nautilus_analysis/statistics/
winner_max.rs1use crate::statistic::PortfolioStatistic;
17
18#[repr(C)]
19#[derive(Debug)]
20#[cfg_attr(
21 feature = "python",
22 pyo3::pyclass(module = "nautilus_trader.core.nautilus_pyo3.analysis")
23)]
24pub struct MaxWinner {}
25
26impl PortfolioStatistic for MaxWinner {
27 type Item = f64;
28
29 fn name(&self) -> String {
30 stringify!(MaxWinner).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 .copied()
41 .filter(|&pnl| pnl > 0.0)
42 .max_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
43 }
44}
45
46#[cfg(test)]
47mod tests {
48 use nautilus_core::approx_eq;
49 use rstest::rstest;
50
51 use super::*;
52
53 #[rstest]
54 fn test_empty_pnls() {
55 let max_winner = MaxWinner {};
56 let result = max_winner.calculate_from_realized_pnls(&[]);
57 assert!(result.is_some());
58 assert!(approx_eq!(f64, result.unwrap(), 0.0, epsilon = 1e-9));
59 }
60
61 #[rstest]
62 fn test_no_winning_trades() {
63 let max_winner = MaxWinner {};
64 let realized_pnls = vec![-100.0, -50.0, -200.0];
65 let result = max_winner.calculate_from_realized_pnls(&realized_pnls);
66 assert!(result.is_none());
67 }
68
69 #[rstest]
70 fn test_all_winning_trades() {
71 let max_winner = MaxWinner {};
72 let realized_pnls = vec![100.0, 50.0, 200.0];
73 let result = max_winner.calculate_from_realized_pnls(&realized_pnls);
74 assert!(result.is_some());
75 assert!(approx_eq!(f64, result.unwrap(), 200.0, epsilon = 1e-9));
76 }
77
78 #[rstest]
79 fn test_mixed_trades() {
80 let max_winner = MaxWinner {};
81 let realized_pnls = vec![100.0, -50.0, 200.0, -100.0];
82 let result = max_winner.calculate_from_realized_pnls(&realized_pnls);
83 assert!(result.is_some());
84 assert!(approx_eq!(f64, result.unwrap(), 200.0, epsilon = 1e-9));
85 }
86
87 #[rstest]
88 fn test_name() {
89 let max_winner = MaxWinner {};
90 assert_eq!(max_winner.name(), "MaxWinner");
91 }
92}