Introduction to QuantLib

QuantLib is an open-source library for quantitative finance, providing tools for valuation, trading, and risk management. It offers a vast array of features including pricing various financial instruments, managing portfolios, and performing quantitative risk assessment.

Key API Examples

1. Date Calculation

  from QuantLib import Date, Months, Period

  date = Date(15, 3, 2023)
  future_date = date + Period(3, Months)
  print(future_date)

2. Day Count Conventions

  from QuantLib import Actual360, Thirty360, Date

  start_date = Date(1, 1, 2023)
  end_date = Date(1, 7, 2023)
  day_count = Actual360()
  t360 = Thirty360()

  print(day_count.yearFraction(start_date, end_date))
  print(t360.yearFraction(start_date, end_date))

3. Interest Rate Curve

  from QuantLib import ZeroCurve, Date, SimpleQuote, RateHelper

  dates = [Date(1,1,2023), Date(1,1,2024), Date(1,1,2025)]
  rates = [SimpleQuote(rate) for rate in [0.01, 0.015, 0.02]]
  helpers = [RateHelper(rate, date) for rate, date in zip(rates, dates)]

  curve = ZeroCurve(dates, rates, dayCounter=Actual360())

4. Bond Pricing

  from QuantLib import Bond, Schedule, Date, SimpleQuote, FixedRateBond, ActualActual, UnitedStates
  from QuantLib import TARGET, Annual

  settlement_days = 3
  face_value = 100
  coupon_rate = 0.045
  start_date = Date(1, 1, 2020)
  maturity_date = Date(1, 1, 2030)
  calendar = TARGET()
  schedule = Schedule(start_date, maturity_date, Period(Annual), calendar, BusinessDayConvention.ModifiedFollowing, BusinessDayConvention.ModifiedFollowing, DateGeneration.Backward, False)
  day_counter = ActualActual()
  bond = FixedRateBond(settlement_days, face_value, schedule, [coupon_rate], day_counter)

  clean_price = bond.cleanPrice(0.04, day_counter, Compounding.Compounded, Frequency.Annual)
  print(clean_price)

5. Option Pricing

  from QuantLib import EuropeanOption, PlainVanillaPayoff, EuropeanExercise, QuoteHandle, SimpleQuote

  spot = QuoteHandle(SimpleQuote(100))
  strike = 100
  payoff = PlainVanillaPayoff(Option.Call, strike)
  maturity = Date(15, 6, 2023)
  exercise = EuropeanExercise(maturity)

  vanilla_option = EuropeanOption(payoff, exercise)
  print(vanilla_option.NPV())

Practical Application Example

  import QuantLib as ql
  
  # Parameters
  spot_price = 100.0
  strike_price = 100.0
  maturity = ql.Date(15, 6, 2023)
  risk_free_rate = 0.05
  dividend_yield = 0.02
  volatility = 0.20
  
  # Creating the Black-Scholes-Merton process
  spot_handle = ql.QuoteHandle(ql.SimpleQuote(spot_price))
  flat_ts = ql.YieldTermStructureHandle(ql.FlatForward(0, ql.NullCalendar(), ql.QuoteHandle(ql.SimpleQuote(risk_free_rate)), ql.Actual360()))
  dividend_yield = ql.YieldTermStructureHandle(ql.FlatForward(0, ql.NullCalendar(), ql.QuoteHandle(ql.SimpleQuote(dividend_yield)), ql.Actual360()))
  flat_vol_ts = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(0, ql.NullCalendar(), ql.QuoteHandle(ql.SimpleQuote(volatility)), ql.Actual360()))
  bsm_process = ql.BlackScholesMertonProcess(spot_handle, dividend_yield, flat_ts, flat_vol_ts)

  # European Option
  payoff = ql.PlainVanillaPayoff(ql.Option.Call, strike_price)
  exercise = ql.EuropeanExercise(maturity)
  european_option = ql.EuropeanOption(payoff, exercise)
  
  # Pricing Engine
  european_option.setPricingEngine(ql.AnalyticEuropeanEngine(bsm_process))
  
  # Price
  price = european_option.NPV()
  print("The option price is:", price)

QuantLib empowers financial engineers with the capability to create sophisticated models and tools for finance. The above APIs and example serve as an introductory guide to get started with QuantLib’s diverse functionalities.

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