FA-61381 / Options payoff and settlement / Open access

Physical delivery on exercise and assignment: every contract is assumed to deliver 100 shares · case 01

Adjusted contracts after corporate actions deliver the wrong share count.

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

ROOT CAUSE

The share count uses a standard 100-share deliverable.

VERIFIED REPAIR

Multiply contracts by the actual deliverable per contract.

Unsuccessful approach: Using the deliverable for shares but 100 for cash splits the two legs.

Case contract

Inputs kind C/P, strike, signed contracts (+long exercised, -short assigned), deliverable shares per contract and whether exercise happened. Long calls and short puts receive shares and pay strike*shares; long puts and short calls deliver shares and receive strike*shares. Return [share delta, cash delta] (cash in currency, computed in cents); unexercised returns [0, 0.0].

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(kind, strike, contracts, deliverable, exercised):
    if not exercised:
        return [0, 0.0]
    n = abs(contracts) * 100
    k = round(strike * 100)
    direction = 1 if kind == 'C' else -1
    if contracts < 0:
        direction = -direction
    shares = direction * n
    cash = -direction * n * k
    return [shares, cash / 100]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression adjusted deliverable 1', ['P', 250, -3, 50, True], [150, -37500.0]], ['regression adjusted deliverable 2', ['C', 101.25, 5, 150, True], [750, -75937.5]], ['partial repair probe 1', ['P', 10, -3, 150, True], [450, -4500.0]], ['partial repair probe 2', ['C', 101.25, 5, 50, True], [250, -25312.5]], ['boundary control 1', ['C', 50, 1, 100, True], [100, -5000.0]], ['boundary control 2', ['P', 50, -1, 100, True], [100, -5000.0]], ['normal control 1', ['C', 10, -3, 50, False], [0, 0.0]], ['normal control 2', ['C', 10, 1, 100, True], [100, -1000.0]]], [['regression adjusted deliverable 1', ['C', 42.5, 5, 50, True], [250, -10625.0]], ['regression adjusted deliverable 2', ['P', 101.25, -3, 50, True], [150, -15187.5]], ['partial repair probe 1', ['C', 101.25, 2, 150, True], [300, -30375.0]], ['partial repair probe 2', ['C', 42.5, -3, 150, True], [-450, 19125.0]], ['boundary control 1', ['C', 50, 1, 100, False], [0, 0.0]], ['boundary control 2', ['P', 50, -1, 100, True], [100, -5000.0]], ['normal