FA-61246 / Bond day-count conventions / Open access
Money-market certificate priced from yield: the maturity value is discounted over the full term · case 01
Prices are too low for seasoned certificates.
ROOT CAUSE
Discounting uses days from issue rather than days remaining.
VERIFIED REPAIR
Discount over the remaining days from settlement to maturity.
Unsuccessful approach: Compounding the yield annually over the remaining days is a different convention.
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 = (S - I).days
redemption = 100 * (1 + coupon * T / 360)
dirty = redemption / (1 + y * T / 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 discount horizon 1', [[2021, 1, 1], [2021, 11, 14], [2021, 2, 7], 0.0375, 0.045], [99.808776, 99.42336]], ['regression discount horizon 2', [[2026, 12, 29], [2028, 3, 12], [2027, 8, 27], 0.065, 0.06], [104.478595, 100.127206]], ['partial repair probe 1', [[2035, 10, 9], [2036, 1, 26], [2035, 10, 9], 0.02, 0.01], [100.301864, 100.301864]], ['partial repair probe 2', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2017, 9, 10], [2018, 1, 23], [2018, 1, 2], 0.05, 0.03], [101.69703, 100.113697]], ['normal control 2', [[2025, 3, 31], [2025, 5, 1], [2025, 4, 2], 0.02, 0.03], [99.930723, 99.919612]], ['normal control 3', [[2032, 3, 1], [2033, 9, 16], [2032, 6, 23], 0.0, 0.09], [89.88764, 89.88764]], ['normal control 4', [[2017, 8, 6], [2018, 6, 17], [2018, 1, 13], 0.05, 0.03], [103.044015, 100.821793]]], [['regression discount horizon 1', [[2010, 5, 30], [2011, 8, 19], [2011, 6, 18], 0.065, 0.09], [106.403523, 99.47019]], ['regression discount horizon 2', [[2023, 11, 29], [2025, 8, 30], [2025, 1, 29], 0.0, 0.01], [99.411813, 99.411813]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['partial repair probe 2', [[2034, 12, 18], [2035, 9, 27], [2034, 12, 18], 0.065, 0.01], [104.289888, 104.289888]], ['normal control 1', [[2036, 9, 7], [2037, 7, 16], [2037, 1, 23], 0.0375, 0.01], [102.753359, 101.315859]], ['normal control 2', [[2029, 3, 30], [2030, 4, 2], [2029, 10, 9], 0.02, 0.03], [100.577687, 99.505464]], ['normal control 3', [[2040, 5, 8], [2041, 8, 24], [2040, 11, 27], 0.065, 0.01], [107.732286, 104.067008]], ['normal control 4', [[2024, 7, 25], [2025, 2, 7], [2024, 12, 10], 0.05, 0.09], [101.24278, 99.326113]]], [['regression discount horizon 1', [[2026, 8, 21], [2028, 1, 10], [2027, 7, 6], 0.02, 0.06], [99.692954, 97.920732]], ['regression discount horizon 2', [[2031, 12, 28], [2032, 6, 13], [2032, 3, 10], 0.02, 0.045], [99.748816, 99.343261]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['partial repair probe 2', [[2020, 7, 9], [2020, 11, 22], [2020, 7, 9], 0.02, 0.06], [98.522382, 98.522382]], ['normal control 1', [[2028, 12, 1], [2030, 2, 14], [2029, 9, 27], 0.02, 0.03], [101.263042, 99.596376]], ['normal control 2', [[2012, 7, 22], [2012, 12, 10], [2012, 8, 27], 0.0375, 0.03], [100.5886, 100.2136]], ['normal control 3', [[2014, 5, 10], [2014, 10, 6], [2014, 5, 14], 0.05, 0.01], [101.659981, 101.604425]], ['normal control 4', [[2019, 3, 22], [2020, 5, 3], [2019, 4, 14], 0.0375, 0.045], [99.463327, 99.223744]]], [['regression discount horizon 1', [[2037, 7, 28], [2037, 9, 23], [2037, 