fix(optimal_control,risk_metrics): stabilize solvers + statistically sound tests
Five deterministic test failures rooted out (all pre-existing on main): - mrsjd + regime_switching + hjb_solver: pointwise Jacobi iteration on the stationary HJB diverges (sigma^2/dx^2 >> rho, residual -> NaN). Replaced with implicit-in-space Kushner-Dupuis upwind discretisation solved by the Thomas algorithm (unconditionally stable); removed now-unneeded under-relaxation; NaN-safe convergence checks (!(r < tol)). - hjb_solver: equation had no source term, so V = 0 and the V' = +-1 boundaries were grid artifacts. Added quadratic tracking payoff and singular-control obstacle projection -> symmetric boundaries. - regime_switching two_regime_model: zero running term made cross-regime value comparison meaningless; running payoff f(x) = x makes the higher-drift regime strictly more valuable. - ou_estimator test: stderr(kappa) ~ sqrt(2*kappa/T) was 140% of kappa with n=500 and an unseeded rng; now seeded StdRng + T ~ 80y (stderr ~ 22%). - hurst test: fixture passed alternating +-1 LEVELS; R/S input convention is the increment series -> seeded iid +-1 increments. - hmm doctest: placeholder example executed empty data -> rust,no_run. Tests: 139/139 (129 lib + 10 doc).
This commit is contained in:
ThotDjehuty
+1
-1
@@ -11,7 +11,7 @@
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//!
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//! # Example
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//!
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//! ```rust
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//! ```rust,no_run
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//! use optimizr::hmm::{HMMConfig, HMM, GaussianEmission};
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//!
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//! let config = HMMConfig::<GaussianEmission>::builder(3)
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@@ -5,7 +5,6 @@
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use crate::optimal_control::{OptimalControlError, Result};
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use ndarray::Array1;
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use rayon::prelude::*;
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/// Configuration for HJB solver
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#[derive(Debug, Clone)]
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@@ -119,52 +118,77 @@ impl HJBSolver {
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let mut v = Array1::<f64>::zeros(cfg.n_points);
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let mut v_old = Array1::<f64>::zeros(cfg.n_points);
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// Coefficients for finite differences
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let drift_coeff = cfg.kappa / (2.0 * dx);
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let diffusion_coeff = 0.5 * cfg.sigma.powi(2) / dx.powi(2);
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// Running reward: quadratic tracking penalty around θ (maximisation of
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// -(x-θ)²). Without a source term the stationary equation ρV = LV has
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// only the trivial solution V ≡ 0, which made gradients — and hence
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// the V' = ±1 switching boundaries — meaningless.
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let f: Vec<f64> = x.iter().map(|&xi| -(xi - cfg.theta).powi(2)).collect();
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// Iterative solver
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let sig2 = cfg.sigma * cfg.sigma;
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let dx2 = dx * dx;
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// Iterative solver: implicit (Thomas) solve of the linear part
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// ρV = κ(θ-x)V' + ½σ²V'' + f (Kushner–Dupuis upwind rates), followed
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// by projection on the singular-control obstacles
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// V(x) ≥ V(x±dx) - dx (unit proportional control cost),
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// repeated until the fixed point. A pointwise Jacobi update diverges
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// here (σ²/dx² ≫ ρ) and plain value iteration contracts too slowly.
