument of type \"str\" cannot be assigned to parameter \"order\" of type \"OrderType\" in function \"reversal_confirmed\"\n Type \"str\" is not assignable to type \"OrderType\"\n \"str\" is not assignable to type \"Literal['entry']\"\n \"str\" is not assignable to type \"Literal['exit']\"",
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
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"rule": "reportArgumentType",
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
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{
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
"message": "Argument of type \"str\" cannot be assigned to parameter \"side\" of type \"TradeDirection\" in function \"reversal_confirmed\"\n Type \"str\" is not assignable to type \"TradeDirection\"\n \"str\" is not assignable to type \"Literal['long']\"\n \"str\" is not assignable to type \"Literal['short']\"",
"rule": "reportArgumentType",
"severity": "error",
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},
{
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
"message": "Argument of type \"str\" cannot be assigned to parameter \"order\" of type \"OrderType\" in function \"reversal_confirmed\"\n Type \"str\" is not assignable to type \"OrderType\"\n \"str\" is not assignable to type \"Literal['entry']\"\n \"str\" is not assignable to type \"Literal['exit']\"",
"rule": "reportArgumentType",
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},
{
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
"message": "Argument of type \"int | float\" cannot be assigned to parameter \"lookback_period_candles\" of type \"int\" in function \"reversal_confirmed\"\n Type \"int | float\" is not assignable to type \"int\"\n \"float\" is not assignable to \"int\"",
"rule": "reportArgumentType",
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{
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
"message": "Argument of type \"datetime | None\" cannot be assigned to parameter \"current_candle_date\" of type \"datetime\" in function \"_normalize_final_take_profit_state\"\n Type \"datetime | None\" is not assignable to type \"datetime\"\n \"None\" is not assignable to \"datetime\"",
"rule": "reportArgumentType",
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{
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
"message": "Argument of type \"Any | None\" cannot be assigned to parameter \"timeframe\" of type \"str\" in function \"get_analyzed_dataframe\"\n Type \"Any | None\" is not assignable to type \"str\"\n \"None\" is not assignable to \"str\"",
"rule": "reportArgumentType",
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},
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
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"rule": "reportArgumentType",
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"message": "Argument of type \"int | float\" cannot be assigned to parameter \"lookback_period_candles\" of type \"int\" in function \"reversal_confirmed\"\n Type \"int | float\" is not assignable to type \"int\"\n \"float\" is not assignable to \"int\"",
"rule": "reportArgumentType",
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{
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
"message": "Argument of type \"LocalTrade\" cannot be assigned to parameter \"trade\" of type \"Trade\" in function \"get_trade_annotation_line_start_date\"\n \"LocalTrade\" is not assignable to \"Trade\"",
"rule": "reportArgumentType",
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},
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
"message": "Argument of type \"LocalTrade\" cannot be assigned to parameter \"trade\" of type \"Trade\" in function \"get_trade_exit_stage\"\n \"LocalTrade\" is not assignable to \"Trade\"",
"rule": "reportArgumentType",
"severity": "error",
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
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"rule": "reportArgumentType",
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{
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
"message": "Argument of type \"LocalTrade\" cannot be assigned to parameter \"trade\" of type \"Trade\" in function \"get_trade_entry_date\"\n \"LocalTrade\" is not assignable to \"Trade\"",
"rule": "reportArgumentType",
"severity": "error",
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{
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"file": "quickadapter/user_data/strategies/QuickAdapterV3.py",
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"rule": "reportArgumentType",
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},
{
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"file": "quickadapter/user_data/strategies/Utils.py",
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],
"filesAnalyzed": 5,
NamedTuple,
assert_never,
cast,
+ get_args,
)
import numpy as np
DensityMethod = Literal["knn", "medoid"]
SelectionMethod = DistanceMethod | ClusterMethod | DensityMethod
ValidationMode = Literal["warn", "raise", "none"]
+_VALIDATION_MODES: Final[tuple[ValidationMode, ...]] = get_args(ValidationMode)
SplitFn = Callable[[pd.DataFrame, pd.DataFrame, "SampleWeightInputs", pd.DataFrame], dict[str, Any]]
warnings.simplefilter(action="ignore", category=FutureWarning)
"cluster": _CLUSTER_METHODS,
"density": _DENSITY_METHODS,
}
+ _CATEGORY_DISTANCE, _CATEGORY_CLUSTER, _CATEGORY_DENSITY = _SELECTION_CATEGORIES
+ _SUPPORT_POLICY_FALLBACK: Final[Literal["fallback"]] = LABEL_WEIGHT_SUPPORT_POLICIES[0]
+ _SUPPORT_POLICY_RAISE: Final[Literal["raise"]] = LABEL_WEIGHT_SUPPORT_POLICIES[1]
