nautilus_analysis/python/statistics/
beta_ratio.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 pyo3::prelude::*;
19
20use super::transform_returns;
21use crate::{statistic::PortfolioStatistic, statistics::beta_ratio::BetaRatio};
22
23#[pymethods]
24#[pyo3_stub_gen::derive::gen_stub_pymethods]
25impl BetaRatio {
26    /// Calculates the beta of portfolio returns relative to a benchmark.
27    ///
28    /// Beta measures the systematic risk (market sensitivity) of a portfolio and is
29    /// calculated as the covariance of the portfolio and benchmark returns divided by
30    /// the variance of the benchmark returns:
31    ///
32    /// `Beta = Cov(portfolio, benchmark) / Var(benchmark)`
33    ///
34    /// Sample (Bessel-corrected, `ddof = 1`) covariance and variance are used to match
35    /// the standard deviation convention elsewhere in this crate. Beta is not annualized.
36    ///
37    /// # References
38    ///
39    /// - Sharpe, W. F. (1964). "Capital Asset Prices: A Theory of Market Equilibrium under
40    ///   Conditions of Risk". *Journal of Finance*, 19(3), 425-442.
41    /// - CFA Institute Investment Foundations, 3rd Edition
42    #[new]
43    fn py_new() -> Self {
44        Self::new()
45    }
46
47    fn __repr__(&self) -> String {
48        self.to_string()
49    }
50
51    #[getter]
52    #[pyo3(name = "name")]
53    fn py_name(&self) -> String {
54        self.name()
55    }
56
57    #[pyo3(name = "calculate_from_returns")]
58    fn py_calculate_from_returns(&self, _returns: BTreeMap<u64, f64>) -> Option<f64> {
59        None
60    }
61
62    #[pyo3(name = "calculate_from_realized_pnls")]
63    fn py_calculate_from_realized_pnls(&self, _realized_pnls: Vec<f64>) -> Option<f64> {
64        None
65    }
66
67    #[pyo3(name = "calculate_from_positions")]
68    fn py_calculate_from_positions(&self, _positions: Vec<Py<PyAny>>) -> Option<f64> {
69        None
70    }
71
72    #[pyo3(name = "calculate_from_returns_with_benchmark")]
73    #[expect(clippy::needless_pass_by_value)]
74    fn py_calculate_from_returns_with_benchmark(
75        &self,
76        returns: BTreeMap<u64, f64>,
77        benchmark: BTreeMap<u64, f64>,
78    ) -> Option<f64> {
79        self.calculate_from_returns_with_benchmark(
80            &transform_returns(&returns),
81            &transform_returns(&benchmark),
82        )
83    }
84}
nautilus_analysis/python/statistics/beta_ratio.rs

nautilus_analysis/python/statistics/
beta_ratio.rs
