Curves

from vade import DiscountCurve, LineCurve, ForwardCurve, SpreadCurve, CompositeCurve, TurnOfYearCurve, FXImpliedCurve, NelsonSiegel, NelsonSiegelSvensson, SmithWilson, IRImpliedCurve, CreditImpliedCurve, FittedBondCurve

DiscountCurve(nodes, *, interpolation="log_linear", id="curve", convention="act360", modifier="modified_following", calendar=None, t=None, endpoints="not_a_knot")

import datetime
from vade import DiscountCurve

nodes = {
    datetime.date(2024, 1, 1): 1.0,
    datetime.date(2025, 1, 1): 0.97,
    datetime.date(2026, 1, 1): 0.94,
}
curve = DiscountCurve(nodes, interpolation="log_linear", convention="act365f")

df = curve.discount_factor(datetime.date(2024, 7, 1))
assert 0.97 < df < 1.0  # interpolated mid-year discount factor

zr = curve.zero_rate(datetime.date(2024, 1, 1), datetime.date(2025, 1, 1))
assert 0.02 < zr < 0.04  # zero rate implied by DF=0.97

shifted = curve.shift(10)  # shift by 10 basis points

LineCurve(nodes, *, interpolation="linear", id="curve", convention="act360", modifier="modified_following", calendar=None, t=None, endpoints="not_a_knot")

import datetime
from vade import LineCurve

nodes = {
    datetime.date(2024, 1, 1): 0.030,
    datetime.date(2025, 1, 1): 0.035,
    datetime.date(2026, 1, 1): 0.040,
}
curve = LineCurve(nodes, interpolation="linear", convention="act365f")

zr = curve.zero_rate(datetime.date(2024, 1, 1), datetime.date(2024, 7, 1))
assert 0.03 <= zr <= 0.035  # interpolated mid-year rate

fr = curve.forward_rate(datetime.date(2024, 1, 1), datetime.date(2025, 1, 1))
assert fr > 0  # positive forward rate

ForwardCurve(nodes, *, interpolation="flat_forward", id="forward_curve", convention="act365f", modifier="modified_following", calendar=None, t=None, endpoints="not_a_knot")

import datetime
from vade import ForwardCurve

# Instantaneous forward rates (per-day units matching internal x-axis)
nodes = {
    datetime.date(2024, 1, 1): 8.2e-5,
    datetime.date(2025, 1, 1): 9.0e-5,
    datetime.date(2026, 1, 1): 1.0e-4,
}
curve = ForwardCurve(nodes, interpolation="flat_forward", convention="act365f")

df = curve.discount_factor(datetime.date(2025, 1, 1))
assert 0.9 < df < 1.0  # discount factor integrated from forward rates

fr = curve.forward_rate(datetime.date(2024, 1, 1), datetime.date(2025, 1, 1))
assert 0.02 < fr < 0.04  # annualized forward rate

SpreadCurve(base_json, spread_nodes, *, mode="additive", spread_interpolation="linear", convention="act365f", id="spread_curve")

import datetime
from vade import DiscountCurve, SpreadCurve

base_nodes = {
    datetime.date(2024, 1, 1): 1.0,
    datetime.date(2025, 1, 1): 0.97,
    datetime.date(2026, 1, 1): 0.94,
}
base = DiscountCurve(base_nodes, interpolation="log_linear", convention="act365f")
base_json = base.to_json()

spread_nodes = {
    datetime.date(2024, 1, 1): 0.001,
    datetime.date(2025, 1, 1): 0.002,
    datetime.date(2026, 1, 1): 0.003,
}
spread = SpreadCurve(base_json, spread_nodes, mode="additive")

df = spread.discount_factor(datetime.date(2025, 1, 1))
assert 0.9 < df < 1.0  # spread-adjusted discount factor

import datetime
from vade import DiscountCurve, SpreadCurve

# Base OIS curve with realistic discount factors
base = DiscountCurve(
    {
        datetime.date(2025, 6, 16): 1.0,
        datetime.date(2026, 6, 16): 0.9615,
        datetime.date(2027, 6, 16): 0.9240,
        datetime.date(2028, 6, 16): 0.8880,
    },
    interpolation="log_linear",
    convention="act360",
)

# Credit spread term structure (in zero-rate space)
spread_nodes = {
    datetime.date(2025, 6, 16): 0.005,
    datetime.date(2026, 6, 16): 0.008,
    datetime.date(2027, 6, 16): 0.012,
    datetime.date(2028, 6, 16): 0.015,
}

