Historical Cryptocurrency Market Data in Flat Files
Access historical cryptocurrency market data through a single platform.
Retrieve trades, quotes, full order books, OHLCV data, exchange datasets, and downloadable flat files from hundreds of exchanges.
Available through S3, Snowflake, and MCP.
What Data Will You Get?
Historical Trade Data
Access individual trade executions across supported exchanges and trading pairs. Each record includes price, traded volume, timestamps, trade identifiers, and aggressor side information.
Historical Quote Data
Retrieve historical best bid and ask updates for supported markets. Analyze spreads, liquidity changes, and market microstructure over time.
Full Limit Order Book Data
Access order book updates and snapshots for supported symbols. Reconstruct historical market depth and study order flow behavior at scale.
OHLCV Market Data
Retrieve normalized OHLCV datasets across multiple timeframes. Build charts, calculate indicators, and analyze market trends using ready-to-use candlestick data.
Daily and Hourly Partitions
Download datasets organized into UTC-based daily and hourly partitions. Retrieve only the time periods required for your analysis.
Exchange-Level Datasets
Access market data grouped by exchange identifiers and venue-specific datasets. Analyze exchange behavior individually or compare activity across venues.
Symbol-Level Historical Files
Retrieve datasets for specific trading pairs using CoinAPI symbol identifiers and exchange-native symbols. Build targeted workflows without downloading unnecessary data.
Compressed CSV Files
Download data as gzip-compressed CSV files optimized for large-scale storage and transfer. Compatible with common analytics, engineering, and research workflows.
Snowflake Data Access
Access selected datasets directly through Snowflake. Perform large-scale SQL analysis without managing file downloads and storage infrastructure.
Dataset Discovery via MCP
Use the Flat Files MCP server to discover buckets, datasets, date partitions, exchanges, and available files before downloading.
Key Features
Massive Historical Coverage
Access hundreds of terabytes of historical cryptocurrency market data collected across hundreds of exchanges and thousands of assets.
Flexible Data Retrieval
Download exactly the exchanges, symbols, datasets, and date ranges you need. Reduce transfer costs and simplify data workflows.
S3-Compatible Infrastructure
Integrate directly with existing S3 tools, SDKs, cloud workflows, and automation systems. No proprietary download infrastructure required.
Consistent File Structure
All datasets follow predictable naming conventions and folder organization. Build automated pipelines with confidence.
Parallel Download Support
Accelerate large-scale ingestion workflows using concurrent downloads across datasets and partitions.
High-Precision Timestamps
All market events include standardized timestamps designed for accurate sequencing and historical analysis.
The Challenge of Historical Market Data
Collecting cryptocurrency market history is harder than it looks. Markets operate across hundreds of exchanges. Each exchange uses different APIs, different formats, different symbol conventions, and different retention policies.
Most organizations start collecting market data themselves. Then they encounter:
- Exchange API limitations
- Historical data gaps
- Storage challenges
- Symbol normalization issues
- Large infrastructure costs
- Data quality inconsistencies
- Massive download requirements
- Ongoing maintenance
CoinAPI solves that problem. We collect, normalize, organize, and maintain historical cryptocurrency market datasets so your team can focus on analysis instead of data collection.
Why Developers Choose CoinAPI Flat Files
Large-Scale Historical Coverage
Access hundreds of terabytes of cryptocurrency market history through a single platform.
Multiple Dataset Types
Retrieve trades, quotes, order books, and OHLCV data from the same infrastructure.
Standardized Data Formats
Work with normalized datasets that follow consistent schemas and naming conventions.
Flexible Access Methods
Choose S3 API, Snowflake, or MCP based on your environment.
Efficient Storage and Transfer
Compressed CSV files help reduce bandwidth usage and storage requirements.
Built for Automation
Consistent folder structures and partitioning simplify ingestion pipelines and large-scale workflows.
Model Context Protocol discovery for flat files.
Model Context Protocol (MCP) lets AI agents discover available Flat Files datasets before downloading anything. The Flat Files MCP server exposes tools for browsing buckets, dataset prefixes, date partitions, and concrete .csv.gz object keys.
- List available storage buckets
- Browse top-level datasets like trades, quotes, order books, and OHLCV
- Explore date partitions
- Discover available file paths
- Check object metadata such as file key, size, and last modified time
- Hand off discovered object keys to the S3 API for download
MCP is designed for discovery and file navigation. Actual file downloads are handled through the standard S3-compatible API.
Build systems on top of large-scale market history.
CoinAPI Flat Files provides infrastructure designed for efficient discovery, retrieval, and storage of market history.
- File downloads
- Automated ingestion
- Data pipelines
- Historical archives
- SQL analysis
- Data warehousing
- Large-scale queries
- Business intelligence workflows
- Dataset discovery
- AI workflows
- Bucket exploration
- File enumeration
- Data availability
- Transfer usage
- Dataset coverage
- File sizes
- Historical completeness
Enterprise deployment options are available for organizations requiring dedicated infrastructure or custom data delivery workflows.
Why CoinAPI Flat Files Instead of Collecting Historical Data Yourself?
Every exchange uses different APIs, different symbol formats, different rate limits, different retention policies, and different levels of historical coverage. CoinAPI removes that burden.
Start Building With CoinAPI Flat Files
Access historical cryptocurrency market data through scalable flat file infrastructure. Build research platforms, machine learning systems, analytics products, backtesting environments, AI applications, and institutional workflows using normalized historical datasets.