FA-84476 / Betting odds conversion / Open access
Half-lose leg settled as a loss or a push · case 01
A half-lost Asian leg kills the parlay, or is ignored.
ROOT CAUSE
The half-lose leg is treated as a full loss.
VERIFIED REPAIR
Use a factor of 1/2 for half-lose legs.
Unsuccessful approach: Treating it as a push keeps the half that was lost.
Case contract
Accumulator settlement. legs rows are [decimal price, result] with results win, lose, push, void, half-win (half the stake wins at the price, half is refunded: factor (1 + d) / 2) and half-lose (half refunded: factor 1/2). push and void legs have factor 1, win factor d, lose makes the whole bet lose. Return [combined factor rounded half up to four decimals as a string, payout in cents rounded down].
Why this case matters
Settlement engines must collapse pushed, voided and Asian half results inside multiples.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
import math
N = 1
observations = []
def solve(legs, stake_cents):
factor = Fraction(1)
for price, result in legs:
d = Fraction(price)
if result == 'lose':
factor = Fraction(0)
break
if result == 'win':
factor *= d
elif result == 'half-win':
factor *= (1 + d) / 2
elif result == 'half-lose':
factor *= 0
q = math.floor(factor * 10000 + Fraction(1, 2))
return ['%d.%04d' % (q // 10000, q % 10000), math.floor(stake_cents * factor)]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
def run(args):
try:
return solve(*args)
except Exception as exc:
return 'raised ' + type(exc).__name__
cases = [[('control two winners', ([['2.00', 'win'], ['1.50', 'win']], 1000), ['3.0000', 3000]),
('boundary pushed leg', ([['2.00', 'win'], ['1.90', 'push']], 1000), ['2.0000', 2000]),
('boundary half-win leg', ([['2.00', 'half-win'], ['2.00', 'win']], 1000), ['3.0000', 3000]),
('boundary half-lose leg', ([['2.00', 'half-lose'], ['3.00', 'win']], 1000), ['1.5000', 1500]),
('control losing leg', ([['2.00', 'win'], ['1.50', 'lose']], 1000), ['0.0000', 0]),
('control payout rounds down', ([['1.33', 'win']], 333), ['1.3300', 442]),
('regression: half lose factor',
([['2.43', 'half-lose'], ['2.66', 'half-lose'], ['1.80', 'win'], ['2.80', 'void']], 250),
['0.4500', 112]),
('variant scenario 1', ([['2.93', 'void'], ['1.52', 'half-win']], 100), ['1.2600', 126]),
('variant scenario 2',
([['2.95', 'win'], ['1.34', 'win'], ['2.38', 'lose'], ['2.73', 'win']], 1000),
['0.0000', 0])],
[('control two winners', ([['2.00', 'win'], ['1.50', 'win']], 1000), ['3.0000', 3000]),
('boundary pushed leg', ([['2.00', 'win'], ['1.90', 'push']], 1000), ['2.0000', 2000]),
('boundary half-win leg', ([['2.00', 'half-win'], ['2.00', 'win']], 1000), ['3.0000', 3000]),
('boundary half-lose leg', ([['2.00', 'half-lose'], ['3.00', 'win']], 1000), ['1.5000', 1500]),
('control losing leg', ([['2.00', 'win'], ['1.50', 'lose']], 1000), ['0.0000', 0]),
('control payout rounds down', ([['1.33', 'win']], 333), ['1.3300', 442]),
('regression: half lose factor',
([['3.95', 'win'], ['2.15', 'half-lose'], ['2.45', 'half-lose']], 333),
['0.9875', 328]),
('variant scenario 1',
([['2.16', 'lose'], ['2.15', 'void'], ['2.36', 'half-win'], ['2.29', 'win']], 1000),
['0.0000', 0]),