control 1', ['P', 42.5, -1, 100, True], [100, -4250.0]], ['normal control 2', ['P', 101.25, -1, 100, True], [100, -10125.0]]], [['regression adjusted deliverable 1', ['C', 250, 2, 150, True], [300, -75000.0]], ['regression adjusted deliverable 2', ['P', 101.25, 1, 50, True], [-50, 5062.5]], ['partial repair probe 1', ['C', 101.25, -1, 150, True], [-150, 15187.5]], ['partial repair probe 2', ['P', 42.5, 5, 50, True], [-250, 10625.0]], ['boundary control 1', ['C', 50, 1, 100, False], [0, 0.0]], ['boundary control 2', ['C', 50, 1, 100, True], [100, -5000.0]], ['normal control 1', ['C', 250, -1, 50, False], [0, 0.0]], ['normal control 2', ['P', 42.5, 2, 100, True], [-200, 8500.0]]], [['regression adjusted deliverable 1', ['P', 250, 5, 50, True], [-250, 62500.0]], ['regression adjusted deliverable 2', ['C', 250, 1, 50, True], [50, -12500.0]], ['partial repair probe 1', ['P', 10, 5, 133, True], [-665, 6650.0]], ['partial repair probe 2', ['P', 250, 1, 50, True], [-50, 12500.0]], ['boundary control 1', ['P', 50, -1, 100, True], [100, -5000.0]], ['boundary control 2', ['C', 50, 1, 100, False], [0, 0.0]], ['normal control 1', ['C', 10, -1, 133, False], [0, 0.0]], ['normal control 2', ['C', 10, -3, 100, False], [0, 0.0]]], [['regression adjusted deliverable 1', ['C', 250, -1, 150, True], [-150, 37500.0]], ['regression adjusted deliverable 2', ['P', 250, 1, 50, True], [-50, 12500.0]], ['partial repair probe 1', ['C', 250, -1, 133, True], [-133, 33250.0]], ['partial repair probe 2', ['C', 42.5, -3, 50, True], [-150, 6375.0]], ['boundary control 1', ['C', 50, 1, 100, True], [100, -5000.0]], ['boundary control 2', ['C', 50, 1, 100, False], [0, 0.0]], ['normal control 1', ['C', 42.5, 2, 100, False], [0, 0.0]], ['normal control 2', ['C', 10, -1, 100, True], [-100, 1000.0]]]]
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 adjusted deliverable 1	`[300, -75000.0]`	`[150, -37500.0]`	Failed
regression adjusted deliverable 2	`[500, -50625.0]`	`[750, -75937.5]`	Failed
partial repair probe 1	`[300, -3000.0]`	`[450, -4500.0]`	Failed
partial repair probe 2	`[500, -50625.0]`	`[250, -25312.5]`	Failed
boundary control 1	`[100, -5000.0]`	`[100, -5000.0]`	Passed
boundary control 2	`[100, -5000.0]`	`[100, -5000.0]`	Passed
normal control 1	`[0, 0.0]`	`[0, 0.0]`	Passed
normal control 2	`[100, -1000.0]`	`[100, -1000.0]`	Passed

