nautilus_analysis/statistics/
loser_min.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 std::fmt::{self, 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")
27)]
28pub struct MinLoser {}
29
30impl Display for MinLoser {
31    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
32        write!(f, "Min Loser")
33    }
34}
35
36impl PortfolioStatistic for MinLoser {
37    type Item = f64;
38
39    fn name(&self) -> String {
40        self.to_string()
41    }
42
43    fn calculate_from_realized_pnls(&self, realized_pnls: &[f64]) -> Option<Self::Item> {
44        if realized_pnls.is_empty() {
45            return Some(0.0);
46        }
47
48        // Match old Python behavior: filters for x <= 0.0 (includes zero)
49        let losers: Vec<f64> = realized_pnls
50            .iter()
51            .filter(|&&pnl| pnl <= 0.0)
52            .copied()
53            .collect();
54
55        if losers.is_empty() {
56            return Some(0.0); // Match old Python behavior
57        }
58
59        losers
60            .iter()
61            .max_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
62            .copied()
63    }
64
65    fn calculate_from_returns(&self, _returns: &Returns) -> Option<Self::Item> {
66        None
67    }
68
69    fn calculate_from_positions(&self, _positions: &[Position]) -> Option<Self::Item> {
70        None
71    }
72}
73
74////////////////////////////////////////////////////////////////////////////////
75// Tests
76////////////////////////////////////////////////////////////////////////////////
77
78#[cfg(test)]
79mod tests {
80    use nautilus_core::approx_eq;
81    use rstest::rstest;
82
83    use super::*;
84
85    #[rstest]
86    fn test_empty_pnls() {
87        let min_loser = MinLoser {};
88        let result = min_loser.calculate_from_realized_pnls(&[]);
89        assert!(result.is_some());
90        assert!(approx_eq!(f64, result.unwrap(), 0.0, epsilon = 1e-9));
91    }
92
93    #[rstest]
94    fn test_all_positive() {
95        let min_loser = MinLoser {};
96        let pnls = vec![10.0, 20.0, 30.0];
97        let result = min_loser.calculate_from_realized_pnls(&pnls);
98        assert!(result.is_some());
99        // Returns 0.0 when no losers (matches old Python behavior)
100        assert!(approx_eq!(f64, result.unwrap(), 0.0, epsilon = 1e-9));
101    }
102
103    #[rstest]
104    fn test_all_negative() {
105        let min_loser = MinLoser {};
106        let pnls = vec![-10.0, -20.0, -30.0];
107        let result = min_loser.calculate_from_realized_pnls(&pnls);
108        assert!(result.is_some());
109        assert!(approx_eq!(f64, result.unwrap(), -10.0, epsilon = 1e-9));
110    }
111
112    #[rstest]
113    fn test_mixed_pnls() {
114        let min_loser = MinLoser {};
115        let pnls = vec![10.0, -20.0, 30.0, -40.0];
116        let result = min_loser.calculate_from_realized_pnls(&pnls);
117        assert!(result.is_some());
118        assert!(approx_eq!(f64, result.unwrap(), -20.0, epsilon = 1e-9));
119    }
120
121    #[rstest]
122    fn test_with_zero() {
123        let min_loser = MinLoser {};
124        let pnls = vec![10.0, 0.0, -20.0, -30.0];
125        let result = min_loser.calculate_from_realized_pnls(&pnls);
126        assert!(result.is_some());
127        // Includes zero in losers (x <= 0.0), so max is 0.0 (matches old Python behavior)
128        assert!(approx_eq!(f64, result.unwrap(), 0.0, epsilon = 1e-9));
129    }
130
131    #[rstest]
132    fn test_single_negative() {
133        let min_loser = MinLoser {};
134        let pnls = vec![-10.0];
135        let result = min_loser.calculate_from_realized_pnls(&pnls);
136        assert!(result.is_some());
137        assert!(approx_eq!(f64, result.unwrap(), -10.0, epsilon = 1e-9));
138    }
139
140    #[rstest]
141    fn test_name() {
142        let min_loser = MinLoser {};
143        assert_eq!(min_loser.name(), "Min Loser");
144    }
145}
nautilus_analysis/statistics/loser_min.rs

nautilus_analysis/statistics/
loser_min.rs
