nautilus_analysis/statistics/
returns_avg_win.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 arithmetic mean of the positive portfolio returns.
23///
24/// Zero returns are excluded (neither wins nor losses). Returns `NaN` for an
25/// empty series or when there are no positive returns.
26#[repr(C)]
27#[derive(Debug, Clone)]
28#[cfg_attr(
29    feature = "python",
30    pyo3::pyclass(module = "nautilus_trader.analysis", from_py_object)
31)]
32#[cfg_attr(
33    feature = "python",
34    pyo3_stub_gen::derive::gen_stub_pyclass(module = "nautilus_trader.analysis")
35)]
36pub struct ReturnsAverageWin {}
37
38impl Display for ReturnsAverageWin {
39    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
40        write!(f, "Average Win (Return)")
41    }
42}
43
44impl PortfolioStatistic for ReturnsAverageWin {
45    type Item = f64;
46
47    fn name(&self) -> String {
48        self.to_string()
49    }
50
51    fn calculate_from_returns(&self, returns: &Returns) -> Option<Self::Item> {
52        if !self.check_valid_returns(returns) {
53            return Some(f64::NAN);
54        }
55
56        let positive_returns: Vec<f64> = returns.values().copied().filter(|&x| x > 0.0).collect();
57
58        if positive_returns.is_empty() {
59            return Some(f64::NAN);
60        }
61
62        let sum: f64 = positive_returns.iter().sum();
63        let count = positive_returns.len() as f64;
64
65        Some(sum / count)
66    }
67    fn calculate_from_realized_pnls(&self, _realized_pnls: &[f64]) -> Option<Self::Item> {
68        None
69    }
70
71    fn calculate_from_positions(&self, _positions: &[Position]) -> Option<Self::Item> {
72        None
73    }
74}
75
76#[cfg(test)]
77mod tests {
78    use std::collections::BTreeMap;
79
80    use nautilus_core::{UnixNanos, approx_eq};
81    use rstest::rstest;
82
83    use super::*;
84
85    fn create_returns(values: &[f64]) -> Returns {
86        let mut new_return = BTreeMap::new();
87        for (i, value) in values.iter().enumerate() {
88            new_return.insert(UnixNanos::from(i as u64), *value);
89        }
90        new_return
91    }
92
93    #[rstest]
94    fn test_empty_returns() {
95        let avg_win = ReturnsAverageWin {};
96        let returns = create_returns(&[]);
97        let result = avg_win.calculate_from_returns(&returns);
98        assert!(result.is_some());
99        assert!(result.unwrap().is_nan());
100    }
101
102    #[rstest]
103    fn test_all_negative() {
104        let avg_win = ReturnsAverageWin {};
105        let returns = create_returns(&[-10.0, -20.0, -30.0]);
106        let result = avg_win.calculate_from_returns(&returns);
107        assert!(result.is_some());
108        assert!(result.unwrap().is_nan());
109    }
110
111    #[rstest]
112    fn test_all_positive() {
113        let avg_win = ReturnsAverageWin {};
114        let returns = create_returns(&[10.0, 20.0, 30.0]);
115        let result = avg_win.calculate_from_returns(&returns);
116        assert!(result.is_some());
117        // Average of [10.0, 20.0, 30.0] = (10 + 20 + 30) / 3 = 20.0
118        assert!(approx_eq!(f64, result.unwrap(), 20.0, epsilon = 1e-9));
119    }
120
121    #[rstest]
122    fn test_mixed_returns() {
123        let avg_win = ReturnsAverageWin {};
124        let returns = create_returns(&[10.0, -20.0, 30.0, -40.0]);
125        let result = avg_win.calculate_from_returns(&returns);
126        assert!(result.is_some());
127        // Average of [10.0, 30.0] = (10 + 30) / 2 = 20.0
128        assert!(approx_eq!(f64, result.unwrap(), 20.0, epsilon = 1e-9));
129    }
130
131    #[rstest]
132    fn test_name() {
133        let avg_win = ReturnsAverageWin {};
134        assert_eq!(avg_win.name(), "Average Win (Return)");
135    }
136}
nautilus_analysis/statistics/returns_avg_win.rs

nautilus_analysis/statistics/
returns_avg_win.rs
