nautilus_analysis/statistics/
win_rate.rs1use 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 WinRate {}
25
26impl PortfolioStatistic for WinRate {
27 type Item = f64;
28
29 fn name(&self) -> String {
30 stringify!(WinRate).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 let (winners, losers): (Vec<f64>, Vec<f64>) =
39 realized_pnls.iter().partition(|&&pnl| pnl > 0.0);
40
41 let total_trades = winners.len() + losers.len();
42 Some(winners.len() as f64 / total_trades.max(1) as f64)
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 win_rate = WinRate {};
55 let result = win_rate.calculate_from_realized_pnls(&[]);
56 assert!(result.is_some());
57 assert_eq!(result.unwrap(), 0.0);
58 }
59
60 #[rstest]
61 fn test_all_winning_trades() {
62 let win_rate = WinRate {};
63 let realized_pnls = vec![100.0, 50.0, 200.0];
64 let result = win_rate.calculate_from_realized_pnls(&realized_pnls);
65 assert!(result.is_some());
66 assert_eq!(result.unwrap(), 1.0);
67 }
68
69 #[rstest]
70 fn test_all_losing_trades() {
71 let win_rate = WinRate {};
72 let realized_pnls = vec![-100.0, -50.0, -200.0];
73 let result = win_rate.calculate_from_realized_pnls(&realized_pnls);
74 assert!(result.is_some());
75 assert_eq!(result.unwrap(), 0.0);
76 }
77
78 #[rstest]
79 fn test_mixed_trades() {
80 let win_rate = WinRate {};
81 let realized_pnls = vec![100.0, -50.0, 200.0, -100.0];
82 let result = win_rate.calculate_from_realized_pnls(&realized_pnls);
83 assert!(result.is_some());
84 assert_eq!(result.unwrap(), 0.5);
85 }
86
87 #[rstest]
88 fn test_name() {
89 let win_rate = WinRate {};
90 assert_eq!(win_rate.name(), "WinRate");
91 }
92}