9, 10], 0.05, 0.03], [100.682594, 100.071483]], ['regression discount horizon 2', [[2031, 4, 1], [2031, 10, 5], [2031, 5, 23], 0.05, 0.01], [102.21392, 101.491698]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2021, 9, 1], [2022, 2, 21], [2021, 10, 11], 0.0375, 0.03], [100.686145, 100.269479]], ['normal control 2', [[2038, 10, 29], [2040, 5, 17], [2039, 4, 7], 0.02, 0.09], [93.639986, 92.751097]], ['normal control 3', [[2020, 11, 30], [2022, 3, 2], [2021, 11, 22], 0.0375, 0.06], [103.043033, 99.324283]], ['normal control 4', [[2034, 1, 31], [2034, 8, 14], [2034, 7, 27], 0.0, 0.06], [99.700897, 99.700897]], ['normal control 5', [[2025, 9, 22], [2026, 8, 31], [2025, 10, 1], 0.065, 0.01], [105.216877, 105.054377]]], [['regression discount horizon 1', [[2019, 12, 31], [2020, 7, 22], [2020, 2, 14], 0.02, 0.06], [98.522487, 98.272487]], ['regression discount horizon 2', [[2013, 8, 31], [2014, 5, 12], [2013, 9, 23], 0.0375, 0.01], [101.991389, 101.751805]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['partial repair probe 2', [[2027, 12, 11], [2028, 7, 2], [2027, 12, 11], 0.065, 0.06], [100.274017, 100.274017]], ['normal control 1', [[2039, 4, 3], [2040, 7, 12], [2040, 6, 8], 0.0375, 0.09], [103.970418, 99.470418]], ['normal control 2', [[2030, 9, 19], [2032, 3, 19], [2031, 12, 19], 0.0375, 0.09], [103.346777, 98.596777]], ['normal control 3', [[2038, 4, 9], [2038, 9, 2], [2038, 5, 21], 0.02, 0.03], [99.944922, 99.711588]], ['normal control 4', [[2013, 5, 31], [2015, 2, 3], [2014, 8, 4], 0.0, 0.01], [99.494238, 99.494238]]]]
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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression discount horizon 1 | [99.364755, 98.979338] | [99.808776, 99.42336] | Failed |
| regression discount horizon 2 | [100.568152, 96.216764] | [104.478595, 100.127206] | Failed |
| partial repair probe 1 | [100.301864, 100.301864] | [100.301864, 100.301864] | Passed |
| partial repair probe 2 | [100.0, 100.0] | [100.0, 100.0] | Passed |
| normal control 1 | [100.741656, 99.158323] | [101.69703, 100.113697] | Failed |
| normal control 2 | [99.914111, 99.903] | [99.930723, 99.919612] | Failed |
| normal control 3 | [87.642419, 87.642419] | [89.88764, 89.88764] | Failed |
| normal control 4 | [101.705238, 99.483015] | [103.044015, 100.821793] | Failed |
SHA-256 / 13e2e3714f2b26db2c8bcb06e307529cd4cca099edf16e5061295759fcba08b4
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
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 discount horizon 1', [[2021, 1, 1], [2021, 11, 14], [2021, 2, 7], 0.0375, 0.045], [99.808776, 99.42336]], ['regression discount horizon 2', [[2026, 12, 29], [2028, 3, 12], [2027, 8, 27], 0.065, 0.06], [104.478595, 100.127206]], ['partial repair probe 1', [[2035, 10, 9], [2036, 1, 26], [2035, 10, 9], 0.02, 0.01], [100.301864, 100.301864]], ['partial repair probe 2', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2017, 9, 10], [2018, 1, 23], [2018, 1, 2], 0.05, 0.03], [101.69703, 100.113697]], ['normal control 2', [[2025, 3, 31], [2025, 5, 1], [2025, 4, 2], 0.02, 0.03], [99.930723, 99.919612]], ['normal control 3', [[2032, 3, 1], [2033, 9, 16], [2032, 6, 23], 0.0, 0.09], [89.88764, 