FA-61636 / Options payoff and settlement / Open access

Exercise assignment allocation across short accounts: fifo assigns the newest positions first · case 01

Recently opened shorts are assigned ahead of older ones.

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

ROOT CAUSE

The fifo sort is reversed.

VERIFIED REPAIR

Sort by ascending open sequence.

Unsuccessful approach: Sorting by account name ignores open order.

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], reverse=True):
            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 fifo ordering 1', [6, [['F6', 1, 10], ['E5', 2, 14], ['B2', 3, 15], ['C3', 3, 16], ['A1', 7, 2]], 'fifo'], [['A1', 6]]], ['regression fifo ordering 2', [5, [['F6', 3, 8], ['D4', 7, 11], ['E5', 10, 5], ['C3', 7, 19], ['B2', 1, 9]], 'fifo'], [['E5', 5]]], ['partial repair probe 1', [4, [['C3', 3, 19], ['E5', 3, 13], ['F6', 10, 10], ['B2', 2, 2]], 'fifo'], [['B2', 2], ['F6', 2]]], ['partial repair probe 2', [16, [['B2', 5, 12], ['E5', 10, 9], ['C3', 7, 11], ['D4', 10, 18]], 'fifo'], [['C3', 6], ['E5', 10]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [10, [['F6', 1, 1], ['C3', 1, 5], ['D4', 3, 12], ['A1', 7, 6], ['B2', 2, 4]], 'pro-rata'], [['A1', 5], ['B2', 1], ['C3', 1], ['D4', 2], ['F6', 1]]], ['normal control 2', [8, [['E5', 2, 16], ['A1', 10, 1]], 'pro-rata'], [['A1', 7], ['E5', 1]]], ['normal control 3', [1, [['D4', 1, 16]], 'pro-rata'], [['D4', 1]]]], [['regression fifo ordering 1', [2, [['C3', 3, 11], ['E5', 10, 6], ['F6', 7, 17], ['A1', 10, 4]], 'fifo'], [['A1', 2]]], ['regression fifo ordering 2', [15, [['D4', 5, 11], ['E5', 10, 3], ['B2', 5, 14], ['C3', 2, 15]], 'fifo'], [['D4', 5], ['E5', 10]]], ['partial repair probe 1', [10, [['F6', 3, 1], ['A1', 10, 6], ['C3', 1, 5]], 'fifo'], [['A1', 6], ['C3', 1], ['F6', 3]]], ['partial repair probe 2', [7, [['A1', 7, 16], ['B2', 3, 14]], 'fifo'], [['A1', 4], ['B2', 3]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [9, [['D4', 10, 17], ['E5', 3, 11]], 'pro-rata'], [['D4', 7], ['E5', 2]]], ['normal control 2', [1, [['F6', 10, 15]], 'fifo'], [['F6', 1]]], ['normal control 3', [12, [['B2', 5, 8], ['D4', 3, 1], ['E5', 1, 2], ['C3', 3, 4], ['A1', 7, 13]], 'pro-rata'], [['A1', 4], ['B2', 3], ['C3', 2], ['D4', 2], ['E5', 1]]]], [['regression fifo ordering 1', [4, [['C3', 10, 15], ['E5', 5, 16], ['F6', 7, 3], ['B2', 1, 9]], 'fifo'], [['F6', 4]]], ['regression fifo ordering 2', [1, [['F6', 3, 17], ['D4', 1, 18]], 'fifo'], [['F6', 1]]], ['partial repair probe 1', [7, [['C3', 1, 4], ['F6', 10, 2], ['D4', 7, 18], ['A1', 5, 8], ['E5', 1, 15]], 'fifo'], [['F6', 7]]], ['partial repair probe 2', [13, [['E5', 7, 7], ['A1', 5, 9], ['D4', 2, 6]], 'fifo'], [['A1', 4], ['D4', 2], ['E5', 