{"abstract":"Tree prices drift from the stated continuous-compounding model.","category":"Options payoff and settlement","checks":8,"contract":"Inputs kind, spot S, strike K, rate r, volatility sigma, maturity T in years and steps. dt=T/steps, u=exp(sigma*sqrt(dt)), d=1/u, p=(exp(r*dt)-d)/(u-d), disc=exp(-r*dt). Roll back from terminal payoffs; at each node take max(continuation, immediate exercise at that node's price). Return the root rounded to 6.","evaluation_group":"w2-options_payoff_and_settlement-crr-american","failed_approach":"Using the full maturity T in the growth factor inflates p.","family":"w2-options_payoff_and_settlement-crr-american-risk-neutral-probability","id":"FA-61521","implementations":{"attempt":{"sha256":"bd64c29a4cb4adbae45feb4baf0f6322d72dfb4f83baec6dcd150e1430354b12","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(kind, S, K, r, sigma, T, steps):\n    dt = T / steps\n    u = math.exp(sigma * math.sqrt(dt))\n    d = 1 / u\n    p = (math.exp(r * T) - d) / (u - d)\n    disc = math.exp(-r * dt)\n    def pay(s):\n        return max(s - K, 0.0) if kind == 'C' else max(K - s, 0.0)\n    vals = [pay(S * u ** j * d ** (steps - j)) for j in range(steps + 1)]\n    for i in range(steps - 1, -1, -1):\n        vals = [max(disc * (p * vals[j + 1] + (1 - p) * vals[j]), pay(S * u ** j * d ** (i - j))) for j in range(i + 1)]\n    return round(vals[0], 6)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression risk-neutral probability 1', ['P', 95.0, 100.0, 0.01, 0.15, 0.5, 3], 6.658835], ['regression risk-neutral probability 2', ['C', 110.0, 90.0, 0.08, 0.25, 0.5, 12], 24.159122], ['partial repair probe 1', ['P', 110.0, 90.0, 0.05, 0.15, 0.25, 12], 0.003613], ['normal control 1', ['P', 110.0, 100.0, 0.0, 0.4, 0.25, 3], 4.073107], ['normal control 2', ['P', 100.0, 90.0, 0.0, 0.25, 0.25, 5], 1.438421], ['normal control 3', ['P', 95.0, 105.0, 0.08, 0.15, 0.5, 3], 10.0], ['normal control 4', ['P', 95.0, 100.0, 0.0, 0.15, 1.0, 12], 8.771401], ['normal control 5', ['C', 110.0, 105.0, 0.0, 0.25, 0.5, 25], 10.332348]], [['regression risk-neutral probability 1', ['P', 80.0, 90.0, 0.08, 0.25, 2.0, 3], 11.993101], ['regression risk-neutral probability 2', ['C', 80.0, 105.0, 0.05, 0.4, 1.0, 25], 6.416395], ['partial repair probe 1', ['C', 80.0, 100.0, 0.01, 0.15, 0.25, 25], 0.002014], ['normal control 1', ['P', 110.0, 105.0, 0.0, 0.15, 0.5, 25], 2.513171], ['normal control 2', ['P', 80.0, 100.0, 0.0, 0.15, 0.25, 5], 20.0], ['normal control 3', ['P', 95.0, 90.0, 0.0, 0.25, 2.0, 5], 11.126274], ['normal control 4', ['P', 110.0, 90.0, 0.0, 0.15, 0.25, 12], 0.006049], ['normal control 5', ['P', 80.0, 105.0, 0.01, 0.15, 0.5, 12], 25.0]], [['regression risk-neutral probability 1', ['C', 110.0, 90.0, 0.08, 0.25, 0.5, 12], 24.159122], ['regression risk-neutral probability 2', ['P', 110.0, 90.0, 0.01, 0.15, 2.0, 3], 1.727866], ['partial repair probe 1', ['C', 80.0, 105.0, 0.01, 0.15, 0.5, 12], 0.01299], ['partial repair probe 2', ['P', 110.0, 90.0, 0.05, 0.15, 0.25, 25], 0.0038], ['normal control 1', ['C', 100.0, 100.0, 0.0, 0.25, 0.5, 12], 6.898148], ['normal control 2', ['P', 80.0, 105.0, 0.01, 0.15, 1.0, 25], 25.0], ['normal control 3', ['P', 80.0, 100.0, 0.0, 0.4, 1.0, 3], 25.451176], ['normal control 4', ['C', 100.0, 100.0, 0.0, 0.25, 2.0, 12], 13.742666]], [['regression risk-neutral probability 1', ['P', 110.0, 100.0, 0.05, 0.15, 1.0, 25], 1.469382], ['regression risk-neutral probability 2', ['P', 100.0, 100.0, 0.01, 0.4, 0.5, 5], 11.583913], ['partial repair probe 1', ['P', 110.0, 90.0, 0.01, 0.15, 0.25, 