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FA-62051 / Currency rounding and FX conversion / Open access

A zero-quantity fill on a flat position invents an average rate · case 01

A flat book reports an average rate after a zero-quantity booking correction.

Verified by executionVariant 1 · 6 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Zero-quantity trades are not skipped, so a flat position opens an average at the trade rate.

VERIFIED REPAIR

Ignore zero-quantity trades entirely.

Unsuccessful approach: Skipping zero trades only while a position is open still assigns an average on a flat book.

Case contract

solve(trades, spot): trades are [qty, rate] in foreign units (buy positive, sell negative) at home-per-foreign decimal rates. Adding in the position's direction updates the weighted average rate; reducing realizes closed_qty * (rate - avg) * sign(position) without changing avg; a flip realizes the closed part and opens the remainder at the trade rate; a flat position has no average. Zero-quantity trades are ignored. Unrealized = position * (spot - avg). Return [position, avg to 6 dp or None, realized to 2 dp, unrealized to 2 dp], rounded half-even only at output with unsigned zeros.

Why this case matters

Currency amounts must be rounded at the right stage and in the right unit, or ledgers, quotes and settlements drift by minor units.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
from decimal import Decimal, ROUND_HALF_EVEN, ROUND_HALF_UP, ROUND_FLOOR, ROUND_CEILING, ROUND_DOWN, ROUND_UP, ROUND_HALF_DOWN
from fractions import Fraction
N = 1
observations = []
def solve(trades, spot):
    def fmt(fr, e):
        d = (Decimal(fr.numerator) / Decimal(fr.denominator)).quantize(Decimal(1).scaleb(-e), rounding=ROUND_HALF_EVEN)
        return format(abs(d) if d == 0 else d, 'f')
    pos, avg, realized = 0, None, Fraction(0)
    for qty, rate in trades:
        r = Fraction(rate)
        pass
        if pos == 0 or (pos > 0) == (qty > 0):
            avg = r if pos == 0 else (abs(pos) * avg + abs(qty) * r) / (abs(pos) + abs(qty))
            pos += qty
        else:
            closed = min(abs(qty), abs(pos))
            sgn = 1 if pos > 0 else -1
            realized += closed * (r - avg) * sgn
            pos += qty
            if pos == 0: avg = None
            elif (pos > 0) != (sgn > 0): avg = r
    unreal = pos * (Fraction(spot) - avg) if pos else Fraction(0)
    return [pos, None if avg is None else fmt(avg, 6), fmt(realized, 2), fmt(unreal, 2)]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
cases = [[('regression zero-quantity-trade 1', ([[0, '1.10']], '1.05'), [0, None, '0.00', '0.00']),
  ('regression zero-quantity-trade 2', ([[50, '1.1'], [-50, '1.2'], [0, '1.3']], '1.5'), [0, None, '5.00', '0.00']),
  ('control: add then reduce', ([[100, '1.10'], [100, '1.20'], [-50, '1.30']], '1.25'),
   [150, '1.150000', '7.50', '15.00']),
  ('control: flip long to short', ([[100, '1.10'], [-150, '1.00']], '1.05'), [-50, '1.000000', '-10.00', '-2.50']),
  ('control: close short', ([[-100, '1.10'], [100, '1.00']], '1.05'), [0, None, '10.00', '0.00']),
  ('control: short add', ([[-30, '1.0833'], [-70, '1.0851']], '1.0800'), [-100, '1.084560', '0.00', '0.46'])],
