Visualization
GridPlotter — Plotly-based grid plotter for quick HTML visualisation of paths, surfaces, and smiles. Feature-gated behind viz.
Visualization
stochastic-rs-viz ships GridPlotter — a thin wrapper over
Plotly that produces self-contained HTML plots
suitable for notebooks, READMEs, and quick visual checks.
The crate is feature-gated behind viz (default off) so a default
build does not pull Plotly's transitive deps.
[dependencies]
stochastic-rs-viz = "2.0.0"
# or via the umbrella:
stochastic-rs = { version = "2.0.0", features = ["viz"] }Examples
Sample-path overlay
use stochastic_rs::viz::GridPlotter;
use stochastic_rs::stochastic::diffusion::ou::Ou;
use stochastic_rs::traits::ProcessExt;
let p = Ou::<f64>::new(2.0, 0.0, 1.0, 1_000, Some(0.0), Some(1.0));
let paths = p.sample_par(8);
let mut plot = GridPlotter::new();
plot.add_paths("OU", &paths);
plot.write_html("ou_paths.html")?;Implied-vol surface heatmap
use stochastic_rs::viz::GridPlotter;
use stochastic_rs::quant::vol_surface::implied::ImpliedVolSurface;
let surface = ImpliedVolSurface::<f64>::from_market(
&strikes, &expiries, &iv_grid, /* spot */ 100.0,
);
let mut plot = GridPlotter::new();
plot.add_iv_surface("IV", &surface);
plot.write_html("iv_surface.html")?;What you get
- Sample-path overlays (single + many paths)
- Distribution histograms with overlaid pdf
- 2-D heatmaps (vol surface, IV grid)
- 3-D surface plots
- Smile / skew comparison panels
For interactive notebooks, prefer the Python bindings — the same data
can be passed straight to matplotlib, plotly.py, or bokeh.
AI surrogates
Neural-network volatility surrogates — Heston, one-factor Bergomi, rough Bergomi. Trained offline, inference at sub-millisecond speeds via candle.
Python bindings
stochastic-rs-py — Python coverage for distributions, processes, pricers, calibrators, copulas, stats. NumPy in / out, 210 entries at v2.0.