Aggregating real-time financial data at scale looks simple from the outside — hit an API, cache it, display it — until you actually try to do it reliably for 10,000+ assets across crypto, stocks, forex, and commodities. Some lessons from building that pipeline for Vextor Capital.
Source attribution is not optional
Every data point needs a clear chain back to its origin. We pull from CoinGecko, Polygon.io, Alpha Vantage, and FRED, and each asset page has to carry the source, the timestamp, and the update cadence. This isn't just a compliance nicety — it's what lets you debug a bad number fast. If a price looks wrong, you need to know in seconds whether it's your pipeline or the upstream provider.
Rate limits shape your architecture more than you'd think
Free and mid-tier financial APIs have rate limits that don't scale with the number of assets you want to track. The naive approach (one request per asset per refresh) falls over immediately at scale. What actually works:
- Batch requests wherever the provider supports it
- Stagger refresh intervals by asset volatility (crypto needs tighter refresh than, say, government bonds)
- Cache aggressively with a TTL matched to how fast that asset class actually moves, not a single global TTL for everything
Delayed vs. real-time is a UX decision, not just a technical one
Not every asset needs to be real-time, and pretending otherwise wastes API budget. Making the delay explicit and visible on every page (rather than hiding it) turned out to matter more for user trust than shaving seconds off latency.
Revalidation methodology has to be public
If you're going to claim "real-time" or "live" data anywhere, you need a public methodology page describing exactly how often each asset class is revalidated and from where. Without that, "real-time" is just a marketing claim nobody can verify — and it's the first thing a skeptical user (or a journalist) will ask about.
None of this is groundbreaking distributed-systems theory, but the constraints are different from a typical SaaS backend: correctness and provenance matter more than raw throughput, because the cost of being wrong is a user making a financial decision on bad data.
Full methodology write-up: https://vextorcapital.com/methodology
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