 [('regression zero-quantity-trade 1', ([[0, '1.1473'], [0, '1.0737']], '1.1228'), [0, None, '0.00', '0.00']),
  ('regression zero-quantity-trade 2', ([[0, '1.1043']], '1.1389'), [0, None, '0.00', '0.00']),
  ('control: zero trade only', ([[0, '1.10']], '1.05'), [0, None, '0.00', '0.00']),
  ('control: short add', ([[-30, '1.0833'], [-70, '1.0851']], '1.0800'), [-100, '1.084560', '0.00', '0.46']),
  ('control: three reductions', ([[300, '1.0711'], [-100, '1.0733'], [-100, '1.0745'], [-100, '1.0719']], '1.07'),
   [0, None, '0.64', '0.00']),
  ('control: zero between', ([[50, '1.1'], [-50, '1.2'], [0, '1.3'], [20, '1.4']], '1.5'),
   [20, '1.400000', '5.00', '2.00'])],
 [('regression zero-quantity-trade 1', ([[0, '1.0667']], '1.1026'), [0, None, '0.00', '0.00']),
  ('regression zero-quantity-trade 2', ([[0, '1.0905']], '1.0908'), [0, None, '0.00', '0.00']),
  ('control: zero between', ([[50, '1.1'], [-50, '1.2'], [0, '1.3'], [20, '1.4']], '1.5'),
   [20, '1.400000', '5.00', '2.00']),
  ('control: flip short to long', ([[-40, '1.2'], [100, '1.1']], '1.15'), [60, '1.100000', '4.00', '3.00']),
  ('control: add then reduce', ([[100, '1.10'], [100, '1.20'], [-50, '1.30']], '1.25'),
   [150, '1.150000', '7.50', '15.00']),
  ('control: flip long to short', ([[100, '1.10'], [-150, '1.00']], '1.05'), [-50, '1.000000', '-10.00', '-2.50'])],
 [('regression zero-quantity-trade 1', ([[0, '1.1082']], '1.0890'), [0, None, '0.00', '0.00']),
  ('regression zero-quantity-trade 2', ([[0, '1.0848']], '1.0930'), [0, None, '0.00', '0.00']),
  ('control: flip long to short', ([[100, '1.10'], [-150, '1.00']], '1.05'), [-50, '1.000000', '-10.00', '-2.50']),
  ('control: close short', ([[-100, '1.10'], [100, '1.00']], '1.05'), [0, None, '10.00', '0.00']),
  ('control: zero trade only', ([[0, '1.10']], '1.05'), [0, None, '0.00', '0.00']),
  ('control: short add', ([[-30, '1.0833'], [-70, '1.0851']], '1.0800'), [-100, '1.084560', '0.00', '0.46'])],
 [('regression zero-quantity-trade 1', ([[0, '1.1213'], [0, '1.0823']], '1.1168'), [0, None, '0.00', '0.00']),
  ('regression zero-quantity-trade 2', ([[0, '1.0532']], '1.1383'), [0, None, '0.00', '0.00']),
  ('control: short add', ([[-30, '1.0833'], [-70, '1.0851']], '1.0800'), [-100, '1.084560', '0.00', '0.46']),
  ('control: three reductions', ([[300, '1.0711'], [-100, '1.0733'], [-100, '1.0745'], [-100, '1.0719']], '1.07'),
   [0, None, '0.64', '0.00']),
  ('control: zero between', ([[50, '1.1'], [-50, '1.2'], [0, '1.3'], [20, '1.4']], '1.5'),
   [20, '1.400000', '5.00', '2.00']),
  ('control: flip short to long', ([[-40, '1.2'], [100, '1.1']], '1.15'), [60, '1.100000', '4.00', '3.00'])]]
for label, args, expected in cases[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 zero-quantity-trade 1[0, '1.100000', '0.00', '0.00'][0, None, '0.00', '0.00']Failed
regression zero-quantity-trade 2[0, '1.300000', '5.00', '0.00'][0, None, '5.00', '0.00']Failed
control: add then reduce[150, '1.150000', '7.50', '15.00'][150, '1.150000', '7.50', '15.00']Passed
control: flip long to short[-50, '1.000000', '-10.00', '-2.50'][-50, '1.000000', '-10.00', '-2.50']Passed
control: close short[0, None, '10.00', '0.00'][0, None, '10.00', '0.00']Passed
control: short add[-100, '1.084560', '0.00', '0.46'][-100, '1.084560', '0.00', '0.46']Passed