# Additive spread: zero_rate = base_zero_rate + spread
additive = SpreadCurve(base.to_json(), spread_nodes, mode="additive")
df_add = additive.discount_factor(datetime.date(2027, 6, 16))
assert 0.85 < float(df_add) < 0.95  # lower DF than base due to spread

# Multiplicative spread: discount_factor = base_df * spread_df
multiplicative = SpreadCurve(base.to_json(), spread_nodes, mode="multiplicative")
df_mult = multiplicative.discount_factor(datetime.date(2027, 6, 16))
assert 0.85 < float(df_mult) < 0.95

# JSON roundtrip preserves all parameters
restored = SpreadCurve.from_json(additive.to_json())
df_restored = restored.discount_factor(datetime.date(2027, 6, 16))
assert abs(float(df_restored) - float(df_add)) < 1e-12

CompositeCurve(curves, id=None)

import datetime
from vade import DiscountCurve, CompositeCurve

nodes1 = {
    datetime.date(2024, 1, 1): 1.0,
    datetime.date(2025, 1, 1): 0.98,
    datetime.date(2026, 1, 1): 0.96,
}
nodes2 = {
    datetime.date(2024, 1, 1): 1.0,
    datetime.date(2025, 1, 1): 0.99,
    datetime.date(2026, 1, 1): 0.98,
}
curve1 = DiscountCurve(nodes1, convention="act365f")
curve2 = DiscountCurve(nodes2, convention="act365f")

composite = CompositeCurve([curve1, curve2])
df = composite.discount_factor(datetime.date(2025, 1, 1))
assert 0.96 < df < 0.98  # product of 0.98 * 0.99

import datetime
from vade import DiscountCurve, CompositeCurve, IRS

# OIS discount curve (risk-free rate ~4%)
ois = DiscountCurve(
    {datetime.date(2025, 6, 16): 1.0, datetime.date(2026, 6, 16): 0.9615,
     datetime.date(2027, 6, 16): 0.9240},
    convention="act360", id="ois",
)

# Funding spread curve (credit spread ~20bps)
spread = DiscountCurve(
    {datetime.date(2025, 6, 16): 1.0, datetime.date(2026, 6, 16): 0.998,
     datetime.date(2027, 6, 16): 0.996},
    convention="act360", id="spread",
)

# Composite = OIS x Spread (DF is product of components)
funding = CompositeCurve([ois, spread], id="funding")
df_composite = funding.discount_factor(datetime.date(2026, 6, 16))
df_ois = ois.discount_factor(datetime.date(2026, 6, 16))
df_spread = spread.discount_factor(datetime.date(2026, 6, 16))
assert abs(float(df_composite) - float(df_ois) * float(df_spread)) < 1e-10

# Use composite curve for IRS discounting
irs = IRS(
    effective=datetime.date(2025, 6, 16),
    termination="2y",
    frequency="s",
    convention="act360",
    fixed_rate=4.0,
    notional=10_000_000,
    currency="usd",
)
npv = irs.npv(funding)
assert isinstance(float(npv), float)

TurnOfYearCurve(inner_json, bumps, *, convention="act365f", calendar=None, id="toy_curve")

import datetime
from vade import DiscountCurve, TurnOfYearCurve

nodes = {
    datetime.date(2024, 1, 1): 1.0,
    datetime.date(2025, 1, 1): 0.97,
    datetime.date(2026, 1, 1): 0.94,
}
inner = DiscountCurve(nodes, interpolation="log_linear", convention="act365f")
inner_json = inner.to_json()

bumps = {
    datetime.date(2024, 12, 31): 5.0,
    datetime.date(2025, 12, 31): 3.0,
}
toy = TurnOfYearCurve(inner_json, bumps, convention="act365f")

df = toy.discount_factor(datetime.date(2025, 1, 1))
assert 0.9 < df < 1.0  # adjusted discount factor

import datetime
from vade import DiscountCurve, TurnOfYearCurve

# Smooth underlying curve
inner = DiscountCurve(
    {
        datetime.date(2025, 6, 16): 1.0,
        datetime.date(2026, 6, 16): 0.9615,
        datetime.date(2027, 6, 16): 0.9240,
        datetime.date(2028, 6, 16): 0.8880,
    },
    interpolation="log_linear",
    convention="act365f",
)

# Year-end bumps in basis points (5bp at 2025 year-end, 3bp at 2026 year-end)
bumps = {
    datetime.date(2025, 12, 31): 5.0,
    datetime.date(2026, 12, 31): 3.0,
}
toy = TurnOfYearCurve(inner.to_json(), bumps, convention="act365f")

