# SpendingAnalysis App Design

## Overview

A desktop application for importing, categorizing, and analyzing personal spending data from bank and credit card CSV exports. Built with Python, PySide6, and SQLite. Designed for a household context where the primary user tracks their own spending alongside shared accounts and family member attribution.

## Architecture

Three-layer architecture:

- **Data Layer** — SQLite via SQLAlchemy. Normalized schema for transactions, accounts, categories, rules, and household members.
- **Service Layer** — Python modules for CSV import/detection, categorization, duplicate detection, recurring charge analysis, and forecasting.
- **UI Layer** — PySide6 with sidebar navigation. Views: Import, Transactions, Analysis, Recurring, Settings.

### Project Structure

```
src/
  models/        # SQLAlchemy models (SQLite)
  services/      # Import, categorization, analysis, forecasting
  ui/            # PySide6 views and components
  db.py          # Database connection/session management
```

### Dependencies

- `PySide6` — Desktop UI framework
- `sqlalchemy` — ORM / database management
- `pandas` — CSV handling and analysis
- `matplotlib` — Charting (embedded in Qt via FigureCanvasQTAgg)

## Data Model

### household_members

- id, name, relationship
- Seeded with the primary user on first run
- Initial household: Andrew (self), Donna (wife), son

### Known income sources

Based on real data:
- **Andrew's salary** — CIM Techniques via QuickBooks direct deposit (biweekly, ~$2,314)
- **Donna's salary** — Oasis Batch payroll (biweekly, varies ~$998–$1,185)
- **Other deposits** — Mobile deposits, Capital One transfers in

These are auto-detected as income and attributed to the corresponding household member via categorization rules.

### accounts

- id, name, institution, type (checking/credit), owner (FK to household_members)
- Shared accounts (e.g., joint bank account) get a "Shared" designation
- Linked to CSV mappings so the app remembers which account a file belongs to

### transactions

- id, date, amount (signed: negative=expense, positive=income), description, account (FK), category (FK), attributed_to (FK to household_members, optional)
- `attributed_to` auto-fills from account owner by default, overridable per transaction or via rules

### categories

- id, name, default_tag (needs/wants/savings), icon
- Ships with standard defaults: Housing, Groceries, Dining Out, Transportation, Utilities, Entertainment, Healthcare, Shopping, Subscriptions, Family, Transfer, Income, etc.
- Fully user-editable: add, rename, merge, delete

### categorization_rules

- id, pattern, category (FK), tag_override (optional), attributed_to (FK, optional), priority
- Matched against transaction description, evaluated in priority order, first match wins

### csv_mappings

- id, name, header_fingerprint, column_map (JSON), amount_logic, account (FK)
- Saved per-source so repeat imports are automatic

## CSV Import & Mapping

### Known Source Formats

Based on real sample data:

**Chase credit card** (e.g., `Chase0372_Activity*.CSV`):
- Has headers: Transaction Date, Post Date, Description, Category, Type, Amount, Memo
- Amount is signed (negative=purchase, positive=payment/return)
- Type column: Sale, Payment, Return
- Includes Chase's own category assignments — import as initial suggestions

**Wells Fargo checking** (e.g., `Checking1.csv`):
- No headers — five positional columns: Date, Amount, Flag ("*"), Check Number, Description
- Amount is signed (negative=debit, positive=credit)
- Descriptions are very verbose with embedded authorization dates, card numbers, and reference IDs
- Requires description normalization to extract core merchant/payee

### Headerless CSV support

The mapping wizard handles files with no headers by showing column previews with positional indices. User assigns meaning by column position rather than header name.

### Description normalization

The service layer strips noise from transaction descriptions before storage:
- Authorization dates ("PURCHASE AUTHORIZED ON 02/06")
- Card numbers ("CARD 5360")
- Reference IDs ("S586033096695382", "REF #OP0WS99NKQ")
- Redundant location/phone details

The cleaned description is stored for display and rule matching. The raw description is preserved in a separate field for reference.

### Source category hints

When a CSV includes pre-assigned categories (like Chase does), the import process can use these as initial category suggestions. The user can accept, override, or ignore them.

### Cross-account transfer detection

The import service detects matching transfer pairs across accounts. For example:
- Checking: "CHASE CREDIT CRD EPAY 260128 ... -$1,461.35"
- Chase: "Payment Thank You - Web ... +$1,461.35"

These are linked as transfers so they don't double-count as spending or income.

