I’m attending Nicole Königstein’s live Agentic AI for Finance Certification bootcamp: building multi-agent AI systems with harness engineering for finance.

Gifted review ticket from Packt Publishing — honest, unpaid opinion below.

On Saturday, August 15, 2026, I spent three hours, effectively, in Stefan Jansen’s (Packt) live workshop, “Machine Learning for Trading in the Age of AI Agents” — two 90-minute parts with a short break.

I recently came across López de Prado et al.’s (2026) treatment of the Sharpe ratio estimator: SR-hat ~ N(SR, σ²[SR] = f(T, ρ, γ₃, γ₄, SR)) — Normal, with the true SR as its mean, and a variance σ²[SR] conditioned on sample size, the excess return series’ autocorrelation, its skewness, its kurtosis, and the level of SR itself.

This is a build-along workshop, not a lecture: attendees go from generating to validating and refining an end-to-end ML trading strategy with real ETF market data — using AI coding agents to build the feature engineering, model training, and backtesting pipeline, and AI research agents to generate, validate, and refine the trading ideas themselves.

How a single-value performance metric can distort the ranking of trading strategies — and how to avoid falling for it and gain deeper insight into strategy comparison.

Gifted review copy from Packt Publishing — honest, unpaid opinion below.

Just finished reviewing Stefan Jansen’s Machine Learning for Trading, 3rd Edition (Packt, 2026), several days after publication.

I wrote my newest paper mainly to challenge the conventional way of judging an investment or trading strategy through a single observational point of its performance metric — an approach that discards precious information about…

Gifted review copy from Packt Publishing — honest, unpaid opinion below. This is a genuinely useful reference for anyone building trading systems in Python. The recipe format is the book’s greatest strength — plenty of self-contained recipes spanning data acquisition, data wrangling, and visualization; building fundamental data screeners; factor trading; backtesting (both vector-based and event-based) and its evaluation; and using the IB API for historical and streaming data in portfolio building and management.

XAI methods enable the use of tools such as AI/ML models or backtesting in finance while gaining explainability of their outputs.

Introduction The Capital Asset Pricing Model (CAPM) represents an asset’s market sensitivity using a single scalar (beta), estimated by ordinary least squares on the full return distribution.