FA-61646 / Options payoff and settlement / Open access

Exercise assignment allocation across short accounts: leftovers go to the smallest remainders · case 01

Accounts closest to deserving another contract are skipped.

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

ROOT CAUSE

The remainder sort key is not negated.

VERIFIED REPAIR

Rank by descending remainder.

Unsuccessful approach: Ranking by position size ignores the actual remainders.

Case contract

Inputs exercised contracts (<= total short), shorts [account, qty, open sequence] and method. fifo assigns in ascending open sequence, each up to its quantity. pro-rata gives floor(exercised*qty/total) and distributes the remaining contracts one each by largest fractional remainder, ties by lower open sequence. Return sorted [account, assigned] for accounts with assignments.

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

N = 1
observations = []
def solve(exercised, shorts, method):
    total = sum(q for _, q, _ in shorts)
    alloc = {}
    if method == 'fifo':
        left = exercised
        for acct, q, seq in sorted(shorts, key=lambda s: s[2]):
            take = min(q, left)
            alloc[acct] = alloc.get(acct, 0) + take
            left -= take
    else:
        rem = []
        for acct, q, seq in shorts:
            alloc[acct] = alloc.get(acct, 0) + exercised * q // total
            rem.append(((exercised * q % total), seq, acct))
        left = exercised - sum(alloc.values())
        for _, seq, acct in sorted(rem)[:left]:
            alloc[acct] += 1
    return sorted([a, n] for a, n in alloc.items() if n > 0)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression remainder ranking 1', [12, [['B2', 5, 6], ['A1', 2, 12], ['C3', 7, 10], ['E5', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 4], ['C3', 6], ['E5', 1]]], ['regression remainder ranking 2', [3, [['C3', 5, 10], ['D4', 2, 5]], 'pro-rata'], [['C3', 2], ['D4', 1]]], ['partial repair probe 1', [2, [['E5', 3, 17], ['A1', 1, 8]], 'pro-rata'], [['A1', 1], ['E5', 1]]], ['partial repair probe 2', [5, [['C3', 1, 5], ['B2', 3, 2], ['E5', 1, 8], ['A1', 5, 7]], 'pro-rata'], [['A1', 2], ['B2', 2], ['C3', 1]]], ['boundary control 1', [2, [['A1', 5, 2], ['B2', 5, 1]], 'fifo'], [['B2', 2]]], ['boundary control 2', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [4, [['D4', 7, 13]], 'pro-rata'], [['D4', 4]]], ['normal control 2', [6, [['E5', 10, 11], ['F6', 7, 19]], 'fifo'], [['E5', 6]]]], [['regression remainder ranking 1', [12, [['A1', 3, 1], ['C3', 7, 18], ['B2', 7, 2], ['E5', 5, 5]], 'pro-rata'], [['A1', 1], ['B2', 4], ['C3', 4], ['E5', 3]]], ['regression remainder ranking 2', [1, [['A1', 7, 3], ['D4', 10, 11]], 'pro-rata'], [['D4', 1]]], ['partial repair probe 1', [9, [['F6', 3, 13], ['A1', 7, 16], ['C3', 3, 9], ['D4', 5, 7]], 'pro-rata'], [['A1', 3], ['C3', 2], ['D4', 3], ['F6', 1]]], ['partial repair probe 2', [8, [['C3', 7, 12], ['D4', 3, 5], ['A1', 3, 2], ['B2', 3, 11]], 'pro-rata'], [['A1', 2], ['B2', 1], ['C3', 3], ['D4', 2]]], ['boundary control 1', [2, [['A1', 5, 2], ['B2', 5, 1]], 'fifo'], [['B2', 2]]], ['boundary control 2', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [3, [['D4', 