('variant scenario 2', ([['1.39', 'void'], ['1.88', 'win']], 1000), ['1.8800', 1880])],
[('control two winners', ([['2.00', 'win'], ['1.50', 'win']], 1000), ['3.0000', 3000]),
('boundary pushed leg', ([['2.00', 'win'], ['1.90', 'push']], 1000), ['2.0000', 2000]),
('boundary half-win leg', ([['2.00', 'half-win'], ['2.00', 'win']], 1000), ['3.0000', 3000]),
('boundary half-lose leg', ([['2.00', 'half-lose'], ['3.00', 'win']], 1000), ['1.5000', 1500]),
('control losing leg', ([['2.00', 'win'], ['1.50', 'lose']], 1000), ['0.0000', 0]),
('control payout rounds down', ([['1.33', 'win']], 333), ['1.3300', 442]),
('regression: half lose factor',
([['3.23', 'void'], ['1.78', 'half-lose'], ['1.60', 'win'], ['3.77', 'win']], 1999),
['3.0160', 6028]),
('variant scenario 1',
([['2.85', 'lose'], ['2.07', 'half-lose'], ['3.95', 'push'], ['2.78', 'void'], ['2.40', 'void']],
250),
['0.0000', 0]),
('variant scenario 2',
([['3.61', 'lose'],
['3.42', 'half-lose'],
['2.22', 'half-win'],
['2.00', 'win'],
['3.00', 'win']],
100),
['0.0000', 0])],
[('control two winners', ([['2.00', 'win'], ['1.50', 'win']], 1000), ['3.0000', 3000]),
('boundary pushed leg', ([['2.00', 'win'], ['1.90', 'push']], 1000), ['2.0000', 2000]),
('boundary half-win leg', ([['2.00', 'half-win'], ['2.00', 'win']], 1000), ['3.0000', 3000]),
('boundary half-lose leg', ([['2.00', 'half-lose'], ['3.00', 'win']], 1000), ['1.5000', 1500]),
('control losing leg', ([['2.00', 'win'], ['1.50', 'lose']], 1000), ['0.0000', 0]),
('control payout rounds down', ([['1.33', 'win']], 333), ['1.3300', 442]),
('regression: half lose factor',
([['1.55', 'half-lose'], ['2.82', 'push'], ['2.89', 'half-win']], 1999),
['0.9725', 1944]),
('variant scenario 1',
([['2.72', 'half-win'],
['2.70', 'push'],
['3.02', 'lose'],
['1.59', 'half-lose'],
['1.30', 'half-lose']],
250),
['0.0000', 0]),
('variant scenario 2',
([['3.97', 'lose'],
['2.37', 'half-lose'],
['3.46', 'half-win'],
['1.36', 'win'],
['2.04', 'half-lose']],
1999),
['0.0000', 0])],
[('control two winners', ([['2.00', 'win'], ['1.50', 'win']], 1000), ['3.0000', 3000]),
('boundary pushed leg', ([['2.00', 'win'], ['1.90', 'push']], 1000), ['2.0000', 2000]),
('boundary half-win leg', ([['2.00', 'half-win'], ['2.00', 'win']], 1000), ['3.0000', 3000]),
('boundary half-lose leg', ([['2.00', 'half-lose'], ['3.00', 'win']], 1000), ['1.5000', 1500]),
('control losing leg', ([['2.00', 'win'], ['1.50', 'lose']], 1000), ['0.0000', 0]),
('control payout rounds down', ([['1.33', 'win']], 333), ['1.3300', 442]),
('regression: half lose factor',
([['3.21', 'half-win'], ['2.12', 'win'], ['1.45', 'half-lose']], 333),
['2.2313', 743]),
('variant scenario 1',
([['3.09', 'half-lose'], ['2.98', 'lose'], ['3.93', 'win'], ['1.31', 'win'], ['1.54', 'win']],
1000),
['0.0000', 0]),
('variant scenario 2', ([['2.51', 'win'], ['1.25', 'lose']], 333), ['0.0000', 0])]]
for label, args, expected in cases[N - 1]:
check(label, run(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 |
|---|---|---|---|
| control two winners | ['3.0000', 3000] | ['3.0000', 3000] | Passed |
| boundary pushed leg | ['2.0000', 2000] | ['2.0000', 2000] | Passed |
| boundary half-win leg | ['3.0000', 3000] | ['3.0000', 3000] | Passed |