89.88764]], ['normal control 4', [[2017, 8, 6], [2018, 6, 17], [2018, 1, 13], 0.05, 0.03], [103.044015, 100.821793]]], [['regression discount horizon 1', [[2010, 5, 30], [2011, 8, 19], [2011, 6, 18], 0.065, 0.09], [106.403523, 99.47019]], ['regression discount horizon 2', [[2023, 11, 29], [2025, 8, 30], [2025, 1, 29], 0.0, 0.01], [99.411813, 99.411813]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['partial repair probe 2', [[2034, 12, 18], [2035, 9, 27], [2034, 12, 18], 0.065, 0.01], [104.289888, 104.289888]], ['normal control 1', [[2036, 9, 7], [2037, 7, 16], [2037, 1, 23], 0.0375, 0.01], [102.753359, 101.315859]], ['normal control 2', [[2029, 3, 30], [2030, 4, 2], [2029, 10, 9], 0.02, 0.03], [100.577687, 99.505464]], ['normal control 3', [[2040, 5, 8], [2041, 8, 24], [2040, 11, 27], 0.065, 0.01], [107.732286, 104.067008]], ['normal control 4', [[2024, 7, 25], [2025, 2, 7], [2024, 12, 10], 0.05, 0.09], [101.24278, 99.326113]]], [['regression discount horizon 1', [[2026, 8, 21], [2028, 1, 10], [2027, 7, 6], 0.02, 0.06], [99.692954, 97.920732]], ['regression discount horizon 2', [[2031, 12, 28], [2032, 6, 13], [2032, 3, 10], 0.02, 0.045], [99.748816, 99.343261]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['partial repair probe 2', [[2020, 7, 9], [2020, 11, 22], [2020, 7, 9], 0.02, 0.06], [98.522382, 98.522382]], ['normal control 1', [[2028, 12, 1], [2030, 2, 14], [2029, 9, 27], 0.02, 0.03], [101.263042, 99.596376]], ['normal control 2', [[2012, 7, 22], [2012, 12, 10], [2012, 8, 27], 0.0375, 0.03], [100.5886, 100.2136]], ['normal control 3', [[2014, 5, 10], [2014, 10, 6], [2014, 5, 14], 0.05, 0.01], [101.659981, 101.604425]], ['normal control 4', [[2019, 3, 22], [2020, 5, 3], [2019, 4, 14], 0.0375, 0.045], [99.463327, 99.223744]]], [['regression discount horizon 1', [[2037, 7, 28], [2037, 9, 23], [2037, 9, 10], 0.05, 0.03], [100.682594, 100.071483]], ['regression discount horizon 2', [[2031, 4, 1], [2031, 10, 5], [2031, 5, 23], 0.05, 0.01], [102.21392, 101.491698]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2021, 9, 1], [2022, 2, 21], [2021, 10, 11], 0.0375, 0.03], [100.686145, 100.269479]], ['normal control 2', [[2038, 10, 29], [2040, 5, 17], [2039, 4, 7], 0.02, 0.09], [93.639986, 92.751097]], ['normal control 3', [[2020, 11, 30], [2022, 3, 2], [2021, 11, 22], 0.0375, 0.06], [103.043033, 99.324283]], ['normal control 4', [[2034, 1, 31], [2034, 8, 14], [2034, 7, 27], 0.0, 0.06], [99.700897, 99.700897]], ['normal control 5', [[2025, 9, 22], [2026, 8, 31], [2025, 10, 1], 0.065, 0.01], [105.216877, 105.054377]]], [['regression discount horizon 1', [[2019, 12, 31], [2020, 7, 22], [2020, 2, 14], 0.02, 0.06], [98.522487, 98.272487]], ['regression discount horizon 2', [[2013, 8, 31], [2014, 5, 12], [2013, 9, 23], 0.0375, 0.01], [101.991389, 101.751805]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['partial repair probe 2', [[2027, 12, 11], [2028, 7, 2], [2027, 12, 11], 0.065, 0.06], [100.274017, 100.274017]], ['normal control 1', [[2039, 4, 3], [2040, 7, 12], [2040, 6, 8], 0.0375, 0.09], [103.970418, 99.470418]], ['normal control 2', [[2030, 9, 19], [2032, 3, 19], [2031, 12, 19], 0.0375, 0.09], [103.346777, 98.596777]], ['normal control 3', [[2038, 4, 9], [2038, 9, 2], [2038, 5, 21], 0.02, 0.03], [99.944922, 99.711588]], ['normal control 4', [[2013, 5, 31], [2015, 2, 3], [2014, 8, 4], 0.0, 0.01], [99.494238, 99.494238]]]]
