FAILURE MAP
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FA-61386 / Options payoff and settlement / Open access

Contract adjustment for splits and special dividends: any forward split is treated as an integral ratio · case 01

A 3-for-2 split leaves strike and contracts unchanged and the deliverable unadjusted.

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

ROOT CAUSE

Integral ratios are detected with new > old instead of divisibility.

VERIFIED REPAIR

Treat a split as integral only when new is a multiple of old.

Unsuccessful approach: Requiring old == 1 misroutes ratios like 4-for-2.

Case contract

Inputs strike, contracts, deliverable, action and value. split [new, old]: if new is a multiple of old (ratio r), strike becomes strike/r rounded half-up to cents and contracts become contracts*r; otherwise the deliverable becomes floor(deliverable*new/old) with strike and contracts unchanged. special_div of at least 0.125 per share reduces the strike by the amount (rounded half-up to cents). Other actions, including ordinary_div, change nothing. Return [strike, contracts, deliverable].

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(strike, contracts, deliverable, action, value):
    k = round(strike * 100)
    if action == 'split':
        new, old = value
        if new > old:
            r = new // old
            k = (2 * k + r) // (2 * r)
            contracts = contracts * r
        else:
            deliverable = deliverable * new // old
    elif action == 'special_div':
        amt = round(value * 1000)
        if amt >= 125:
            k = (k * 10 - amt + 5) // 10
    return [k / 100, contracts, deliverable]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression integral ratio detection 1', [47.5, 1, 100, 'split', [5, 3]], [47.5, 1, 166]], ['regression integral ratio detection 2', [101.01, 1, 150, 'split', [5, 3]], [101.01, 1, 250]], ['partial repair probe 1', [87.75, 1, 100, 'split', [6, 3]], [43.88, 2, 100]], ['partial repair probe 2', [25.25, 1, 150, 'split', [6, 3]], [12.63, 2, 150]], ['boundary control 1', [40, 1, 100, 'ordinary_div', 1.0], [40.0, 1, 100]], ['boundary control 2', [25.25, 1, 100, 'split', [2, 1]], [12.63, 2, 100]], ['normal control 1', [87.75, 10, 150, 'special_div', 0.124], [87.75, 10, 150]], ['normal control 2', [47.5, 10, 100, 'special_div', 2.375], [45.13, 10, 100]]], [['regression integral ratio detection 1', [150.05, 3, 150, 'split', [5, 4]], [150.05, 3, 187]], ['regression integral ratio detection 2', [47.5, 10, 100, 'split', [3, 2]], [47.5, 10, 150]], ['partial repair probe 1', [12.5, 3, 150, 'split', [4, 2]], [6.25, 6, 150]], ['partial repair probe 2', [40, 10, 100, 'split', [4, 2]], [20.0, 20, 100]], ['boundary control 1', [40, 1, 100, 'special_div', 