Enterprise-grade data customization for institutions and quant teams, delivering CoinGlass data in flexible, tailored formats that integrate directly with your systems and strategies.
CoinGlass provides enterprise-grade data customization services for institutional clients, quantitative teams, and professional data-driven use cases.
In addition to standard API solutions, we support flexible customization of data access methods, historical range, output structure, and delivery formats based on your specific requirements.
As long as the data is covered by CoinGlass, we can tailor it to better fit your system and business needs.
Supported Customization Capabilities
Custom Rate Limits
Based on different system scales and usage requirements, we offer tailored rate limit solutions to support high-frequency access, batch requests, and production-level integrations.
Custom Data Granularity
Customize data granularity to better suit your needs in research, monitoring, modeling, or strategy development.
Custom Historical Range
Extend historical data access to support long-term research, backtesting, strategy validation, and trend analysis.
CSV & Bulk Export
In addition to API access, we support CSV export and bulk data delivery, enabling offline analysis, batch processing, database ingestion, and internal system integration.
Dedicated Technical Support
Enterprise clients receive prioritized technical support to assist with integration, debugging, issue resolution, and future scaling.
Flexible Combination of Multiple Datasets
Combine multiple historical datasets into unified structures tailored for research, analytics, or system consumption.
Data Structure Customization for Institutional & Quant Systems
Design stable, clear, and scalable data structures tailored for institutional platforms, quantitative research systems, and automated trading systems, reducing integration complexity and improving reliability.
Customization Examples
The following examples demonstrate how CoinGlass enterprise customization can be applied in real-world data scenarios.
Example 1: Liquidation Heatmap Snapshot
Provides historical snapshots of liquidation heatmaps, not just real-time views:
- Full heatmap distribution at specific timestamps
- Layered by price bins
- Supports multi-timepoint replay
Customizable options:
- Snapshot frequency (e.g., 1min / 5min / custom)
- Data precision (price granularity)
- Multi-exchange aggregation
Use cases:
- Liquidation-driven strategies
- Market structure analysis
- Heatmap backtesting and visualization
Example 2: Orderbook Depth Snapshot
Provides structured historical orderbook depth data:
- Bid / Ask depth distribution
- Aggregation by % distance from mid price
- Historical snapshot series
Customizable options:
- Depth levels (Level 10 / 50 / 100)
- Aggregation method (absolute price / percentage bins)
- Multi-exchange weighted aggregation
Use cases:
- Market making strategies
- Liquidity analysis
- Slippage modeling
Example 3: Liquidation Cluster Detection
Outputs structured “high-risk zones” based on liquidation distribution:
- High-density liquidation price ranges
- Long/short imbalance metrics
- Dynamic trend tracking
Supports:
- Custom thresholds
- Real-time or historical output
- Price-linked analysis
Use cases:
- Risk management systems
- Liquidation alert models
- High-frequency trading signals
Example 4: Cross-Exchange Weighted Metrics
Build unified metrics across exchanges:
- Weighted Funding Rate
- Weighted Open Interest
- Weighted volume / depth
Customizable options:
- Weighting methods (OI / Volume / custom)
- Exchange selection
- Real-time or historical modes
Use cases:
- Noise-reduced analytics
- Macro market structure analysis
- Stable signal generation
Example 5: Multi-Dimensional Derivatives Feed
Combine multiple derivatives metrics into a unified data feed:
- Funding Rate
- Liquidation
- OI
- Volume
- Basis
Supports:
- Single endpoint delivery
- Timestamp alignment
- Custom field structures
Use cases:
- Quant system data pipelines
- Real-time signal computation
- Multi-factor models
Example 6: Custom Market Structure Metrics
Build composite indicators based on client requirements, such as:
- Market Sentiment Index
- Leverage Ratio
- Long/Short Pressure Index
Supports:
- Multi-source data fusion
- Custom formulas
- API or file-based output
Use cases:
- Proprietary trading systems
- Data products
- Research platforms
Example 7: Funding Rate + Open Interest Unified Feed
Combine core derivatives metrics into a single structured output:
- Funding Rate
- Open Interest
- Price
Supports:
- Unified timestamp alignment
- Single endpoint response
- High-frequency updates (e.g., 1s / 5s)
Use cases:
Quant strategies, arbitrage models, real-time monitoring systems
Example 8: Liquidation Aggregation Across Exchanges
Aggregate liquidation data across multiple exchanges:
- Binance / OKX / Bybit / Deribit, etc.
- Long / Short breakdown
- Time-based aggregation (e.g., 1min / 5min / 1h)
Customizable options:
- Per-exchange breakdown
- Cumulative vs point-in-time values
- Optional price and volume fields
Use cases:
Market sentiment analysis, risk control systems, anomaly detection
Example 9: ETF Flow + BTC Price Dataset
Combine ETF flows with market price data:
- Bitcoin Spot ETF Netflow
- Total Net Assets
- BTC Price
Supports:
- Daily / hourly granularity
- Aggregated or per-ETF output
- Historical backfill
Use cases:
Institutional research, macro analysis, flow-driven models
Example 10: Options Data Combination (IV + Volume + OI)
Structured options market dataset:
- Implied Volatility
- Options Volume
- Open Interest
Customizable options:
- By strike
- By expiry
- Aggregated or full-level structure
Use cases:
Volatility trading strategies, options research
Example 11: Long-Term Historical Data Export
Access extended historical datasets for research and backtesting:
- Funding Rate history (multi-year)
- Liquidation history
- Open Interest time series
Supports:
- CSV bulk download
- Segmented export (monthly / yearly)
- One-time or scheduled delivery
Use cases:
Quant backtesting, factor research, data warehouse building
Example 12: Custom Metrics Structure
Build tailored data structures based on client needs:
- Aggregated Market Sentiment Index
- Exchange Weighted Funding Rate
- Combined derivatives dashboard feed
Supports:
- Multi-metric fusion
- Custom field naming
- API or file output
Use cases:
Internal strategy systems, data products, analytics platforms
Flexible Data Delivery
Whether you need:
- Higher-frequency data access
- Longer historical coverage
- Custom field structures for internal systems
- Unified formats across multiple datasets
- Bulk export or offline delivery
CoinGlass can provide enterprise-grade customization based on its existing data infrastructure.
We don’t just provide data — we help you integrate it seamlessly into your products, strategies, and systems.
Use Cases
Quant Research & Backtesting
Requires longer history, finer granularity, and multi-metric datasets for strategy development and validation.
Institutional System Integration
Requires higher rate limits and stable data structures to support production-level systems.
Data Platforms & Internal Databases
Use CSV or bulk delivery for database ingestion, BI systems, or internal analytics platforms.
Fintech Product Integration
Integrate CoinGlass data into proprietary products, terminals, or data services.
Why Choose CoinGlass Enterprise Customization
- Proven data coverage and quality
- Flexible customization based on real use cases
- Data structures optimized for institutional systems
- Reduced development and integration costs
- Reliable and scalable data delivery
Get an Enterprise Plan
If you need higher-performance data access, more flexible data combinations, or structures tailored for institutional systems, contact the CoinGlass team for a customized solution.