Implementation:Avhz RustQuant Curve new

Knowledge Sources	RustQuant
Domains	Fixed_Income, Yield_Curves, Market_Data
Last Updated	2026-02-07 20:00 GMT

Overview

Concrete tool for constructing yield curves from market data provided by the RustQuant data crate.

Description

The Curve struct stores rate data as a BTreeMap<Date, f64> and supports multiple interpolation backends (Linear, Exponential). It optionally fits a Nelson-Siegel-Svensson parametric model via particle swarm optimization. The struct is PyO3-annotated for Python interop.

Construction is available via new() (from separate date and rate vectors) or from_nodes() (from a BTreeMap). The curve lazily fits the NSS model on first interpolation request for out-of-sample dates.

Usage

Import Curve when you need to build a yield curve from market data. Specify the curve type (Flat, Spot, Forward, Discount) and interpolation method (Linear, Exponential).

Code Reference

Source Location

Repository: RustQuant
File: crates/RustQuant_data/src/curves.rs
Lines: L46-167 (Curve struct, CurveType, InterpolationMethod, new, from_nodes)

Signature

pub enum CurveType { Flat, Spot, Forward, Discount }
pub enum InterpolationMethod { Linear, Exponential, CubicSpline, Lagrange }

#[pyclass]
pub struct Curve {
    nodes: BTreeMap<Date, f64>,
    curve_type: CurveType,
    interpolation_method: InterpolationMethod,
    interpolator: Arc<dyn Interpolator<Date, f64>>,
    nss: Option<NelsonSiegelSvensson>,
}

impl Curve {
    pub fn new(
        dates: Vec<Date>,
        rates: Vec<f64>,
        curve_type: CurveType,
        interpolation_method: InterpolationMethod,
    ) -> PyResult<Self>

    pub fn from_nodes(
        nodes: BTreeMap<Date, f64>,
        curve_type: CurveType,
        interpolation_method: InterpolationMethod,
    ) -> PyResult<Self>
}

Import

use RustQuant::data::{Curve, CurveType, InterpolationMethod};
use time::Date;

I/O Contract

Inputs

Name	Type	Required	Description
dates	Vec<Date>	Yes (for new())	Vector of observation dates
rates	Vec<f64>	Yes (for new())	Vector of observed rates corresponding to dates
nodes	BTreeMap<Date, f64>	Yes (for from_nodes())	Pre-built date-rate mapping
curve_type	CurveType	Yes	Flat, Spot, Forward, or Discount
interpolation_method	InterpolationMethod	Yes	Linear or Exponential (CubicSpline/Lagrange are TODO)

Outputs

Name	Type	Description
return	PyResult<Curve>	Constructed curve ready for rate queries and interpolation

Usage Examples

Basic Spot Curve

use RustQuant::data::{Curve, CurveType, InterpolationMethod};
use time::macros::date;

let dates = vec![
    date!(2024 - 01 - 01),
    date!(2024 - 07 - 01),
    date!(2025 - 01 - 01),
    date!(2026 - 01 - 01),
    date!(2029 - 01 - 01),
];

let rates = vec![0.04, 0.042, 0.045, 0.048, 0.05];

let curve = Curve::new(
    dates,
    rates,
    CurveType::Spot,
    InterpolationMethod::Linear,
).unwrap();

Query Rates

// Get an exact rate
let rate = curve.get_rate(date!(2025 - 01 - 01)); // Some(0.045)

// Get an interpolated rate
let rate = curve.get_rate(date!(2024 - 10 - 01)); // Interpolated

// Get rate and add to curve nodes
let mut curve = curve;
let rate = curve.get_rate_and_insert(date!(2024 - 10 - 01));

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