FA-61336 / Bond day-count conventions / Open access

Deposit maturity with the end-to-end rule: the end-to-end maturity is the calendar month end · case 01

Maturities fall on weekends and holidays at month end.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The target is the calendar last day of the target month without business-day adjustment.

VERIFIED REPAIR

Mature on the last business day of the target month.

Unsuccessful approach: Stepping back only over weekends ignores month-end holidays.

Case contract

Inputs a start business day, tenor in months, holidays, principal and rate. If the start is the last business day of its month, maturity is the last business day of the target month; otherwise maturity is the start day clamped into the target month and adjusted modified following. Interest = principal*rate*days/360 rounded to 2. Return [maturity, interest].

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(start, tenor, holidays, principal, rate):
    S = datetime.date(*start)
    H = {datetime.date(*h) for h in holidays}
    def biz(x):
        return x.weekday() < 5 and x not in H
    def mlen(y, m):
        if m == 2:
            return 29 if (y % 4 == 0 and y % 100 != 0) or y % 400 == 0 else 28
        return 30 if m in (4, 6, 9, 11) else 31
    def last_biz(y, m):
        x = datetime.date(y, m, mlen(y, m))
        while not biz(x):
            x -= datetime.timedelta(days=1)
        return x
    def mf(x):
        f = x
        while not biz(f):
            f += datetime.timedelta(days=1)
        if f.month != x.month:
            f = x
            while not biz(f):
                f -= datetime.timedelta(days=1)
        return f
    t = S.year * 12 + S.month - 1 + tenor
    y, m = t // 12, t % 12 + 1
    if S == last_biz(S.year, S.month):
        M = datetime.date(y, m, mlen(y, m))
    else:
        M = mf(datetime.date(y, m, min(S.day, mlen(y, m))))
    interest = round(principal * rate * (M - S).days / 360, 2)
    return [[M.year, M.month, M.day], interest]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression end-end target 1', [[2024, 5, 31], 6, [[2024, 11, 30], [2024, 11, 28], [2024, 11, 27]], 1000000, 0.0125], [[2024, 11, 29], 6319.44]], ['regression end-end target 2', [[2022, 6, 30], 1, [[2022, 7, 29], [2022, 7, 27], [2022, 7, 29]], 250000, 0.053], [[2022, 7, 28], 1030.56]], ['partial repair probe 1', [[2019, 2, 28], 2, [[2019, 4, 30], [2019, 4, 26], [2019, 4, 26]], 1000000, 0.053], [[2019, 4, 29], 8833.33]], ['partial repair probe 