3, 1]], 'pro-rata'], [['D4', 3]]], ['normal control 2', [7, [['E5', 7, 12]], 'pro-rata'], [['E5', 7]]]], [['regression remainder ranking 1', [13, [['E5', 7, 5], ['F6', 7, 1], ['D4', 10, 7], ['B2', 3, 10]], 'pro-rata'], [['B2', 2], ['D4', 5], ['E5', 3], ['F6', 3]]], ['regression remainder ranking 2', [6, [['B2', 2, 16], ['E5', 5, 6]], 'pro-rata'], [['B2', 2], ['E5', 4]]], ['partial repair probe 1', [5, [['E5', 3, 8], ['D4', 7, 18]], 'pro-rata'], [['D4', 3], ['E5', 2]]], ['partial repair probe 2', [4, [['B2', 3, 5], ['A1', 5, 6]], 'pro-rata'], [['A1', 2], ['B2', 2]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['boundary control 2', [2, [['A1', 5, 2], ['B2', 5, 1]], 'fifo'], [['B2', 2]]], ['normal control 1', [2, [['B2', 3, 1]], 'fifo'], [['B2', 2]]], ['normal control 2', [2, [['F6', 5, 1]], 'pro-rata'], [['F6', 2]]]], [['regression remainder ranking 1', [11, [['A1', 3, 18], ['D4', 7, 8], ['F6', 5, 12]], 'pro-rata'], [['A1', 2], ['D4', 5], ['F6', 4]]], ['regression remainder ranking 2', [7, [['D4', 3, 19], ['B2', 5, 11], ['F6', 7, 12]], 'pro-rata'], [['B2', 2], ['D4', 2], ['F6', 3]]], ['partial repair probe 1', [5, [['D4', 7, 6], ['F6', 3, 10], ['C3', 7, 18], ['E5', 3, 1]], 'pro-rata'], [['C3', 1], ['D4', 2], ['E5', 1], ['F6', 1]]], ['partial repair probe 2', [2, [['C3', 1, 2], ['E5', 3, 17]], 'pro-rata'], [['C3', 1], ['E5', 1]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['boundary control 2', [2, [['A1', 5, 2], ['B2', 5, 1]], 'fifo'], [['B2', 2]]], ['normal control 1', [6, [['A1', 5, 2], ['B2', 10, 4]], 'pro-rata'], [['A1', 2], ['B2', 4]]], ['normal control 2', [1, [['A1', 1, 1]], 'pro-rata'], [['A1', 1]]]], [['regression remainder ranking 1', [12, [['B2', 5, 16], ['A1', 2, 18], ['F6', 10, 12], ['C3', 10, 17], ['E5', 2, 3]], 'pro-rata'], [['A1', 1], ['B2', 2], ['C3', 4], ['E5', 1], ['F6', 4]]], ['regression remainder ranking 2', [1, [['A1', 10, 12], ['B2', 3, 11], ['E5', 3, 1], ['D4', 2, 5], ['F6', 7, 18]], 'pro-rata'], [['A1', 1]]], ['partial repair probe 1', [3, [['E5', 1, 1], ['A1', 5, 13]], 'pro-rata'], [['A1', 2], ['E5', 1]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['boundary control 2', [2, [['A1', 5, 2], ['B2', 5, 1]], 'fifo'], [['B2', 2]]], ['normal control 1', [9, [['D4', 10, 2]], 'pro-rata'], [['D4', 9]]], ['normal control 2', [7, [['C3', 3, 7], ['F6', 2, 2], ['B2', 2, 6]], 'fifo'], [['B2', 2], ['C3', 3], ['F6', 2]]], ['normal control 3', [6, [['F6', 5, 19], ['A1', 1, 6]], 'pro-rata'], [['A1', 1], ['F6', 5]]]]]
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 remainder ranking 1	`[['A1', 1], ['B2', 5], ['C3', 6]]`	`[['A1', 1], ['B2', 4], ['C3', 6], ['E5', 1]]`	Failed
regression remainder ranking 2	`[['C3', 3]]`	`[['C3', 2], ['D4', 1]]`	Failed
partial repair probe 1	`[['A1', 1], ['E5', 1]]`	`[['A1', 1], ['E5', 1]]`	Passed
partial repair probe 2	`[['A1', 2], ['B2', 2], ['C3', 1]]`	`[['A1', 2], ['B2', 2], ['C3', 1]]`	Passed
boundary control 1	`[['B2', 2]]`	`[['B2', 2]]`	Passed
boundary control 2	`[['A1', 1], ['B2', 1], ['C3', 1]]`	`[['A1', 1], ['B2', 1], ['C3', 1]]`	Passed
normal control 1	`[['D4', 4]]`	`[['D4', 4]]`	Passed
normal control 2	`[['E5', 6]]`	`[['E5', 6]]`	Passed

