stochastic-rs-quant 2.5.3

Quantitative finance: pricing, calibration, vol surfaces, instruments.
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

use pyo3::exceptions::PyValueError;
use pyo3::prelude::*;

#[pyclass(name = "DiscountCurve", unsendable)]
pub struct PyDiscountCurve {
  pub(super) inner: crate::curves::discount_curve::DiscountCurve<f64>,
}

#[pymethods]
impl PyDiscountCurve {
  /// Build from `(maturities, zero_rates)` arrays under continuous compounding.
  /// `interp`: "linear" / "log_df" / "cubic" / "monotone_convex".
  #[staticmethod]
  #[pyo3(signature = (maturities, zero_rates, interp="linear"))]
  fn from_zero_rates<'py>(
    maturities: numpy::PyReadonlyArray1<'py, f64>,
    zero_rates: numpy::PyReadonlyArray1<'py, f64>,
    interp: &str,
  ) -> PyResult<Self> {
    use crate::curves::types::InterpolationMethod;
    let im = match interp.to_ascii_lowercase().as_str() {
      "linear" | "linear_zr" => InterpolationMethod::LinearOnZeroRates,
      "log_df" | "loglinear_df" => InterpolationMethod::LogLinearOnDiscountFactors,
      "cubic" | "cubic_zr" => InterpolationMethod::CubicSplineOnZeroRates,
      "monotone_convex" | "mc" => InterpolationMethod::MonotoneConvex,
      o => {
        return Err(PyValueError::new_err(format!(
          "interp must be linear/log_df/cubic/monotone_convex, got '{o}'"
        )));
      }
    };
    let mat = maturities.as_array().to_owned();
    let zr = zero_rates.as_array().to_owned();
    Ok(Self {
      inner: crate::curves::discount_curve::DiscountCurve::from_zero_rates(&mat, &zr, im),
    })
  }

  fn discount_factor(&self, t: f64) -> f64 {
    self.inner.discount_factor(t)
  }
  fn zero_rate(&self, t: f64) -> f64 {
    self.inner.zero_rate(t)
  }
  fn forward_rate(&self, t1: f64, t2: f64) -> f64 {
    self.inner.forward_rate(t1, t2)
  }
  fn par_rate(&self, maturity: f64, frequency: u32) -> f64 {
    self.inner.par_rate(maturity, frequency)
  }

  /// Vectorised zero rates on a maturity array.
  fn zero_rates<'py>(
    &self,
    py: Python<'py>,
    maturities: numpy::PyReadonlyArray1<'py, f64>,
  ) -> pyo3::Bound<'py, numpy::PyArray1<f64>> {
    use numpy::IntoPyArray;
    let mat = maturities.as_array().to_owned();
    self.inner.zero_rates(&mat).into_pyarray(py)
  }
}

#[pyclass(name = "NelsonSiegel", unsendable)]
pub struct PyNelsonSiegel {
  inner: crate::curves::nelson_siegel::NelsonSiegel<f64>,
}

#[pymethods]
impl PyNelsonSiegel {
  #[new]
  fn new(beta0: f64, beta1: f64, beta2: f64, lambda: f64) -> Self {
    Self {
      inner: crate::curves::nelson_siegel::NelsonSiegel::new(beta0, beta1, beta2, lambda),
    }
  }

  /// Fit Nelson-Siegel parameters to market zero rates (requires openblas feature).
  #[cfg(feature = "openblas")]
  #[staticmethod]
  fn fit_curve<'py>(
    maturities: numpy::PyReadonlyArray1<'py, f64>,
    market_rates: numpy::PyReadonlyArray1<'py, f64>,
  ) -> Self {
    let mat = maturities.as_array().to_owned();
    let mr = market_rates.as_array().to_owned();
    Self {
      inner: <crate::curves::nelson_siegel::NelsonSiegel<f64>>::fit(&mat, &mr),
    }
  }

  fn zero_rate(&self, tau: f64) -> f64 {
    self.inner.zero_rate(tau)
  }
  fn forward_rate(&self, tau: f64) -> f64 {
    self.inner.forward_rate(tau)
  }
  fn discount_factor(&self, tau: f64) -> f64 {
    self.inner.discount_factor(tau)
  }
}

#[pyclass(name = "ZeroCouponInflationCurve", unsendable)]
pub struct PyZeroCouponInflationCurve {
  inner: crate::inflation::curve::ZeroCouponInflationCurve<f64>,
}

#[pymethods]
impl PyZeroCouponInflationCurve {
  /// Build a zero-coupon inflation curve from `(pillars, breakevens)`.
  #[new]
  fn new<'py>(
    pillars: numpy::PyReadonlyArray1<'py, f64>,
    breakevens: numpy::PyReadonlyArray1<'py, f64>,
  ) -> Self {
    Self {
      inner: crate::inflation::curve::ZeroCouponInflationCurve::new(
        pillars.as_array().to_owned(),
        breakevens.as_array().to_owned(),
      ),
    }
  }

  /// Forward CPI index ratio $I(0, T)/I(0, 0) = (1 + b(T))^T$.
  fn forward_index_ratio(&self, t: f64) -> f64 {
    use crate::inflation::curve::InflationCurve;
    self.inner.forward_index_ratio(t)
  }
}