Vector-Based
Reinforcement Learning
VBRL solves the "Black Box" problem in financial AI. Our engine recognizes market patterns with forensic precision, providing transparent, institutional-grade execution.
The Technology Stack
Glass-Box AI
Traditional AI is opaque. VBRL is forensic. For every trade, the system retrieves historical evidence, allowing human oversight and manageable risk.
Retuned in Real-Time
Markets change regimes instantly. VBRL uses HNSW vector indices to pivot strategy dynamically, retrieving experiences to match current volatility.
Zero-Latency Evolution
No more costly re-training loops. VBRL agents learn in milliseconds, converting new market experiences into searchable vectors instantly.
Systemic Workflow
Recognition Pipeline Lifecycle
Real-time market events are encoded into high-dimensional vector embeddings.
HNSW engine scans millions of historical regimes to find structural analogues.
Retrieved experiences form the episodic memory bank for decision analysis.
The VBRL agent synthesizes evidence into actionable, risk-managed trade plans.
Built for Trust.
AI Price Patterns converts chaotic price streams into structurally aware, probability‑ranked scenario space. By recalling analogous regimes through visual matching, it reframes decision‑making around ranges and risk asymmetry.
Deep Dive Further
Technical Whitepaper
Complete mathematical framework, backtest methodology, and research behind VBRL.
API Reference
REST and WebSocket endpoints for integrating pattern search into your systems.
Try It Live
Experience the pattern matching engine in action with real market data.