# Discount factors reflect year-end bumps
df_after_ye = toy.discount_factor(datetime.date(2026, 1, 2))
inner_df_after = inner.discount_factor(datetime.date(2026, 1, 2))

# TurnOfYear curve produces lower DFs than inner curve around year-end
assert float(df_after_ye) < float(inner_df_after)

# JSON roundtrip preserves bumps
restored = TurnOfYearCurve.from_json(toy.to_json())
df_check = restored.discount_factor(datetime.date(2026, 1, 2))
assert abs(float(df_check) - float(df_after_ye)) < 1e-12

FXImpliedCurve(cashflow_ccy, collateral_ccy, fx_rates, fx_settlements, collateral_curve, cashflow_curve, id=None)

import datetime
from vade import DiscountCurve, FXRates, FXImpliedCurve

usd_nodes = {
    datetime.date(2024, 1, 1): 1.0,
    datetime.date(2025, 1, 1): 0.96,
    datetime.date(2026, 1, 1): 0.92,
}
eur_nodes = {
    datetime.date(2024, 1, 1): 1.0,
    datetime.date(2025, 1, 1): 0.97,
    datetime.date(2026, 1, 1): 0.94,
}
usd_curve = DiscountCurve(usd_nodes, convention="act365f", id="usd")
eur_curve = DiscountCurve(eur_nodes, convention="act365f", id="eur")

fx = FXRates({"eurusd": 1.10}, datetime.date(2024, 1, 1))
settlements = {"usd": datetime.date(2024, 1, 3), "eur": datetime.date(2024, 1, 3)}

fx_implied = FXImpliedCurve("eur", "usd", fx, settlements, usd_curve, eur_curve)
df = fx_implied.discount_factor(datetime.date(2025, 1, 1))
assert 0.9 < float(df) < 1.0  # FX-adjusted discount factor (returns Dual due to FXRates AD)

NelsonSiegel(beta0, beta1, beta2, tau, base_date, convention="act365f", id="")

import datetime
from vade import NelsonSiegel

ns = NelsonSiegel(
    beta0=0.05,
    beta1=-0.02,
    beta2=0.01,
    tau=1.5,
    base_date=datetime.date(2024, 1, 1),
    convention="act365f",
)

df = ns.discount_factor(datetime.date(2025, 1, 1))
assert 0.9 < df < 1.0  # discount factor from parametric model

zr = ns.zero_rate(datetime.date(2024, 1, 1), datetime.date(2034, 1, 1))
assert 0.04 < zr < 0.06  # long-term rate converges to beta0

NelsonSiegelSvensson(beta0, beta1, beta2, beta3, tau1, tau2, base_date, convention="act365f", id="")

import datetime
from vade import NelsonSiegelSvensson

nss = NelsonSiegelSvensson(
    beta0=0.05,
    beta1=-0.02,
    beta2=0.01,
    beta3=0.005,
    tau1=1.5,
    tau2=5.0,
    base_date=datetime.date(2024, 1, 1),
    convention="act365f",
)

df = nss.discount_factor(datetime.date(2025, 1, 1))
assert 0.9 < df < 1.0  # discount factor from parametric model

zr = nss.zero_rate(datetime.date(2024, 1, 1), datetime.date(2034, 1, 1))
assert 0.04 < zr < 0.06  # long-term rate converges to beta0

SmithWilson(dates, rates, ufr, alpha, base_date, convention="act365f", id="")

import datetime
from vade import SmithWilson

sw = SmithWilson(
    dates=[
        datetime.date(2025, 1, 1),
        datetime.date(2029, 1, 1),
        datetime.date(2034, 1, 1),
        datetime.date(2044, 1, 1),
    ],
    rates=[0.030, 0.032, 0.035, 0.038],
    ufr=0.035,
    alpha=0.1,
    base_date=datetime.date(2024, 1, 1),
    convention="act365f",
)

df = sw.discount_factor(datetime.date(2029, 1, 1))
assert 0.8 < df < 1.0  # discount factor from Smith-Wilson model

zr = sw.zero_rate(datetime.date(2024, 1, 1), datetime.date(2029, 1, 1))
assert 0.02 < zr < 0.05  # zero rate at 5-year point

