Our research team pushes the limits of machine learning and signal discovery across global asset classes.
Deep learning, ensemble methods, and reinforcement learning applied to financial time series and cross‑sectional data.
Satellite imagery, social sentiment, supply chain analytics, and other non‑traditional information sources.
Order flow analysis, liquidity modeling, and execution optimization across global markets.
Systematic transformation of raw data into predictive signals using domain expertise and automated discovery.
Rigorous backtesting, walk‑forward analysis, and out‑of‑sample testing across multiple market regimes.
Decomposition of returns into systematic factors and idiosyncratic components for better risk understanding.
Developing interpretable models that provide insight into decision‑making processes and risk drivers.
Low‑latency data processing and model inference for rapid adaptation to changing market conditions.
Identifying relationships and spillover effects across equity, fixed income, currency, and commodity markets.