fastFE

A Python library that simplifies QuantLib for pricing exotic derivatives. It provides high-level abstractions for yield curve bootstrapping, volatility surface construction, financial model calibration, and Monte Carlo simulation — so you can focus on the economics of a trade rather than QuantLib's low-level API.

Features

• Curve bootstrapping — build yield curves from deposits, swaps, OIS, FRAs, bonds, and SOFR futures for USD, EUR, JPY, TWD, CHF, and GBP with a single function call
• Volatility surfaces — construct 1D vol curves and 2D vol surfaces; create calibration helpers for swaptions and Heston models
• Financial models — Hull-White (interest rate), Heston (stochastic vol equity), Black-Scholes-Merton (equity/FX), Garman-Kohlhagen (FX), and multi-asset correlation models
• Monte Carlo paths — all models expose a uniform monte_carlo_paths() interface returning pandas DataFrames indexed by ql.Date
• Longstaff-Schwartz — built-in LSM engine for Bermudan and American-style early-exercise products
• Market conventions — pre-defined fixed and floating leg conventions for major currencies via Conventions
• Schedule utilities — helpers for combining schedules, computing year fractions, and resolving fixing dates

Installation

pip install fastFE

QuantLib must be installed separately (see QuantLib-Python installation guide):

pip install QuantLib

Quick Start

Build a yield curve

import QuantLib as ql
import pandas as pd
from fastFE import bootstrap_curve

today = ql.Date().todaysDate()

df_deposit = pd.DataFrame({
    'tenor': ['1M', '2M', '3M', '6M', '9M'],
    'rates': [0.015, 0.018, 0.02, 0.022, 0.025]
})
df_swap = pd.DataFrame({
    'tenor': ['1Y', '2Y', '5Y', '7Y', '10Y'],
    'rate':  [0.015, 0.018, 0.02, 0.022, 0.025]
})

curve = bootstrap_curve(today, deposit=df_deposit, swap=df_swap)
print(curve.discount(today + ql.Period('1Y')))

bootstrap_curve automatically applies the correct market conventions for the currency inferred from the helpers. Pass currency='EUR' to override.

See examples/examples.py for a complete reference of all rate helper types (OIS, FRA, bond, SOFR futures).

---

Construct a volatility surface

import QuantLib as ql
import pandas as pd
from fastFE import create_black_vol_curve, create_black_vol_surface

today = ql.Date().todaysDate()

# 1D vol curve
vol_series = pd.Series(
    [0.15, 0.18, 0.20, 0.22, 0.25],
    index=['1M', '2M', '3M', '6M', '9M']
)
vol_curve = create_black_vol_curve(vol_series, today)

# 2D vol surface (rows = strikes, columns = expiry tenors)
vol_df = pd.DataFrame(
    [[0.20, 0.21, 0.22],
     [0.22, 0.23, 0.24],
     [0.24, 0.25, 0.26]],
    index=[90.0, 100.0, 110.0],
    columns=['1M', '3M', '6M']
)
vol_surface = create_black_vol_surface(vol_df, today)

---

Price an equity derivative with Black-Scholes-Merton

import QuantLib as ql
from fastFE import BlackScholesMertonModel, create_black_vol_curve

today = ql.Date().todaysDate()
dayCount = ql.Actual365Fixed()
calendar = ql.WeekendsOnly()
spot = 100.0

risk_free = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.04, dayCount))
dividend  = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.01, dayCount))

vol_curve = create_black_vol_curve(
    pd.Series([0.20, 0.21, 0.22, 0.23], index=['3M', '6M', '9M', '1Y']),
    today
)

model = BlackScholesMertonModel(risk_free, dividend, vol_curve, spot)

fixing_schedule = ql.MakeSchedule(
    today, today + ql.Period('1Y'), ql.Period('1M'), calendar=calendar
)
paths = model.monte_carlo_paths(fixing_schedule, numPaths=1000)
# paths: pd.DataFrame, shape (len(fixing_schedule), 1000)

See examples/equity_link_example.py for a complete equity-linked structured product (Heston model, Bermudan knock-out, European knock-in, auto-call).

