FA-61416 / Options payoff and settlement / Open access

Arithmetic Asian payoff with disrupted fixings: disrupted fixings are dropped from the average · case 01

Averages over fewer observations overweight the remaining fixings.

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

ROOT CAUSE

The average is taken over valid fixings only instead of filling disruptions.

VERIFIED REPAIR

Fill each disrupted fixing and average over the full schedule.

Unsuccessful approach: Counting disruptions as zero drags the average down.

Case contract

Inputs kind (price-call, price-put, strike-call), fixings (None = disrupted), strike and final price. A disrupted fixing takes the last preceding filled value; if none precedes, the next valid fixing. avg = mean of the filled fixings. price-call pays max(avg-K,0), price-put max(K-avg,0), strike-call max(final-avg,0). Round to 6.

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, fixings, strike, final):
    vals = list(fixings)
    filled = []
    for i, v in enumerate(vals):
        if v is None:
            prev = [x for x in filled if x is not None]
            later = [x for x in vals[i + 1:] if x is not None]
            v = prev[-1] if prev else later[0]
        filled.append(v)
    avg = sum(v for v in fixings if v is not None) / len([v for v in fixings if v is not None])
    if kind == 'price-call':
        pay = max(avg - strike, 0.0)
    elif kind == 'price-put':
        pay = max(strike - avg, 0.0)
    else:
        pay = max(final - avg, 0.0)
    return round(pay, 6)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression disrupted fixing handling 1', ['strike-call', [None, 87.52, 112.04, 81.76, 108.63, 98.66, 109.85, None], 110, 119.83], 20.35125], ['regression disrupted fixing handling 2', ['price-call', [None, 117.16, 81.52], 90, 95.95], 15.28], ['partial repair probe 1', ['price-put', [None, 92.96], 100, 104.63], 7.04], ['partial repair probe 2', ['price-call', [None, 111.0], 105, 107.34], 6.0], ['normal control 1', ['price-call', [107.13, None, None, 105.03, None, 100.25, None], 110, 104.13], 0.0], ['normal control 2', ['price-call', [104.5, 99.92, 114.23, 95.28], 105, 89.1], 0.0], ['normal control 3', ['price-put', [114.44, 99.53, 94.81], 105, 117.35], 2.073333], ['normal control 4', ['price-call', [90.6, None, 99.94, 92.36, 97.01, 110.32, 105.44, 114.87], 105, 99.53], 0.0]], [['regression disrupted fixing handling 1', ['price-put', [111.81, 89.43, 91.67, None], 100, 119.46], 3.855], ['regression disrupted fixing handling 2', ['price-put', [93.24, 117.39, 94.36, 88.27, None], 105, 96.62], 8.694], ['partial repair probe 1', ['price-put', [114.77, 90.96, 110.13, None, 116.07], 90, 91.76], 0.0], ['partial repair probe 2', ['strike-call', [110.79, 87.87, 85.41, 113.03, None, 93.27], 100, 84.57], 0.0], ['normal control 1', ['price-put', [93.25, 93.38], 110, 115.05], 16.685], ['normal control 2', ['price-call', [101.0, 102.82, 91.23], 90, 82.29], 8.35], ['normal control 3', ['strike-call', [84.17, 