nautilus_analysis/statistics/
loser_min.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 MinLoser {}
25
26impl PortfolioStatistic for MinLoser {
27    type Item = f64;
28
29    fn name(&self) -> String {
30        stringify!(MinLoser).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        realized_pnls
39            .iter()
40            .filter(|&&pnl| pnl < 0.0)
41            .max_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
42            .copied()
43    }
44}
45
46#[cfg(test)]
47mod tests {
48    use rstest::rstest;
49
50    use super::*;
51
52    #[rstest]
53    fn test_empty_pnls() {
54        let min_loser = MinLoser {};
55        let result = min_loser.calculate_from_realized_pnls(&[]);
56        assert!(result.is_some());
57        assert_eq!(result.unwrap(), 0.0);
58    }
59
60    #[rstest]
61    fn test_all_positive() {
62        let min_loser = MinLoser {};
63        let pnls = vec![10.0, 20.0, 30.0];
64        let result = min_loser.calculate_from_realized_pnls(&pnls);
65        assert!(result.is_none());
66    }
67
68    #[rstest]
69    fn test_all_negative() {
70        let min_loser = MinLoser {};
71        let pnls = vec![-10.0, -20.0, -30.0];
72        let result = min_loser.calculate_from_realized_pnls(&pnls);
73        assert!(result.is_some());
74        assert_eq!(result.unwrap(), -10.0);
75    }
76
77    #[rstest]
78    fn test_mixed_pnls() {
79        let min_loser = MinLoser {};
80        let pnls = vec![10.0, -20.0, 30.0, -40.0];
81        let result = min_loser.calculate_from_realized_pnls(&pnls);
82        assert!(result.is_some());
83        assert_eq!(result.unwrap(), -20.0);
84    }
85
86    #[rstest]
87    fn test_with_zero() {
88        let min_loser = MinLoser {};
89        let pnls = vec![10.0, 0.0, -20.0, -30.0];
90        let result = min_loser.calculate_from_realized_pnls(&pnls);
91        assert!(result.is_some());
92        assert_eq!(result.unwrap(), -20.0);
93    }
94
95    #[rstest]
96    fn test_single_negative() {
97        let min_loser = MinLoser {};
98        let pnls = vec![-10.0];
99        let result = min_loser.calculate_from_realized_pnls(&pnls);
100        assert!(result.is_some());
101        assert_eq!(result.unwrap(), -10.0);
102    }
103
104    #[rstest]
105    fn test_name() {
106        let min_loser = MinLoser {};
107        assert_eq!(min_loser.name(), "MinLoser");
108    }
109}
nautilus_analysis/statistics/loser_min.rs

nautilus_analysis/statistics/
loser_min.rs
