FA-61601 / Options payoff and settlement / Open access
Listed option expiration date calendar: a quarter-end month maps to the next quarter · case 01
March, June and September quarterly contracts expire three months late.
ROOT CAUSE
The quarter month is computed from m // 3 instead of (m - 1) // 3.
VERIFIED REPAIR
Map months 1-3 to March, 4-6 to June and so on.
Unsuccessful approach: Clamping the result at December fixes only the December overflow.
Case contract
Inputs year, month, kind and holidays. Monthly expiry is the third Friday of the month; quarterly expiry is the last calendar day of the quarter-end month (Mar/Jun/Sep/Dec) containing the month. Either date rolls back to the preceding business day (weekday not in holidays) when it is not a business day. Return [y,m,d].
Why this case matters
Option expiry, exercise and settlement engines move cash and shares; a wrong branch misstates obligations.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
N = 1
observations = []
def solve(y, m, kind, holidays):
H = {datetime.date(*h) for h in holidays}
def biz(x):
return x.weekday() < 5 and x not in H
def mlen(yy, mm):
if mm == 2:
return 29 if (yy % 4 == 0 and yy % 100 != 0) or yy % 400 == 0 else 28
return 30 if mm in (4, 6, 9, 11) else 31
if kind == 'quarterly':
qm = (m // 3 + 1) * 3
x = datetime.date(y, qm, mlen(y, qm))
else:
first = datetime.date(y, m, 1)
offset = (4 - first.weekday()) % 7
x = first + datetime.timedelta(days=offset + 14)
while not biz(x):
x -= datetime.timedelta(days=1)
return [x.year, x.month, x.day]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression quarter month mapping 1', [2021, 3, 'quarterly', [[2021, 3, 29]]], [2021, 3, 31]], ['regression quarter month mapping 2', [2026, 6, 'quarterly', [[2026, 6, 19], [2026, 6, 18]]], [2026, 6, 30]], ['partial repair probe 1', [2030, 9, 'quarterly', []], [2030, 9, 30]], ['partial repair probe 2', [2025, 9, 'quarterly', []], [2025, 9, 30]], ['boundary control 1', [2024, 3, 'monthly', []], [2024, 3, 15]], ['boundary control 2', [2024, 6, 'monthly', []], [2024, 6, 21]], ['normal control 1', [2031, 3, 'monthly', []], [2031, 3, 21]], ['normal control 2', [2024, 3, 'monthly', [[2024, 3, 15], [2024, 3, 14]]], [2024, 3, 13]]], [['regression quarter month mapping 1', [2030, 3, 'quarterly', []], [2030, 3, 29]], ['regression quarter month mapping 2', [2027, 6, 'quarterly', [[2027, 6, 27]]], [2027, 6, 30]], ['partial repair probe 1', [2020, 6, 'quarterly', [[2020, 6, 19], [2020, 6, 18]]], [2020, 6, 30]], ['partial repair probe 2', [2029, 3, 'quarterly', [[2029, 3, 28]]], [2029, 3, 30]], ['boundary control 1', [2024, 6, 'monthly', []], [2024, 6, 21]], ['boundary control 2', [2024, 3, 'monthly', []], [2024, 3, 15]], ['normal control 1', [2028, 11, 'monthly', [[2028, 11, 17], [2028, 11, 16]]], [2028, 11, 15]], ['normal control 2', [2030, 11, 'quarterly', []], [2030, 12, 31]]], [['regression quarter month mapping 1', [2026, 9, 'quarterly', []], [2026, 9, 30]], ['regression quarter