113.94, 108.91, 109.85, 112.59, 112.54], 90, 93.23], 0.0], ['normal control 4', ['price-put', [86.22, 80.69, 101.45, 101.34, 86.33, 103.13], 100, 87.56], 6.806667]], [['regression disrupted fixing handling 1', ['price-call', [106.0, 108.3, 96.37, None], 90, 103.03], 11.76], ['regression disrupted fixing handling 2', ['price-call', [99.86, None, 85.17, None, 106.07, 119.67, None, 115.98], 105, 102.54], 0.0], ['partial repair probe 1', ['price-put', [98.13, 107.51, None, 98.65, None], 100, 104.68], 0.0], ['partial repair probe 2', ['strike-call', [None, None, 106.81, 111.64, None, None], 90, 116.42], 7.195], ['normal control 1', ['price-call', [None, None, None, 96.07], 100, 115.84], 0.0], ['normal control 2', ['price-call', [84.48, 87.17, 106.28], 105, 89.71], 0.0], ['normal control 3', ['price-put', [110.16, 105.89, 83.97], 105, 95.8], 4.993333], ['normal control 4', ['price-call', [117.25, 109.64, 94.89, 91.58, 110.73], 105, 84.9], 0.0]], [['regression disrupted fixing handling 1', ['strike-call', [80.95, 115.9, 96.86, None, 91.95, None, None], 105, 102.79], 7.587143], ['regression disrupted fixing handling 2', ['price-call', [None, None, None, 105.25, None, None, 101.32, 112.49], 100, 92.33], 5.66375], ['partial repair probe 1', ['strike-call', [None, 86.49, 95.99, None, 98.83, None, 102.22, None], 105, 119.83], 23.9475], ['partial repair probe 2', ['strike-call', [None, 113.88], 100, 91.48], 0.0], ['normal control 1', ['price-call', [108.16, 89.04, None, 118.06, 104.39, None], 110, 107.4], 0.0], ['normal control 2', ['price-call', [117.04, 101.03, 116.99], 110, 86.66], 1.686667], ['normal control 3', ['strike-call', [109.16, 113.24, 117.9], 105, 84.08], 0.0], ['normal control 4', ['price-call', [113.22, 83.42, 84.09, 87.55, 101.17, 82.4, 102.33, None], 100, 105.59], 0.0]], [['regression disrupted fixing handling 1', ['price-put', [None, 80.26, 110.32, 91.41, 93.3, 83.63, None, None], 100, 96.26], 11.695], ['regression disrupted fixing handling 2', ['strike-call', [80.07, None, 84.32, None, None, 101.78, 117.54, None], 105, 101.59], 7.845], ['partial repair probe 1', ['strike-call', [None, 90.87, 88.7], 90, 81.72], 0.0], ['partial repair probe 2', ['strike-call', [None, 111.73, None, 100.17, 92.24], 105, 87.14], 0.0], ['normal control 1', ['price-call', [100.0, None], 100, 94.42], 0.0], ['normal control 2', ['price-call', [99.74, 116.93], 105, 95.76], 3.335], ['normal control 3', ['price-call', [114.28, 88.93], 105, 88.52], 0.0], ['normal control 4', ['price-call', [118.94, 103.57, None, 86.07, 107.68, None, 98.85], 110, 98.85], 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 disrupted fixing handling 1	`20.086667`	`20.35125`	Failed
regression disrupted fixing handling 2	`9.34`	`15.28`	Failed
partial repair probe 1	`7.04`	`7.04`	Passed
partial repair probe 2	`6.0`	`6.0`	Passed
normal control 1	`0.0`	`0.0`	Passed
normal control 2	`0.0`	`0.0`	Passed
normal control 3	`2.073333`	`2.073333`	Passed
normal control 4	`0.0`	`0.0`	Passed