25], 0.005888], ['partial repair probe 2', ['C', 80.0, 100.0, 0.05, 0.15, 0.25, 12], 0.001397], ['normal control 1', ['P', 100.0, 105.0, 0.0, 0.15, 1.0, 25], 8.986901], ['normal control 2', ['C', 100.0, 105.0, 0.0, 0.4, 2.0, 25], 20.54431], ['normal control 3', ['P', 95.0, 100.0, 0.0, 0.25, 0.25, 25], 7.804053], ['normal control 4', ['P', 80.0, 100.0, 0.0, 0.4, 1.0, 12], 26.184178]], [['regression risk-neutral probability 1', ['P', 100.0, 100.0, 0.08, 0.15, 2.0, 12], 4.019753], ['regression risk-neutral probability 2', ['P', 95.0, 90.0, 0.01, 0.15, 0.5, 5], 1.671132], ['partial repair probe 1', ['C', 80.0, 100.0, 0.05, 0.15, 0.25, 12], 0.001397], ['partial repair probe 2', ['P', 110.0, 90.0, 0.05, 0.15, 0.25, 25], 0.0038], ['normal control 1', ['P', 95.0, 105.0, 0.08, 0.15, 1.0, 5], 10.0], ['normal control 2', ['P', 80.0, 100.0, 0.0, 0.25, 0.25, 12], 20.134901], ['normal control 3', ['P', 95.0, 105.0, 0.0, 0.4, 0.25, 5], 13.698971], ['normal control 4', ['C', 100.0, 100.0, 0.0, 0.15, 1.0, 25], 6.038372]]]\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":"06ed991952ff4bb05e3e10521b0c9fc4a4259d8800baa89550efd6ea34f5af84","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(kind, S, K, r, sigma, T, steps):\n    dt = T / steps\n    u = math.exp(sigma * math.sqrt(dt))\n    d = 1 / u\n    p = (1 + r * dt - d) / (u - d)\n    disc = math.exp(-r * dt)\n    def pay(s):\n        return max(s - K, 0.0) if kind == 'C' else max(K - s, 0.0)\n    vals = [pay(S * u ** j * d ** (steps - j)) for j in range(steps + 1)]\n    for i in range(steps - 1, -1, -1):\n        vals = [max(disc * (p * vals[j + 1] + (1 - p) * vals[j]), pay(S * u ** j * d ** (i - j))) for j in range(i + 1)]\n    return round(vals[0], 6)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression risk-neutral probability 1', ['P', 95.0, 100.0, 0.01, 0.15, 0.5, 3], 6.658835], ['regression risk-neutral probability 2', ['C', 110.0, 90.0, 0.08, 0.25, 0.5, 12], 24.159122], ['partial repair probe 1', ['P', 110.0, 90.0, 0.05, 0.15, 0.25, 12], 0.003613], ['normal control 1', ['P', 110.0, 100.0, 0.0, 0.4, 0.25, 3], 4.073107], ['normal control 2', ['P', 100.0, 90.0, 0.0, 0.25, 0.25, 5], 1.438421], ['normal control 3', ['P', 95.0, 105.0, 0.08, 0.15, 0.5, 3], 10.0], ['normal control 4', ['P', 95.0, 100.0, 0.0, 0.15, 1.0, 12], 8.771401], ['normal control 5', ['C', 110.0, 105.0, 0.0, 0.25, 0.5, 25], 10.332348]], [['regression risk-neutral probability 1', ['P', 80.0, 90.0, 0.08, 0.25, 2.0, 3], 11.993101], ['regression risk-neutral probability 2', ['C', 80.0, 105.0, 0.05, 0.4, 1.0, 25], 6.416395], ['partial repair probe 1', ['C', 80.0, 100.0, 0.01, 0.15, 0.25, 25], 0.002014], ['normal control 1', ['P', 110.0, 105.0, 0.0, 0.15, 0.5, 25], 2.513171], ['normal control 2', ['P', 80.0, 100.0, 0.0, 0.15, 0.25, 5], 20.0], ['normal control 3', ['P', 95.0, 90.0, 0.0, 0.25, 2.0, 5], 11.126274], ['normal control 4', ['P', 110.0, 90.0, 0.0, 0.15, 0.25, 12], 0.006049], ['normal control 5', ['P', 80.0, 105.0, 0.01, 0.15, 0.5, 12], 25.0]], [['regression risk-neutral probability 1', ['C', 110.0, 90.0, 0.08, 0.25, 0.5, 12], 24.159122], ['regression risk-neutral probability 2', ['P', 110.0, 90.0, 0.01, 0.15, 2.0, 3], 1.727866], ['partial repair probe 1', ['C', 80.0, 105.0, 0.01, 0.15, 0.5, 12], 0.01299], ['partial repair probe 2', ['P', 110.0, 90.0, 0.05, 0.15, 0.25, 25], 0.0038], ['normal control 1', ['C', 100.0, 100.0, 0.0, 0.25, 0.5, 12], 6.898148], ['normal control 2', ['P', 80.0, 105.0, 0.01, 0.15, 1.0, 25], 25.0], ['normal control 3', ['P', 80.0, 100.0, 0.0, 0.4, 1.0, 3], 25.451176], ['normal