nautilus_analysis/statistics/
loser_avg.rs

1// -------------------------------------------------------------------------------------------------
2//  Copyright (C) 2015-2025 Posei Systems Pty Ltd. All rights reserved.
3//  https://poseitrader.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 = "posei_trader.core.nautilus_pyo3.analysis")
23)]
24pub struct AvgLoser {}
25
26impl PortfolioStatistic for AvgLoser {
27    type Item = f64;
28
29    fn name(&self) -> String {
30        stringify!(AvgLoser).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 losers: Vec<f64> = realized_pnls
39            .iter()
40            .filter(|&&pnl| pnl <= 0.0)
41            .copied()
42            .collect();
43
44        if losers.is_empty() {
45            return Some(0.0);
46        }
47
48        let sum: f64 = losers.iter().sum();
49        Some(sum / losers.len() as f64)
50    }
51}
52
53#[cfg(test)]
54mod tests {
55    use rstest::rstest;
56
57    use super::*;
58
59    #[rstest]
60    fn test_empty_pnls() {
61        let avg_loser = AvgLoser {};
62        let result = avg_loser.calculate_from_realized_pnls(&[]);
63        assert!(result.is_some());
64        assert_eq!(result.unwrap(), 0.0);
65    }
66
67    #[rstest]
68    fn test_no_losers() {
69        let avg_loser = AvgLoser {};
70        let pnls = vec![10.0, 20.0, 30.0];
71        let result = avg_loser.calculate_from_realized_pnls(&pnls);
72        assert!(result.is_some());
73        assert_eq!(result.unwrap(), 0.0);
74    }
75
76    #[rstest]
77    fn test_only_losers() {
78        let avg_loser = AvgLoser {};
79        let pnls = vec![-10.0, -20.0, -30.0];
80        let result = avg_loser.calculate_from_realized_pnls(&pnls);
81        assert!(result.is_some());
82        assert_eq!(result.unwrap(), -20.0);
83    }
84
85    #[rstest]
86    fn test_mixed_pnls() {
87        let avg_loser = AvgLoser {};
88        let pnls = vec![10.0, -20.0, 30.0, -40.0];
89        let result = avg_loser.calculate_from_realized_pnls(&pnls);
90        assert!(result.is_some());
91        assert_eq!(result.unwrap(), -30.0);
92    }
93
94    #[rstest]
95    fn test_zero_included() {
96        let avg_loser = AvgLoser {};
97        let pnls = vec![10.0, 0.0, -20.0, -30.0];
98        let result = avg_loser.calculate_from_realized_pnls(&pnls);
99        assert!(result.is_some());
100        // Average of [0.0, -20.0, -30.0]
101        assert_eq!(result.unwrap(), -16.666666666666668);
102    }
103
104    #[rstest]
105    fn test_single_loser() {
106        let avg_loser = AvgLoser {};
107        let pnls = vec![-10.0];
108        let result = avg_loser.calculate_from_realized_pnls(&pnls);
109        assert!(result.is_some());
110        assert_eq!(result.unwrap(), -10.0);
111    }
112
113    #[rstest]
114    fn test_name() {
115        let avg_loser = AvgLoser {};
116        assert_eq!(avg_loser.name(), "AvgLoser");
117    }
118}
nautilus_analysis/statistics/loser_avg.rs

nautilus_analysis/statistics/
loser_avg.rs
