The QuantLib C++ library
C-Rank Grade: A (Welcoming) - 64/100
External PR Merge Rate: 72%
Response Time: -
First Timer Success: 58%
lballabio/QuantLib has a recorded first-timer success rate of 57.9%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in lballabio/QuantLib respond to incoming external pull requests in approximately 0.0 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 64.1 places lballabio/QuantLib in the A tier.
The external contributor pull request merge rate for lballabio/QuantLib is 71.6%, based on public PR activity from non-core contributors.
lballabio/QuantLib currently has 11 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Highly welcoming maintainers in lballabio/QuantLib. Prompt code reviews with positive guidance for new contributors.
Average Response Latency
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External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
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Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Maintainers are most responsive around 08:00 UTC (08:00 UTC in your timezone). Recommended submission window is Monday - Wednesday from 07:00 to 11:00 UTC (07:00 - 11:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~16 hours (90% approval). Large PRs (>500 lines) take ~115 hours (32% approval). Keep your diff compact for rapid review.
Environment QuantLib 1.43 (QuantLib-Python 1.43 wheel from PyPI), Python 3.11, Linux x86-64. The lines cited below are unchanged on master (966a4cc). Reproduction A 10y-minus-2y CMS spread coupon fixing in 5 years (paid a year later), flat 3% curve, shifted-lognormal volatility of 20% on both legs, correlation 0.5, and a SwaptionVolatilityMatrix with a shift per swap tenor (d1 for the 10y leg, d2 for the 2y leg). Caplet and floorlet rates are read from the pricer. import QuantLib as ql ref = ql.Date(21, 9, 2026) ql.Settings.instance().evaluationDate = ref dc, cal = ql.Actual365Fixed(), ql.NullCalendar() fixing = ref + ql.Period(5, ql.Years) pay = fixing + ql.Period(1, ql.Years) curve = ql.YieldTermStructureHandle(ql.FlatForward(ref, 0.03, dc)) ibor = ql.IborIndex("I6M", ql.Period(6, ql.Months), 0, ql.USDCurrency(), cal, ql.Unadjusted, False, ql.Actual360(), curve) swap_index = lambda y: ql.SwapIndex("S", ql.Period(y, ql.Years), 0, ql.USDCurrency(), cal,
Environment QuantLib 1.43 (QuantLib-Python 1.43 wheel from PyPI), Python 3.11, Linux x86-64. The lines cited below are unchanged on master (966a4cc). Reproduction import math import QuantLib as ql today = ql.Date(21, 9, 2026) ql.Settings.instance().evaluationDate = today dc = ql.Actual365Fixed() lag = ql.Period(3, ql.Months) discount = ql.YieldTermStructureHandle(ql.FlatForward(today, 0.03, dc)) # flat 2.4% year-on-year curve from the July 2026 base month, and its index dates = [ql.Date(1, 7, 2026) + ql.Period(m, ql.Months) for m in (0, 12, 120)] curve = ql.YoYInflationCurve(today, dates, [0.024] * 3, ql.Monthly, dc) handle = ql.RelinkableYoYInflationTermStructureHandle() handle.linkTo(curve) underlying = ql.ZeroInflationIndex("X", ql.CustomRegion("R", "RR"), False, ql.Monthly, ql.Period(2, ql.Months), ql.EURCurrency()) index = ql.YoYInflationIndex(underlying, handle) # annual cap, strike 2.25%; the first live coupon pays 2026-11-15 and fixes 2026-
Environment QuantLib 1.43 (QuantLib-Python 1.43 wheel from PyPI), Python 3.11, Linux x86-64. The lines cited below are unchanged in substance on master (966a4cc). Reproduction import QuantLib as ql today = ql.Date(21, 9, 2026) ql.Settings.instance().evaluationDate = today calendar = ql.WeekendsOnly() tenor_days = [91, 182, 365, 730, 1095, 1826, 2557, 3653, 5479, 7305, 10958] zeros = [0.0310, 0.0318, 0.0325, 0.0334, 0.0340, 0.0352, 0.0365, 0.0372, 0.0380, 0.0384, 0.0390] curve = ql.ZeroCurve([today] + [today + d for d in tenor_days], [zeros[0]] + zeros, ql.Actual365Fixed(), calendar, ql.Linear()) curve.enableExtrapolation() handle = ql.YieldTermStructureHandle(curve) def bond(entries): start, end = ql.Date(28, 3, 2026), ql.Date(28, 3, 2033) schedule = ql.Schedule(start, end, ql.Period(6, ql.Months), calendar, ql.Unadjusted, ql.Unadjusted, ql.DateGeneration.Backward, False) calls = ql.CallabilitySchedule() for kin
Environment QuantLib 1.43 (QuantLib-Python 1.43 wheel from PyPI), Python 3.11, Linux x86-64. The lines cited below are unchanged on master (966a4cc). Reproduction import QuantLib as ql rho = 0.707 drezner = ql.BivariateCumulativeNormalDistributionDr78(rho) genz = ql.BivariateCumulativeNormalDistributionWe04DP(rho) print(drezner(5.6e-17, 0.766), drezner(1e-9, 0.766), drezner(3e-9, 0.766)) # nan nan 0.4753807154659074 print(drezner(0.0, 0.766), genz(5.6e-17, 0.766)) # 0.47538071269320853 0.47538055107656235 # the same function at rho = 1 and rho = -1, which the constructor accepts print(ql.BivariateCumulativeNormalDistributionDr78(1.0)(0.1, 0.0)) # nan (expected 0.5) print(ql.BivariateCumulativeNormalDistributionDr78(-1.0)(-0.5, 0.3)) # nan (expected 0) try: ql.BivariateCumulativeNormalDistributionDr78(1.0)(0.5, 0.5) # expected N(0.5) = 0.6915 except RuntimeError as error: print(error) # rho must be >= -1.0 (-nan not allowed) Expecte
Environment QuantLib 1.43 (QuantLib-Python 1.43 wheel from PyPI), Python 3.11, Linux x86-64. The lines cited below are unchanged on master (966a4cc). Reproduction A soft barrier option is the average over the band of single-barrier options (Hart and Ross); the expected value below is that average over AnalyticBarrierEngine prices (Simpson, 128 intervals). import QuantLib as ql today = ql.Date(1, 1, 2026) ql.Settings.instance().evaluationDate = today dc = ql.Actual365Fixed() p = ql.BlackScholesMertonProcess( ql.QuoteHandle(ql.SimpleQuote(100.0)), ql.YieldTermStructureHandle(ql.FlatForward(today, 0.04, dc, ql.Continuous)), ql.YieldTermStructureHandle(ql.FlatForward(today, 0.08, dc, ql.Continuous)), ql.BlackVolTermStructureHandle(ql.BlackConstantVol(today, ql.NullCalendar(), 0.25, dc))) expiry = ql.EuropeanExercise(today + 365) def soft(kind, flag, lower, upper): o = ql.SoftBarrierOption(kind, lower, upper, ql.PlainVanillaPayoff(flag, 100.0), expiry) o.setPricingE
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When evaluating whether to contribute to lballabio/QuantLib, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of lballabio/QuantLib acknowledge new external contributions in approximately -. Out of all submitted pull requests from non-core authors in the last 180-day window, 71.6% were successfully merged into the primary branch.
lballabio/QuantLib has a recorded first-timer success rate of 57.9%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~-. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 64.1 places lballabio/QuantLib in the Welcoming tier.
The external pull request merge rate is 71.6%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, lballabio/QuantLib currently has 11 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for lballabio/QuantLib are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.