●Python + Fintech

Python dominates quantitative finance for one reason: the numerical computing ecosystem has no equivalent. NumPy provides vectorized operations that make financial calculations practical at scale—a risk calculation across a million positions runs in seconds, not hours. Pandas handles time-series data (market prices, transaction histories) with built-in resampling, rolling windows, and alignment operations that financial analysis requires. FastAPI brings this computational power to production services. Traditional Python web frameworks (Django, Flask) use synchronous request handling, blocking on every database query and API call. FastAPI's async architecture handles concurrent requests efficiently—critical when your service needs to call banking partners, payment processors, and market data providers simultaneously. The automatic OpenAPI documentation isn't just convenience; it's a compliance asset. For regulatory audits, you can provide machine-readable API specifications showing exactly what data your service accepts and returns. Pydantic models serve as both validation logic and documentation, enforcing financial data types (decimal precision, currency codes, valid date ranges) at the API boundary. This combination of computational power and production-readiness makes Python the natural choice for fintech. You can prototype financial models in Jupyter notebooks, then deploy them as FastAPI services without rewriting in a different language. The scientists building models and the engineers deploying them work in the same ecosystem.

Fintech projects require balancing rapid iteration with regulatory compliance. I start by mapping the compliance landscape: What regulations apply? What audit requirements exist? What data retention policies are needed? This shapes the architecture from day one—adding compliance later is expensive and risky. The FastAPI application structure encodes financial domain concepts explicitly. Pydantic models define transaction types with validation rules: amounts as Decimal with explicit precision, currency codes validated against ISO 4217, dates that can't be in the future for historical transactions. These constraints are enforced at the API boundary, preventing bad data from entering the system. For financial calculations, I use Python's decimal module throughout, never float. Database columns are NUMERIC with explicit precision. The codebase has no place where floating point could introduce rounding errors—this is enforced through type annotations and linting rules. Audit logging is infrastructure, not an afterthought. FastAPI middleware captures complete request/response for every operation. Background tasks log to append-only tables with full context: who, what, when, why. These logs ship to immutable storage meeting regulatory retention requirements. Testing fintech applications requires realistic scenarios. I build test fixtures with edge cases from domain experts: leap years in interest calculations, currency conversions with rounding, daylight saving time transitions for scheduled jobs. Integration tests verify complete flows against sandboxed banking and payment APIs.