FA-61631 / Options payoff and settlement / Open access
Uncovered short option margin requirement: premium is only part of the percentage branch · case 01
Options at the minimum requirement exclude their own premium.
ROOT CAUSE
The premium is placed inside the first argument of max().
VERIFIED REPAIR
Add the premium outside the max().
Unsuccessful approach: Dropping the premium entirely understates every requirement.
Case contract
Inputs kind, underlying price, strike, premium, contracts and multiplier. Out-of-the-money amount is max(K-S,0) for calls and max(S-K,0) for puts. Per-unit requirement = premium + max(20% of underlying - OTM amount, 10% of floor base) where the floor base is the underlying for calls and the strike for puts. Return requirement*multiplier*contracts rounded to cents.
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
from fractions import Fraction
N = 1
observations = []
def solve(kind, underlying, strike, premium, contracts, multiplier):
S = Fraction(str(underlying))
K = Fraction(str(strike))
P = Fraction(str(premium))
otm = max(K - S, 0) if kind == 'C' else max(S - K, 0)
floor_base = S if kind == 'C' else K
req = max(P + S * Fraction(20, 100) - otm, floor_base * Fraction(10, 100))
return float(round(req * multiplier * contracts, 2))
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression premium inclusion 1', ['P', 120.0, 95, 3.4, 1, 100], 1290.0], ['regression premium inclusion 2', ['C', 80.0, 90, 0.5, 2, 10], 170.0], ['partial repair probe 1', ['P', 104.25, 110, 3.4, 1, 100], 2425.0], ['partial repair probe 2', ['P', 100.0, 95, 3.4, 1, 10], 184.0], ['normal control 1', ['P', 80.0, 90, 8.0, 1, 10], 240.0], ['normal control 2', ['P', 80.0, 105, 15.1, 2, 10], 622.0], ['normal control 3', ['P', 100.0, 100, 15.1, 1, 100], 3510.0], ['normal control 4', ['C', 100.0, 110, 1.25, 1, 100], 1125.0]], [['regression premium inclusion 1', ['P', 120.0, 95, 1.25, 1, 100], 1075.0], ['regression premium inclusion 2', ['C', 92.5, 110, 0.5, 10, 100], 9750.0], ['partial repair probe 1', ['P', 80.0, 95, 1.25, 1, 100], 1725.0], ['partial repair probe 2', ['P', 92.5, 105, 8.0, 1, 10], 265.0], ['normal control 1', ['C', 100.0, 95, 0.5, 10, 10], 2050.0], ['normal control 2', ['C', 100.0, 110, 1.25, 2, 100], 2250.0], ['normal control 3', ['C', 100.0, 110, 15.1, 1, 10], 251.0], ['normal control 4', ['P', 120.0, 95, 0.5, 2, 100], 2000.0]], [['regression premium inclusion 1', ['P', 120.0, 95, 3.4, 1, 100], 1290.0], ['regression premium inclusion 2', ['P', 120.0, 95, 1.25, 10, 10], 1075.0], ['partial repair probe 1', ['C', 92.5, 90, 3.4, 10, 100], 21900.0], ['partial repair probe 2', ['P', 80.0, 95, 8.0, 10, 10], 2400.0], ['normal control 1', ['C', 92.5, 95, 8.0, 2, 10], 480.0], ['normal control 2', ['C', 104.25, 105, 1.25, 1, 10], 213.5], ['normal control 3', ['P', 92.5, 95, 1.25, 10, 100], 19750.0], ['normal control 4', ['C', 80.0, 95, 3.4, 1, 100], 1140.0]], [['regression premium inclusion 1', ['C', 80.0, 90, 15.1, 2, 10], 