2', [[2024, 12, 31], 3, [[2025, 3, 30], [2025, 3, 31]], 1000000, 0.053], [[2025, 3, 28], 12808.33]], ['normal control 1', [[2025, 3, 17], 2, [[2025, 5, 28], [2025, 5, 27]], 250000, 0.0425], [[2025, 5, 19], 1859.38]], ['normal control 2', [[2024, 3, 4], 2, [[2024, 5, 30], [2024, 5, 27], [2024, 5, 31]], 250000, 0.0125], [[2024, 5, 6], 546.88]], ['normal control 3', [[2023, 7, 7], 3, [[2023, 10, 29], [2023, 10, 30], [2023, 10, 28]], 1000000, 0.0425], [[2023, 10, 9], 11097.22]], ['normal control 4', [[2025, 5, 30], 1, [[2025, 6, 27]], 250000, 0.053], [[2025, 6, 30], 1140.97]]], [['regression end-end target 1', [[2025, 12, 31], 1, [], 1000000, 0.0125], [[2026, 1, 30], 1041.67]], ['regression end-end target 2', [[2019, 3, 29], 3, [[2019, 6, 27]], 1000000, 0.0425], [[2019, 6, 28], 10743.06]], ['partial repair probe 1', [[2029, 11, 30], 1, [[2029, 12, 31]], 250000, 0.0125], [[2029, 12, 28], 243.06]], ['partial repair probe 2', [[2030, 4, 30], 12, [[2031, 4, 27], [2031, 4, 30], [2031, 4, 27]], 1000000, 0.0125], [[2031, 4, 29], 12638.89]], ['normal control 1', [[2021, 5, 14], 3, [[2021, 8, 31]], 250000, 0.0425], [[2021, 8, 16], 2774.31]], ['normal control 2', [[2019, 2, 27], 2, [[2019, 4, 30], [2019, 4, 29]], 250000, 0.053], [[2019, 4, 26], 2134.72]], ['normal control 3', [[2024, 1, 31], 12, [[2025, 1, 29], [2025, 1, 29], [2025, 1, 30]], 1000000, 0.053], [[2025, 1, 31], 53883.33]], ['normal control 4', [[2027, 2, 26], 6, [[2027, 8, 29]], 250000, 0.053], [[2027, 8, 31], 6845.83]]], [['regression end-end target 1', [[2030, 3, 29], 1, [[2030, 4, 30], [2030, 4, 27], [2030, 4, 30]], 1000000, 0.0425], [[2030, 4, 29], 3659.72]], ['regression end-end target 2', [[2026, 4, 30], 1, [], 250000, 0.053], [[2026, 5, 29], 1067.36]], ['partial repair probe 1', [[2027, 7, 30], 3, [[2027, 10, 29], [2027, 10, 29]], 250000, 0.0425], [[2027, 10, 28], 2656.25]], ['partial repair probe 2', [[2025, 6, 30], 2, [[2025, 8, 31], [2025, 8, 29], [2025, 8, 31]], 250000, 0.0425], [[2025, 8, 28], 1741.32]], ['normal control 1', [[2030, 6, 17], 1, [], 250000, 0.0425], [[2030, 7, 17], 885.42]], ['normal control 2', [[2023, 7, 12], 1, [], 1000000, 0.0125], [[2023, 8, 14], 1145.83]], ['normal control 3', [[2021, 8, 16], 1, [[2021, 9, 26], [2021, 9, 29], [2021, 9, 26]], 1000000, 0.053], [[2021, 9, 16], 4563.89]], ['normal control 4', [[2027, 6, 17], 2, [[2027, 8, 28], [2027, 8, 27], [2027, 8, 29]], 250000, 0.0125], [[2027, 8, 17], 529.51]]], [['regression end-end target 1', [[2028, 3, 31], 6, [[2028, 9, 30], [2028, 9, 30], [2028, 