{"abstract":"Continuously compounded outputs are too high.","category":"Bond day-count conventions","checks":8,"contract":"Inputs a nominal rate, from/to day-count year basis (360 or 365), from/to compounding frequency (0 = continuous). First rescale the nominal rate by to_basis/from_basis, then convert compounding through the effective annual rate: EAR = (1+r/m)^m - 1 or e^r - 1; target nominal = m*((1+EAR)^(1/m)-1) or ln(1+EAR). Return rounded to 10 decimals.","evaluation_group":"w2-bond_day_count_conventions-rate-basis-conversion","failed_approach":"Applying exp to the EAR uses the forward conversion in the inverse direction.","family":"w2-bond_day_count_conventions-rate-basis-conversion-continuous-target","id":"FA-61281","implementations":{"attempt":{"sha256":"13c97984313da47bc8ac6fd9f7a0d549d6e8842308bd2b57489bad0758b36cb5","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(rate, from_basis, to_basis, from_comp, to_comp):\n    def ear(r, m):\n        return math.exp(r) - 1 if m == 0 else (1 + r / m) ** m - 1\n    def nominal(e, m):\n        return math.exp(e) - 1 if m == 0 else m * ((1 + e) ** (1 / m) - 1)\n    scaled = rate * to_basis / from_basis\n    return round(nominal(ear(scaled, from_comp), to_comp), 10)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression continuous target 1', [0.08, 365, 360, 0, 0], 0.0789041096], ['regression continuous target 2', [0.15, 360, 365, 2, 0], 0.1465783693], ['partial repair probe 1', [0.0475, 360, 360, 4, 0], 0.0472201818], ['partial repair probe 2', [0.15, 365, 365, 1, 0], 0.1397619424], ['boundary control 1', [0.05, 360, 360, 2, 2], 0.05], ['boundary control 2', [0.05, 360, 365, 1, 1], 0.0506944444], ['normal control 1', [0.03, 360, 365, 2, 4], 0.0303018911], ['normal control 2', [0.0475, 360, 360, 4, 2], 0.0477820313]], [['regression continuous target 1', [0.0475, 365, 365, 1, 0], 0.0464063728], ['regression continuous target 2', [0.0125, 365, 360, 0, 0], 0.0123287671], ['partial repair probe 1', [0.08, 365, 360, 12, 0], 0.0786458304], ['partial repair probe 2', [0.0125, 360, 360, 0, 0], 0.0125], ['boundary control 1', [0.05, 360, 365, 1, 1], 0.0506944444], ['boundary control 2', [0.05, 360, 360, 2, 2], 0.05], ['normal control 1', [0.03, 365, 360, 1, 12], 0.0291951909], ['normal control 2', [0.0125, 365, 365, 2, 4], 0.0124805295]], [['regression continuous target 1', [0.0475, 365, 360, 1, 0], 0.0457850009], ['regression continuous target 2', [0.0475, 365, 365, 2, 0], 0.0469447124], ['partial repair probe 1', [0.08, 360, 365, 2, 0], 0.0795095171], ['partial repair probe 2', [0.0475, 360, 360, 12, 0], 0.0474062369], ['boundary control 1', [0.05, 360, 365, 1, 1], 0.0506944444], ['boundary control 2', [0.05, 360, 360, 2, 2], 0.05], ['normal control 1', [0.15, 365, 365, 12, 1], 0.1607545177], ['normal control 2', [0.0475, 360, 365, 0, 2], 0.0487442442]], [['regression continuous target 1', [0.001, 360, 365, 2, 0], 0.001013632], ['regression continuous target 2', [0.0125, 360, 365, 4, 0], 0.0126535759], ['partial repair probe 1', [0.001, 365, 360, 1, 0], 0.0009858153], ['partial repair probe 2', [0.001, 360, 360, 1, 0], 0.0009995003], ['boundary control 1', [0.05, 360, 360, 2, 2], 0.05], ['boundary control 2', [0.05, 360, 365, 1, 1], 0.0506944444], ['normal control 1', [0.001, 365, 365, 12, 1], 0.0010004585], ['normal control 2', [0.001, 365, 365, 1, 2], 0.0009997501]], [['regression continuous target 1', [0.0125, 365, 365, 4, 0], 0.0124805093], ['regression continuous target 2', [0.0125, 360, 365, 2, 0], 0.0126336248], ['partial repair probe 1', [0.15, 365, 365, 12, 0], 0.14907024], ['partial repair probe 2', [0.08, 365, 365, 0, 0], 0.08], ['boundary control 1', [0.05, 360, 365, 1, 1], 0.0506944444], ['boundary control 2', [0.05, 360, 360, 2, 2], 0.05], ['normal control 1', [0.0125, 365, 360, 4, 2], 0.0123477669], ['normal control 2', [0.08, 360, 360, 0, 1], 