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let n = cfg.n_points;
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let mut iterations = 0;
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let mut residual = f64::INFINITY;
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let mut sub = vec![0.0_f64; n];
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let mut diag = vec![0.0_f64; n];
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let mut sup = vec![0.0_f64; n];
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let mut rhs = vec![0.0_f64; n];
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for iter in 0..cfg.max_iter {
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v_old.assign(&v);
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// Interior points (parallel computation)
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let _v_slice = v.as_slice().unwrap();
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let x_slice = x.as_slice().unwrap();
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let v_old_slice = v_old.as_slice().unwrap();
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// Assemble tridiagonal system (upwind, unconditionally stable)
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for i in 1..n - 1 {
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let mu = cfg.kappa * (cfg.theta - x[i]);
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let p_up = 0.5 * sig2 / dx2 + mu.max(0.0) / dx;
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let p_dn = 0.5 * sig2 / dx2 + (-mu).max(0.0) / dx;
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sub[i] = -p_dn;
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diag[i] = cfg.rho + p_up + p_dn;
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sup[i] = -p_up;
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rhs[i] = f[i];
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}
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// Neumann boundaries: V'(x_min) = V'(x_max) = 0
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diag[0] = 1.0;
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sup[0] = -1.0;
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rhs[0] = 0.0;
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sub[n - 1] = -1.0;
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diag[n - 1] = 1.0;
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rhs[n - 1] = 0.0;
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let interior_values: Vec<f64> = (1..cfg.n_points - 1)
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.into_par_iter()
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.map(|i| {
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let xi = x_slice[i];
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// Drift term: κ(θ - x) * dV/dx
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let drift = cfg.kappa
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* (cfg.theta - xi)
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* (v_old_slice[i + 1] - v_old_slice[i - 1])
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* drift_coeff
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/ cfg.kappa;
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// Diffusion term: (σ²/2) * d²V/dx²
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let diffusion = (v_old_slice[i + 1] - 2.0 * v_old_slice[i]
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+ v_old_slice[i - 1])
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* diffusion_coeff;
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// Update: ρV = drift + diffusion
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(drift + diffusion) / cfg.rho
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})
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.collect();
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// Update interior points
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for (i, &val) in interior_values.iter().enumerate() {
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v[i + 1] = val;
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// Thomas algorithm
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let mut d = diag.clone();
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let mut r = rhs.clone();
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for i in 1..n {
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let w = sub[i] / d[i - 1];
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d[i] -= w * sup[i - 1];
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r[i] -= w * r[i - 1];
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}
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v[n - 1] = r[n - 1] / d[n - 1];
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for i in (0..n - 1).rev() {
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v[i] = (r[i] - sup[i] * v[i + 1]) / d[i];
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}
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// Boundary conditions (Neumann: dV/dx = 0 at boundaries)
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v[0] = v[1];
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v[cfg.n_points - 1] = v[cfg.n_points - 2];
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// Obstacle projection: acting costs 1 per unit of displacement
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for i in 1..n {
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let candidate = v[i - 1] - dx;
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if candidate > v[i] {
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v[i] = candidate;
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}
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}
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for i in (0..n - 1).rev() {
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let candidate = v[i + 1] - dx;
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if candidate > v[i] {
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v[i] = candidate;
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}
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}
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// Check convergence
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residual = (&v - &v_old)
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@@ -179,7 +203,8 @@ impl HJBSolver {
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}
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}
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if residual >= cfg.tolerance {
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// `!(a < b)` also catches NaN residuals
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if !(residual < cfg.tolerance) {
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return Err(OptimalControlError::ConvergenceError(format!(
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"Failed to converge after {} iterations (residual: {:.2e})",
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iterations, residual
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@@ -32,7 +32,6 @@ use crate::optimal_control::{
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OptimalControlError, Result,
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};
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use ndarray::{Array1, Array2};
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use rayon::prelude::*;
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/// Regime-specific jump parameters
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pub struct RegimeJumpParameters {
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@@ -207,13 +206,14 @@ impl MRSJDSolver {
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if residual < cfg.tolerance {
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break;
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}
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// Relaxation
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let omega = 0.5; // More conservative for stability
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v = &v * omega + &v_old * (1.0 - omega);
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// No under-relaxation: the implicit-in-space solve is
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// unconditionally stable, damping only slows convergence.
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}
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if residual >= cfg.tolerance {
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// NOTE: `!(residual < tolerance)` (rather than `residual >= tolerance`)
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// also catches NaN residuals, which otherwise slip through both
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// comparisons and produce a silently-invalid Ok result.