9, 29]], 1000000, 0.0425], [[2028, 9, 28], 21368.06]], ['regression end-end target 2', [[2029, 8, 31], 6, [[2030, 2, 28]], 250000, 0.053], [[2030, 2, 27], 6625.0]], ['partial repair probe 1', [[2030, 10, 31], 3, [[2031, 1, 31], [2031, 1, 27], [2031, 1, 31]], 1000000, 0.0425], [[2031, 1, 30], 10743.06]], ['partial repair probe 2', [[2020, 11, 30], 2, [[2021, 1, 29], [2021, 1, 29], [2021, 1, 30]], 1000000, 0.0425], [[2021, 1, 28], 6965.28]], ['normal control 1', [[2024, 1, 15], 3, [], 250000, 0.0125], [[2024, 4, 15], 789.93]], ['normal control 2', [[2022, 10, 20], 6, [[2023, 4, 30], [2023, 4, 29]], 250000, 0.053], [[2023, 4, 20], 6698.61]], ['normal control 3', [[2027, 9, 21], 12, [[2028, 9, 26]], 250000, 0.053], [[2028, 9, 21], 13470.83]], ['normal control 4', [[2021, 3, 3], 12, [[2022, 3, 30], [2022, 3, 29], [2022, 3, 29]], 250000, 0.0425], [[2022, 3, 3], 10772.57]]], [['regression end-end target 1', [[2020, 12, 31], 1, [[2021, 1, 28], [2021, 1, 28], [2021, 1, 28]], 1000000, 0.0125], [[2021, 1, 29], 1006.94]], ['regression end-end target 2', [[2024, 4, 30], 2, [[2024, 6, 30], [2024, 6, 26], [2024, 6, 28]], 250000, 0.053], [[2024, 6, 27], 2134.72]], ['partial repair probe 1', [[2022, 7, 29], 3, [[2022, 10, 31], [2022, 10, 31], [2022, 10, 30]], 250000, 0.053], [[2022, 10, 28], 3349.31]], ['partial repair probe 2', [[2022, 2, 28], 1, [[2022, 3, 31], [2022, 3, 29]], 1000000, 0.0125], [[2022, 3, 30], 1041.67]], ['normal control 1', [[2020, 11, 10], 2, [[2021, 1, 27], [2021, 1, 28]], 1000000, 0.0425], [[2021, 1, 11], 7319.44]], ['normal control 2', [[2021, 5, 31], 1, [], 250000, 0.053], [[2021, 6, 30], 1104.17]], ['normal control 3', [[2029, 10, 22], 6, [], 1000000, 0.0125], [[2030, 4, 22], 6319.44]], ['normal control 4', [[2027, 4, 2], 2, [], 1000000, 0.0125], [[2027, 6, 2], 2118.06]]]]
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 end-end target 1	`[[2024, 11, 30], 6354.17]`	`[[2024, 11, 29], 6319.44]`	Failed
regression end-end target 2	`[[2022, 7, 31], 1140.97]`	`[[2022, 7, 28], 1030.56]`	Failed
partial repair probe 1	`[[2019, 4, 30], 8980.56]`	`[[2019, 4, 29], 8833.33]`	Failed
partial repair probe 2	`[[2025, 3, 31], 13250.0]`	`[[2025, 3, 28], 12808.33]`	Failed
normal control 1	`[[2025, 5, 19], 1859.38]`	`[[2025, 5, 19], 1859.38]`	Passed
normal control 2	`[[2024, 5, 6], 546.88]`	`[[2024, 5, 6], 546.88]`	Passed
normal control 3	`[[2023, 10, 9], 11097.22]`	`[[2023, 10, 9], 11097.22]`	Passed
normal control 4	`[[2025, 6, 30], 1140.97]`	`[[2025, 6, 30], 1140.97]`	Passed