0.125], [39.88, 1, 100]], ['boundary control 2', [25.25, 1, 100, 'split', [2, 1]], [12.63, 2, 100]], ['normal control 1', [33.33, 10, 100, 'split', [3, 1]], [11.11, 30, 100]], ['normal control 2', [101.01, 1, 150, 'ordinary_div', 2.375], [101.01, 1, 150]]], [['regression integral ratio detection 1', [25.25, 10, 150, 'split', [5, 3]], [25.25, 10, 250]], ['regression integral ratio detection 2', [101.01, 1, 100, 'split', [3, 2]], [101.01, 1, 150]], ['partial repair probe 1', [87.75, 3, 100, 'split', [4, 2]], [43.88, 6, 100]], ['partial repair probe 2', [150.05, 3, 100, 'split', [4, 2]], [75.03, 6, 100]], ['boundary control 1', [40, 1, 100, 'special_div', 0.125], [39.88, 1, 100]], ['boundary control 2', [25.25, 1, 100, 'split', [2, 1]], [12.63, 2, 100]], ['normal control 1', [40, 3, 100, 'special_div', 0.1], [40.0, 3, 100]], ['normal control 2', [12.5, 3, 100, 'split', [1, 2]], [12.5, 3, 50]]], [['regression integral ratio detection 1', [101.01, 1, 100, 'split', [5, 4]], [101.01, 1, 125]], ['regression integral ratio detection 2', [87.75, 3, 150, 'split', [3, 2]], [87.75, 3, 225]], ['partial repair probe 1', [101.01, 3, 100, 'split', [4, 2]], [50.51, 6, 100]], ['partial repair probe 2', [12.5, 3, 100, 'split', [6, 3]], [6.25, 6, 100]], ['boundary control 1', [40, 1, 100, 'ordinary_div', 1.0], [40.0, 1, 100]], ['boundary control 2', [40, 1, 100, 'special_div', 0.125], [39.88, 1, 100]], ['normal control 1', [40, 3, 100, 'merger', 0.126], [40.0, 3, 100]], ['normal control 2', [33.33, 10, 100, 'split', [2, 1]], [16.67, 20, 100]]], [['regression integral ratio detection 1', [40, 3, 150, 'split', [5, 3]], [40.0, 3, 250]], ['regression integral ratio detection 2', [12.5, 10, 150, 'split', [5, 4]], [12.5, 10, 187]], ['partial repair probe 1', [33.33, 3, 100, 'split', [4, 2]], [16.67, 6, 100]], ['partial repair probe 2', [101.01, 1, 100, 'split', [4, 2]], [50.51, 2, 100]], ['boundary control 1', [40, 1, 100, 'special_div', 0.125], [39.88, 1, 100]], ['boundary control 2', [40, 1, 100, 'ordinary_div', 1.0], [40.0, 1, 100]], ['normal control 1', [150.05, 1, 100, 'special_div', 2.375], [147.68, 1, 100]], ['normal control 2', [12.5, 1, 100, 'merger', 0.124], [12.5, 1, 100]]]]
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 fixtureActualExpectedOutcome
regression integral ratio detection 1[47.5, 1, 100][47.5, 1, 166]Failed
regression integral ratio detection 2[101.01, 1, 150][101.01, 1, 250]Failed
partial repair probe 1[43.88, 2, 100][43.88, 2, 100]Passed
partial repair probe 2[12.63, 2, 150][12.63, 2, 150]Passed
boundary control 1[40.0, 1, 100][40.0, 1, 100]Passed
boundary control 2[12.63, 2, 100][12.63, 2, 100]Passed
normal control 1[87.75, 10, 150][87.75, 10, 150]Passed
normal control 2[45.13, 10, 100][45.13, 10, 100]Passed