month mapping 2', [2027, 6, 'quarterly', [[2027, 6, 18], [2027, 6, 28]]], [2027, 6, 30]], ['partial repair probe 1', [2030, 9, 'quarterly', []], [2030, 9, 30]], ['partial repair probe 2', [2029, 9, 'quarterly', [[2029, 9, 21], [2029, 9, 28]]], [2029, 9, 27]], ['boundary control 1', [2024, 6, 'monthly', []], [2024, 6, 21]], ['boundary control 2', [2024, 3, 'monthly', []], [2024, 3, 15]], ['normal control 1', [2029, 8, 'monthly', []], [2029, 8, 17]], ['normal control 2', [2022, 4, 'monthly', [[2022, 4, 15]]], [2022, 4, 14]]], [['regression quarter month mapping 1', [2031, 9, 'quarterly', [[2031, 9, 27]]], [2031, 9, 30]], ['regression quarter month mapping 2', [2032, 3, 'quarterly', [[2032, 3, 28]]], [2032, 3, 31]], ['partial repair probe 1', [2029, 6, 'quarterly', []], [2029, 6, 29]], ['partial repair probe 2', [2027, 9, 'quarterly', [[2027, 9, 17], [2027, 9, 27]]], [2027, 9, 30]], ['boundary control 1', [2024, 6, 'monthly', []], [2024, 6, 21]], ['boundary control 2', [2024, 3, 'monthly', []], [2024, 3, 15]], ['normal control 1', [2023, 3, 'monthly', []], [2023, 3, 17]], ['normal control 2', [2032, 5, 'quarterly', [[2032, 6, 28]]], [2032, 6, 30]]], [['regression quarter month mapping 1', [2029, 3, 'quarterly', [[2029, 3, 16], [2029, 3, 30]]], [2029, 3, 29]], ['regression quarter month mapping 2', [2029, 3, 'quarterly', []], [2029, 3, 30]], ['partial repair probe 1', [2029, 3, 'quarterly', [[2029, 3, 16], [2029, 3, 15], [2029, 3, 30]]], [2029, 3, 29]], ['partial repair probe 2', [2019, 9, 'quarterly', [[2019, 9, 20], [2019, 9, 30]]], [2019, 9, 27]], ['boundary control 1', [2024, 3, 'monthly', []], [2024, 3, 15]], ['boundary control 2', [2024, 6, 'monthly', []], [2024, 6, 21]], ['normal control 1', [2020, 7, 'quarterly', [[2020, 9, 30]]], [2020, 9, 29]], ['normal control 2', [2026, 9, 'monthly', [[2026, 9, 18]]], [2026, 9, 17]]]]
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 quarter month mapping 1 | [2021, 6, 30] | [2021, 3, 31] | Failed |
| regression quarter month mapping 2 | [2026, 9, 30] | [2026, 6, 30] | Failed |
| partial repair probe 1 | [2030, 12, 31] | [2030, 9, 30] | Failed |
| partial repair probe 2 | [2025, 12, 31] | [2025, 9, 30] | Failed |
| boundary control 1 | [2024, 3, 15] | [2024, 3, 15] | Passed |
| boundary control 2 | [2024, 6, 21] | [2024, 6, 21] | Passed |
| normal control 1 | [2031, 3, 21] | [2031, 3, 21] | Passed |
| normal control 2 | [2024, 3, 13] | [2024, 3, 13] | Passed |
SHA-256 / 9b58c2bc3dcc93fc07adc5983b7942494ed8970132e74088f1d6927dff23b6f9
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
N = 1
observations = []
def solve(y, m, kind, holidays):
H = {datetime.date(*h) for h in holidays}
def biz(x):
return x.weekday() < 5 and x not in H
def mlen(yy, mm):
if mm == 2:
return 29 if (yy % 4 == 0 and yy % 100 != 0) or yy % 400 == 0 else 28
return 30 if mm in (4, 6, 9, 11) else 31
if kind == 'quarterly':
qm = min(12, (m // 3 + 1) * 3)
x = datetime.date(y, qm, mlen(y, qm))
else:
first = datetime.date(y, m, 1)
