The Science Behind Our Strategies
Every strategy starts with a testable hypothesis and ends with live market validation. Read how we separate signal from noise.
3
Transformer models in research pipeline
13TB+
Institutional-grade tick data from DataBento
5
Exchanges: CME, Eurex, ICE, NASDAQ, OPRA
65.0%
Directional prediction from 42K+ experiments
Publications
Technical papers and whitepapers documenting our methodology and systems
AEON & HYPERION Grid: Multi-Agent Architecture for Algorithmic Trading
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.
Large Language Models in Algorithmic Trading: Applications and Methodology
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.
Research-Driven Development
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.
Every strategy begins with a clear, testable hypothesis grounded in market microstructure theory.
All models undergo extensive backtesting, walk-forward analysis, and out-of-sample validation.
Production systems are monitored for drift and updated based on ongoing research findings.