""" Afton — Reverse 2024 Over-Cleanup (Prior Period Adjustment) ============================================================ Today's JE 27061 ($22K dup cleanup) and JE 27062 ($203K UF plug) both reversed phantom income for entries spanning 2024-2026. Per the 2025/2026-only scope locked 2026-05-23, the pre-2025 portion ($114,939) was out of scope. This JE surgically reverses just the 2024 income reversal portion of today's work by debiting Retained Earnings (proper prior-period adjustment per GAAP) and crediting back the Income accounts proportionally to what today's JEs hit. Effect: - 2026 P&L income: +$114,939 (cancels the 2024-related portion of today's reversals) - Retained Earnings: -$114,939 (recognizes the prior-period adjustment) - UF: UNCHANGED (cleaned state preserved) - 2024 P&L: UNCHANGED (we never touch closed period directly) Net result: 2026 P&L only shows in-scope cleanup (~$110K). 2024 books unchanged. Equity reflects the prior-period error. Defaults to DRY RUN. Pass --apply to stage. """ from __future__ import annotations import argparse import json import sys from collections import defaultdict from datetime import datetime from pathlib import Path if hasattr(sys.stdout, "reconfigure"): sys.stdout.reconfigure(encoding="utf-8", errors="replace") SCRIPT_DIR = Path(__file__).resolve().parent QBO_APP_DIR = SCRIPT_DIR.parent.parent / "Atlas" / "app" QUEUE_DIR = SCRIPT_DIR.parent.parent / "Team" / "Riv" / "j2_qbo_mcp" sys.path.insert(0, str(QBO_APP_DIR)) sys.path.insert(0, str(QUEUE_DIR)) import qbo_client as qbo # type: ignore from queue_backend import append as queue_append # type: ignore CLIENT = "afton" RETAINED_EARNINGS_ACCT_ID = "2" TXN_DATE = "2026-05-22" DOC_NUMBER = "AFTON-PPA-2024" # 14 chars FORENSICS_JSON = SCRIPT_DIR / "afton_uf_forensics.json" SURVEY_JSON = SCRIPT_DIR / "afton_je_topology_survey.json" # JE 27062's actual income account allocations (from the proportional stager output) JE_27062_ALLOCATIONS = { "49": {"name": "Services", "amount": 100659.05}, "1": {"name": "Sales", "amount": 92392.75}, "55": {"name": "Uncategorized Income", "amount": 6232.87}, "118": {"name": "QuickBooks Payments Sales", "amount": 3766.21}, } JE_27062_TOTAL = sum(v["amount"] for v in JE_27062_ALLOCATIONS.values()) # $203,050.88 def main() -> None: ap = argparse.ArgumentParser(description=__doc__) ap.add_argument("--apply", action="store_true") args = ap.parse_args() # ── Compute JE 27062's 2024 portion ────────────────────────────────── forensics = json.load(open(FORENSICS_JSON, encoding="utf-8")) no_method = [r for r in forensics["rows"] if not r.get("payment_method_id")] # Replicate the auto-classification used in the stager def is_auto(r): if r.get("create_time") and r.get("txn_date"): try: ct = datetime.fromisoformat(r["create_time"].replace("Z", "+00:00")).date() td = datetime.fromisoformat(r["txn_date"]).date() if (ct - td).days >= 30: return False except Exception: pass return True auto = [r for r in no_method if is_auto(r)] je27062_2024_amount = sum(r["amount"] for r in auto if (r.get("txn_date") or "")[:4] == "2024") print(f"JE 27062 2024 portion: ${je27062_2024_amount:,.2f}") # Proportional share of each income acct attributable to 2024 portion je27062_2024_share = je27062_2024_amount / JE_27062_TOTAL je27062_reversals: dict[str, float] = {} for acct_id, v in JE_27062_ALLOCATIONS.items(): amt = round(v["amount"] * je27062_2024_share, 2) je27062_reversals[acct_id] = {"name": v["name"], "amount": amt} # ── Compute JE 27061's 2024 portion (per-pair invoice-routed) ──────── survey = json.load(open(SURVEY_JSON, encoding="utf-8")) je27061_reversals: dict[str, dict] = defaultdict(lambda: {"name": "", "amount": 0.0}) je27061_2024_amount = 0.0 for r in survey["rows"]: # Survey contains all 35 dup pairs. Filter to 2024 untouched. if r["track"] != "untouched": continue if (r.get("target_invoice_date") or "")[:4] != "2024": continue amt = r["target_invoice_total"] je27061_2024_amount += amt # Each pair has 1 income line income_lines = r["income_lines"] if len(income_lines) != 1: continue iar = income_lines[0]["item_account_ref"] acct_id = str(iar["value"]) je27061_reversals[acct_id]["name"] = iar["name"] je27061_reversals[acct_id]["amount"] += amt print(f"JE 27061 2024 portion: ${je27061_2024_amount:,.2f}") # ── Combine both reversals ─────────────────────────────────────────── combined: dict[str, dict] = defaultdict(lambda: {"name": "", "amount": 0.0}) for acct_id, v in je27062_reversals.items(): combined[acct_id]["name"] = v["name"] combined[acct_id]["amount"] += v["amount"] for acct_id, v in je27061_reversals.items(): combined[acct_id]["name"] = v["name"] or combined[acct_id]["name"] combined[acct_id]["amount"] += v["amount"] total_reversal = sum(v["amount"] for v in combined.values()) print(f"\nTotal 2024 over-cleanup to reverse: ${total_reversal:,.2f}") print("\nProposed Cr Income (reversal) by account:") for acct_id, v in sorted(combined.items(), key=lambda x: -x[1]["amount"]): print(f" {acct_id:>4s} {v['name']:<40s} ${v['amount']:>12,.2f}") # Rounding correction so Dr = sum of Crs exactly total_cr = round(sum(round(v["amount"], 2) for v in combined.values()), 2) print(f"\n Total Cr: ${total_cr:,.2f}") print(f" Dr Retained Earnings: ${total_cr:,.2f}") # ── Build JE ────────────────────────────────────────────────────────── je_lines = [] je_lines.append({ "Description": ( "Prior Period Adjustment — restore 2026 P&L to in-scope only. " "Reverses the 2024-related portion of JE 27061 + JE 27062 income " "reversals. Per 2025/2026-only scope locked 2026-05-23." ), "Amount": total_cr, "PostingType": "Debit", "AccountRef": {"value": RETAINED_EARNINGS_ACCT_ID, "name": "Retained Earnings"}, }) for acct_id, v in sorted(combined.items(), key=lambda x: -x[1]["amount"]): amt = round(v["amount"], 2) if amt < 0.01: continue je_lines.append({ "Description": ( f"PPA reversal — restore {v['name']} income from 2024-related " f"portion of today's UF cleanup" ), "Amount": amt, "PostingType": "Credit", "AccountRef": {"value": acct_id, "name": v["name"]}, }) dr_total = sum(l["Amount"] for l in je_lines if l["PostingType"] == "Debit") cr_total = sum(l["Amount"] for l in je_lines if l["PostingType"] == "Credit") diff = round(dr_total - cr_total, 2) if abs(diff) > 0.001: # Adjust the largest Cr line to absorb rounding for l in je_lines: if l["PostingType"] == "Credit": l["Amount"] = round(l["Amount"] + diff, 2) print(f"\nRounding correction of ${diff:.2f} applied to {l['AccountRef']['name']}") break cr_total = sum(l["Amount"] for l in je_lines if l["PostingType"] == "Credit") print(f"\nJE: {len(je_lines)} lines Dr=${dr_total:,.2f} Cr=${cr_total:,.2f}") if abs(dr_total - cr_total) > 0.01: sys.exit(f"JE UNBALANCED: Dr=${dr_total:.2f} Cr=${cr_total:.2f}") if not args.apply: print("\nDRY RUN — pass --apply to stage") return private_note = ( f"Prior Period Adjustment — restores 2026 P&L to in-scope-only " f"by reversing the 2024-portion of JE 27061 + JE 27062 income reversals. " f"Per Jimmie 2026-05-23 scope decision: 2025/2026 only. " f"This JE: Dr Retained Earnings ${dr_total:,.2f}, Cr Income accts " f"(proportional split). Net 2026 P&L income +${dr_total:,.2f}; " f"UF unchanged at ~$60K; 2024 books unchanged." )[:3900] payload = { "TxnDate": TXN_DATE, "Line": je_lines, "Adjustment": True, "DocNumber": DOC_NUMBER, "PrivateNote": private_note, } row_id = queue_append({ "timestamp": datetime.now().isoformat(timespec="seconds"), "client": CLIENT, "tool_called": "create_journal_entry", "payload_json": json.dumps(payload, default=str), "requested_by": "Ledger (afton_2024_overclean_reversal_stager.py)", "status": "Pending", "tier": 1, }) print(f"\nQUEUED as row {row_id}") if __name__ == "__main__": main()