What Is QuantLib?
QuantLib is a free, open-source C++ library for quantitative finance - and the most comprehensive one available. Its Python bindings (via SWIG) give you access to hundreds of pricing models, yield curve construction tools, date handling utilities, and numerical methods without writing a single line of C++.
The library covers fixed income, derivatives, credit, and risk management. It implements everything from basic Black-Scholes pricing to multi-factor interest rate models, exotic option payoffs, and credit default swap valuation. Banks, hedge funds, and risk departments around the world use it in production systems.
What makes QuantLib different from writing your own pricing functions in NumPy or SciPy is the sheer breadth of implementation. You could spend months coding a proper yield curve bootstrapper, a bond pricing engine with accrued interest and day count conventions, and a lattice-based American option pricer. QuantLib has all of this ready to go, tested, and maintained by an active community since 2000.
If you're working in Python for finance, QuantLib fills the gap between simple textbook implementations and the institutional-grade libraries used on trading desks.
Installing QuantLib Python
Installation is straightforward with pip. The QuantLib package on PyPI ships pre-built wheels for most platforms, so you don't need to compile C++ yourself.
pip install QuantLib
On older systems or unusual architectures, you might need to install from source. In that case you'll need the Boost C++ libraries and SWIG. But for most users on Windows, macOS, or Linux with Python 3.8 - 3.12, the pip install just works.
Verify the installation:
import QuantLib as ql print(ql.__version__)
If that prints a version string (something like 1.34 or later), you're ready to go. The entire library is accessed through the ql namespace.
QuantLib Basics: Dates, Calendars, and Schedules
Before you price anything, you need to understand how QuantLib handles dates. Financial calculations are sensitive to exact dates - a bond's price depends on its coupon schedule, an option's price depends on exactly how many days remain until expiry, and yield curves are built from instruments with specific settlement dates. QuantLib provides a full date arithmetic system that handles all of this.
Dates
QuantLib's Date object works with day, month, and year. Months use named constants to avoid ambiguity.
import QuantLib as ql # Create a date: 15th June 2026 date1 = ql.Date(15, ql.June, 2026) print(date1) # June 15th, 2026 # Date arithmetic date2 = date1 + ql.Period(6, ql.Months) print(date2) # December 15th, 2026 date3 = date1 + ql.Period(30, ql.Days) print(date3) # July 15th, 2026 # Access components print(date1.dayOfWeek()) # 2 (Monday=1 ... Friday=5) print(date1.month()) # 6 print(date1.year()) # 2026 # Difference between dates diff = date2 - date1 print(f"Days between: {diff}") # 183 # Today's date (from QuantLib's evaluation date) today = ql.Date.todaysDate() print(today)
Calendars
Calendars determine which days are business days and which are holidays. This matters for settlement dates, coupon payments, and exercise dates.
uk_calendar = ql.UnitedKingdom() us_calendar = ql.UnitedStates(ql.UnitedStates.GovernmentBond) date = ql.Date(25, ql.December, 2026) print(uk_calendar.isBusinessDay(date)) # False (Christmas) print(uk_calendar.isHoliday(date)) # True # Advance by 5 business days from a given date start = ql.Date(20, ql.December, 2026) settlement = uk_calendar.advance(start, ql.Period(5, ql.Days)) print(settlement) # Skips Christmas and Boxing Day # Count business days between two dates bdays = uk_calendar.businessDaysBetween( ql.Date(1, ql.January, 2026), ql.Date(31, ql.December, 2026) ) print(f"Business days in 2026: {bdays}")
Day Count Conventions
Day count conventions determine how year fractions are calculated. Different markets use different conventions, and getting this wrong will produce incorrect prices.
dc_act365 = ql.Actual365Fixed() dc_act360 = ql.ActualActual(ql.ActualActual.ISDA) dc_30360 = ql.Thirty360(ql.Thirty360.BondBasis) d1 = ql.Date(1, ql.March, 2026) d2 = ql.Date(1, ql.September, 2026) print(f"Actual/365: {dc_act365.yearFraction(d1, d2):.6f}") print(f"Act/Act ISDA: {dc_act360.yearFraction(d1, d2):.6f}") print(f"30/360: {dc_30360.yearFraction(d1, d2):.6f}")
Schedules
A Schedule generates a series of dates - typically coupon dates for a bond. You specify the start and end, the frequency, and how to handle dates that fall on holidays.
