nautilus_analysis/statistics/
expectancy.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 super::{loser_avg::AvgLoser, winner_avg::AvgWinner};
17use crate::statistic::PortfolioStatistic;
18
19#[repr(C)]
20#[derive(Debug)]
21#[cfg_attr(
22    feature = "python",
23    pyo3::pyclass(module = "nautilus_trader.core.nautilus_pyo3.analysis")
24)]
25pub struct Expectancy {}
26
27impl PortfolioStatistic for Expectancy {
28    type Item = f64;
29
30    fn name(&self) -> String {
31        stringify!(Expectancy).to_string()
32    }
33
34    fn calculate_from_realized_pnls(&self, realized_pnls: &[f64]) -> Option<Self::Item> {
35        if realized_pnls.is_empty() {
36            return Some(0.0);
37        }
38
39        let avg_winner = AvgWinner {}
40            .calculate_from_realized_pnls(realized_pnls)
41            .unwrap_or(0.0);
42        let avg_loser = AvgLoser {}
43            .calculate_from_realized_pnls(realized_pnls)
44            .unwrap_or(0.0);
45
46        let (winners, losers): (Vec<f64>, Vec<f64>) =
47            realized_pnls.iter().partition(|&&pnl| pnl > 0.0);
48
49        let total_trades = winners.len() + losers.len();
50        let win_rate = winners.len() as f64 / total_trades.max(1) as f64;
51        let loss_rate = 1.0 - win_rate;
52
53        Some(avg_winner.mul_add(win_rate, avg_loser * loss_rate))
54    }
55}
56
57#[cfg(test)]
58mod tests {
59    use super::*;
60
61    #[test]
62    fn test_empty_pnl_list() {
63        let expectancy = Expectancy {};
64        let result = expectancy.calculate_from_realized_pnls(&[]);
65        assert!(result.is_some());
66        assert_eq!(result.unwrap(), 0.0);
67    }
68
69    #[test]
70    fn test_all_winners() {
71        let expectancy = Expectancy {};
72        let pnls = vec![10.0, 20.0, 30.0];
73        let result = expectancy.calculate_from_realized_pnls(&pnls);
74
75        assert!(result.is_some());
76        // Expected: avg_winner = 20.0, win_rate = 1.0, loss_rate = 0.0
77        // Expectancy = (20.0 * 1.0) + (0.0 * 0.0) = 20.0
78        assert_eq!(result.unwrap(), 20.0);
79    }
80
81    #[test]
82    fn test_all_losers() {
83        let expectancy = Expectancy {};
84        let pnls = vec![-10.0, -20.0, -30.0];
85        let result = expectancy.calculate_from_realized_pnls(&pnls);
86
87        assert!(result.is_some());
88        // Expected: avg_loser = -20.0, win_rate = 0.0, loss_rate = 1.0
89        // Expectancy = (0.0 * 0.0) + (-20.0 * 1.0) = -20.0
90        assert_eq!(result.unwrap(), -20.0);
91    }
92
93    #[test]
94    fn test_mixed_pnls() {
95        let expectancy = Expectancy {};
96        let pnls = vec![10.0, -5.0, 15.0, -10.0];
97        let result = expectancy.calculate_from_realized_pnls(&pnls);
98
99        assert!(result.is_some());
100        // Expected:
101        // avg_winner = 12.5 (average of 10.0 and 15.0)
102        // avg_loser = -7.5 (average of -5.0 and -10.0)
103        // win_rate = 0.5 (2 winners out of 4 trades)
104        // loss_rate = 0.5
105        // Expectancy = (12.5 * 0.5) + (-7.5 * 0.5) = 2.5
106        assert_eq!(result.unwrap(), 2.5);
107    }
108
109    #[test]
110    fn test_single_trade() {
111        let expectancy = Expectancy {};
112        let pnls = vec![10.0];
113        let result = expectancy.calculate_from_realized_pnls(&pnls);
114
115        assert!(result.is_some());
116        // Expected: avg_winner = 10.0, win_rate = 1.0, loss_rate = 0.0
117        // Expectancy = (10.0 * 1.0) + (0.0 * 0.0) = 10.0
118        assert_eq!(result.unwrap(), 10.0);
119    }
120
121    #[test]
122    fn test_name() {
123        let expectancy = Expectancy {};
124        assert_eq!(expectancy.name(), "Expectancy");
125    }
126}
nautilus_analysis/statistics/expectancy.rs

nautilus_analysis/statistics/
expectancy.rs