control 4', ['C', 100.0, 100.0, 0.0, 0.25, 2.0, 12], 13.742666]], [['regression risk-neutral probability 1', ['P', 110.0, 100.0, 0.05, 0.15, 1.0, 25], 1.469382], ['regression risk-neutral probability 2', ['P', 100.0, 100.0, 0.01, 0.4, 0.5, 5], 11.583913], ['partial repair probe 1', ['P', 110.0, 90.0, 0.01, 0.15, 0.25, 25], 0.005888], ['partial repair probe 2', ['C', 80.0, 100.0, 0.05, 0.15, 0.25, 12], 0.001397], ['normal control 1', ['P', 100.0, 105.0, 0.0, 0.15, 1.0, 25], 8.986901], ['normal control 2', ['C', 100.0, 105.0, 0.0, 0.4, 2.0, 25], 20.54431], ['normal control 3', ['P', 95.0, 100.0, 0.0, 0.25, 0.25, 25], 7.804053], ['normal control 4', ['P', 80.0, 100.0, 0.0, 0.4, 1.0, 12], 26.184178]], [['regression risk-neutral probability 1', ['P', 100.0, 100.0, 0.08, 0.15, 2.0, 12], 4.019753], ['regression risk-neutral probability 2', ['P', 95.0, 90.0, 0.01, 0.15, 0.5, 5], 1.671132], ['partial repair probe 1', ['C', 80.0, 100.0, 0.05, 0.15, 0.25, 12], 0.001397], ['partial repair probe 2', ['P', 110.0, 90.0, 0.05, 0.15, 0.25, 25], 0.0038], ['normal control 1', ['P', 95.0, 105.0, 0.08, 0.15, 1.0, 5], 10.0], ['normal control 2', ['P', 80.0, 100.0, 0.0, 0.25, 0.25, 12], 20.134901], ['normal control 3', ['P', 95.0, 105.0, 0.0, 0.4, 0.25, 5], 13.698971], ['normal control 4', ['C', 100.0, 100.0, 0.0, 0.15, 1.0, 25], 6.038372]]]\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":"d355e370e818bf5140b998f4a70edeeb1447e53124f983a7d80f95a910209cd6","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(kind, S, K, r, sigma, T, steps):\n    dt = T / steps\n    u = math.exp(sigma * math.sqrt(dt))\n    d = 1 / u\n    p = (math.exp(r * dt) - d) / (u - d)\n    disc = math.exp(-r * dt)\n    def pay(s):\n        return max(s - K, 0.0) if kind == 'C' else max(K - s, 0.0)\n    vals = [pay(S * u ** j * d ** (steps - j)) for j in range(steps + 1)]\n    for i in range(steps - 1, -1, -1):\n        vals = [max(disc * (p * vals[j + 1] + (1 - p) * vals[j]), pay(S * u ** j * d ** (i - j))) for j in range(i + 1)]\n    return round(vals[0], 6)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression risk-neutral probability 1', ['P', 95.0, 100.0, 0.01, 0.15, 0.5, 3], 6.658835], ['regression risk-neutral probability 2', ['C', 110.0, 90.0, 0.08, 0.25, 0.5, 12], 24.159122], ['partial repair probe 1', ['P', 110.0, 90.0, 0.05, 0.15, 0.25, 12], 0.003613], ['normal control 1', ['P', 110.0, 100.0, 0.0, 0.4, 0.25, 3], 4.073107], ['normal control 2', ['P', 100.0, 90.0, 0.0, 0.25, 0.25, 5], 1.438421], ['normal control 3', ['P', 95.0, 105.0, 0.08, 0.15, 0.5, 3], 10.0], ['normal control 4', ['P', 95.0, 100.0, 0.0, 0.15, 1.0, 12], 8.771401], ['normal control 5', ['C', 110.0, 105.0, 0.0, 0.25, 0.5, 25], 10.332348]], [['regression risk-neutral probability 1', ['P', 80.0, 90.0, 0.08, 0.25, 2.0, 3], 11.993101], ['regression risk-neutral probability 2', ['C', 80.0, 105.0, 0.05, 0.4, 1.0, 25], 6.416395], ['partial repair probe 1', ['C', 80.0, 100.0, 0.01, 0.15, 0.25, 25], 0.002014], ['normal control 1', ['P', 110.0, 105.0, 0.0, 0.15, 0.5, 25], 2.513171], ['normal control 2', ['P', 80.0, 100.0, 0.0, 0.15, 0.25, 5], 20.0], ['normal control 3', ['P', 95.0, 90.0, 0.0, 0.25, 2.0, 5], 11.126274], ['normal control 4', ['P', 110.0, 90.0, 0.0, 0.15, 0.25, 12], 0.006049], ['normal control 5', ['P', 80.0, 105.0, 0.01, 0.15, 0.5, 12], 25.0]], [['regression risk-neutral probability 1', ['C', 110.0, 90.0, 0.08, 0.25, 0.5, 12], 24.159122], ['regression risk-neutral probability 2', ['P', 110.0, 90.0, 0.01, 0.15, 2.0, 3], 1.727866], ['partial repair probe 1', ['C', 