SHA-256 / d2715468c45221c3a5df6592cdae6cb19578f2dc9a7068ac2f00dd41b71a2a95

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(strike, contracts, deliverable, action, value):
    k = round(strike * 100)
    if action == 'split':
        new, old = value
        if old == 1:
            r = new // old
            k = (2 * k + r) // (2 * r)
            contracts = contracts * r
        else:
            deliverable = deliverable * new // old
    elif action == 'special_div':
        amt = round(value * 1000)
        if amt >= 125:
            k = (k * 10 - amt + 5) // 10
    return [k / 100, contracts, deliverable]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression integral ratio detection 1', [47.5, 1, 100, 'split', [5, 3]], [47.5, 1, 166]], ['regression integral ratio detection 2', [101.01, 1, 150, 'split', [5, 3]], [101.01, 1, 250]], ['partial repair probe 1', [87.75, 1, 100, 'split', [6, 3]], [43.88, 2, 100]], ['partial repair probe 2', [25.25, 1, 150, 'split', [6, 3]], [12.63, 2, 150]], ['boundary control 1', [40, 1, 100, 'ordinary_div', 1.0], [40.0, 1, 100]], ['boundary control 2', [25.25, 1, 100, 'split', [2, 1]], [12.63, 2, 100]], ['normal control 1', [87.75, 10, 150, 'special_div', 0.124], [87.75, 10, 150]], ['normal control 2', [47.5, 10, 100, 'special_div', 2.375], [45.13, 10, 100]]], [['regression integral ratio detection 1', [150.05, 3, 150, 'split', [5, 4]], [150.05, 3, 187]], ['regression integral ratio detection 2', [47.5, 10, 100, 'split', [3, 2]], [47.5, 10, 150]], ['partial repair probe 1', [12.5, 3, 150, 'split', [4, 2]], [6.25, 6, 150]], ['partial repair probe 2', [40, 10, 100, 'split', [4, 2]], [20.0, 20, 100]], ['boundary control 1', [40, 1, 100, 'special_div', 0.125], [39.88, 1, 100]], ['boundary control 2', [25.25, 1, 100, 'split', [2, 1]], [12.63, 2, 100]], ['normal control 1', [33.33, 10, 100, 'split', [3, 1]], [11.11, 30, 100]], ['normal control 2', [101.01, 1, 150, 'ordinary_div', 2.375], [101.01, 1, 150]]], [['regression integral ratio detection 1', [25.25, 10, 150, 'split', [5, 3]], [25.25, 10, 250]], ['regression integral ratio detection 2', [101.01, 1, 100, 'split', [3, 2]], [101.01, 1, 150]], ['partial repair probe 1', [87.75, 3, 100, 'split', [4, 2]], [43.88, 6, 100]], ['partial repair probe 2', [150.05, 3, 100, 'split', [4, 2]], [75.03, 6, 100]], ['boundary control 1', [40, 1, 100, 'special_div', 0.125], [39.88, 1, 100]], ['boundary control 2', [25.25, 1, 100, 'split', [2, 1]], [12.63, 2, 100]], ['normal control 1', [40, 3, 100, 'special_div', 0.1], [40.0, 3, 100]], ['normal control 2', [12.5, 3, 100, 'split', [1, 2]], [12.5, 3, 50]]], [['regression integral ratio detection 1', [101.01, 1, 100, 'split', [5, 4]], [101.01, 1, 125]], ['regression integral ratio detection 2', [87.75, 3, 150, 'split', [3, 2]], [87.75, 3, 225]], ['partial repair probe 1', [101.01, 3, 100, 'split', [4, 2]], [50.51, 6, 100]], ['partial repair probe 2', [12.5, 3, 100, 'split', [6, 3]], [6.25, 6, 100]], ['boundary control 1', [40, 1, 100, 'ordinary_div', 1.0], [40.0, 1, 100]], ['boundary control 2', [40, 1, 100, 'special_div', 0.125], [39.88, 1, 100]], ['normal control 1', [40, 3, 100, 'merger', 0.126], [40.0, 3, 100]], ['normal control 2', [33.33, 10, 100, 'split', [2, 1]], [16.67, 20, 100]]], [['regression integral ratio detection 1', [40, 3, 150, 'split', [5, 3]], [40.0, 3, 250]], ['regression integral ratio detection 2', [12.5, 10, 150, 'split', [5, 4]], [12.5, 10, 187]], ['partial repair probe 1', [33.33, 3, 100, 'split', [4, 2]], [16.67, 6, 100]], ['partial repair probe 2', [101.01, 1, 100, 'split', [4, 2]], [50.51, 2, 100]], ['boundary control 1', [40, 1, 100, 'special_div', 0.125], [39.88, 1, 100]], ['boundary control 2', [40, 1, 100, 'ordinary_div', 1.0], [40.0, 1, 100]], ['normal control 1', [150.05, 1, 100, 'special_div', 2.375], [147.68, 1, 100]], ['normal control 2', [12.5, 1, 100, 'merger', 0.124], [12.5, 1, 100]]]]
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 fixtureActualExpectedOutcome
regression integral ratio detection 1[47.5, 1, 166][47.5, 1, 166]Passed
regression integral ratio detection 2[101.01, 1, 250][101.01, 1, 250]Passed
partial repair probe 1[87.75, 1, 200][43.88, 2, 100]Failed
partial repair probe 2[25.25, 1, 300][12.63, 2, 150]Failed
boundary control 1[40.0, 1, 100][40.0, 1, 100]Passed
boundary control 2[12.63, 2, 100][12.63, 2, 100]Passed
normal control 1[87.75, 10, 150][87.75, 10, 150]Passed
normal control 2[45.13, 10, 100][45.13, 10, 100]Passed