### First import from a new source

1. App reads CSV, detects delimiter, and checks for headers vs. headerless format
2. Mapping wizard shows a preview of the first few rows
3. User maps columns to normalized fields: date, amount, description, and optionally reference number, balance, source category
4. User specifies amount logic (single signed column, separate debit/credit columns, type column)
5. User selects or creates the account this CSV belongs to
6. Mapping saved to `csv_mappings` keyed on column header fingerprint (or column count + sample patterns for headerless files)

### Subsequent imports

1. App matches CSV headers (or structure) to a saved mapping
2. Confirmation screen shows: transaction count, date range, duplicates detected
3. User confirms before committing

### Duplicate detection

Composite key: date + amount + description + account. Potential duplicates are flagged for user confirmation rather than silently skipped. Handles overlapping date range re-imports.

### Normalization

Amounts normalized to signed values. The mapping wizard captures the amount logic per source to handle different bank formats.

### Post-import

Categorization engine runs on new transactions. Unmatched transactions flagged for manual review.

## Categorization Engine

### Rule-based categorization

Rules match patterns against transaction descriptions and assign: category, optional tag override, optional person attribution. Examples from real data:

- `CIMTECHNIQUES` -> Income, attributed to Andrew
- `OASISBATCH PAYROLL` -> Income, attributed to Donna
- `CHASE CREDIT CRD EPAY` -> Transfer
- `CAPITAL ONE TRANSFER` -> Transfer
- `AMEX EPAYMENT` -> Transfer, attributed to Donna
- `FREEDOM MTG PYMTS` -> Housing, Needs (mortgage)
- `DOMINION ENERGY` -> Utilities, Needs
- `HELLOFRESH` -> Groceries, Needs
- `Netflix.com` -> Subscriptions, Wants
- `PUBLIX|ALDI|PIGGLY WIGGLY|WAL-MART` -> Groceries, Needs
- `CHICK-FIL-A|KFC|MCDONALD` -> Dining Out, Wants
- `VW CREDIT` -> Transportation, Needs (auto loan)
- `FARM BUREAU INS` -> Insurance, Needs
- `WSFS LOAN` -> Debt Payment, Needs
- `WAY2SAVE SAVINGS` -> Transfer (savings)

### Auto-rule creation

When a user manually categorizes a transaction, the app offers to create a rule from the merchant name. Rule set grows naturally with use.

### Extensible interface

```python
class Categorizer(Protocol):
    def categorize(self, transaction) -> CategoryResult | None
```

Rule-based engine implements this. Future AI implementation can be chained as a fallback without changing the rest of the app.

### Uncategorized queue

Transactions that don't match any rule appear in a review queue in the Transactions view for manual categorization.

## Analysis & Visualization

### Spending Over Time

- Bar/line chart showing total spending by day, week, or month (toggle)
- Filterable by account, person, category, tag
- Stacked bar variant for category proportions over time
- Hover for details

### Category Breakdowns

- Donut chart for a selected time period
- Click a slice to drill into that category's transactions
- Horizontal bar chart ranking categories by total spend
- Needs/wants/savings summary bar showing the ratio

### Recurring Charges

- Pattern detection: same merchant, similar amount (within ~10%), regular intervals
- Results list: merchant, typical amount, frequency, estimated annual cost
- User confirms or dismisses each detection
- Summary card showing total monthly/annual subscription burden

### Forecasting

- Combines historical category averages (weighted toward recent months) with confirmed recurring charges
- Month-ahead: stacked bar alongside recent actual months
- Year-ahead: extrapolation with trend line
- Clearly labels high-confidence (recurring) vs. lower-confidence (historical average) projections
- "What if" mode: toggle recurring charges on/off, adjust category sliders, live projection updates
- Explicitly labeled as estimates, no false precision

## UI Layout & Styling

### Navigation

Fixed left sidebar with icon + label: Import, Transactions, Analysis, Recurring, Settings. Collapses to icons-only on narrow windows.

### Transactions view

Searchable, sortable table. Columns: date, description, amount, account, category, tags, person. Inline category editing with auto-rule prompt. Top filters for date range, account, person, category, uncategorized-only toggle.

### Theming

Dark theme by default, light theme toggle. Qt stylesheets (QSS), one file per theme. Modern typography, subtle borders, consistent spacing. Accent color for interactive elements.

### Charts

Matplotlib embedded via FigureCanvasQTAgg. Styled to match app theme. Consistent color palette across all charts (same category = same color everywhere).

### Performance

Background threads for import and analysis operations with progress indicators. Drag-and-drop CSV file import supported.

## Scope

### V1

- CSV import with mapping wizard
- Rule-based auto-categorization (extensible for future AI)
- Household member attribution
- Spending over time trends
- Category breakdowns with needs/wants/savings
- Recurring charge detection
- Forecasting with "what if" mode
- Dark/light theming

### Deferred

- AI-based categorization
- Budget vs. actual tracking
- OFX/QFX file format support