7]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [5, [['F6', 3, 7], ['B2', 2, 19]], 'pro-rata'], [['B2', 2], ['F6', 3]]], ['normal control 2', [4, [['F6', 1, 8], ['B2', 2, 4], ['D4', 3, 15], ['C3', 7, 16]], 'pro-rata'], [['B2', 1], ['C3', 2], ['D4', 1]]], ['normal control 3', [2, [['D4', 2, 1]], 'pro-rata'], [['D4', 2]]]], [['regression fifo ordering 1', [22, [['F6', 7, 10], ['B2', 5, 7], ['C3', 2, 19], ['A1', 10, 14], ['E5', 5, 8]], 'fifo'], [['A1', 5], ['B2', 5], ['E5', 5], ['F6', 7]]], ['regression fifo ordering 2', [2, [['F6', 2, 5], ['C3', 1, 6]], 'fifo'], [['F6', 2]]], ['partial repair probe 1', [8, [['D4', 5, 5], ['C3', 7, 9]], 'fifo'], [['C3', 3], ['D4', 5]]], ['partial repair probe 2', [7, [['F6', 5, 16], ['D4', 3, 18], ['B2', 3, 17]], 'fifo'], [['B2', 2], ['F6', 5]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [16, [['F6', 10, 18], ['B2', 1, 12], ['D4', 10, 3]], 'pro-rata'], [['B2', 1], ['D4', 8], ['F6', 7]]], ['normal control 2', [1, [['E5', 1, 17]], 'pro-rata'], [['E5', 1]]], ['normal control 3', [7, [['C3', 7, 13], ['B2', 3, 8]], 'pro-rata'], [['B2', 2], ['C3', 5]]]], [['regression fifo ordering 1', [9, [['D4', 1, 12], ['B2', 7, 9], ['F6', 7, 19], ['C3', 10, 18]], 'fifo'], [['B2', 7], ['C3', 1], ['D4', 1]]], ['regression fifo ordering 2', [12, [['E5', 1, 5], ['F6', 3, 1], ['B2', 10, 3], ['C3', 3, 8]], 'fifo'], [['B2', 9], ['F6', 3]]], ['partial repair probe 1', [8, [['F6', 1, 14], ['B2', 2, 2], ['C3', 1, 18], ['A1', 7, 15]], 'fifo'], [['A1', 5], ['B2', 2], ['F6', 1]]], ['partial repair probe 2', [3, [['F6', 2, 8], ['D4', 2, 17], ['E5', 10, 15]], 'fifo'], [['E5', 1], ['F6', 2]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [17, [['A1', 10, 11], ['D4', 10, 16], ['F6', 1, 5]], 'pro-rata'], [['A1', 8], ['D4', 8], ['F6', 1]]], ['normal control 2', [4, [['A1', 2, 4], ['B2', 2, 2], ['E5', 2, 1], ['D4', 2, 10]], 'pro-rata'], [['A1', 1], ['B2', 1], ['D4', 1], ['E5', 1]]], ['normal control 3', [7, [['B2', 2, 11], ['F6', 2, 6], ['D4', 3, 14]], 'pro-rata'], [['B2', 2], ['D4', 3], ['F6', 2]]]]]
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 fifo ordering 1	`[['B2', 3], ['C3', 3]]`	`[['A1', 6]]`	Failed
regression fifo ordering 2	`[['C3', 5]]`	`[['E5', 5]]`	Failed
partial repair probe 1	`[['C3', 3], ['E5', 1]]`	`[['B2', 2], ['F6', 2]]`	Failed
partial repair probe 2	`[['B2', 5], ['C3', 1], ['D4', 10]]`	`[['C3', 6], ['E5', 10]]`	Failed
boundary control 1	`[['A1', 1], ['B2', 1], ['C3', 1]]`	`[['A1', 1], ['B2', 1], ['C3', 1]]`	Passed
normal control 1	`[['A1', 5], ['B2', 1], ['C3', 1], ['D4', 2], ['F6', 1]]`	`[['A1', 5], ['B2', 1], ['C3', 1], ['D4', 2], ['F6', 1]]`	Passed
normal control 2	`[['A1', 7], ['E5', 1]]`	`[['A1', 7], ['E5', 1]]`	Passed
normal control 3	`[['D4', 1]]`	`[['D4', 1]]`	Passed

