nautilus_analysis/python/statistics/
value_at_risk.rs

1// -------------------------------------------------------------------------------------------------
2//  Copyright (C) 2015-2026 Nautech Systems Pty Ltd. All rights reserved.
3//  https://nautechsystems.io
4//
5//  Licensed under the GNU Lesser General Public License Version 3.0 (the "License");
6//  You may not use this file except in compliance with the License.
7//  You may obtain a copy of the License at https://www.gnu.org/licenses/lgpl-3.0.en.html
8//
9//  Unless required by applicable law or agreed to in writing, software
10//  distributed under the License is distributed on an "AS IS" BASIS,
11//  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12//  See the License for the specific language governing permissions and
13//  limitations under the License.
14// -------------------------------------------------------------------------------------------------
15
16use std::collections::BTreeMap;
17
18use nautilus_core::python::to_pyvalue_err;
19use nautilus_model::position::Position;
20use pyo3::prelude::*;
21
22use super::transform_returns;
23use crate::{statistic::PortfolioStatistic, statistics::value_at_risk::ValueAtRisk};
24
25#[pymethods]
26#[pyo3_stub_gen::derive::gen_stub_pymethods]
27impl ValueAtRisk {
28    /// Calculates the historical Value at Risk (`VaR`) of portfolio returns.
29    ///
30    /// `VaR` is the loss threshold that returns are not expected to exceed at a given
31    /// confidence level. This is the non-parametric (historical) estimator: the
32    /// empirical quantile of the return distribution at `1 - confidence`.
33    ///
34    /// `VaR(c) = quantile(returns, 1 - c)`
35    ///
36    /// The quantile uses linear interpolation between closest ranks (matching
37    /// `numpy.percentile`). `confidence` defaults to `0.95`. The result is expressed
38    /// as a return (e.g. `-0.03` is a 3% loss threshold); more negative means greater
39    /// risk. Returns `NaN` for an empty series.
40    ///
41    /// # References
42    ///
43    /// - Jorion, P. (2007). *Value at Risk: The New Benchmark for Managing Financial Risk*
44    ///   (3rd ed.). McGraw-Hill.
45    /// - J.P. Morgan/Reuters (1996). *RiskMetrics Technical Document* (4th ed.).
46    #[expect(
47        clippy::doc_markdown,
48        reason = "citation contains proper nouns with intra-word capitals"
49    )]
50    #[new]
51    #[pyo3(signature = (confidence=None))]
52    fn py_new(confidence: Option<f64>) -> PyResult<Self> {
53        Self::new_checked(confidence).map_err(to_pyvalue_err)
54    }
55
56    fn __repr__(&self) -> String {
57        self.to_string()
58    }
59
60    #[getter]
61    #[pyo3(name = "name")]
62    fn py_name(&self) -> String {
63        self.name()
64    }
65
66    #[pyo3(name = "calculate_from_returns")]
67    #[expect(clippy::needless_pass_by_value)]
68    fn py_calculate_from_returns(&mut self, raw_returns: BTreeMap<u64, f64>) -> Option<f64> {
69        self.calculate_from_returns(&transform_returns(&raw_returns))
70    }
71
72    #[pyo3(name = "calculate_from_realized_pnls")]
73    fn py_calculate_from_realized_pnls(&mut self, _realized_pnls: Vec<f64>) -> Option<f64> {
74        None
75    }
76
77    #[pyo3(name = "calculate_from_positions")]
78    fn py_calculate_from_positions(&mut self, _positions: Vec<Position>) -> Option<f64> {
79        None
80    }
81}
nautilus_analysis/python/statistics/value_at_risk.rs

nautilus_analysis/python/statistics/
value_at_risk.rs
