FA-61661 / Options payoff and settlement / Open access

FIFO realized P&L for option trades: closing a short lot books the price change with the long sign · case 01

Buying back a short option at a lower premium records a loss.

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

ROOT CAUSE

The realized amount ignores the direction of the lot being closed.

VERIFIED REPAIR

Multiply by +1 for long lots and -1 for short lots.

Unsuccessful approach: Always using open minus close inverts the long-lot result instead.

Case contract

Inputs trades [side B/S, contracts, premium], multiplier and fee per contract. Positions may be long or short. Each trade first closes opposite-direction lots oldest first; realized P&L per matched contract is (close - open) for long lots and (open - close) for short lots, times the multiplier. Any remainder opens a new lot in the trade direction. Fees = contracts traded * fee. Work in cents; return [net position, realized, fees].

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 collections
N = 1
observations = []
def solve(trades, multiplier, fee):
    lots = collections.deque()
    realized = 0
    fees = 0
    fee_c = round(fee * 100)
    for side, qty, price in trades:
        p = round(price * 100)
        sgn = 1 if side == 'B' else -1
        fees += qty * fee_c
        left = qty
        while left and lots and (lots[0][0] > 0) != (sgn > 0):
            lot = lots[0]
            m = min(left, abs(lot[0]))
            direction = 1 if lot[0] > 0 else -1
            realized += (p - lot[1]) * m
            lot[0] -= m * direction
            left -= m
            if lot[0] == 0:
                lots.popleft()
        if left:
            lots.append([sgn * left, p])
    pos = sum(l[0] for l in lots)
    return [pos, realized * multiplier / 100, fees / 100]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression short lot pnl sign 1', [[['B', 2, 4.1], ['S', 3, 0.05], ['S', 5, 0.05], ['B', 1, 0.85], ['S', 3, 0.85]], 100, 0.0], [-8, -890.0, 0.0]], ['regression short lot pnl sign 2', [[['S', 3, 4.1], ['B', 5, 1.2], ['S', 2, 0.85]], 100, 1.0], [0, 800.0, 10.0]], ['partial repair probe 1', [[['B', 5, 0.05], ['B', 1, 3.5], ['B', 5, 0.05], ['B', 2, 1.2], ['S', 3, 3.5], ['B', 5, 2.05]], 100, 0.0], [15, 1035.0, 0.0]], ['partial repair probe 2', [[['B', 1, 1.2], ['B', 3, 