SHA-256 / bca21324bb88f3c1e32774f92052c80efb8832498f9dc98923748cb5239add34

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[0]):
            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 fifo ordering 1', [6, [['F6', 1, 10], ['E5', 2, 14], ['B2', 3, 15], ['C3', 3, 16], ['A1', 7, 2]], 'fifo'], [['A1', 6]]], ['regression fifo ordering 2', [5, [['F6', 3, 8], ['D4', 7, 11], ['E5', 10, 5], ['C3', 7, 19], ['B2', 1, 9]], 'fifo'], [['E5', 5]]], ['partial repair probe 1', [4, [['C3', 3, 19], ['E5', 3, 13], ['F6', 10, 10], ['B2', 2, 2]], 'fifo'], [['B2', 2], ['F6', 2]]], ['partial repair probe 2', [16, [['B2', 5, 12], ['E5', 10, 9], ['C3', 7, 11], ['D4', 10, 18]], 'fifo'], [['C3', 6], ['E5', 10]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [10, [['F6', 1, 1], ['C3', 1, 5], ['D4', 3, 12], ['A1', 7, 6], ['B2', 2, 4]], 'pro-rata'], [['A1', 5], ['B2', 1], ['C3', 1], ['D4', 2], ['F6', 1]]], ['normal control 2', [8, [['E5', 2, 16], ['A1', 10, 1]], 'pro-rata'], [['A1', 7], ['E5', 1]]], ['normal control 3', [1, [['D4', 1, 16]], 'pro-rata'], [['D4', 1]]]], [['regression fifo ordering 1', [2, [['C3', 3, 11], ['E5', 10, 6], ['F6', 7, 17], ['A1', 10, 4]], 'fifo'], [['A1', 2]]], ['regression fifo ordering 2', [15, [['D4', 5, 11], ['E5', 10, 3], ['B2', 5, 14], ['C3', 2, 15]], 'fifo'], [['D4', 5], ['E5', 10]]], ['partial repair probe 1', [10, [['F6', 3, 1], ['A1', 10, 6], ['C3', 1, 5]], 'fifo'], [['A1', 6], ['C3', 1], ['F6', 3]]], ['partial repair probe 2', [7, [['A1', 7, 16], ['B2', 3, 14]], 'fifo'], [['A1', 4], ['B2', 3]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [9, [['D4', 10, 17], ['E5', 3, 11]], 'pro-rata'], [['D4', 7], ['E5', 2]]], ['normal control 2', [1, [['F6', 10, 15]], 'fifo'], [['F6', 1]]], ['normal control 3', [12, [['B2', 5, 8], ['D4', 3, 1], ['E5', 1, 2], ['C3', 3, 4], ['A1', 7, 13]], 'pro-rata'], [['A1', 4], ['B2', 3], ['C3', 2], ['D4', 2], ['E5', 1]]]], [['regression fifo ordering 1', [4, [['C3', 10, 15], ['E5', 5, 16], ['F6', 7, 3], ['B2', 1, 9]], 'fifo'], [['F6', 4]]], ['regression fifo ordering 2', [1, [['F6', 3, 17], ['D4', 1, 18]], 'fifo'], [['F6', 1]]], ['partial repair probe 1', [7, [['C3', 1, 4], ['F6', 10, 2], ['D4', 7, 18], ['A1', 5, 8], ['E5', 1, 15]], 'fifo'], [['F6', 7]]], ['partial repair probe 2', [13, [['E5', 7, 7], ['A1', 5, 9], ['D4', 2, 6]], 'fifo'], [['A1', 4], ['D4', 2], ['E5', 7]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [5, [['F6', 3, 7], ['B2', 2, 19]], 'pro-rata'], [['B2', 2], ['F6', 3]]], ['normal control 2', [4, [['F6', 1, 8], ['B2', 2, 4], ['D4', 3, 15], ['C3', 