SHA-256 / 9e1db56880ea48fc3357ee6bf769a42f61cbb7a6c30d37a982fb5b1d0e6e4b72

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(kind, strike, contracts, deliverable, exercised):
    if not exercised:
        return [0, 0.0]
    n = abs(contracts) * deliverable
    k = round(strike * 100)
    direction = 1 if kind == 'C' else -1
    if contracts < 0:
        direction = -direction
    shares = direction * n
    cash = -direction * abs(contracts) * 100 * k
    return [shares, cash / 100]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression adjusted deliverable 1', ['P', 250, -3, 50, True], [150, -37500.0]], ['regression adjusted deliverable 2', ['C', 101.25, 5, 150, True], [750, -75937.5]], ['partial repair probe 1', ['P', 10, -3, 150, True], [450, -4500.0]], ['partial repair probe 2', ['C', 101.25, 5, 50, True], [250, -25312.5]], ['boundary control 1', ['C', 50, 1, 100, True], [100, -5000.0]], ['boundary control 2', ['P', 50, -1, 100, True], [100, -5000.0]], ['normal control 1', ['C', 10, -3, 50, False], [0, 0.0]], ['normal control 2', ['C', 10, 1, 100, True], [100, -1000.0]]], [['regression adjusted deliverable 1', ['C', 42.5, 5, 50, True], [250, -10625.0]], ['regression adjusted deliverable 2', ['P', 101.25, -3, 50, True], [150, -15187.5]], ['partial repair probe 1', ['C', 101.25, 2, 150, True], [300, -30375.0]], ['partial repair probe 2', ['C', 42.5, -3, 150, True], [-450, 19125.0]], ['boundary control 1', ['C', 50, 1, 100, False], [0, 0.0]], ['boundary control 2', ['P', 50, -1, 100, True], [100, -5000.0]], ['normal control 1', ['P', 42.5, -1, 100, True], [100, -4250.0]], ['normal control 2', ['P', 101.25, -1, 100, True], [100, -10125.0]]], [['regression adjusted deliverable 1', ['C', 250, 2, 150, True], [300, -75000.0]], ['regression adjusted deliverable 2', ['P', 101.25, 1, 50, True], [-50, 5062.5]], ['partial repair probe 1', ['C', 101.25, -1, 150, True], [-150, 15187.5]], ['partial repair probe 2', ['P', 42.5, 5, 50, True], [-250, 10625.0]], ['boundary control 1', ['C', 50, 1, 100, False], [0, 0.0]], ['boundary control 2', ['C', 50, 1, 100, True], [100, -5000.0]], ['normal control 1', ['C', 250, -1, 50, False], [0, 0.0]], ['normal control 2', ['P', 42.5, 2, 100, True], [-200, 8500.0]]], [['regression adjusted deliverable 1', ['P', 250, 5, 50, True], [-250, 62500.0]], ['regression adjusted deliverable 2', ['C', 250, 1, 50, True], [50, -12500.0]], ['partial repair probe 1', ['P', 10, 5, 133, True], [-665, 6650.0]], ['partial repair probe 2', ['P', 250, 1, 50, True], [-50, 12500.0]], ['boundary control 1', ['P', 50, -1, 100, True], [100, -5000.0]], ['boundary control 2', ['C', 50, 1, 100, False], [0, 0.0]], ['normal control 1', ['C', 10, -1, 133, False], [0, 0.0]], ['normal control 2', ['C', 10, -3, 100, False], [0, 0.0]]], [['regression adjusted deliverable 1', ['C', 250, -1, 150, True], [-150, 37500.0]], ['regression adjusted deliverable 2', ['P', 250, 1, 50, True], [-50, 12500.0]], ['partial repair probe 1', ['C', 250, -1, 133, True], [-133, 33250.0]], ['partial repair probe 2', ['C', 42.5, -3, 50, True], [-150, 6375.0]], ['boundary control 1', ['C', 50, 1, 100, True], [100, -5000.0]], ['boundary control 2', ['C', 50, 1, 100, False], [0, 0.0]], ['normal control 1', ['C', 42.5, 2, 100, False], [0, 0.0]], ['normal control 2', ['C', 10, -1, 100, True], [-100, 1000.0]]]]
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 adjusted deliverable 1	`[150, -75000.0]`	`[150, -37500.0]`	Failed
regression adjusted deliverable 2	`[750, -50625.0]`	`[750, -75937.5]`	Failed
partial repair probe 1	`[450, -3000.0]`	`[450, -4500.0]`	Failed
partial repair probe 2	`[250, -50625.0]`	`[250, -25312.5]`	Failed
boundary control 1	`[100, -5000.0]`	`[100, -5000.0]`	Passed
boundary control 2	`[100, -5000.0]`	`[100, -5000.0]`	Passed
normal control 1	`[0, 0.0]`	`[0, 0.0]`	Passed
normal control 2	`[100, -1000.0]`	`[100, -1000.0]`	Passed