_SELECTION_METHODS: Final[tuple[SelectionMethod, ...]] = (
*_DISTANCE_METHODS,
# SciPy-compatible distance metrics: the first 8 entries of
# ``_DISTANCE_METRICS`` route to ``scipy.spatial.distance.cdist``.
_SCIPY_METRICS_SET: Final[frozenset[str]] = frozenset(_DISTANCE_METRICS[:8])
+ _STANDARDIZED_DISTANCE_METRICS_SET: Final[frozenset[str]] = frozenset(_DISTANCE_METRICS[5:7])
_UNSUPPORTED_WEIGHTS_METRICS: Final[tuple[str, ...]] = (
_DISTANCE_METRICS[6], # "mahalanobis"
)
_PROBABILITY_DISTANCE_METRICS: Final[tuple[str, ...]] = (
- "jensenshannon",
- "hellinger",
- "shellinger",
+ _DISTANCE_METRICS[7],
+ _METRIC_HELLINGER,
+ _METRIC_SHELLINGER,
)
_PROBABILITY_DISTANCE_METRICS_SET: Final[frozenset[str]] = frozenset(
_PROBABILITY_DISTANCE_METRICS
)
_POWER_MEAN_MAP: Final[dict[str, float]] = {
- "harmonic_mean": -1.0,
- "geometric_mean": 0.0,
- "arithmetic_mean": 1.0,
- "quadratic_mean": 2.0,
- "cubic_mean": 3.0,
+ _DISTANCE_METRICS[10]: -1.0,
+ _DISTANCE_METRICS[11]: 0.0,
+ _DISTANCE_METRICS[12]: 1.0,
+ _DISTANCE_METRICS[13]: 2.0,
+ _DISTANCE_METRICS[14]: 3.0,
}
_POWER_MEAN_METRICS_SET: Final[frozenset[str]] = frozenset(_POWER_MEAN_MAP)
label_weights,
logger=logger,
context=context,
- on_collapse="fallback",
+ on_collapse=LABEL_WEIGHT_SUPPORT_POLICIES[0],
)
except LabelWeightSupportError as exc:
logger.warning(
"""
reason_text = "; ".join(reasons)
match policy:
- case "raise":
+ case QuickAdapterRegressorV3._SUPPORT_POLICY_RAISE:
raise ValueError(
f"{context}: label weighting support failed ({reason_text}); "
"support_policy='raise'"
)
- case "fallback":
+ case QuickAdapterRegressorV3._SUPPORT_POLICY_FALLBACK:
logger.warning(
"%s: label weighting support failed (%s); "
"falling back to sanitized base weights (support_policy='fallback')",
) -> float | None:
if value is None:
return None
- if mode == "none":
+ if mode == _VALIDATION_MODES[2]:
return (
float(value)
if (np.isfinite(value) and (predicate is None or predicate(value)))
if not np.isfinite(value):
msg = f"Invalid {ctx} value {value!r}: must be finite"
- if mode == "raise":
+ if mode == _VALIDATION_MODES[1]:
raise ValueError(msg)
logger.warning(f"{msg}, using default")
return None
if predicate is not None and not predicate(value):
msg = f"Invalid {ctx} value {value!r}: {constraint}"
- if mode == "raise":
+ if mode == _VALIDATION_MODES[1]:
raise ValueError(msg)
logger.warning(f"{msg}, using default")
return None
@staticmethod
def _validate_minkowski_p(
- p: float | None, *, ctx: str, mode: ValidationMode = "raise"
+ p: float | None, *, ctx: str, mode: ValidationMode = _VALIDATION_MODES[1]
) -> float | None:
return QuickAdapterRegressorV3._validate_scalar(
p, ctx=ctx, mode=mode, predicate=lambda v: v > 0, constraint="must be > 0"
distance_metric: str,
weights: NDArray[np.floating] | None = None,
p: float | None = None,
- mode: ValidationMode = "none",
+ mode: ValidationMode = _VALIDATION_MODES[2],
metric_ctx: str = "distance_metric",
p_ctx: str = "p",
*,
if validated_p is not None:
kwargs["p"] = validated_p
- if distance_metric in {"seuclidean", "mahalanobis"}:
+ if distance_metric in QuickAdapterRegressorV3._STANDARDIZED_DISTANCE_METRICS_SET:
if reference_matrix.shape[0] < 2 or not np.all(np.isfinite(reference_matrix)):
raise ValueError(
"Standardized distances require a finite full front with at least two rows"
raise ValueError(