462.0], ['regression premium inclusion 2', ['P', 120.0, 105, 1.25, 10, 10], 1175.0], ['partial repair probe 1', ['P', 100.0, 95, 3.4, 1, 10], 184.0], ['partial repair probe 2', ['P', 100.0, 100, 1.25, 2, 10], 425.0], ['normal control 1', ['C', 80.0, 100, 1.25, 1, 10], 92.5], ['normal control 2', ['P', 120.0, 95, 0.5, 2, 10], 200.0], ['normal control 3', ['P', 80.0, 100, 15.1, 2, 10], 622.0], ['normal control 4', ['C', 80.0, 105, 0.5, 2, 100], 1700.0]], [['regression premium inclusion 1', ['C', 80.0, 100, 15.1, 2, 100], 4620.0], ['regression premium inclusion 2', ['P', 104.25, 90, 8.0, 1, 100], 1700.0], ['partial repair probe 1', ['P', 80.0, 95, 3.4, 10, 100], 19400.0], ['partial repair probe 2', ['P', 80.0, 105, 0.5, 1, 10], 165.0], ['normal control 1', ['C', 120.0, 105, 3.4, 2, 100], 5480.0], ['normal control 2', ['C', 80.0, 95, 8.0, 10, 10], 1600.0], ['normal control 3', ['C', 100.0, 90, 0.5, 10, 100], 20500.0], ['normal control 4', ['P', 120.0, 100, 15.1, 10, 10], 2510.0]]]
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 premium inclusion 1 | 950.0 | 1290.0 | Failed |
| regression premium inclusion 2 | 160.0 | 170.0 | Failed |
| partial repair probe 1 | 2425.0 | 2425.0 | Passed |
| partial repair probe 2 | 184.0 | 184.0 | Passed |
| normal control 1 | 240.0 | 240.0 | Passed |
| normal control 2 | 622.0 | 622.0 | Passed |
| normal control 3 | 3510.0 | 3510.0 | Passed |
| normal control 4 | 1125.0 | 1125.0 | Passed |
SHA-256 / 8b082d5a6c55508c3b8364a5a403ef347498831c8dfc0c2b01fcf7202339fbe4
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(kind, underlying, strike, premium, contracts, multiplier):
S = Fraction(str(underlying))
K = Fraction(str(strike))
P = Fraction(str(premium))
otm = max(K - S, 0) if kind == 'C' else max(S - K, 0)
floor_base = S if kind == 'C' else K
req = max(S * Fraction(20, 100) - otm, floor_base * Fraction(10, 100))
return float(round(req * multiplier * contracts, 2))
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression premium inclusion 1', ['P', 120.0, 95, 3.4, 1, 100], 1290.0], ['regression premium inclusion 2', ['C', 80.0, 90, 0.5, 2, 10], 170.0], ['partial repair probe 1', ['P', 104.25, 110, 3.4, 1, 100], 2425.0], ['partial repair probe 2', ['P', 100.0, 95, 3.4, 1, 10], 184.0], ['normal control 1', ['P', 80.0, 90, 8.0, 1, 10], 240.0], ['normal control 2', ['P', 80.0, 105, 15.1, 2, 10], 622.0], ['normal control 3', ['P', 100.0, 100, 15.1, 1, 100], 3510.0], ['normal control 4', ['C', 100.0, 110, 1.25, 1, 100], 1125.0]], [['regression premium inclusion 1', ['P', 120.0, 95, 1.25, 1, 100], 1075.0], ['regression premium inclusion 2', ['C', 92.5, 110, 0.5, 10, 100], 9750.0], ['partial repair probe 1', ['P', 80.0, 95, 1.25, 1, 100], 1725.0], ['partial repair probe 2', ['P', 92.5, 105, 8.0, 1, 10], 265.0], ['normal control 1', ['C', 100.0, 95, 0.5, 10, 10], 2050.0], ['normal control 2', ['C', 100.0, 110, 1.25, 2, 100], 2250.0], ['normal control 3', ['C', 100.0, 110, 15.1, 1, 10], 251.0], ['normal control 