FA-61821 / Options payoff and settlement / Open access

Option order price tick rounding: the 3.00 threshold is expressed in cents while prices are in mills · case 01

Almost every order is priced on the large tick.

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

ROOT CAUSE

The threshold constant 300 is compared against a price in mills.

VERIFIED REPAIR

Compare the price in mills against 3000.

Unsuccessful approach: Scaling the threshold to 30000 puts every realistic price on the small tick.

Case contract

Inputs a limit price (up to 3 decimals), side and penny-program flag. Work in mills. Below 3.000 the tick is 0.01 for penny classes and 0.05 otherwise; at or above 3.000 it is 0.05 for penny classes and 0.10 otherwise. Buy orders round down to a tick, sell orders round up; prices already on a tick are unchanged. Return the price as a float.

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(price, side, penny):
    m = round(price * 1000)
    small = m < 300
    tick = (10 if penny else 50) if small else (50 if penny else 100)
    if side == 'buy':
        out = m // tick * tick
    else:
        out = -(-m // tick) * tick
    return out / 1000
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression tick threshold units 1', [0.549, 'buy', True], 0.54], ['regression tick threshold units 2', [1.242, 'buy', True], 1.24], ['partial repair probe 1', [3.075, 'buy', True], 3.05], ['partial repair probe 2', [4.125, 'sell', True], 4.15], ['boundary control 1', [3.0, 'buy', False], 3.0], ['normal control 1', [2.995, 'sell', False], 3.0], ['normal control 2', [3.002, 'buy', True], 3.0], ['normal control 3', [1.255, 'sell', False], 1.3]], [['regression tick threshold units 1', [1.28, 'buy', True], 1.28], ['regression tick threshold units 2', [1.242, 'buy', True], 1.24], ['partial repair probe 1', [3.14, 'sell', True], 3.15], ['partial repair probe 2', [4.149, 'sell', False], 4.2], ['boundary control 1', [3.0, 'buy', False], 3.0], ['normal control 1', [12.389, 'sell', False], 12.4], ['normal control 2', [3.05, 'sell', True], 3.05], ['normal control 3', [0.549, 'sell', True], 0.55]], [['regression tick threshold units 1', [2.99, 'sell', True], 2.99], ['regression tick threshold units 2', [1.28, 'buy', True], 1.28], ['partial repair probe 1', [3.002, 'sell', True], 3.05], ['partial repair probe 2', [3.039, 'sell', True], 3.05], ['boundary control 1', [3.0, 'buy', False], 3.0], ['normal control 1', [3.049, 'sell', True], 3.05], ['normal control 2', [3.0, 'sell', True], 3.0], ['normal control 3', [1.242, 'sell', True], 1.25]], [['regression tick threshold units 1', [1.231, 'sell', True], 1.24], ['regression tick threshold units 2', [2.954, 'buy', False], 2.95], ['partial repair probe 1', [12.39, 'sell', True], 12.4], ['partial repair probe 2', [12.37, 'buy', False], 12.3], ['boundary control 1', [3.0, 'buy', False], 3.0], ['normal control 1', [0.549, 'sell', True], 0.55], ['normal control 2', [3.1, 'sell', False], 3.1], ['normal control 3', [3.1, 'sell', True], 3.1]], [['regression tick threshold units 1', [0.504, 'sell', False], 0.55], ['regression tick threshold units 2', [2.995, 'buy', True], 2.99], ['partial repair probe 1', [3.082, 'buy', True], 3.05], ['partial repair probe 2', [4.17, 'sell', True], 4.2], ['boundary control 1', [3.0, 'buy', False], 3.0], ['normal control 1', [4.104, 'buy', False], 4.1], ['normal control 2', [1.255, 'buy', True], 1.25], ['normal control 3', [3.12, 'buy', False], 3.1]]]
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 tick threshold units 1	`0.5`	`0.54`	Failed
regression tick threshold units 2	`1.2`	`1.24`	Failed
partial repair probe 1	`3.05`	`3.05`	Passed
partial repair probe 2	`4.15`	`4.15`	Passed
boundary control 1	`3.0`	`3.0`	Passed
normal control 1	`3.0`	`3.0`	Passed
normal control 2	`3.0`	`3.0`	Passed
normal control 3	`1.3`	`1.3`	Passed