SHA-256 / 3dd5e9e9af42f54237559b480bc98560f4757e97413f88f4461f398878a0f402

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(exercised, shorts, method):
    total = sum(q for _, q, _ in shorts)
    alloc = {}
    if method == 'fifo':
        left = exercised
        for acct, q, seq in sorted(shorts, key=lambda s: s[2]):
            take = min(q, left)
            alloc[acct] = alloc.get(acct, 0) + take
            left -= take
    else:
        rem = []
        for acct, q, seq in shorts:
            alloc[acct] = alloc.get(acct, 0) + exercised * q // total
            rem.append((-q, seq, acct))
        left = exercised - sum(alloc.values())
        for _, seq, acct in sorted(rem)[:left]:
            alloc[acct] += 1
    return sorted([a, n] for a, n in alloc.items() if n > 0)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression remainder ranking 1', [12, [['B2', 5, 6], ['A1', 2, 12], ['C3', 7, 10], ['E5', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 4], ['C3', 6], ['E5', 1]]], ['regression remainder ranking 2', [3, [['C3', 5, 10], ['D4', 2, 5]], 'pro-rata'], [['C3', 2], ['D4', 1]]], ['partial repair probe 1', [2, [['E5', 3, 17], ['A1', 1, 8]], 'pro-rata'], [['A1', 1], ['E5', 1]]], ['partial repair probe 2', [5, [['C3', 1, 5], ['B2', 3, 2], ['E5', 1, 8], ['A1', 5, 7]], 'pro-rata'], [['A1', 2], ['B2', 2], ['C3', 1]]], ['boundary control 1', [2, [['A1', 5, 2], ['B2', 5, 1]], 'fifo'], [['B2', 2]]], ['boundary control 2', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [4, [['D4', 7, 13]], 'pro-rata'], [['D4', 4]]], ['normal control 2', [6, [['E5', 10, 11], ['F6', 7, 19]], 'fifo'], [['E5', 6]]]], [['regression remainder ranking 1', [12, [['A1', 3, 1], ['C3', 7, 18], ['B2', 7, 2], ['E5', 5, 5]], 'pro-rata'], [['A1', 1], ['B2', 4], ['C3', 4], ['E5', 3]]], ['regression remainder ranking 2', [1, [['A1', 7, 3], ['D4', 10, 11]], 'pro-rata'], [['D4', 1]]], ['partial repair probe 1', [9, [['F6', 3, 13], ['A1', 7, 16], ['C3', 3, 9], ['D4', 5, 7]], 'pro-rata'], [['A1', 3], ['C3', 2], ['D4', 3], ['F6', 1]]], ['partial repair probe 2', [8, [['C3', 7, 12], ['D4', 3, 5], ['A1', 3, 2], ['B2', 3, 11]], 'pro-rata'], [['A1', 2], ['B2', 1], ['C3', 3], ['D4', 2]]], ['boundary control 1', [2, [['A1', 5, 2], ['B2', 5, 1]], 'fifo'], [['B2', 2]]], ['boundary control 2', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [3, [['D4', 3, 1]], 'pro-rata'], [['D4', 3]]], ['normal control 2', [7, [['E5', 7, 12]], 'pro-rata'], [['E5', 7]]]], [['regression remainder ranking 1', [13, [['E5', 7, 5], ['F6', 7, 1], ['D4', 10, 7], ['B2', 3, 10]], 'pro-rata'], [['B2', 2], ['D4', 5], ['E5', 3], ['F6', 3]]], ['regression remainder ranking 2', [6, [['B2', 2, 16], ['E5', 5, 6]], 'pro-rata'], [['B2', 2], ['E5', 4]]], ['partial