0.0832870677]]]\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":"144793b22fa51e4fd5aae342586c62b0ef8da1a2348d4c692f19db7b23a22936","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(rate, from_basis, to_basis, from_comp, to_comp):\n    def ear(r, m):\n        return math.exp(r) - 1 if m == 0 else (1 + r / m) ** m - 1\n    def nominal(e, m):\n        return e if m == 0 else m * ((1 + e) ** (1 / m) - 1)\n    scaled = rate * to_basis / from_basis\n    return round(nominal(ear(scaled, from_comp), to_comp), 10)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression continuous target 1', [0.08, 365, 360, 0, 0], 0.0789041096], ['regression continuous target 2', [0.15, 360, 365, 2, 0], 0.1465783693], ['partial repair probe 1', [0.0475, 360, 360, 4, 0], 0.0472201818], ['partial repair probe 2', [0.15, 365, 365, 1, 0], 0.1397619424], ['boundary control 1', [0.05, 360, 360, 2, 2], 0.05], ['boundary control 2', [0.05, 360, 365, 1, 1], 0.0506944444], ['normal control 1', [0.03, 360, 365, 2, 4], 0.0303018911], ['normal control 2', [0.0475, 360, 360, 4, 2], 0.0477820313]], [['regression continuous target 1', [0.0475, 365, 365, 1, 0], 0.0464063728], ['regression continuous target 2', [0.0125, 365, 360, 0, 0], 0.0123287671], ['partial repair probe 1', [0.08, 365, 360, 12, 0], 0.0786458304], ['partial repair probe 2', [0.0125, 360, 360, 0, 0], 0.0125], ['boundary control 1', [0.05, 360, 365, 1, 1], 0.0506944444], ['boundary control 2', [0.05, 360, 360, 2, 2], 0.05], ['normal control 1', [0.03, 365, 360, 1, 12], 0.0291951909], ['normal control 2', [0.0125, 365, 365, 2, 4], 0.0124805295]], [['regression continuous target 1', [0.0475, 365, 360, 1, 0], 0.0457850009], ['regression continuous target 2', [0.0475, 365, 365, 2, 0], 0.0469447124], ['partial repair probe 1', [0.08, 360, 365, 2, 0], 0.0795095171], ['partial repair probe 2', [0.0475, 360, 360, 12, 0], 0.0474062369], ['boundary control 1', [0.05, 360, 365, 1, 1], 0.0506944444], ['boundary control 2', [0.05, 360, 360, 2, 2], 0.05], ['normal control 1', [0.15, 365, 365, 12, 1], 0.1607545177], ['normal control 2', [0.0475, 360, 365, 0, 2], 0.0487442442]], [['regression continuous target 1', [0.001, 360, 365, 2, 0], 0.001013632], ['regression continuous target 2', [0.0125, 360, 365, 4, 0], 0.0126535759], ['partial repair probe 1', [0.001, 365, 360, 1, 0], 0.0009858153], ['partial repair probe 2', [0.001, 360, 360, 1, 0], 0.0009995003], ['boundary control 1', [0.05, 360, 360, 2, 2], 0.05], ['boundary control 2', [0.05, 360, 365, 1, 1], 0.0506944444], ['normal control 1', [0.001, 365, 365, 12, 1], 0.0010004585], ['normal control 2', [0.001, 365, 365, 1, 2], 0.0009997501]], [['regression continuous target 1', [0.0125, 365, 365, 4, 0], 0.0124805093], ['regression continuous target 2', [0.0125, 360, 365, 2, 0], 0.0126336248], ['partial repair probe 1', [0.15, 365, 365, 12, 0], 0.14907024], ['partial repair probe 2', [0.08, 365, 365, 0, 0], 0.08], ['boundary control 1', [0.05, 360, 365, 1, 1], 0.0506944444], ['boundary control 2', [0.05, 360, 360, 2, 2], 0.05], ['normal control 1', [0.0125, 365, 360, 4, 2], 0.0123477669], ['normal control 2', [0.08, 360, 360, 0, 1], 0.0832870677]]]\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"},"fixed":{"sha256":"b9529d93687c014be1e5f3a3be938a3ab71cdfc5f07f2fa1d4d66fec354c1e6b","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(rate, from_basis, to_basis, from_comp, to_comp):\n    def ear(r, m):\n        return math.exp(r) - 1 if m == 0 else (1 + r / m) ** m - 1\n    def nominal(e, m):\n        return math.log(1 + e) if m == 0 else m * ((1 + e) ** (1 / m) - 1)\n    scaled = rate * to_basis / from_basis\n    return round(nominal(ear(scaled, from_comp), to_comp), 10)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression continuous target 1', [0.08, 365, 360, 0, 0], 0.0789041096], ['regression continuous target 2', [0.15, 360, 365, 2, 0], 0.1465783693], ['partial repair probe 1', [0.0475, 360, 360, 4, 0], 0.0472201818], ['partial repair probe 2', [0.15, 365, 365, 1, 0], 0.1397619424], ['boundary control 1', [0.05, 360, 