SHA-256 / f493b80e020840c9e638517ce001c6516970bb8577c42811f32b17a300fc2aad

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
N = 1
observations = []
def solve(start, tenor, holidays, principal, rate):
    S = datetime.date(*start)
    H = {datetime.date(*h) for h in holidays}
    def biz(x):
        return x.weekday() < 5 and x not in H
    def mlen(y, m):
        if m == 2:
            return 29 if (y % 4 == 0 and y % 100 != 0) or y % 400 == 0 else 28
        return 30 if m in (4, 6, 9, 11) else 31
    def last_biz(y, m):
        x = datetime.date(y, m, mlen(y, m))
        while not biz(x):
            x -= datetime.timedelta(days=1)
        return x
    def mf(x):
        f = x
        while not biz(f):
            f += datetime.timedelta(days=1)
        if f.month != x.month:
            f = x
            while not biz(f):
                f -= datetime.timedelta(days=1)
        return f
    t = S.year * 12 + S.month - 1 + tenor
    y, m = t // 12, t % 12 + 1
    if S == last_biz(S.year, S.month):
        M = datetime.date(y, m, mlen(y, m))
        while M.weekday() >= 5:
            M -= datetime.timedelta(days=1)
    else:
        M = mf(datetime.date(y, m, min(S.day, mlen(y, m))))
    interest = round(principal * rate * (M - S).days / 360, 2)
    return [[M.year, M.month, M.day], interest]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression end-end target 1', [[2024, 5, 31], 6, [[2024, 11, 30], [2024, 11, 28], [2024, 11, 27]], 1000000, 0.0125], [[2024, 11, 29], 6319.44]], ['regression end-end target 2', [[2022, 6, 30], 1, [[2022, 7, 29], [2022, 7, 27], [2022, 7, 29]], 250000, 0.053], [[2022, 7, 28], 1030.56]], ['partial repair probe 1', [[2019, 2, 28], 2, [[2019, 4, 30], [2019, 4, 26], [2019, 4, 26]], 1000000, 0.053], [[2019, 4, 29], 8833.33]], ['partial repair probe 2', [[2024, 12, 31], 3, [[2025, 3, 30], [2025, 3, 31]], 1000000, 0.053], [[2025, 3, 28], 12808.33]], ['normal control 1', [[2025, 3, 17], 2, [[2025, 5, 28], [2025, 5, 27]], 250000, 0.0425], [[2025, 5, 19], 1859.38]], ['normal control 2', [[2024, 3, 4], 2, [[2024, 5, 30], [2024, 5, 27], [2024, 5, 31]], 250000, 0.0125], [[2024, 5, 6], 546.88]], ['normal control 3', [[2023, 7, 7], 3, [[2023, 10, 29], [2023, 10, 30], [2023, 10, 28]], 1000000, 0.0425], [[2023, 10, 9], 11097.22]], ['normal control 4', [[2025, 5, 30], 1, [[2025, 6, 27]], 250000, 0.053], [[2025, 6, 30], 1140.97]]], [['regression end-end target 1', [[2025, 12, 31], 1, [], 1000000, 0.0125], [[2026, 1, 30], 1041.67]], ['regression end-end target 2', [[2019, 3, 29], 3, [[2019, 6, 27]], 1000000, 0.0425], [[2019, 6, 28], 10743.06]], ['partial repair probe 1', [[2029, 11, 30], 1, [[2029, 12, 31]], 250000, 0.0125], [[2029, 12, 28], 243.06]], ['partial repair probe 2', [[2030, 4, 30], 12, [[2031, 4, 27], [2031, 4, 30], [2031, 4, 27]], 1000000, 0.0125], [[2031, 4, 29], 12638.89]], ['normal control 1', [[2021, 5, 14], 3, [[2021, 8, 31]], 250000, 0.0425], [[2021, 8, 16], 2774.31]], ['normal control 2', [[2019, 2, 27], 2, [[2019, 4, 30], [2019, 4, 29]], 250000, 0.053], [[2019, 4, 26], 2134.72]], ['normal control 3', [[2024, 1, 31], 12, [[2025, 1, 29], [2025, 1, 29], [2025, 1, 30]], 1000000, 0.053], [[2025, 1, 31], 53883.33]], ['normal control 4', [[2027, 2, 26], 6, [[2027, 8, 29]], 250000, 0.053], [[2027, 8, 31], 6845.83]]], [['regression end-end target 1', [[2030, 3, 29], 1, [[2030, 4, 30], [2030, 4, 27], [2030, 4, 30]], 1000000, 0.0425], [[2030, 