repair probe 1', [5, [['E5', 3, 8], ['D4', 7, 18]], 'pro-rata'], [['D4', 3], ['E5', 2]]], ['partial repair probe 2', [4, [['B2', 3, 5], ['A1', 5, 6]], 'pro-rata'], [['A1', 2], ['B2', 2]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['boundary control 2', [2, [['A1', 5, 2], ['B2', 5, 1]], 'fifo'], [['B2', 2]]], ['normal control 1', [2, [['B2', 3, 1]], 'fifo'], [['B2', 2]]], ['normal control 2', [2, [['F6', 5, 1]], 'pro-rata'], [['F6', 2]]]], [['regression remainder ranking 1', [11, [['A1', 3, 18], ['D4', 7, 8], ['F6', 5, 12]], 'pro-rata'], [['A1', 2], ['D4', 5], ['F6', 4]]], ['regression remainder ranking 2', [7, [['D4', 3, 19], ['B2', 5, 11], ['F6', 7, 12]], 'pro-rata'], [['B2', 2], ['D4', 2], ['F6', 3]]], ['partial repair probe 1', [5, [['D4', 7, 6], ['F6', 3, 10], ['C3', 7, 18], ['E5', 3, 1]], 'pro-rata'], [['C3', 1], ['D4', 2], ['E5', 1], ['F6', 1]]], ['partial repair probe 2', [2, [['C3', 1, 2], ['E5', 3, 17]], 'pro-rata'], [['C3', 1], ['E5', 1]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['boundary control 2', [2, [['A1', 5, 2], ['B2', 5, 1]], 'fifo'], [['B2', 2]]], ['normal control 1', [6, [['A1', 5, 2], ['B2', 10, 4]], 'pro-rata'], [['A1', 2], ['B2', 4]]], ['normal control 2', [1, [['A1', 1, 1]], 'pro-rata'], [['A1', 1]]]], [['regression remainder ranking 1', [12, [['B2', 5, 16], ['A1', 2, 18], ['F6', 10, 12], ['C3', 10, 17], ['E5', 2, 3]], 'pro-rata'], [['A1', 1], ['B2', 2], ['C3', 4], ['E5', 1], ['F6', 4]]], ['regression remainder ranking 2', [1, [['A1', 10, 12], ['B2', 3, 11], ['E5', 3, 1], ['D4', 2, 5], ['F6', 7, 18]], 'pro-rata'], [['A1', 1]]], ['partial repair probe 1', [3, [['E5', 1, 1], ['A1', 5, 13]], 'pro-rata'], [['A1', 2], ['E5', 1]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['boundary control 2', [2, [['A1', 5, 2], ['B2', 5, 1]], 'fifo'], [['B2', 2]]], ['normal control 1', [9, [['D4', 10, 2]], 'pro-rata'], [['D4', 9]]], ['normal control 2', [7, [['C3', 3, 7], ['F6', 2, 2], ['B2', 2, 6]], 'fifo'], [['B2', 2], ['C3', 3], ['F6', 2]]], ['normal control 3', [6, [['F6', 5, 19], ['A1', 1, 6]], 'pro-rata'], [['A1', 1], ['F6', 5]]]]]
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 remainder ranking 1	`[['A1', 1], ['B2', 5], ['C3', 6]]`	`[['A1', 1], ['B2', 4], ['C3', 6], ['E5', 1]]`	Failed
regression remainder ranking 2	`[['C3', 3]]`	`[['C3', 2], ['D4', 1]]`	Failed
partial repair probe 1	`[['E5', 2]]`	`[['A1', 1], ['E5', 1]]`	Failed
partial repair probe 2	`[['A1', 3], ['B2', 2]]`	`[['A1', 2], ['B2', 2], ['C3', 1]]`	Failed
boundary control 1	`[['B2', 2]]`	`[['B2', 2]]`	Passed
boundary control 2	`[['A1', 1], ['B2', 1], ['C3', 1]]`	`[['A1', 1], ['B2', 1], ['C3', 1]]`	Passed
normal control 1	`[['D4', 4]]`	`[['D4', 4]]`	Passed
normal control 2	`[['E5', 6]]`	`[['E5', 6]]`	Passed

