S3
Spot Market Data (S3)
Futures Market Data (S3)
Options Market Data (S3)
Snowflake Tables
All Snowflake tables are versioned. Below are the latest available versions.Spot (Snowflake)
Available in theMARKET_SPOT schema.
Futures (Snowflake)
Available in theMARKET_FUTURES schema.
Options (Snowflake)
Available in theMARKET_OPTIONS schema.
Data Field Descriptions
Spot Market Fields
Order Book Snapshots
Spot Trades
Spot OHLCV
Futures Market Fields
Funding Rates
Liquidations
Open Interest
Options Market Fields
Options Trades
Use Cases and Applications
Spot Market Applications
- Trading Strategy Development: Backtest strategies using historical spot market data
- Market Making: Analyze order book dynamics for algorithmic trading
- Price Discovery Research: Study how prices form across different exchanges
- Arbitrage Detection: Identify price differences across exchanges and timeframes
- Liquidity Analysis: Understand market depth and trading patterns
Futures Market Applications
- Funding Rate Analysis: Study perpetual swap funding patterns and arbitrage opportunities
- Liquidation Monitoring: Track large liquidation events and market impact
- Open Interest Tracking: Monitor position changes and market sentiment
- Risk Management: Calculate exposure and portfolio risk across futures positions
- Contango/Backwardation Studies: Analyze futures curve structures
Options Market Applications
- Volatility Analysis: Study implied volatility patterns and surfaces
- Options Flow Analysis: Track institutional and retail options activity
- Risk Management: Monitor Greeks exposure and hedge portfolios
- Strategy Backtesting: Test options strategies with historical data
- Market Structure Research: Understand options market efficiency and pricing
Integration Benefits
- Multi-Asset Analysis: Correlate spot, futures, and options data
- Cross-Exchange Research: Compare data across multiple exchanges
- Historical Depth: Years of tick-level data for robust analysis
- Real-time Applications: Fresh data for live trading and monitoring
- Academic Research: Clean datasets for empirical finance studies