offset = (4 - first.weekday()) % 7
x = first + datetime.timedelta(days=offset + 14)
while not biz(x):
x -= datetime.timedelta(days=1)
return [x.year, x.month, x.day]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression quarter month mapping 1', [2021, 3, 'quarterly', [[2021, 3, 29]]], [2021, 3, 31]], ['regression quarter month mapping 2', [2026, 6, 'quarterly', [[2026, 6, 19], [2026, 6, 18]]], [2026, 6, 30]], ['partial repair probe 1', [2030, 9, 'quarterly', []], [2030, 9, 30]], ['partial repair probe 2', [2025, 9, 'quarterly', []], [2025, 9, 30]], ['boundary control 1', [2024, 3, 'monthly', []], [2024, 3, 15]], ['boundary control 2', [2024, 6, 'monthly', []], [2024, 6, 21]], ['normal control 1', [2031, 3, 'monthly', []], [2031, 3, 21]], ['normal control 2', [2024, 3, 'monthly', [[2024, 3, 15], [2024, 3, 14]]], [2024, 3, 13]]], [['regression quarter month mapping 1', [2030, 3, 'quarterly', []], [2030, 3, 29]], ['regression quarter month mapping 2', [2027, 6, 'quarterly', [[2027, 6, 27]]], [2027, 6, 30]], ['partial repair probe 1', [2020, 6, 'quarterly', [[2020, 6, 19], [2020, 6, 18]]], [2020, 6, 30]], ['partial repair probe 2', [2029, 3, 'quarterly', [[2029, 3, 28]]], [2029, 3, 30]], ['boundary control 1', [2024, 6, 'monthly', []], [2024, 6, 21]], ['boundary control 2', [2024, 3, 'monthly', []], [2024, 3, 15]], ['normal control 1', [2028, 11, 'monthly', [[2028, 11, 17], [2028, 11, 16]]], [2028, 11, 15]], ['normal control 2', [2030, 11, 'quarterly', []], [2030, 12, 31]]], [['regression quarter month mapping 1', [2026, 9, 'quarterly', []], [2026, 9, 30]], ['regression quarter month mapping 2', [2027, 6, 'quarterly', [[2027, 6, 18], [2027, 6, 28]]], [2027, 6, 30]], ['partial repair probe 1', [2030, 9, 'quarterly', []], [2030, 9, 30]], ['partial repair probe 2', [2029, 9, 'quarterly', [[2029, 9, 21], [2029, 9, 28]]], [2029, 9, 27]], ['boundary control 1', [2024, 6, 'monthly', []], [2024, 6, 21]], ['boundary control 2', [2024, 3, 'monthly', []], [2024, 3, 15]], ['normal control 1', [2029, 8, 'monthly', []], [2029, 8, 17]], ['normal control 2', [2022, 4, 'monthly', [[2022, 4, 15]]], [2022, 4, 14]]], [['regression quarter month mapping 1', [2031, 9, 'quarterly', [[2031, 9, 27]]], [2031, 9, 30]], ['regression quarter month mapping 2', [2032, 3, 'quarterly', [[2032, 3, 28]]], [2032, 3, 31]], ['partial repair probe 1', [2029, 6, 'quarterly', []], [2029, 6, 29]], ['partial repair probe 2', [2027, 9, 'quarterly', [[2027, 9, 17], [2027, 9, 27]]], [2027, 9, 30]], ['boundary control 1', [2024, 6, 'monthly', []], [2024, 6, 21]], ['boundary control 2', [2024, 3, 'monthly', []], [2024, 3, 15]], ['normal control 1', [2023, 3, 'monthly', []], [2023, 3, 17]], ['normal control 2', [2032, 5, 'quarterly', [[2032, 6, 28]]], [2032, 6, 30]]], [['regression quarter month mapping 1', [2029, 3, 'quarterly', [[2029, 3, 16], [2029, 3, 30]]], [2029, 3, 29]], ['regression quarter month mapping 2', [2029, 3, 'quarterly', []], [2029, 3, 30]], ['partial repair