finance_risk¶

Submodules¶

Classes¶

`CharacteristicFactorModel`	!!! abstract "Usage Documentation"
`CovarianceEstimate`	!!! abstract "Usage Documentation"
`CointegrationSpread`	!!! abstract "Usage Documentation"
`ExecutionTrajectory`	!!! abstract "Usage Documentation"
`MarketImpactEstimate`	!!! abstract "Usage Documentation"
`FactorRiskDecomposition`	!!! abstract "Usage Documentation"

Functions¶

`clean_characteristics`(→ numpy.ndarray)	Winsorize and z-score characteristic matrix column-wise.
`fit_characteristic_factor_model`(...)	Fit cross-sectional returns on cleaned characteristics.
`correlation_to_covariance`(→ numpy.ndarray)
`covariance_to_correlation`(→ numpy.ndarray)
`ledoit_wolf_covariance`(→ CovarianceEstimate)	Ledoit-Wolf constant-variance shrinkage covariance estimate.
`nearest_positive_semidefinite`(→ numpy.ndarray)	Project a symmetric matrix onto the positive-semidefinite cone.
`oas_covariance`(→ CovarianceEstimate)	Oracle Approximating Shrinkage covariance estimate.
`sample_covariance`(→ CovarianceEstimate)	Sample covariance of asset returns, annualized by `frequency`.
`cointegration_spread`(→ CointegrationSpread)	OLS hedge ratio and demeaned residual spread for a two-asset pair.
`rolling_correlation`(→ numpy.ndarray)	Rolling Pearson correlation with leading `nan` values before the first full window.
`transfer_entropy`(→ float)	Discrete transfer entropy `source[t-lag] -> target[t]` in nats.
`almgren_chriss_market_impact`(→ MarketImpactEstimate)	Simple Almgren-Chriss style spread, temporary, and permanent cost estimate.
`optimal_execution_trajectory`(→ ExecutionTrajectory)	Monotone liquidation trajectory with risk-aversion controlled front-loading.
`twap_schedule`(→ numpy.ndarray)
`vwap_schedule`(→ numpy.ndarray)
`factor_risk_decomposition`(→ FactorRiskDecomposition)	Decompose portfolio variance into systematic factor and specific risk.

Package Contents¶

class finance_risk.CharacteristicFactorModel(/, **data: Any)[source]¶

Bases: pydantic.BaseModel

!!! abstract “Usage Documentation”: [Models](../concepts/models.md)

A base class for creating Pydantic models.

__class_vars__¶: The names of the class variables defined on the model.

__private_attributes__¶: Metadata about the private attributes of the model.

__signature__¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__¶: The core schema of the model.

__pydantic_custom_init__¶: Whether the model has a custom __init__ function.

__pydantic_decorators__¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__¶: A dictionary containing metadata about generic Pydantic models. The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

__pydantic_parent_namespace__¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__¶: The name of the post-init method for the model, if defined.

__pydantic_root_model__¶: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__¶: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_fields__¶: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects.

__pydantic_computed_fields__¶: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_fields_set__¶: The names of fields explicitly set during instantiation.

__pydantic_private__¶: Values of private attributes set on the model instance.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

model_config¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

factor_returns: tuple[float, ...]¶

residual_variance: tuple[float, ...]¶

finance_risk.clean_characteristics(values: Any, *, winsor_z: float = 3.0) → numpy.ndarray[source]¶: Winsorize and z-score characteristic matrix column-wise.

finance_risk.fit_characteristic_factor_model(returns: Any, characteristics: Any, *, ridge: float = 1e-06) → CharacteristicFactorModel[source]¶: Fit cross-sectional returns on cleaned characteristics.

class finance_risk.CovarianceEstimate(/, **data: Any)[source]¶

Bases: pydantic.BaseModel

!!! abstract “Usage Documentation”: [Models](../concepts/models.md)

A base class for creating Pydantic models.

__class_vars__¶: The names of the class variables defined on the model.

__private_attributes__¶: Metadata about the private attributes of the model.

__signature__¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__¶: The core schema of the model.

__pydantic_custom_init__¶: Whether the model has a custom __init__ function.

__pydantic_decorators__¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__¶: A dictionary containing metadata about generic Pydantic models. The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

__pydantic_parent_namespace__¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__¶: The name of the post-init method for the model, if defined.

__pydantic_root_model__¶: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__¶: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_fields__¶: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects.

__pydantic_computed_fields__¶: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_fields_set__¶: The names of fields explicitly set during instantiation.

__pydantic_private__¶: Values of private attributes set on the model instance.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

model_config¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

matrix: numpy.ndarray¶

assets: tuple[str, ...]¶

method: str¶

frequency: int = 252¶

shrinkage: float | None = None¶

finance_risk.correlation_to_covariance(correlation: Any, volatility: Any) → numpy.ndarray[source]¶

finance_risk.covariance_to_correlation(covariance: Any) → numpy.ndarray[source]¶

finance_risk.ledoit_wolf_covariance(data: Any, *, assets: collections.abc.Sequence[str] | None = None, frequency: int = 252) → CovarianceEstimate[source]¶: Ledoit-Wolf constant-variance shrinkage covariance estimate.

finance_risk.nearest_positive_semidefinite(matrix: Any, *, epsilon: float = 0.0) → numpy.ndarray[source]¶: Project a symmetric matrix onto the positive-semidefinite cone.

finance_risk.oas_covariance(data: Any, *, assets: collections.abc.Sequence[str] | None = None, frequency: int = 252) → CovarianceEstimate[source]¶: Oracle Approximating Shrinkage covariance estimate.

finance_risk.sample_covariance(data: Any, *, assets: collections.abc.Sequence[str] | None = None, frequency: int = 252) → CovarianceEstimate[source]¶: Sample covariance of asset returns, annualized by frequency.

class finance_risk.CointegrationSpread(/, **data: Any)[source]¶

Bases: pydantic.BaseModel

!!! abstract “Usage Documentation”: [Models](../concepts/models.md)

A base class for creating Pydantic models.

