FA-61626 / Options payoff and settlement / Open access

Uncovered short option margin requirement: the OTM reduction is subtracted after the minimum test · case 01

Far out-of-the-money options fall below the minimum requirement.

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

ROOT CAUSE

The OTM amount is subtracted outside the max() that enforces the minimum.

VERIFIED REPAIR

Subtract OTM inside the first branch before comparing with the minimum.

Unsuccessful approach: Halving the OTM reduction still leaves the minimum comparison unchanged in form.

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 = P + max(S * Fraction(20, 100), floor_base * Fraction(10, 100)) - otm
    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 otm reduction placement 1', ['C', 80.0, 110, 8.0, 10, 100], 16000.0], ['regression otm reduction placement 2', ['P', 120.0, 100, 0.5, 2, 100], 2100.0], ['partial repair probe 1', ['P', 100.0, 90, 15.1, 1, 10], 251.0], ['partial repair probe 2', ['P', 104.25, 100, 15.1, 2, 100], 6340.0], ['boundary control 1', ['C', 100.0, 100, 2.0, 1, 100], 2200.0], ['boundary control 2', ['P', 80.0, 100, 20.5, 1, 100], 3650.0], ['normal control 1', ['C', 100.0, 95, 0.5, 10, 10], 2050.0], ['normal control 2', ['C', 104.25, 90, 3.4, 10, 10], 2425.0]], [['regression otm reduction placement 1', ['P', 104.25, 90, 0.5, 2, 10], 190.0], ['regression otm reduction placement 2', ['P', 120.0, 90, 0.5, 2, 100], 1900.0], ['partial repair probe 1', ['C', 100.0, 110, 1.25, 2, 100], 2250.0], ['partial repair probe 2', ['P', 92.5, 90, 8.0, 2, 100], 4800.0], ['boundary control 1', ['C', 100.0, 100, 2.0, 1, 100], 2200.0], ['boundary control 2', ['P', 80.0, 100, 20.5, 1, 100], 3650.0], ['normal control 1', ['C', 92.5, 90, 1.25, 10, 100], 19750.0], ['normal control 2', ['P', 100.0, 105, 8.0, 10, 10], 2800.0]], [['regression otm reduction placement 1', ['C', 92.5, 110, 1.25, 10, 100], 10500.0], ['regression otm reduction placement 2', ['P', 104.25, 90, 0.5, 1, 10], 95.0], ['partial repair probe 1', ['P', 104.25, 100, 8.0, 10, 100], 24600.0], ['partial repair probe 2', ['C', 104.25, 105, 1.25, 10, 10], 2135.0], ['boundary control 1', ['P', 80.0, 100, 20.5, 1, 100], 3650.0], ['boundary control 2', ['C', 100.0, 100, 2.0, 1, 100], 2200.0], ['normal control 1', ['C', 92.5, 90, 15.1, 10, 100], 33600.0], ['normal control 2', ['P', 80.0, 95, 0.5, 10, 10], 1650.0]], [['regression otm reduction placement 1', ['P', 104.25, 90, 3.4, 1, 100], 1240.0], ['regression otm reduction placement 2', ['C', 80.0, 105, 3.4, 2, 100], 2280.0], ['partial repair probe 1', ['P', 104.25, 95, 3.4, 10, 100], 15000.0], ['partial repair probe 2', ['P', 92.5, 90, 1.25, 2, 100], 3450.0], ['boundary control 1', ['C', 100.0, 100, 2.0, 1, 100], 2200.0], ['boundary control 2', ['P', 80.0, 100, 20.5, 1, 100], 3650.0], ['normal control 1', ['P', 80.0, 100, 8.0, 2, 100], 4800.0], ['normal control 2', ['P', 92.5, 100, 1.25, 1, 100], 1975.0]], [['regression otm reduction placement 1', ['C', 80.0, 95, 1.25, 10, 100], 9250.0], ['regression otm reduction placement 2', ['C', 80.0, 95, 0.5, 1, 10], 85.0], ['partial repair probe 1', ['P', 100.0, 95, 0.5, 10, 100], 15500.0], ['partial repair probe 2', ['P', 104.25, 95, 8.0, 2, 100], 3920.0], ['boundary control 1', ['C', 100.0, 100, 2.0, 1, 100], 2200.0], ['boundary control 2', ['P', 80.0, 100, 20.5, 1, 100], 3650.0], ['normal control 1', ['C', 120.0, 110, 0.5, 10, 100], 24500.0], ['normal control 2', ['P', 92.5, 110, 1.25, 2, 100], 3950.0]]]
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 otm reduction placement 1	`-6000.0`	`16000.0`	Failed
regression otm reduction placement 2	`900.0`	`2100.0`	Failed
partial repair probe 1	`251.0`	`251.0`	Passed
partial repair probe 2	`6340.0`	`6340.0`	Passed
boundary control 1	`2200.0`	`2200.0`	Passed
boundary control 2	`3650.0`	`3650.0`	Passed
normal control 1	`2050.0`	`2050.0`	Passed
normal control 2	`2425.0`	`2425.0`	Passed

