transactionsyncing

TransactionSyncing

Refresh financial activity into family_office.db: investment activities (transactions table) and card/bank spending (bank_transactions table), auto-categorized for budget review.

family_office.db is the system of record. The Google Sheets export was retired 2026-07-31.

Step 0: Refresh (sync-first, mandatory)

Both halves read from the local DB, refreshed FIRST so nothing is stale. Follow the shared Sync-First + DB-Read pattern. This skill needs two sources:

uv run python -m src.integrations.snaptrade.sync_transactions_db          # investment activities -> transactions
uv run python -m src.integrations.simplefin.sync_expenses_db --months 3   # card/bank spending -> bank_transactions
# or refresh everything at once:
uv run python -m src.integrations.refresh_all --months 3

Completion criterion: both sync commands report success. Do not gate on MAX(synced_at) advancing: both layers are idempotent and only stamp rows they actually write, so a run with no new activity leaves the timestamp untouched. Read the command output instead (N inserted, M updated, K skipped).

⚠️ The two feeds do not run at the same speed

bank_transactions (SimpleFIN) is generally fresher than transactions (SnapTrade), which lags by days, but freshness is per account: a connected card can go silent for months (see Stale connections below), so check the account's own MAX(date) before trusting its cash leg. Verified 2026-08-04: the positions table showed PLTR and TSLA puts opened on 2026-08-03, while transactions had no row for either buy and its latest activity date was still 2026-07-31. SnapTrade updated holdings before it published the activity that created them.

Consequence: never conclude "X did not happen" from an absence in transactions. A missing row means "not published yet" at least as often as it means "did not occur". To answer whether something executed:

1. Check positions for a holdings change (fastest signal).
2. Check bank_transactions for the cash leg, which is current.
3. Only then read transactions, and treat the tail few days as incomplete.

The same applies to month-boundary reporting: the last several days of any period may still be missing from the investment side.

Direction and sign

bank_transactions.direction is resolved by resolve_direction() in src/integrations/simplefin/sync_expenses_db.py. Explicit feed wording wins over amount sign, because Fidelity's CMA reports inbound payroll with the same negative sign it uses for outflows. amount is signed to match direction, so SUM(amount) is real cash flow: credits positive, debits negative.

⚠️ Household spending lives in THREE streams, in two different tables

A spending review that reads only bank_transactions is wrong. Measured for July 2026: that table showed well under two thirds of personal spend. The missing 41% was in the other two streams.

Stream 2 is the one that gets forgotten. The Fidelity brokerage pays the mortgage, insurance, utilities, phone, tuition, and has a debit card used for groceries. Those rows are type='WITHDRAWAL' in transactions, not in bank_transactions at all.

sqlite3 family_office.db \
  "SELECT date, description, -amount FROM transactions
   WHERE type = 'WITHDRAWAL' AND date LIKE '2026-07%' ORDER BY -amount DESC;"

Categorize those rows with the same categorize_expense(), then separate:

• Household consumption: mortgage, insurance, groceries, utilities, tuition.
• Not consumption: MARGIN INTEREST (financing cost, report separately), card bill payments, and JOURNALED internal moves.

Check for double counting across the two tables by matching amount and date within a couple of days. In July exactly one pair collided ($50 on 7/24) and inspection showed two genuinely separate payments, not a duplicate. Inspect; do not assume either way.

Income totals have the mirror-image problem. Credits into bank_transactions include transfers between the principal's own accounts, so summing them overstates income. Net internal movement out before quoting a surplus.

Spending reviews: count purchases, exclude bill payments, then AUDIT COVERAGE

Count card purchases. Exclude card bill payments. Both are debits, but a purchase and the payment of that same purchase are one dollar of spending, not two. Exclude the categories in NON_SPEND_CATEGORIES, and exclude BUSINESS_CATEGORIES plus the business accounts when the question is household spending.