SHA-256 / c851637daa720d17ee04ba3e8fdaf1ec06c5953e1475732e55b80ae17b843160

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(kind, fixings, strike, final):
    vals = list(fixings)
    filled = []
    for i, v in enumerate(vals):
        if v is None:
            prev = [x for x in filled if x is not None]
            later = [x for x in vals[i + 1:] if x is not None]
            v = prev[-1] if prev else later[0]
        filled.append(v)
    avg = sum(v or 0 for v in fixings) / len(fixings)
    if kind == 'price-call':
        pay = max(avg - strike, 0.0)
    elif kind == 'price-put':
        pay = max(strike - avg, 0.0)
    else:
        pay = max(final - avg, 0.0)
    return round(pay, 6)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression disrupted fixing handling 1', ['strike-call', [None, 87.52, 112.04, 81.76, 108.63, 98.66, 109.85, None], 110, 119.83], 20.35125], ['regression disrupted fixing handling 2', ['price-call', [None, 117.16, 81.52], 90, 95.95], 15.28], ['partial repair probe 1', ['price-put', [None, 92.96], 100, 104.63], 7.04], ['partial repair probe 2', ['price-call', [None, 111.0], 105, 107.34], 6.0], ['normal control 1', ['price-call', [107.13, None, None, 105.03, None, 100.25, None], 110, 104.13], 0.0], ['normal control 2', ['price-call', [104.5, 99.92, 114.23, 95.28], 105, 89.1], 0.0], ['normal control 3', ['price-put', [114.44, 99.53, 94.81], 105, 117.35], 2.073333], ['normal control 4', ['price-call', [90.6, None, 99.94, 92.36, 97.01, 110.32, 105.44, 114.87], 105, 99.53], 0.0]], [['regression disrupted fixing handling 1', ['price-put', [111.81, 89.43, 91.67, None], 100, 119.46], 3.855], ['regression disrupted fixing handling 2', ['price-put', [93.24, 117.39, 94.36, 88.27, None], 105, 96.62], 8.694], ['partial repair probe 1', ['price-put', [114.77, 90.96, 110.13, None, 116.07], 90, 91.76], 0.0], ['partial repair probe 2', ['strike-call', [110.79, 87.87, 85.41, 113.03, None, 93.27], 100, 84.57], 0.0], ['normal control 1', ['price-put', [93.25, 93.38], 110, 115.05], 16.685], ['normal control 2', ['price-call', [101.0, 102.82, 91.23], 90, 82.29], 8.35], ['normal control 3', ['strike-call', [84.17, 113.94, 108.91, 109.85, 112.59, 112.54], 90, 93.23], 0.0], ['normal control 4', ['price-put', [86.22, 80.69, 101.45, 101.34, 86.33, 103.13], 100, 87.56], 6.806667]], [['regression disrupted fixing handling 1', ['price-call', [106.0, 108.3, 96.37, None], 90, 103.03], 11.76], ['regression disrupted fixing handling 2', ['price-call', [99.86, None, 85.17, None, 106.07, 119.67, None, 115.98], 105, 102.54], 0.0], ['partial repair probe 1', ['price-put', [98.13, 107.51, None, 98.65, None], 100, 104.68], 0.0], ['partial repair probe 2', ['strike-call', [None, None, 106.81, 111.64, None, None], 90, 116.42], 7.195], ['normal control 1', ['price-call', [None, None, None, 96.07], 100, 115.84], 0.0], ['normal control 2', ['price-call', [84.48, 87.17, 106.28], 105, 89.71], 0.0], ['normal control 3', ['price-put', [110.16, 105.89, 83.97], 105, 95.8], 4.993333], ['normal control 4', ['price-call', [117.25, 109.64, 94.89, 91.58, 110.73], 105, 84.9], 0.0]], [['regression disrupted fixing handling 1', ['strike-call', [80.95, 115.9, 96.86, None, 91.95, None, None], 105, 102.79], 7.587143], ['regression disrupted fixing handling 2', ['price-call', [None, None, None, 105.25, None, None, 101.32, 112.49], 100, 92.33], 5.66375], ['partial repair probe 1', ['strike-call', [None, 86.49, 95.99, None, 98.83, None, 102.22, None], 105, 119.83], 23.9475], ['partial repair probe 2', ['strike-call', [None, 113.88], 100, 91.48], 0.0], ['normal control 1', ['price-call', [108.16, 89.04, None, 118.06, 104.39, None], 110, 107.4], 0.0], ['normal control 2', ['price-call', [117.04, 101.03, 116.99], 110, 86.66], 1.686667], ['normal control 3', ['strike-call', [109.16, 113.24, 117.9], 105, 84.08], 0.0], ['normal control 4', ['price-call', [113.22, 83.42, 84.09, 87.55, 101.17, 82.4, 102.33, None], 100, 105.59], 0.0]], [['regression disrupted fixing handling 1', ['price-put', [None, 80.26, 110.32, 91.41, 93.3, 83.63, None, None], 100, 96.26], 11.695], ['regression disrupted fixing handling 2', ['strike-call', [80.07, None, 84.32, None, None, 101.78, 117.54, None], 105, 101.59], 7.845], ['partial repair probe 1', ['strike-call', [None, 90.87, 88.7], 90, 81.72], 0.0], ['partial repair probe 2', ['strike-call', [None, 111.73, None, 100.17, 92.24], 105, 87.14], 0.0], ['normal control 1', ['price-call', [100.0, None], 100, 94.42], 0.0], ['normal control 2', ['price-call', [99.74, 116.93], 105, 95.76], 3.335], ['normal control 3', ['price-call', [114.28, 88.93], 105, 88.52], 0.0], ['normal control 4', ['price-call', [118.94, 103.57, None, 86.07, 107.68, None, 98.85], 110, 98.85], 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 disrupted fixing handling 1	`45.0225`	`20.35125`	Failed
regression disrupted fixing handling 2	`0.0`	`15.28`	Failed
partial repair probe 1	`53.52`	`7.04`	Failed
partial repair probe 2	`0.0`	`6.0`	Failed
normal control 1	`0.0`	`0.0`	Passed
normal control 2	`0.0`	`0.0`	Passed
normal control 3	`2.073333`	`2.073333`	Passed
normal control 4	`0.0`	`0.0`	Passed