4, 29], 3659.72]], ['regression end-end target 2', [[2026, 4, 30], 1, [], 250000, 0.053], [[2026, 5, 29], 1067.36]], ['partial repair probe 1', [[2027, 7, 30], 3, [[2027, 10, 29], [2027, 10, 29]], 250000, 0.0425], [[2027, 10, 28], 2656.25]], ['partial repair probe 2', [[2025, 6, 30], 2, [[2025, 8, 31], [2025, 8, 29], [2025, 8, 31]], 250000, 0.0425], [[2025, 8, 28], 1741.32]], ['normal control 1', [[2030, 6, 17], 1, [], 250000, 0.0425], [[2030, 7, 17], 885.42]], ['normal control 2', [[2023, 7, 12], 1, [], 1000000, 0.0125], [[2023, 8, 14], 1145.83]], ['normal control 3', [[2021, 8, 16], 1, [[2021, 9, 26], [2021, 9, 29], [2021, 9, 26]], 1000000, 0.053], [[2021, 9, 16], 4563.89]], ['normal control 4', [[2027, 6, 17], 2, [[2027, 8, 28], [2027, 8, 27], [2027, 8, 29]], 250000, 0.0125], [[2027, 8, 17], 529.51]]], [['regression end-end target 1', [[2028, 3, 31], 6, [[2028, 9, 30], [2028, 9, 30], [2028, 9, 29]], 1000000, 0.0425], [[2028, 9, 28], 21368.06]], ['regression end-end target 2', [[2029, 8, 31], 6, [[2030, 2, 28]], 250000, 0.053], [[2030, 2, 27], 6625.0]], ['partial repair probe 1', [[2030, 10, 31], 3, [[2031, 1, 31], [2031, 1, 27], [2031, 1, 31]], 1000000, 0.0425], [[2031, 1, 30], 10743.06]], ['partial repair probe 2', [[2020, 11, 30], 2, [[2021, 1, 29], [2021, 1, 29], [2021, 1, 30]], 1000000, 0.0425], [[2021, 1, 28], 6965.28]], ['normal control 1', [[2024, 1, 15], 3, [], 250000, 0.0125], [[2024, 4, 15], 789.93]], ['normal control 2', [[2022, 10, 20], 6, [[2023, 4, 30], [2023, 4, 29]], 250000, 0.053], [[2023, 4, 20], 6698.61]], ['normal control 3', [[2027, 9, 21], 12, [[2028, 9, 26]], 250000, 0.053], [[2028, 9, 21], 13470.83]], ['normal control 4', [[2021, 3, 3], 12, [[2022, 3, 30], [2022, 3, 29], [2022, 3, 29]], 250000, 0.0425], [[2022, 3, 3], 10772.57]]], [['regression end-end target 1', [[2020, 12, 31], 1, [[2021, 1, 28], [2021, 1, 28], [2021, 1, 28]], 1000000, 0.0125], [[2021, 1, 29], 1006.94]], ['regression end-end target 2', [[2024, 4, 30], 2, [[2024, 6, 30], [2024, 6, 26], [2024, 6, 28]], 250000, 0.053], [[2024, 6, 27], 2134.72]], ['partial repair probe 1', [[2022, 7, 29], 3, [[2022, 10, 31], [2022, 10, 31], [2022, 10, 30]], 250000, 0.053], [[2022, 10, 28], 3349.31]], ['partial repair probe 2', [[2022, 2, 28], 1, [[2022, 3, 31], [2022, 3, 29]], 1000000, 0.0125], [[2022, 3, 30], 1041.67]], ['normal control 1', [[2020, 11, 10], 2, [[2021, 1, 27], [2021, 1, 28]], 1000000, 0.0425], [[2021, 1, 11], 7319.44]], ['normal control 2', [[2021, 5, 31], 1, [], 250000, 0.053], [[2021, 6, 30], 1104.17]], ['normal control 3', [[2029, 10, 22], 6, [], 1000000, 0.0125], [[2030, 4, 22], 6319.44]], ['normal control 4', [[2027, 4, 2], 2, [], 1000000, 0.0125], [[2027, 6, 2], 2118.06]]]]
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 end-end target 1	`[[2024, 11, 29], 6319.44]`	`[[2024, 11, 29], 6319.44]`	Passed
regression end-end target 2	`[[2022, 7, 29], 1067.36]`	`[[2022, 7, 28], 1030.56]`	Failed
partial repair probe 1	`[[2019, 4, 30], 8980.56]`	`[[2019, 4, 29], 8833.33]`	Failed
partial repair probe 2	`[[2025, 3, 31], 13250.0]`	`[[2025, 3, 28], 12808.33]`	Failed
normal control 1	`[[2025, 5, 19], 1859.38]`	`[[2025, 5, 19], 1859.38]`	Passed
normal control 2	`[[2024, 5, 6], 546.88]`	`[[2024, 5, 6], 546.88]`	Passed
normal control 3	`[[2023, 10, 9], 11097.22]`	`[[2023, 10, 9], 11097.22]`	Passed
normal control 4	`[[2025, 6, 30], 1140.97]`	`[[2025, 6, 30], 1140.97]`	Passed