SHA-256 / 605b0051416adc413fd043e885f9191a61855fd2057467f8520d297b846bbf62

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(price, side, penny):
    m = round(price * 1000)
    small = m < 30000
    tick = (10 if penny else 50) if small else (50 if penny else 100)
    if side == 'buy':
        out = m // tick * tick
    else:
        out = -(-m // tick) * tick
    return out / 1000
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression tick threshold units 1', [0.549, 'buy', True], 0.54], ['regression tick threshold units 2', [1.242, 'buy', True], 1.24], ['partial repair probe 1', [3.075, 'buy', True], 3.05], ['partial repair probe 2', [4.125, 'sell', True], 4.15], ['boundary control 1', [3.0, 'buy', False], 3.0], ['normal control 1', [2.995, 'sell', False], 3.0], ['normal control 2', [3.002, 'buy', True], 3.0], ['normal control 3', [1.255, 'sell', False], 1.3]], [['regression tick threshold units 1', [1.28, 'buy', True], 1.28], ['regression tick threshold units 2', [1.242, 'buy', True], 1.24], ['partial repair probe 1', [3.14, 'sell', True], 3.15], ['partial repair probe 2', [4.149, 'sell', False], 4.2], ['boundary control 1', [3.0, 'buy', False], 3.0], ['normal control 1', [12.389, 'sell', False], 12.4], ['normal control 2', [3.05, 'sell', True], 3.05], ['normal control 3', [0.549, 'sell', True], 0.55]], [['regression tick threshold units 1', [2.99, 'sell', True], 2.99], ['regression tick threshold units 2', [1.28, 'buy', True], 1.28], ['partial repair probe 1', [3.002, 'sell', True], 3.05], ['partial repair probe 2', [3.039, 'sell', True], 3.05], ['boundary control 1', [3.0, 'buy', False], 3.0], ['normal control 1', [3.049, 'sell', True], 3.05], ['normal control 2', [3.0, 'sell', True], 3.0], ['normal control 3', [1.242, 'sell', True], 1.25]], [['regression tick threshold units 1', [1.231, 'sell', True], 1.24], ['regression tick threshold units 2', [2.954, 'buy', False], 2.95], ['partial repair probe 1', [12.39, 'sell', True], 12.4], ['partial repair probe 2', [12.37, 'buy', False], 12.3], ['boundary control 1', [3.0, 'buy', False], 3.0], ['normal control 1', [0.549, 'sell', True], 0.55], ['normal control 2', [3.1, 'sell', False], 3.1], ['normal control 3', [3.1, 'sell', True], 3.1]], [['regression tick threshold units 1', [0.504, 'sell', False], 0.55], ['regression tick threshold units 2', [2.995, 'buy', True], 2.99], ['partial repair probe 1', [3.082, 'buy', True], 3.05], ['partial repair probe 2', [4.17, 'sell', True], 4.2], ['boundary control 1', [3.0, 'buy', False], 3.0], ['normal control 1', [4.104, 'buy', False], 4.1], ['normal control 2', [1.255, 'buy', True], 1.25], ['normal control 3', [3.12, 'buy', False], 3.1]]]
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 tick threshold units 1	`0.54`	`0.54`	Passed
regression tick threshold units 2	`1.24`	`1.24`	Passed
partial repair probe 1	`3.07`	`3.05`	Failed
partial repair probe 2	`4.13`	`4.15`	Failed
boundary control 1	`3.0`	`3.0`	Passed
normal control 1	`3.0`	`3.0`	Passed
normal control 2	`3.0`	`3.0`	Passed
normal control 3	`1.3`	`1.3`	Passed

