FAILURE MAP
← Case archive

FA-61251 / Bond day-count conventions / Open access

Money-market certificate priced from yield: accrued interest counts days remaining instead of days elapsed · case 01

Clean prices are wrong except at the midpoint.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Accrued days use days from settlement to maturity.

THE FAILURE

Accrued days use days from settlement to maturity.

Unsuccessful approach: Adding a day to the elapsed count includes the settlement date.

Case contract

Inputs issue, maturity and settlement dates, coupon and yield (decimals). The instrument pays 100*(1 + coupon*T/360) at maturity where T = days(issue, maturity). Dirty = redemption/(1 + y*R/360) with R = days(settle, maturity); accrued = 100*coupon*A/360 with A = days(issue, settle). Return [dirty, dirty-accrued] rounded to 6.

Why this case matters

Bond accrual and pricing systems depend on exact day-count arithmetic; a single-day error changes settlement cash.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
N = 1
observations = []
def solve(issue, maturity, settle, coupon, y):
    I = datetime.date(*issue)
    M = datetime.date(*maturity)
    S = datetime.date(*settle)
    T = (M - I).days
    R = (M - S).days
    A = (M - S).days
    redemption = 100 * (1 + coupon * T / 360)
    dirty = redemption / (1 + y * R / 360)
    accrued = 100 * coupon * A / 360
    return [round(dirty, 6), round(dirty - accrued, 6)]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression accrued days 1', [[2021, 12, 31], [2023, 4, 27], [2022, 2, 12], 0.065, 0.06], [101.291608, 100.51522]], ['regression accrued days 2', [[2028, 5, 30], [2028, 12, 26], [2028, 11, 21], 0.0375, 0.01], [102.088248, 100.265331]], ['partial repair probe 1', [[2036, 5, 23], [2038, 2, 25], [2037, 6, 1], 0.02, 0.01], [102.804048, 100.72627]], ['partial repair probe 2', [[2030, 9, 27], [2032, 7, 9], [2032, 6, 26], 0.0375, 0.01], [106.742704, 100.096871]], ['normal control 1', [[2022, 9, 30], [2023, 10, 5], [2022, 11, 27], 0.0, 0.045], [96.246391, 96.246391]], ['normal control 2', [[2014, 12, 28], [2016, 7, 26], [2016, 3, 31], 0.0, 0.06], [98.087298, 98.087298]], ['normal control 3', [[2039, 7, 31], [2040, 1, 25], [2039, 10, 22], 0.0, 0.06], [98.441345, 98.441345]], ['normal control 4', [[2023, 3, 31], [2023, 7, 22], [2023, 6, 2], 0.0, 0.045], [99.378882, 99.378882]]], [['regression accrued days 1', [[2030, 3, 31], [2032, 1, 23], [2030, 11, 12], 0.02, 0.01], [102.439828, 101.184272]], ['regression accrued days 2', [[2020, 12, 9], [2021, 8, 7], [2020, 12, 17], 0.0375, 0.045], [99.609296, 99.525963]], ['partial repair probe 1', [[2011, 6, 30], [2011, 8, 3], [2011, 7, 17], 0.02, 0.06], [99.905822, 99.811378]], ['partial repair probe 2', [[2021, 9, 15], [2021, 12, 22], [2021, 11, 3], 0.065, 0.03], [101.355576, 100.470854]], ['normal control 1', [[2025, 8, 12], [2027, 5, 8], [2025, 10, 1], 0.0, 0.01], [98.403674, 98.403674]], ['normal control 2', [[2034, 2, 28], [2034, 9, 17], [2034, 7, 14], 0.0, 0.03], [99.461252, 99.461252]], ['normal control 3', [[2029, 11, 19], [2031, 6, 23], [2031, 5, 13], 0.0, 0.03], [99.659497, 99.659497]], ['normal control 4', [[2030, 9, 16], [2032, 8, 16], [2032, 3, 2], 0.0, 0.03], [98.627435, 98.627435]]], [['regression accrued