That exclusion is only valid if every card that receives a payment also reports its purchases. When a card is paid but not connected, its purchases are invisible and its payment was excluded, so the spending vanishes entirely. Run this before quoting any total:

uv run python -c "
import sqlite3
c = sqlite3.connect('family_office.db')
print('Cards receiving payments:')
for r in c.execute(\"SELECT DISTINCT COALESCE(payee,description) FROM bank_transactions WHERE category='Credit Card Payment'\"):
    print('  ', r[0])
print('Card accounts reporting purchases (with last activity):')
for r in c.execute(\"SELECT account_name, MAX(date), COUNT(*) FROM bank_transactions WHERE LOWER(COALESCE(org,'')) LIKE '%credit%' OR LOWER(COALESCE(org,'')) LIKE '%express%' GROUP BY account_id\"):
    print(f'   {r[0]:<38} last {r[1]}  ({r[2]} rows)')
"

Two failure modes, both found 2026-08-04:

1. Paid but not connected. An Apple Card bill cleared on 2026-08-03 with zero Apple purchases anywhere in the feed. That understated July household spending by roughly 8%. Backfill from the bill amount and say so, or connect the account.
2. Connected but stale. Chase Sapphire Preferred stopped reporting on 2026-07-17 and an older card had produced nothing since 2026-04-21. A stale connection looks identical to genuinely low spending. Treat any card whose last activity is more than about a week old as suspect.

Always state the coverage caveat alongside the total. A household number carrying an unquantified gap is worse than one carrying a stated one.

Workflow Routing

CSV ingest is an archive and fallback path. The primary flow is the Step 0 refresh above.

Examples

Example 1: Sync after downloading Fidelity transaction history

User: "sync transactions"
-> Reads History_for_Account_{account_id}.csv from imports/transactions/
-> Creates/updates Transactions tab with full Fidelity data
-> Routes DEBIT CARD PURCHASE entries to Expense Tracker
-> Auto-categorizes expenses (H-E-B -> Groceries, Tesla -> Auto & Transport)
-> Reports: "Added 45 transactions, 12 expenses categorized"

Example 2: Import new transaction export

User: "import the transaction history"
-> Invokes SyncTransactions workflow
-> Detects duplicates by date + action + amount
-> Skips existing entries, adds only new ones
-> Flags uncategorized expenses for manual review

Example 3: Check recent transactions

User: "import fidelity transactions and update expense tracker"
-> Full sync with expense routing
-> Generates summary of dividends received, purchases, margin interest

Architecture Overview

Data Flow

SnapTrade activities            SimpleFIN dump (bun run src/dump.ts)
        |                                |
        v                                v
  sync_transactions_db            sync_expenses_db (categorize.py)
        |                                |
        v                                v
+------------------+           +--------------------+
| transactions     |           | bank_transactions  |  <- categorized, upserted
| table (DB)       |           | table (DB)         |
+------------------+           +--------------------+

family_office.db is the terminus. Query the tables directly for review; there is no downstream export.

Transaction Types Handled

Smart Categorization

Categorization is executable and runs inside the expense adapter, so the category column arrives pre-filled on every bank_transactions row. Household merchants (local businesses, the daycare, the church) are private and live in the instance as merchant-rules.yaml, merged into the public table at sync time; the public table holds national brands only. The rules live in code at src/integrations/simplefin/categorize.py (categorize_expense(text, amount)), which is the source of truth mirroring the human-readable CategoryRules.md. Keep the two in sync when adding patterns.

Sample patterns:

• H-E-B, KROGER, COSTCO, WAL-MART -> Groceries
• Tesla, SUPERCHA -> Auto & Transport
• BENIHANA, GOLDEN CORRAL, PAPA JOHN -> Dining Out
• CVS, PHARMACY -> Health & Wellness
• amount < $1.00 or verification text -> Exempt; no match -> Uncategorized

Input Sources: the local DB (primary)

Half A: Investment activities (transactions table)

After Step 0's refresh, read investment activity from the DB:

sqlite3 family_office.db \
  "SELECT date, ty

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