SHA-256 / a96a599f4b5edff42a5b58bf34d073e659d8a37f364350a53a43ed48ad958098

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
from decimal import Decimal, ROUND_HALF_EVEN, ROUND_HALF_UP, ROUND_FLOOR, ROUND_CEILING, ROUND_DOWN, ROUND_UP, ROUND_HALF_DOWN
from fractions import Fraction
N = 1
observations = []
def solve(trades, spot):
    def fmt(fr, e):
        d = (Decimal(fr.numerator) / Decimal(fr.denominator)).quantize(Decimal(1).scaleb(-e), rounding=ROUND_HALF_EVEN)
        return format(abs(d) if d == 0 else d, 'f')
    pos, avg, realized = 0, None, Fraction(0)
    for qty, rate in trades:
        r = Fraction(rate)
        if qty == 0 and pos != 0: continue
        if pos == 0 or (pos > 0) == (qty > 0):
            avg = r if pos == 0 else (abs(pos) * avg + abs(qty) * r) / (abs(pos) + abs(qty))
            pos += qty
        else:
            closed = min(abs(qty), abs(pos))
            sgn = 1 if pos > 0 else -1
            realized += closed * (r - avg) * sgn
            pos += qty
            if pos == 0: avg = None
            elif (pos > 0) != (sgn > 0): avg = r
    unreal = pos * (Fraction(spot) - avg) if pos else Fraction(0)
    return [pos, None if avg is None else fmt(avg, 6), fmt(realized, 2), fmt(unreal, 2)]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
cases = [[('regression zero-quantity-trade 1', ([[0, '1.10']], '1.05'), [0, None, '0.00', '0.00']),
  ('regression zero-quantity-trade 2', ([[50, '1.1'], [-50, '1.2'], [0, '1.3']], '1.5'), [0, None, '5.00', '0.00']),
  ('control: add then reduce', ([[100, '1.10'], [100, '1.20'], [-50, '1.30']], '1.25'),
   [150, '1.150000', '7.50', '15.00']),
  ('control: flip long to short', ([[100, '1.10'], [-150, '1.00']], '1.05'), [-50, '1.000000', '-10.00', '-2.50']),
  ('control: close short', ([[-100, '1.10'], [100, '1.00']], '1.05'), [0, None, '10.00', '0.00']),
  ('control: short add', ([[-30, '1.0833'], [-70, '1.0851']], '1.0800'), [-100, '1.084560', '0.00', '0.46'])],
 [('regression zero-quantity-trade 1', ([[0, '1.1473'], [0, '1.0737']], '1.1228'), [0, None, '0.00', '0.00']),
  ('regression zero-quantity-trade 2', ([[0, '1.1043']], '1.1389'), [0, None, '0.00', '0.00']),
  ('control: zero trade only', ([[0, '1.10']], '1.05'), [0, None, '0.00', '0.00']),
  ('control: short add', ([[-30, '1.0833'], [-70, '1.0851']], '1.0800'), [-100, '1.084560', '0.00', '0.46']),
  ('control: three reductions', ([[300, '1.0711'], [-100, '1.0733'], [-100, '1.0745'], [-100, '1.0719']], '1.07'),
   [0, None, '0.64', '0.00']),
  ('control: zero between', ([[50, '1.1'], [-50, '1.2'], [0, '1.3'], [20, '1.4']], '1.5'),
   [20, '1.400000', '5.00', '2.00'])],
 [('regression zero-quantity-trade 1', ([[0, '1.0667']], '1.1026'), [0, None, '0.00', '0.00']),
  ('regression zero-quantity-trade 2', ([[0, '1.0905']], '1.0908'), [0, None, '0.00', '0.00']),
  ('control: zero between', ([[50, '1.1'], [-50, '1.2'], [0, '1.3'], [20, '1.4']], '1.5'),
   [20, '1.400000', '5.00', '2.00']),
  ('control: flip short to long', ([[-40, '1.2'], [100, '1.1']], '1.15'), [60, '1.100000', '4.00', '3.00']),
  ('control: add then reduce', ([[100, '1.10'], [100, '1.20'], [-50, '1.30']], '1.25'),
   [150, '1.150000', '7.50', '15.00']),
  ('control: flip long to short', ([[100, '1.10'], [-150, '1.00']], '1.05'), [-50, '1.000000', '-10.00', '-2.50'])],
 [('regression zero-quantity-trade 1', ([[0, '1.1082']], '1.0890'), [0, None, '0.00', '0.00']),
  ('regression zero-quantity-trade 2', ([[0, '1.0848']], '1.0930'), [0, None, '0.00', '0.00']),
  ('control: flip long to short', ([[100, '1.10'], [-150, '1.00']], '1.05'), [-50, '1.000000', '-10.00', '-2.50']),
  ('control: close short', ([[-100, '1.10'], [100, '1.00']], '1.05'), [0, None, '10.00', '0.00']),
  ('control: zero trade only', ([[0, '1.10']], '1.05'), [0, None, '0.00', '0.00']),
  ('control: short add', ([[-30, '1.0833'], [-70, '1.0851']], '1.0800'), [-100, '1.084560', '0.00', '0.46'])],
 [('regression zero-quantity-trade 1', ([[0, '1.1213'], [0, '1.0823']], '1.1168'), [0, None, '0.00', '0.00']),
  ('regression zero-quantity-trade 2', ([[0, '1.0532']], '1.1383'), [0, None, '0.00', '0.00']),
  ('control: short add', ([[-30, '1.0833'], [-70, '1.0851']], '1.0800'), [-100, '1.084560', '0.00', '0.46']),
  ('control: three reductions', ([[300, '1.0711'], [-100, '1.0733'], [-100, '1.0745'], [-100, '1.0719']], '1.07'),
   [0, None, '0.64', '0.00']),
  ('control: zero between', ([[50, '1.1'], [-50, '1.2'], [0, '1.3'], [20, '1.4']], '1.5'),
   [20, '1.400000', '5.00', '2.00']),
  ('control: flip short to long', ([[-40, '1.2'], [100, '1.1']], '1.15'), [60, '1.100000', '4.00', '3.00'])]]
for label, args, expected in cases[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 zero-quantity-trade 1[0, '1.100000', '0.00', '0.00'][0, None, '0.00', '0.00']Failed
regression zero-quantity-trade 2[0, '1.300000', '5.00', '0.00'][0, None, '5.00', '0.00']Failed
control: add then reduce[150, '1.150000', '7.50', '15.00'][150, '1.150000', '7.50', '15.00']Passed
control: flip long to short[-50, '1.000000', '-10.00', '-2.50'][-50, '1.000000', '-10.00', '-2.50']Passed
control: close short[0, None, '10.00', '0.00'][0, None, '10.00', '0.00']Passed
control: short add[-100, '1.084560', '0.00', '0.46'][-100, '1.084560', '0.00', '0.46']Passed