SHA-256 / e8e13f9079ab97e5d77c4b425137fc9295d7bcec4947a92503058552cba17062

3 / The verified repair

Exit 0

"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
N = 1
observations = []
def solve(start, tenor, holidays, principal, rate):
    S = datetime.date(*start)
    H = {datetime.date(*h) for h in holidays}
    def biz(x):
        return x.weekday() < 5 and x not in H
    def mlen(y, m):
        if m == 2:
            return 29 if (y % 4 == 0 and y % 100 != 0) or y % 400 == 0 else 28
        return 30 if m in (4, 6, 9, 11) else 31
    def last_biz(y, m):
        x = datetime.date(y, m, mlen(y, m))
        while not biz(x):
            x -= datetime.timedelta(days=1)
        return x
    def mf(x):
        f = x
        while not biz(f):
            f += datetime.timedelta(days=1)
        if f.month != x.month:
            f = x
            while not biz(f):
                f -= datetime.timedelta(days=1)
        return f
    t = S.year * 12 + S.month - 1 + tenor
    y, m = t // 12, t % 12 + 1
    if S == last_biz(S.year, S.month):
        M = last_biz(y, m)
    else:
        M = mf(datetime.date(y, m, min(S.day, mlen(y, m))))
    interest = round(principal * rate * (M - S).days / 360, 2)
    return [[M.year, M.month, M.day], interest]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression end-end target 1', [[2024, 5, 31], 6, [[2024, 11, 30], [2024, 11, 28], [2024, 11, 27]], 1000000, 0.0125], [[2024, 11, 29], 6319.44]], ['regression end-end target 2', [[2022, 6, 30], 1, [[2022, 7, 29], [2022, 7, 27], [2022, 7, 29]], 250000, 0.053], [[2022, 7, 28], 1030.56]], ['partial repair probe 1', [[2019, 2, 28], 2, [[2019, 4, 30], [2019, 4, 26], [2019, 4, 26]], 1000000, 0.053], [[2019, 4, 29], 8833.33]], ['partial repair probe 2', [[2024, 12, 31], 3, [[2025, 3, 30], [2025, 3, 31]], 1000000, 0.053], [[2025, 3, 28], 12808.33]], ['normal control 1', [[2025, 3, 17], 2, [[2025, 5, 28], [2025, 5, 27]], 250000, 0.0425], [[2025, 5, 19], 1859.38]], ['normal control 2', [[2024, 3, 4], 2, [[2024, 5, 30], [2024, 5, 27], [2024, 5, 31]], 250000, 0.0125], [[2024, 5, 6], 546.88]], ['normal control 3', [[2023, 7, 7], 3, [[2023, 10, 29], [2023, 10, 30], [2023, 10, 28]], 1000000, 0.0425], [[2023, 10, 9], 11097.22]], ['normal control 4', [[2025, 5, 30], 1, [[2025, 6, 27]], 250000, 0.053], [[2025, 6, 30], 1140.97]]], [['regression end-end target 1', [[2025, 12, 31], 1, [], 1000000, 0.0125], [[2026, 1, 30], 1041.67]], ['regression end-end target 2', [[2019, 3, 29], 3, [[2019, 6, 27]], 1000000, 0.0425], [[2019, 6, 28], 10743.06]], ['partial repair probe 1', [[2029, 11, 30], 1, [[2029, 12, 31]], 250000, 0.0125], [[2029, 12, 28], 243.06]], ['partial repair probe 2', [[2030, 4, 30], 12, [[2031, 4, 27], [2031, 4, 30], [2031, 4, 27]], 1000000, 0.0125], [[2031, 4, 29], 12638.89]], ['normal control 1', [[2021, 5, 14], 3, [[2021, 8, 31]], 250000, 0.0425], [[2021, 8, 16], 2774.31]], ['normal control 2', [[2019, 2, 27], 2, [[2019, 4, 30], [2019, 4, 29]], 250000, 0.053], [[2019, 4, 26], 2134.72]], ['normal control 3', [[2024, 1, 31], 12, [[2025, 1, 29], [2025, 1, 29], [2025, 1, 30]], 1000000, 0.053], [[2025, 1, 31], 53883.33]], ['normal control 4', [[2027, 2, 26], 6, [[2027, 8, 29]], 250000, 0.053], [[2027, 8, 31], 6845.83]]], [['regression end-end target 1', [[2030, 3, 29], 1, [[2030, 4, 30], [2030, 4, 27], [2030, 4, 30]], 1000000, 0.0425], [[2030, 4, 29], 3659.72]], ['regression end-end target 2', [[2026, 4, 30], 1, [], 250000, 0.053], [[2026, 5, 29], 1067.36]], ['partial repair probe 