issue_date = ql.Date(15, ql.March, 2026) maturity_date = ql.Date(15, ql.March, 2031) tenor = ql.Period(ql.Semiannual) calendar = ql.UnitedKingdom() convention = ql.ModifiedFollowing termination_convention = ql.ModifiedFollowing rule = ql.DateGeneration.Backward schedule = ql.Schedule( issue_date, maturity_date, tenor, calendar, convention, termination_convention, rule, False # end of month ) print("Coupon dates:") for i, date in enumerate(schedule): print(f" {i}: {date}")
Pricing a European Option
This is the classic QuantLib Python example. We'll price a European call option using the analytic Black-Scholes engine - the same formula you'd find in any options pricing textbook, but implemented with proper financial conventions.
import QuantLib as ql # Set the evaluation date today = ql.Date(9, ql.April, 2026) ql.Settings.instance().evaluationDate = today # Option parameters option_type = ql.Option.Call strike = 100.0 expiry = ql.Date(9, ql.October, 2026) # Market data spot_price = 105.0 volatility = 0.20 # 20% annualised vol risk_free_rate = 0.045 # 4.5% risk-free rate dividend_yield = 0.01 # 1% continuous dividend yield # Build the market data handles spot_handle = ql.QuoteHandle(ql.SimpleQuote(spot_price)) vol_handle = ql.BlackVolTermStructureHandle( ql.BlackConstantVol(today, ql.NullCalendar(), volatility, ql.Actual365Fixed()) ) rate_handle = ql.YieldTermStructureHandle( ql.FlatForward(today, risk_free_rate, ql.Actual365Fixed()) ) dividend_handle = ql.YieldTermStructureHandle( ql.FlatForward(today, dividend_yield, ql.Actual365Fixed()) ) # Construct the Black-Scholes-Merton process bsm_process = ql.BlackScholesMertonProcess( spot_handle, dividend_handle, rate_handle, vol_handle ) # Define the option payoff = ql.PlainVanillaPayoff(option_type, strike) exercise = ql.EuropeanExercise(expiry) option = ql.VanillaOption(payoff, exercise) # Attach the pricing engine option.setPricingEngine(ql.AnalyticEuropeanEngine(bsm_process)) # Results print(f"Option price: {option.NPV():.4f}") print(f"Delta: {option.delta():.4f}") print(f"Gamma: {option.gamma():.4f}") print(f"Vega: {option.vega():.4f}") print(f"Theta: {option.theta():.4f}") print(f"Rho: {option.rho():.4f}")
A few things to notice. First, QuantLib uses a global evaluation date via ql.Settings.instance().evaluationDate. Every calculation in your session uses this date unless you override it. Second, market data is wrapped in "handles" - this is a C++ pattern that allows lazy recalculation when inputs change. Third, the option doesn't know how to price itself until you attach an engine. This separation of instrument and pricing method is fundamental to QuantLib's architecture.
The output gives you both the price and the Greeks in one go. No need to compute finite-difference bumps manually - the analytic engine calculates them from the closed-form solution.
Pricing an American Option
American options can be exercised at any time before expiry, which means there's no closed-form solution like Black-Scholes. QuantLib provides several numerical methods: binomial trees, finite difference grids, and least-squares Monte Carlo.
