# PR1 Summary: Project Scaffold + DB + Price Loader

**Status**: ✅ Complete  
**Date**: 2025-12-13

## What was built

### 1. Project scaffold
- `pyproject.toml` with all dependencies (SQLAlchemy, Alembic, yfinance, pandas, pytest, ruff, black, etc.)
- `src/pote/` layout with config, db, and ingestion modules
- `.gitignore`, `.env.example`, `Makefile` for dev workflow
- Docs: `README.md` + 6 `.md` files in `docs/` covering MVP, architecture, schema, sources, safety/ethics, and dev setup

### 2. Database models (SQLAlchemy 2.0)
- **Officials**: Congress members (name, chamber, party, state, bioguide_id)
- **Securities**: stocks/bonds (ticker, name, exchange, sector)
- **Trades**: disclosed transactions (official_id, security_id, transaction_date, filing_date, side, value ranges)
- **Prices**: daily OHLCV (security_id, date, open/high/low/close/volume)
- **Metrics stubs**: `metrics_official` and `metrics_trade` (Phase 2)

Includes proper indexes, unique constraints, and relationships.

### 3. Alembic migrations
- Initialized Alembic with `env.py` wired to our config
- Generated and applied initial migration (`66fd166195e8`)
- DB file: `pote.db` (SQLite for dev)

### 4. Price loader (`PriceLoader`)
- Fetches daily price data from **yfinance**
- Idempotent: skips existing dates, resumes from gaps
- Upsert logic (insert or update on conflict)
- Handles single ticker or bulk fetches
- Logging + basic error handling

### 5. Tests (pytest)
- `tests/conftest.py`: fixtures for in-memory DB, sample officials/securities/trades/prices
- `tests/test_models.py`: model creation, relationships, unique constraints, queries (7 tests)
- `tests/test_price_loader.py`: loader logic, idempotency, upsert, mocking yfinance (8 tests)
- **Result**: 15 tests, all passing ✅

### 6. Tooling
- **Black** + **ruff** configured and run (all code formatted + linted)
- `Makefile` with targets: `install`, `test`, `lint`, `format`, `migrate`, `clean`
- Smoke-test script: `scripts/fetch_sample_prices.py` (verified live with AAPL/MSFT/TSLA)

## What works now
- You can spin up the DB, run migrations, fetch price data, and query it
- All core Phase 1 foundations are in place
- Tests confirm models and ingestion work correctly

## Next steps (PR2+)
Per `docs/00_mvp.md`:
- **PR2**: QuiverQuant or FMP client for Congress trades
- **PR3**: ETL job to populate `officials` and `trades` tables
- **PR4+**: Analytics (abnormal returns, clustering, signals)

## How to run
```bash
# Install
make install
source venv/bin/activate

# Run migrations
make migrate

# Fetch sample prices
python scripts/fetch_sample_prices.py

# Run tests
make test

# Lint + format
make lint
make format
```

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**Research-only reminder**: This tool is for transparency and descriptive analytics using public data. Not investment advice.