nautilus_analysis/statistics/
loser_avg.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#[repr(C)]
23#[derive(Debug, Clone)]
24#[cfg_attr(
25    feature = "python",
26    pyo3::pyclass(module = "nautilus_trader.core.nautilus_pyo3.analysis", from_py_object)
27)]
28#[cfg_attr(
29    feature = "python",
30    pyo3_stub_gen::derive::gen_stub_pyclass(module = "nautilus_trader.analysis")
31)]
32pub struct AvgLoser {}
33
34impl Display for AvgLoser {
35    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
36        write!(f, "Avg Loser")
37    }
38}
39
40impl PortfolioStatistic for AvgLoser {
41    type Item = f64;
42
43    fn name(&self) -> String {
44        self.to_string()
45    }
46
47    fn calculate_from_realized_pnls(&self, realized_pnls: &[f64]) -> Option<Self::Item> {
48        if realized_pnls.is_empty() {
49            return Some(f64::NAN);
50        }
51
52        let losers: Vec<f64> = realized_pnls
53            .iter()
54            .filter(|&&pnl| pnl < 0.0)
55            .copied()
56            .collect();
57
58        if losers.is_empty() {
59            return Some(f64::NAN);
60        }
61
62        let sum: f64 = losers.iter().sum();
63        Some(sum / losers.len() as f64)
64    }
65
66    fn calculate_from_returns(&self, _returns: &Returns) -> Option<Self::Item> {
67        None
68    }
69
70    fn calculate_from_positions(&self, _positions: &[Position]) -> Option<Self::Item> {
71        None
72    }
73}
74
75#[cfg(test)]
76mod tests {
77    use nautilus_core::approx_eq;
78    use rstest::rstest;
79
80    use super::*;
81
82    #[rstest]
83    fn test_empty_pnls() {
84        let avg_loser = AvgLoser {};
85        let result = avg_loser.calculate_from_realized_pnls(&[]);
86        assert!(result.is_some());
87        assert!(result.unwrap().is_nan());
88    }
89
90    #[rstest]
91    fn test_no_losers() {
92        let avg_loser = AvgLoser {};
93        let pnls = vec![10.0, 20.0, 30.0];
94        let result = avg_loser.calculate_from_realized_pnls(&pnls);
95        assert!(result.is_some());
96        assert!(result.unwrap().is_nan());
97    }
98
99    #[rstest]
100    fn test_only_losers() {
101        let avg_loser = AvgLoser {};
102        let pnls = vec![-10.0, -20.0, -30.0];
103        let result = avg_loser.calculate_from_realized_pnls(&pnls);
104        assert!(result.is_some());
105        assert!(approx_eq!(f64, result.unwrap(), -20.0, epsilon = 1e-9));
106    }
107
108    #[rstest]
109    fn test_mixed_pnls() {
110        let avg_loser = AvgLoser {};
111        let pnls = vec![10.0, -20.0, 30.0, -40.0];
112        let result = avg_loser.calculate_from_realized_pnls(&pnls);
113        assert!(result.is_some());
114        assert!(approx_eq!(f64, result.unwrap(), -30.0, epsilon = 1e-9));
115    }
116
117    #[rstest]
118    fn test_zero_excluded() {
119        let avg_loser = AvgLoser {};
120        let pnls = vec![10.0, 0.0, -20.0, -30.0];
121        let result = avg_loser.calculate_from_realized_pnls(&pnls);
122        assert!(result.is_some());
123        // Zero excluded, average of [-20.0, -30.0]
124        assert!(approx_eq!(f64, result.unwrap(), -25.0, epsilon = 1e-9));
125    }
126
127    #[rstest]
128    fn test_single_loser() {
129        let avg_loser = AvgLoser {};
130        let pnls = vec![-10.0];
131        let result = avg_loser.calculate_from_realized_pnls(&pnls);
132        assert!(result.is_some());
133        assert!(approx_eq!(f64, result.unwrap(), -10.0, epsilon = 1e-9));
134    }
135
136    #[rstest]
137    fn test_name() {
138        let avg_loser = AvgLoser {};
139        assert_eq!(avg_loser.name(), "Avg Loser");
140    }
141}
nautilus_analysis/statistics/loser_avg.rs

nautilus_analysis/statistics/
loser_avg.rs