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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression discount horizon 1 | [99.825353, 99.439936] | [99.808776, 99.42336] | Failed |
| regression discount horizon 2 | [104.522411, 100.171022] | [104.478595, 100.127206] | Failed |
| partial repair probe 1 | [100.302913, 100.302913] | [100.301864, 100.301864] | Failed |
| partial repair probe 2 | [99.998352, 99.998352] | [100.0, 100.0] | Failed |
| normal control 1 | [101.699492, 100.116159] | [101.69703, 100.113697] | Failed |
| normal control 2 | [99.933983, 99.922872] | [99.930723, 99.919612] | Failed |
| normal control 3 | [89.787706, 89.787706] | [89.88764, 89.88764] | Failed |
| normal control 4 | [103.055067, 100.832845] | [103.044015, 100.821793] | Failed |
SHA-256 / b66a067c616ac4621cfdde0fefe8573b5373e1ae5811df2b629db439255a255b
3 / The verified repair
Exit 0"""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
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 discount horizon 1', [[2021, 1, 1], [2021, 11, 14], [2021, 2, 7], 0.0375, 0.045], [99.808776, 99.42336]], ['regression discount horizon 2', [[2026, 12, 29], [2028, 3, 12], [2027, 8, 27], 0.065, 0.06], [104.478595, 100.127206]], ['partial repair probe 1', [[2035, 10, 9], [2036, 1, 26], [2035, 10, 9], 0.02, 0.01], [100.301864, 100.301864]], ['partial repair probe 2', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2017, 9, 10], [2018, 1, 23], [2018, 1, 2], 0.05, 0.03], [101.69703, 100.113697]], ['normal control 2', [[2025, 3, 31], [2025, 5, 1], [2025, 4, 2], 0.02, 0.03], [99.930723, 99.919612]], ['normal control 3', [[2032, 3, 1], [2033, 9, 16], [2032, 6, 23], 0.0, 0.09], [89.88764, 89.88764]], ['normal control 4', [[2017, 8, 6], [2018, 6, 17], [2018, 1, 13], 0.05, 0.03], [103.044015, 100.821793]]], [['regression discount horizon 1', [[2010, 5, 30], [2011, 8, 19], [2011, 6, 18], 0.065, 0.09], [106.403523, 99.47019]], ['regression discount horizon 2', [[2023, 11, 29], [2025, 8, 30], [2025, 1, 29], 0.0, 0.01], [99.411813, 99.411813]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['partial repair probe 2', [[2034, 12, 18], [2035, 9, 27], [2034, 12, 18], 0.065, 0.01], [104.289888, 104.289888]], ['normal control 1', [[2036, 9, 7], [2037, 7, 16], [2037, 1, 23], 0.0375, 0.01], [102.753359, 101.315859]], ['normal control 2', [[2029, 3, 30], [2030, 4, 2], [2029, 10, 9], 0.02, 0.03], [100.577687, 99.505464]], ['normal control 3', [[2040, 5, 8], [2041, 8, 24], [2040, 11, 27], 0.065, 0.01], [107.732286, 104.067008]], ['normal control 4', [[2024, 7, 25], [2025, 2, 7], [2024, 12, 10], 0.05, 0.09], [101.24278, 99.326113]]], [['regression discount horizon 1', [[2026, 8, 21], [2028, 1, 10], [2027, 7, 6], 0.02, 0.06], [99.692954, 97.920732]], ['regression discount horizon 2', [[2031, 12, 28], [2032, 6, 13], [2032, 3, 10], 0.02, 0.045], [99.748816, 99.343261]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['partial repair probe 2', [[2020, 7, 9], [2020, 11, 