"Standardized distances require positive active-objective variances"
)
- if distance_metric == "seuclidean":
+ if distance_metric == QuickAdapterRegressorV3._DISTANCE_METRICS[5]:
kwargs["V"] = variances
else:
covariance = np.atleast_2d(np.cov(reference_matrix, rowvar=False, ddof=1))
@staticmethod
def _validate_quantile_q(
- q: float | None, *, ctx: str, mode: ValidationMode = "raise"
+ q: float | None, *, ctx: str, mode: ValidationMode = _VALIDATION_MODES[1]
) -> float | None:
return QuickAdapterRegressorV3._validate_scalar(
q,
@staticmethod
def _validate_power_mean_p(
- p: float | None, *, ctx: str, mode: ValidationMode = "raise"
+ p: float | None, *, ctx: str, mode: ValidationMode = _VALIDATION_MODES[1]
) -> float | None:
return QuickAdapterRegressorV3._validate_scalar(p, ctx=ctx, mode=mode)
@staticmethod
def _validate_metric_weights_support(
- metric: str, *, ctx: str, mode: ValidationMode = "warn"
+ metric: str, *, ctx: str, mode: ValidationMode = _VALIDATION_MODES[0]
) -> str | None:
if metric not in QuickAdapterRegressorV3._UNSUPPORTED_WEIGHTS_METRICS_SET:
return metric
- if mode == "none":
+ if mode == _VALIDATION_MODES[2]:
return None
msg = f"Invalid {ctx} value {metric!r}: does not support custom weights"
- if mode == "raise":
+ if mode == _VALIDATION_MODES[1]:
raise ValueError(msg)
logger.warning(f"{msg}, using uniform weights")
return None
n_objectives: int,
*,
ctx: str,
- mode: ValidationMode = "raise",
+ mode: ValidationMode = _VALIDATION_MODES[1],
) -> NDArray[np.floating] | None:
if weights is None:
return np.full(n_objectives, 1.0 / n_objectives)
return normalized
msg = f"Invalid {ctx} value: sum is zero"
- if mode == "raise":
+ if mode == _VALIDATION_MODES[1]:
raise ValueError(msg)
- if mode == "warn":
+ if mode == _VALIDATION_MODES[0]:
logger.warning(f"{msg}, using uniform weights")
return np.full(n_objectives, 1.0 / n_objectives)
valid_options: tuple[str, ...],
*,
ctx: str,
- mode: ValidationMode = "raise",
+ mode: ValidationMode = _VALIDATION_MODES[1],
default: str | None = None,
) -> str | None:
if isinstance(value, str) and value in valid_set:
return value
- if mode == "none":
+ if mode == _VALIDATION_MODES[2]:
return default
msg = enum_error_message(ctx, value, valid_options)
- if mode == "raise":
+ if mode == _VALIDATION_MODES[1]:
raise ValueError(msg)
logger.warning(f"{msg}, using {default!r}")
return default
ctx: str,
default: str,
aggregate_allowed: bool,
- mode: ValidationMode = "warn",
+ mode: ValidationMode = _VALIDATION_MODES[0],
) -> str:
if aggregate_allowed:
valid_metrics = QuickAdapterRegressorV3._LABEL_SELECTION_DISTANCE_METRICS_SET
label_p_order: float | None,
*,
ctx: str,
- mode: ValidationMode = "raise",
+ mode: ValidationMode = _VALIDATION_MODES[1],
) -> float | None:
p = (
label_p_order
QuickAdapterRegressorV3._SELECTION_METHODS_SET,
QuickAdapterRegressorV3._SELECTION_METHODS,
ctx="label_method",
- mode="raise",
+ mode=_VALIDATION_MODES[1],
)
category = QuickAdapterRegressorV3._get_selection_category(label_method)
"method": label_method,
}
- if category == "distance":
+ if category == QuickAdapterRegressorV3._CATEGORY_DISTANCE:
distance_metric = self.ft_params.get(
"label_distance_metric",
QuickAdapterRegressorV3.LABEL_DISTANCE_METRIC_DEFAULT,
distance_metric = QuickAdapterRegressorV3._validate_label_selection_metric(