---

Price an FX derivative with Garman-Kohlhagen

import QuantLib as ql
from fastFE import GarmanKohlagenProcessModel, create_black_vol_surface

today = ql.Date().todaysDate()
dayCount = ql.Actual365Fixed()
spot = 1.10  # EURUSD

domestic = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.04, dayCount))
foreign  = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.02, dayCount))

# vol_surface: see "Construct a volatility surface" above
fx_model = GarmanKohlagenProcessModel(domestic, foreign, vol_surface, spot)

daily_schedule = ql.MakeSchedule(
    today, today + ql.Period('1Y'), ql.Period('1D'), calendar=ql.WeekendsOnly()
)
fx_paths = fx_model.monte_carlo_paths(daily_schedule, numPaths=1000)

See examples/fx_linked_example.py for an FX range accrual note, and examples/target_redemption_example.py for a Target Redemption Forward (TRF).

---

Price an interest rate derivative with Hull-White

import QuantLib as ql
import pandas as pd
from fastFE import HullWhiteModel, bootstrap_curve

today = ql.Date().todaysDate()
ql.Settings.instance().evaluationDate = today

curve = bootstrap_curve(today, deposit=df_deposit, swap=df_swap)

hw_model = HullWhiteModel(today, curve)
hw_model.calibrate(df_swaption)  # df_swaption: DataFrame with maturity, length, volatility

def create_libor_6M(ts):
    return ql.IborIndex(
        'Libor6M', ql.Period('6M'), 2, ql.USDCurrency(),
        ql.UnitedStates(ql.UnitedStates.Settlement),
        ql.ModifiedFollowing, True, ql.Thirty360(ql.Thirty360.USA), ts
    )

underlying_path, fixings, discount_factors = hw_model.monte_carlo_paths(
    index_factories={'libor_6M': create_libor_6M},
    fixingSchedule=fixing_schedule,
    paymentSchedule=payment_schedule,
    numPaths=1000
)
libor_fixings = fixings['libor_6M']  # pd.DataFrame

See examples/irs_examle.py for a cancellable Libor IRS, examples/sofr_irs_example.py for a cancellable SOFR IRS with daily compounding, and examples/ir_linked_example.py for a CMS spread cancellable IRS.

---

Bermudan / American optionality with Longstaff-Schwartz

import numpy as np
import pandas as pd
from fastFE import LongstaffSchwartz, subset_to_bool

# cashflows: pd.DataFrame (payment_dates x paths)
# discount_factors: pd.DataFrame (payment_dates x 1)
# exercise_payoff: callable, receives array of path values, returns cashflow array

exercisable = subset_to_bool(exercise_dates, cashflows.index)

lsm = LongstaffSchwartz(
    cashflows=cashflows,
    discountFactors=discount_factors,
    exercisable=exercisable,
    exercise_payoff=lambda x: np.zeros(len(x)),
    observable=libor_fixings
)

lsm.backward_induction()
print(lsm.confidence_interval())       # (lower, upper) at 95%
print(lsm.survival_probability())      # probability of not exercising early
print(lsm.exercise_cashflows())        # expected cashflows from early exercise
print(lsm.exercise_mask())             # bool DataFrame: exercise decision per path

See examples/examples.py for a standalone LSM example with mock data.

---

Multi-asset Monte Carlo

from fastFE import MultiAssetModel

corr_matrix = [[1.0, 0.6], [0.6, 1.0]]

# Combine a BSM equity process and a GK FX process
multi_model = MultiAssetModel(
    processes=[equity_model.process, fx_model.process],
    corrMatrix=corr_matrix
)

fixings = multi_model.monte_carlo_paths(fixing_schedule, numPaths=1000)
equity_paths = fixings[0]   # pd.DataFrame
fx_paths     = fixings[1]   # pd.DataFrame

See examples/multi_stock_examle.py for a multi-asset example.