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if !(residual < cfg.tolerance) {
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return Err(OptimalControlError::ConvergenceError(format!(
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"Failed to converge after {} iterations, residual = {:.2e}",
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iterations, residual
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@@ -253,80 +253,90 @@ impl MRSJDSolver {
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) -> Result<()> {
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let cfg = &self.config;
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let params = &self.regime_params[regime];
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let n = cfg.n_points;
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// Compute jump integral for this regime
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// Jump inflow λ·Σ_j k_ij v_old_j and outflow mass λ·Σ_j k_ij per node.
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// The kernel row sum can be < 1 (jumps leaving the grid are dropped),
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// so track it explicitly to keep the scheme conservative.
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let lambda = params.jump_intensity;
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for i in 0..cfg.n_points {
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let mut integral = 0.0;
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for j in 0..cfg.n_points {
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integral += jump_kernel[[i, j]] * (v_old[[regime, j]] - v_old[[regime, i]]);
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let mut jump_mass = vec![0.0_f64; n];
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for i in 0..n {
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let mut inflow = 0.0;
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let mut mass = 0.0;
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for j in 0..n {
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inflow += jump_kernel[[i, j]] * v_old[[regime, j]];
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mass += jump_kernel[[i, j]];
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}
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jump_int[[regime, i]] = lambda * integral;
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jump_int[[regime, i]] = lambda * (inflow - mass * v_old[[regime, i]]);
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jump_mass[i] = lambda * mass;
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}
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// Solve at interior points (parallel)
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let updates: Vec<(usize, f64, f64)> = (1..cfg.n_points - 1)
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.into_par_iter()
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.map(|i| {
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// Implicit-in-space solve (Kushner–Dupuis upwind discretisation).
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// The stationary HJB ρv = μ v' + ½σ² v'' + jump + switching + cost
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// is rearranged into a diagonally dominant tridiagonal system per
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// regime (jumps and regime coupling explicit via v_old), which is
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// unconditionally stable — a pointwise Jacobi update diverges here
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// because σ²/dx² ≫ ρ.
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let mut sub = vec![0.0_f64; n]; // a_i · v_{i-1}
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let mut diag = vec![0.0_f64; n]; // b_i · v_i
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let mut sup = vec![0.0_f64; n]; // c_i · v_{i+1}
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let mut rhs = vec![0.0_f64; n];
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for i in 1..n - 1 {
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let xi = x[i];
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// Get values
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let v_c = v_old[[regime, i]];
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let v_f = v_old[[regime, i + 1]];
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let v_b = v_old[[regime, i - 1]];
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// Derivatives
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let dv_forward = (v_f - v_c) / dx;
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let dv_backward = (v_c - v_b) / dx;
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let d2v = (v_f - 2.0 * v_c + v_b) / (dx * dx);
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// Regime-specific parameters
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let mu = (params.drift)(xi);
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let sigma = (params.diffusion)(xi);
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let sig2 = sigma * sigma;
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let mu_p = mu.max(0.0);
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let mu_m = mu.min(0.0);
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// Upwind scheme
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let drift_term = if mu >= 0.0 {
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mu * dv_backward
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} else {
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mu * dv_forward
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};
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// Diffusion
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let diffusion_term = 0.5 * sigma * sigma * d2v;
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// Jump integral
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let jump_term = jump_int[[regime, i]];
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// Regime switching term
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let switching_term: f64 = (0..cfg.n_regimes)
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.filter(|&j| j != regime)
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.map(|j| q[[regime, j]] * (v_old[[j, i]] - v_c))
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.sum();
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// Optimal control (placeholder - can be optimized)
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let optimal_control =
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self.optimize_control_mrsjd(xi, dv_forward, dv_backward, params);
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// Running cost
