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Machine learning models, backtesting frameworks, and data-driven trading strategies — from research to deployment.
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.
NautilusTrader Review 2026: Best Open-Source Algo Engine?
An independent review of NautilusTrader, the open-source, Rust-native algorithmic trading engine, covering architecture, backtesting, adapters, and pricing.
How to Model Transaction Costs in a Backtest (Python)
How to model transaction costs in a backtest — build spread, commission and power-law impact into your Python simulator so your net results reflect real fills.
Fed Hike Odds Hit 87%: Recalibrate Your Quant Models
Fed rate hike odds surged after a hot CPI amid oil-driven inflation. Adjust your model's rate, energy and correlation inputs before the September FOMC meeting.