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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::value_at_risk::ValueAtRisk};
#[pymethods]
#[pyo3_stub_gen::derive::gen_stub_pymethods]
impl ValueAtRisk {
/// 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.).
#[expect(
clippy::doc_markdown,
reason = "citation contains proper nouns with intra-word capitals"
)]
#[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
}
}