Here's how to price an American put using a binomial tree and a finite difference engine, and compare the results:
import QuantLib as ql today = ql.Date(9, ql.April, 2026) ql.Settings.instance().evaluationDate = today # Option parameters strike = 100.0 expiry = ql.Date(9, ql.April, 2027) option_type = ql.Option.Put # Market data spot = 95.0 vol = 0.25 rate = 0.045 div_yield = 0.02 # Build the BSM process spot_handle = ql.QuoteHandle(ql.SimpleQuote(spot)) rate_handle = ql.YieldTermStructureHandle( ql.FlatForward(today, rate, ql.Actual365Fixed()) ) div_handle = ql.YieldTermStructureHandle( ql.FlatForward(today, div_yield, ql.Actual365Fixed()) ) vol_handle = ql.BlackVolTermStructureHandle( ql.BlackConstantVol(today, ql.NullCalendar(), vol, ql.Actual365Fixed()) ) process = ql.BlackScholesMertonProcess( spot_handle, div_handle, rate_handle, vol_handle ) # Define the American option payoff = ql.PlainVanillaPayoff(option_type, strike) exercise = ql.AmericanExercise(today, expiry) american_option = ql.VanillaOption(payoff, exercise) # Method 1: Binomial tree (Cox-Ross-Rubinstein) steps = 500 american_option.setPricingEngine( ql.BinomialVanillaEngine(process, "crr", steps) ) binomial_price = american_option.NPV() print(f"Binomial CRR ({steps} steps): {binomial_price:.4f}") # Method 2: Finite difference american_option.setPricingEngine( ql.FdBlackScholesVanillaEngine(process, 200, 200) ) fd_price = american_option.NPV() print(f"Finite difference: {fd_price:.4f}") # Compare with the European price (lower bound for the American) euro_payoff = ql.PlainVanillaPayoff(option_type, strike) euro_exercise = ql.EuropeanExercise(expiry) euro_option = ql.VanillaOption(euro_payoff, euro_exercise) euro_option.setPricingEngine(ql.AnalyticEuropeanEngine(process)) euro_price = euro_option.NPV() print(f"European price (lower bound): {euro_price:.4f}") print(f"Early exercise premium: {fd_price - euro_price:.4f}")
The binomial tree method builds a recombining price lattice and works backwards from expiry, checking at each node whether early exercise is optimal. More steps give higher accuracy but take longer. With 500 steps, you'll typically get 4 - 5 decimal places of accuracy.
The finite difference method solves the Black-Scholes PDE on a grid. The two arguments after the process control the number of time steps and spatial grid points. For most cases, both methods agree to within a fraction of a penny.
Building a Yield Curve
Yield curve construction is one of QuantLib's strongest features. In practice, you bootstrap a curve from market instruments - deposit rates for the short end, futures for the middle, and swap rates for the long end. Here's a complete example.
import QuantLib as ql today = ql.Date(9, ql.April, 2026) ql.Settings.instance().evaluationDate = today calendar = ql.UnitedKingdom() day_count = ql.Actual365Fixed() # Deposit rates (short end: overnight to 6 months) deposit_helpers = [] deposit_data = [ (ql.Period(1, ql.Days), 0.0430), # overnight (ql.Period(1, ql.Weeks), 0.0432), (ql.Period(1, ql.Months), 0.0435), (ql.Period(3, ql.Months), 0.0440), (ql.Period(6, ql.Months), 0.0445), ] for tenor, rate in deposit_data: helper = ql.DepositRateHelper( ql.QuoteHandle(ql.SimpleQuote(rate)), tenor, 2, # settlement days calendar, ql.ModifiedFollowing, False, # end of month day_count ) deposit_helpers.append(helper) # Swap rates (long end: 2 years to 30 years) swap_helpers = [] swap_data = [ (ql.Period(2, ql.Years), 0.0420), (ql.Period(3, ql.Years), 0.0415), (ql.Period(5, ql.Years), 0.0405), (ql.Period(7, ql.Years), 0.0400), (ql.Period(10, ql.Years), 0.0395), (ql.Period(15, ql.Years), 0.0390), (ql.Period(20, ql.Years), 0.0388), (ql.Period(30, ql.Years), 0.0385), ] for tenor, rate in swap_data: helper = ql.SwapRateHelper( ql.QuoteHandle(ql.SimpleQuote(rate)), tenor, calendar, ql.Annual, ql.ModifiedFollowing, day_count, ql.Euribor6M() # floating leg index ) swap_helpers.append(helper) # Combine all helpers and bootstrap the curve helpers = deposit_helpers + swap_helpers curve = ql.PiecewiseLogLinearDiscount(today, helpers, day_count) curve.enableExtrapolation() # Extract zero rates and discount factors at various tenors print("Tenor Zero Rate Discount Factor") print("-" * 42) tenors = [0.25, 0.5, 1, 2, 3, 5, 7, 10, 15, 20, 30] for t in tenors: zero = curve.zeroRate(t, ql.Compounded, ql.Annual).rate() df = curve.discount(t) print(f"{t:5.2f}y {zero:.4%} {df:.6f}")
This builds a proper piecewise-bootstrapped curve - the same methodology used on fixed income trading desks. The PiecewiseLogLinearDiscount interpolation method ensures smooth discount factors while fitting market instruments exactly. QuantLib also offers cubic spline, log-cubic, and other interpolation methods if you need them.
