nautilus_analysis/python/statistics/
profit_factor.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_model::position::Position;
19use pyo3::prelude::*;
20
21use super::transform_returns;
22use crate::{statistic::PortfolioStatistic, statistics::profit_factor::ProfitFactor};
23
24#[pymethods]
25#[pyo3_stub_gen::derive::gen_stub_pymethods]
26impl ProfitFactor {
27    /// Calculates the profit factor based on portfolio returns.
28    ///
29    /// Profit factor is defined as the ratio of gross profits to gross losses:
30    /// `Sum(Positive Returns) / Abs(Sum(Negative Returns))`
31    ///
32    /// A profit factor greater than 1.0 indicates a profitable strategy, while
33    /// a factor less than 1.0 indicates losses exceed gains.
34    ///
35    /// Generally:
36    /// - 1.0-1.5: Modest profitability
37    /// - 1.5-2.0: Good profitability
38    /// - > 2.0: Excellent profitability
39    ///
40    /// # References
41    ///
42    /// - Tharp, V. K. (1998). *Trade Your Way to Financial Freedom*. McGraw-Hill.
43    /// - Kaufman, P. J. (2013). *Trading Systems and Methods* (5th ed.). Wiley.
44    #[new]
45    fn py_new() -> Self {
46        Self {}
47    }
48
49    fn __repr__(&self) -> String {
50        self.to_string()
51    }
52
53    #[getter]
54    #[pyo3(name = "name")]
55    fn py_name(&self) -> String {
56        self.name()
57    }
58
59    #[pyo3(name = "calculate_from_returns")]
60    #[expect(clippy::needless_pass_by_value)]
61    fn py_calculate_from_returns(&mut self, raw_returns: BTreeMap<u64, f64>) -> Option<f64> {
62        self.calculate_from_returns(&transform_returns(&raw_returns))
63    }
64
65    #[pyo3(name = "calculate_from_realized_pnls")]
66    fn py_calculate_from_realized_pnls(&mut self, _realized_pnls: Vec<f64>) -> Option<f64> {
67        None
68    }
69
70    #[pyo3(name = "calculate_from_positions")]
71    fn py_calculate_from_positions(&mut self, _positions: Vec<Position>) -> Option<f64> {
72        None
73    }
74}
nautilus_analysis/python/statistics/profit_factor.rs

nautilus_analysis/python/statistics/
profit_factor.rs