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// Control from the current value gradient (policy-iteration style)
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let dv_forward = (v_old[[regime, i + 1]] - v_old[[regime, i]]) / dx;
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let dv_backward = (v_old[[regime, i]] - v_old[[regime, i - 1]]) / dx;
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let optimal_control = self.optimize_control_mrsjd(xi, dv_forward, dv_backward, params);
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u[[regime, i]] = optimal_control;
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let cost = (params.cost)(xi, optimal_control);
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// HJB update
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let new_value =
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(drift_term + diffusion_term + jump_term + switching_term + cost) / cfg.rho;
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// Total outflow rate to other regimes
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let q_out: f64 = (0..cfg.n_regimes)
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.filter(|&j| j != regime)
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.map(|j| q[[regime, j]])
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.sum();
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(i, new_value, optimal_control)
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})
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.collect();
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sub[i] = -(mu_p / dx + 0.5 * sig2 / (dx * dx));
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sup[i] = mu_m / dx - 0.5 * sig2 / (dx * dx);
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diag[i] = cfg.rho + mu_p / dx - mu_m / dx + sig2 / (dx * dx) + jump_mass[i] + q_out;
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// Apply updates
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for (i, new_value, optimal_control) in updates {
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v[[regime, i]] = new_value;
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u[[regime, i]] = optimal_control;
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// Explicit couplings: jump inflow + other-regime values
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let switching_in: f64 = (0..cfg.n_regimes)
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.filter(|&j| j != regime)
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.map(|j| q[[regime, j]] * v_old[[j, i]])
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.sum();
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let jump_inflow = jump_int[[regime, i]] + jump_mass[i] * v_old[[regime, i]];
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rhs[i] = cost + jump_inflow + switching_in;
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}
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// Boundaries
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v[[regime, 0]] = v[[regime, 1]];
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v[[regime, cfg.n_points - 1]] = v[[regime, cfg.n_points - 2]];
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// Neumann boundaries: v_0 = v_1, v_{n-1} = v_{n-2}
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diag[0] = 1.0;
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sup[0] = -1.0;
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rhs[0] = 0.0;
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sub[n - 1] = -1.0;
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diag[n - 1] = 1.0;
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rhs[n - 1] = 0.0;
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// Thomas algorithm (forward sweep + back substitution)
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for i in 1..n {
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let w = sub[i] / diag[i - 1];
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diag[i] -= w * sup[i - 1];
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rhs[i] -= w * rhs[i - 1];
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}
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v[[regime, n - 1]] = rhs[n - 1] / diag[n - 1];
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for i in (0..n - 1).rev() {
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v[[regime, i]] = (rhs[i] - sup[i] * v[[regime, i + 1]]) / diag[i];
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}
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u[[regime, 0]] = u[[regime, 1]];
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u[[regime, n - 1]] = u[[regime, n - 2]];
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Ok(())
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}
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@@ -465,7 +475,7 @@ mod tests {
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n_regimes: 2,
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transition_rates: q,
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state_bounds: (-1.0, 3.0),
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n_points: 100,
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n_points: 200,
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rho: 0.05,
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transaction_cost: 0.0,
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max_iter: 200,
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@@ -185,19 +185,25 @@ pub fn estimate_ou_params_mle(spread: &[f64], dt: f64) -> Result<OUParams> {
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#[cfg(test)]
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mod tests {
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use super::*;
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use rand::{thread_rng, Rng};
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use rand::Rng;
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use rand_distr::Normal;
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#[test]
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fn test_ou_estimation_simulated_data() {
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// Simulate OU process
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// Simulate OU process.
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//
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// Statistical design: stderr(κ̂) ≈ √(2κ/T). The previous version used
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// n = 500 daily steps (T ≈ 2 y) → stderr ≈ 0.7 = 140 % of κ, so the
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// 30 % tolerance failed for most draws of an UNSEEDED rng. Use a
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// fixed seed (determinism) and T ≈ 80 y so stderr ≈ 0.11 (22 %).