4.1], ['B', 3, 3.5], ['B', 1, 2.05], ['S', 5, 0.05], ['S', 5, 1.2]], 100, 0.0], [-2, -2220.0, 0.0]], ['normal control 1', [[['S', 1, 2.05], ['S', 5, 3.5], ['S', 1, 1.2], ['S', 3, 4.1], ['S', 3, 4.1]], 100, 1.0], [-13, 0.0, 13.0]], ['normal control 2', [[['B', 5, 0.85], ['B', 2, 4.1], ['B', 3, 0.85], ['B', 3, 0.85], ['B', 2, 2.05]], 10, 0.0], [15, 0.0, 0.0]], ['normal control 3', [[['S', 5, 0.05], ['S', 1, 2.05], ['B', 5, 0.05]], 10, 1.0], [-1, 0.0, 11.0]], ['normal control 4', [[['S', 2, 0.05], ['B', 2, 0.05]], 100, 0.65], [0, 0.0, 2.6]]], [['regression short lot pnl sign 1', [[['S', 3, 2.05], ['B', 5, 4.1]], 100, 0.0], [2, -615.0, 0.0]], ['regression short lot pnl sign 2', [[['S', 2, 0.05], ['B', 5, 2.05], ['S', 5, 1.2], ['S', 5, 1.2], ['S', 3, 0.85]], 10, 1.0], [-10, -65.5, 20.0]], ['partial repair probe 1', [[['B', 3, 1.2], ['B', 5, 0.85], ['S', 3, 0.05], ['S', 3, 2.05]], 100, 1.0], [2, 15.0, 14.0]], ['partial repair probe 2', [[['S', 1, 2.05], ['B', 5, 2.05], ['B', 3, 3.5], ['S', 2, 4.1]], 100, 1.0], [5, 410.0, 11.0]], ['normal control 1', [[['B', 3, 0.05], ['B', 1, 4.1]], 10, 0.65], [4, 0.0, 2.6]], ['normal control 2', [[['S', 5, 0.05], ['S', 5, 0.85], ['S', 2, 4.1], ['S', 2, 3.5]], 10, 0.65], [-14, 0.0, 9.1]], ['normal control 3', [[['B', 1, 0.05], ['B', 2, 2.05]], 100, 1.0], [3, 0.0, 3.0]], ['normal control 4', [[['B', 1, 4.1]], 100, 0.65], [1, 0.0, 0.65]]], [['regression short lot pnl sign 1', [[['B', 3, 4.1], ['S', 5, 0.05], ['S', 1, 2.05], ['S', 1, 4.1], ['B', 2, 0.85], ['B', 5, 0.05]], 100, 1.0], [3, -770.0, 17.0]], ['regression short lot pnl sign 2', [[['S', 3, 3.5], ['B', 1, 2.05], ['B', 5, 3.5], ['S', 3, 3.5], ['S', 5, 0.05]], 100, 0.0], [-5, 145.0, 0.0]], ['partial repair probe 1', [[['B', 3, 1.2], ['S', 1, 4.1], ['S', 2, 3.5]], 10, 0.0], [0, 75.0, 0.0]], ['partial repair probe 2', [[['B', 5, 3.5], ['B', 3, 2.05], ['B', 5, 0.85], ['S', 1, 3.5], ['B', 1, 4.1], ['S', 3, 0.85]], 10, 1.0], [10, -79.5, 18.0]], ['normal control 1', [[['B', 1, 0.85], ['B', 5, 0.05]], 10, 0.0], [6, 0.0, 0.0]], ['normal control 2', [[['S', 2, 0.05]], 10, 0.0], [-2, 0.0, 0.0]], ['normal control 3', [[['S', 5, 0.85], ['S', 5, 0.85], ['S', 2, 3.5], ['B', 5, 0.85], ['S', 3, 1.2], ['B', 3, 0.85]], 100, 1.0], [-7, 0.0, 23.0]], ['normal control 4', [[['B', 1, 0.85], ['B', 3, 4.1], ['B', 2, 2.05], ['B', 3, 0.85], ['B', 3, 3.5]], 