| boundary half-lose leg | ['0.0000', 0] | ['1.5000', 1500] | Failed |
| control losing leg | ['0.0000', 0] | ['0.0000', 0] | Passed |
| control payout rounds down | ['1.3300', 442] | ['1.3300', 442] | Passed |
| regression: half lose factor | ['0.0000', 0] | ['0.4500', 112] | Failed |
| variant scenario 1 | ['1.2600', 126] | ['1.2600', 126] | Passed |
| variant scenario 2 | ['0.0000', 0] | ['0.0000', 0] | Passed |
SHA-256 / 0d9b71788abd76d0237cb597eb83bfef2dfab69006c452b61adf7d4d33fe1c60
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
import math
N = 1
observations = []
def solve(legs, stake_cents):
factor = Fraction(1)
for price, result in legs:
d = Fraction(price)
if result == 'lose':
factor = Fraction(0)
break
if result == 'win':
factor *= d
elif result == 'half-win':
factor *= (1 + d) / 2
elif result == 'half-lose':
factor *= 1
q = math.floor(factor * 10000 + Fraction(1, 2))
return ['%d.%04d' % (q // 10000, q % 10000), math.floor(stake_cents * factor)]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
def run(args):
try:
return solve(*args)
except Exception as exc:
return 'raised ' + type(exc).__name__
cases = [[('control two winners', ([['2.00', 'win'], ['1.50', 'win']], 1000), ['3.0000', 3000]),
('boundary pushed leg', ([['2.00', 'win'], ['1.90', 'push']], 1000), ['2.0000', 2000]),
('boundary half-win leg', ([['2.00', 'half-win'], ['2.00', 'win']], 1000), ['3.0000', 3000]),
('boundary half-lose leg', ([['2.00', 'half-lose'], ['3.00', 'win']], 1000), ['1.5000', 1500]),
('control losing leg', ([['2.00', 'win'], ['1.50', 'lose']], 1000), ['0.0000', 0]),
('control payout rounds down', ([['1.33', 'win']], 333), ['1.3300', 442]),
('regression: half lose factor',
([['2.43', 'half-lose'], ['2.66', 'half-lose'], ['1.80', 'win'], ['2.80', 'void']], 250),
['0.4500', 112]),
('variant scenario 1', ([['2.93', 'void'], ['1.52', 'half-win']], 100), ['1.2600', 126]),
('variant scenario 2',
([['2.95', 'win'], ['1.34', 'win'], ['2.38', 'lose'], ['2.73', 'win']], 1000),
['0.0000', 0])],
[('control two winners', ([['2.00', 'win'], ['1.50', 'win']], 1000), ['3.0000', 3000]),
('boundary pushed leg', ([['2.00', 'win'], ['1.90', 'push']], 1000), ['2.0000', 2000]),
('boundary half-win leg', ([['2.00', 'half-win'], ['2.00', 'win']], 1000), ['3.0000', 3000]),
('boundary half-lose leg', ([['2.00', 'half-lose'], ['3.00', 'win']], 1000), ['1.5000', 1500]),
('control losing leg', ([['2.00', 'win'], ['1.50', 'lose']], 1000), ['0.0000', 0]),
('control payout rounds down', ([['1.33', 'win']], 333), ['1.3300', 442]),
('regression: half lose factor',
([['3.95', 'win'], ['2.15', 'half-lose'], ['2.45', 'half-lose']], 333),
['0.9875', 328]),
('variant scenario 1',
([['2.16', 'lose'], ['2.15', 'void'], ['2.36', 'half-win'], ['2.29', 'win']], 1000),
['0.0000', 0]),
('variant scenario 2', ([['1.39', 'void'], ['1.88', 'win']], 1000), ['1.8800', 1880])],
[('control two winners', ([['2.00', 'win'], ['1.50', 'win']], 1000), ['3.0000', 3000]),
('boundary pushed leg', ([['2.00', 'win'], ['1.90', 'push']], 1000), ['2.0000', 2000]),
('boundary half-win leg', ([['2.00', 'half-win'], ['2.00', 'win']], 1000), ['3.0000', 3000]),
('boundary half-lose leg', ([['2.00', 'half-lose'], ['3.00', 'win']], 1000), ['1.5000', 1500]),