---

Market conventions

Conventions provides pre-built fixed and floating leg convention dictionaries for USD, EUR, JPY, TWD, CHF, and GBP, ready to pass directly into rate helper constructors.

from fastFE import Conventions, create_deposit_rate_helpers, create_swap_rate_helpers

deposit_helpers = create_deposit_rate_helpers(df_deposit, Conventions.USFixedLegConventions())
swap_helpers    = create_swap_rate_helpers(
    df_swap,
    fixed_leg_conventions=Conventions.USFixedLegConventions(),
    floating_leg_conventions=Conventions.USFloatingLegConventions()
)

---

Examples

Disclaimer: All example files import from the market_data module, which provides mock/synthetic data generated programmatically for testing and demonstration purposes only. All rates, prices, and volatilities are fictional and do not represent real market data. Do not use for actual trading or financial decision-making.

File	Description
examples/examples.py	Comprehensive reference — dates, schedules, all rate helpers, all model types, LSM
examples/irs_examle.py	Cancellable fixed-vs-Libor IRS using Hull-White + LSM
examples/sofr_irs_example.py	Cancellable fixed-vs-SOFR IRS with daily compounding
examples/ir_linked_example.py	CMS 5Y-2Y spread cancellable IRS (Hull-White + LSM)
examples/equity_link_example.py	Equity-linked note with Heston model, knock-out, knock-in, auto-call
examples/fx_linked_example.py	FX daily range accrual note (Garman-Kohlhagen)
examples/target_redemption_example.py	Target Redemption Forward (TRF) in EURUSD
examples/multi_stock_examle.py	Multi-asset correlated Monte Carlo

API Reference

bootstrap_curve(today, *, deposit=None, swap=None, OIS=None, fra=None, bond=None, sofr=None, currency='USD')

Bootstraps a yield curve from any combination of instrument DataFrames. Returns a ql.YieldTermStructureHandle.

HullWhiteModel(today, curve)

Interest rate model. Call .calibrate(df_swaption) to fit to market swaptions, then .monte_carlo_paths(index_factories, fixingSchedule, paymentSchedule, numPaths) to generate short-rate paths and index fixings.

HestonModel(risk_free_curve, dividend_curve, calendar)

Stochastic volatility equity model. Call .calibrate(vol_df, spot), then .monte_carlo_paths(schedule, numPaths).

BlackScholesMertonModel(risk_free_curve, dividend_curve, vol, spot)

Equity/index model. Accepts constant vol, vol curve, or vol surface. Call .monte_carlo_paths(schedule, numPaths, seed=None); pass a non-zero seed for reproducible paths.

GarmanKohlagenProcessModel(domestic_curve, foreign_curve, vol, spot)

FX model. Same vol options and monte_carlo_paths interface as BSM.

MultiAssetModel(processes, corrMatrix)

Combines multiple GBM processes with a correlation matrix. Returns a list of DataFrames from .monte_carlo_paths(schedule, numPaths).

LongstaffSchwartz(cashflows, discountFactors, exercisable, exercise_payoff, observable)

Longstaff-Schwartz LSM engine. Call .backward_induction() first, then access .valuations(), .confidence_interval(), .survival_probability(), .exercise_cashflows(), .exercise_mask().

Conventions

Class with class methods: USFixedLegConventions(), USFloatingLegConventions(), EURFixedLegConventions(), EURFloatingLegConventions(), JPYFixedLegConventions(), JPYFloatingLegConventions(), and equivalents for TWD, CHF, GBP.

Requirements

• Python >= 3.9
• QuantLib >= 1.32
• pandas >= 1.5
• numpy >= 1.23
• scikit-learn >= 1.2

License

MIT