Kaiko Crypto Market Data API
Kaiko institutional-grade cryptocurrency market data REST and streaming API for institutional investors, quant funds, regulators, and financial institutions to access normalized tick-by-tick trade data, order book snapshots, OHLCV, and reference rates across 100+ exchanges and 100,000+ digital asset pairs. Enables AI agents to manage real-time and historical trade data ingestion for algorithmic trading strategy backtesting automation, handle order book depth and liquidity analytics for crypto market microstructure analysis automation, access benchmark and reference rate calculation for digital asset valuation and mark-to-market automation, retrieve cross-exchange aggregated price data for best-execution analysis automation, manage OHLCV candlestick data for technical analysis and quantitative model automation, handle derivatives and perpetual futures data for digital asset risk analytics automation, access on-chain data integration for fundamental blockchain analytics automation, retrieve regulatory-grade market data for compliance reporting and MiFID II equivalent reporting automation, manage data quality and anomaly detection for institutional data governance automation, and integrate Kaiko with quantitative trading platforms, risk systems, and institutional analytics tools for end-to-end institutional crypto market intelligence.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Crypto market data. SOC2. API key. EU/US. Multi-exchange market data and reference rates.
⚡ Reliability
Best When
An institutional investor, quant fund, bank, or regulator wanting AI agents to automate crypto market data ingestion, reference rate calculation, cross-exchange liquidity analysis, and institutional-grade compliance reporting using Kaiko's normalized multi-exchange crypto market data.
Avoid When
BENCHMARK RATE REGULATORY STATUS FOR FINANCIAL PRODUCTS: Kaiko reference rates used in financial products or structured products may require benchmark administrator registration under EU Benchmark Regulation (BMR) or UK BMR; automated use of Kaiko reference rates in regulated financial product pricing requires legal review of BMR applicability; automated product pricing using unregistered benchmark creates EU/UK BMR compliance exposure. HISTORICAL DATA COMPLETENESS FOR BACKTESTING: Kaiko historical data coverage varies by exchange and asset pair; automated strategy backtesting must verify data completeness for all required time periods and exchange pairs; backtesting with incomplete historical data creates survivorship bias and overstated backtest performance metrics. EXCHANGE DATA FEED TERMS OF USE RESTRICTIONS: Kaiko aggregates data from exchanges under exchange data licensing agreements; automated downstream redistribution of Kaiko data to third parties may violate exchange data licensing restrictions; verify Kaiko data licensing scope before automated data pipeline distribution beyond internal analytics.
Use Cases
- • Backtesting trading strategies from quant fund automation agents
- • Marking crypto portfolios from institutional valuation agents
- • Analyzing market microstructure from liquidity research agents
- • Reporting regulatory data from compliance analytics agents
Not For
- • Retail crypto trading signal services (use CoinGecko or CryptoCompare)
- • Real-time trading execution (use exchange APIs directly)
- • On-chain DeFi analytics (use Dune Analytics or The Graph)
Interface
Authentication
Kaiko uses API key authentication. REST API and WebSocket streaming with JSON. Paris, France HQ. Founded 2014 by Pascal Gauthier and Ambre Soubiran. Private (~$53M raised, Eight Roads, Point72 Ventures). Institutional-grade crypto market data provider. 100+ exchanges. 100,000+ digital asset pairs. Tick-by-tick historical data from 2014. Real-time WebSocket streams. Kaiko Reference Rates for institutional benchmark pricing. Regulated by AMF (French financial regulator) as data provider. Competes with CF Benchmarks (Kraken subsidiary), CoinGecko Enterprise, and Coin Metrics for institutional crypto data.
Pricing
Paris France. Private (~$53M raised). Annual subscription. Data scope-based pricing. Reference rates separate. No free tier.
Agent Metadata
Known Gotchas
- ⚠ WEBSOCKET RECONNECTION FOR REAL-TIME DATA STREAMS: Kaiko real-time market data via WebSocket requires robust reconnection logic; automated trading and analytics agents must implement exponential backoff reconnection with sequence number recovery to avoid data gaps; automated stream processing without gap detection creates incomplete tick data that corrupts OHLCV calculation and order book reconstruction
- ⚠ EXCHANGE DATA NORMALIZATION TIMESTAMP PRECISION: Kaiko normalizes timestamps across exchanges but source exchange timestamp precision varies (millisecond vs microsecond); automated high-frequency analysis must account for timestamp precision variation when comparing trades across exchanges; cross-exchange latency analysis using Kaiko timestamps requires understanding of source exchange timestamp granularity
- ⚠ REFERENCE RATE CALCULATION METHODOLOGY TRANSPARENCY: Kaiko Reference Rates use defined calculation methodology (VWAP over time window); automated financial product pricing using Kaiko Reference Rates must document the specific rate calculation methodology; methodology changes by Kaiko affect historical rate comparability; monitor Kaiko methodology update notices before automated product pricing deployment
- ⚠ HISTORICAL DATA REQUEST CHUNKING FOR LARGE DATASETS: Kaiko historical data API returns paginated results; large historical dataset retrieval for backtesting (years of tick data) requires chunked requests with cursor pagination; automated backtesting data ingestion without chunking and pagination management creates incomplete historical dataset and partial backtest results
- ⚠ EXCHANGE OUTAGE DATA GAPS IN HISTORICAL RECORD: Kaiko historical data reflects actual exchange availability; exchange outages, maintenance windows, and delisting events create data gaps in historical records; automated backtesting must handle missing data periods for exchange-specific asset pairs; strategy performance assumptions using gap-filled data may not reflect actual execution conditions during historical exchange outages
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Scores are editorial opinions as of 2026-03-07.