7, 16]], 'pro-rata'], [['B2', 1], ['C3', 2], ['D4', 1]]], ['normal control 3', [2, [['D4', 2, 1]], 'pro-rata'], [['D4', 2]]]], [['regression fifo ordering 1', [22, [['F6', 7, 10], ['B2', 5, 7], ['C3', 2, 19], ['A1', 10, 14], ['E5', 5, 8]], 'fifo'], [['A1', 5], ['B2', 5], ['E5', 5], ['F6', 7]]], ['regression fifo ordering 2', [2, [['F6', 2, 5], ['C3', 1, 6]], 'fifo'], [['F6', 2]]], ['partial repair probe 1', [8, [['D4', 5, 5], ['C3', 7, 9]], 'fifo'], [['C3', 3], ['D4', 5]]], ['partial repair probe 2', [7, [['F6', 5, 16], ['D4', 3, 18], ['B2', 3, 17]], 'fifo'], [['B2', 2], ['F6', 5]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [16, [['F6', 10, 18], ['B2', 1, 12], ['D4', 10, 3]], 'pro-rata'], [['B2', 1], ['D4', 8], ['F6', 7]]], ['normal control 2', [1, [['E5', 1, 17]], 'pro-rata'], [['E5', 1]]], ['normal control 3', [7, [['C3', 7, 13], ['B2', 3, 8]], 'pro-rata'], [['B2', 2], ['C3', 5]]]], [['regression fifo ordering 1', [9, [['D4', 1, 12], ['B2', 7, 9], ['F6', 7, 19], ['C3', 10, 18]], 'fifo'], [['B2', 7], ['C3', 1], ['D4', 1]]], ['regression fifo ordering 2', [12, [['E5', 1, 5], ['F6', 3, 1], ['B2', 10, 3], ['C3', 3, 8]], 'fifo'], [['B2', 9], ['F6', 3]]], ['partial repair probe 1', [8, [['F6', 1, 14], ['B2', 2, 2], ['C3', 1, 18], ['A1', 7, 15]], 'fifo'], [['A1', 5], ['B2', 2], ['F6', 1]]], ['partial repair probe 2', [3, [['F6', 2, 8], ['D4', 2, 17], ['E5', 10, 15]], 'fifo'], [['E5', 1], ['F6', 2]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [17, [['A1', 10, 11], ['D4', 10, 16], ['F6', 1, 5]], 'pro-rata'], [['A1', 8], ['D4', 8], ['F6', 1]]], ['normal control 2', [4, [['A1', 2, 4], ['B2', 2, 2], ['E5', 2, 1], ['D4', 2, 10]], 'pro-rata'], [['A1', 1], ['B2', 1], ['D4', 1], ['E5', 1]]], ['normal control 3', [7, [['B2', 2, 11], ['F6', 2, 6], ['D4', 3, 14]], 'pro-rata'], [['B2', 2], ['D4', 3], ['F6', 2]]]]]
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 fifo ordering 1	`[['A1', 6]]`	`[['A1', 6]]`	Passed
regression fifo ordering 2	`[['B2', 1], ['C3', 4]]`	`[['E5', 5]]`	Failed
partial repair probe 1	`[['B2', 2], ['C3', 2]]`	`[['B2', 2], ['F6', 2]]`	Failed
partial repair probe 2	`[['B2', 5], ['C3', 7], ['D4', 4]]`	`[['C3', 6], ['E5', 10]]`	Failed
boundary control 1	`[['A1', 1], ['B2', 1], ['C3', 1]]`	`[['A1', 1], ['B2', 1], ['C3', 1]]`	Passed
normal control 1	`[['A1', 5], ['B2', 1], ['C3', 1], ['D4', 2], ['F6', 1]]`	`[['A1', 5], ['B2', 1], ['C3', 1], ['D4', 2], ['F6', 1]]`	Passed
normal control 2	`[['A1', 7], ['E5', 1]]`	`[['A1', 7], ['E5', 1]]`	Passed
normal control 3	`[['D4', 1]]`	`[['D4', 1]]`	Passed