SHA-256 / b5d2dc75743f891dae4bee9539f18aa0dd5c73448780c257c7fd4c9ddb91e8e3

3 / The verified repair

Exit 0

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(exercised, shorts, method):
    total = sum(q for _, q, _ in shorts)
    alloc = {}
    if method == 'fifo':
        left = exercised
        for acct, q, seq in sorted(shorts, key=lambda s: s[2]):
            take = min(q, left)
            alloc[acct] = alloc.get(acct, 0) + take
            left -= take
    else:
        rem = []
        for acct, q, seq in shorts:
            alloc[acct] = alloc.get(acct, 0) + exercised * q // total
            rem.append((-(exercised * q % total), seq, acct))
        left = exercised - sum(alloc.values())
        for _, seq, acct in sorted(rem)[:left]:
            alloc[acct] += 1
    return sorted([a, n] for a, n in alloc.items() if n > 0)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression remainder ranking 1', [12, [['B2', 5, 6], ['A1', 2, 12], ['C3', 7, 10], ['E5', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 4], ['C3', 6], ['E5', 1]]], ['regression remainder ranking 2', [3, [['C3', 5, 10], ['D4', 2, 5]], 'pro-rata'], [['C3', 2], ['D4', 1]]], ['partial repair probe 1', [2, [['E5', 3, 17], ['A1', 1, 8]], 'pro-rata'], [['A1', 1], ['E5', 1]]], ['partial repair probe 2', [5, [['C3', 1, 5], ['B2', 3, 2], ['E5', 1, 8], ['A1', 5, 7]], 'pro-rata'], [['A1', 2], ['B2', 2], ['C3', 1]]], ['boundary control 1', [2, [['A1', 5, 2], ['B2', 5, 1]], 'fifo'], [['B2', 2]]], ['boundary control 2', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [4, [['D4', 7, 13]], 'pro-rata'], [['D4', 4]]], ['normal control 2', [6, [['E5', 10, 11], ['F6', 7, 19]], 'fifo'], [['E5', 6]]]], [['regression remainder ranking 1', [12, [['A1', 3, 1], ['C3', 7, 18], ['B2', 7, 2], ['E5', 5, 5]], 'pro-rata'], [['A1', 1], ['B2', 4], ['C3', 4], ['E5', 3]]], ['regression remainder ranking 2', [1, [['A1', 7, 3], ['D4', 10, 11]], 'pro-rata'], [['D4', 1]]], ['partial repair probe 1', [9, [['F6', 3, 13], ['A1', 7, 16], ['C3', 3, 9], ['D4', 5, 7]], 'pro-rata'], [['A1', 3], ['C3', 2], ['D4', 3], ['F6', 1]]], ['partial repair probe 2', [8, [['C3', 7, 12], ['D4', 3, 5], ['A1', 3, 2], ['B2', 3, 11]], 'pro-rata'], [['A1', 2], ['B2', 1], ['C3', 3], ['D4', 2]]], ['boundary control 1', [2, [['A1', 5, 2], ['B2', 5, 1]], 'fifo'], [['B2', 2]]], ['boundary control 2', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [3, [['D4', 3, 1]], 'pro-rata'], [['D4', 3]]], ['normal control 2', [7, [['E5', 7, 12]], 'pro-rata'], [['E5', 7]]]], [['regression remainder ranking 1', [13, [['E5', 7, 5], ['F6', 7, 1], ['D4', 10, 7], ['B2', 3, 10]], 'pro-rata'], [['B2', 2], ['D4', 5], ['E5', 3], ['F6', 3]]], ['regression remainder ranking 2', [6, [['B2', 2, 16], ['E5', 5, 6]], 'pro-rata'], [['B2', 2], ['E5', 4]]], ['partial repair probe 1', [5, [['E5', 3, 8], ['D4', 7, 18]], 'pro-rata'], [['D4', 3], ['E5', 2]]], ['partial repair probe 