The enableExtrapolation() call allows you to query the curve beyond the last instrument's maturity. Without it, QuantLib throws an error if you ask for a rate at 31 years when your longest instrument is 30 years.
Pricing a Fixed-Rate Bond
With a yield curve in hand, you can price bonds. Here's how to create a fixed-rate bond, link it to the curve, and extract its clean price, dirty price, and yield to maturity.
import QuantLib as ql today = ql.Date(9, ql.April, 2026) ql.Settings.instance().evaluationDate = today # Build a flat yield curve for simplicity flat_rate = 0.04 curve = ql.FlatForward(today, flat_rate, ql.Actual365Fixed()) curve_handle = ql.YieldTermStructureHandle(curve) # Bond parameters face_value = 100.0 issue_date = ql.Date(15, ql.March, 2024) maturity_date = ql.Date(15, ql.March, 2034) coupon_rate = 0.045 # 4.5% annual coupon settlement_days = 2 calendar = ql.UnitedKingdom() day_count = ql.Actual365Fixed() # Generate the coupon schedule schedule = ql.Schedule( issue_date, maturity_date, ql.Period(ql.Semiannual), calendar, ql.ModifiedFollowing, ql.ModifiedFollowing, ql.DateGeneration.Backward, False ) # Create the bond bond = ql.FixedRateBond( settlement_days, face_value, schedule, [coupon_rate], day_count ) # Attach a discounting engine engine = ql.DiscountingBondEngine(curve_handle) bond.setPricingEngine(engine) # Results print(f"Clean price: {bond.cleanPrice():.4f}") print(f"Dirty price: {bond.dirtyPrice():.4f}") print(f"Accrued: {bond.accruedAmount():.4f}") print(f"Yield (semi): {bond.bondYield(day_count, ql.Compounded, ql.Semiannual):.4%}") # Cash flows print("\nCash flows:") print(f"{'Date':<22} {'Amount':>10}") print("-" * 32) for cf in bond.cashflows(): print(f"{cf.date()} {cf.amount():>10.4f}")
The distinction between clean and dirty price matters in practice. The dirty price is what you actually pay. The clean price strips out accrued interest and is what gets quoted in the market. QuantLib handles the accrued interest calculation automatically based on the day count convention and coupon schedule.
Computing Greeks for Options
You already saw Greeks as part of the European option example, but let's look at this more carefully. Here's a dedicated example that prices an option and extracts all the standard Greeks, plus shows how to bump-and-reprice for custom sensitivities.
import QuantLib as ql today = ql.Date(9, ql.April, 2026) ql.Settings.instance().evaluationDate = today # Setup spot_quote = ql.SimpleQuote(100.0) vol_quote = ql.SimpleQuote(0.20) rate = 0.045 div_yield = 0.01 spot_handle = ql.QuoteHandle(spot_quote) vol_handle = ql.BlackVolTermStructureHandle( ql.BlackConstantVol(today, ql.NullCalendar(), ql.QuoteHandle(vol_quote), ql.Actual365Fixed()) ) rate_handle = ql.YieldTermStructureHandle( ql.FlatForward(today, rate, ql.Actual365Fixed()) ) div_handle = ql.YieldTermStructureHandle( ql.FlatForward(today, div_yield, ql.Actual365Fixed()) ) process = ql.BlackScholesMertonProcess( spot_handle, div_handle, rate_handle, vol_handle ) # Create and price the option payoff = ql.PlainVanillaPayoff(ql.Option.Call, 100.0) exercise = ql.EuropeanExercise(ql.Date(9, ql.October, 2026)) option = ql.VanillaOption(payoff, exercise) option.setPricingEngine(ql.AnalyticEuropeanEngine(process)) # Analytic Greeks print("=== Analytic Greeks ===") print(f"Price: {option.NPV():.4f}") print(f"Delta: {option.delta():.4f}") print(f"Gamma: {option.gamma():.6f}") print(f"Vega: {option.vega():.4f}") print(f"Theta: {option.theta():.4f}") print(f"Rho: {option.rho():.4f}") # Bump-and-reprice for custom sensitivities # Useful when analytic Greeks aren't available (e.g. exotic options) bump = 0.01 # 1% relative bump # Spot delta via finite difference base_price = option.NPV() spot_quote.setValue(100.0 * (1 + bump)) up_price = option.NPV() spot_quote.setValue(100.0 * (1 - bump)) down_price = option.NPV() spot_quote.setValue(100.0) # reset fd_delta = (up_price - down_price) / (2 * 100.0 * bump) print(f"\nFD Delta: {fd_delta:.4f}") # Vega via vol bump vol_quote.setValue(0.21) up_vol_price = option.NPV() vol_quote.setValue(0.19) down_vol_price = option.NPV() vol_quote.setValue(0.20) # reset fd_vega = (up_vol_price - down_vol_price) / 0.02 print(f"FD Vega: {fd_vega:.4f}")
The bump-and-reprice approach is important because many exotic instruments don't have analytic Greeks. By changing the value of a SimpleQuote, QuantLib automatically recalculates everything downstream - you don't need to rebuild the process or reattach the engine. This "observable" pattern is one of the library's best design decisions.