SHA-256 / 66147ccc110f8af2460d8080d6949973e6da94b19724f48df35063f9e0d9261a

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 fifo ordering 1', [6, [['F6', 1, 10], ['E5', 2, 14], ['B2', 3, 15], ['C3', 3, 16], ['A1', 7, 2]], 'fifo'], [['A1', 6]]], ['regression fifo ordering 2', [5, [['F6', 3, 8], ['D4', 7, 11], ['E5', 10, 5], ['C3', 7, 19], ['B2', 1, 9]], 'fifo'], [['E5', 5]]], ['partial repair probe 1', [4, [['C3', 3, 19], ['E5', 3, 13], ['F6', 10, 10], ['B2', 2, 2]], 'fifo'], [['B2', 2], ['F6', 2]]], ['partial repair probe 2', [16, [['B2', 5, 12], ['E5', 10, 9], ['C3', 7, 11], ['D4', 10, 18]], 'fifo'], [['C3', 6], ['E5', 10]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [10, [['F6', 1, 1], ['C3', 1, 5], ['D4', 3, 12], ['A1', 7, 6], ['B2', 2, 4]], 'pro-rata'], [['A1', 5], ['B2', 1], ['C3', 1], ['D4', 2], ['F6', 1]]], ['normal control 2', [8, [['E5', 2, 16], ['A1', 10, 1]], 'pro-rata'], [['A1', 7], ['E5', 1]]], ['normal control 3', [1, [['D4', 1, 16]], 'pro-rata'], [['D4', 1]]]], [['regression fifo ordering 1', [2, [['C3', 3, 11], ['E5', 10, 6], ['F6', 7, 17], ['A1', 10, 4]], 'fifo'], [['A1', 2]]], ['regression fifo ordering 2', [15, [['D4', 5, 11], ['E5', 10, 3], ['B2', 5, 14], ['C3', 2, 15]], 'fifo'], [['D4', 5], ['E5', 10]]], ['partial repair probe 1', [10, [['F6', 3, 1], ['A1', 10, 6], ['C3', 1, 5]], 'fifo'], [['A1', 6], ['C3', 1], ['F6', 3]]], ['partial repair probe 2', [7, [['A1', 7, 16], ['B2', 3, 14]], 'fifo'], [['A1', 4], ['B2', 3]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [9, [['D4', 10, 17], ['E5', 3, 11]], 'pro-rata'], [['D4', 7], ['E5', 2]]], ['normal control 2', [1, [['F6', 10, 15]], 'fifo'], [['F6', 1]]], ['normal control 3', [12, [['B2', 5, 8], ['D4', 3, 1], ['E5', 1, 2], ['C3', 3, 4], ['A1', 7, 13]], 'pro-rata'], [['A1', 4], ['B2', 3], ['C3', 2], ['D4', 2], ['E5', 1]]]], [['regression fifo ordering 1', [4, [['C3', 10, 15], ['E5', 5, 16], ['F6', 7, 3], ['B2', 1, 9]], 'fifo'], [['F6', 4]]], ['regression fifo ordering 2', [1, [['F6', 3, 17], ['D4', 1, 18]], 'fifo'], [['F6', 1]]], ['partial repair probe 1', [7, [['C3', 1, 4], ['F6', 10, 2], ['D4', 7, 18], ['A1', 5, 8], ['E5', 1, 15]], 'fifo'], [['F6', 7]]], ['partial repair probe 2', [13, [['E5', 7, 7], ['A1', 5, 9], ['D4', 2, 6]], 'fifo'], [['A1', 4], ['D4', 2], ['E5', 7]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [5, [['F6', 3, 7], ['B2', 2, 19]], 'pro-rata'], [['B2', 2], ['F6', 3]]], ['normal control 2', [4, [['F6', 1, 8], ['B2', 2, 4], ['D4', 3, 15], ['C3', 7, 16]], 'pro-rata'], [['B2', 1], ['C3', 2], ['D4', 1]]], ['normal control 3', [2, [['D4', 2, 1]], 'pro-rata'], [['D4', 2]]]], [['regression fifo ordering 1', [22, [['F6', 7, 10], ['B2', 