distance_metric,
ctx="label_distance_metric",
- mode="warn",
+ mode=_VALIDATION_MODES[0],
default=QuickAdapterRegressorV3.LABEL_DISTANCE_METRIC_DEFAULT,
aggregate_allowed=True,
)
config["distance_metric"] = distance_metric
- elif category == "cluster":
+ elif category == QuickAdapterRegressorV3._CATEGORY_CLUSTER:
distance_metric = self.ft_params.get(
"label_cluster_metric",
QuickAdapterRegressorV3.LABEL_CLUSTER_METRIC_DEFAULT,
distance_metric = QuickAdapterRegressorV3._validate_label_selection_metric(
distance_metric,
ctx="label_cluster_metric",
- mode="warn",
+ mode=_VALIDATION_MODES[0],
default=QuickAdapterRegressorV3.LABEL_CLUSTER_METRIC_DEFAULT,
aggregate_allowed=False,
)
ctx="label_cluster_trial_selection_method",
)
config["trial_selection_method"] = trial_selection_method
- elif category == "density":
+ elif category == QuickAdapterRegressorV3._CATEGORY_DENSITY:
density_method = cast("DensityMethod", label_method)
density_metric_default = QuickAdapterRegressorV3._get_label_density_metric_default(
density_method
distance_metric = QuickAdapterRegressorV3._validate_label_selection_metric(
distance_metric,
ctx="label_density_metric",
- mode="warn",
+ mode=_VALIDATION_MODES[0],
default=density_metric_default,
aggregate_allowed=False,
)
QuickAdapterRegressorV3._SCALER_TYPES_SET,
QuickAdapterRegressorV3._SCALER_TYPES,
ctx="scaler",
- mode="raise",
+ mode=_VALIDATION_MODES[1],
)
feature_range = self.ft_params.get("range", QuickAdapterRegressorV3.RANGE_DEFAULT)
)
match method:
- case "train_test_split":
+ case QuickAdapterRegressorV3.DATA_SPLIT_METHOD_DEFAULT:
split_builder = self._make_train_test_split_datasets
- case "timeseries_split":
+ case QuickAdapterRegressorV3._DATA_SPLIT_TIMESERIES:
split_builder = self._make_timeseries_split_datasets
case _:
raise ValueError(
*,
weights: NDArray[np.floating] | None = None,
p: float | None = None,
- mode: ValidationMode = "none",
+ mode: ValidationMode = _VALIDATION_MODES[2],
p_ctx: str = "p",
) -> NDArray[np.floating]:
if distance_metric in QuickAdapterRegressorV3._POWER_MEAN_METRICS_SET:
distance_metric,
weights=weights,
p=p,
- mode="warn",
+ mode=_VALIDATION_MODES[0],
p_ctx="label_distance_p",
)
return np.abs(distances) if apply_abs else distances
label_weights,
n_objectives,
ctx="label_weights",
- mode="raise",
+ mode=_VALIDATION_MODES[1],
)
if n_samples == 1 and method in {
distance_metric = label_config["distance_metric"]
p = QuickAdapterRegressorV3._resolve_p_order(
- distance_metric, label_p_order, ctx=f"label_p_order for {method}", mode="none"
+ distance_metric,
+ label_p_order,
+ ctx=f"label_p_order for {method}",
+ mode=_VALIDATION_MODES[2],
)
distance_kwargs = (
QuickAdapterRegressorV3._prepare_distance_kwargs(
distance_metric,
weights=weights,
p=p,
- mode="warn",
+ mode=_VALIDATION_MODES[0],
metric_ctx=f"label_{category}_metric",
reference_matrix=normalized_matrix,
)
else {}
)
- if category == "distance":
+ if category == QuickAdapterRegressorV3._CATEGORY_DISTANCE:
if method == QuickAdapterRegressorV3._METHOD_COMPROMISE_PROGRAMMING:
return QuickAdapterRegressorV3._compromise_programming_scores(
normalized_matrix,
distance_kwargs=distance_kwargs,
)
- if category == "cluster":
+ if category == QuickAdapterRegressorV3._CATEGORY_CLUSTER:
cluster_metric = label_config["distance_metric"]
cluster_selection_method = label_config["selection_method"]