('control losing leg', ([['2.00', 'win'], ['1.50', 'lose']], 1000), ['0.0000', 0]),
('control payout rounds down', ([['1.33', 'win']], 333), ['1.3300', 442]),
('regression: half lose factor',
([['3.23', 'void'], ['1.78', 'half-lose'], ['1.60', 'win'], ['3.77', 'win']], 1999),
['3.0160', 6028]),
('variant scenario 1',
([['2.85', 'lose'], ['2.07', 'half-lose'], ['3.95', 'push'], ['2.78', 'void'], ['2.40', 'void']],
250),
['0.0000', 0]),
('variant scenario 2',
([['3.61', 'lose'],
['3.42', 'half-lose'],
['2.22', 'half-win'],
['2.00', 'win'],
['3.00', 'win']],
100),
['0.0000', 0])],
[('control two winners', ([['2.00', 'win'], ['1.50', 'win']], 1000), ['3.0000', 3000]),
('boundary pushed leg', ([['2.00', 'win'], ['1.90', 'push']], 1000), ['2.0000', 2000]),
('boundary half-win leg', ([['2.00', 'half-win'], ['2.00', 'win']], 1000), ['3.0000', 3000]),
('boundary half-lose leg', ([['2.00', 'half-lose'], ['3.00', 'win']], 1000), ['1.5000', 1500]),
('control losing leg', ([['2.00', 'win'], ['1.50', 'lose']], 1000), ['0.0000', 0]),
('control payout rounds down', ([['1.33', 'win']], 333), ['1.3300', 442]),
('regression: half lose factor',
([['1.55', 'half-lose'], ['2.82', 'push'], ['2.89', 'half-win']], 1999),
['0.9725', 1944]),
('variant scenario 1',
([['2.72', 'half-win'],
['2.70', 'push'],
['3.02', 'lose'],
['1.59', 'half-lose'],
['1.30', 'half-lose']],
250),
['0.0000', 0]),
('variant scenario 2',
([['3.97', 'lose'],
['2.37', 'half-lose'],
['3.46', 'half-win'],
['1.36', 'win'],
['2.04', 'half-lose']],
1999),
['0.0000', 0])],
[('control two winners', ([['2.00', 'win'], ['1.50', 'win']], 1000), ['3.0000', 3000]),
('boundary pushed leg', ([['2.00', 'win'], ['1.90', 'push']], 1000), ['2.0000', 2000]),
('boundary half-win leg', ([['2.00', 'half-win'], ['2.00', 'win']], 1000), ['3.0000', 3000]),
('boundary half-lose leg', ([['2.00', 'half-lose'], ['3.00', 'win']], 1000), ['1.5000', 1500]),
('control losing leg', ([['2.00', 'win'], ['1.50', 'lose']], 1000), ['0.0000', 0]),
('control payout rounds down', ([['1.33', 'win']], 333), ['1.3300', 442]),
('regression: half lose factor',
([['3.21', 'half-win'], ['2.12', 'win'], ['1.45', 'half-lose']], 333),
['2.2313', 743]),
('variant scenario 1',
([['3.09', 'half-lose'], ['2.98', 'lose'], ['3.93', 'win'], ['1.31', 'win'], ['1.54', 'win']],
1000),
['0.0000', 0]),
('variant scenario 2', ([['2.51', 'win'], ['1.25', 'lose']], 333), ['0.0000', 0])]]
for label, args, expected in cases[N - 1]:
check(label, run(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 |
|---|---|---|---|
| control two winners | ['3.0000', 3000] | ['3.0000', 3000] | Passed |
| boundary pushed leg | ['2.0000', 2000] | ['2.0000', 2000] | Passed |
| boundary half-win leg | ['3.0000', 3000] | ['3.0000', 3000] | Passed |
| boundary half-lose leg | ['3.0000', 3000] | ['1.5000', 1500] | Failed |
| control losing leg | ['0.0000', 0] | ['0.0000', 0] | Passed |
| control payout rounds down | ['1.3300', 442] | ['1.3300', 442] | Passed |
| regression: half lose factor | ['1.8000', 450] | ['0.4500', 112] | Failed |
| variant scenario 1 | ['1.2600', 126] | ['1.2600', 126] | Passed |
| variant scenario 2 | ['0.0000', 0] | ['0.0000', 0] | Passed |
