FA-60951 / Bond day-count conventions / Open access

Act/365L denominator selection: the end year is excluded from the leap-day search · case 01

An annual period ending after 29 February of a leap end year uses 365 days.

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

ROOT CAUSE

The search over years stops before the end year.

VERIFIED REPAIR

Search every year from the start year through the end year inclusive.

Unsuccessful approach: Starting the search one year later misses leap days in the start year.

Case contract

Inputs start, end ([y,m,d]) and coupon frequency. Days are actual days. For annual frequency the denominator is 366 if any 29 February lies in (start, end], else 365. For other frequencies the denominator is 366 if the end date year is a leap year, else 365. Return days/denominator rounded to 9 decimals.

Why this case matters

Bond accrual and pricing systems depend on exact day-count arithmetic; a single-day error changes settlement cash.

1 / The failure

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
from fractions import Fraction
N = 1
observations = []
def solve(a, b, freq):
    A = datetime.date(*a)
    B = datetime.date(*b)
    def leap(y):
        return (y % 4 == 0 and y % 100 != 0) or y % 400 == 0
    days = (B - A).days
    if freq == 1:
        has29 = any(leap(y) and A < datetime.date(y, 2, 29) <= B for y in range(A.year, B.year))
        den = 366 if has29 else 365
    else:
        den = 366 if leap(B.year) else 365
    return round(days / den, 9)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression candidate year range 1', [[2023, 2, 28], [2024, 2, 29], 1], 1.0], ['regression candidate year range 2', [[1915, 8, 27], [1916, 9, 13], 1], 1.046448087], ['partial repair probe 1', [[2024, 1, 29], [2025, 1, 29], 1], 1.0], ['partial repair probe 2', [[1924, 1, 30], [1927, 1, 30], 1], 2.994535519], ['boundary control 1', [[2024, 2, 29], [2025, 2, 28], 1], 1.0], ['boundary control 2', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['normal control 1', [[2059, 4, 30], [2059, 12, 6], 4], 0.602739726], ['normal control 2', [[2095, 2, 1], [2095, 8, 1], 2], 0.495890411]], [['regression candidate year range 1', [[2055, 5, 28], [2056, 5, 28], 1], 1.0], ['regression candidate year range 2', [[1907, 5, 8], [1908, 5, 8], 1], 1.0], ['partial repair probe 1', [[2104, 1, 1], [2106, 1, 1], 1], 1.99726776], ['partial repair probe 2', [[2004, 1, 1], [2005, 1, 1], 1], 1.0], ['boundary control 1', [[2024, 