360, 2, 2], 0.05], ['boundary control 2', [0.05, 360, 365, 1, 1], 0.0506944444], ['normal control 1', [0.03, 360, 365, 2, 4], 0.0303018911], ['normal control 2', [0.0475, 360, 360, 4, 2], 0.0477820313]], [['regression continuous target 1', [0.0475, 365, 365, 1, 0], 0.0464063728], ['regression continuous target 2', [0.0125, 365, 360, 0, 0], 0.0123287671], ['partial repair probe 1', [0.08, 365, 360, 12, 0], 0.0786458304], ['partial repair probe 2', [0.0125, 360, 360, 0, 0], 0.0125], ['boundary control 1', [0.05, 360, 365, 1, 1], 0.0506944444], ['boundary control 2', [0.05, 360, 360, 2, 2], 0.05], ['normal control 1', [0.03, 365, 360, 1, 12], 0.0291951909], ['normal control 2', [0.0125, 365, 365, 2, 4], 0.0124805295]], [['regression continuous target 1', [0.0475, 365, 360, 1, 0], 0.0457850009], ['regression continuous target 2', [0.0475, 365, 365, 2, 0], 0.0469447124], ['partial repair probe 1', [0.08, 360, 365, 2, 0], 0.0795095171], ['partial repair probe 2', [0.0475, 360, 360, 12, 0], 0.0474062369], ['boundary control 1', [0.05, 360, 365, 1, 1], 0.0506944444], ['boundary control 2', [0.05, 360, 360, 2, 2], 0.05], ['normal control 1', [0.15, 365, 365, 12, 1], 0.1607545177], ['normal control 2', [0.0475, 360, 365, 0, 2], 0.0487442442]], [['regression continuous target 1', [0.001, 360, 365, 2, 0], 0.001013632], ['regression continuous target 2', [0.0125, 360, 365, 4, 0], 0.0126535759], ['partial repair probe 1', [0.001, 365, 360, 1, 0], 0.0009858153], ['partial repair probe 2', [0.001, 360, 360, 1, 0], 0.0009995003], ['boundary control 1', [0.05, 360, 360, 2, 2], 0.05], ['boundary control 2', [0.05, 360, 365, 1, 1], 0.0506944444], ['normal control 1', [0.001, 365, 365, 12, 1], 0.0010004585], ['normal control 2', [0.001, 365, 365, 1, 2], 0.0009997501]], [['regression continuous target 1', [0.0125, 365, 365, 4, 0], 0.0124805093], ['regression continuous target 2', [0.0125, 360, 365, 2, 0], 0.0126336248], ['partial repair probe 1', [0.15, 365, 365, 12, 0], 0.14907024], ['partial repair probe 2', [0.08, 365, 365, 0, 0], 0.08], ['boundary control 1', [0.05, 360, 365, 1, 1], 0.0506944444], ['boundary control 2', [0.05, 360, 360, 2, 2], 0.05], ['normal control 1', [0.0125, 365, 360, 4, 2], 0.0123477669], ['normal control 2', [0.08, 360, 360, 0, 1], 0.0832870677]]]\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-rate-basis-conversion-continuous-target","generated_at":"2026-09-29T14:46:53.783360+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.","repair":"Return ln(1 + EAR) for a continuous target.","root_cause":"The continuous branch of the inverse conversion returns EAR without taking the logarithm.","sha256":"d59431bb5c4f409eb8a7a68c199072b9f875b06ad0772438c4fc85bbef225cb3","title":"Rate conversion across day-count basis and compounding: a continuous target returns the effective annual rate · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":39.447,"exit_code":1,"observations":[{"actual":0.0855649623,"check":"regression continuous target 1","expected":0.0789041096,"passed":false},{"actual":0.1710088806,"check":"regression continuous target 2","expected":0.1465783693,"passed":false},{"actual":0.0495408805,"check":"partial repair probe 1","expected":0.0472201818,"passed":false},{"actual":0.1618342427,"check":"partial repair probe 2","expected":0.1397619424,"passed":false},{"actual":0.05,"check":"boundary control 1","expected":0.05,"passed":true},{"actual":0.0506944444,"check":"boundary control 2","expected":0.0506944444,"passed":true},{"actual":0.0303018911,"check":"normal control 1","expected":0.0303018911,"passed":true},{"actual":0.0477820313,"check":"normal control 2","expected":0.0477820313,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression continuous target 1\", \"actual\": 0.0855649623, \"expected\": 0.0789041096, \"passed\": false}, {\"check\": \"regression continuous target 2\", \"actual\": 0.1710088806, \"expected\": 0.1465783693, \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": 