nautilus_analysis/statistics/
winner_avg.rs

1// -------------------------------------------------------------------------------------------------
2//  Copyright (C) 2015-2025 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 crate::statistic::PortfolioStatistic;
17
18#[repr(C)]
19#[derive(Debug)]
20#[cfg_attr(
21    feature = "python",
22    pyo3::pyclass(module = "nautilus_trader.core.nautilus_pyo3.analysis")
23)]
24pub struct AvgWinner {}
25
26impl PortfolioStatistic for AvgWinner {
27    type Item = f64;
28
29    fn name(&self) -> String {
30        stringify!(AvgWinner).to_string()
31    }
32
33    fn calculate_from_realized_pnls(&self, realized_pnls: &[f64]) -> Option<Self::Item> {
34        if realized_pnls.is_empty() {
35            return Some(0.0);
36        }
37
38        let winners: Vec<f64> = realized_pnls
39            .iter()
40            .filter(|&&pnl| pnl > 0.0)
41            .copied()
42            .collect();
43
44        if winners.is_empty() {
45            return Some(0.0);
46        }
47
48        let sum: f64 = winners.iter().sum();
49        Some(sum / winners.len() as f64)
50    }
51}
52
53#[cfg(test)]
54mod tests {
55    use nautilus_core::approx_eq;
56    use rstest::rstest;
57
58    use super::*;
59
60    #[rstest]
61    fn test_empty_pnls() {
62        let avg_winner = AvgWinner {};
63        let result = avg_winner.calculate_from_realized_pnls(&[]);
64        assert!(result.is_some());
65        assert!(approx_eq!(f64, result.unwrap(), 0.0, epsilon = 1e-9));
66    }
67
68    #[rstest]
69    fn test_no_winning_trades() {
70        let avg_winner = AvgWinner {};
71        let realized_pnls = vec![-100.0, -50.0, -200.0];
72        let result = avg_winner.calculate_from_realized_pnls(&realized_pnls);
73        assert!(result.is_some());
74        assert!(approx_eq!(f64, result.unwrap(), 0.0, epsilon = 1e-9));
75    }
76
77    #[rstest]
78    fn test_all_winning_trades() {
79        let avg_winner = AvgWinner {};
80        let realized_pnls = vec![100.0, 50.0, 200.0];
81        let result = avg_winner.calculate_from_realized_pnls(&realized_pnls);
82        assert!(result.is_some());
83        assert!(approx_eq!(
84            f64,
85            result.unwrap(),
86            116.66666666666667,
87            epsilon = 1e-9
88        ));
89    }
90
91    #[rstest]
92    fn test_mixed_trades() {
93        let avg_winner = AvgWinner {};
94        let realized_pnls = vec![100.0, -50.0, 200.0, -100.0];
95        let result = avg_winner.calculate_from_realized_pnls(&realized_pnls);
96        assert!(result.is_some());
97        assert!(approx_eq!(f64, result.unwrap(), 150.0, epsilon = 1e-9));
98    }
99
100    #[rstest]
101    fn test_name() {
102        let avg_winner = AvgWinner {};
103        assert_eq!(avg_winner.name(), "AvgWinner");
104    }
105}
nautilus_analysis/statistics/winner_avg.rs

nautilus_analysis/statistics/
winner_avg.rs
