nautilus_analysis/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::fmt::Display;
17
18use nautilus_model::position::Position;
19
20use crate::{Returns, statistic::PortfolioStatistic};
21
22/// Calculates the profit factor based on portfolio returns.
23///
24/// Profit factor is defined as the ratio of gross profits to gross losses:
25/// `Sum(Positive Returns) / Abs(Sum(Negative Returns))`
26///
27/// A profit factor greater than 1.0 indicates a profitable strategy, while
28/// a factor less than 1.0 indicates losses exceed gains.
29///
30/// Generally:
31/// - 1.0-1.5: Modest profitability
32/// - 1.5-2.0: Good profitability
33/// - > 2.0: Excellent profitability
34///
35/// # References
36///
37/// - Tharp, V. K. (1998). *Trade Your Way to Financial Freedom*. McGraw-Hill.
38/// - Kaufman, P. J. (2013). *Trading Systems and Methods* (5th ed.). Wiley.
39#[repr(C)]
40#[derive(Debug, Clone)]
41#[cfg_attr(
42    feature = "python",
43    pyo3::pyclass(module = "nautilus_trader.core.nautilus_pyo3.analysis", from_py_object)
44)]
45#[cfg_attr(
46    feature = "python",
47    pyo3_stub_gen::derive::gen_stub_pyclass(module = "nautilus_trader.analysis")
48)]
49pub struct ProfitFactor {}
50
51impl Display for ProfitFactor {
52    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
53        write!(f, "Profit Factor")
54    }
55}
56
57impl PortfolioStatistic for ProfitFactor {
58    type Item = f64;
59
60    fn name(&self) -> String {
61        self.to_string()
62    }
63
64    fn calculate_from_returns(&self, returns: &Returns) -> Option<Self::Item> {
65        if !self.check_valid_returns(returns) {
66            return Some(f64::NAN);
67        }
68
69        // Zero returns are excluded from both sums (neither profit nor loss)
70        let (positive_returns_sum, negative_returns_sum) =
71            returns
72                .values()
73                .fold((0.0, 0.0), |(pos_sum, neg_sum), &pnl| {
74                    if pnl > 0.0 {
75                        (pos_sum + pnl, neg_sum)
76                    } else if pnl < 0.0 {
77                        (pos_sum, neg_sum + pnl)
78                    } else {
79                        (pos_sum, neg_sum)
80                    }
81                });
82
83        if negative_returns_sum == 0.0 {
84            return Some(f64::NAN);
85        }
86        Some((positive_returns_sum / negative_returns_sum).abs())
87    }
88    fn calculate_from_realized_pnls(&self, _realized_pnls: &[f64]) -> Option<Self::Item> {
89        None
90    }
91
92    fn calculate_from_positions(&self, _positions: &[Position]) -> Option<Self::Item> {
93        None
94    }
95}
96
97#[cfg(test)]
98mod profit_factor_tests {
99    use std::collections::BTreeMap;
100
101    use nautilus_core::{UnixNanos, approx_eq};
102    use rstest::rstest;
103
104    use super::*;
105
106    fn create_returns(values: &[f64]) -> Returns {
107        let mut new_return = BTreeMap::new();
108        for (i, value) in values.iter().enumerate() {
109            new_return.insert(UnixNanos::from(i as u64), *value);
110        }
111
112        new_return
113    }
114
115    #[rstest]
116    fn test_empty_returns() {
117        let profit_factor = ProfitFactor {};
118        let returns = create_returns(&[]);
119        let result = profit_factor.calculate_from_returns(&returns);
120        assert!(result.is_some());
121        assert!(result.unwrap().is_nan());
122    }
123
124    #[rstest]
125    fn test_all_positive() {
126        let profit_factor = ProfitFactor {};
127        let returns = create_returns(&[10.0, 20.0, 30.0]);
128        let result = profit_factor.calculate_from_returns(&returns);
129        assert!(result.is_some());
130        assert!(result.unwrap().is_nan());
131    }
132
133    #[rstest]
134    fn test_all_negative() {
135        let profit_factor = ProfitFactor {};
136        let returns = create_returns(&[-10.0, -20.0, -30.0]);
137        let result = profit_factor.calculate_from_returns(&returns);
138        assert!(result.is_some());
139        assert!(approx_eq!(f64, result.unwrap(), 0.0, epsilon = 1e-9));
140    }
141
142    #[rstest]
143    fn test_mixed_returns() {
144        let profit_factor = ProfitFactor {};
145        let returns = create_returns(&[10.0, -20.0, 30.0, -40.0]);
146        let result = profit_factor.calculate_from_returns(&returns);
147        assert!(result.is_some());
148        // (10.0 + 30.0) / |-20.0 + -40.0| = 40 / 60 = 0.666...
149        assert!(approx_eq!(
150            f64,
151            result.unwrap(),
152            0.6666666666666666,
153            epsilon = 1e-9
154        ));
155    }
156
157    #[rstest]
158    fn test_with_zero() {
159        let profit_factor = ProfitFactor {};
160        let returns = create_returns(&[10.0, 0.0, -20.0, -30.0]);
161        let result = profit_factor.calculate_from_returns(&returns);
162        assert!(result.is_some());
163        // Zero excluded: 10.0 / |-20.0 + -30.0| = 10 / 50 = 0.2
164        assert!(approx_eq!(f64, result.unwrap(), 0.2, epsilon = 1e-9));
165    }
166
167    #[rstest]
168    fn test_equal_positive_negative() {
169        let profit_factor = ProfitFactor {};
170        let returns = create_returns(&[20.0, -20.0]);
171        let result = profit_factor.calculate_from_returns(&returns);
172        assert!(result.is_some());
173        assert!(approx_eq!(f64, result.unwrap(), 1.0, epsilon = 1e-9));
174    }
175
176    #[rstest]
177    fn test_name() {
178        let profit_factor = ProfitFactor {};
179        assert_eq!(profit_factor.name(), "Profit Factor");
180    }
181}
nautilus_analysis/statistics/profit_factor.rs

nautilus_analysis/statistics/
profit_factor.rs
