fixed-income

Install

python3 -m pip install -e .

python3 -m pip install -e '.[data]'   # datasets[data] (FRED, Treasury.gov, NY Fed, CME)
python3 -m pip install -e '.[viz]'    # plotly, dash, matplotlib, …
python3 -m pip install -e '.[dev]'    # pytest, ruff, build
python3 -m pip install -e '.[dev,data,viz]'

Bond risk

from fixed_income import Bond
from fixed_income.risk import macaulay, modified

b = Bond(c=0.05, F=100, T=10, P=95.0)
y = b.ytm()
print(macaulay(b, ytm=y), modified(b, ytm=y))

Nelson–Siegel and NSS on Treasury par

from datasets.data import TreasuryParCurveSource
from fixed_income import NelsonSiegel, NelsonSiegelSvensson

par = TreasuryParCurveSource().fetch()
t = [p.maturity_years for p in par.points]
r = [p.par_yield for p in par.points]

ns = NelsonSiegel(t=t, r=r)
ns.fit()
print("NS tau:", ns.tau)

nss = NelsonSiegelSvensson(t=t, r=r)
nss.fit()
print("NSS tau1/tau2:", nss.tau1, nss.tau2)

SOFR OIS curve, forwards, and Hull–White calibration

from datetime import date
from datasets.data import NyFedSofrSource, CmeSofrSettleBundleSource
from fixed_income import bootstrap_ois_from_sofr, HullWhite, forward_rate_from_discount

as_of = date(2026, 5, 22)
sofr = NyFedSofrSource().fetch(as_of=as_of)
ois = bootstrap_ois_from_sofr(sofr, pillars=[0.25, 0.5, 1.0, 2.0, 5.0])

fwd = forward_rate_from_discount(ois, 0.25, 1.0)
print(f"1y forward (from OIS): {fwd:.4%}")

bundle = CmeSofrSettleBundleSource(trade_date=as_of).fetch(as_of=as_of)
cal = HullWhite.calibrate_to_futures(bundle.sr3, ois)
print(f"HW a={cal.a:.4f}, sigma={cal.sigma:.4f}, RMSE={cal.rmse:.4f}")

Examples

• examples/rates_models.ipynb
• examples/bonds.ipynb

Dash dashboard

python3 -m pip install -e '.[viz]'
python3 -c "from fixed_income.viz import create_dash_app; create_dash_app().run_server(debug=True)"

Build / test

./scripts/build_test.sh
python3 -m pytest tests/test_rates.py -q