trial_selection_method = label_config["trial_selection_method"]
distance_kwargs=distance_kwargs,
)
- if category == "density":
+ if category == QuickAdapterRegressorV3._CATEGORY_DENSITY:
density_method = cast("DensityMethod", method)
density_metric = label_config["distance_metric"]
series.loc[result.indices] = result.directions
metrics: dict[str, list[float]] = {
- "amplitude": result.amplitudes,
- "amplitude_threshold_ratio": result.amplitude_threshold_ratios,
- "volume_rate": result.volume_rates,
- "speed": result.speeds,
- "efficiency_ratio": result.efficiency_ratios,
- "volume_weighted_efficiency_ratio": result.volume_weighted_efficiency_ratios,
+ COMBINED_METRICS[0]: result.amplitudes,
+ COMBINED_METRICS[1]: result.amplitude_threshold_ratios,
+ COMBINED_METRICS[2]: result.volume_rates,
+ COMBINED_METRICS[3]: result.speeds,
+ COMBINED_METRICS[4]: result.efficiency_ratios,
+ COMBINED_METRICS[5]: result.volume_weighted_efficiency_ratios,
}
known_at_lookahead = pd.Series(
return int(4.0 * sigma + 0.5)
if method == SMOOTHING_METHODS[7]: # "savgol"
polyorder = max(int(config.get("polyorder", DEFAULTS_LABEL_SMOOTHING["polyorder"])), 0)
- effective_window, _, _ = get_savgol_params(raw_window, polyorder, "mirror")
+ effective_window, _, _ = get_savgol_params(raw_window, polyorder, SMOOTHING_MODES[0])
elif method == SMOOTHING_METHODS[3]: # "kaiser_bessel_derived"
effective_window = get_even_window(raw_window)
else:
center=True,
min_periods=1,
).max()
- if method == "savgol" and mode == "interp":
+ if method == SMOOTHING_METHODS[7] and mode == SMOOTHING_MODES[4]:
smoothed_known_at_positions.iloc[:kernel_half_width] = known_at_positions.iloc[
: 2 * kernel_half_width + 1
].max()
*,
logger: Logger,
context: str,
- on_collapse: Literal["raise", "fallback"] = "raise",
+ on_collapse: Literal["raise", "fallback"] = LABEL_WEIGHT_SUPPORT_POLICIES[1],
) -> NDArray[np.floating]:
"""Combine base sample weights with the label importance weights.
return 2 * series - series.shift(int(lag))
+MA_MODES: Final[tuple[str, ...]] = ("sma", "ema", "wma", "dema", "tema", "trima", "kama", "t3")
+PRICE_MODES: Final[tuple[str, ...]] = ("average", "median", "typical", "weighted-close", "close")
+
+
@lru_cache(maxsize=_CACHE_MAXSIZE_SMALL)
def get_ma_fn(
mamode: str,
str,
Callable[[pd.Series | NDArray[np.floating], int], pd.Series | NDArray[np.floating]],
] = {
- "sma": ta.SMA,
- "ema": ta.EMA,
- "wma": ta.WMA,
- "dema": ta.DEMA,
- "tema": ta.TEMA,
- "trima": ta.TRIMA,
- "kama": ta.KAMA,
- "t3": ta.T3,
+ MA_MODES[0]: ta.SMA,
+ MA_MODES[1]: ta.EMA,
+ MA_MODES[2]: ta.WMA,
+ MA_MODES[3]: ta.DEMA,
+ MA_MODES[4]: ta.TEMA,
+ MA_MODES[5]: ta.TRIMA,
+ MA_MODES[6]: ta.KAMA,
+ MA_MODES[7]: ta.T3,
}
- return mamodes.get(mamode, mamodes["sma"])
+ return mamodes.get(mamode, mamodes[MA_MODES[0]])
@lru_cache(maxsize=_CACHE_MAXSIZE_SMALL)
@lru_cache(maxsize=_CACHE_MAXSIZE_SMALL)
def get_price_fn(pricemode: str) -> Callable[[pd.DataFrame], pd.Series]:
pricemodes = {
- "average": ta.AVGPRICE,
- "median": ta.MEDPRICE,
- "typical": ta.TYPPRICE,
- "weighted-close": ta.WCLPRICE,
- "close": lambda df: df.get("close"),
+ PRICE_MODES[0]: ta.AVGPRICE,
+ PRICE_MODES[1]: ta.MEDPRICE,
+ PRICE_MODES[2]: ta.TYPPRICE,
+ PRICE_MODES[3]: ta.WCLPRICE,
+ PRICE_MODES[4]: lambda df: df.get("close"),
}
- return pricemodes.get(pricemode, pricemodes["close"])
