QuantSupport for Python

This Python package provides QuantSupport's native instruments, market construction, valuation and risk calculations. Define a contract and trade, configure its market, then evaluate the position inside a pricing context. Results are available as scalar properties and pandas tables.

Documentation and computational studies

The Python user guide is self-contained: installation, a complete valuation, financial conventions, market construction, product/model selection, scenarios, and exposure/XVA. Each workflow includes the financial conventions needed to interpret its results.

• First valuation and lifecycle
• Curves, market data, calibration, and history
• Bonds, loans, options, and scripting
• Sensitivities, netting, XVA, and custom pricers
• Official computational studies: rendered notebooks organized by financial topic
• Exact API reference: constructors/factories, parameters, defaults, properties, protocols, and errors

Public classes are grouped in quantsupport.time, .market, .instruments, .pricing, and .risk, and re-exported at the root for compatibility. quantsupport.Swap and quantsupport.instruments.Swap are the same class.

Install

Released wheels require Python 3.9 or later and install pandas as a dependency:

python -m pip install quantsupport

Use quantsupport[typing] for optional editor typing dependencies. A wheel does not require Rust. For a development build from the repository root:

python -m venv .venv
. .venv/bin/activate
python -m pip install "maturin>=1.5,<2.0"
maturin develop -m bindings/python/Cargo.toml --release

On Windows activate .venv\Scripts\Activate.ps1. Source builds require the stable Rust toolchain. Select this same interpreter in Jupyter; an older installed wheel can lack classes present in the checkout.

An in-memory valuation

This example defines a five-year receive-fixed swap and an explicit flat continuous zero-rate curve using in-memory inputs:

import quantsupport as qs

reference = qs.Date(2026, 2, 24)
quotes = qs.QuoteStore.from_dict({
    "reference_date": str(reference), "quotes": [],
})
curve = qs.CurveConfiguration(
    market_index="SOFR", day_counter="Actual360", interpolator="Linear",
    construction=qs.CurveConstruction.zero_rate(
        pillars=["0D", "10Y"], rates=[0.03, 0.03],
    ),
)
context = qs.PricingContext(
    quotes, [curve], csa_policy=qs.SingleCurveCSADiscountPolicy("USD", "SOFR"),
)
swap = qs.Swap(
    "USD-IRS", reference, reference + "5Y",
    10_000_000, 0.03, "USD", "SOFR",
    fixed_leg_frequency="Semiannual", floating_leg_frequency="Quarterly",
)
position = qs.Trade(swap, reference, qs.Side.LongReceive)
with context:
    result = context.evaluate(position, ["Value", "FairRate", "Sensitivities"])
    print(result.price, result.fair_rate)
    print(result.sensitivities)

The continuous curve implies simple forwards slightly above 3%, so the receive-fixed swap has a small negative value. Rates and spreads use annual decimals unless specified otherwise. Risk values are derivatives per unit input; multiply decimal-rate derivatives by 0.0001 for the approximate monetary change from a one-basis-point increase. Generic swap cashflow tables report signed amounts, positive for receipts and negative for payments, including initial advances. They contain no PV column.

Interface conventions

For portfolio aggregation, use the same explicit trades and receive standard EvaluationResults:

book = qs.Portfolio("PORTFOLIO", [position, another_position])
with context:
    result = context.evaluate(
        book, ["Value", "Sensitivities", "Cashflows"],
    )
    print(result.price)
    print(result.sensitivities)
    print(result.coupons)  # type, market_index, fixing_index, dates, notional, rate/spread, conventions
    print(result.cashflow_summary)  # payment_date, currency, interest, capital, other, total

Start with How a portfolio works for position construction, requests, result tables and numerical reconciliation. Open the portfolio aggregation notebook for an editable deposit/swap book, weighted quotes, distinct fixing indices, individual-position reconciliation and optional CSV exports.