days 1', [[2012, 5, 14], [2013, 1, 15], [2012, 8, 31], 0.065, 0.01], [104.045715, 102.077659]], ['regression accrued days 2', [[2013, 10, 29], [2014, 1, 15], [2013, 11, 22], 0.0375, 0.06], [99.91328, 99.66328]], ['partial repair probe 1', [[2033, 7, 3], [2035, 4, 26], [2034, 4, 3], 0.0375, 0.045], [101.9512, 99.097033]], ['partial repair probe 2', [[2020, 10, 14], [2021, 7, 14], [2020, 11, 3], 0.0375, 0.01], [102.126031, 101.917698]], ['normal control 1', [[2023, 7, 13], [2025, 4, 14], [2023, 11, 4], 0.0, 0.045], [93.819632, 93.819632]], ['normal control 2', [[2010, 12, 4], [2012, 1, 14], [2011, 11, 20], 0.0, 0.09], [98.64365, 98.64365]], ['normal control 3', [[2011, 1, 31], [2012, 8, 15], [2011, 5, 28], 0.0, 0.01], [98.778982, 98.778982]], ['normal control 4', [[2012, 1, 13], [2012, 11, 21], [2012, 11, 12], 0.0, 0.06], [99.850225, 99.850225]]], [['regression accrued days 1', [[2032, 4, 1], [2034, 1, 25], [2033, 8, 16], 0.0375, 0.045], [104.794576, 99.56541]], ['regression accrued days 2', [[2012, 9, 28], [2013, 3, 23], [2012, 12, 17], 0.02, 0.01], [100.70922, 100.264775]], ['partial repair probe 1', [[2031, 11, 30], [2032, 2, 4], [2032, 1, 2], 0.065, 0.01], [101.098993, 100.503159]], ['normal control 1', [[2036, 4, 3], [2036, 11, 25], [2036, 11, 21], 0.0, 0.03], [99.966678, 99.966678]], ['normal control 2', [[2025, 9, 30], [2026, 12, 8], [2026, 4, 20], 0.0, 0.03], [98.103336, 98.103336]], ['normal control 3', [[2029, 5, 16], [2030, 2, 19], [2030, 1, 13], 0.0, 0.09], [99.083478, 99.083478]], ['normal control 4', [[2022, 1, 10], [2023, 2, 23], [2022, 4, 3], 0.0, 0.06], [94.846665, 94.846665]], ['normal control 5', [[2022, 4, 11], [2023, 9, 11], [2022, 12, 4], 0.0, 0.045], [96.60669, 96.60669]]], [['regression accrued days 1', [[2023, 6, 10], [2023, 11, 27], [2023, 10, 10], 0.065, 0.06], [102.251433, 100.048655]], ['regression accrued days 2', [[2021, 4, 4], [2022, 6, 11], [2022, 1, 14], 0.02, 0.045], [100.545464, 98.962131]], ['partial repair probe 1', [[2015, 11, 24], [2016, 2, 16], [2016, 1, 5], 0.02, 0.09], [99.422728, 99.189395]], ['normal control 1', [[2016, 12, 31], [2017, 4, 21], [2017, 2, 18], 0.0, 0.045], [99.23096, 99.23096]], ['normal control 2', [[2029, 10, 18], [2030, 8, 8], [2029, 11, 5], 0.0, 0.03], [97.751711, 97.751711]], ['normal control 3', [[2038, 1, 30], [2038, 5, 9], [2038, 4, 5], 0.0, 0.01], [99.905645, 99.905645]], ['normal control 4', [[2013, 4, 23], [2014, 6, 5], [2014, 4, 30], 0.0, 0.045], [99.552016, 99.552016]], ['normal control 5', [[2026, 8, 27], [2028, 2, 14], [2027, 7, 18], 0.0, 0.03], [98.27205, 98.27205]]]]
for label, args, expected in fixtures[N-1]:
    check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
regression accrued days 1[101.291608, 93.36522][101.291608, 100.51522]Failed
regression accrued days 2[102.088248, 101.723664][102.088248, 100.265331]Failed
partial repair probe 1[102.804048, 101.309603][102.804048, 100.72627]Failed
partial repair probe 2[106.742704, 106.607287][106.742704, 100.096871]Failed
normal control 1[96.246391, 96.246391][96.246391, 96.246391]Passed
normal control 2[98.087298, 98.087298][98.087298, 98.087298]Passed
normal control 3[98.441345, 98.441345][98.441345, 98.441345]Passed
normal control 4[99.378882, 99.378882][99.378882, 99.378882]Passed