10, 0.65], [12, 0.0, 7.8]]], [['regression short lot pnl sign 1', [[['B', 5, 4.1], ['S', 5, 0.85], ['S', 3, 0.05], ['S', 2, 1.2], ['B', 5, 3.5], ['B', 3, 4.1]], 10, 1.0], [3, -312.0, 23.0]], ['regression short lot pnl sign 2', [[['B', 1, 1.2], ['S', 2, 3.5], ['S', 1, 1.2], ['B', 3, 1.2]], 100, 1.0], [1, 460.0, 7.0]], ['partial repair probe 1', [[['B', 3, 0.05], ['B', 3, 0.05], ['S', 5, 2.05], ['B', 5, 3.5], ['B', 2, 3.5], ['B', 2, 0.85]], 10, 0.0], [10, 100.0, 0.0]], ['partial repair probe 2', [[['B', 1, 4.1], ['B', 1, 1.2], ['S', 2, 3.5]], 100, 0.65], [0, 170.0, 2.6]], ['normal control 1', [[['B', 5, 0.05]], 100, 0.65], [5, 0.0, 3.25]], ['normal control 2', [[['B', 5, 0.05], ['B', 1, 2.05]], 10, 0.0], [6, 0.0, 0.0]], ['normal control 3', [[['B', 5, 4.1], ['B', 2, 4.1]], 10, 0.0], [7, 0.0, 0.0]], ['normal control 4', [[['S', 5, 2.05], ['B', 3, 2.05], ['B', 5, 2.05]], 100, 0.65], [3, 0.0, 8.45]]], [['regression short lot pnl sign 1', [[['S', 2, 0.05], ['B', 3, 2.05], ['B', 5, 0.05]], 100, 1.0], [6, -400.0, 10.0]], ['regression short lot pnl sign 2', [[['S', 2, 1.2], ['B', 2, 2.05], ['S', 2, 0.85]], 100, 0.0], [-2, -170.0, 0.0]], ['partial repair probe 1', [[['S', 1, 0.05], ['B', 1, 0.05], ['B', 2, 2.05], ['S', 5, 1.2], ['S', 3, 3.5]], 10, 1.0], [-6, -17.0, 12.0]], ['partial repair probe 2', [[['B', 5, 1.2], ['S', 2, 0.85], ['S', 5, 0.85]], 10, 1.0], [-2, -17.5, 12.0]], ['normal control 1', [[['B', 5, 0.05], ['B', 1, 0.85], ['B', 3, 0.05], ['B', 2, 2.05]], 10, 0.0], [11, 0.0, 0.0]], ['normal control 2', [[['S', 5, 1.2], ['S', 5, 4.1]], 100, 0.65], [-10, 0.0, 6.5]], ['normal control 3', [[['B', 1, 0.05], ['B', 2, 1.2], ['B', 2, 0.05]], 100, 0.0], [5, 0.0, 0.0]], ['normal control 4', [[['S', 1, 4.1], ['S', 3, 4.1]], 10, 0.0], [-4, 0.0, 0.0]]]]
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 short lot pnl sign 1	`[-8, -730.0, 0.0]`	`[-8, -890.0, 0.0]`	Failed
regression short lot pnl sign 2	`[0, -940.0, 10.0]`	`[0, 800.0, 10.0]`	Failed
partial repair probe 1	`[15, 1035.0, 0.0]`	`[15, 1035.0, 0.0]`	Passed
partial repair probe 2	`[-2, -2220.0, 0.0]`	`[-2, -2220.0, 0.0]`	Passed
normal control 1	`[-13, 0.0, 13.0]`	`[-13, 0.0, 13.0]`	Passed
normal control 2	`[15, 0.0, 0.0]`	`[15, 0.0, 0.0]`	Passed
normal control 3	`[-1, 0.0, 11.0]`	`[-1, 0.0, 11.0]`	Passed
normal control 4	`[0, 0.0, 2.6]`	`[0, 0.0, 2.6]`	Passed