SHA-256 / fb7208c6a87db3337044be993c1b2bcda497fbddcde6f8a2ed7ec9ae10a1dfe6

3 / The verified repair

Exit 0

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

N = 1
observations = []
def solve(kind, strike, contracts, deliverable, exercised):
    if not exercised:
        return [0, 0.0]
    n = abs(contracts) * deliverable
    k = round(strike * 100)
    direction = 1 if kind == 'C' else -1
    if contracts < 0:
        direction = -direction
    shares = direction * n
    cash = -direction * n * k
    return [shares, cash / 100]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression adjusted deliverable 1', ['P', 250, -3, 50, True], [150, -37500.0]], ['regression adjusted deliverable 2', ['C', 101.25, 5, 150, True], [750, -75937.5]], ['partial repair probe 1', ['P', 10, -3, 150, True], [450, -4500.0]], ['partial repair probe 2', ['C', 101.25, 5, 50, True], [250, -25312.5]], ['boundary control 1', ['C', 50, 1, 100, True], [100, -5000.0]], ['boundary control 2', ['P', 50, -1, 100, True], [100, -5000.0]], ['normal control 1', ['C', 10, -3, 50, False], [0, 0.0]], ['normal control 2', ['C', 10, 1, 100, True], [100, -1000.0]]], [['regression adjusted deliverable 1', ['C', 42.5, 5, 50, True], [250, -10625.0]], ['regression adjusted deliverable 2', ['P', 101.25, -3, 50, True], [150, -15187.5]], ['partial repair probe 1', ['C', 101.25, 2, 150, True], [300, -30375.0]], ['partial repair probe 2', ['C', 42.5, -3, 150, True], [-450, 19125.0]], ['boundary control 1', ['C', 50, 1, 100, False], [0, 0.0]], ['boundary control 2', ['P', 50, -1, 100, True], [100, -5000.0]], ['normal control 1', ['P', 42.5, -1, 100, True], [100, -4250.0]], ['normal control 2', ['P', 101.25, -1, 100, True], [100, -10125.0]]], [['regression adjusted deliverable 1', ['C', 250, 2, 150, True], [300, -75000.0]], ['regression adjusted deliverable 2', ['P', 101.25, 1, 50, True], [-50, 5062.5]], ['partial repair probe 1', ['C', 101.25, -1, 150, True], [-150, 15187.5]], ['partial repair probe 2', ['P', 42.5, 5, 50, True], [-250, 10625.0]], ['boundary control 1', ['C', 50, 1, 100, False], [0, 0.0]], ['boundary control 2', ['C', 50, 1, 100, True], [100, -5000.0]], ['normal control 1', ['C', 250, -1, 50, False], [0, 0.0]], ['normal control 2', ['P', 42.5, 2, 100, True], [-200, 8500.0]]], [['regression adjusted deliverable 1', ['P', 250, 5, 50, True], [-250, 62500.0]], ['regression adjusted deliverable 2', ['C', 250, 1, 50, True], [50, -12500.0]], ['partial repair probe 1', ['P', 10, 5, 133, True], [-665, 6650.0]], ['partial repair probe 2', ['P', 250, 1, 50, True], [-50, 12500.0]], ['boundary control 1', ['P', 50, -1, 100, True], [100, -5000.0]], ['boundary control 2', ['C', 50, 1, 100, False], [0, 0.0]], ['normal control 1', ['C', 10, -1, 133, False], [0, 0.0]], ['normal control 2', ['C', 10, -3, 100, False], [0, 0.0]]], [['regression adjusted deliverable 1', ['C', 250, -1, 150, True], [-150, 37500.0]], ['regression adjusted deliverable 2', ['P', 250, 1, 50, True], [-50, 12500.0]], ['partial repair probe 1', ['C', 250, -1, 133, True], [-133, 33250.0]], ['partial repair probe 2', ['C', 42.5, -3, 50, True], [-150, 6375.0]], ['boundary control 1', ['C', 50, 1, 100, True], [100, -5000.0]], ['boundary control 2', ['C', 50, 1, 100, False], [0, 0.0]], ['normal control 1', ['C', 42.5, 2, 100, False], [0, 0.0]], ['normal control 2', ['C', 10, -1, 100, True], [-100, 1000.0]]]]
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 adjusted deliverable 1	`[150, -37500.0]`	`[150, -37500.0]`	Passed
regression adjusted deliverable 2	`[750, -75937.5]`	`[750, -75937.5]`	Passed
partial repair probe 1	`[450, -4500.0]`	`[450, -4500.0]`	Passed
partial repair probe 2	`[250, -25312.5]`	`[250, -25312.5]`	Passed
boundary control 1	`[100, -5000.0]`	`[100, -5000.0]`	Passed
boundary control 2	`[100, -5000.0]`	`[100, -5000.0]`	Passed
normal control 1	`[0, 0.0]`	`[0, 0.0]`	Passed
normal control 2	`[100, -1000.0]`	`[100, -1000.0]`	Passed

SHA-256 / fcc7a22bb6904f47c74e235ee928bb4182670d59622b7a0649e4f57b3e0a5a55

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 2026-09-29T14:46:54.801619+00:00.

Case digest / a7cd4411f8321ed046dbeca2e02844bf3e1559f42741f4a933c9eb8554ef4f94