2, 29], [2025, 2, 28], 1], 1.0], ['boundary control 2', [[2024, 11, 15], [2025, 2, 15], 4], 0.252054795], ['normal control 1', [[1902, 3, 28], [1902, 12, 28], 4], 0.753424658], ['normal control 2', [[2023, 2, 1], [2023, 8, 1], 2], 0.495890411]], [['regression candidate year range 1', [[2003, 1, 29], [2004, 3, 3], 1], 1.090163934], ['regression candidate year range 2', [[2091, 1, 30], [2092, 3, 29], 1], 1.158469945], ['partial repair probe 1', [[2024, 2, 15], [2025, 2, 15], 1], 1.0], ['partial repair probe 2', [[2096, 1, 15], [2098, 1, 15], 1], 1.99726776], ['boundary control 1', [[2024, 2, 29], [2025, 2, 28], 1], 1.0], ['boundary control 2', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['normal control 1', [[1956, 5, 23], [1959, 5, 23], 1], 3.0], ['normal control 2', [[2050, 7, 28], [2051, 7, 28], 2], 1.0]], [['regression candidate year range 1', [[1983, 3, 1], [1984, 3, 1], 1], 1.0], ['regression candidate year range 2', [[1972, 1, 31], [1972, 10, 26], 1], 0.734972678], ['partial repair probe 1', [[2000, 1, 29], [2002, 1, 29], 1], 1.99726776], ['partial repair probe 2', [[2000, 1, 29], [2001, 1, 29], 1], 1.0], ['boundary control 1', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['boundary control 2', [[2024, 11, 15], [2025, 2, 15], 4], 0.252054795], ['normal control 1', [[2095, 2, 28], [2096, 2, 28], 1], 1.0], ['normal control 2', [[1947, 3, 30], [1948, 7, 15], 2], 1.292349727]], [['regression candidate year range 1', [[1995, 12, 1], [1996, 12, 1], 1], 1.0], ['regression candidate year range 2', [[1991, 4, 30], [1992, 4, 30], 1], 1.0], ['partial repair probe 1', [[2024, 2, 15], [2025, 2, 15], 1], 1.0], ['partial repair probe 2', [[2028, 2, 1], [2031, 2, 1], 1], 2.994535519], ['boundary control 1', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['boundary control 2', [[2024, 11, 15], [2025, 2, 15], 4], 0.252054795], ['normal control 1', [[2096, 1, 28], [2096, 4, 28], 4], 0.24863388], ['normal control 2', [[1943, 1, 22], [1943, 4, 22], 4], 0.246575342]]]
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 candidate year range 1	`1.002739726`	`1.0`	Failed
regression candidate year range 2	`1.049315068`	`1.046448087`	Failed
partial repair probe 1	`1.0`	`1.0`	Passed
partial repair probe 2	`2.994535519`	`2.994535519`	Passed
boundary control 1	`1.0`	`1.0`	Passed
boundary control 2	`0.24863388`	`0.24863388`	Passed
normal control 1	`0.602739726`	`0.602739726`	Passed
normal control 2	`0.495890411`	`0.495890411`	Passed