SHA-256 / a563c0f4da7ca57ded021bd0c984e3747f226492a6b4be63a52073f2cd58bbf9

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
N = 1
observations = []
def solve(issue, maturity, settle, coupon, y):
    I = datetime.date(*issue)
    M = datetime.date(*maturity)
    S = datetime.date(*settle)
    T = (M - I).days
    R = (M - S).days
    A = (S - I).days + 1
    redemption = 100 * (1 + coupon * T / 360)
    dirty = redemption / (1 + y * R / 360)
    accrued = 100 * coupon * A / 360
    return [round(dirty, 6), round(dirty - accrued, 6)]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression accrued days 1', [[2021, 12, 31], [2023, 4, 27], [2022, 2, 12], 0.065, 0.06], [101.291608, 100.51522]], ['regression accrued days 2', [[2028, 5, 30], [2028, 12, 26], [2028, 11, 21], 0.0375, 0.01], [102.088248, 100.265331]], ['partial repair probe 1', [[2036, 5, 23], [2038, 2, 25], [2037, 6, 1], 0.02, 0.01], [102.804048, 100.72627]], ['partial repair probe 2', [[2030, 9, 27], [2032, 7, 9], [2032, 6, 26], 0.0375, 0.01], [106.742704, 100.096871]], ['normal control 1', [[2022, 9, 30], [2023, 10, 5], [2022, 11, 27], 0.0, 0.045], [96.246391, 96.246391]], ['normal control 2', [[2014, 12, 28], [2016, 7, 26], [2016, 3, 31], 0.0, 0.06], [98.087298, 98.087298]], ['normal control 3', [[2039, 7, 31], [2040, 1, 25], [2039, 10, 22], 0.0, 0.06], [98.441345, 98.441345]], ['normal control 4', [[2023, 3, 31], [2023, 7, 22], [2023, 6, 2], 0.0, 0.045], [99.378882, 99.378882]]], [['regression accrued days 1', [[2030, 3, 31], [2032, 1, 23], [2030, 11, 12], 0.02, 0.01], [102.439828, 101.184272]], ['regression accrued days 2', [[2020, 12, 9], [2021, 8, 7], [2020, 12, 17], 0.0375, 0.045], [99.609296, 99.525963]], ['partial repair probe 1', [[2011, 6, 30], [2011, 8, 3], [2011, 7, 17], 0.02, 0.06], [99.905822, 99.811378]], ['partial repair probe 2', [[2021, 9, 15], [2021, 12, 22], [2021, 11, 3], 0.065, 0.03], [101.355576, 100.470854]], ['normal control 1', [[2025, 8, 12], [2027, 5, 8], [2025, 10, 1], 0.0, 0.01], [98.403674, 98.403674]], ['normal control 2', [[2034, 2, 28], [2034, 9, 17], [2034, 7, 14], 0.0, 0.03], [99.461252, 99.461252]], ['normal control 3', [[2029, 11, 19], [2031, 6, 23], [2031, 5, 13], 0.0, 0.03], [99.659497, 99.659497]], ['normal control 4', [[2030, 9, 16], [2032, 8, 16], [2032, 3, 2], 0.0, 0.03], [98.627435, 98.627435]]], [['regression accrued days 1', [[2012, 5, 14], [2013, 1, 15], [2012, 8, 31], 0.065, 0.01], [104.045715, 102.077659]], ['regression accrued days 2', [[2013, 10, 29], [2014, 1, 15], [2013, 11, 22], 0.0375, 0.06], [99.91328, 99.66328]], ['partial repair probe 1', [[2033, 7, 3], [2035, 4, 26], [2034, 4, 3], 0.0375, 0.045], [101.9512, 99.097033]], ['partial repair probe 2', [[2020, 10, 14], [2021, 7, 14], [2020, 11, 3], 0.0375, 0.01], [102.126031, 101.917698]], ['normal control 1', [[2023, 7, 13], [2025, 4, 14], [2023, 11, 4], 0.0, 0.045], [93.819632, 93.819632]], ['normal control 2', [[2010, 12, 4], [2012, 1, 14], [2011, 11, 20], 0.0, 0.09], [98.64365, 98.64365]], ['normal control 3', [[2011, 1, 31], [2012, 8, 15], [2011, 5, 28], 0.0, 0.01], [98.778982, 98.778982]], ['normal control 4', [[2012, 1, 