SHA-256 / 8bd00dc45845d744ba8a0d2a12ce1d29b9b2cbcf4b5dd552a2cb53be0afba087
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
import math
N = 1
observations = []
def solve(legs, stake_cents):
factor = Fraction(1)
for price, result in legs:
d = Fraction(price)
if result == 'lose':
factor = Fraction(0)
break
if result == 'win':
factor *= d
elif result == 'half-win':
factor *= (1 + d) / 2
elif result == 'half-lose':
factor *= Fraction(1, 2)
q = math.floor(factor * 10000 + Fraction(1, 2))
return ['%d.%04d' % (q // 10000, q % 10000), math.floor(stake_cents * factor)]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
def run(args):
try:
return solve(*args)
except Exception as exc:
return 'raised ' + type(exc).__name__
cases = [[('control two winners', ([['2.00', 'win'], ['1.50', 'win']], 1000), ['3.0000', 3000]),
('boundary pushed leg', ([['2.00', 'win'], ['1.90', 'push']], 1000), ['2.0000', 2000]),
('boundary half-win leg', ([['2.00', 'half-win'], ['2.00', 'win']], 1000), ['3.0000', 3000]),
('boundary half-lose leg', ([['2.00', 'half-lose'], ['3.00', 'win']], 1000), ['1.5000', 1500]),
('control losing leg', ([['2.00', 'win'], ['1.50', 'lose']], 1000), ['0.0000', 0]),
('control payout rounds down', ([['1.33', 'win']], 333), ['1.3300', 442]),
('regression: half lose factor',
([['2.43', 'half-lose'], ['2.66', 'half-lose'], ['1.80', 'win'], ['2.80', 'void']], 250),
['0.4500', 112]),
('variant scenario 1', ([['2.93', 'void'], ['1.52', 'half-win']], 100), ['1.2600', 126]),
('variant scenario 2',
([['2.95', 'win'], ['1.34', 'win'], ['2.38', 'lose'], ['2.73', 'win']], 1000),
['0.0000', 0])],
[('control two winners', ([['2.00', 'win'], ['1.50', 'win']], 1000), ['3.0000', 3000]),
('boundary pushed leg', ([['2.00', 'win'], ['1.90', 'push']], 1000), ['2.0000', 2000]),
('boundary half-win leg', ([['2.00', 'half-win'], ['2.00', 'win']], 1000), ['3.0000', 3000]),
('boundary half-lose leg', ([['2.00', 'half-lose'], ['3.00', 'win']], 1000), ['1.5000', 1500]),
('control losing leg', ([['2.00', 'win'], ['1.50', 'lose']], 1000), ['0.0000', 0]),
('control payout rounds down', ([['1.33', 'win']], 333), ['1.3300', 442]),
('regression: half lose factor',
([['3.95', 'win'], ['2.15', 'half-lose'], ['2.45', 'half-lose']], 333),
['0.9875', 328]),
('variant scenario 1',
([['2.16', 'lose'], ['2.15', 'void'], ['2.36', 'half-win'], ['2.29', 'win']], 1000),
['0.0000', 0]),
('variant scenario 2', ([['1.39', 'void'], ['1.88', 'win']], 1000), ['1.8800', 1880])],
[('control two winners', ([['2.00', 'win'], ['1.50', 'win']], 1000), ['3.0000', 3000]),
('boundary pushed leg', ([['2.00', 'win'], ['1.90', 'push']], 1000), ['2.0000', 2000]),
('boundary half-win leg', ([['2.00', 'half-win'], ['2.00', 'win']], 1000), ['3.0000', 3000]),
('boundary half-lose leg', ([['2.00', 'half-lose'], ['3.00', 'win']], 1000), ['1.5000', 1500]),
('control losing leg', ([['2.00', 'win'], ['1.50', 'lose']], 1000), ['0.0000', 0]),
('control payout rounds down', ([['1.33', 'win']], 333), ['1.3300', 442]),
('regression: half lose factor',
([['3.23', 'void'], ['1.78', 'half-lose'], ['1.60', 'win'], ['3.77', 'win']], 1999),
['3.0160', 6028]),
('variant scenario 1',
([['2.85', 'lose'], ['2.07', 'half-lose'], ['3.95', 'push'], ['2.78', 'void'], ['2.40', 'void']],
