How to Evaluate a Market Data API Before Going to Production
A vendor-neutral checklist for evaluating a market data API: price accuracy, latency percentiles, WebSocket stability, gaps, rate limits, and licensing.
Tag · data quality
data quality
6 posts on data quality from the SiftingIO market data blog, spanning real-time and historical coverage across stocks, forex, crypto, commodities, and on-chain venues.
Posts tagged data quality
How accurate is real-time market data? A live spread snapshot across crypto, FX, and gold
Consolidated bid/ask spreads measured live on BTCUSD, EURUSD, XAUUSD and more: why one price is really a band, and how to reproduce the numbers yourself.
Why two backtests of the same strategy disagree: the historical price data faults to check first
Five ways historical price data silently corrupts a backtest: adjustment method, venue-dependent highs and lows, gap policy, session cuts, and restated bars.
Bid ask spread: how to read a quote and flag a wide or stale one in code
How to read bid and ask from a quote snapshot, compute the bid ask spread in basis points, and flag wide or stale quotes in Python before users see them.
Why financial platforms need a clear market data methodology
A market data methodology explains where a price comes from and how it is validated. Here is why that documentation matters and how to read it in an API.
Detecting a stale or manipulated quote with a cross-venue consensus price
How to use a robust cross-venue fair price as a validation layer to flag when one venue is printing a stale, thin, or manipulated quote.