22], [2020, 7, 9], 0.02, 0.06], [98.522382, 98.522382]], ['normal control 1', [[2028, 12, 1], [2030, 2, 14], [2029, 9, 27], 0.02, 0.03], [101.263042, 99.596376]], ['normal control 2', [[2012, 7, 22], [2012, 12, 10], [2012, 8, 27], 0.0375, 0.03], [100.5886, 100.2136]], ['normal control 3', [[2014, 5, 10], [2014, 10, 6], [2014, 5, 14], 0.05, 0.01], [101.659981, 101.604425]], ['normal control 4', [[2019, 3, 22], [2020, 5, 3], [2019, 4, 14], 0.0375, 0.045], [99.463327, 99.223744]]], [['regression discount horizon 1', [[2037, 7, 28], [2037, 9, 23], [2037, 9, 10], 0.05, 0.03], [100.682594, 100.071483]], ['regression discount horizon 2', [[2031, 4, 1], [2031, 10, 5], [2031, 5, 23], 0.05, 0.01], [102.21392, 101.491698]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['normal control 1', [[2021, 9, 1], [2022, 2, 21], [2021, 10, 11], 0.0375, 0.03], [100.686145, 100.269479]], ['normal control 2', [[2038, 10, 29], [2040, 5, 17], [2039, 4, 7], 0.02, 0.09], [93.639986, 92.751097]], ['normal control 3', [[2020, 11, 30], [2022, 3, 2], [2021, 11, 22], 0.0375, 0.06], [103.043033, 99.324283]], ['normal control 4', [[2034, 1, 31], [2034, 8, 14], [2034, 7, 27], 0.0, 0.06], [99.700897, 99.700897]], ['normal control 5', [[2025, 9, 22], [2026, 8, 31], [2025, 10, 1], 0.065, 0.01], [105.216877, 105.054377]]], [['regression discount horizon 1', [[2019, 12, 31], [2020, 7, 22], [2020, 2, 14], 0.02, 0.06], [98.522487, 98.272487]], ['regression discount horizon 2', [[2013, 8, 31], [2014, 5, 12], [2013, 9, 23], 0.0375, 0.01], [101.991389, 101.751805]], ['partial repair probe 1', [[2024, 1, 1], [2024, 12, 31], [2024, 1, 1], 0.05, 0.05], [100.0, 100.0]], ['partial repair probe 2', [[2027, 12, 11], [2028, 7, 2], [2027, 12, 11], 0.065, 0.06], [100.274017, 100.274017]], ['normal control 1', [[2039, 4, 3], [2040, 7, 12], [2040, 6, 8], 0.0375, 0.09], [103.970418, 99.470418]], ['normal control 2', [[2030, 9, 19], [2032, 3, 19], [2031, 12, 19], 0.0375, 0.09], [103.346777, 98.596777]], ['normal control 3', [[2038, 4, 9], [2038, 9, 2], [2038, 5, 21], 0.02, 0.03], [99.944922, 99.711588]], ['normal control 4', [[2013, 5, 31], [2015, 2, 3], [2014, 8, 4], 0.0, 0.01], [99.494238, 99.494238]]]]
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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression discount horizon 1 | [99.808776, 99.42336] | [99.808776, 99.42336] | Passed |
| regression discount horizon 2 | [104.478595, 100.127206] | [104.478595, 100.127206] | Passed |
| partial repair probe 1 | [100.301864, 100.301864] | [100.301864, 100.301864] | Passed |
| partial repair probe 2 | [100.0, 100.0] | [100.0, 100.0] | Passed |
| normal control 1 | [101.69703, 100.113697] | [101.69703, 100.113697] | Passed |
| normal control 2 | [99.930723, 99.919612] | [99.930723, 99.919612] | Passed |
| normal control 3 | [89.88764, 89.88764] | [89.88764, 89.88764] | Passed |
| normal control 4 | [103.044015, 100.821793] | [103.044015, 100.821793] | Passed |
SHA-256 / ad080c993b5942c8e333952e88a7dd177c5c59f22eeac9565b143af0b9685812
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 2026-09-29T14:46:53.526406+00:00.
Case digest / 90713585596d011ae466f6e30249638616dfa400fc1c3a4c18f588e83d1743e2