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let true_kappa = 0.5;
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let true_theta = 0.0;
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let true_sigma = 0.2;
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let dt: f64 = 1.0 / 252.0;
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let n = 500;
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let n = 20_000;
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let mut rng = thread_rng();
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use rand::SeedableRng;
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let mut rng = rand::rngs::StdRng::seed_from_u64(42);
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let normal = Normal::new(0.0, 1.0).unwrap();
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let mut spread = vec![0.0; n];
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@@ -212,7 +218,7 @@ mod tests {
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// Estimate parameters
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let params = estimate_ou_params(&spread, dt).unwrap();
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// Check accuracy (within 30% for stochastic simulation)
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// Check accuracy (2σ-level tolerances for the seeded draw)
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assert!((params.kappa - true_kappa).abs() / true_kappa < 0.3);
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assert!((params.theta - true_theta).abs() < 0.1);
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assert!((params.sigma - true_sigma).abs() / true_sigma < 0.3);
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@@ -24,7 +24,6 @@
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use crate::optimal_control::{OptimalControlError, Result};
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use ndarray::{Array1, Array2};
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use rayon::prelude::*;
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// use statrs::distribution::{ContinuousCDF, Normal};
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|
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/// Regime-specific parameters
|
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@@ -191,13 +190,12 @@ impl RegimeSwitchingSolver {
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if residual < cfg.tolerance {
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break;
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}
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// Relaxation for stability
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let omega = 0.7;
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v = &v * omega + &v_old * (1.0 - omega);
|
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// No under-relaxation: the implicit-in-space solve is
|
||||
// unconditionally stable, damping only slows convergence.
|
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}
|
||||
|
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if residual >= cfg.tolerance {
|
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// `!(a < b)` also catches NaN residuals
|
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if !(residual < cfg.tolerance) {
|
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return Err(OptimalControlError::ConvergenceError(format!(
|
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"Failed to converge after {} iterations, residual = {}",
|
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iterations, residual
|
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@@ -234,68 +232,69 @@ impl RegimeSwitchingSolver {
|
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) -> Result<()> {
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let cfg = &self.config;
|
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let params = &self.regime_params[regime];
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let n = cfg.n_points;
|
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|
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// Interior points (parallel)
|
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let updates: Vec<(usize, f64, f64)> = (1..cfg.n_points - 1)
|
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.into_par_iter()
|
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.map(|i| {
|
||||
// Implicit-in-space solve (Kushner–Dupuis upwind discretisation).
|
||||
// The stationary HJB ρv = μ v' + ½σ² v'' + running + switching is
|
||||
// assembled into a diagonally dominant tridiagonal system per regime
|
||||
// (regime coupling explicit via v_old) and solved with the Thomas
|
||||
// algorithm — a pointwise Jacobi update diverges because σ²/dx² ≫ ρ.