SHA-256 / a7e3cd06775a93744dcde65a226a1fca10b40a8d75d1b19d9223e952cd8650ec

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(strike, contracts, deliverable, action, value):
    k = round(strike * 100)
    if action == 'split':
        new, old = value
        if new % old == 0:
            r = new // old
            k = (2 * k + r) // (2 * r)
            contracts = contracts * r
        else:
            deliverable = deliverable * new // old
    elif action == 'special_div':
        amt = round(value * 1000)
        if amt >= 125:
            k = (k * 10 - amt + 5) // 10
    return [k / 100, contracts, deliverable]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression integral ratio detection 1', [47.5, 1, 100, 'split', [5, 3]], [47.5, 1, 166]], ['regression integral ratio detection 2', [101.01, 1, 150, 'split', [5, 3]], [101.01, 1, 250]], ['partial repair probe 1', [87.75, 1, 100, 'split', [6, 3]], [43.88, 2, 100]], ['partial repair probe 2', [25.25, 1, 150, 'split', [6, 3]], [12.63, 2, 150]], ['boundary control 1', [40, 1, 100, 'ordinary_div', 1.0], [40.0, 1, 100]], ['boundary control 2', [25.25, 1, 100, 'split', [2, 1]], [12.63, 2, 100]], ['normal control 1', [87.75, 10, 150, 'special_div', 0.124], [87.75, 10, 150]], ['normal control 2', [47.5, 10, 100, 'special_div', 2.375], [45.13, 10, 100]]], [['regression integral ratio detection 1', [150.05, 3, 150, 'split', [5, 4]], [150.05, 3, 187]], ['regression integral ratio detection 2', [47.5, 10, 100, 'split', [3, 2]], [47.5, 10, 150]], ['partial repair probe 1', [12.5, 3, 150, 'split', [4, 2]], [6.25, 6, 150]], ['partial repair probe 2', [40, 10, 100, 'split', [4, 2]], [20.0, 20, 100]], ['boundary control 1', [40, 1, 100, 'special_div', 0.125], [39.88, 1, 100]], ['boundary control 2', [25.25, 1, 100, 'split', [2, 1]], [12.63, 2, 100]], ['normal control 1', [33.33, 10, 100, 'split', [3, 1]], [11.11, 30, 100]], ['normal control 2', [101.01, 1, 150, 'ordinary_div', 2.375], [101.01, 1, 150]]], [['regression integral ratio detection 1', [25.25, 10, 150, 'split', [5, 3]], [25.25, 10, 250]], ['regression integral ratio detection 2', [101.01, 1, 100, 'split', [3, 2]], [101.01, 1, 150]], ['partial repair probe 1', [87.75, 3, 100, 'split', [4, 2]], [43.88, 6, 100]], ['partial repair probe 2', [150.05, 3, 100, 'split', [4, 2]], [75.03, 6, 100]], ['boundary control 1', [40, 1, 100, 'special_div', 0.125], [39.88, 1, 100]], ['boundary control 2', [25.25, 1, 100, 'split', [2, 1]], [12.63, 2, 100]], ['normal control 1', [40, 3, 100, 'special_div', 0.1], [40.0, 3, 100]], ['normal control 2', [12.5, 3, 100, 'split', [1, 2]], [12.5, 3, 50]]], [['regression integral ratio detection 1', [101.01, 1, 100, 'split', [5, 4]], [101.01, 1, 125]], ['regression integral ratio detection 2', [87.75, 3, 150, 'split', [3, 2]], [87.75, 3, 225]], ['partial repair probe 1', [101.01, 3, 100, 'split', [4, 2]], [50.51, 6, 100]], ['partial repair probe 2', [12.5, 3, 100, 'split', [6, 3]], [6.25, 6, 100]], ['boundary control 1', [40, 1, 100, 'ordinary_div', 1.0], [40.0, 1, 100]], ['boundary control 2', [40, 1, 100, 'special_div', 0.125], [39.88, 1, 100]], ['normal control 1', [40, 3, 100, 'merger', 0.126], [40.0, 3, 100]], ['normal control 2', [33.33, 10, 100, 'split', [2, 1]], [16.67, 20, 100]]], [['regression integral ratio detection 1', [40, 3, 150, 'split', [5, 3]], [40.0, 3, 250]], ['regression integral ratio detection 2', [12.5, 10, 150, 'split', [5, 4]], [12.5, 10, 187]], ['partial repair probe 1', [33.33, 3, 100, 'split', [4, 2]], [16.67, 6, 100]], ['partial repair probe 2', [101.01, 1, 100, 'split', [4, 2]], [50.51, 2, 100]], ['boundary control 1', [40, 1, 100, 'special_div', 0.125], [39.88, 1, 100]], ['boundary control 2', [40, 1, 100, 'ordinary_div', 1.0], [40.0, 1, 100]], ['normal control 1', [150.05, 1, 100, 'special_div', 2.375], [147.68, 1, 100]], ['normal control 2', [12.5, 1, 100, 'merger', 0.124], [12.5, 1, 100]]]]
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 fixtureActualExpectedOutcome
regression integral ratio detection 1[47.5, 1, 166][47.5, 1, 166]Passed
regression integral ratio detection 2[101.01, 1, 250][101.01, 1, 250]Passed
partial repair probe 1[43.88, 2, 100][43.88, 2, 100]Passed
partial repair probe 2[12.63, 2, 150][12.63, 2, 150]Passed
boundary control 1[40.0, 1, 100][40.0, 1, 100]Passed
boundary control 2[12.63, 2, 100][12.63, 2, 100]Passed
normal control 1[87.75, 10, 150][87.75, 10, 150]Passed
normal control 2[45.13, 10, 100][45.13, 10, 100]Passed

SHA-256 / 85ceb7a2b0e7892b8b077783d10a3c6976da1e406312630fa1e6661d406df662

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 2026-09-29T14:46:54.856730+00:00.

Case digest / 7954890c438d4bcce056542878efd6926443ab4e0b060534b166b635ab3b032f