Struct ValueAtRisk 

pub struct ValueAtRisk { /* private fields */ }

Calculates the historical Value at Risk (VaR) of portfolio returns.

VaR is the loss threshold that returns are not expected to exceed at a given confidence level. This is the non-parametric (historical) estimator: the empirical quantile of the return distribution at 1 - confidence.

VaR(c) = quantile(returns, 1 - c)

The quantile uses linear interpolation between closest ranks (matching numpy.percentile). confidence defaults to 0.95. The result is expressed as a return (e.g. -0.03 is a 3% loss threshold); more negative means greater risk. Returns NaN for an empty series.

References

• Jorion, P. (2007). Value at Risk: The New Benchmark for Managing Financial Risk (3rd ed.). McGraw-Hill.
• J.P. Morgan/Reuters (1996). RiskMetrics Technical Document (4th ed.).

Implementations

impl ValueAtRisk

pub fn new_checked(confidence: Option<f64>) -> Result<Self>

Creates a new checked ValueAtRisk instance.

Errors

Returns an error if confidence is not finite and in the range (0, 1).

pub fn new(confidence: Option<f64>) -> Self

Creates a new ValueAtRisk instance.

Panics

Panics if confidence is not finite and in the range (0, 1).

Trait Implementations

impl Clone for ValueAtRisk

fn clone(&self) -> ValueAtRisk

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ValueAtRisk

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl DerefToPyAny for ValueAtRisk

impl Display for ValueAtRisk

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'a, 'py> FromPyObject<'a, 'py> for ValueAtRisk

where Self: Clone,

type Error = PyClassGuardError<'a, 'py>

The type returned in the event of a conversion error.

fn extract( obj: Borrowed<'a, 'py, PyAny>, ) -> Result<Self, <Self as FromPyObject<'a, 'py>>::Error>

Extracts Self from the bound smart pointer obj.

impl<'py> IntoPyObject<'py> for ValueAtRisk

type Target = ValueAtRisk

The Python output type

type Output = Bound<'py, <ValueAtRisk as IntoPyObject<'py>>::Target>

The smart pointer type to use.

type Error = PyErr

The type returned in the event of a conversion error.

fn into_pyobject( self, py: Python<'py>, ) -> Result<<Self as IntoPyObject<'_>>::Output, <Self as IntoPyObject<'_>>::Error>

Performs the conversion.

impl PortfolioStatistic for ValueAtRisk

type Item = f64

fn name(&self) -> String

Returns the name of this statistic for display and identification purposes.

fn calculate_from_returns(&self, raw_returns: &Returns) -> Option<Self::Item>

Calculates the statistic from time-indexed returns data.

fn calculate_from_realized_pnls( &self, _realized_pnls: &[f64], ) -> Option<Self::Item>

Calculates the statistic from realized profit and loss values.

fn calculate_from_positions( &self, _positions: &[Position], ) -> Option<Self::Item>

Calculates the statistic from position data.

fn calculate_from_orders( &self, orders: Vec<Box<dyn Order>>, ) -> Option<Self::Item>

Calculates the statistic from order data.

fn calculate_from_returns_with_benchmark( &self, returns: &Returns, benchmark: &Returns, ) -> Option<Self::Item>

Calculates the statistic from time-indexed strategy returns relative to a benchmark.

fn align_returns(&self, a: &Returns, b: &Returns) -> (Vec<f64>, Vec<f64>)

Aligns two returns series onto a common daily grid.

fn check_valid_returns(&self, returns: &Returns) -> bool

Validates that returns data is not empty.

fn downsample_to_daily_bins(&self, returns: &Returns) -> Returns

Downsamples high-frequency returns to daily bins by geometric compounding.

fn calculate_std(&self, returns: &Returns) -> f64

Calculates the standard deviation of returns with Bessel’s correction.

impl PyClass for ValueAtRisk

const NAME: &str = "ValueAtRisk"

Name of the class.

type Frozen = False

Whether the pyclass is frozen.

impl PyClassImpl for ValueAtRisk

const MODULE: Option<&str>

Module which the class will be associated with.