SHA-256 / be317b9d6506d6b7778a9b5b23e982328101beec9d25444da06862ed718394d4

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
import collections
N = 1
observations = []
def solve(trades, multiplier, fee):
    lots = collections.deque()
    realized = 0
    fees = 0
    fee_c = round(fee * 100)
    for side, qty, price in trades:
        p = round(price * 100)
        sgn = 1 if side == 'B' else -1
        fees += qty * fee_c
        left = qty
        while left and lots and (lots[0][0] > 0) != (sgn > 0):
            lot = lots[0]
            m = min(left, abs(lot[0]))
            direction = 1 if lot[0] > 0 else -1
            realized += (lot[1] - p) * m
            lot[0] -= m * direction
            left -= m
            if lot[0] == 0:
                lots.popleft()
        if left:
            lots.append([sgn * left, p])
    pos = sum(l[0] for l in lots)
    return [pos, realized * multiplier / 100, fees / 100]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression short lot pnl sign 1', [[['B', 2, 4.1], ['S', 3, 0.05], ['S', 5, 0.05], ['B', 1, 0.85], ['S', 3, 0.85]], 100, 0.0], [-8, -890.0, 0.0]], ['regression short lot pnl sign 2', [[['S', 3, 4.1], ['B', 5, 1.2], ['S', 2, 0.85]], 100, 1.0], [0, 800.0, 10.0]], ['partial repair probe 1', [[['B', 5, 0.05], ['B', 1, 3.5], ['B', 5, 0.05], ['B', 2, 1.2], ['S', 3, 3.5], ['B', 5, 2.05]], 100, 0.0], [15, 1035.0, 0.0]], ['partial repair probe 2', [[['B', 1, 1.2], ['B', 3, 4.1], ['B', 3, 3.5], ['B', 1, 2.05], ['S', 5, 0.05], ['S', 5, 1.2]], 100, 0.0], [-2, -2220.0, 0.0]], ['normal control 1', [[['S', 1, 2.05], ['S', 5, 3.5], ['S', 1, 1.2], ['S', 3, 4.1], ['S', 3, 4.1]], 100, 1.0], [-13, 0.0, 13.0]], ['normal control 2', [[['B', 5, 0.85], ['B', 2, 4.1], ['B', 3, 0.85], ['B', 3, 0.85], ['B', 2, 2.05]], 10, 0.0], [15, 0.0, 0.0]], ['normal control 3', [[['S', 5, 0.05], ['S', 1, 2.05], ['B', 5, 0.05]], 10, 1.0], [-1, 0.0, 11.0]], ['normal control 4', [[['S', 2, 0.05], ['B', 2, 0.05]], 100, 0.65], [0, 0.0, 2.6]]], [['regression short lot pnl sign 1', [[['S', 3, 2.05], ['B', 5, 4.1]], 100, 0.0], [2, -615.0, 0.0]], ['regression short lot pnl sign 2', [[['S', 2, 0.05], ['B', 5, 2.05], ['S', 5, 1.2], ['S', 5, 1.2], ['S', 3, 0.85]], 10, 1.0], [-10, -65.5, 20.0]], ['partial repair probe 1', [[['B', 3, 1.2], ['B', 5, 0.85], ['S', 3, 0.05], ['S', 3, 2.05]], 100, 1.0], [2, 15.0, 14.0]], ['partial repair probe 2', [[['S', 1, 2.05], ['B', 5, 2.05], ['B', 3, 3.5], ['S', 2, 4.1]], 100, 1.0], [5, 410.0, 11.0]], ['normal control 1', [[['B', 3, 0.05], ['B', 1, 4.1]], 10, 0.65], [4, 0.0, 2.6]], ['normal control 2', [[['S', 5, 0.05], ['S', 5, 0.85], ['S', 2, 4.1], ['S', 2, 3.5]], 10, 0.65], [-14, 0.0, 9.1]], ['normal control 3', [[['B', 1, 0.05], ['B', 2, 2.05]], 100, 1.0], [3, 0.0, 3.0]], ['normal control 4', [[['B', 1, 4.1]], 100, 0.65], [1, 0.0, 0.65]]], [['regression short lot pnl sign 1', [[['B', 3, 4.1], ['S', 5, 0.05], ['S', 1, 2.05], ['S', 1, 4.1], ['B', 2, 0.85], ['B', 5, 0.05]], 100, 1.0], [3, -770.0, 17.0]], ['regression short lot pnl sign 2', [[['S', 3, 3.5], ['B', 1, 2.05], ['B', 