80.0, 105.0, 0.01, 0.15, 0.5, 12], 0.01299], ['partial repair probe 2', ['P', 110.0, 90.0, 0.05, 0.15, 0.25, 25], 0.0038], ['normal control 1', ['C', 100.0, 100.0, 0.0, 0.25, 0.5, 12], 6.898148], ['normal control 2', ['P', 80.0, 105.0, 0.01, 0.15, 1.0, 25], 25.0], ['normal control 3', ['P', 80.0, 100.0, 0.0, 0.4, 1.0, 3], 25.451176], ['normal control 4', ['C', 100.0, 100.0, 0.0, 0.25, 2.0, 12], 13.742666]], [['regression risk-neutral probability 1', ['P', 110.0, 100.0, 0.05, 0.15, 1.0, 25], 1.469382], ['regression risk-neutral probability 2', ['P', 100.0, 100.0, 0.01, 0.4, 0.5, 5], 11.583913], ['partial repair probe 1', ['P', 110.0, 90.0, 0.01, 0.15, 0.25, 25], 0.005888], ['partial repair probe 2', ['C', 80.0, 100.0, 0.05, 0.15, 0.25, 12], 0.001397], ['normal control 1', ['P', 100.0, 105.0, 0.0, 0.15, 1.0, 25], 8.986901], ['normal control 2', ['C', 100.0, 105.0, 0.0, 0.4, 2.0, 25], 20.54431], ['normal control 3', ['P', 95.0, 100.0, 0.0, 0.25, 0.25, 25], 7.804053], ['normal control 4', ['P', 80.0, 100.0, 0.0, 0.4, 1.0, 12], 26.184178]], [['regression risk-neutral probability 1', ['P', 100.0, 100.0, 0.08, 0.15, 2.0, 12], 4.019753], ['regression risk-neutral probability 2', ['P', 95.0, 90.0, 0.01, 0.15, 0.5, 5], 1.671132], ['partial repair probe 1', ['C', 80.0, 100.0, 0.05, 0.15, 0.25, 12], 0.001397], ['partial repair probe 2', ['P', 110.0, 90.0, 0.05, 0.15, 0.25, 25], 0.0038], ['normal control 1', ['P', 95.0, 105.0, 0.08, 0.15, 1.0, 5], 10.0], ['normal control 2', ['P', 80.0, 100.0, 0.0, 0.25, 0.25, 12], 20.134901], ['normal control 3', ['P', 95.0, 105.0, 0.0, 0.4, 0.25, 5], 13.698971], ['normal control 4', ['C', 100.0, 100.0, 0.0, 0.15, 1.0, 25], 6.038372]]]\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 exchange or clearing rulebook. 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-options_payoff_and_settlement-crr-american-risk-neutral-probability","generated_at":"2026-09-29T14:46:56.077533+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Option expiry, exercise and settlement engines move cash and shares; a wrong branch misstates obligations.","repair":"Use p = (exp(r*dt) - d)/(u - d).","root_cause":"p uses 1 + r*dt instead of exp(r*dt).","sha256":"4f04865517e9b7af6baf3e4cb422c4dfdb91a5e40ddb70a618146cfeb8dd2194","title":"Cox-Ross-Rubinstein American option tree: the growth factor uses simple interest · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":41.985,"exit_code":1,"observations":[{"actual":6.293341,"check":"regression risk-neutral probability 1","expected":6.658835,"passed":false},{"actual":84.326751,"check":"regression risk-neutral probability 2","expected":24.159122,"passed":false},{"actual":1e-06,"check":"partial repair probe 1","expected":0.003613,"passed":false},{"actual":4.073107,"check":"normal control 1","expected":4.073107,"passed":true},{"actual":1.438421,"check":"normal control 2","expected":1.438421,"passed":true},{"actual":10.0,"check":"normal control 3","expected":10.0,"passed":true},{"actual":8.771401,"check":"normal control 4","expected":8.771401,"passed":true},{"actual":10.332348,"check":"normal control 5","expected":10.332348,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression risk-neutral probability 1\", \"actual\": 6.293341, \"expected\": 6.658835, \"passed\": false}, {\"check\": \"regression risk-neutral probability 2\", \"actual\": 84.326751, \"expected\": 24.159122, \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": 1e-06, \"expected\": 0.003613, \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": 4.073107, \"expected\": 