250),
['0.0000', 0]),
('variant scenario 2',
([['3.61', 'lose'],
['3.42', 'half-lose'],
['2.22', 'half-win'],
['2.00', 'win'],
['3.00', 'win']],
100),
['0.0000', 0])],
[('control two winners', ([['2.00', 'win'], ['1.50', 'win']], 1000), ['3.0000', 3000]),
('boundary pushed leg', ([['2.00', 'win'], ['1.90', 'push']], 1000), ['2.0000', 2000]),
('boundary half-win leg', ([['2.00', 'half-win'], ['2.00', 'win']], 1000), ['3.0000', 3000]),
('boundary half-lose leg', ([['2.00', 'half-lose'], ['3.00', 'win']], 1000), ['1.5000', 1500]),
('control losing leg', ([['2.00', 'win'], ['1.50', 'lose']], 1000), ['0.0000', 0]),
('control payout rounds down', ([['1.33', 'win']], 333), ['1.3300', 442]),
('regression: half lose factor',
([['1.55', 'half-lose'], ['2.82', 'push'], ['2.89', 'half-win']], 1999),
['0.9725', 1944]),
('variant scenario 1',
([['2.72', 'half-win'],
['2.70', 'push'],
['3.02', 'lose'],
['1.59', 'half-lose'],
['1.30', 'half-lose']],
250),
['0.0000', 0]),
('variant scenario 2',
([['3.97', 'lose'],
['2.37', 'half-lose'],
['3.46', 'half-win'],
['1.36', 'win'],
['2.04', 'half-lose']],
1999),
['0.0000', 0])],
[('control two winners', ([['2.00', 'win'], ['1.50', 'win']], 1000), ['3.0000', 3000]),
('boundary pushed leg', ([['2.00', 'win'], ['1.90', 'push']], 1000), ['2.0000', 2000]),
('boundary half-win leg', ([['2.00', 'half-win'], ['2.00', 'win']], 1000), ['3.0000', 3000]),
('boundary half-lose leg', ([['2.00', 'half-lose'], ['3.00', 'win']], 1000), ['1.5000', 1500]),
('control losing leg', ([['2.00', 'win'], ['1.50', 'lose']], 1000), ['0.0000', 0]),
('control payout rounds down', ([['1.33', 'win']], 333), ['1.3300', 442]),
('regression: half lose factor',
([['3.21', 'half-win'], ['2.12', 'win'], ['1.45', 'half-lose']], 333),
['2.2313', 743]),
('variant scenario 1',
([['3.09', 'half-lose'], ['2.98', 'lose'], ['3.93', 'win'], ['1.31', 'win'], ['1.54', 'win']],
1000),
['0.0000', 0]),
('variant scenario 2', ([['2.51', 'win'], ['1.25', 'lose']], 333), ['0.0000', 0])]]
for label, args, expected in cases[N - 1]:
check(label, run(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 |
|---|---|---|---|
| control two winners | ['3.0000', 3000] | ['3.0000', 3000] | Passed |
| boundary pushed leg | ['2.0000', 2000] | ['2.0000', 2000] | Passed |
| boundary half-win leg | ['3.0000', 3000] | ['3.0000', 3000] | Passed |
| boundary half-lose leg | ['1.5000', 1500] | ['1.5000', 1500] | Passed |
| control losing leg | ['0.0000', 0] | ['0.0000', 0] | Passed |
| control payout rounds down | ['1.3300', 442] | ['1.3300', 442] | Passed |
| regression: half lose factor | ['0.4500', 112] | ['0.4500', 112] | Passed |
| variant scenario 1 | ['1.2600', 126] | ['1.2600', 126] | Passed |
| variant scenario 2 | ['0.0000', 0] | ['0.0000', 0] | Passed |
SHA-256 / e40d52cdb13944287cf77657337bd0ae2fb090cffad73276135af331947c6f37
Verification & scope
Stipulated, bounded toy contract stated in the contract field; not a claim of conformance with any operator, exchange or regulator rule set. 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 2026-09-29T14:50:31.204759+00:00.
Case digest / 14cabbd43ca1461b8e723b16f794d250f691e5ee140c0cc76cb1a98afe259769