+ return pricemodes.get(pricemode, pricemodes[PRICE_MODES[4]])
def ewo(
dataframe: pd.DataFrame,
ma1_length: int = 5,
ma2_length: int = 34,
- pricemode: str = "close",
- mamode: str = "sma",
+ pricemode: str = PRICE_MODES[4],
+ mamode: str = MA_MODES[0],
zero_lag: bool = False,
normalize: bool = False,
*,
prices = get_price_fn(pricemode)(dataframe)
if zero_lag:
- if mamode == "ema":
+ if mamode == MA_MODES[1]:
def ma_fn(series, timeperiod):
return zlema(series, period=timeperiod)
jaw_shift: int = 8,
teeth_shift: int = 5,
lips_shift: int = 3,
- pricemode: str = "median",
+ pricemode: str = PRICE_MODES[1],
zero_lag: bool = False,
) -> tuple[pd.Series, pd.Series, pd.Series]:
"""
_EARLY_STOPPING_ROUNDS_DEFAULT: Final[int] = 50
+_REGRESSOR_EVAL_METRIC: Final[str] = "rmse"
+_XGBOOST_BOOSTERS: Final[tuple[str, ...]] = ("gbtree", "dart")
+_XGBOOST_GROW_POLICIES: Final[tuple[str, ...]] = ("depthwise", "lossguide")
+_LIGHTGBM_BOOSTING_TYPES: Final[tuple[str, ...]] = ("gbdt", "dart")
+_NGBOOST_DISTRIBUTIONS: Final[tuple[str, ...]] = (
+ "normal",
+ "lognormal",
+ "exponential",
+ "laplace",
+ "t",
+)
+_CATBOOST_TASK_TYPES: Final[tuple[str, ...]] = ("CPU", "GPU")
+_CATBOOST_DEFAULT_LOSS: Final[str] = "RMSE"
+_CATBOOST_BOOTSTRAP_TYPES: Final[tuple[str, ...]] = ("Bayesian", "Bernoulli", "MVS")
+_CATBOOST_BOOSTING_TYPES: Final[tuple[str, ...]] = ("Plain", "Ordered")
+_CATBOOST_GROW_POLICIES: Final[tuple[str, ...]] = ("SymmetricTree", "Depthwise", "Lossguide")
+
_CATBOOST_GPU_RSM_LOSS_FUNCTIONS: Final[tuple[str, ...]] = (
"PairLogit",
"PairLogitPairwise",
from ngboost.distns import Exponential, Laplace, LogNormal, Normal, T
dist_map = {
- "normal": Normal,
- "lognormal": LogNormal,
- "exponential": Exponential,
- "laplace": Laplace,
- "t": T,
+ _NGBOOST_DISTRIBUTIONS[0]: Normal,
+ _NGBOOST_DISTRIBUTIONS[1]: LogNormal,
+ _NGBOOST_DISTRIBUTIONS[2]: Exponential,
+ _NGBOOST_DISTRIBUTIONS[3]: Laplace,
+ _NGBOOST_DISTRIBUTIONS[4]: T,
}
if dist_name not in dist_map:
fit_callbacks.append(
EarlyStopping(
rounds=early_stopping_rounds,
- metric_name="rmse",
+ metric_name=_REGRESSOR_EVAL_METRIC,
data_name="validation_0",
save_best=True,
)
model = XGBRegressor(
objective="reg:squarederror",
- eval_metric="rmse",
+ eval_metric=_REGRESSOR_EVAL_METRIC,
callbacks=fit_callbacks if fit_callbacks else None,
**model_training_parameters,
)
if trial is not None and has_eval_set:
fit_callbacks.append(
- optuna.integration.LightGBMPruningCallback(trial, "rmse", valid_name="valid_0")
+ optuna.integration.LightGBMPruningCallback(
+ trial, _REGRESSOR_EVAL_METRIC, valid_name="valid_0"
+ )
)
model = LGBMRegressor(objective="regression", **model_training_parameters)
sample_weight=train_weights,
eval_set=eval_set,
eval_sample_weight=eval_weights,
- eval_metric="rmse",
+ eval_metric=_REGRESSOR_EVAL_METRIC,
init_model=init_model,
callbacks=fit_callbacks if fit_callbacks else None,
)
early_stopping_rounds = _pop_early_stopping_rounds(model_training_parameters, has_eval_set)
- dist = model_training_parameters.pop("dist", "normal")
- if dist == "lognormal":
+ dist = model_training_parameters.pop("dist", _NGBOOST_DISTRIBUTIONS[0])
+ if dist == _NGBOOST_DISTRIBUTIONS[1]:
label_sets = [y] + ([labels for _, labels in eval_set] if eval_set else [])
if any(
not np.all(np.isfinite(values) & (values > 0))
