{"abstract":"Prices are too low for seasoned certificates.","category":"Bond day-count conventions","checks":8,"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.","contract_signature":"issue, maturity, settle, coupon, y","evaluation_group":"w2-bond_day_count_conventions-cd-interest-at-maturity","failed_approach":"Compounding the yield annually over the remaining days is a different convention.","family":"w2-bond_day_count_conventions-cd-interest-at-maturity-discount-horizon","id":"FA-61246","implementations":{"attempt":{"sha256":"b66a067c616ac4621cfdde0fefe8573b5373e1ae5811df2b629db439255a255b","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport datetime\nN = 1\nobservations = []\ndef solve(issue, maturity, settle, coupon, y):\n    I = datetime.date(*issue)\n    M = datetime.date(*maturity)\n    S = datetime.date(*settle)\n    T = (M - I).days\n    R = (M - S).days\n    A = (S - I).days\n    redemption = 100 * (1 + coupon * T / 360)\n    dirty = redemption / (1 + y) ** (R / 360)\n    accrued = 100 * coupon * A / 360\n    return [round(dirty, 6), round(dirty - accrued, 6)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['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]]]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"broken":{"sha256":"13e2e3714f2b26db2c8bcb06e307529cd4cca099edf16e5061295759fcba08b4","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport datetime\nN = 1\nobservations = []\ndef solve(issue, maturity, settle, coupon, y):\n    I = datetime.date(*issue)\n    M = datetime.date(*maturity)\n    S = datetime.date(*settle)\n    T = (M - I).days\n    R = (M - S).days\n    A = (S - I).days\n    redemption = 100 * (1 + coupon * T / 360)\n    dirty = redemption / (1 + y * T / 360)\n    accrued = 100 * coupon * A / 360\n    return [round(dirty, 6), round(dirty - accrued, 6)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['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]]]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"}},"limitations":"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.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"w2-bond_day_count_conventions-cd-interest-at-maturity-discount-horizon","generated_at":"2026-09-29T14:46:53.526406+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Bond accrual and pricing systems depend on exact day-count arithmetic; a single-day error changes settlement cash.","root_cause":"Discounting uses days from issue rather than days remaining.","sha256":"a7f411cdcc58e7604f01825fb2f07e6e191a4ba71a9c6c32ebc84ac23c55ab9a","title":"Money-market certificate priced from yield: the maturity value is discounted over the full term · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verified":true,"visibility":"public","verification":{"attempt":{"elapsed_ms":51.743,"exit_code":1,"observations":[{"actual":[99.825353,99.439936],"check":"regression discount horizon 1","expected":[99.808776,99.42336],"passed":false},{"actual":[104.522411,100.171022],"check":"regression discount horizon 2","expected":[104.478595,100.127206],"passed":false},{"actual":[100.302913,100.302913],"check":"partial repair probe 1","expected":[100.301864,100.301864],"passed":false},{"actual":[99.998352,99.998352],"check":"partial repair probe 2","expected":[100.0,100.0],"passed":false},{"actual":[101.699492,100.116159],"check":"normal control 1","expected":[101.69703,100.113697],"passed":false},{"actual":[99.933983,99.922872],"check":"normal control 2","expected":[99.930723,99.919612],"passed":false},{"actual":[89.787706,89.787706],"check":"normal control 3","expected":[89.88764,89.88764],"passed":false},{"actual":[103.055067,100.832845],"check":"normal control 4","expected":[103.044015,100.821793],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression discount horizon 1\", \"actual\": [99.825353, 99.439936], \"expected\": [99.808776, 99.42336], \"passed\": false}, {\"check\": \"regression discount horizon 2\", \"actual\": [104.522411, 100.171022], \"expected\": [104.478595, 100.127206], \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": [100.302913, 100.302913], \"expected\": [100.301864, 100.301864], \"passed\": false}, {\"check\": \"partial repair probe 2\", \"actual\": [99.998352, 99.998352], \"expected\": [100.0, 100.0], \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": [101.699492, 100.116159], \"expected\": [101.69703, 100.113697], \"passed\": false}, {\"check\": \"normal control 2\", \"actual\": [99.933983, 99.922872], \"expected\": [99.930723, 99.919612], \"passed\": false}, {\"check\": \"normal control 3\", \"actual\": [89.787706, 89.787706], \"expected\": [89.88764, 89.88764], \"passed\": false}, {\"check\": \"normal control 4\", \"actual\": [103.055067, 100.832845], \"expected\": [103.044015, 100.821793], \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":45.331,"exit_code":1,"observations":[{"actual":[99.364755,98.979338],"check":"regression discount horizon 1","expected":[99.808776,99.42336],"passed":false},{"actual":[100.568152,96.216764],"check":"regression discount horizon 2","expected":[104.478595,100.127206],"passed":false},{"actual":[100.301864,100.301864],"check":"partial repair probe 1","expected":[100.301864,100.301864],"passed":true},{"actual":[100.0,100.0],"check":"partial repair probe 2","expected":[100.0,100.0],"passed":true},{"actual":[100.741656,99.158323],"check":"normal control 1","expected":[101.69703,100.113697],"passed":false},{"actual":[99.914111,99.903],"check":"normal control 2","expected":[99.930723,99.919612],"passed":false},{"actual":[87.642419,87.642419],"check":"normal control 3","expected":[89.88764,89.88764],"passed":false},{"actual":[101.705238,99.483015],"check":"normal control 4","expected":[103.044015,100.821793],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression discount horizon 1\", \"actual\": [99.364755, 98.979338], \"expected\": [99.808776, 99.42336], \"passed\": false}, {\"check\": \"regression discount horizon 2\", \"actual\": [100.568152, 96.216764], \"expected\": [104.478595, 100.127206], \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": [100.301864, 100.301864], \"expected\": [100.301864, 100.301864], \"passed\": true}, {\"check\": \"partial repair probe 2\", \"actual\": [100.0, 100.0], \"expected\": [100.0, 100.0], \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": [100.741656, 99.158323], \"expected\": [101.69703, 100.113697], \"passed\": false}, {\"check\": \"normal control 2\", \"actual\": [99.914111, 99.903], \"expected\": [99.930723, 99.919612], \"passed\": false}, {\"check\": \"normal control 3\", \"actual\": [87.642419, 87.642419], \"expected\": [89.88764, 89.88764], \"passed\": false}, {\"check\": \"normal control 4\", \"actual\": [101.705238, 99.483015], \"expected\": [103.044015, 100.821793], \"passed\": false}], \"passed\": false}\n"}},"member_only":{"stages":["fixed"],"fields":["implementations.fixed","verification.fixed","harness","repair"],"note":"The verified repair, its recorded checks, the repair description, and the scoring harness are available to members."}}