Alpha Research with ML
(Signal Discovery)
Our primary alpha engine utilizes deep representation learning and gradient boosting to extract non-linear, orthogonal signals from immense datasets. We specialize in cross-sectional equity forecasting by modeling complex interactions between price-volume dynamics, granular fundamental metrics, and unstructured alternative data sources like satellite imaging and NLP-processed news sentiment. Stringent anti-overfit controls, including purged cross-validation strategies and combinatorial purged cross-validation (CPCV), guarantee robust out-of-sample edge.
Key Competencies:
- Unstructured Data Ingestion: Transforming raw text, geospatial, and chain-of-supply data into dense vector embeddings.
- Non-linear Factor Discovery: Leveraging transformer-based architectures to identify transient market mispricings.
- Strict Regime Conditioning: Dynamically weighting alpha signals based on macroeconomic and microstructure regimes.