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// -------------------------------------------------------------------------------------------------
// Copyright (C) 2015-2026 Nautech Systems Pty Ltd. All rights reserved.
// https://nautechsystems.io
//
// Licensed under the GNU Lesser General Public License Version 3.0 (the "License");
// You may not use this file except in compliance with the License.
// You may obtain a copy of the License at https://www.gnu.org/licenses/lgpl-3.0.en.html
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// -------------------------------------------------------------------------------------------------
use std::collections::BTreeMap;
use nautilus_core::python::to_pyvalue_err;
use pyo3::prelude::*;
use super::transform_returns;
use crate::{statistic::PortfolioStatistic, statistics::expected_shortfall::ExpectedShortfall};
#[pymethods]
#[pyo3_stub_gen::derive::gen_stub_pymethods]
impl ExpectedShortfall {
/// Calculates the historical Expected Shortfall (Conditional Value at Risk) of
/// portfolio returns.
///
/// Expected Shortfall is the average of the losses that occur beyond the
/// `ValueAtRisk` threshold at a
/// given confidence level - the mean of the worst
/// `1 - confidence` tail of the return distribution. It is a coherent risk
/// measure and captures tail severity that `VaR` alone does not.
///
/// `ES(c) = mean( r | r <= VaR(c) )`
///
/// `confidence` defaults to `0.95`. The result is expressed as a return (e.g.
/// `-0.05` is a 5% expected tail loss); it is always less than or equal to the
/// corresponding `VaR`. Returns `NaN` for an empty series.
///
/// # References
///
/// - Acerbi, C., & Tasche, D. (2002). "Expected Shortfall: A Natural Coherent Alternative
/// to Value at Risk". *Economic Notes*, 31(2), 379-388.
/// - Rockafellar, R. T., & Uryasev, S. (2000). "Optimization of Conditional Value-at-Risk".
/// *Journal of Risk*, 2(3), 21-41.
#[new]
#[pyo3(signature = (confidence=None))]
fn py_new(confidence: Option<f64>) -> PyResult<Self> {
Self::new_checked(confidence).map_err(to_pyvalue_err)
}
fn __repr__(&self) -> String {
self.to_string()
}
#[getter]
#[pyo3(name = "name")]
fn py_name(&self) -> String {
self.name()
}
#[pyo3(name = "calculate_from_returns")]
#[expect(clippy::needless_pass_by_value)]
fn py_calculate_from_returns(&mut self, raw_returns: BTreeMap<u64, f64>) -> Option<f64> {
self.calculate_from_returns(&transform_returns(&raw_returns))
}
#[pyo3(name = "calculate_from_realized_pnls")]
fn py_calculate_from_realized_pnls(&mut self, _realized_pnls: Vec<f64>) -> Option<f64> {
None
}
#[pyo3(name = "calculate_from_positions")]
fn py_calculate_from_positions(&mut self, _positions: Vec<Py<PyAny>>) -> Option<f64> {
None
}
}