The context prices one member at a time and sums completed numeric results and duplicate risk factors in portfolio order. Built-in pricers reuse the per-trade AD tape; no portfolio-wide AD graph or collection of member results is retained. Native Rust pricers report monetary PVs and derivatives in the context base currency, automatically using its FX store and including FX risk. Cashflow currencies remain separate. Python pricers report monetary results in the context base currency. Unsupported aggregate measures remain absent, as does any measure omitted by a member. Repeated entries count as repeated positions. Cashflows preserve coupon types, contractual and effective fixing indices (including leg overrides), accrual dates, day counts, overnight conventions and payoffs. Compatible fixed coupons use notional-weighted rates; compatible linear floating coupons use notional-weighted spreads. Pay and receive directions remain separate; daily-compounded spreads and different option payoffs are kept distinct. Coupons without contractual metadata remain individual rows. result.coupons exposes these contractual terms; historical payments are retained. The separate summary view separates net coupon interest from signed redemptions and advances, leaving unknown payment types in Other. It does not allocate accrual NII.

Fixed-rate deposits already use the shared Rust MakeFixedRateDeposit / FixedRateDeposit instrument and are exposed as qs.FixedRateDeposit (also qs.instruments.FixedRateDeposit). Create a qs.Trade to place one in a portfolio.

BondPricer leaves FairRate absent. For bond price/spread anchors, use YieldToMaturity, ModifiedDuration, ZSpread or the selected model's OAS request instead.

• Built-in instruments contain terms, not positions. Trade owns execution date, side, size overrides, and bond anchors. evaluate() and NettingSet require explicit trades. Do not put side or trade_date in product dicts.
• Context entry validates and enables lazy construction. initialize() eagerly constructs all configured objects. Final scope exit releases constructed state; do not retain AD-backed views after context/scenario exit.
• Context collection/store getters return copies. Assign changed inputs back to invalidate dependent construction.
• Many financial inputs accept enums or names, but typed shock/calendar/ schedule parameters retain their documented restrictions. For equity use qs.MarketIndex.equity("SPX") or {"Equity": "SPX"}, not "EQ:SPX".
• Python from_json(path) reads a file. Configuration from_dict returns one configuration; documented list loaders return lists. Product dictionaries instead follow constructor keywords.
• Bond trades require pricing_anchor: clean/dirty prices use quotation units; straight bonds use Z-spread, issuer-call bonds OAS. Discounting is a policy choice, not an automatic inference from currency.
• Loan Value is future expected receipt PV, excluding the initial advance. Loan fair-rate and prepayment choices are explained in the guide.
• Python scripting returns averaged numeric variables, not sensitivity/ cashflow tables. Rate models are explicit; finite-path estimator comparisons need not reconcile raw cashflow PVs with a control-adjusted selected variable.
• High-level XVA produces CVA/FVA for the documented contingent-claim subset. ENE is signed; profiles are pre-margin pointwise means, not PFE quantiles. Curve-driven adjustment risk uses survival/derived funding-spread pillars, not original CDS/funding quotes; those aggregator paths do not replay the original bootstrap Jacobians.
• Historical selection preserves one complete market's valuation date. Fixings are separate and not filtered by publication vintage.

Running the studies and tests

The examples guide gives environment setup and reading order. From the repository root, after building this checkout's binding:

python -m pip install -r bindings/python/examples/requirements.txt
python -m bindings.python.examples.run_notebooks
python -m bindings.python.examples.run_notebooks valuation/swaps --write
python -m unittest discover -s bindings/python/tests

The runner uses a fresh kernel per notebook, verifies the actual interpreter and binding exports, and rejects stale bindings before executing cells. Saved sanitized execution_environment metadata records Python/package versions and whether the kernel matched its caller. Without --write it leaves sources unchanged; Sphinx renders saved outputs without executing notebooks. The legacy external sensitivity benchmark has additional ORE/CurveEngine prerequisites and is not part of that runner's default directory. To validate all sixteen studies:

python -m pip install "open-source-risk-engine==1.8.14.1" "curveengine @ git+https://github.com/jmelo11/curveengine.git@d7e000da626e08ff2be5d34561597065340b017e"
python -m bindings.python.examples.run_notebooks --include-benchmark

Use the benchmark selector to run it alone.

Native harness checks include:

cargo check -p quantsupport-py
cargo test -p quantsupport-py python_dict_and_script_contracts -- --nocapture

Do not enable extension-module for Rust tests. Set PYO3_PYTHON when Cargo needs a particular interpreter; Windows linking/loading also requires its runtime DLL directories on PATH. New native product implementations belong under bindings/python/src/instruments/ with dispatch in src/pricing/.

See the documentation build guide for standalone Sphinx builds, which require neither this native module nor notebook execution.