5, 3.5], ['S', 3, 3.5], ['S', 5, 0.05]], 100, 0.0], [-5, 145.0, 0.0]], ['partial repair probe 1', [[['B', 3, 1.2], ['S', 1, 4.1], ['S', 2, 3.5]], 10, 0.0], [0, 75.0, 0.0]], ['partial repair probe 2', [[['B', 5, 3.5], ['B', 3, 2.05], ['B', 5, 0.85], ['S', 1, 3.5], ['B', 1, 4.1], ['S', 3, 0.85]], 10, 1.0], [10, -79.5, 18.0]], ['normal control 1', [[['B', 1, 0.85], ['B', 5, 0.05]], 10, 0.0], [6, 0.0, 0.0]], ['normal control 2', [[['S', 2, 0.05]], 10, 0.0], [-2, 0.0, 0.0]], ['normal control 3', [[['S', 5, 0.85], ['S', 5, 0.85], ['S', 2, 3.5], ['B', 5, 0.85], ['S', 3, 1.2], ['B', 3, 0.85]], 100, 1.0], [-7, 0.0, 23.0]], ['normal control 4', [[['B', 1, 0.85], ['B', 3, 4.1], ['B', 2, 2.05], ['B', 3, 0.85], ['B', 3, 3.5]], 10, 0.65], [12, 0.0, 7.8]]], [['regression short lot pnl sign 1', [[['B', 5, 4.1], ['S', 5, 0.85], ['S', 3, 0.05], ['S', 2, 1.2], ['B', 5, 3.5], ['B', 3, 4.1]], 10, 1.0], [3, -312.0, 23.0]], ['regression short lot pnl sign 2', [[['B', 1, 1.2], ['S', 2, 3.5], ['S', 1, 1.2], ['B', 3, 1.2]], 100, 1.0], [1, 460.0, 7.0]], ['partial repair probe 1', [[['B', 3, 0.05], ['B', 3, 0.05], ['S', 5, 2.05], ['B', 5, 3.5], ['B', 2, 3.5], ['B', 2, 0.85]], 10, 0.0], [10, 100.0, 0.0]], ['partial repair probe 2', [[['B', 1, 4.1], ['B', 1, 1.2], ['S', 2, 3.5]], 100, 0.65], [0, 170.0, 2.6]], ['normal control 1', [[['B', 5, 0.05]], 100, 0.65], [5, 0.0, 3.25]], ['normal control 2', [[['B', 5, 0.05], ['B', 1, 2.05]], 10, 0.0], [6, 0.0, 0.0]], ['normal control 3', [[['B', 5, 4.1], ['B', 2, 4.1]], 10, 0.0], [7, 0.0, 0.0]], ['normal control 4', [[['S', 5, 2.05], ['B', 3, 2.05], ['B', 5, 2.05]], 100, 0.65], [3, 0.0, 8.45]]], [['regression short lot pnl sign 1', [[['S', 2, 0.05], ['B', 3, 2.05], ['B', 5, 0.05]], 100, 1.0], [6, -400.0, 10.0]], ['regression short lot pnl sign 2', [[['S', 2, 1.2], ['B', 2, 2.05], ['S', 2, 0.85]], 100, 0.0], [-2, -170.0, 0.0]], ['partial repair probe 1', [[['S', 1, 0.05], ['B', 1, 0.05], ['B', 2, 2.05], ['S', 5, 1.2], ['S', 3, 3.5]], 10, 1.0], [-6, -17.0, 12.0]], ['partial repair probe 2', [[['B', 5, 1.2], ['S', 2, 0.85], ['S', 5, 0.85]], 10, 1.0], [-2, -17.5, 12.0]], ['normal control 1', [[['B', 5, 0.05], ['B', 1, 0.85], ['B', 3, 0.05], ['B', 2, 2.05]], 10, 0.0], [11, 0.0, 0.0]], ['normal control 2', [[['S', 5, 1.2], ['S', 5, 4.1]], 100, 0.65], [-10, 0.0, 6.5]], ['normal control 3', [[['B', 1, 0.05], ['B', 2, 1.2], ['B', 2, 0.05]], 100, 0.0], [5, 0.0, 0.0]], ['normal control 4', [[['S', 1, 4.1], ['S', 3, 4.1]], 10, 0.0], [-4, 0.0, 0.0]]]]
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 short lot pnl sign 1	`[-8, 730.0, 0.0]`	`[-8, -890.0, 0.0]`	Failed
regression short lot pnl sign 2	`[0, 940.0, 10.0]`	`[0, 800.0, 10.0]`	Failed
partial repair probe 1	`[15, -1035.0, 0.0]`	`[15, 1035.0, 0.0]`	Failed
partial repair probe 2	`[-2, 2220.0, 0.0]`	`[-2, -2220.0, 0.0]`	Failed
normal control 1	`[-13, 0.0, 13.0]`	`[-13, 0.0, 13.0]`	Passed
normal control 2	`[15, 0.0, 0.0]`	`[15, 0.0, 0.0]`	Passed
normal control 3	`[-1, 0.0, 11.0]`	`[-1, 0.0, 11.0]`	Passed
normal control 4	`[0, 0.0, 2.6]`	`[0, 0.0, 2.6]`	Passed