1', [[2027, 7, 30], 3, [[2027, 10, 29], [2027, 10, 29]], 250000, 0.0425], [[2027, 10, 28], 2656.25]], ['partial repair probe 2', [[2025, 6, 30], 2, [[2025, 8, 31], [2025, 8, 29], [2025, 8, 31]], 250000, 0.0425], [[2025, 8, 28], 1741.32]], ['normal control 1', [[2030, 6, 17], 1, [], 250000, 0.0425], [[2030, 7, 17], 885.42]], ['normal control 2', [[2023, 7, 12], 1, [], 1000000, 0.0125], [[2023, 8, 14], 1145.83]], ['normal control 3', [[2021, 8, 16], 1, [[2021, 9, 26], [2021, 9, 29], [2021, 9, 26]], 1000000, 0.053], [[2021, 9, 16], 4563.89]], ['normal control 4', [[2027, 6, 17], 2, [[2027, 8, 28], [2027, 8, 27], [2027, 8, 29]], 250000, 0.0125], [[2027, 8, 17], 529.51]]], [['regression end-end target 1', [[2028, 3, 31], 6, [[2028, 9, 30], [2028, 9, 30], [2028, 9, 29]], 1000000, 0.0425], [[2028, 9, 28], 21368.06]], ['regression end-end target 2', [[2029, 8, 31], 6, [[2030, 2, 28]], 250000, 0.053], [[2030, 2, 27], 6625.0]], ['partial repair probe 1', [[2030, 10, 31], 3, [[2031, 1, 31], [2031, 1, 27], [2031, 1, 31]], 1000000, 0.0425], [[2031, 1, 30], 10743.06]], ['partial repair probe 2', [[2020, 11, 30], 2, [[2021, 1, 29], [2021, 1, 29], [2021, 1, 30]], 1000000, 0.0425], [[2021, 1, 28], 6965.28]], ['normal control 1', [[2024, 1, 15], 3, [], 250000, 0.0125], [[2024, 4, 15], 789.93]], ['normal control 2', [[2022, 10, 20], 6, [[2023, 4, 30], [2023, 4, 29]], 250000, 0.053], [[2023, 4, 20], 6698.61]], ['normal control 3', [[2027, 9, 21], 12, [[2028, 9, 26]], 250000, 0.053], [[2028, 9, 21], 13470.83]], ['normal control 4', [[2021, 3, 3], 12, [[2022, 3, 30], [2022, 3, 29], [2022, 3, 29]], 250000, 0.0425], [[2022, 3, 3], 10772.57]]], [['regression end-end target 1', [[2020, 12, 31], 1, [[2021, 1, 28], [2021, 1, 28], [2021, 1, 28]], 1000000, 0.0125], [[2021, 1, 29], 1006.94]], ['regression end-end target 2', [[2024, 4, 30], 2, [[2024, 6, 30], [2024, 6, 26], [2024, 6, 28]], 250000, 0.053], [[2024, 6, 27], 2134.72]], ['partial repair probe 1', [[2022, 7, 29], 3, [[2022, 10, 31], [2022, 10, 31], [2022, 10, 30]], 250000, 0.053], [[2022, 10, 28], 3349.31]], ['partial repair probe 2', [[2022, 2, 28], 1, [[2022, 3, 31], [2022, 3, 29]], 1000000, 0.0125], [[2022, 3, 30], 1041.67]], ['normal control 1', [[2020, 11, 10], 2, [[2021, 1, 27], [2021, 1, 28]], 1000000, 0.0425], [[2021, 1, 11], 7319.44]], ['normal control 2', [[2021, 5, 31], 1, [], 250000, 0.053], [[2021, 6, 30], 1104.17]], ['normal control 3', [[2029, 10, 22], 6, [], 1000000, 0.0125], [[2030, 4, 22], 6319.44]], ['normal control 4', [[2027, 4, 2], 2, [], 1000000, 0.0125], [[2027, 6, 2], 2118.06]]]]
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 end-end target 1	`[[2024, 11, 29], 6319.44]`	`[[2024, 11, 29], 6319.44]`	Passed
regression end-end target 2	`[[2022, 7, 28], 1030.56]`	`[[2022, 7, 28], 1030.56]`	Passed
partial repair probe 1	`[[2019, 4, 29], 8833.33]`	`[[2019, 4, 29], 8833.33]`	Passed
partial repair probe 2	`[[2025, 3, 28], 12808.33]`	`[[2025, 3, 28], 12808.33]`	Passed
normal control 1	`[[2025, 5, 19], 1859.38]`	`[[2025, 5, 19], 1859.38]`	Passed
normal control 2	`[[2024, 5, 6], 546.88]`	`[[2024, 5, 6], 546.88]`	Passed
normal control 3	`[[2023, 10, 9], 11097.22]`	`[[2023, 10, 9], 11097.22]`	Passed
normal control 4	`[[2025, 6, 30], 1140.97]`	`[[2025, 6, 30], 1140.97]`	Passed

SHA-256 / 46ebb5f1f20d51c15da352535679e9053511ef5d539bbd2cbfe00179e31abc4c

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 2026-09-29T14:46:54.249440+00:00.

Case digest / bebdd667e06ead6530579212ce71c4b1508330340d8cdb8deff761bae37bea9a