nautilus_analysis/statistics/
max_drawdown.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
16//! Maximum Drawdown statistic.
17
18use std::collections::BTreeMap;
19
20use nautilus_core::UnixNanos;
21
22use crate::statistic::PortfolioStatistic;
23
24/// Calculates the Maximum Drawdown for returns.
25///
26/// Maximum Drawdown is the maximum observed loss from a peak to a trough,
27/// before a new peak is attained. It is an indicator of downside risk over
28/// a specified time period.
29///
30/// Formula: Max((Peak - Trough) / Peak) for all peak-trough sequences
31#[repr(C)]
32#[derive(Debug, Clone, Default)]
33#[cfg_attr(
34    feature = "python",
35    pyo3::pyclass(module = "nautilus_trader.core.nautilus_pyo3.analysis")
36)]
37pub struct MaxDrawdown {}
38
39impl MaxDrawdown {
40    /// Creates a new [`MaxDrawdown`] instance.
41    #[must_use]
42    pub fn new() -> Self {
43        Self {}
44    }
45}
46
47impl PortfolioStatistic for MaxDrawdown {
48    type Item = f64;
49
50    fn name(&self) -> String {
51        "Max Drawdown".to_string()
52    }
53
54    fn calculate_from_returns(&self, returns: &BTreeMap<UnixNanos, f64>) -> Option<Self::Item> {
55        if returns.is_empty() {
56            return Some(0.0);
57        }
58
59        // Calculate cumulative returns starting from 1.0
60        let mut cumulative = 1.0;
61        let mut running_max = 1.0;
62        let mut max_drawdown = 0.0;
63
64        for &ret in returns.values() {
65            cumulative *= 1.0 + ret;
66
67            // Update running maximum
68            if cumulative > running_max {
69                running_max = cumulative;
70            }
71
72            // Calculate drawdown from running max
73            let drawdown = (running_max - cumulative) / running_max;
74
75            // Update maximum drawdown
76            if drawdown > max_drawdown {
77                max_drawdown = drawdown;
78            }
79        }
80
81        // Return as negative percentage
82        Some(-max_drawdown)
83    }
84}
85
86////////////////////////////////////////////////////////////////////////////////
87// Tests
88////////////////////////////////////////////////////////////////////////////////
89
90#[cfg(test)]
91mod tests {
92    use rstest::rstest;
93
94    use super::*;
95
96    fn create_returns(values: Vec<f64>) -> BTreeMap<UnixNanos, f64> {
97        values
98            .into_iter()
99            .enumerate()
100            .map(|(i, v)| (UnixNanos::from(i as u64), v))
101            .collect()
102    }
103
104    #[rstest]
105    fn test_name() {
106        let stat = MaxDrawdown::new();
107        assert_eq!(stat.name(), "Max Drawdown");
108    }
109
110    #[rstest]
111    fn test_empty_returns() {
112        let stat = MaxDrawdown::new();
113        let returns = BTreeMap::new();
114        let result = stat.calculate_from_returns(&returns);
115        assert_eq!(result, Some(0.0));
116    }
117
118    #[rstest]
119    fn test_no_drawdown() {
120        let stat = MaxDrawdown::new();
121        // Only positive returns, no drawdown
122        let returns = create_returns(vec![0.01, 0.02, 0.01, 0.015]);
123        let result = stat.calculate_from_returns(&returns).unwrap();
124        assert_eq!(result, 0.0);
125    }
126
127    #[rstest]
128    fn test_simple_drawdown() {
129        let stat = MaxDrawdown::new();
130        // Start at 1.0, go to 1.1 (+10%), then drop to 0.99 (-10% from peak)
131        // Max DD = (1.1 - 0.99) / 1.1 = 0.1 / 1.1 = 0.0909 (9.09%)
132        let returns = create_returns(vec![0.10, -0.10]);
133        let result = stat.calculate_from_returns(&returns).unwrap();
134
135        // Should be approximately -0.10 (reported as negative)
136        assert!((result + 0.10).abs() < 0.01);
137    }
138
139    #[rstest]
140    fn test_multiple_drawdowns() {
141        let stat = MaxDrawdown::new();
142        // Peak at 1.5, trough at 1.0
143        // DD1: 10% from 1.0
144        // DD2: 20% from 1.5
145        let returns = create_returns(vec![0.10, -0.10, 0.50, -0.20, 0.10]);
146        let result = stat.calculate_from_returns(&returns).unwrap();
147
148        // Max DD should be the larger one (20%)
149        assert!((result + 0.20).abs() < 0.01);
150    }
151
152    #[rstest]
153    fn test_initial_loss() {
154        let stat = MaxDrawdown::new();
155        // Start with 40% loss
156        let returns = create_returns(vec![-0.40, -0.10]);
157        let result = stat.calculate_from_returns(&returns).unwrap();
158
159        // From 1.0 -> 0.6 -> 0.54
160        // Max DD from initial 1.0 is 46%
161        assert!((result + 0.46).abs() < 0.01);
162    }
163}
nautilus_analysis/statistics/max_drawdown.rs

nautilus_analysis/statistics/
max_drawdown.rs
