nautilus_analysis/statistics/
winner_max.rs1use std::fmt::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", from_py_object)
27)]
28#[cfg_attr(
29 feature = "python",
30 pyo3_stub_gen::derive::gen_stub_pyclass(module = "nautilus_trader.analysis")
31)]
32pub struct MaxWinner {}
33
34impl Display for MaxWinner {
35 fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
36 write!(f, "Max Winner")
37 }
38}
39
40impl PortfolioStatistic for MaxWinner {
41 type Item = f64;
42
43 fn name(&self) -> String {
44 self.to_string()
45 }
46
47 fn calculate_from_realized_pnls(&self, realized_pnls: &[f64]) -> Option<Self::Item> {
48 if realized_pnls.is_empty() {
49 return Some(f64::NAN);
50 }
51
52 let winners: Vec<f64> = realized_pnls
53 .iter()
54 .filter(|&&pnl| pnl > 0.0)
55 .copied()
56 .collect();
57
58 if winners.is_empty() {
59 return Some(f64::NAN);
60 }
61
62 winners
63 .iter()
64 .max_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
65 .copied()
66 }
67
68 fn calculate_from_returns(&self, _returns: &Returns) -> Option<Self::Item> {
69 None
70 }
71
72 fn calculate_from_positions(&self, _positions: &[Position]) -> Option<Self::Item> {
73 None
74 }
75}
76
77#[cfg(test)]
78mod tests {
79 use nautilus_core::approx_eq;
80 use rstest::rstest;
81
82 use super::*;
83
84 #[rstest]
85 fn test_empty_pnls() {
86 let max_winner = MaxWinner {};
87 let result = max_winner.calculate_from_realized_pnls(&[]);
88 assert!(result.is_some());
89 assert!(result.unwrap().is_nan());
90 }
91
92 #[rstest]
93 fn test_no_winning_trades() {
94 let max_winner = MaxWinner {};
95 let realized_pnls = vec![-100.0, -50.0, -200.0];
96 let result = max_winner.calculate_from_realized_pnls(&realized_pnls);
97 assert!(result.is_some());
98 assert!(result.unwrap().is_nan());
99 }
100
101 #[rstest]
102 fn test_all_winning_trades() {
103 let max_winner = MaxWinner {};
104 let realized_pnls = vec![100.0, 50.0, 200.0];
105 let result = max_winner.calculate_from_realized_pnls(&realized_pnls);
106 assert!(result.is_some());
107 assert!(approx_eq!(f64, result.unwrap(), 200.0, epsilon = 1e-9));
108 }
109
110 #[rstest]
111 fn test_mixed_trades() {
112 let max_winner = MaxWinner {};
113 let realized_pnls = vec![100.0, -50.0, 200.0, -100.0];
114 let result = max_winner.calculate_from_realized_pnls(&realized_pnls);
115 assert!(result.is_some());
116 assert!(approx_eq!(f64, result.unwrap(), 200.0, epsilon = 1e-9));
117 }
118
119 #[rstest]
120 fn test_name() {
121 let max_winner = MaxWinner {};
122 assert_eq!(max_winner.name(), "Max Winner");
123 }
124}