4', ['P', 120.0, 95, 0.5, 2, 100], 2000.0]], [['regression premium inclusion 1', ['P', 120.0, 95, 3.4, 1, 100], 1290.0], ['regression premium inclusion 2', ['P', 120.0, 95, 1.25, 10, 10], 1075.0], ['partial repair probe 1', ['C', 92.5, 90, 3.4, 10, 100], 21900.0], ['partial repair probe 2', ['P', 80.0, 95, 8.0, 10, 10], 2400.0], ['normal control 1', ['C', 92.5, 95, 8.0, 2, 10], 480.0], ['normal control 2', ['C', 104.25, 105, 1.25, 1, 10], 213.5], ['normal control 3', ['P', 92.5, 95, 1.25, 10, 100], 19750.0], ['normal control 4', ['C', 80.0, 95, 3.4, 1, 100], 1140.0]], [['regression premium inclusion 1', ['C', 80.0, 90, 15.1, 2, 10], 462.0], ['regression premium inclusion 2', ['P', 120.0, 105, 1.25, 10, 10], 1175.0], ['partial repair probe 1', ['P', 100.0, 95, 3.4, 1, 10], 184.0], ['partial repair probe 2', ['P', 100.0, 100, 1.25, 2, 10], 425.0], ['normal control 1', ['C', 80.0, 100, 1.25, 1, 10], 92.5], ['normal control 2', ['P', 120.0, 95, 0.5, 2, 10], 200.0], ['normal control 3', ['P', 80.0, 100, 15.1, 2, 10], 622.0], ['normal control 4', ['C', 80.0, 105, 0.5, 2, 100], 1700.0]], [['regression premium inclusion 1', ['C', 80.0, 100, 15.1, 2, 100], 4620.0], ['regression premium inclusion 2', ['P', 104.25, 90, 8.0, 1, 100], 1700.0], ['partial repair probe 1', ['P', 80.0, 95, 3.4, 10, 100], 19400.0], ['partial repair probe 2', ['P', 80.0, 105, 0.5, 1, 10], 165.0], ['normal control 1', ['C', 120.0, 105, 3.4, 2, 100], 5480.0], ['normal control 2', ['C', 80.0, 95, 8.0, 10, 10], 1600.0], ['normal control 3', ['C', 100.0, 90, 0.5, 10, 100], 20500.0], ['normal control 4', ['P', 120.0, 100, 15.1, 10, 10], 2510.0]]]
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 premium inclusion 1 | 950.0 | 1290.0 | Failed |
| regression premium inclusion 2 | 160.0 | 170.0 | Failed |
| partial repair probe 1 | 2085.0 | 2425.0 | Failed |
| partial repair probe 2 | 150.0 | 184.0 | Failed |
| normal control 1 | 160.0 | 240.0 | Failed |
| normal control 2 | 320.0 | 622.0 | Failed |
| normal control 3 | 2000.0 | 3510.0 | Failed |
| normal control 4 | 1000.0 | 1125.0 | Failed |
SHA-256 / 3ca2599ecd1bc307cdc0b5b9ace200c23ac896208d6275a8e20220d5a6a16d48
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(kind, underlying, strike, premium, contracts, multiplier):
S = Fraction(str(underlying))
K = Fraction(str(strike))
P = Fraction(str(premium))
otm = max(K - S, 0) if kind == 'C' else max(S - K, 0)
floor_base = S if kind == 'C' else K
req = P + max(S * Fraction(20, 100) - otm, floor_base * Fraction(10, 100))
return float(round(req * multiplier * contracts, 2))