SHA-256 / 8d3e38bfe184b68446c88b6107c6a0c61e9bcc4428543080af334a0822ebc90d

3 / The verified repair

Exit 0

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

N = 1
observations = []
def solve(price, side, penny):
    m = round(price * 1000)
    small = m < 3000
    tick = (10 if penny else 50) if small else (50 if penny else 100)
    if side == 'buy':
        out = m // tick * tick
    else:
        out = -(-m // tick) * tick
    return out / 1000
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression tick threshold units 1', [0.549, 'buy', True], 0.54], ['regression tick threshold units 2', [1.242, 'buy', True], 1.24], ['partial repair probe 1', [3.075, 'buy', True], 3.05], ['partial repair probe 2', [4.125, 'sell', True], 4.15], ['boundary control 1', [3.0, 'buy', False], 3.0], ['normal control 1', [2.995, 'sell', False], 3.0], ['normal control 2', [3.002, 'buy', True], 3.0], ['normal control 3', [1.255, 'sell', False], 1.3]], [['regression tick threshold units 1', [1.28, 'buy', True], 1.28], ['regression tick threshold units 2', [1.242, 'buy', True], 1.24], ['partial repair probe 1', [3.14, 'sell', True], 3.15], ['partial repair probe 2', [4.149, 'sell', False], 4.2], ['boundary control 1', [3.0, 'buy', False], 3.0], ['normal control 1', [12.389, 'sell', False], 12.4], ['normal control 2', [3.05, 'sell', True], 3.05], ['normal control 3', [0.549, 'sell', True], 0.55]], [['regression tick threshold units 1', [2.99, 'sell', True], 2.99], ['regression tick threshold units 2', [1.28, 'buy', True], 1.28], ['partial repair probe 1', [3.002, 'sell', True], 3.05], ['partial repair probe 2', [3.039, 'sell', True], 3.05], ['boundary control 1', [3.0, 'buy', False], 3.0], ['normal control 1', [3.049, 'sell', True], 3.05], ['normal control 2', [3.0, 'sell', True], 3.0], ['normal control 3', [1.242, 'sell', True], 1.25]], [['regression tick threshold units 1', [1.231, 'sell', True], 1.24], ['regression tick threshold units 2', [2.954, 'buy', False], 2.95], ['partial repair probe 1', [12.39, 'sell', True], 12.4], ['partial repair probe 2', [12.37, 'buy', False], 12.3], ['boundary control 1', [3.0, 'buy', False], 3.0], ['normal control 1', [0.549, 'sell', True], 0.55], ['normal control 2', [3.1, 'sell', False], 3.1], ['normal control 3', [3.1, 'sell', True], 3.1]], [['regression tick threshold units 1', [0.504, 'sell', False], 0.55], ['regression tick threshold units 2', [2.995, 'buy', True], 2.99], ['partial repair probe 1', [3.082, 'buy', True], 3.05], ['partial repair probe 2', [4.17, 'sell', True], 4.2], ['boundary control 1', [3.0, 'buy', False], 3.0], ['normal control 1', [4.104, 'buy', False], 4.1], ['normal control 2', [1.255, 'buy', True], 1.25], ['normal control 3', [3.12, 'buy', False], 3.1]]]
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 tick threshold units 1	`0.54`	`0.54`	Passed
regression tick threshold units 2	`1.24`	`1.24`	Passed
partial repair probe 1	`3.05`	`3.05`	Passed
partial repair probe 2	`4.15`	`4.15`	Passed
boundary control 1	`3.0`	`3.0`	Passed
normal control 1	`3.0`	`3.0`	Passed
normal control 2	`3.0`	`3.0`	Passed
normal control 3	`1.3`	`1.3`	Passed

SHA-256 / e56c1524aa3cf778c320465cf9c93caf19a6878052c8bb8f757a90abcf27a1d2

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 2026-09-29T14:46:58.782341+00:00.

Case digest / 6f34591eb34afeb552e55175d8321b48c6d897a67ed8901b3ccedb0e306a9300