stochastic-rs

Development rules for stochastic-rs — enforces project conventions when writing new modules, adding dependencies, or implementing algorithms

Conventions for writing and maintaining stochastic-rs documentation pages under website/content/docs/. Eight page templates (process / distribution / copula / pricer / calibrator / estimator / AI surrogate / concept), frontmatter schema, KaTeX gotchas, meta.json sidebar wiring, doctest-backed examples, and the audit-script contract. Invoke whenever a new public type ships and needs a docs page, or when fixing rot in existing pages.

How to add a new diffusion / SDE process to stochastic-rs-stochastic. Invoke when implementing GBM-like, OU-like, Vasicek-like, CIR-like, Heston-like models that satisfy `dX_t = drift dt + diffusion dW_t`.

How to add a Hurst-parameterised (rough / fractional) process to stochastic-rs-stochastic. Invoke when wrapping fBm / fGn / Volterra-kernel processes — fOU, rBergomi, rough Heston, fBates, fractional CIR, etc.

How to add a GPU-accelerated sampler (CUDA / Metal) for a process or distribution. Invoke when porting a CPU sampler to GPU for the long-horizon / many-paths regime where CPU SIMD saturates.

How to add a jump-diffusion / Lévy / compound-Poisson process to stochastic-rs-stochastic. Invoke for Merton-jump, Kou-jump, Bates-style models, or for layering jumps onto an existing diffusion (GBM → MJD, Heston → Bates).

How to add a Monte Carlo variance-reduction technique to stochastic-rs-quant. Covers antithetic, control-variate, stratified, importance, quasi-MC (Halton/Sobol), and MLMC. Returns McEstimate<T> with 95% CI.

How to add a univariate distribution to stochastic-rs-distributions. Covers SimdXxx struct, sampling pattern (transformation / ziggurat / rejection / inversion), DistributionExt closed-form moments/pdf/cdf/cf, KS-test, and the py_distribution! macro.

Step-by-step recipe for exposing a new Rust type to Python via stochastic-rs-py. Quickstart companion to the comprehensive `python-bindings` SKILL — invoke for the "what files do I touch and in what order" view.

Conventions for writing criterion benchmarks in stochastic-rs. Group naming, parameter sweep, [[bench]] required-features gating, no-println / no-dead-helper rules. Invoke when adding a new bench or refactoring an existing one.

How to implement a model calibrator in stochastic-rs (BSM, Heston, SABR, SVI, …). Invoke when adding a new calibration routine that fits a model to market option prices or implied vols.

How to add a bivariate copula to stochastic-rs-copulas. Invoke when implementing Clayton, Frank, Gumbel, Joe, Plackett, FGM-style families, or any 2-d Archimedean / extreme-value copula.

Conventions for adding / propagating Cargo features across the stochastic-rs workspace. Invoke when adding a new optional dependency, gating a sub-module, or debugging "feature X enabled in crate A but not B" build errors.

How to expose first- and second-order Greeks via the GreeksExt trait in stochastic-rs. Invoke when adding a new pricer that needs delta/gamma/vega/theta/rho/vanna/charm/volga/veta, or when a Monte Carlo pricer is missing the single-pass `greeks()` override.

Conventions for writing integration tests in stochastic-rs. Pinned-seed mandate, golden-numerics paper-reference pattern, no-debug-test rule, feature-gating discipline. Invoke when adding tests under tests/ or when a test is flaky.

Step-by-step release workflow for stochastic-rs (rc → stable → patch). Invoke when bumping versions, cutting a tag, publishing to crates.io, or shipping wheels to PyPI.

How to add a statistical estimator to stochastic-rs-stats. Covers ArrayView1<T> input shape, *Result struct conventions, parametric vs bootstrap p-values, openblas gating, paper-citation requirements, and reference-comparison tests.

How to add a neural-network volatility surrogate to stochastic-rs-ai. Covers StochVolModelSpec, BoundedScaler / StandardScaler conventions, gzip-npy training-set loading, train_save_load roundtrip test, and predict_surface integration with ImpliedVolSurface::from_flat_iv_grid.

Maintenance and extension rules for stochastic-rs Python (PyO3) bindings — invoke when adding/updating distributions, processes, pricers, calibrators, or estimators that need Python exposure

Enforces scalability, integration, and compatibility requirements when creating any new module in stochastic-rs — covers stochastic, quant, stats, distributions, copulas, and ai