AI-powered stock analysis & quantitative research
Machine learning models, backtesting frameworks, and data-driven trading strategies — from research to deployment.
Optimizing an AI-Silicon Portfolio: Markowitz Mean-Variance with pypfopt, Done Right
Mean-variance optimization on NVDA, AMD, AVGO, TSM, ASML, MU, INTC, QCOM, TXN and ARM with pypfopt — Ledoit-Wolf shrinkage, weight bounds and L2 regularization, and when to reach for HRP, Black-Litterman or CVaR instead.
Quant & AI Markets News Roundup: Oct 4, 2026 — Agentic Retail Scales as Institutional Quant Adapts
Robinhood's embedded trading agents, the Fed's first hike since 2023, a new SEC crypto-custody proposal and a September hedge-fund split define this week in quant and AI.
PyBroker Review 2026: ML-Native Backtesting in Python
PyBroker 2.0 folds ML training, walk-forward evaluation and bootstrap metrics into one Python engine, but its Commons Clause licence limits commercial use.
Triple-Barrier Labeling and Meta-Labeling in Python: A 2026 Implementation Guide
A practical Python guide to path-aware trade labels, secondary prediction filters, overlap controls, and reproducible evaluation for quantitative research.
AI Markets Weekly: SEC Tokenized Stocks, AI Exposure, Agents
QuantBrainAI's AI-markets roundup separates audited facts from vendor claims across SEC tokenized-stock rules, an AI-exposure factor, and agent execution rails.
OpenBB Open Data Platform Review 2026: Open-Sourced, AGPL, and What It Means for Quants
Desk-research review of the OpenBB Open Data Platform after its open-sourcing announcement, covering surfaces, pricing, licensing, and adoption verdicts.
Purged K-Fold Cross-Validation Python: Stop Label Leakage
Learn how purged k-fold cross-validation in Python removes overlapping-label leakage and why a trailing embargo matters for financial machine learning.