max_depth=model_training_parameters.pop("max_depth", None),
min_samples_split=model_training_parameters.pop("min_samples_split", 2),
min_samples_leaf=model_training_parameters.pop("min_samples_leaf", 1),
- random_state=model_training_parameters["random_state"],
+ random_state=model_training_parameters[spec.seed_param],
),
**model_training_parameters,
)
elif regressor == _REGRESSOR_SPECS.catboost.name:
from catboost import CatBoostRegressor, Pool
- model_training_parameters.setdefault("loss_function", "RMSE")
+ model_training_parameters.setdefault("loss_function", _CATBOOST_DEFAULT_LOSS)
if model_path is not None and "train_dir" not in model_training_parameters:
if trial is not None:
else:
model_training_parameters["train_dir"] = str(model_path / "catboost_info")
- task_type = model_training_parameters.get("task_type", "CPU")
- loss_function = model_training_parameters.get("loss_function", "RMSE")
- if task_type == "GPU":
+ task_type = model_training_parameters.get("task_type", _CATBOOST_TASK_TYPES[0])
+ loss_function = model_training_parameters.get("loss_function", _CATBOOST_DEFAULT_LOSS)
+ if task_type == _CATBOOST_TASK_TYPES[1]:
model_training_parameters.pop("gpu_vram_gb", None)
model_training_parameters.pop("n_jobs", None)
model_training_parameters.setdefault("max_ctr_complexity", 4)
_apply_verbosity_alias(model_training_parameters)
pruning_callback = None
- if trial is not None and has_eval_set and task_type != "GPU":
- pruning_callback = optuna.integration.CatBoostPruningCallback(trial, "RMSE")
+ if trial is not None and has_eval_set and task_type != _CATBOOST_TASK_TYPES[1]:
+ pruning_callback = optuna.integration.CatBoostPruningCallback(
+ trial, _CATBOOST_DEFAULT_LOSS
+ )
fit_callbacks.append(pruning_callback)
model = CatBoostRegressor(**model_training_parameters)
def resolve_optuna_model_parameters(regressor: Regressor, params: dict[str, Any]) -> dict[str, Any]:
"""Reconstruct estimator parameters from replayable raw Optuna suggestions."""
resolved = params.copy()
- if regressor == _REGRESSOR_SPECS.xgboost.name and resolved.get("grow_policy") == "lossguide":
+ if (
+ regressor == _REGRESSOR_SPECS.xgboost.name
+ and resolved.get("grow_policy") == _XGBOOST_GROW_POLICIES[1]
+ ):
resolved["max_depth"] = 0
elif regressor == _REGRESSOR_SPECS.histgradientboostingregressor.name and resolved.pop(
"l2_regularization_zero", False
ranges = _build_ranges(default_ranges, log_scaled_params)
- booster = trial.suggest_categorical("booster", ["gbtree", "dart"])
- grow_policy = trial.suggest_categorical("grow_policy", ["depthwise", "lossguide"])
+ booster = trial.suggest_categorical("booster", _XGBOOST_BOOSTERS)
+ grow_policy = trial.suggest_categorical("grow_policy", _XGBOOST_GROW_POLICIES)
params: dict[str, Any] = {
# Boosting/Training
trial, "max_leaves", ranges["max_leaves"], min_val=2, log=True
),
}
- if grow_policy == "lossguide"
+ if grow_policy == _XGBOOST_GROW_POLICIES[1]
else {
"max_depth": _optuna_suggest_int_from_range(
trial, "max_depth", ranges["max_depth"], min_val=1
),
}
- if booster == "dart":
+ if booster == _XGBOOST_BOOSTERS[1]:
params["sample_type"] = trial.suggest_categorical(
"sample_type", ["uniform", "weighted"]
)
ranges = _build_ranges(default_ranges, log_scaled_params)
- boosting_type = trial.suggest_categorical("boosting_type", ["gbdt", "dart"])