Using the Heston Model for Option Pricing
QuantLib also supports more advanced models beyond Black-Scholes. The Heston stochastic volatility model treats volatility as a random process rather than a constant, which better captures the volatility smile observed in real markets.
import QuantLib as ql today = ql.Date(9, ql.April, 2026) ql.Settings.instance().evaluationDate = today # Market data spot = 100.0 rate = 0.045 div_yield = 0.01 spot_handle = ql.QuoteHandle(ql.SimpleQuote(spot)) rate_handle = ql.YieldTermStructureHandle( ql.FlatForward(today, rate, ql.Actual365Fixed()) ) div_handle = ql.YieldTermStructureHandle( ql.FlatForward(today, div_yield, ql.Actual365Fixed()) ) # Heston model parameters v0 = 0.04 # initial variance (vol^2) kappa = 2.0 # mean reversion speed theta = 0.04 # long-run variance sigma = 0.3 # vol of vol rho = -0.7 # correlation between spot and vol heston_process = ql.HestonProcess( rate_handle, div_handle, spot_handle, v0, kappa, theta, sigma, rho ) heston_model = ql.HestonModel(heston_process) heston_engine = ql.AnalyticHestonEngine(heston_model) # Price options at different strikes to see the volatility smile strikes = [80, 85, 90, 95, 100, 105, 110, 115, 120] expiry = ql.Date(9, ql.April, 2027) print(f"{'Strike':>8} {'Price':>10} {'Implied Vol':>12}") print("-" * 32) for K in strikes: payoff = ql.PlainVanillaPayoff(ql.Option.Call, K) exercise = ql.EuropeanExercise(expiry) option = ql.VanillaOption(payoff, exercise) option.setPricingEngine(heston_engine) price = option.NPV() # Back out the BS implied vol from the Heston price iv = option.impliedVolatility( price, ql.BlackScholesMertonProcess( spot_handle, div_handle, rate_handle, ql.BlackVolTermStructureHandle( ql.BlackConstantVol(today, ql.NullCalendar(), 0.2, ql.Actual365Fixed()) ) ) ) print(f"{K:>8.0f} {price:>10.4f} {iv:>11.2%}")
You'll see that out-of-the-money puts (low strikes) have higher implied volatilities than at-the-money options - the classic volatility smile. This is exactly the pattern that Black-Scholes with a flat vol cannot produce but which appears in real market data.
QuantLib vs Other Libraries
How does QuantLib compare to other Python tools for quantitative finance?
QuantLib vs SciPy / NumPy
SciPy and NumPy are general-purpose numerical libraries. You can absolutely implement Black-Scholes pricing with scipy.stats.norm and a few lines of code. But QuantLib gives you the financial infrastructure that SciPy doesn't - calendars, day count conventions, proper yield curve bootstrapping, coupon schedules, and dozens of pricing engines. If you're pricing a single European option for a homework assignment, SciPy is fine. If you're building a system that needs to price bonds with irregular coupons, handle holiday calendars across multiple countries, and bootstrap curves from live market data, QuantLib saves you months of work.
