Agentic AI for Finance Bootcamp: Days 3 & 4
I am back for the second half of Nicole Königstein’s live 4-day “Agentic AI for Finance Certification” bootcamp (Packt Publishing), which teaches you how to:
GraphRAG with Neo4j: A Quant’s Preview
On September 19, from 15:30 to 19:30 CEST, I’ll be joining a hands-on, virtual workshop presented by Packt Publishing and led by Dr. Alessandro Negro, Chief Scientist at GraphAware — and I wanted to share why I’m looking forward to it.
Numerical Hedging of Portfolio Risk via Approximated Derivatives
This post presents a conceptual method for constructing hedging strategies using numerical approximations of derivatives with respect to tradable risk factors. Focusing on a selected factor, we show how first- and second-order sensitivities of portfolio value can be estimated from models or data-driven sources. These are then used to build hedges that neutralize both linear and curvature risk, enabling robust, model-agnostic risk management.
Sharpe Ratio Distribution: When Normal’s a No-Go, Why Not SHASHo? (3/4)
Post 2/4 (Block 3) landed on the exact, unconditional result: P(SR-hat ≤ SR-hat*) = Φ(R*) — SHASHo’s probability that the SR estimator falls at or below any value collapses exactly to a standard Normal one.
Five slides attached, being the main content of this post, follow: the first sets up the test’s conditional probability, the remaining four present the test-related measures built on it.
Agentic AI for Finance: First Impressions of Nicole Königstein’s Packt Course
Gifted review ticket from Packt Publishing — honest, unpaid opinion below.
I completed the first two of four 4-hour live sessions in Nicole Königstein’s “Agentic AI for Finance” certification course with Packt, and want to share, as an educator and researcher in quantitative finance, an honest first impression, with more sessions still to come.A Performance Metric Isn’t a Fixed Number, It’s a Random Variable
This post builds on my earlier post, where I discussed comparing trading strategies from a distributional perspective. Here I show a simple implementation using Pyfolio Reloaded’s bootstrap tools and seaborn. For readers interested in a more rigorous statistical treatment—modeling the full conditional distribution of the performance metric—I discuss the approach using GAMLSS
Sharpe Ratio Distribution: When Normal’s a No-Go, Why Not SHASHo? (2/4)
In the first post (1/4), a GAMLSS fit picked SHASHo over Normal for the Sharpe ratio estimator (López de Prado et al., 2026) — all four parameters statistically significant, a better GAIC than Normal, and an exact transform (R transform) to standard Normal already in hand.
Attending Nicole Königstein’s Agentic AI for Finance Certification
I’m attending Nicole Königstein’s live Agentic AI for Finance Certification bootcamp: building multi-agent AI systems with harness engineering for finance.
Machine Learning for Trading in the Age of AI Agents — Workshop Review
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.
Sharpe Ratio Distribution: When Normal’s a No-Go, Why Not SHASHo? (1/4)
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.