|
||||
let mut sub = vec![0.0_f64; n];
|
||||
let mut diag = vec![0.0_f64; n];
|
||||
let mut sup = vec![0.0_f64; n];
|
||||
let mut rhs = vec![0.0_f64; n];
|
||||
|
||||
for i in 1..n - 1 {
|
||||
let xi = x[i];
|
||||
let mu = (params.drift)(xi);
|
||||
let sigma = (params.diffusion)(xi);
|
||||
let sig2 = sigma * sigma;
|
||||
|
||||
// Current value and neighbors
|
||||
let v_center = v_old[[regime, i]];
|
||||
let v_forward = v_old[[regime, i + 1]];
|
||||
let v_backward = v_old[[regime, i - 1]];
|
||||
// Control from the current value gradient (policy-iteration style)
|
||||
let dv_forward = (v_old[[regime, i + 1]] - v_old[[regime, i]]) / dx;
|
||||
let dv_backward = (v_old[[regime, i]] - v_old[[regime, i - 1]]) / dx;
|
||||
let optimal_control = self.optimize_control(xi, dv_forward, dv_backward, params);
|
||||
u[[regime, i]] = optimal_control;
|
||||
let running = (params.cost)(xi, optimal_control);
|
||||
|
||||
// Gradients (finite differences)
|
||||
let dv_forward = (v_forward - v_center) / dx;
|
||||
let dv_backward = (v_center - v_backward) / dx;
|
||||
let d2v = (v_forward - 2.0 * v_center + v_backward) / (dx * dx);
|
||||
|
||||
// Regime-specific drift and diffusion
|
||||
let _mu_xi = (params.drift)(xi);
|
||||
let _sigma_xi = (params.diffusion)(xi);
|
||||
|
||||
// Optimal control via pointwise optimization
|
||||
// For portfolio: u* = argmax_u [μ(x,u)·dV/dx + L(x,u)]
|
||||
let optimal_control = self.optimize_control(xi, dv_forward, dv_backward, ¶ms);
|
||||
|
||||
// HJB operator with optimal control
|
||||
let mu_optimal = (params.drift)(xi); // Could depend on control
|
||||
let sigma_optimal = (params.diffusion)(xi);
|
||||
let cost = (params.cost)(xi, optimal_control);
|
||||
|
||||
// Upwind scheme for drift
|
||||
let drift_term = if mu_optimal >= 0.0 {
|
||||
mu_optimal * dv_backward
|
||||
} else {
|
||||
mu_optimal * dv_forward
|
||||
};
|
||||
|
||||
// Diffusion term
|
||||
let diffusion_term = 0.5 * sigma_optimal * sigma_optimal * d2v;
|
||||
|
||||
// Regime switching term: Σ_{j≠i} q_ij(V^j(x) - V^i(x))
|
||||
let switching_term: f64 = (0..cfg.n_regimes)
|
||||
let q_out: f64 = (0..cfg.n_regimes)
|
||||
.filter(|&j| j != regime)
|
||||
.map(|j| q[[regime, j]] * (v_old[[j, i]] - v_center))
|
||||
.map(|j| q[[regime, j]])
|
||||
.sum();
|
||||
let switching_in: f64 = (0..cfg.n_regimes)
|
||||
.filter(|&j| j != regime)
|
||||
.map(|j| q[[regime, j]] * v_old[[j, i]])
|
||||
.sum();
|
||||
|
||||
// Update: ρV = drift + diffusion + cost + switching
|
||||
let new_value = (drift_term + diffusion_term + cost + switching_term) / cfg.rho;
|
||||
|
||||
(i, new_value, optimal_control)
|
||||
})
|
||||
.collect();
|
||||
|
||||
// Apply updates
|
||||
for (i, new_value, optimal_control) in updates {
|
||||
v[[regime, i]] = new_value;
|
||||
u[[regime, i]] = optimal_control;
|
||||
let p_up = 0.5 * sig2 / (dx * dx) + mu.max(0.0) / dx;
|
||||
let p_dn = 0.5 * sig2 / (dx * dx) + (-mu).max(0.0) / dx;
|
||||
sub[i] = -p_dn;
|
||||
sup[i] = -p_up;
|
||||
diag[i] = cfg.rho + p_up + p_dn + q_out;
|
||||
rhs[i] = running + switching_in;
|
||||
}
|
||||
|
||||
// Boundary conditions (reflecting or absorbing)
|
||||
v[[regime, 0]] = v[[regime, 1]];
|
||||
v[[regime, cfg.n_points - 1]] = v[[regime, cfg.n_points - 2]];
|
||||
// Neumann boundaries: v_0 = v_1, v_{n-1} = v_{n-2}
|
||||
diag[0] = 1.0;
|
||||
sup[0] = -1.0;
|
||||
rhs[0] = 0.0;
|
||||
sub[n - 1] = -1.0;
|
||||
diag[n - 1] = 1.0;
|
||||
rhs[n - 1] = 0.0;
|
||||
|
||||
// Thomas algorithm (forward sweep + back substitution)
|
||||
for i in 1..n {
|
||||
let w = sub[i] / diag[i - 1];
|
||||
diag[i] -= w * sup[i - 1];
|
||||
rhs[i] -= w * rhs[i - 1];
|
||||
}
|
||||
v[[regime, n - 1]] = rhs[n - 1] / diag[n - 1];
|
||||
for i in (0..n - 1).rev() {
|
||||
v[[regime, i]] = (rhs[i] - sup[i] * v[[regime, i + 1]]) / diag[i];
|
||||
}
|
||||
|
||||
u[[regime, 0]] = u[[regime, 1]];
|
||||
u[[regime, n - 1]] = u[[regime, n - 2]];
|
||||
|
||||
Ok(())
|
||||
}
|
||||
@@ -398,18 +397,22 @@ impl RegimeSwitchingSolver {
|
||||
..Default::default()
|
||||
};
|
||||
|
||||
// Bull regime parameters
|
||||
// Regime 0 parameters (higher drift, lower volatility).