const IS_BASETYPE: bool = false

#[pyclass(subclass)]

const IS_SUBCLASS: bool = false

#[pyclass(extends=…)]

const IS_MAPPING: bool = false

#[pyclass(mapping)]

const IS_SEQUENCE: bool = false

#[pyclass(sequence)]

const IS_IMMUTABLE_TYPE: bool = false

#[pyclass(immutable_type)]

const RAW_DOC: &'static CStr = /// Calculates the historical Value at Risk (`VaR`) of portfolio returns. /// /// `VaR` is the loss threshold that returns are not expected to exceed at a given /// confidence level. This is the non-parametric (historical) estimator: the /// empirical quantile of the return distribution at `1 - confidence`. /// /// `VaR(c) = quantile(returns, 1 - c)` /// /// The quantile uses linear interpolation between closest ranks (matching /// `numpy.percentile`). `confidence` defaults to `0.95`. The result is expressed /// as a return (e.g. `-0.03` is a 3% loss threshold); more negative means greater /// risk. Returns `NaN` for an empty series. /// /// # References /// /// - Jorion, P. (2007). *Value at Risk: The New Benchmark for Managing Financial Risk* /// (3rd ed.). McGraw-Hill. /// - J.P. Morgan/Reuters (1996). *RiskMetrics Technical Document* (4th ed.).

Docstring for the class provided on the struct or enum.

const DOC: &'static CStr

Fully rendered class doc, including the text_signature if a constructor is defined.

type Layout = <<ValueAtRisk as PyClassImpl>::BaseNativeType as PyClassBaseType>::Layout<ValueAtRisk>

Description of how this class is laid out in memory

type BaseType = PyAny

Base class

type ThreadChecker = NoopThreadChecker

This handles following two situations:

type Inventory = Pyo3MethodsInventoryForValueAtRisk

type PyClassMutability = <<PyAny as PyClassBaseType>::PyClassMutability as PyClassMutability>::MutableChild

Immutable or mutable

type Dict = PyClassDummySlot

Specify this class has #[pyclass(dict)] or not.

type WeakRef = PyClassDummySlot

Specify this class has #[pyclass(weakref)] or not.

type BaseNativeType = PyAny

The closest native ancestor. This is PyAny by default, and when you declare #[pyclass(extends=PyDict)], it’s PyDict.

fn items_iter() -> PyClassItemsIter

fn lazy_type_object() -> &'static LazyTypeObject<Self>

fn dict_offset() -> Option<PyObjectOffset>

Used to provide the dictoffset slot (equivalent to tp_dictoffset)

fn weaklist_offset() -> Option<PyObjectOffset>

Used to provide the weaklistoffset slot (equivalent to tp_weaklistoffset

impl PyClassNewTextSignature for ValueAtRisk

const TEXT_SIGNATURE: &'static str = "(confidence=None)"

impl PyStubType for ValueAtRisk

fn type_output() -> TypeInfo

The type to be used in the output signature, i.e. return type of the Python function or methods.

fn type_input() -> TypeInfo

The type to be used in the input signature, i.e. the arguments of the Python function or methods.

impl PyTypeInfo for ValueAtRisk

const NAME: &str = <Self as ::pyo3::PyClass>::NAME

👎Deprecated since 0.28.0:

prefer using ::type_object(py).name() to get the correct runtime value

Class name.

const MODULE: Option<&str> = <Self as ::pyo3::impl_::pyclass::PyClassImpl>::MODULE

👎Deprecated since 0.28.0:

prefer using ::type_object(py).module() to get the correct runtime value

Module name, if any.

fn type_object_raw(py: Python<'_>) -> *mut PyTypeObject

Returns the PyTypeObject instance for this type.

fn type_object(py: Python<'_>) -> Bound<'_, PyType>

Returns the safe abstraction over the type object.

fn is_type_of(object: &Bound<'_, PyAny>) -> bool

Checks if object is an instance of this type or a subclass of this type.

fn is_exact_type_of(object: &Bound<'_, PyAny>) -> bool

Checks if object is an instance of this type.