probe 1', [2029, 3, 'quarterly', [[2029, 3, 16], [2029, 3, 15], [2029, 3, 30]]], [2029, 3, 29]], ['partial repair probe 2', [2019, 9, 'quarterly', [[2019, 9, 20], [2019, 9, 30]]], [2019, 9, 27]], ['boundary control 1', [2024, 3, 'monthly', []], [2024, 3, 15]], ['boundary control 2', [2024, 6, 'monthly', []], [2024, 6, 21]], ['normal control 1', [2020, 7, 'quarterly', [[2020, 9, 30]]], [2020, 9, 29]], ['normal control 2', [2026, 9, 'monthly', [[2026, 9, 18]]], [2026, 9, 17]]]]
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 quarter month mapping 1 | [2021, 6, 30] | [2021, 3, 31] | Failed |
| regression quarter month mapping 2 | [2026, 9, 30] | [2026, 6, 30] | Failed |
| partial repair probe 1 | [2030, 12, 31] | [2030, 9, 30] | Failed |
| partial repair probe 2 | [2025, 12, 31] | [2025, 9, 30] | Failed |
| boundary control 1 | [2024, 3, 15] | [2024, 3, 15] | Passed |
| boundary control 2 | [2024, 6, 21] | [2024, 6, 21] | Passed |
| normal control 1 | [2031, 3, 21] | [2031, 3, 21] | Passed |
| normal control 2 | [2024, 3, 13] | [2024, 3, 13] | Passed |
SHA-256 / dd5666a1aa0336178aa7c01f413a572ee154ba168f706888be3913e05c6f9c9f
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
N = 1
observations = []
def solve(y, m, kind, holidays):
H = {datetime.date(*h) for h in holidays}
def biz(x):
return x.weekday() < 5 and x not in H
def mlen(yy, mm):
if mm == 2:
return 29 if (yy % 4 == 0 and yy % 100 != 0) or yy % 400 == 0 else 28
return 30 if mm in (4, 6, 9, 11) else 31
if kind == 'quarterly':
qm = ((m - 1) // 3 + 1) * 3
x = datetime.date(y, qm, mlen(y, qm))
else:
first = datetime.date(y, m, 1)
offset = (4 - first.weekday()) % 7
x = first + datetime.timedelta(days=offset + 14)
while not biz(x):
x -= datetime.timedelta(days=1)
return [x.year, x.month, x.day]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression quarter month mapping 1', [2021, 3, 'quarterly', [[2021, 3, 29]]], [2021, 3, 31]], ['regression quarter month mapping 2', [2026, 6, 'quarterly', [[2026, 6, 19], [2026, 6, 18]]], [2026, 6, 30]], ['partial repair probe 1', [2030, 9, 'quarterly', []], [2030, 9, 30]], ['partial repair probe 2', [2025, 9, 'quarterly', []], [2025, 9, 30]], ['boundary control 1', [2024, 3, 'monthly', []], [2024, 3, 15]], ['boundary control 2', [2024, 6, 'monthly', []], [2024, 6, 21]], ['normal control 1', [2031, 3, 'monthly', []], [2031, 3, 21]], ['normal control 2', [2024, 3, 'monthly', [[2024, 3, 15], [2024, 3, 14]]], [2024, 3, 13]]], [['regression quarter month mapping 1', [2030, 3, 'quarterly', []], [2030, 3, 29]], ['regression quarter month mapping 2', [2027, 6, 'quarterly', [[2027, 6, 27]]], [2027, 6, 30]], ['partial repair probe 1', [2020, 6, 'quarterly', [[2020, 6, 19], [2020, 6, 18]]], [2020, 6, 30]], ['partial repair probe 2', [2029, 3, 'quarterly', [[2029, 3, 28]]], [2029, 3, 30]], ['boundary control 1', [2024, 6, 'monthly', []], [2024, 6, 21]], ['boundary control 2', [2024, 3, 'monthly', []], [2024, 3, 15]], ['normal control 1', [2028, 11, 