4.073107, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 1.438421, \"expected\": 1.438421, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": 10.0, \"expected\": 10.0, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": 8.771401, \"expected\": 8.771401, \"passed\": true}, {\"check\": \"normal control 5\", \"actual\": 10.332348, \"expected\": 10.332348, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":40.449,"exit_code":1,"observations":[{"actual":6.658986,"check":"regression risk-neutral probability 1","expected":6.658835,"passed":false},{"actual":24.152308,"check":"regression risk-neutral probability 2","expected":24.159122,"passed":false},{"actual":0.003613,"check":"partial repair probe 1","expected":0.003613,"passed":true},{"actual":4.073107,"check":"normal control 1","expected":4.073107,"passed":true},{"actual":1.438421,"check":"normal control 2","expected":1.438421,"passed":true},{"actual":10.0,"check":"normal control 3","expected":10.0,"passed":true},{"actual":8.771401,"check":"normal control 4","expected":8.771401,"passed":true},{"actual":10.332348,"check":"normal control 5","expected":10.332348,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression risk-neutral probability 1\", \"actual\": 6.658986, \"expected\": 6.658835, \"passed\": false}, {\"check\": \"regression risk-neutral probability 2\", \"actual\": 24.152308, \"expected\": 24.159122, \"passed\": false}, {\"check\": \"partial repair probe 1\", \"actual\": 0.003613, \"expected\": 0.003613, \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": 4.073107, \"expected\": 4.073107, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 1.438421, \"expected\": 1.438421, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": 10.0, \"expected\": 10.0, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": 8.771401, \"expected\": 8.771401, \"passed\": true}, {\"check\": \"normal control 5\", \"actual\": 10.332348, \"expected\": 10.332348, \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":39.61,"exit_code":0,"observations":[{"actual":6.658835,"check":"regression risk-neutral probability 1","expected":6.658835,"passed":true},{"actual":24.159122,"check":"regression risk-neutral probability 2","expected":24.159122,"passed":true},{"actual":0.003613,"check":"partial repair probe 1","expected":0.003613,"passed":true},{"actual":4.073107,"check":"normal control 1","expected":4.073107,"passed":true},{"actual":1.438421,"check":"normal control 2","expected":1.438421,"passed":true},{"actual":10.0,"check":"normal control 3","expected":10.0,"passed":true},{"actual":8.771401,"check":"normal control 4","expected":8.771401,"passed":true},{"actual":10.332348,"check":"normal control 5","expected":10.332348,"passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression risk-neutral probability 1\", \"actual\": 6.658835, \"expected\": 6.658835, \"passed\": true}, {\"check\": \"regression risk-neutral probability 2\", \"actual\": 24.159122, \"expected\": 24.159122, \"passed\": true}, {\"check\": \"partial repair probe 1\", \"actual\": 0.003613, \"expected\": 0.003613, \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": 4.073107, \"expected\": 4.073107, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": 1.438421, \"expected\": 1.438421, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": 10.0, \"expected\": 10.0, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": 8.771401, \"expected\": 8.771401, \"passed\": true}, {\"check\": \"normal control 5\", \"actual\": 10.332348, \"expected\": 10.332348, \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}