Struct ReturnsAverageLoss 

#[repr(C)]
pub struct ReturnsAverageLoss {}

Calculates the arithmetic mean of the negative portfolio returns.

Zero returns are excluded (neither wins nor losses). Returns NaN for an empty series or when there are no negative returns.

Trait Implementations

impl Clone for ReturnsAverageLoss

fn clone(&self) -> Self

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for ReturnsAverageLoss

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

Formats the value using the given formatter.

impl DerefToPyAny for ReturnsAverageLoss

impl Display for ReturnsAverageLoss

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

Formats the value using the given formatter.

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

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 ReturnsAverageLoss

type Target = ReturnsAverageLoss

The Python output type

type Output = Bound<'py, ReturnsAverageLoss>

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>) -> PyResult<Bound<'py, Self>>

Performs the conversion.

impl PortfolioStatistic for ReturnsAverageLoss

type Item = f64

fn name(&self) -> String

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

fn calculate_from_returns(&self, 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_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 ReturnsAverageLoss

const NAME: &str = "ReturnsAverageLoss"

Name of the class.

type Frozen = False

Whether the pyclass is frozen.

impl PyClassImpl for ReturnsAverageLoss

const MODULE: Option<&str>

Module which the class will be associated with.

const RAW_DOC: &'static CStr = /// Calculates the arithmetic mean of the negative portfolio returns. /// /// Zero returns are excluded (neither wins nor losses). Returns `NaN` for an /// empty series or when there are no negative returns.

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 = <<ReturnsAverageLoss as PyClassImpl>::BaseNativeType as PyClassBaseType>::Layout<ReturnsAverageLoss>

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 = Pyo3MethodsInventoryForReturnsAverageLoss

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 __traverse__(&self, visit: PyVisit<'_>) -> Result<(), PyTraverseError>

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)]

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 ReturnsAverageLoss

const TEXT_SIGNATURE: &'static str = "()"

impl PyStubType for ReturnsAverageLoss

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 ReturnsAverageLoss

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.