Alternative Data Streams
Price and volume are no longer enough. We process millions of unstructured data points daily—from SEC filings to GitHub commits—delivering normalized, machine-readable alpha vectors directly into your evaluation models.
Real-Time NLP Parsing
Our proprietary Natural Language Processing (NLP) engine monitors over 5,000 global news outlets, financial subreddits, and crypto Twitter feeds.
Instead of giving you raw text, we deliver structured JSON payloads containing identified entities, ticker symbols, and composite sentiment scores (ranging from -1.0 to 1.0) with microsecond latency.
- ✓ Named Entity Recognition (NER) for global equities and tokens
- ✓ Sarcasm and financial-jargon-aware sentiment models
- ✓ Volume velocity alerts (e.g., "mentions up 400% in 5 mins")
Developer & Network Activity (Web3)
For digital assets, price often follows utility. We maintain dedicated infrastructure to index blockchain events, GitHub commit velocity, and smart contract deployments in real-time, providing fundamental metrics for algorithmic evaluation.
GitHub Commit Velocity
Quantify developer engagement. We track daily commits, active contributors, and major core updates across top 500 Web3 protocols, outputting a normalized "Development Health Score".
On-Chain Capital Flows
Track the whales. Real-time alerting for massive wallet movements, exchange inflows/outflows, and stablecoin minting events across Ethereum, Solana, and Arbitrum.
Macro Economic Feeds
Instantaneous API delivery of CPI, NFP, and Fed rate decisions directly from source, parsed into JSON less than 2 milliseconds after publication.
Sentiment-to-Price Correlation Matrices
Raw sentiment is useless without context. Our analytical engine automatically maps historical sentiment spikes against asset price action to generate predictive correlation matrices. We calculate the exact decay rate of news impact: does a positive SEC filing affect the price for 10 minutes, or 10 days?
- Decay profiling for localized news events
- Lead-lag analysis between Twitter sentiment and volume spikes
- Automated backtesting of sentiment-driven moving averages
Deterministic Entity Resolution
Financial language is ambiguous. Our machine learning models are trained specifically to disambiguate terms contextually. The engine instantly differentiates between "Apple" (agricultural commodity) and "AAPL" (equity), or "Polygon" (geometry) and "MATIC" (blockchain).
This strict entity resolution ensures your algorithmic models are never fed false-positive data vectors, preventing catastrophic automated trading errors based on misunderstood context.
Enterprise Ingestion Architecture
Processing unstructured data at global scale requires zero-tolerance infrastructure. We do not rely on third-party aggregators; we ingest directly from the firehoses.