nautilus_analysis/statistics/
returns_avg_loss.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/// Calculates the arithmetic mean of the negative portfolio returns.
23///
24/// Zero returns are excluded (neither wins nor losses). Returns `NaN` for an
25/// empty series or when there are no negative returns.
26#[repr(C)]
27#[derive(Debug, Clone)]
28#[cfg_attr(
29    feature = "python",
30    pyo3::pyclass(module = "nautilus_trader.analysis", from_py_object)
31)]
32#[cfg_attr(
33    feature = "python",
34    pyo3_stub_gen::derive::gen_stub_pyclass(module = "nautilus_trader.analysis")
35)]
36pub struct ReturnsAverageLoss {}
37
38impl Display for ReturnsAverageLoss {
39    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
40        write!(f, "Average Loss (Return)")
41    }
42}
43
44impl PortfolioStatistic for ReturnsAverageLoss {
45    type Item = f64;
46
47    fn name(&self) -> String {
48        self.to_string()
49    }
50
51    fn calculate_from_returns(&self, returns: &Returns) -> Option<Self::Item> {
52        if !self.check_valid_returns(returns) {
53            return Some(f64::NAN);
54        }
55
56        let negative_returns: Vec<f64> = returns.values().copied().filter(|&x| x < 0.0).collect();
57
58        if negative_returns.is_empty() {
59            return Some(f64::NAN);
60        }
61
62        let sum: f64 = negative_returns.iter().sum();
63        let count = negative_returns.len() as f64;
64
65        Some(sum / count)
66    }
67    fn calculate_from_realized_pnls(&self, _realized_pnls: &[f64]) -> Option<Self::Item> {
68        None
69    }
70
71    fn calculate_from_positions(&self, _positions: &[Position]) -> Option<Self::Item> {
72        None
73    }
74}
75
76#[cfg(test)]
77mod tests {
78    use std::collections::BTreeMap;
79
80    use nautilus_core::{UnixNanos, approx_eq};
81    use rstest::rstest;
82
83    use super::*;
84
85    fn create_returns(values: &[f64]) -> Returns {
86        let mut new_return = BTreeMap::new();
87        for (i, value) in values.iter().enumerate() {
88            new_return.insert(UnixNanos::from(i as u64), *value);
89        }
90
91        new_return
92    }
93
94    #[rstest]
95    fn test_empty_returns() {
96        let avg_loss = ReturnsAverageLoss {};
97        let returns = create_returns(&[]);
98        let result = avg_loss.calculate_from_returns(&returns);
99        assert!(result.is_some());
100        assert!(result.unwrap().is_nan());
101    }
102
103    #[rstest]
104    fn test_all_positive() {
105        let avg_loss = ReturnsAverageLoss {};
106        let returns = create_returns(&[10.0, 20.0, 30.0]);
107        let result = avg_loss.calculate_from_returns(&returns);
108        assert!(result.is_some());
109        assert!(result.unwrap().is_nan());
110    }
111
112    #[rstest]
113    fn test_all_negative() {
114        let avg_loss = ReturnsAverageLoss {};
115        let returns = create_returns(&[-10.0, -20.0, -30.0]);
116        let result = avg_loss.calculate_from_returns(&returns);
117        assert!(result.is_some());
118        // Average of [-10.0, -20.0, -30.0] = (-10 + -20 + -30) / 3 = -20.0
119        assert!(approx_eq!(f64, result.unwrap(), -20.0, epsilon = 1e-9));
120    }
121
122    #[rstest]
123    fn test_mixed_returns() {
124        let avg_loss = ReturnsAverageLoss {};
125        let returns = create_returns(&[10.0, -20.0, 30.0, -40.0]);
126        let result = avg_loss.calculate_from_returns(&returns);
127        assert!(result.is_some());
128        // Average of [-20.0, -40.0] = (-20 + -40) / 2 = -30.0
129        assert!(approx_eq!(f64, result.unwrap(), -30.0, epsilon = 1e-9));
130    }
131
132    #[rstest]
133    fn test_with_zero() {
134        let avg_loss = ReturnsAverageLoss {};
135        let returns = create_returns(&[10.0, 0.0, -20.0, -30.0]);
136        let result = avg_loss.calculate_from_returns(&returns);
137        assert!(result.is_some());
138        // Average of [-20.0, -30.0] = (-20 + -30) / 2 = -25.0
139        assert!(approx_eq!(f64, result.unwrap(), -25.0, epsilon = 1e-9));
140    }
141
142    #[rstest]
143    fn test_name() {
144        let avg_loss = ReturnsAverageLoss {};
145        assert_eq!(avg_loss.name(), "Average Loss (Return)");
146    }
147}
nautilus_analysis/statistics/returns_avg_loss.rs

nautilus_analysis/statistics/
returns_avg_loss.rs
