use ndarray::Array1;
use stochastic_rs_core::simd_rng::Deterministic;
use stochastic_rs_core::simd_rng::SeedExt;
use stochastic_rs_core::simd_rng::Unseeded;
use stochastic_rs_distributions::normal::SimdNormal;
use crate::traits::FloatExt;
use crate::traits::ProcessExt;
#[inline]
fn validate_drift_args<T: FloatExt>(
mu: Option<T>,
b: Option<T>,
r: Option<T>,
r_f: Option<T>,
type_name: &'static str,
) {
let has_r_pair = r.is_some() && r_f.is_some();
if !(has_r_pair || b.is_some() || mu.is_some()) {
panic!("{type_name}: one of (r and r_f), b, or mu must be provided");
}
}
pub struct HestonLog<T: FloatExt, S: SeedExt = Unseeded> {
pub mu: Option<T>,
pub b: Option<T>,
pub r: Option<T>,
pub r_f: Option<T>,
pub kappa: T,
pub theta: T,
pub xi: T,
pub rho: T,
pub n: usize,
pub s0: Option<T>,
pub v0: Option<T>,
pub t: Option<T>,
pub use_sym: Option<bool>,
pub seed: S,
}
impl<T: FloatExt> HestonLog<T> {
pub fn new(
mu: Option<T>,
b: Option<T>,
r: Option<T>,
r_f: Option<T>,
kappa: T,
theta: T,
xi: T,
rho: T,
n: usize,
s0: Option<T>,
v0: Option<T>,
t: Option<T>,
use_sym: Option<bool>,
) -> Self {
assert!(n >= 2, "n must be at least 2");
assert!(kappa >= T::zero(), "kappa must be >= 0");
assert!(theta >= T::zero(), "theta must be >= 0");
assert!(xi >= T::zero(), "xi must be >= 0");
assert!(
rho >= -T::one() && rho <= T::one(),
"rho must be in [-1, 1]"
);
if let Some(v0) = v0 {
assert!(v0 >= T::zero(), "v0 must be >= 0");
}
validate_drift_args(mu, b, r, r_f, "HestonLog");
Self {
mu,
b,
r,
r_f,
kappa,
theta,
xi,
rho,
n,
s0,
v0,
t,
use_sym,
seed: Unseeded,
}
}
}
impl<T: FloatExt> HestonLog<T, Deterministic> {
pub fn seeded(
mu: Option<T>,
b: Option<T>,
r: Option<T>,
r_f: Option<T>,
kappa: T,
theta: T,
xi: T,
rho: T,
n: usize,
s0: Option<T>,
v0: Option<T>,
t: Option<T>,
use_sym: Option<bool>,
seed: u64,
) -> Self {
assert!(n >= 2, "n must be at least 2");
assert!(kappa >= T::zero(), "kappa must be >= 0");
assert!(theta >= T::zero(), "theta must be >= 0");
assert!(xi >= T::zero(), "xi must be >= 0");
assert!(
rho >= -T::one() && rho <= T::one(),
"rho must be in [-1, 1]"
);
if let Some(v0) = v0 {
assert!(v0 >= T::zero(), "v0 must be >= 0");
}
validate_drift_args(mu, b, r, r_f, "HestonLog");
Self {
mu,
b,
r,
r_f,
kappa,
theta,
xi,
rho,
n,
s0,
v0,
t,
use_sym,
seed: Deterministic::new(seed),
}
}
}
impl<T: FloatExt, S: SeedExt> HestonLog<T, S> {
#[inline]
fn drift(&self) -> T {
match (self.r, self.r_f, self.b, self.mu) {
(Some(r), Some(r_f), _, _) => r - r_f,
(_, _, Some(b), _) => b,
(_, _, _, Some(mu)) => mu,
_ => unreachable!("validate_drift_args ensures at least one of (r+r_f), b, mu is set"),
}
}
}
impl<T: FloatExt, S: SeedExt> ProcessExt<T> for HestonLog<T, S> {
type Output = [Array1<T>; 2];
fn sample(&self) -> Self::Output {
let mut s = Array1::<T>::zeros(self.n);
let mut v = Array1::<T>::zeros(self.n);
if self.n == 0 {
return [s, v];
}
let s0 = self.s0.unwrap_or(T::one());
assert!(s0 > T::zero(), "s0 must be > 0 for log-price simulation");
s[0] = s0;
v[0] = self.v0.unwrap_or(self.theta).max(T::zero());
if self.n == 1 {
return [s, v];
}
let n_increments = self.n - 1;
let dt = self.t.unwrap_or(T::one()) / T::from_usize_(n_increments);
let sqrt_dt = dt.sqrt();
let mut dws = vec![T::zero(); n_increments];
let mut z = vec![T::zero(); n_increments];
let mut dwv = vec![T::zero(); n_increments];
let n1 = SimdNormal::<T>::from_seed_source(T::zero(), sqrt_dt, &self.seed);
let n2 = SimdNormal::<T>::from_seed_source(T::zero(), sqrt_dt, &self.seed);
n1.fill_slice_fast(&mut dws);
n2.fill_slice_fast(&mut z);
let corr_scale = (T::one() - self.rho * self.rho).sqrt();
for i in 0..n_increments {
dwv[i] = self.rho * dws[i] + corr_scale * z[i];
}
let drift = self.drift();
let half = T::from_f64_fast(0.5);
for i in 1..self.n {
let v_prev = if self.use_sym.unwrap_or(false) {
v[i - 1].abs()
} else {
v[i - 1].max(T::zero())
};
let sqrt_v = v_prev.sqrt();
let log_inc = (drift - half * v_prev) * dt + sqrt_v * dws[i - 1];
s[i] = s[i - 1] * log_inc.exp();
let dv = self.kappa * (self.theta - v_prev) * dt + self.xi * sqrt_v * dwv[i - 1];
v[i] = if self.use_sym.unwrap_or(false) {
(v_prev + dv).abs()
} else {
(v_prev + dv).max(T::zero())
};
}
[s, v]
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn price_stays_positive() {
let p = HestonLog::new(
Some(0.05_f64),
None,
None,
None,
1.5,
0.04,
0.3,
-0.7,
256,
Some(100.0),
Some(0.04),
Some(1.0),
Some(false),
);
let [s, _v] = p.sample();
assert!(s.iter().all(|x| *x > 0.0));
}
#[test]
fn variance_stays_non_negative() {
let p = HestonLog::new(
Some(0.05_f64),
None,
None,
None,
1.5,
0.04,
0.5,
-0.7,
256,
Some(100.0),
Some(0.04),
Some(1.0),
Some(false),
);
let [_s, v] = p.sample();
assert!(v.iter().all(|x| *x >= 0.0));
}
}
py_process_2x1d!(PyHestonLog, HestonLog,
sig: (mu=None, b=None, r=None, r_f=None, *, kappa, theta, xi, rho, n, s0=None, v0=None, t=None, use_sym=None, seed=None, dtype=None),
params: (mu: Option<f64>, b: Option<f64>, r: Option<f64>, r_f: Option<f64>, kappa: f64, theta: f64, xi: f64, rho: f64, n: usize, s0: Option<f64>, v0: Option<f64>, t: Option<f64>, use_sym: Option<bool>)
);