SHA-256 / deff69521fd891ddbb5acd03c9d855b8ce9e1fec85cfdb95920cb679e1cb53b7

3 / The verified repair

Exit 0

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

N = 1
observations = []
def solve(kind, fixings, strike, final):
    vals = list(fixings)
    filled = []
    for i, v in enumerate(vals):
        if v is None:
            prev = [x for x in filled if x is not None]
            later = [x for x in vals[i + 1:] if x is not None]
            v = prev[-1] if prev else later[0]
        filled.append(v)
    avg = sum(filled) / len(filled)
    if kind == 'price-call':
        pay = max(avg - strike, 0.0)
    elif kind == 'price-put':
        pay = max(strike - avg, 0.0)
    else:
        pay = max(final - avg, 0.0)
    return round(pay, 6)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression disrupted fixing handling 1', ['strike-call', [None, 87.52, 112.04, 81.76, 108.63, 98.66, 109.85, None], 110, 119.83], 20.35125], ['regression disrupted fixing handling 2', ['price-call', [None, 117.16, 81.52], 90, 95.95], 15.28], ['partial repair probe 1', ['price-put', [None, 92.96], 100, 104.63], 7.04], ['partial repair probe 2', ['price-call', [None, 111.0], 105, 107.34], 6.0], ['normal control 1', ['price-call', [107.13, None, None, 105.03, None, 100.25, None], 110, 104.13], 0.0], ['normal control 2', ['price-call', [104.5, 99.92, 114.23, 95.28], 105, 89.1], 0.0], ['normal control 3', ['price-put', [114.44, 99.53, 94.81], 105, 117.35], 2.073333], ['normal control 4', ['price-call', [90.6, None, 99.94, 92.36, 97.01, 110.32, 105.44, 114.87], 105, 99.53], 0.0]], [['regression disrupted fixing handling 1', ['price-put', [111.81, 89.43, 91.67, None], 100, 119.46], 3.855], ['regression disrupted fixing handling 2', ['price-put', [93.24, 117.39, 94.36, 88.27, None], 105, 96.62], 8.694], ['partial repair probe 1', ['price-put', [114.77, 90.96, 110.13, None, 116.07], 90, 91.76], 0.0], ['partial repair probe 2', ['strike-call', [110.79, 87.87, 85.41, 113.03, None, 93.27], 100, 84.57], 0.0], ['normal control 1', ['price-put', [93.25, 93.38], 110, 115.05], 16.685], ['normal control 2', ['price-call', [101.0, 102.82, 91.23], 90, 82.29], 8.35], ['normal control 3', ['strike-call', [84.17, 113.94, 108.91, 109.85, 112.59, 112.54], 90, 93.23], 0.0], ['normal control 4', ['price-put', [86.22, 80.69, 101.45, 101.34, 86.33, 103.13], 100, 87.56], 6.806667]], [['regression disrupted fixing handling 1', ['price-call', [106.0, 108.3, 96.37, None], 90, 103.03], 11.76], ['regression disrupted fixing handling 2', ['price-call', [99.86, None, 85.17, None, 106.07, 119.67, None, 115.98], 105, 102.54], 0.0], ['partial repair probe 1', ['price-put', [98.13, 107.51, None, 98.65, None], 100, 104.68], 