nautilus-analysis 0.59.0

Performance analysis and statistics for the Nautilus trading engine
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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 pyo3::prelude::*;

use super::transform_returns;
use crate::{statistic::PortfolioStatistic, statistics::beta_ratio::BetaRatio};

#[pymethods]
#[pyo3_stub_gen::derive::gen_stub_pymethods]
impl BetaRatio {
    /// Calculates the beta of portfolio returns relative to a benchmark.
    ///
    /// Beta measures the systematic risk (market sensitivity) of a portfolio and is
    /// calculated as the covariance of the portfolio and benchmark returns divided by
    /// the variance of the benchmark returns:
    ///
    /// `Beta = Cov(portfolio, benchmark) / Var(benchmark)`
    ///
    /// Sample (Bessel-corrected, `ddof = 1`) covariance and variance are used to match
    /// the standard deviation convention elsewhere in this crate. Beta is not annualized.
    ///
    /// # References
    ///
    /// - Sharpe, W. F. (1964). "Capital Asset Prices: A Theory of Market Equilibrium under
    ///   Conditions of Risk". *Journal of Finance*, 19(3), 425-442.
    /// - CFA Institute Investment Foundations, 3rd Edition
    #[new]
    fn py_new() -> Self {
        Self::new()
    }

    fn __repr__(&self) -> String {
        self.to_string()
    }

    #[getter]
    #[pyo3(name = "name")]
    fn py_name(&self) -> String {
        self.name()
    }

    #[pyo3(name = "calculate_from_returns")]
    fn py_calculate_from_returns(&self, _returns: BTreeMap<u64, f64>) -> Option<f64> {
        None
    }

    #[pyo3(name = "calculate_from_realized_pnls")]
    fn py_calculate_from_realized_pnls(&self, _realized_pnls: Vec<f64>) -> Option<f64> {
        None
    }

    #[pyo3(name = "calculate_from_positions")]
    fn py_calculate_from_positions(&self, _positions: Vec<Py<PyAny>>) -> Option<f64> {
        None
    }

    #[pyo3(name = "calculate_from_returns_with_benchmark")]
    #[expect(clippy::needless_pass_by_value)]
    fn py_calculate_from_returns_with_benchmark(
        &self,
        returns: BTreeMap<u64, f64>,
        benchmark: BTreeMap<u64, f64>,
    ) -> Option<f64> {
        self.calculate_from_returns_with_benchmark(
            &transform_returns(&returns),
            &transform_returns(&benchmark),
        )
    }
}