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/// Calculates the expectancy of a trading strategy based on realized PnLs.
20///
21/// Expectancy is defined as: (Average Win × Win Rate) - (Average Loss × Loss Rate)
22/// This metric provides insight into the expected profitability per trade and helps
23/// evaluate the overall edge of a trading strategy.
24#[repr(C)]
25#[derive(Debug)]
26#[cfg_attr(
27    feature = "python",
28    pyo3::pyclass(module = "nautilus_trader.core.nautilus_pyo3.analysis")
29)]
30pub struct Expectancy {}
31
32impl PortfolioStatistic for Expectancy {
33    type Item = f64;
34
35    fn name(&self) -> String {
36        stringify!(Expectancy).to_string()
37    }
38
39    fn calculate_from_realized_pnls(&self, realized_pnls: &[f64]) -> Option<Self::Item> {
40        if realized_pnls.is_empty() {
41            return Some(0.0);
42        }
43
44        let avg_winner = AvgWinner {}
45            .calculate_from_realized_pnls(realized_pnls)
46            .unwrap_or(0.0);
47        let avg_loser = AvgLoser {}
48            .calculate_from_realized_pnls(realized_pnls)
49            .unwrap_or(0.0);
50
51        let (winners, losers): (Vec<f64>, Vec<f64>) =
52            realized_pnls.iter().partition(|&&pnl| pnl > 0.0);
53
54        let total_trades = winners.len() + losers.len();
55        let win_rate = winners.len() as f64 / total_trades.max(1) as f64;
56        let loss_rate = 1.0 - win_rate;
57
58        Some(avg_winner.mul_add(win_rate, avg_loser * loss_rate))
59    }
60}
61
62#[cfg(test)]
63mod tests {
64    use nautilus_core::approx_eq;
65    use rstest::rstest;
66
67    use super::*;
68
69    #[rstest]
70    fn test_empty_pnl_list() {
71        let expectancy = Expectancy {};
72        let result = expectancy.calculate_from_realized_pnls(&[]);
73        assert!(result.is_some());
74        assert!(approx_eq!(f64, result.unwrap(), 0.0, epsilon = 1e-9));
75    }
76
77    #[rstest]
78    fn test_all_winners() {
79        let expectancy = Expectancy {};
80        let pnls = vec![10.0, 20.0, 30.0];
81        let result = expectancy.calculate_from_realized_pnls(&pnls);
82
83        assert!(result.is_some());
84        // Expected: avg_winner = 20.0, win_rate = 1.0, loss_rate = 0.0
85        // Expectancy = (20.0 * 1.0) + (0.0 * 0.0) = 20.0
86        assert!(approx_eq!(f64, result.unwrap(), 20.0, epsilon = 1e-9));
87    }
88
89    #[rstest]
90    fn test_all_losers() {
91        let expectancy = Expectancy {};
92        let pnls = vec![-10.0, -20.0, -30.0];
93        let result = expectancy.calculate_from_realized_pnls(&pnls);
94
95        assert!(result.is_some());
96        // Expected: avg_loser = -20.0, win_rate = 0.0, loss_rate = 1.0
97        // Expectancy = (0.0 * 0.0) + (-20.0 * 1.0) = -20.0
98        assert!(approx_eq!(f64, result.unwrap(), -20.0, epsilon = 1e-9));
99    }
100
101    #[rstest]
102    fn test_mixed_pnls() {
103        let expectancy = Expectancy {};
104        let pnls = vec![10.0, -5.0, 15.0, -10.0];
105        let result = expectancy.calculate_from_realized_pnls(&pnls);
106
107        assert!(result.is_some());
108        // Expected:
109        // avg_winner = 12.5 (average of 10.0 and 15.0)
110        // avg_loser = -7.5 (average of -5.0 and -10.0)
111        // win_rate = 0.5 (2 winners out of 4 trades)
112        // loss_rate = 0.5
113        // Expectancy = (12.5 * 0.5) + (-7.5 * 0.5) = 2.5
114        assert!(approx_eq!(f64, result.unwrap(), 2.5, epsilon = 1e-9));
115    }
116
117    #[rstest]
118    fn test_single_trade() {
119        let expectancy = Expectancy {};
120        let pnls = vec![10.0];
121        let result = expectancy.calculate_from_realized_pnls(&pnls);
122
123        assert!(result.is_some());
124        // Expected: avg_winner = 10.0, win_rate = 1.0, loss_rate = 0.0
125        // Expectancy = (10.0 * 1.0) + (0.0 * 0.0) = 10.0
126        assert!(approx_eq!(f64, result.unwrap(), 10.0, epsilon = 1e-9));
127    }
128
129    #[rstest]
130    fn test_name() {
131        let expectancy = Expectancy {};
132        assert_eq!(expectancy.name(), "Expectancy");
133    }
134}
nautilus_analysis/statistics/expectancy.rs

nautilus_analysis/statistics/
expectancy.rs