13], [2012, 11, 21], [2012, 11, 12], 0.0, 0.06], [99.850225, 99.850225]]], [['regression accrued days 1', [[2032, 4, 1], [2034, 1, 25], [2033, 8, 16], 0.0375, 0.045], [104.794576, 99.56541]], ['regression accrued days 2', [[2012, 9, 28], [2013, 3, 23], [2012, 12, 17], 0.02, 0.01], [100.70922, 100.264775]], ['partial repair probe 1', [[2031, 11, 30], [2032, 2, 4], [2032, 1, 2], 0.065, 0.01], [101.098993, 100.503159]], ['normal control 1', [[2036, 4, 3], [2036, 11, 25], [2036, 11, 21], 0.0, 0.03], [99.966678, 99.966678]], ['normal control 2', [[2025, 9, 30], [2026, 12, 8], [2026, 4, 20], 0.0, 0.03], [98.103336, 98.103336]], ['normal control 3', [[2029, 5, 16], [2030, 2, 19], [2030, 1, 13], 0.0, 0.09], [99.083478, 99.083478]], ['normal control 4', [[2022, 1, 10], [2023, 2, 23], [2022, 4, 3], 0.0, 0.06], [94.846665, 94.846665]], ['normal control 5', [[2022, 4, 11], [2023, 9, 11], [2022, 12, 4], 0.0, 0.045], [96.60669, 96.60669]]], [['regression accrued days 1', [[2023, 6, 10], [2023, 11, 27], [2023, 10, 10], 0.065, 0.06], [102.251433, 100.048655]], ['regression accrued days 2', [[2021, 4, 4], [2022, 6, 11], [2022, 1, 14], 0.02, 0.045], [100.545464, 98.962131]], ['partial repair probe 1', [[2015, 11, 24], [2016, 2, 16], [2016, 1, 5], 0.02, 0.09], [99.422728, 99.189395]], ['normal control 1', [[2016, 12, 31], [2017, 4, 21], [2017, 2, 18], 0.0, 0.045], [99.23096, 99.23096]], ['normal control 2', [[2029, 10, 18], [2030, 8, 8], [2029, 11, 5], 0.0, 0.03], [97.751711, 97.751711]], ['normal control 3', [[2038, 1, 30], [2038, 5, 9], [2038, 4, 5], 0.0, 0.01], [99.905645, 99.905645]], ['normal control 4', [[2013, 4, 23], [2014, 6, 5], [2014, 4, 30], 0.0, 0.045], [99.552016, 99.552016]], ['normal control 5', [[2026, 8, 27], [2028, 2, 14], [2027, 7, 18], 0.0, 0.03], [98.27205, 98.27205]]]]
for label, args, expected in fixtures[N-1]:
    check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
regression accrued days 1[101.291608, 100.497164][101.291608, 100.51522]Failed
regression accrued days 2[102.088248, 100.254914][102.088248, 100.265331]Failed
partial repair probe 1[102.804048, 100.720714][102.804048, 100.72627]Failed
partial repair probe 2[106.742704, 100.086454][106.742704, 100.096871]Failed
normal control 1[96.246391, 96.246391][96.246391, 96.246391]Passed
normal control 2[98.087298, 98.087298][98.087298, 98.087298]Passed
normal control 3[98.441345, 98.441345][98.441345, 98.441345]Passed
normal control 4[99.378882, 99.378882][99.378882, 99.378882]Passed

SHA-256 / dcab41a30321db8dca92a9e9c9833599c764992d67ff3e33a3820f10bf17028b

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 8 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

Member access is invitation-based. Sign in with your invited account to inspect the repair.

Sign in to the archive ↗

Verification & scope

A deterministic toy contract stated explicitly in the contract field; no claim of conformance to any published convention text. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.

Observations recorded using Python 3.12.14 at 2026-09-29T14:46:53.524804+00:00.

Case digest / 0fa59c0a9e9ac268f37843830b054badb3dc1480ab0499ddfd69cda99d6c712e