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
- Built-in instruments contain terms, not positions.
Tradeowns execution date, side, size overrides, and bond anchors.evaluate()andNettingSetrequire explicit trades. Do not putsideortrade_datein 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. Configurationfrom_dictreturns 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 fourteen 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.
1 maintainer
Metadata
1 maintainer
Release files for quantsupport 0.1.12
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