libitofin 0.7.0

A ground-up Rust port of QuantLib: quantitative-finance primitives for pricing, risk, and numerical methods.
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136

//! Shared fixtures for the decomposition tests: the `setup()` matrices and
//! `norm` helpers from `test-suite/matrices.cpp`.

use crate::math::matrix::Matrix;
use crate::types::{Real, Size};

pub(crate) fn matrices_m1() -> Matrix {
    Matrix::from([[1.0, 0.9, 0.7], [0.9, 1.0, 0.4], [0.7, 0.4, 1.0]])
}

pub(crate) fn matrices_m2() -> Matrix {
    Matrix::from([[1.0, 0.9, 0.7], [0.9, 1.0, 0.3], [0.7, 0.3, 1.0]])
}

pub(crate) fn matrices_m3() -> Matrix {
    Matrix::from([
        [1.0, 2.0, 3.0, 4.0],
        [2.0, 0.0, 2.0, 1.0],
        [0.0, 1.0, 0.0, 0.0],
    ])
}

pub(crate) fn matrices_m4() -> Matrix {
    Matrix::from([
        [1.0, 2.0, 400.0],
        [2.0, 0.0, 1.0],
        [30.0, 2.0, 0.0],
        [2.0, 0.0, 1.05],
    ])
}

pub(crate) fn matrices_m5() -> Matrix {
    Matrix::from([
        [2.0, -1.0, 0.0, 0.0],
        [-1.0, 2.0, -1.0, 0.0],
        [0.0, -1.0, 2.0, -1.0],
        [0.0, 0.0, -1.0, 2.0],
    ])
}

pub(crate) fn matrices_m6() -> Matrix {
    Matrix::from([
        [1.0, -0.8084124981, 0.1915875019, 0.106775049],
        [-0.8084124981, 1.0, -0.6562326948, 0.1915875019],
        [0.1915875019, -0.6562326948, 1.0, -0.8084124981],
        [0.106775049, 0.1915875019, -0.8084124981, 1.0],
    ])
}

pub(crate) fn matrices_m7() -> Matrix {
    let mut m = matrices_m1();
    m[(0, 1)] = 0.3;
    m[(0, 2)] = 0.2;
    m[(2, 1)] = 1.2;
    m
}

pub(crate) fn identity(n: usize) -> Matrix {
    let mut m = Matrix::with_size(n, n);
    for i in 0..n {
        m[(i, i)] = 1.0;
    }
    m
}

/// A small deterministic PCG-style generator standing in for the QuantLib
/// MersenneTwisterUniformRng used by the C++ suite (ported separately as
/// QL-1.11); the decomposition tests only need reproducible values in [0, 1).
pub(crate) struct TestRng(u64);

impl TestRng {
    pub(crate) fn new(seed: u64) -> Self {
        TestRng(seed)
    }

    pub(crate) fn next_real(&mut self) -> Real {
        self.0 = self
            .0
            .wrapping_mul(6364136223846793005)
            .wrapping_add(1442695040888963407);
        (self.0 >> 11) as Real / (1_u64 << 53) as Real
    }
}

/// The deterministic correlation matrix from
/// `MatrixTests::createTestCorrelationMatrix` in `test-suite/matrices.cpp`.
pub(crate) fn create_test_correlation_matrix(n: Size) -> Matrix {
    let mut rho = Matrix::with_size(n, n);
    for i in 0..n {
        for j in i..n {
            let value = (-0.1 * (i as Real - j as Real).abs()
                - if i != j { 0.02 * (i + j) as Real } else { 0.0 })
            .exp();
            rho[(i, j)] = value;
            rho[(j, i)] = value;
        }
    }
    rho
}

/// Element-wise relative comparison matching `QL_CHECK_CLOSE_MATRIX_TOL` in
/// `test-suite/matrices.cpp`: `tol_pct` is a percentage tolerance checked
/// against both operands, as in `BOOST_CHECK_CLOSE`.
pub(crate) fn assert_close_matrix(actual: &Matrix, expected: &Matrix, tol_pct: Real) {
    assert!(
        actual.rows() == expected.rows() && actual.columns() == expected.columns(),
        "matrix dimensions do not match: {}x{} vs {}x{}",
        actual.rows(),
        actual.columns(),
        expected.rows(),
        expected.columns()
    );
    let fraction = tol_pct / 100.0;
    for i in 0..actual.rows() {
        for j in 0..actual.columns() {
            let a = actual[(i, j)];
            let e = expected[(i, j)];
            let diff = (a - e).abs();
            assert!(
                diff <= fraction * a.abs() && diff <= fraction * e.abs(),
                "matrices differ at ({i}, {j}): {a} vs {e} (tolerance {tol_pct}%)"
            );
        }
    }
}

/// The Frobenius norm of `m`, matching `norm(const Matrix&)` in the C++ suite.
pub(crate) fn norm_matrix(m: &Matrix) -> Real {
    let mut sum = 0.0;
    for i in 0..m.rows() {
        for j in 0..m.columns() {
            sum += m[(i, j)] * m[(i, j)];
        }
    }
    sum.sqrt()
}