'monthly', [[2028, 11, 17], [2028, 11, 16]]], [2028, 11, 15]], ['normal control 2', [2030, 11, 'quarterly', []], [2030, 12, 31]]], [['regression quarter month mapping 1', [2026, 9, 'quarterly', []], [2026, 9, 30]], ['regression quarter month mapping 2', [2027, 6, 'quarterly', [[2027, 6, 18], [2027, 6, 28]]], [2027, 6, 30]], ['partial repair probe 1', [2030, 9, 'quarterly', []], [2030, 9, 30]], ['partial repair probe 2', [2029, 9, 'quarterly', [[2029, 9, 21], [2029, 9, 28]]], [2029, 9, 27]], ['boundary control 1', [2024, 6, 'monthly', []], [2024, 6, 21]], ['boundary control 2', [2024, 3, 'monthly', []], [2024, 3, 15]], ['normal control 1', [2029, 8, 'monthly', []], [2029, 8, 17]], ['normal control 2', [2022, 4, 'monthly', [[2022, 4, 15]]], [2022, 4, 14]]], [['regression quarter month mapping 1', [2031, 9, 'quarterly', [[2031, 9, 27]]], [2031, 9, 30]], ['regression quarter month mapping 2', [2032, 3, 'quarterly', [[2032, 3, 28]]], [2032, 3, 31]], ['partial repair probe 1', [2029, 6, 'quarterly', []], [2029, 6, 29]], ['partial repair probe 2', [2027, 9, 'quarterly', [[2027, 9, 17], [2027, 9, 27]]], [2027, 9, 30]], ['boundary control 1', [2024, 6, 'monthly', []], [2024, 6, 21]], ['boundary control 2', [2024, 3, 'monthly', []], [2024, 3, 15]], ['normal control 1', [2023, 3, 'monthly', []], [2023, 3, 17]], ['normal control 2', [2032, 5, 'quarterly', [[2032, 6, 28]]], [2032, 6, 30]]], [['regression quarter month mapping 1', [2029, 3, 'quarterly', [[2029, 3, 16], [2029, 3, 30]]], [2029, 3, 29]], ['regression quarter month mapping 2', [2029, 3, 'quarterly', []], [2029, 3, 30]], ['partial repair probe 1', [2029, 3, 'quarterly', [[2029, 3, 16], [2029, 3, 15], [2029, 3, 30]]], [2029, 3, 29]], ['partial repair probe 2', [2019, 9, 'quarterly', [[2019, 9, 20], [2019, 9, 30]]], [2019, 9, 27]], ['boundary control 1', [2024, 3, 'monthly', []], [2024, 3, 15]], ['boundary control 2', [2024, 6, 'monthly', []], [2024, 6, 21]], ['normal control 1', [2020, 7, 'quarterly', [[2020, 9, 30]]], [2020, 9, 29]], ['normal control 2', [2026, 9, 'monthly', [[2026, 9, 18]]], [2026, 9, 17]]]]
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 quarter month mapping 1 | [2021, 3, 31] | [2021, 3, 31] | Passed |
| regression quarter month mapping 2 | [2026, 6, 30] | [2026, 6, 30] | Passed |
| partial repair probe 1 | [2030, 9, 30] | [2030, 9, 30] | Passed |
| partial repair probe 2 | [2025, 9, 30] | [2025, 9, 30] | Passed |
| boundary control 1 | [2024, 3, 15] | [2024, 3, 15] | Passed |
| boundary control 2 | [2024, 6, 21] | [2024, 6, 21] | Passed |
| normal control 1 | [2031, 3, 21] | [2031, 3, 21] | Passed |
| normal control 2 | [2024, 3, 13] | [2024, 3, 13] | Passed |
SHA-256 / 5cf8a421a3dd94b9045cc63732a2277c7be09695e4bf4d782b74b742decac770
Verification & scope
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.
Observations recorded using Python 3.12.14 at 2026-09-29T14:46:56.789011+00:00.
Case digest / 4a547f2a87554cded586364c7c10584a1ed2767e634f1a20116f37ad3c300798