QuantLib vs Manual Python Implementations
Writing your own Black-Scholes function is a good learning exercise. But production pricing needs more than a formula. You need to handle edge cases (what happens at expiry?), implement proper day counting, support multiple exercise styles, and ensure numerical stability. QuantLib has been tested against these edge cases for over two decades. Your hand-rolled implementation hasn't.
QuantLib vs Commercial Libraries
Bloomberg's DLIB and Numerix are the main commercial alternatives. They offer better documentation, professional support, and integration with market data feeds. QuantLib's advantage is that it's free, open-source, and has a large community. For learning, research, and many production use cases, QuantLib is more than sufficient. If you need phone support and guaranteed SLAs, you'll need a commercial product.
When to Use QuantLib
QuantLib is the right choice when you need:
- Proper fixed income analytics (yield curves, bond pricing, swap valuation)
- Multiple option pricing models with consistent interfaces
- Financial date handling and calendar support
- A reference implementation to validate your own models against
- A free library for research, teaching, or startup-stage production systems
It's probably overkill if you just want to calculate a Black-Scholes price in a Jupyter notebook. And it may not be enough if you need ultra-low-latency pricing for a high-frequency trading system (in that case, you'd use the C++ library directly or write custom code).
Frequently Asked Questions
Is QuantLib Python fast enough for production use?
QuantLib Python is a thin wrapper around compiled C++ code, so the heavy numerical work runs at native speed. Pricing a single European option takes microseconds. Building a yield curve from 20 instruments takes a few milliseconds. For batch pricing of a few thousand instruments, it's more than fast enough. Where you'll hit limits is in very large-scale Monte Carlo simulations (millions of paths) or real-time pricing of tens of thousands of instruments per second. In those cases, calling the C++ library directly or using QuantLib's built-in multithreading support from C++ will be faster. For most quant research, risk reporting, and end-of-day pricing workflows, the Python bindings add negligible overhead.
What Python version works with QuantLib?
As of 2026, the QuantLib Python package on PyPI provides pre-built wheels for Python 3.8 through 3.12 on Windows, macOS, and Linux. Python 3.13 support is typically available within a few weeks of a new QuantLib release. If you're using a very new Python version and pre-built wheels aren't available yet, you can install from source with pip install QuantLib --no-binary :all:, though this requires a C++ compiler and the Boost libraries. For the smoothest experience, use Python 3.10 or 3.11.
How does QuantLib handle the evaluation date?
The evaluation date is a global setting accessed through ql.Settings.instance().evaluationDate. Every pricing calculation uses this date as "today" - it determines time to expiry for options, settlement dates for bonds, and the starting point for yield curve queries. You set it once at the start of your script and it applies everywhere. If you need to reprice instruments on a different date (for historical analysis or scenario testing), just change the evaluation date and reprice. All handles and instruments will automatically recalculate. Be careful in multi-threaded environments - the evaluation date is shared across all threads. QuantLib provides a SavedSettings context manager to handle this safely.
Can I use QuantLib for exotic option pricing?
Yes. QuantLib supports a wide range of exotic options including barrier options (up-and-in, up-and-out, down-and-in, down-and-out), Asian options (arithmetic and geometric average), lookback options, cliquet options, and basket options. The library provides both analytic engines (where closed-form solutions exist) and numerical engines (Monte Carlo, finite difference) for exotics that require simulation. The interface is consistent across all instrument types - you create a payoff, define the exercise style, build the instrument, and attach a pricing engine. The learning curve for adding new instrument types is shallow once you understand the basic pattern.
Where can I learn more about QuantLib?
The best starting point is Luigi Ballabio's book "Implementing QuantLib" (freely available online), which explains the library's design philosophy and architecture. The official QuantLib documentation at quantlib.org covers the C++ API comprehensively, and most class names map directly to Python. Dimitri Reiswich's QuantLib Python Cookbook provides practical recipes. For specific problems, the quantlib-users mailing list is active and helpful. The library's test suite (available on GitHub) is also a rich source of examples - if you want to know how to use a particular class, search the test files for usage patterns. Finally, practising with the code is the most effective way to learn. Start with the examples in this tutorial, then gradually work through more complex instruments as your needs grow.
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