5, 7], ['C3', 2, 19], ['A1', 10, 14], ['E5', 5, 8]], 'fifo'], [['A1', 5], ['B2', 5], ['E5', 5], ['F6', 7]]], ['regression fifo ordering 2', [2, [['F6', 2, 5], ['C3', 1, 6]], 'fifo'], [['F6', 2]]], ['partial repair probe 1', [8, [['D4', 5, 5], ['C3', 7, 9]], 'fifo'], [['C3', 3], ['D4', 5]]], ['partial repair probe 2', [7, [['F6', 5, 16], ['D4', 3, 18], ['B2', 3, 17]], 'fifo'], [['B2', 2], ['F6', 5]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [16, [['F6', 10, 18], ['B2', 1, 12], ['D4', 10, 3]], 'pro-rata'], [['B2', 1], ['D4', 8], ['F6', 7]]], ['normal control 2', [1, [['E5', 1, 17]], 'pro-rata'], [['E5', 1]]], ['normal control 3', [7, [['C3', 7, 13], ['B2', 3, 8]], 'pro-rata'], [['B2', 2], ['C3', 5]]]], [['regression fifo ordering 1', [9, [['D4', 1, 12], ['B2', 7, 9], ['F6', 7, 19], ['C3', 10, 18]], 'fifo'], [['B2', 7], ['C3', 1], ['D4', 1]]], ['regression fifo ordering 2', [12, [['E5', 1, 5], ['F6', 3, 1], ['B2', 10, 3], ['C3', 3, 8]], 'fifo'], [['B2', 9], ['F6', 3]]], ['partial repair probe 1', [8, [['F6', 1, 14], ['B2', 2, 2], ['C3', 1, 18], ['A1', 7, 15]], 'fifo'], [['A1', 5], ['B2', 2], ['F6', 1]]], ['partial repair probe 2', [3, [['F6', 2, 8], ['D4', 2, 17], ['E5', 10, 15]], 'fifo'], [['E5', 1], ['F6', 2]]], ['boundary control 1', [3, [['A1', 1, 1], ['B2', 1, 2], ['C3', 1, 3]], 'pro-rata'], [['A1', 1], ['B2', 1], ['C3', 1]]], ['normal control 1', [17, [['A1', 10, 11], ['D4', 10, 16], ['F6', 1, 5]], 'pro-rata'], [['A1', 8], ['D4', 8], ['F6', 1]]], ['normal control 2', [4, [['A1', 2, 4], ['B2', 2, 2], ['E5', 2, 1], ['D4', 2, 10]], 'pro-rata'], [['A1', 1], ['B2', 1], ['D4', 1], ['E5', 1]]], ['normal control 3', [7, [['B2', 2, 11], ['F6', 2, 6], ['D4', 3, 14]], 'pro-rata'], [['B2', 2], ['D4', 3], ['F6', 2]]]]]
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 fifo ordering 1	`[['A1', 6]]`	`[['A1', 6]]`	Passed
regression fifo ordering 2	`[['E5', 5]]`	`[['E5', 5]]`	Passed
partial repair probe 1	`[['B2', 2], ['F6', 2]]`	`[['B2', 2], ['F6', 2]]`	Passed
partial repair probe 2	`[['C3', 6], ['E5', 10]]`	`[['C3', 6], ['E5', 10]]`	Passed
boundary control 1	`[['A1', 1], ['B2', 1], ['C3', 1]]`	`[['A1', 1], ['B2', 1], ['C3', 1]]`	Passed
normal control 1	`[['A1', 5], ['B2', 1], ['C3', 1], ['D4', 2], ['F6', 1]]`	`[['A1', 5], ['B2', 1], ['C3', 1], ['D4', 2], ['F6', 1]]`	Passed
normal control 2	`[['A1', 7], ['E5', 1]]`	`[['A1', 7], ['E5', 1]]`	Passed
normal control 3	`[['D4', 1]]`	`[['D4', 1]]`	Passed

SHA-256 / 3f9daf01bd1227cc6e70fe855e2ffd959ffef3ab17f287483cd014a70cd44372

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 2026-09-29T14:46:57.084043+00:00.

Case digest / 9fefe183aa30c309c26ece47b7f56a3245ef700caff8e415b54d6dabd275da9f