0.0], ['partial repair probe 2', ['strike-call', [None, None, 106.81, 111.64, None, None], 90, 116.42], 7.195], ['normal control 1', ['price-call', [None, None, None, 96.07], 100, 115.84], 0.0], ['normal control 2', ['price-call', [84.48, 87.17, 106.28], 105, 89.71], 0.0], ['normal control 3', ['price-put', [110.16, 105.89, 83.97], 105, 95.8], 4.993333], ['normal control 4', ['price-call', [117.25, 109.64, 94.89, 91.58, 110.73], 105, 84.9], 0.0]], [['regression disrupted fixing handling 1', ['strike-call', [80.95, 115.9, 96.86, None, 91.95, None, None], 105, 102.79], 7.587143], ['regression disrupted fixing handling 2', ['price-call', [None, None, None, 105.25, None, None, 101.32, 112.49], 100, 92.33], 5.66375], ['partial repair probe 1', ['strike-call', [None, 86.49, 95.99, None, 98.83, None, 102.22, None], 105, 119.83], 23.9475], ['partial repair probe 2', ['strike-call', [None, 113.88], 100, 91.48], 0.0], ['normal control 1', ['price-call', [108.16, 89.04, None, 118.06, 104.39, None], 110, 107.4], 0.0], ['normal control 2', ['price-call', [117.04, 101.03, 116.99], 110, 86.66], 1.686667], ['normal control 3', ['strike-call', [109.16, 113.24, 117.9], 105, 84.08], 0.0], ['normal control 4', ['price-call', [113.22, 83.42, 84.09, 87.55, 101.17, 82.4, 102.33, None], 100, 105.59], 0.0]], [['regression disrupted fixing handling 1', ['price-put', [None, 80.26, 110.32, 91.41, 93.3, 83.63, None, None], 100, 96.26], 11.695], ['regression disrupted fixing handling 2', ['strike-call', [80.07, None, 84.32, None, None, 101.78, 117.54, None], 105, 101.59], 7.845], ['partial repair probe 1', ['strike-call', [None, 90.87, 88.7], 90, 81.72], 0.0], ['partial repair probe 2', ['strike-call', [None, 111.73, None, 100.17, 92.24], 105, 87.14], 0.0], ['normal control 1', ['price-call', [100.0, None], 100, 94.42], 0.0], ['normal control 2', ['price-call', [99.74, 116.93], 105, 95.76], 3.335], ['normal control 3', ['price-call', [114.28, 88.93], 105, 88.52], 0.0], ['normal control 4', ['price-call', [118.94, 103.57, None, 86.07, 107.68, None, 98.85], 110, 98.85], 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 disrupted fixing handling 1	`20.35125`	`20.35125`	Passed
regression disrupted fixing handling 2	`15.28`	`15.28`	Passed
partial repair probe 1	`7.04`	`7.04`	Passed
partial repair probe 2	`6.0`	`6.0`	Passed
normal control 1	`0.0`	`0.0`	Passed
normal control 2	`0.0`	`0.0`	Passed
normal control 3	`2.073333`	`2.073333`	Passed
normal control 4	`0.0`	`0.0`	Passed

SHA-256 / e5cbe48221982620bd1454f5b294eea71a4f7e34a51a5121589c275615a24772

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 2026-09-29T14:46:55.056235+00:00.

Case digest / 66c041f6a624824d5d79a62270e3567d775b8a384f2674c970288b4292ccdcbc