0.0495408805, \"expected\": 0.0472201818, \"passed\": false}, {\"check\": \"partial repair probe 2\", \"actual\": 0.1618342427, \"expected\": 0.1397619424, \"passed\": false}, {\"check\": \"boundary control 1\", \"actual\": 0.05, \"expected\": 0.05, \"passed\": true}, {\"check\": \"boundary control 2\", \"actual\": 0.0506944444, \"expected\": 0.0506944444, \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": 0.0303018911, \"expected\": 0.0303018911, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 0.0477820313, \"expected\": 0.0477820313, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":40.956,"exit_code":1,"observations":[{"actual":0.082100554,"check":"regression continuous target 1","expected":0.0789041096,"passed":false},{"actual":0.1578656684,"check":"regression continuous target 2","expected":0.1465783693,"passed":false},{"actual":0.0483528119,"check":"partial repair probe 1","expected":0.0472201818,"passed":false},{"actual":0.15,"check":"partial repair probe 2","expected":0.1397619424,"passed":false},{"actual":0.05,"check":"boundary control 1","expected":0.05,"passed":true},{"actual":0.0506944444,"check":"boundary control 2","expected":0.0506944444,"passed":true},{"actual":0.0303018911,"check":"normal control 1","expected":0.0303018911,"passed":true},{"actual":0.0477820313,"check":"normal control 2","expected":0.0477820313,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression continuous target 1\", \"actual\": 0.082100554, \"expected\": 0.0789041096, \"passed\": false}, {\"check\": \"regression continuous target 2\", \"actual\": 0.1578656684, \"expected\": 0.1465783693, \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": 0.0483528119, \"expected\": 0.0472201818, \"passed\": false}, {\"check\": \"partial repair probe 2\", \"actual\": 0.15, \"expected\": 0.1397619424, \"passed\": false}, {\"check\": \"boundary control 1\", \"actual\": 0.05, \"expected\": 0.05, \"passed\": true}, {\"check\": \"boundary control 2\", \"actual\": 0.0506944444, \"expected\": 0.0506944444, \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": 0.0303018911, \"expected\": 0.0303018911, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 0.0477820313, \"expected\": 0.0477820313, \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":43.486,"exit_code":0,"observations":[{"actual":0.0789041096,"check":"regression continuous target 1","expected":0.0789041096,"passed":true},{"actual":0.1465783693,"check":"regression continuous target 2","expected":0.1465783693,"passed":true},{"actual":0.0472201818,"check":"partial repair probe 1","expected":0.0472201818,"passed":true},{"actual":0.1397619424,"check":"partial repair probe 2","expected":0.1397619424,"passed":true},{"actual":0.05,"check":"boundary control 1","expected":0.05,"passed":true},{"actual":0.0506944444,"check":"boundary control 2","expected":0.0506944444,"passed":true},{"actual":0.0303018911,"check":"normal control 1","expected":0.0303018911,"passed":true},{"actual":0.0477820313,"check":"normal control 2","expected":0.0477820313,"passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression continuous target 1\", \"actual\": 0.0789041096, \"expected\": 0.0789041096, \"passed\": true}, {\"check\": \"regression continuous target 2\", \"actual\": 0.1465783693, \"expected\": 0.1465783693, \"passed\": true}, {\"check\": \"partial repair probe 1\", \"actual\": 0.0472201818, \"expected\": 0.0472201818, \"passed\": true}, {\"check\": \"partial repair probe 2\", \"actual\": 0.1397619424, \"expected\": 0.1397619424, \"passed\": true}, {\"check\": \"boundary control 1\", \"actual\": 0.05, \"expected\": 0.05, \"passed\": true}, {\"check\": \"boundary control 2\", \"actual\": 0.0506944444, \"expected\": 0.0506944444, \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": 0.0303018911, \"expected\": 0.0303018911, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 0.0477820313, \"expected\": 0.0477820313, \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}