SHA-256 / 5c3622434973bfcc2cda459b1460238b26ff18f6ca1981dd7eea96139aadf2c8

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
from fractions import Fraction
N = 1
observations = []
def solve(a, b, freq):
    A = datetime.date(*a)
    B = datetime.date(*b)
    def leap(y):
        return (y % 4 == 0 and y % 100 != 0) or y % 400 == 0
    days = (B - A).days
    if freq == 1:
        has29 = any(leap(y) and A < datetime.date(y, 2, 29) <= B for y in range(A.year + 1, B.year + 1))
        den = 366 if has29 else 365
    else:
        den = 366 if leap(B.year) else 365
    return round(days / den, 9)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression candidate year range 1', [[2023, 2, 28], [2024, 2, 29], 1], 1.0], ['regression candidate year range 2', [[1915, 8, 27], [1916, 9, 13], 1], 1.046448087], ['partial repair probe 1', [[2024, 1, 29], [2025, 1, 29], 1], 1.0], ['partial repair probe 2', [[1924, 1, 30], [1927, 1, 30], 1], 2.994535519], ['boundary control 1', [[2024, 2, 29], [2025, 2, 28], 1], 1.0], ['boundary control 2', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['normal control 1', [[2059, 4, 30], [2059, 12, 6], 4], 0.602739726], ['normal control 2', [[2095, 2, 1], [2095, 8, 1], 2], 0.495890411]], [['regression candidate year range 1', [[2055, 5, 28], [2056, 5, 28], 1], 1.0], ['regression candidate year range 2', [[1907, 5, 8], [1908, 5, 8], 1], 1.0], ['partial repair probe 1', [[2104, 1, 1], [2106, 1, 1], 1], 1.99726776], ['partial repair probe 2', [[2004, 1, 1], [2005, 1, 1], 1], 1.0], ['boundary control 1', [[2024, 2, 29], [2025, 2, 28], 1], 1.0], ['boundary control 2', [[2024, 11, 15], [2025, 2, 15], 4], 0.252054795], ['normal control 1', [[1902, 3, 28], [1902, 12, 28], 4], 0.753424658], ['normal control 2', [[2023, 2, 1], [2023, 8, 1], 2], 0.495890411]], [['regression candidate year range 1', [[2003, 1, 29], [2004, 3, 3], 1], 1.090163934], ['regression candidate year range 2', [[2091, 1, 30], [2092, 3, 29], 1], 1.158469945], ['partial repair probe 1', [[2024, 2, 15], [2025, 2, 15], 1], 1.0], ['partial repair probe 2', [[2096, 1, 15], [2098, 1, 15], 1], 1.99726776], ['boundary control 1', [[2024, 2, 29], [2025, 2, 28], 1], 1.0], ['boundary control 2', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['normal control 1', [[1956, 5, 23], [1959, 5, 23], 1], 3.0], ['normal control 2', [[2050, 7, 28], [2051, 7, 28], 2], 1.0]], [['regression candidate year range 1', [[1983, 3, 1], [1984, 3, 1], 1], 1.0], ['regression candidate year range 2', [[1972, 1, 31], [1972, 10, 26], 1], 0.734972678], ['partial repair probe 1', [[2000, 1, 29], [2002, 1, 29], 1], 1.99726776], ['partial repair probe 2', [[2000, 1, 29], [2001, 1, 29], 1], 1.0], ['boundary control 1', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['boundary control 2', [[2024, 11, 15], [2025, 2, 15], 4], 0.252054795], ['normal control 1', [[2095, 2, 28], [2096, 2, 28], 1], 1.0], ['normal control 2', [[1947, 3, 30], [1948, 7, 15], 2], 1.292349727]], [['regression candidate year range 1', [[1995, 12, 1], [1996, 12, 1], 1], 1.0], ['regression candidate year range 2', [[1991, 4, 30], [1992, 4, 30], 1], 1.0], ['partial repair probe 1', [[2024, 2, 15], [2025, 2, 15], 1], 1.0], ['partial repair probe 2', [[2028, 2, 1], [2031, 2, 1], 1], 2.994535519], ['boundary control 1', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['boundary control 2', [[2024, 11, 15], [2025, 2, 15], 4], 0.252054795], ['normal control 1', [[2096, 1, 28], [2096, 4, 28], 4], 0.24863388], ['normal control 2', [[1943, 1, 22], [1943, 4, 22], 4], 0.246575342]]]
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 candidate year range 1	`1.0`	`1.0`	Passed
regression candidate year range 2	`1.046448087`	`1.046448087`	Passed
partial repair probe 1	`1.002739726`	`1.0`	Failed
partial repair probe 2	`3.002739726`	`2.994535519`	Failed
boundary control 1	`1.0`	`1.0`	Passed
boundary control 2	`0.24863388`	`0.24863388`	Passed
normal control 1	`0.602739726`	`0.602739726`	Passed
normal control 2	`0.495890411`	`0.495890411`	Passed