2', [4, [['B2', 3, 5], ['A1', 5, 6]], 'pro-rata'], [['A1', 2], ['B2', 2]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['boundary control 2', [2, [['A1', 5, 2], ['B2', 5, 1]], 'fifo'], [['B2', 2]]], ['normal control 1', [2, [['B2', 3, 1]], 'fifo'], [['B2', 2]]], ['normal control 2', [2, [['F6', 5, 1]], 'pro-rata'], [['F6', 2]]]], [['regression remainder ranking 1', [11, [['A1', 3, 18], ['D4', 7, 8], ['F6', 5, 12]], 'pro-rata'], [['A1', 2], ['D4', 5], ['F6', 4]]], ['regression remainder ranking 2', [7, [['D4', 3, 19], ['B2', 5, 11], ['F6', 7, 12]], 'pro-rata'], [['B2', 2], ['D4', 2], ['F6', 3]]], ['partial repair probe 1', [5, [['D4', 7, 6], ['F6', 3, 10], ['C3', 7, 18], ['E5', 3, 1]], 'pro-rata'], [['C3', 1], ['D4', 2], ['E5', 1], ['F6', 1]]], ['partial repair probe 2', [2, [['C3', 1, 2], ['E5', 3, 17]], 'pro-rata'], [['C3', 1], ['E5', 1]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['boundary control 2', [2, [['A1', 5, 2], ['B2', 5, 1]], 'fifo'], [['B2', 2]]], ['normal control 1', [6, [['A1', 5, 2], ['B2', 10, 4]], 'pro-rata'], [['A1', 2], ['B2', 4]]], ['normal control 2', [1, [['A1', 1, 1]], 'pro-rata'], [['A1', 1]]]], [['regression remainder ranking 1', [12, [['B2', 5, 16], ['A1', 2, 18], ['F6', 10, 12], ['C3', 10, 17], ['E5', 2, 3]], 'pro-rata'], [['A1', 1], ['B2', 2], ['C3', 4], ['E5', 1], ['F6', 4]]], ['regression remainder ranking 2', [1, [['A1', 10, 12], ['B2', 3, 11], ['E5', 3, 1], ['D4', 2, 5], ['F6', 7, 18]], 'pro-rata'], [['A1', 1]]], ['partial repair probe 1', [3, [['E5', 1, 1], ['A1', 5, 13]], 'pro-rata'], [['A1', 2], ['E5', 1]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['boundary control 2', [2, [['A1', 5, 2], ['B2', 5, 1]], 'fifo'], [['B2', 2]]], ['normal control 1', [9, [['D4', 10, 2]], 'pro-rata'], [['D4', 9]]], ['normal control 2', [7, [['C3', 3, 7], ['F6', 2, 2], ['B2', 2, 6]], 'fifo'], [['B2', 2], ['C3', 3], ['F6', 2]]], ['normal control 3', [6, [['F6', 5, 19], ['A1', 1, 6]], 'pro-rata'], [['A1', 1], ['F6', 5]]]]]
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 remainder ranking 1	`[['A1', 1], ['B2', 4], ['C3', 6], ['E5', 1]]`	`[['A1', 1], ['B2', 4], ['C3', 6], ['E5', 1]]`	Passed
regression remainder ranking 2	`[['C3', 2], ['D4', 1]]`	`[['C3', 2], ['D4', 1]]`	Passed
partial repair probe 1	`[['A1', 1], ['E5', 1]]`	`[['A1', 1], ['E5', 1]]`	Passed
partial repair probe 2	`[['A1', 2], ['B2', 2], ['C3', 1]]`	`[['A1', 2], ['B2', 2], ['C3', 1]]`	Passed
boundary control 1	`[['B2', 2]]`	`[['B2', 2]]`	Passed
boundary control 2	`[['A1', 1], ['B2', 1], ['C3', 1]]`	`[['A1', 1], ['B2', 1], ['C3', 1]]`	Passed
normal control 1	`[['D4', 4]]`	`[['D4', 4]]`	Passed
normal control 2	`[['E5', 6]]`	`[['E5', 6]]`	Passed

SHA-256 / 087901700e41beff83d389d1201cc8527491b2020869a3c4e7be1c403fff19a3

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 2026-09-29T14:46:57.181623+00:00.

Case digest / 8e4fa67c57316c7684fa47704c68827e3c17c0578cc35a68c543cced26403da7