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

nautilus_analysis/python/statistics/
profit_factor.rs
