Don't Just Predict.
Recall.
The market has been here before. Instantly retrieval structural analogues from deep history using high-dimensional vector search.
Test the Engine:
Sketch-to-Search
Markets exhibit structural symmetry. Draw a hypothetical price trajectory and our engine will instantly surface the most similar moments from Bitcoin's history.
Sketch pattern
Draw a trajectory from left to right. The sketch will be sampled into 40 bars and scaled around the anchor price.
The "Glass Box" Advantage
Modern markets operate in regimes that legacy models fail to recognize. Neural networks provide signals but act as "Black Boxes"—lacking explainability.
How VBRL Works
1. Retrieval
Engine scans 2.5M+ vectors to find structural analogues (KNN) matching current price action.
2. Optimization
VBRL Agent refines the forecast, calculating Expected Value (EV) and optimal risk parameters.
3. Execution
Outputs a transparent Trade Plan with precise Stops, Targets, and holding horizons.
⚡ Python SDK Now Live
Integrate vector-based market intelligence.
→ Why It Matters.
"Black Box" AI
Opaque signals, no "why"
Linear Models
Fail in novel regimes
"Glass Box" Search
Evidence-based & auditable
Technical Advantages
Forensic Validation
Strictly audited walk-forward backtesting with **zero look-ahead bias**. Our Rust engine enforces physical separation between "search time" and "outcome data."
Zero-Lag Learning
Unlike neural networks that require heavy retraining for new regimes, our VBRL memory bank adapts **instantly** to market shifts.
High-Performance HNSW
Custom-built Rust-HNSW core delivers sub-millisecond retrieval across millions of vectors.
RL Feature Factory
Accelerate AI training with episode sampling. Bridge raw price action to Reinforcement Learning pipelines.
Narrative
A living market memory built on vector similarity search: continuously identifying nuanced historical patterns that conventional models miss.
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, risk asymmetry and adaptive sizing.
Don't Sell Alpha.
Sell Intelligence.
Discover how our "Context-Aware Memory" infrastructure empowers Quants to validate hypotheses in seconds.
Platform Architecture
Context-Aware Search
Search historical regimes by structural similarity. Find 'looks like' and 'behaves like' patterns instantly.
Adaptive Envelopes
Forward scenario envelopes aggregated from similar patterns. See the full range of outcomes, not just a guess.
Regime-Based Risk
Dynamic guardrails & sizing based on 'Novel' vs 'Known' regime detection. Risk is context-dependent.
Glass Box Transparency
Inspect every matched cohort. Full auditability of why a signal was generated. Zero black-box opacity.
Get In Touch
Need enterprise integration, latency profiles or API throughput specs? We tailor coverage (assets, depth, horizon) & delivery (websocket / batch). Open the form and outline your workflow.
Ready to Elevate Your Edge?
Join quantitative teams using proprietary pattern intelligence for resilient decision workflows.