nautilus_analysis/python/
mod.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
16//! Python bindings from [PyO3](https://pyo3.rs).
17
18#![allow(
19    clippy::unused_self,
20    reason = "PyO3 statistic methods take &self for Python API parity even when stateless"
21)]
22
23pub mod analyzer;
24pub mod statistics;
25
26use pyo3::{prelude::*, pymodule};
27
28/// Initializes the Python `analysis` module.
29///
30/// Adds the `PortfolioAnalyzer` class and all portfolio statistics.
31///
32/// # Errors
33///
34/// Returns a Python exception if adding any class fails.
35#[pymodule]
36pub fn analysis(_: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> {
37    m.add_class::<crate::analyzer::PortfolioAnalyzer>()?;
38
39    // Statistics - Returns-based
40    m.add_class::<crate::statistics::cagr::CAGR>()?;
41    m.add_class::<crate::statistics::calmar_ratio::CalmarRatio>()?;
42    m.add_class::<crate::statistics::max_drawdown::MaxDrawdown>()?;
43    m.add_class::<crate::statistics::profit_factor::ProfitFactor>()?;
44    m.add_class::<crate::statistics::returns_avg::ReturnsAverage>()?;
45    m.add_class::<crate::statistics::returns_avg_loss::ReturnsAverageLoss>()?;
46    m.add_class::<crate::statistics::returns_avg_win::ReturnsAverageWin>()?;
47    m.add_class::<crate::statistics::returns_volatility::ReturnsVolatility>()?;
48    m.add_class::<crate::statistics::risk_return_ratio::RiskReturnRatio>()?;
49    m.add_class::<crate::statistics::sharpe_ratio::SharpeRatio>()?;
50    m.add_class::<crate::statistics::sortino_ratio::SortinoRatio>()?;
51
52    // Statistics - PnL-based
53    m.add_class::<crate::statistics::expectancy::Expectancy>()?;
54    m.add_class::<crate::statistics::loser_avg::AvgLoser>()?;
55    m.add_class::<crate::statistics::loser_max::MaxLoser>()?;
56    m.add_class::<crate::statistics::loser_min::MinLoser>()?;
57    m.add_class::<crate::statistics::win_rate::WinRate>()?;
58    m.add_class::<crate::statistics::winner_avg::AvgWinner>()?;
59    m.add_class::<crate::statistics::winner_max::MaxWinner>()?;
60    m.add_class::<crate::statistics::winner_min::MinWinner>()?;
61
62    // Statistics - Position-based
63    m.add_class::<crate::statistics::long_ratio::LongRatio>()?;
64
65    Ok(())
66}
nautilus_analysis/python/mod.rs

nautilus_analysis/python/
mod.rs
