Endpoint families
100+
Coverage across stocks, crypto, FX, futures, indices, options, macro, news and fundamentals.
+ Pravial Data
Multi-asset market data APIs for research, execution and AI.
Pravial Data delivers live and historical market data across U.S. stocks, forex, indices, commodities, futures, options and crypto, with normalized access for dashboards, backtests, execution engines and AI models.
+What You Get
Production-ready capability stack
Each deployment is engineered for measurable outcomes, not just feature delivery.
- +Market by order, full order book and L3 data where supported.
- +MBP-10 and market depth for 10 levels of price-book visibility.
- +Tick-by-tick trades, last sale, quotes, snapshots and previous aggregates.
- +OHLCV aggregates by second, minute, hour or day for research and production workloads.
- +Instrument definitions, auction imbalance, order imbalance, intraday and EOD statistics.
- +News, analyst insights, ratings, earnings, guidance, events, macro data, sentiment and alternative intelligence.
+Performance Lens
Execution and operating metrics
Representative benchmarks used to define architecture priorities and delivery standards.
Depth formats
L2/L3
MBP-10 market depth and MBO order-book structures where feed availability supports it.
Data modes
Live + Hist
Real-time and historical consumption patterns for research, dashboards, bots and AI systems.
+Implementation Focus
Built around engineering rigor and operational clarity
01
Normalized Market Data
Consistent schemas and interfaces across asset classes so engineering teams can move faster without rebuilding data adapters for every feed.
SymbolsTimestampsSchemas
02
Research-to-Production Pipeline
Data designed to support backtesting, analytics, live trading systems, monitoring dashboards and AI feature generation.
BacktestsStreamingDashboards
03
Alternative Intelligence Layer
Market context beyond price: analyst data, news, events, macro indicators, earnings, sentiment and corporate actions.
NewsEarningsSentiment
+Delivery Process
Market data integration path
Structured execution cadence to minimize integration risk and maximize speed to value.
01
Feed Scope
We define instruments, depth requirements, latency targets, history needs and alternative-data sources.
02
API Integration
Data is connected to dashboards, research stacks, bots, AI models or broker infrastructure.
03
Quality + Scaling
We tune throughput, monitoring, error handling, caching, retention and cost efficiency.
+FAQ
Key questions before launch
Common points discussed during architecture and implementation planning.
+ Next Step