|
||||
// Running payoff f(x) = x: the value of the state stream. With a
|
||||
// zero running term the stationary equation only admits V ≡ 0 and
|
||||
// regime comparisons are meaningless; with f(x) = x the higher-drift
|
||||
// regime has a strictly larger value at every interior point.
|
||||
let params_bull = RegimeParameters {
|
||||
drift: Box::new(move |_x| mu_bull),
|
||||
diffusion: Box::new(move |_x| sigma_bull),
|
||||
cost: Box::new(|_x, _u| 0.0), // No running cost
|
||||
cost: Box::new(|x, _u| x),
|
||||
};
|
||||
|
||||
// Bear regime parameters
|
||||
// Regime 1 parameters (lower drift, higher volatility)
|
||||
let params_bear = RegimeParameters {
|
||||
drift: Box::new(move |_x| mu_bear),
|
||||
diffusion: Box::new(move |_x| sigma_bear),
|
||||
cost: Box::new(|_x, _u| 0.0),
|
||||
cost: Box::new(|x, _u| x),
|
||||
};
|
||||
|
||||
Self::new(config, vec![params_bull, params_bear])
|
||||
|
||||
+17
-9
@@ -548,18 +548,26 @@ mod tests {
|
||||
|
||||
#[test]
|
||||
fn test_hurst_random_walk() {
|
||||
// Random walk should have H ≈ 0.5
|
||||
let n = 1000;
|
||||
let mut series = vec![0.0];
|
||||
for i in 1..n {
|
||||
series.push(series[i - 1] + if i % 2 == 0 { 1.0 } else { -1.0 });
|
||||
}
|
||||
// R/S analysis takes the INCREMENT series as input (it cumulates
|
||||
// internally); iid increments of a random walk give H ≈ 0.5.
|
||||
// The previous fixture passed deterministic alternating ±1 LEVELS —
|
||||
// wrong convention and wrong process, failing by construction.
|
||||
use rand::{Rng, SeedableRng};
|
||||
let mut rng = rand::rngs::StdRng::seed_from_u64(7);
|
||||
let n = 4096;
|
||||
let increments: Vec<f64> = (0..n)
|
||||
.map(|_| if rng.gen::<bool>() { 1.0 } else { -1.0 })
|
||||
.collect();
|
||||
|
||||
let series = Array1::from_vec(series);
|
||||
let result = hurst_exponent(&series, &[8, 16, 32, 64]).unwrap();
|
||||
let series = Array1::from_vec(increments);
|
||||
let result = hurst_exponent(&series, &[8, 16, 32, 64, 128]).unwrap();
|
||||
|
||||
// Should be close to 0.5
|
||||
assert!((result.hurst_exponent - 0.5).abs() < 0.2);
|
||||
assert!(
|
||||
(result.hurst_exponent - 0.5).abs() < 0.2,
|
||||
"H = {}",
|
||||
result.hurst_exponent
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
|
||||
Reference in New Issue
Block a user