SHA-256 / 1b6f1a5304954e6464a758ab69b7db49969636aae60ad91e310fe6f16e091e19

3 / The verified repair

Exit 0

"""Failure Map reference implementation. Python standard library only."""
import json
import collections
N = 1
observations = []
def solve(trades, multiplier, fee):
    lots = collections.deque()
    realized = 0
    fees = 0
    fee_c = round(fee * 100)
    for side, qty, price in trades:
        p = round(price * 100)
        sgn = 1 if side == 'B' else -1
        fees += qty * fee_c
        left = qty
        while left and lots and (lots[0][0] > 0) != (sgn > 0):
            lot = lots[0]
            m = min(left, abs(lot[0]))
            direction = 1 if lot[0] > 0 else -1
            realized += (p - lot[1]) * m * direction
            lot[0] -= m * direction
            left -= m
            if lot[0] == 0:
                lots.popleft()
        if left:
            lots.append([sgn * left, p])
    pos = sum(l[0] for l in lots)
    return [pos, realized * multiplier / 100, fees / 100]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression short lot pnl sign 1', [[['B', 2, 4.1], ['S', 3, 0.05], ['S', 5, 0.05], ['B', 1, 0.85], ['S', 3, 0.85]], 100, 0.0], [-8, -890.0, 0.0]], ['regression short lot pnl sign 2', [[['S', 3, 4.1], ['B', 5, 1.2], ['S', 2, 0.85]], 100, 1.0], [0, 800.0, 10.0]], ['partial repair probe 1', [[['B', 5, 0.05], ['B', 1, 3.5], ['B', 5, 0.05], ['B', 2, 1.2], ['S', 3, 3.5], ['B', 5, 2.05]], 100, 0.0], [15, 1035.0, 0.0]], ['partial repair probe 2', [[['B', 1, 1.2], ['B', 3, 4.1], ['B', 3, 3.5], ['B', 1, 2.05], ['S', 5, 0.05], ['S', 5, 1.2]], 100, 0.0], [-2, -2220.0, 0.0]], ['normal control 1', [[['S', 1, 2.05], ['S', 5, 3.5], ['S', 1, 1.2], ['S', 3, 4.1], ['S', 3, 4.1]], 100, 1.0], [-13, 0.0, 13.0]], ['normal control 2', [[['B', 5, 0.85], ['B', 2, 4.1], ['B', 3, 0.85], ['B', 3, 0.85], ['B', 2, 2.05]], 10, 0.0], [15, 0.0, 0.0]], ['normal control 3', [[['S', 5, 0.05], ['S', 1, 2.05], ['B', 5, 0.05]], 10, 1.0], [-1, 0.0, 11.0]], ['normal control 4', [[['S', 2, 0.05], ['B', 2, 0.05]], 100, 0.65], [0, 0.0, 2.6]]], [['regression short lot pnl sign 1', [[['S', 3, 2.05], ['B', 5, 4.1]], 100, 0.0], [2, -615.0, 0.0]], ['regression short lot pnl sign 2', [[['S', 2, 0.05], ['B', 5, 2.05], ['S', 5, 1.2], ['S', 5, 1.2], ['S', 3, 0.85]], 10, 1.0], [-10, -65.5, 20.0]], ['partial repair probe 1', [[['B', 3, 1.2], ['B', 5, 0.85], ['S', 3, 0.05], ['S', 3, 2.05]], 100, 1.0], [2, 15.0, 14.0]], ['partial repair probe 2', [[['S', 1, 2.05], ['B', 5, 2.05], ['B', 3, 3.5], ['S', 2, 4.1]], 100, 1.0], [5, 410.0, 11.0]], ['normal control 1', [[['B', 3, 0.05], ['B', 1, 4.1]], 10, 0.65], [4, 0.0, 2.6]], ['normal control 2', [[['S', 5, 0.05], ['S', 5, 0.85], ['S', 2, 4.1], ['S', 2, 3.5]], 10, 0.65], [-14, 0.0, 9.1]], ['normal control 3', [[['B', 1, 0.05], ['B', 2, 2.05]], 100, 1.0], [3, 0.0, 3.0]], ['normal control 4', [[['B', 1, 4.1]], 100, 0.65], [1, 0.0, 0.65]]], [['regression short lot pnl sign 1', [[['B', 3, 4.1], ['S', 5, 0.05], ['S', 1, 2.05], ['S', 1, 4.1], ['B', 2, 0.85], ['B', 5, 0.05]], 100, 1.0], [3, -770.0, 17.0]], ['regression short lot pnl sign 2', [[['S', 3, 3.5], ['B', 1, 2.05], ['B', 5, 3.5], ['S', 3, 3.5], ['S', 5, 0.05]], 100, 0.0], [-5, 145.0, 0.0]], ['partial