nautilus_analysis/python/statistics/
information_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::information_ratio::InformationRatio};
22
23#[pymethods]
24#[pyo3_stub_gen::derive::gen_stub_pymethods]
25impl InformationRatio {
26    /// Calculates the information ratio of portfolio returns relative to a benchmark.
27    ///
28    /// The information ratio measures active return per unit of active risk (tracking error):
29    ///
30    /// `IR = mean(active) / std(active) * sqrt(period)`
31    ///
32    /// where `active_i = portfolio_i - benchmark_i`, `std` uses Bessel's correction
33    /// (`ddof = 1`), and the ratio is annualized by the square root of the specified period
34    /// (default: 252 trading days).
35    ///
36    /// # References
37    ///
38    /// - Goodwin, T. H. (1998). "The Information Ratio". *Financial Analysts Journal*, 54(4), 34-43.
39    /// - CFA Institute Investment Foundations, 3rd Edition
40    #[new]
41    #[pyo3(signature = (period=None))]
42    fn py_new(period: Option<usize>) -> Self {
43        Self::new(period)
44    }
45
46    fn __repr__(&self) -> String {
47        self.to_string()
48    }
49
50    #[getter]
51    #[pyo3(name = "name")]
52    fn py_name(&self) -> String {
53        self.name()
54    }
55
56    #[pyo3(name = "calculate_from_returns")]
57    fn py_calculate_from_returns(&self, _returns: BTreeMap<u64, f64>) -> Option<f64> {
58        None
59    }
60
61    #[pyo3(name = "calculate_from_realized_pnls")]
62    fn py_calculate_from_realized_pnls(&self, _realized_pnls: Vec<f64>) -> Option<f64> {
63        None
64    }
65
66    #[pyo3(name = "calculate_from_positions")]
67    fn py_calculate_from_positions(&self, _positions: Vec<Py<PyAny>>) -> Option<f64> {
68        None
69    }
70
71    #[pyo3(name = "calculate_from_returns_with_benchmark")]
72    #[expect(clippy::needless_pass_by_value)]
73    fn py_calculate_from_returns_with_benchmark(
74        &self,
75        returns: BTreeMap<u64, f64>,
76        benchmark: BTreeMap<u64, f64>,
77    ) -> Option<f64> {
78        self.calculate_from_returns_with_benchmark(
79            &transform_returns(&returns),
80            &transform_returns(&benchmark),
81        )
82    }
83}
nautilus_analysis/python/statistics/information_ratio.rs

nautilus_analysis/python/statistics/
information_ratio.rs