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression premium inclusion 1', ['P', 120.0, 95, 3.4, 1, 100], 1290.0], ['regression premium inclusion 2', ['C', 80.0, 90, 0.5, 2, 10], 170.0], ['partial repair probe 1', ['P', 104.25, 110, 3.4, 1, 100], 2425.0], ['partial repair probe 2', ['P', 100.0, 95, 3.4, 1, 10], 184.0], ['normal control 1', ['P', 80.0, 90, 8.0, 1, 10], 240.0], ['normal control 2', ['P', 80.0, 105, 15.1, 2, 10], 622.0], ['normal control 3', ['P', 100.0, 100, 15.1, 1, 100], 3510.0], ['normal control 4', ['C', 100.0, 110, 1.25, 1, 100], 1125.0]], [['regression premium inclusion 1', ['P', 120.0, 95, 1.25, 1, 100], 1075.0], ['regression premium inclusion 2', ['C', 92.5, 110, 0.5, 10, 100], 9750.0], ['partial repair probe 1', ['P', 80.0, 95, 1.25, 1, 100], 1725.0], ['partial repair probe 2', ['P', 92.5, 105, 8.0, 1, 10], 265.0], ['normal control 1', ['C', 100.0, 95, 0.5, 10, 10], 2050.0], ['normal control 2', ['C', 100.0, 110, 1.25, 2, 100], 2250.0], ['normal control 3', ['C', 100.0, 110, 15.1, 1, 10], 251.0], ['normal control 4', ['P', 120.0, 95, 0.5, 2, 100], 2000.0]], [['regression premium inclusion 1', ['P', 120.0, 95, 3.4, 1, 100], 1290.0], ['regression premium inclusion 2', ['P', 120.0, 95, 1.25, 10, 10], 1075.0], ['partial repair probe 1', ['C', 92.5, 90, 3.4, 10, 100], 21900.0], ['partial repair probe 2', ['P', 80.0, 95, 8.0, 10, 10], 2400.0], ['normal control 1', ['C', 92.5, 95, 8.0, 2, 10], 480.0], ['normal control 2', ['C', 104.25, 105, 1.25, 1, 10], 213.5], ['normal control 3', ['P', 92.5, 95, 1.25, 10, 100], 19750.0], ['normal control 4', ['C', 80.0, 95, 3.4, 1, 100], 1140.0]], [['regression premium inclusion 1', ['C', 80.0, 90, 15.1, 2, 10], 462.0], ['regression premium inclusion 2', ['P', 120.0, 105, 1.25, 10, 10], 1175.0], ['partial repair probe 1', ['P', 100.0, 95, 3.4, 1, 10], 184.0], ['partial repair probe 2', ['P', 100.0, 100, 1.25, 2, 10], 425.0], ['normal control 1', ['C', 80.0, 100, 1.25, 1, 10], 92.5], ['normal control 2', ['P', 120.0, 95, 0.5, 2, 10], 200.0], ['normal control 3', ['P', 80.0, 100, 15.1, 2, 10], 622.0], ['normal control 4', ['C', 80.0, 105, 0.5, 2, 100], 1700.0]], [['regression premium inclusion 1', ['C', 80.0, 100, 15.1, 2, 100], 4620.0], ['regression premium inclusion 2', ['P', 104.25, 90, 8.0, 1, 100], 1700.0], ['partial repair probe 1', ['P', 80.0, 95, 3.4, 10, 100], 19400.0], ['partial repair probe 2', ['P', 80.0, 105, 0.5, 1, 10], 165.0], ['normal control 1', ['C', 120.0, 105, 3.4, 2, 100], 5480.0], ['normal control 2', ['C', 80.0, 95, 8.0, 10, 10], 1600.0], ['normal control 3', ['C', 100.0, 90, 0.5, 10, 100], 20500.0], ['normal control 4', ['P', 120.0, 100, 15.1, 10, 10], 2510.0]]]
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 premium inclusion 1 | 1290.0 | 1290.0 | Passed |
| regression premium inclusion 2 | 170.0 | 170.0 | Passed |
| partial repair probe 1 | 2425.0 | 2425.0 | Passed |
| partial repair probe 2 | 184.0 | 184.0 | Passed |
| normal control 1 | 240.0 | 240.0 | Passed |
| normal control 2 | 622.0 | 622.0 | Passed |
| normal control 3 | 3510.0 | 3510.0 | Passed |
| normal control 4 | 1125.0 | 1125.0 | Passed |
SHA-256 / b18272baced6de3a7bb6376d50af91a679800999f02fc423cb96ed11413b20a5
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 2026-09-29T14:46:57.056578+00:00.
Case digest / ec65fec9a174c3a842e7253dd51e70a49ee0e44d79577322843c1fd21b2f1520