SHA-256 / 33cd45e419bb339e6fa589783bee7922a0909bebc714563767ea988a57bf0bc2

3 / The verified repair

Exit 0

"""Failure Map reference implementation. Python standard library only."""
import json
import datetime
from fractions import Fraction
N = 1
observations = []
def solve(a, b, freq):
    A = datetime.date(*a)
    B = datetime.date(*b)
    def leap(y):
        return (y % 4 == 0 and y % 100 != 0) or y % 400 == 0
    days = (B - A).days
    if freq == 1:
        has29 = any(leap(y) and A < datetime.date(y, 2, 29) <= B for y in range(A.year, B.year + 1))
        den = 366 if has29 else 365
    else:
        den = 366 if leap(B.year) else 365
    return round(days / den, 9)
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['regression candidate year range 1', [[2023, 2, 28], [2024, 2, 29], 1], 1.0], ['regression candidate year range 2', [[1915, 8, 27], [1916, 9, 13], 1], 1.046448087], ['partial repair probe 1', [[2024, 1, 29], [2025, 1, 29], 1], 1.0], ['partial repair probe 2', [[1924, 1, 30], [1927, 1, 30], 1], 2.994535519], ['boundary control 1', [[2024, 2, 29], [2025, 2, 28], 1], 1.0], ['boundary control 2', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['normal control 1', [[2059, 4, 30], [2059, 12, 6], 4], 0.602739726], ['normal control 2', [[2095, 2, 1], [2095, 8, 1], 2], 0.495890411]], [['regression candidate year range 1', [[2055, 5, 28], [2056, 5, 28], 1], 1.0], ['regression candidate year range 2', [[1907, 5, 8], [1908, 5, 8], 1], 1.0], ['partial repair probe 1', [[2104, 1, 1], [2106, 1, 1], 1], 1.99726776], ['partial repair probe 2', [[2004, 1, 1], [2005, 1, 1], 1], 1.0], ['boundary control 1', [[2024, 2, 29], [2025, 2, 28], 1], 1.0], ['boundary control 2', [[2024, 11, 15], [2025, 2, 15], 4], 0.252054795], ['normal control 1', [[1902, 3, 28], [1902, 12, 28], 4], 0.753424658], ['normal control 2', [[2023, 2, 1], [2023, 8, 1], 2], 0.495890411]], [['regression candidate year range 1', [[2003, 1, 29], [2004, 3, 3], 1], 1.090163934], ['regression candidate year range 2', [[2091, 1, 30], [2092, 3, 29], 1], 1.158469945], ['partial repair probe 1', [[2024, 2, 15], [2025, 2, 15], 1], 1.0], ['partial repair probe 2', [[2096, 1, 15], [2098, 1, 15], 1], 1.99726776], ['boundary control 1', [[2024, 2, 29], [2025, 2, 28], 1], 1.0], ['boundary control 2', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['normal control 1', [[1956, 5, 23], [1959, 5, 23], 1], 3.0], ['normal control 2', [[2050, 7, 28], [2051, 7, 28], 2], 1.0]], [['regression candidate year range 1', [[1983, 3, 1], [1984, 3, 1], 1], 1.0], ['regression candidate year range 2', [[1972, 1, 31], [1972, 10, 26], 1], 0.734972678], ['partial repair probe 1', [[2000, 1, 29], [2002, 1, 29], 1], 1.99726776], ['partial repair probe 2', [[2000, 1, 29], [2001, 1, 29], 1], 1.0], ['boundary control 1', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['boundary control 2', [[2024, 11, 15], [2025, 2, 15], 4], 0.252054795], ['normal control 1', [[2095, 2, 28], [2096, 2, 28], 1], 1.0], ['normal control 2', [[1947, 3, 30], [1948, 7, 15], 2], 1.292349727]], [['regression candidate year range 1', [[1995, 12, 1], [1996, 12, 1], 1], 1.0], ['regression candidate year range 2', [[1991, 4, 30], [1992, 4, 30], 1], 1.0], ['partial repair probe 1', [[2024, 2, 15], [2025, 2, 15], 1], 1.0], ['partial repair probe 2', [[2028, 2, 1], [2031, 2, 1], 1], 2.994535519], ['boundary control 1', [[2023, 12, 15], [2024, 3, 15], 4], 0.24863388], ['boundary control 2', [[2024, 11, 15], [2025, 2, 15], 4], 0.252054795], ['normal control 1', [[2096, 1, 28], [2096, 4, 28], 4], 0.24863388], ['normal control 2', [[1943, 1, 22], [1943, 4, 22], 4], 0.246575342]]]
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 candidate year range 1	`1.0`	`1.0`	Passed
regression candidate year range 2	`1.046448087`	`1.046448087`	Passed
partial repair probe 1	`1.0`	`1.0`	Passed
partial repair probe 2	`2.994535519`	`2.994535519`	Passed
boundary control 1	`1.0`	`1.0`	Passed
boundary control 2	`0.24863388`	`0.24863388`	Passed
normal control 1	`0.602739726`	`0.602739726`	Passed
normal control 2	`0.495890411`	`0.495890411`	Passed

SHA-256 / c0443261084dbcae7d6769b0648082c9aff1f2879028faa6bdfcf24d447a70bd

Verification & scope

A deterministic toy contract stated explicitly in the contract field; no claim of conformance to any published convention text. 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 2026-09-29T14:46:50.510902+00:00.

Case digest / 8f77aa6dfd3fe4b064cbc6513130d96c75d9291c595405e943c26029aec3a5cf