SHA-256 / 08a36fde0dc0df0b0d3193df150358e16403c3e6a27834090a60e09fb9fae578

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json
from decimal import Decimal, ROUND_HALF_EVEN, ROUND_HALF_UP, ROUND_FLOOR, ROUND_CEILING, ROUND_DOWN, ROUND_UP, ROUND_HALF_DOWN
from fractions import Fraction
N = 1
observations = []
def solve(trades, spot):
    def fmt(fr, e):
        d = (Decimal(fr.numerator) / Decimal(fr.denominator)).quantize(Decimal(1).scaleb(-e), rounding=ROUND_HALF_EVEN)
        return format(abs(d) if d == 0 else d, 'f')
    pos, avg, realized = 0, None, Fraction(0)
    for qty, rate in trades:
        r = Fraction(rate)
        if qty == 0: continue
        if pos == 0 or (pos > 0) == (qty > 0):
            avg = r if pos == 0 else (abs(pos) * avg + abs(qty) * r) / (abs(pos) + abs(qty))
            pos += qty
        else:
            closed = min(abs(qty), abs(pos))
            sgn = 1 if pos > 0 else -1
            realized += closed * (r - avg) * sgn
            pos += qty
            if pos == 0: avg = None
            elif (pos > 0) != (sgn > 0): avg = r
    unreal = pos * (Fraction(spot) - avg) if pos else Fraction(0)
    return [pos, None if avg is None else fmt(avg, 6), fmt(realized, 2), fmt(unreal, 2)]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
cases = [[('regression zero-quantity-trade 1', ([[0, '1.10']], '1.05'), [0, None, '0.00', '0.00']),
  ('regression zero-quantity-trade 2', ([[50, '1.1'], [-50, '1.2'], [0, '1.3']], '1.5'), [0, None, '5.00', '0.00']),
  ('control: add then reduce', ([[100, '1.10'], [100, '1.20'], [-50, '1.30']], '1.25'),
   [150, '1.150000', '7.50', '15.00']),
  ('control: flip long to short', ([[100, '1.10'], [-150, '1.00']], '1.05'), [-50, '1.000000', '-10.00', '-2.50']),
  ('control: close short', ([[-100, '1.10'], [100, '1.00']], '1.05'), [0, None, '10.00', '0.00']),
  ('control: short add', ([[-30, '1.0833'], [-70, '1.0851']], '1.0800'), [-100, '1.084560', '0.00', '0.46'])],
 [('regression zero-quantity-trade 1', ([[0, '1.1473'], [0, '1.0737']], '1.1228'), [0, None, '0.00', '0.00']),
  ('regression zero-quantity-trade 2', ([[0, '1.1043']], '1.1389'), [0, None, '0.00', '0.00']),
  ('control: zero trade only', ([[0, '1.10']], '1.05'), [0, None, '0.00', '0.00']),
  ('control: short add', ([[-30, '1.0833'], [-70, '1.0851']], '1.0800'), [-100, '1.084560', '0.00', '0.46']),
  ('control: three reductions', ([[300, '1.0711'], [-100, '1.0733'], [-100, '1.0745'], [-100, '1.0719']], '1.07'),
   [0, None, '0.64', '0.00']),
  ('control: zero between', ([[50, '1.1'], [-50, '1.2'], [0, '1.3'], [20, '1.4']], '1.5'),
   [20, '1.400000', '5.00', '2.00'])],
 [('regression zero-quantity-trade 1', ([[0, '1.0667']], '1.1026'), [0, None, '0.00', '0.00']),
  ('regression zero-quantity-trade 2', ([[0, '1.0905']], '1.0908'), [0, None, '0.00', '0.00']),