SHA-256 / 2dcd1625dcc61240527f39bd8722e9fc785223a6dc48214f5b2db15ff8c5a5e7

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 = P + max(S * Fraction(20, 100) - otm / 2, 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 otm reduction placement 1', ['C', 80.0, 110, 8.0, 10, 100], 16000.0], ['regression otm reduction placement 2', ['P', 120.0, 100, 0.5, 2, 100], 2100.0], ['partial repair probe 1', ['P', 100.0, 90, 15.1, 1, 10], 251.0], ['partial repair probe 2', ['P', 104.25, 100, 15.1, 2, 100], 6340.0], ['boundary control 1', ['C', 100.0, 100, 2.0, 1, 100], 2200.0], ['boundary control 2', ['P', 80.0, 100, 20.5, 1, 100], 3650.0], ['normal control 1', ['C', 100.0, 95, 0.5, 10, 10], 2050.0], ['normal control 2', ['C', 104.25, 90, 3.4, 10, 10], 2425.0]], [['regression otm reduction placement 1', ['P', 104.25, 90, 0.5, 2, 10], 190.0], ['regression otm reduction placement 2', ['P', 120.0, 90, 0.5, 2, 100], 1900.0], ['partial repair probe 1', ['C', 100.0, 110, 1.25, 2, 100], 2250.0], ['partial repair probe 2', ['P', 92.5, 90, 8.0, 2, 100], 4800.0], ['boundary control 1', ['C', 100.0, 100, 2.0, 1, 100], 2200.0], ['boundary control 2', ['P', 80.0, 100, 20.5, 1, 100], 3650.0], ['normal control 1', ['C', 92.5, 90, 1.25, 10, 100], 19750.0], ['normal control 2', ['P', 100.0, 105, 8.0, 10, 10], 2800.0]], [['regression otm reduction placement 1', ['C', 92.5, 110, 1.25, 10, 100], 10500.0], ['regression otm reduction placement 2', ['P', 104.25, 90, 0.5, 1, 10], 95.0], ['partial repair probe 1', ['P', 104.25, 100, 8.0, 10, 100], 24600.0], ['partial repair probe 2', ['C', 104.25, 105, 1.25, 10, 10], 2135.0], ['boundary control 1', ['P', 80.0, 100, 20.5, 1, 100], 3650.0], ['boundary control 2', ['C', 100.0, 100, 2.0, 1, 100], 2200.0], ['normal control 1', ['C', 92.5, 90, 15.1, 10, 100], 33600.0], ['normal control 2', ['P', 80.0, 95, 0.5, 10, 10], 1650.0]], [['regression otm reduction placement 1', ['P', 104.25, 90, 3.4, 1, 100], 1240.0], ['regression otm reduction placement 2', ['C', 80.0, 105, 3.4, 2, 100], 2280.0], ['partial repair probe 1', ['P', 104.25, 95, 3.4, 10, 100], 15000.0], ['partial repair probe 2', ['P', 92.5, 90, 1.25, 2, 100], 3450.0], ['boundary control 1', ['C', 100.0, 100, 2.0, 1, 100], 2200.0], ['boundary control 2', ['P', 80.0, 100, 20.5, 1, 100], 3650.0], ['normal control 1', ['P', 80.0, 100, 8.0, 2, 100], 4800.0], ['normal control 2', ['P', 92.5, 100, 1.25, 1, 100], 1975.0]], [['regression otm reduction placement 1', ['C', 80.0, 95, 1.25, 10, 100], 9250.0], ['regression otm reduction placement 2', ['C', 80.0, 95, 0.5, 1, 10], 85.0], ['partial repair probe 1', ['P', 100.0, 95, 0.5, 10, 100], 15500.0], ['partial repair probe 2', ['P', 104.25, 95, 8.0, 2, 100], 3920.0], ['boundary control 1', ['C', 100.0, 100, 2.0, 1, 100], 2200.0], ['boundary control 2', ['P', 80.0, 100, 20.5, 1, 100], 3650.0], ['normal control 1', ['C', 120.0, 110, 0.5, 10, 100], 24500.0], ['normal control 2', ['P', 92.5, 110, 1.25, 2, 100], 3950.0]]]
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 otm reduction placement 1	`16000.0`	`16000.0`	Passed
regression otm reduction placement 2	`2900.0`	`2100.0`	Failed
partial repair probe 1	`301.0`	`251.0`	Failed
partial repair probe 2	`6765.0`	`6340.0`	Failed
boundary control 1	`2200.0`	`2200.0`	Passed
boundary control 2	`3650.0`	`3650.0`	Passed
normal control 1	`2050.0`	`2050.0`	Passed
normal control 2	`2425.0`	`2425.0`	Passed