__class_vars__¶: The names of the class variables defined on the model.

__private_attributes__¶: Metadata about the private attributes of the model.

__signature__¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__¶: The core schema of the model.

__pydantic_custom_init__¶: Whether the model has a custom __init__ function.

__pydantic_decorators__¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__¶: A dictionary containing metadata about generic Pydantic models. The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

__pydantic_parent_namespace__¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__¶: The name of the post-init method for the model, if defined.

__pydantic_root_model__¶: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__¶: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_fields__¶: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects.

__pydantic_computed_fields__¶: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_fields_set__¶: The names of fields explicitly set during instantiation.

__pydantic_private__¶: Values of private attributes set on the model instance.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

model_config¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

hedge_ratio: float¶

intercept: float¶

spread: numpy.ndarray¶

finance_risk.cointegration_spread(y: Any, x: Any) → CointegrationSpread[source]¶: OLS hedge ratio and demeaned residual spread for a two-asset pair.

finance_risk.rolling_correlation(x: Any, y: Any, *, window: int) → numpy.ndarray[source]¶: Rolling Pearson correlation with leading nan values before the first full window.

finance_risk.transfer_entropy(source: Any, target: Any, *, bins: int = 3, lag: int = 1) → float[source]¶: Discrete transfer entropy source[t-lag] -> target[t] in nats.

class finance_risk.ExecutionTrajectory(/, **data: Any)[source]¶

Bases: pydantic.BaseModel

!!! abstract “Usage Documentation”: [Models](../concepts/models.md)

A base class for creating Pydantic models.

__class_vars__¶: The names of the class variables defined on the model.

__private_attributes__¶: Metadata about the private attributes of the model.

__signature__¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__¶: The core schema of the model.

__pydantic_custom_init__¶: Whether the model has a custom __init__ function.

__pydantic_decorators__¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__¶: A dictionary containing metadata about generic Pydantic models. The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

__pydantic_parent_namespace__¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__¶: The name of the post-init method for the model, if defined.

__pydantic_root_model__¶: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__¶: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_fields__¶: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects.

__pydantic_computed_fields__¶: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_fields_set__¶: The names of fields explicitly set during instantiation.

__pydantic_private__¶: Values of private attributes set on the model instance.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

model_config¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

remaining: numpy.ndarray¶

trades: numpy.ndarray¶

class finance_risk.MarketImpactEstimate(/, **data: Any)[source]¶

Bases: pydantic.BaseModel

!!! abstract “Usage Documentation”: [Models](../concepts/models.md)

A base class for creating Pydantic models.

__class_vars__¶: The names of the class variables defined on the model.

__private_attributes__¶: Metadata about the private attributes of the model.

__signature__¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__¶: The core schema of the model.

__pydantic_custom_init__¶: Whether the model has a custom __init__ function.

__pydantic_decorators__¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__¶: A dictionary containing metadata about generic Pydantic models. The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

__pydantic_parent_namespace__¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__¶: The name of the post-init method for the model, if defined.

__pydantic_root_model__¶: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__¶: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_fields__¶: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects.

__pydantic_computed_fields__¶: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_fields_set__¶: The names of fields explicitly set during instantiation.

__pydantic_private__¶: Values of private attributes set on the model instance.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

model_config¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

spread_cost: float¶

temporary_impact: float¶

permanent_impact: float¶

total_cost: float¶

finance_risk.almgren_chriss_market_impact(*, quantity: float, volatility: float, daily_volume: float, spread: float = 0.0, temporary_impact: float = 0.1, permanent_impact: float = 0.1) → MarketImpactEstimate[source]¶: Simple Almgren-Chriss style spread, temporary, and permanent cost estimate.

finance_risk.optimal_execution_trajectory(*, total_quantity: float, periods: int, risk_aversion: float = 1.0) → ExecutionTrajectory[source]¶: Monotone liquidation trajectory with risk-aversion controlled front-loading.

finance_risk.twap_schedule(*, total_quantity: float, periods: int) → numpy.ndarray[source]¶

finance_risk.vwap_schedule(*, total_quantity: float, volume_curve: Any) → numpy.ndarray[source]¶

class finance_risk.FactorRiskDecomposition(/, **data: Any)[source]¶

Bases: pydantic.BaseModel

!!! abstract “Usage Documentation”: [Models](../concepts/models.md)

A base class for creating Pydantic models.

__class_vars__¶: The names of the class variables defined on the model.

__private_attributes__¶: Metadata about the private attributes of the model.

__signature__¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__¶: The core schema of the model.

__pydantic_custom_init__¶: Whether the model has a custom __init__ function.

__pydantic_decorators__¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__¶: A dictionary containing metadata about generic Pydantic models. The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

__pydantic_parent_namespace__¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__¶: The name of the post-init method for the model, if defined.

__pydantic_root_model__¶: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__¶: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_fields__¶: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects.

__pydantic_computed_fields__¶: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_fields_set__¶: The names of fields explicitly set during instantiation.

__pydantic_private__¶: Values of private attributes set on the model instance.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

model_config¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

total_variance: float¶

systematic_variance: float¶

specific_variance: float¶

factor_contributions: dict[str, float]¶

finance_risk.factor_risk_decomposition(weights: Any, factor_loadings: Any, factor_covariance: Any, specific_variance: Any, *, factors: collections.abc.Sequence[str] | None = None) → FactorRiskDecomposition[source]¶: Decompose portfolio variance into systematic factor and specific risk.