Combine market data with sentiment signals to understand market behavior
Build sentiment analysis platforms that connect news, social media, AI models, and external sentiment indicators with real-time and historical crypto market data.
Sentiment analysis measures how investors, traders, and the broader market feel about cryptocurrencies by analyzing news, social media, research, and other external information sources.
These signals become more valuable when they are compared with actual market behavior. Combining sentiment with market data helps analysts understand whether market participants acted on those signals.
Sentiment alone rarely explains what happened in the market.
Positive news may have little impact if liquidity is low, while negative sentiment can trigger sharp price movements during volatile market conditions. Understanding whether sentiment influenced the market requires accurate prices, trading activity, liquidity, and historical context alongside external sentiment data.
Stream real-time trades, quotes, OHLCV, and Level 1, Level 2, and Level 3 order books to evaluate how markets respond after sentiment changes or major news events.
Combine external sentiment datasets with years of historical trades, quotes, OHLCV, and order books using Historical APIs and Flat Files to determine whether sentiment signals consistently predicted market behavior.
Use one standardized market data model to analyze how different exchanges and trading venues respond to the same sentiment event.
Use the Exchange Rates API to compare assets and market reactions across different reporting currencies using one consistent valuation method.
Deliver live and historical market data to AI assistants, research platforms, and analytics applications through REST APIs, WebSocket streams, and hosted MCP servers alongside your preferred sentiment data provider.
| What You Need | Before CoinAPI | After CoinAPI |
|---|---|---|
| Measure the impact of sentiment | Analyze sentiment without market context | Correlate sentiment signals with real-time and historical market data |
| Validate sentiment models | Test against limited historical datasets | Compare sentiment with years of historical market activity |
| Compare market reactions | Normalize exchange data manually | Analyze standardized market data across hundreds of exchanges |
| Study global market behavior | Reconcile prices across currencies manually | Normalize valuations using the Exchange Rates API |
| Build AI sentiment applications | Maintain separate market data integrations | Connect applications through REST APIs, WebSocket streams, and hosted MCP servers |
| Expand market coverage | Integrate exchanges individually | Access normalized market data across hundreds of exchanges |