  ('control: zero between', ([[50, '1.1'], [-50, '1.2'], [0, '1.3'], [20, '1.4']], '1.5'),
   [20, '1.400000', '5.00', '2.00']),
  ('control: flip short to long', ([[-40, '1.2'], [100, '1.1']], '1.15'), [60, '1.100000', '4.00', '3.00']),
  ('control: add then reduce', ([[100, '1.10'], [100, '1.20'], [-50, '1.30']], '1.25'),
   [150, '1.150000', '7.50', '15.00']),
  ('control: flip long to short', ([[100, '1.10'], [-150, '1.00']], '1.05'), [-50, '1.000000', '-10.00', '-2.50'])],
 [('regression zero-quantity-trade 1', ([[0, '1.1082']], '1.0890'), [0, None, '0.00', '0.00']),
  ('regression zero-quantity-trade 2', ([[0, '1.0848']], '1.0930'), [0, None, '0.00', '0.00']),
  ('control: flip long to short', ([[100, '1.10'], [-150, '1.00']], '1.05'), [-50, '1.000000', '-10.00', '-2.50']),
  ('control: close short', ([[-100, '1.10'], [100, '1.00']], '1.05'), [0, None, '10.00', '0.00']),
  ('control: zero trade only', ([[0, '1.10']], '1.05'), [0, None, '0.00', '0.00']),
  ('control: short add', ([[-30, '1.0833'], [-70, '1.0851']], '1.0800'), [-100, '1.084560', '0.00', '0.46'])],
 [('regression zero-quantity-trade 1', ([[0, '1.1213'], [0, '1.0823']], '1.1168'), [0, None, '0.00', '0.00']),
  ('regression zero-quantity-trade 2', ([[0, '1.0532']], '1.1383'), [0, None, '0.00', '0.00']),
  ('control: short add', ([[-30, '1.0833'], [-70, '1.0851']], '1.0800'), [-100, '1.084560', '0.00', '0.46']),
  ('control: three reductions', ([[300, '1.0711'], [-100, '1.0733'], [-100, '1.0745'], [-100, '1.0719']], '1.07'),
   [0, None, '0.64', '0.00']),
  ('control: zero between', ([[50, '1.1'], [-50, '1.2'], [0, '1.3'], [20, '1.4']], '1.5'),
   [20, '1.400000', '5.00', '2.00']),
  ('control: flip short to long', ([[-40, '1.2'], [100, '1.1']], '1.15'), [60, '1.100000', '4.00', '3.00'])]]
for label, args, expected in cases[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 zero-quantity-trade 1[0, None, '0.00', '0.00'][0, None, '0.00', '0.00']Passed
regression zero-quantity-trade 2[0, None, '5.00', '0.00'][0, None, '5.00', '0.00']Passed
control: add then reduce[150, '1.150000', '7.50', '15.00'][150, '1.150000', '7.50', '15.00']Passed
control: flip long to short[-50, '1.000000', '-10.00', '-2.50'][-50, '1.000000', '-10.00', '-2.50']Passed
control: close short[0, None, '10.00', '0.00'][0, None, '10.00', '0.00']Passed
control: short add[-100, '1.084560', '0.00', '0.46'][-100, '1.084560', '0.00', '0.46']Passed

SHA-256 / ff95ed2f2538e797183e3267e3c626a36588a9dd315686364dafd003c3192697

Verification & scope

A deterministic, bounded teaching model with a stipulated toy contract; it makes no claim of conformance to any real regulation, standard, or institution's rules. 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 2026-09-29T14:47:01.017973+00:00.

Case digest / dc1546750921eec03cdfb9f0979bd521712e0dde7f10f9bd2f5b54c1fe616531