Research & Publications
Every strategy starts with a testable hypothesis and ends with live market validation. Read how we separate signal from noise.
ML Models
3
Transformer models in research pipeline
Market Data
13TB+
Institutional-grade tick data
Markets
5
CME, Eurex, ICE, NASDAQ, OPRA
Best Accuracy
65.0%
Directional prediction accuracy
Publications
Documenting our methodology, architecture decisions, and empirical findings.
This paper presents HYPERION, a distributed cognitive system utilizing specialized agents for strategy direction, pattern recognition, risk management, and execution routing. We demonstrate how multi-agent coordination achieves superior risk-adjusted returns compared to monolithic systems.
This research explores the integration of large language models into trading systems for sentiment analysis, news processing, and decision support. We present a framework for combining LLM insights with traditional quantitative signals.
Our Approach
Every system we build starts with rigorous research. We believe in understanding the theoretical foundations before writing a single line of production code. This approach ensures our systems are not just performant, but fundamentally sound.
01
Every strategy begins with a clear, testable hypothesis grounded in market microstructure theory.
02
All models undergo extensive backtesting, walk-forward analysis, and out-of-sample validation.
03
Production systems are monitored for drift and updated based on ongoing research findings.