Fetch, store, and serve real-time Argentine peso (ARS) exchange rates via a simple REST API.

ArChange connects to DolarAPI, persists historical rates in PostgreSQL, and exposes them through documented FastAPI endpoints. A built-in scheduler keeps the data fresh automatically.

• 7 exchange-rate types — Oficial, Blue, Bolsa (MEP), CCL, Mayorista, Cripto, Tarjeta
• Automatic updates — APScheduler fetches rates every 2 hours (configurable)
• PostgreSQL storage — full history with connection pooling via psycopg 3
• OpenAPI docs — Swagger UI & ReDoc out of the box
• CLI tools — standalone fetch script and batch processor

• Python 3.8+
• Docker & Docker Compose

git clone https://github.com/facundocarrizo99/ArChange.git
cd ArChange
python3 -m venv venv && source venv/bin/activate
pip install -r requirements.txt

docker compose up -d

cp .env.example .env   # edit values if needed

python -m app.main

The API is now live at http://localhost:8000.

Interactive docs: localhost:8000/docs · localhost:8000/redoc

# Fetch and store latest rates
curl -X POST http://localhost:8000/api/exchange/fetch

# Query stored rates
curl http://localhost:8000/api/exchange

{
  "status": "ok",
  "data": [
    {
      "id": 1,
      "type": "blue",
      "buy": 1415.0,
      "sell": 1435.0,
      "rate": 1425.0,
      "diff": 20.0,
      "created_at": "2025-11-06T19:58:00"
    }
  ]
}

All variables have sensible defaults for local development. Override them via .env or shell exports.

ArChange/
├── app/
│   ├── config.py            # Centralized environment configuration
│   ├── main.py              # FastAPI application & routes
│   ├── models.py            # Database model (Exchange)
│   ├── schemas.py           # Pydantic request/response schemas
│   ├── db.py                # Connection pool & CRUD helpers
│   ├── dolar_api.py         # DolarAPI response data class
│   ├── fetch_exchange.py    # Fetch from DolarAPI → store in DB
│   └── job.py               # Scheduled job wrapper
├── migrations/
│   └── 001_create_exchange_rates.sql
├── scripts/
│   └── batch_process.py     # CLI batch processor with custom DB args
├── tests/
│   ├── test_exchange.py
│   ├── test_fetch_exchange.py
│   ├── test_job.py
│   └── test_batch.py
├── run_fetch.py             # Standalone fetch script
├── docker-compose.yml
├── requirements.txt
├── pyproject.toml
├── pytest.ini
├── .env.example
└── .editorconfig

DolarAPI ──► fetch_exchange.py ──► db.py ──► PostgreSQL
                 ▲                              │
                 │                              ▼
            job.py / scheduler            GET /api/exchange
                 ▲
                 │
            POST /run-job
            POST /api/exchange/fetch

config.py feeds database credentials and scheduler settings to every layer, ensuring a single source of truth.

Migrations run automatically on startup.

CREATE TABLE IF NOT EXISTS exchange_rates (
    id         BIGSERIAL PRIMARY KEY,
    type       VARCHAR(50) NOT NULL,   -- 'blue', 'oficial', …
    buy        DECIMAL(10,2),
    sell       DECIMAL(10,2),
    rate       DECIMAL(10,2),          -- (buy + sell) / 2
    diff       DECIMAL(10,2),          -- sell − buy
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

# Standalone fetch (uses env vars)
python run_fetch.py

# Batch processor with explicit DB args
python scripts/batch_process.py --db-host localhost --db-port 5433

# Run all tests
pytest

# With coverage
pytest --cov=app tests/

# Single file
pytest tests/test_fetch_exchange.py -v

1. Set environment variables for your production database.
2. Run docker compose up -d (or point to a managed PostgreSQL instance).
3. Start the app: uvicorn app.main:app --host 0.0.0.0 --port 8000.
4. The scheduler will begin fetching automatically on startup.

1. Fork the repo & create a feature branch.
2. Write tests in tests/.
3. Ensure pytest passes.
4. Submit a pull request.

MIT