SHA-256 / 70b63bb0042b2a8921eeb99480927344bf0dae730168309e8406b1e5c6a0e1f2

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 otm reduction placement 1', ['C', 80.0, 110, 8.0, 10, 100], 16000.0], ['regression otm reduction placement 2', ['P', 120.0, 100, 0.5, 2, 100], 2100.0], ['partial repair probe 1', ['P', 100.0, 90, 15.1, 1, 10], 251.0], ['partial repair probe 2', ['P', 104.25, 100, 15.1, 2, 100], 6340.0], ['boundary control 1', ['C', 100.0, 100, 2.0, 1, 100], 2200.0], ['boundary control 2', ['P', 80.0, 100, 20.5, 1, 100], 3650.0], ['normal control 1', ['C', 100.0, 95, 0.5, 10, 10], 2050.0], ['normal control 2', ['C', 104.25, 90, 3.4, 10, 10], 2425.0]], [['regression otm reduction placement 1', ['P', 104.25, 90, 0.5, 2, 10], 190.0], ['regression otm reduction placement 2', ['P', 120.0, 90, 0.5, 2, 100], 1900.0], ['partial repair probe 1', ['C', 100.0, 110, 1.25, 2, 100], 2250.0], ['partial repair probe 2', ['P', 92.5, 90, 8.0, 2, 100], 4800.0], ['boundary control 1', ['C', 100.0, 100, 2.0, 1, 100], 2200.0], ['boundary control 2', ['P', 80.0, 100, 20.5, 1, 100], 3650.0], ['normal control 1', ['C', 92.5, 90, 1.25, 10, 100], 19750.0], ['normal control 2', ['P', 100.0, 105, 8.0, 10, 10], 2800.0]], [['regression otm reduction placement 1', ['C', 92.5, 110, 1.25, 10, 100], 10500.0], ['regression otm reduction placement 2', ['P', 104.25, 90, 0.5, 1, 10], 95.0], ['partial repair probe 1', ['P', 104.25, 100, 8.0, 10, 100], 24600.0], ['partial repair probe 2', ['C', 104.25, 105, 1.25, 10, 10], 2135.0], ['boundary control 1', ['P', 80.0, 100, 20.5, 1, 100], 3650.0], ['boundary control 2', ['C', 100.0, 100, 2.0, 1, 100], 2200.0], ['normal control 1', ['C', 92.5, 90, 15.1, 10, 100], 33600.0], ['normal control 2', ['P', 80.0, 95, 0.5, 10, 10], 1650.0]], [['regression otm reduction placement 1', ['P', 104.25, 90, 3.4, 1, 100], 1240.0], ['regression otm reduction placement 2', ['C', 80.0, 105, 3.4, 2, 100], 2280.0], ['partial repair probe 1', ['P', 104.25, 95, 3.4, 10, 100], 15000.0], ['partial repair probe 2', ['P', 92.5, 90, 1.25, 2, 100], 3450.0], ['boundary control 1', ['C', 100.0, 100, 2.0, 1, 100], 2200.0], ['boundary control 2', ['P', 80.0, 100, 20.5, 1, 100], 3650.0], ['normal control 1', ['P', 80.0, 100, 8.0, 2, 100], 4800.0], ['normal control 2', ['P', 92.5, 100, 1.25, 1, 100], 1975.0]], [['regression otm reduction placement 1', ['C', 80.0, 95, 1.25, 10, 100], 9250.0], ['regression otm reduction placement 2', ['C', 80.0, 95, 0.5, 1, 10], 85.0], ['partial repair probe 1', ['P', 100.0, 95, 0.5, 10, 100], 15500.0], ['partial repair probe 2', ['P', 104.25, 95, 8.0, 2, 100], 3920.0], ['boundary control 1', ['C', 100.0, 100, 2.0, 1, 100], 2200.0], ['boundary control 2', ['P', 80.0, 100, 20.5, 1, 100], 3650.0], ['normal control 1', ['C', 120.0, 110, 0.5, 10, 100], 24500.0], ['normal control 2', ['P', 92.5, 110, 1.25, 2, 100], 3950.0]]]
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 otm reduction placement 1	`16000.0`	`16000.0`	Passed
regression otm reduction placement 2	`2100.0`	`2100.0`	Passed
partial repair probe 1	`251.0`	`251.0`	Passed
partial repair probe 2	`6340.0`	`6340.0`	Passed
boundary control 1	`2200.0`	`2200.0`	Passed
boundary control 2	`3650.0`	`3650.0`	Passed
normal control 1	`2050.0`	`2050.0`	Passed
normal control 2	`2425.0`	`2425.0`	Passed

SHA-256 / 08ab6805f9a42e1d574509cecb794c772754a82564a0c657aee010d2dac39eb5

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 2026-09-29T14:46:57.041314+00:00.

Case digest / 8eb3eafdf17b32a37998e3e15ea1239ffe6c037b6b53ec8f193c82d8a6ae30e2