+ boosting_type = trial.suggest_categorical("boosting_type", _LIGHTGBM_BOOSTING_TYPES)
params: dict[str, Any] = {
# Boosting/Training
),
}
- if boosting_type == "dart":
+ if boosting_type == _LIGHTGBM_BOOSTING_TYPES[1]:
params["xgboost_dart_mode"] = trial.suggest_categorical(
"xgboost_dart_mode", [False, True]
)
log=True,
),
# Distribution
- "dist": trial.suggest_categorical("dist", ["normal", "lognormal"]),
+ "dist": trial.suggest_categorical("dist", _NGBOOST_DISTRIBUTIONS[:2]),
}
elif regressor == _REGRESSOR_SPECS.catboost.name:
# Parameter order: boosting -> tree structure -> regularization -> sampling
- task_type = model_training_parameters.get("task_type", "CPU")
- loss_function = model_training_parameters.get("loss_function", "RMSE")
+ task_type = model_training_parameters.get("task_type", _CATBOOST_TASK_TYPES[0])
+ loss_function = model_training_parameters.get("loss_function", _CATBOOST_DEFAULT_LOSS)
- if task_type == "GPU":
+ if task_type == _CATBOOST_TASK_TYPES[1]:
gpu_vram_gb = model_training_parameters.get("gpu_vram_gb", _CATBOOST_GPU_VRAM_DEFAULT)
matched_vram_gb = max(
(v for v in _CATBOOST_GPU_VRAM_PARAM_RANGES if v <= gpu_vram_gb),
"rsm": (0.5, 1.0),
"subsample": (0.6, 1.0),
}
- bootstrap_options = ["Bayesian", "Bernoulli"]
- boosting_type_options = ["Plain"]
+ bootstrap_options = _CATBOOST_BOOTSTRAP_TYPES[:2]
+ boosting_type_options = _CATBOOST_BOOSTING_TYPES[:1]
else: # CPU
default_ranges: dict[str, tuple[float, float]] = {
# Boosting/Training
"rsm": (0.5, 1.0),
"subsample": (0.6, 1.0),
}
- bootstrap_options = ["Bayesian", "Bernoulli", "MVS"]
- boosting_type_options = ["Plain", "Ordered"]
+ bootstrap_options = _CATBOOST_BOOTSTRAP_TYPES
+ boosting_type_options = _CATBOOST_BOOSTING_TYPES
log_scaled_params = {
"iterations",
boosting_type = trial.suggest_categorical("boosting_type", boosting_type_options)
bootstrap_type = trial.suggest_categorical("bootstrap_type", bootstrap_options)
- grow_policy = trial.suggest_categorical(
- "grow_policy", ["SymmetricTree", "Depthwise", "Lossguide"]
- )
- if boosting_type == "Ordered" and grow_policy != "SymmetricTree":
+ grow_policy = trial.suggest_categorical("grow_policy", _CATBOOST_GROW_POLICIES)
+ if (
+ boosting_type == _CATBOOST_BOOSTING_TYPES[1]
+ and grow_policy != _CATBOOST_GROW_POLICIES[0]
+ ):
raise optuna.TrialPruned("Ordered boosting is not supported for nonsymmetric trees")
params: dict[str, Any] = {
),
}
- if task_type == "CPU" or loss_function in _CATBOOST_GPU_RSM_LOSS_FUNCTIONS:
+ if (
+ task_type == _CATBOOST_TASK_TYPES[0]
+ or loss_function in _CATBOOST_GPU_RSM_LOSS_FUNCTIONS
+ ):
params["rsm"] = trial.suggest_float(
"rsm",
ranges["rsm"][0],
ranges["rsm"][1],
)
- if bootstrap_type == "Bayesian":
+ if bootstrap_type == _CATBOOST_BOOTSTRAP_TYPES[0]:
params["bagging_temperature"] = trial.suggest_float(
"bagging_temperature",
ranges["bagging_temperature"][0],
ranges["bagging_temperature"][1],
)
- if bootstrap_type in ["Bernoulli", "MVS"]:
+ if bootstrap_type in _CATBOOST_BOOTSTRAP_TYPES[1:]:
params["subsample"] = trial.suggest_float(
"subsample",
ranges["subsample"][0],
ranges["subsample"][1],
)
- if task_type == "GPU":
+ if task_type == _CATBOOST_TASK_TYPES[1]:
params["border_count"] = _optuna_suggest_int_from_range(
trial, "border_count", ranges["border_count"], min_val=1
)
repositories / freqai-strategies.git / commitdiff
