Econometric Feature Hierarchy, Automated Bot Heuristics & Dual-Track RAG Architecture

Author: Quantitative Strategy & Machine Learning Division

#713 Features#Econometrics#VIX Spillover#HMM Regimes#RAG

1. Context & Problem Statement

Machine learning trading signals without structured econometric interpretability produce black-box skepticism and prevent traders from validating conviction during macro regime shifts.

2. Decision

We codified an explicit 713-feature econometric hierarchy spanning volatility regimes (HMM Bear/Bull), orderbook imbalances, macro cross-asset correlations, and VIX spillover metrics, coupled with a dual-track retrieval-augmented generation (RAG) pipeline translating mathematical state vectors into natural-language decision explanations.

3. Consequences & Trade-Offs

Positive Outcomes

Full quantitative interpretability for every trade signal emitted by deep neural networks

Real-time econometric state awareness combining macro, microstructure, and trend factors

Transparent decision rationale accessible via Copilot chat and prediction inspection

Negative / Trade-Offs

Computing 713 features across 13 assets per bar requires robust caching

Mitigation Strategies

Redis time-series caching and vectorized feature computation pipelines

Standards & References

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