repair probe 1', [[['B', 3, 1.2], ['S', 1, 4.1], ['S', 2, 3.5]], 10, 0.0], [0, 75.0, 0.0]], ['partial repair probe 2', [[['B', 5, 3.5], ['B', 3, 2.05], ['B', 5, 0.85], ['S', 1, 3.5], ['B', 1, 4.1], ['S', 3, 0.85]], 10, 1.0], [10, -79.5, 18.0]], ['normal control 1', [[['B', 1, 0.85], ['B', 5, 0.05]], 10, 0.0], [6, 0.0, 0.0]], ['normal control 2', [[['S', 2, 0.05]], 10, 0.0], [-2, 0.0, 0.0]], ['normal control 3', [[['S', 5, 0.85], ['S', 5, 0.85], ['S', 2, 3.5], ['B', 5, 0.85], ['S', 3, 1.2], ['B', 3, 0.85]], 100, 1.0], [-7, 0.0, 23.0]], ['normal control 4', [[['B', 1, 0.85], ['B', 3, 4.1], ['B', 2, 2.05], ['B', 3, 0.85], ['B', 3, 3.5]], 10, 0.65], [12, 0.0, 7.8]]], [['regression short lot pnl sign 1', [[['B', 5, 4.1], ['S', 5, 0.85], ['S', 3, 0.05], ['S', 2, 1.2], ['B', 5, 3.5], ['B', 3, 4.1]], 10, 1.0], [3, -312.0, 23.0]], ['regression short lot pnl sign 2', [[['B', 1, 1.2], ['S', 2, 3.5], ['S', 1, 1.2], ['B', 3, 1.2]], 100, 1.0], [1, 460.0, 7.0]], ['partial repair probe 1', [[['B', 3, 0.05], ['B', 3, 0.05], ['S', 5, 2.05], ['B', 5, 3.5], ['B', 2, 3.5], ['B', 2, 0.85]], 10, 0.0], [10, 100.0, 0.0]], ['partial repair probe 2', [[['B', 1, 4.1], ['B', 1, 1.2], ['S', 2, 3.5]], 100, 0.65], [0, 170.0, 2.6]], ['normal control 1', [[['B', 5, 0.05]], 100, 0.65], [5, 0.0, 3.25]], ['normal control 2', [[['B', 5, 0.05], ['B', 1, 2.05]], 10, 0.0], [6, 0.0, 0.0]], ['normal control 3', [[['B', 5, 4.1], ['B', 2, 4.1]], 10, 0.0], [7, 0.0, 0.0]], ['normal control 4', [[['S', 5, 2.05], ['B', 3, 2.05], ['B', 5, 2.05]], 100, 0.65], [3, 0.0, 8.45]]], [['regression short lot pnl sign 1', [[['S', 2, 0.05], ['B', 3, 2.05], ['B', 5, 0.05]], 100, 1.0], [6, -400.0, 10.0]], ['regression short lot pnl sign 2', [[['S', 2, 1.2], ['B', 2, 2.05], ['S', 2, 0.85]], 100, 0.0], [-2, -170.0, 0.0]], ['partial repair probe 1', [[['S', 1, 0.05], ['B', 1, 0.05], ['B', 2, 2.05], ['S', 5, 1.2], ['S', 3, 3.5]], 10, 1.0], [-6, -17.0, 12.0]], ['partial repair probe 2', [[['B', 5, 1.2], ['S', 2, 0.85], ['S', 5, 0.85]], 10, 1.0], [-2, -17.5, 12.0]], ['normal control 1', [[['B', 5, 0.05], ['B', 1, 0.85], ['B', 3, 0.05], ['B', 2, 2.05]], 10, 0.0], [11, 0.0, 0.0]], ['normal control 2', [[['S', 5, 1.2], ['S', 5, 4.1]], 100, 0.65], [-10, 0.0, 6.5]], ['normal control 3', [[['B', 1, 0.05], ['B', 2, 1.2], ['B', 2, 0.05]], 100, 0.0], [5, 0.0, 0.0]], ['normal control 4', [[['S', 1, 4.1], ['S', 3, 4.1]], 10, 0.0], [-4, 0.0, 0.0]]]]
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 short lot pnl sign 1	`[-8, -890.0, 0.0]`	`[-8, -890.0, 0.0]`	Passed
regression short lot pnl sign 2	`[0, 800.0, 10.0]`	`[0, 800.0, 10.0]`	Passed
partial repair probe 1	`[15, 1035.0, 0.0]`	`[15, 1035.0, 0.0]`	Passed
partial repair probe 2	`[-2, -2220.0, 0.0]`	`[-2, -2220.0, 0.0]`	Passed
normal control 1	`[-13, 0.0, 13.0]`	`[-13, 0.0, 13.0]`	Passed
normal control 2	`[15, 0.0, 0.0]`	`[15, 0.0, 0.0]`	Passed
normal control 3	`[-1, 0.0, 11.0]`	`[-1, 0.0, 11.0]`	Passed
normal control 4	`[0, 0.0, 2.6]`	`[0, 0.0, 2.6]`	Passed

SHA-256 / 039146e25a07a178da680fb76755c25551bfc4ab9b59d77cfa9c9d71bac92717

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 2026-09-29T14:46:57.375691+00:00.

Case digest / 1edc10605d518cd2834173b6c4b1d26259d44d2a36b324767f66556d7ee9002f