IRImpliedCurve(source, ref_date, tenors, calendar, convention="act365f")

import datetime
from vade import DiscountCurve, IRImpliedCurve, BusinessCalendar

nodes = {
    datetime.date(2024, 1, 1): 1.0,
    datetime.date(2025, 1, 1): 0.97,
    datetime.date(2026, 1, 1): 0.94,
    datetime.date(2029, 1, 1): 0.85,
    datetime.date(2034, 1, 1): 0.72,
}
source = DiscountCurve(nodes, interpolation="log_linear", convention="act365f")

implied = IRImpliedCurve(
    source=source,
    ref_date=datetime.date(2024, 1, 1),
    tenors=["1Y", "2Y", "5Y", "10Y"],
    calendar=BusinessCalendar("NYC"),
)

implied.tenor_labels()  # ['1Y', '2Y', '5Y', '10Y']

df = implied.discount_factor(datetime.date(2025, 1, 1))
assert 0.96 < df < 0.98  # consistent with source curve

CreditImpliedCurve(nodes, convention="act360", recovery_rate=0.4, id="credit_curve")

import datetime
from vade import CreditImpliedCurve

nodes = {
    datetime.date(2024, 1, 1): 1.0,
    datetime.date(2025, 1, 1): 0.98,
    datetime.date(2026, 1, 1): 0.95,
    datetime.date(2029, 1, 1): 0.88,
}
credit = CreditImpliedCurve(nodes, convention="act360", recovery_rate=0.4)

sp = credit.survival_probability(datetime.date(2025, 1, 1))
assert 0.0 < sp < 1.0  # survival probability at 1 year

credit.id  # 'credit_curve'
credit.recovery_rate  # 0.4

FittedBondCurve(bonds, clean_prices, settlement, model="NS", *, convention="act365f", base_date=None, weights=None, initial_params=None, max_iter=200, func_tol=1e-10, ufr=None, alpha=None, id="")

import datetime
from vade import FittedBondCurve, FixedRateBond, NelsonSiegel

settlement = datetime.date(2024, 1, 15)

# Create a portfolio of bonds with different maturities
bonds = []
for maturity_year, coupon in [(2026, 0.03), (2027, 0.035), (2029, 0.04), (2031, 0.042), (2034, 0.045)]:
    bond = FixedRateBond(
        effective=settlement,
        termination=datetime.date(maturity_year, 1, 15),
        frequency="s",
        convention="act365f",
        coupon=coupon,
        face_value=100.0,
    )
    bonds.append(bond)

# Observed clean prices
clean_prices = [99.5, 99.0, 98.5, 97.0, 95.0]

# Fit Nelson-Siegel model
fc = FittedBondCurve(bonds, clean_prices, settlement, model="NS")

assert fc.converged
assert fc.rmse < 1.0  # good fit quality

# Query the fitted curve
df = fc.discount_factor(datetime.date(2025, 1, 15))
assert 0.9 < df < 1.0  # reasonable 1Y discount factor

zr = fc.zero_rate(settlement, datetime.date(2029, 1, 15))
assert 0.0 < zr < 0.10  # positive 5Y zero rate

# Per-bond pricing errors
errors = fc.pricing_errors()
assert len(errors) == 5  # one per bond

# Extract the fitted inner curve
inner = fc.curve()
assert isinstance(inner, NelsonSiegel)

import datetime
from vade import FittedBondCurve, FixedRateBond

bonds = [
    FixedRateBond(effective=datetime.date(2024, 6, 15), termination="2y",
                  fixed_rate=3.5, frequency="s", convention="act365f", currency="usd"),
    FixedRateBond(effective=datetime.date(2024, 6, 15), termination="3y",
                  fixed_rate=3.75, frequency="s", convention="act365f", currency="usd"),
    FixedRateBond(effective=datetime.date(2024, 6, 15), termination="5y",
                  fixed_rate=4.0, frequency="s", convention="act365f", currency="usd"),
    FixedRateBond(effective=datetime.date(2024, 6, 15), termination="7y",
                  fixed_rate=4.25, frequency="s", convention="act365f", currency="usd"),
    FixedRateBond(effective=datetime.date(2024, 6, 15), termination="10y",
                  fixed_rate=4.5, frequency="s", convention="act365f", currency="usd"),
]

clean_prices = [99.5, 99.0, 98.5, 97.5, 96.0]

fc = FittedBondCurve(
    bonds=bonds,
    clean_prices=clean_prices,
    settlement=datetime.date(2025, 6, 16),
    model="NS",
)

# Check convergence diagnostics
assert fc.converged
assert fc.iterations < 100
assert fc.rmse < 1.0  # RMSE in price terms

# pricing_errors() returns per-bond fitting errors
errors = fc.pricing_errors()
assert len(errors) == 5
for err in errors:
    assert abs(err) < 2.0  # each bond fits within 2 price points

# .curve() extracts the underlying parametric curve
inner_curve = fc.curve()
df = inner_curve.discount_factor(datetime.date(2030, 6, 16))
assert 0.70 < float(df) < 1.0