TL;DR: On March 27, I’m joining my co-authors Jared Broad, Vivek Singh, and Philip Sun via Zoom to deliver a guest lecture for Boston University’s Questrom School of Business on our book Hands-On AI Trading with Python, QuantConnect, and AWS. We’re covering alpha generation with deep learning, AI-driven risk management, and the real-world mess of deploying models into live markets with Python, and QuantConnect.
Why Am I Guest-Lecturing at BU from the Other Side of the World?
Writing a book about AI trading is one thing. Defending your approach in real time in front of a room full of master’s students in mathematical finance — that’s a different game entirely. The fact that we’re doing it via Zoom from four different time zones (I’ll be dialling in from Singapore at 1:30 AM local time) only makes it more fitting. This is how global finance actually works — distributed teams, async collaboration, and technology that erases borders.
When Philip Sun — who teaches Algorithmic and High-Frequency Trading in BU’s MS in Mathematical Finance & Financial Technology programme — invited the full author team for a guest lecture in his MF821 course, I said yes immediately. Not because it’s convenient (it isn’t — hello, 1:30 AM), but because the gap between academic finance and production AI trading systems is still enormous. Most quant finance programmes teach elegant theory. Very few teach you what happens when your reinforcement learning agent encounters a volatility spike at 3 AM and your stop-loss logic has a race condition. That’s the gap our book — and this lecture — aims to close.
What Are We Covering?
The session runs Friday, March 27, 2026, from 1:30 PM to 4:15 PM EDT via Zoom, followed by an interactive Q&A. Here’s what we’ll dig into:
- Alpha Generation with Deep Learning — How to extract signals from noisy market data. Not the textbook version — the version where your feature engineering matters more than your model architecture.
- AI-Driven Risk Management — Using ML to predict volatility and protect portfolios. I’ll share a lesson I learned the hard way: a model that’s 94% accurate on backtest data can still blow up your P&L in live markets if your regime-detection logic doesn’t adapt fast enough.
- The “Hands-On” Approach — Real-world deployment challenges. Backtests lie. Paper trading reveals some truth. Production trading reveals all of it. We’ll talk about the ugly parts nobody writes about.
- The Future of Finance in the Age of Generative AI — Where the industry is heading over the next 12–24 months. I have strong opinions on this one.
The quote from our book that anchors the whole session: “In the world of AI trading, the difference between profit and loss often comes down to the quality of your features and the robustness of your backtest.”
Who’s Presenting?
This is what makes the lecture unusual — you’re getting four distinct perspectives from people who build in production, not just publish papers. All joining via Zoom from different corners of the world.
| Speaker | Role | Brings to the Table |
|---|---|---|
| Jiri Pik (me) | Founder & CEO, RocketEdge; Author | AI trading systems architecture, cloud deployment, Python/C# implementation |
| Jared Broad | CEO & Founder, QuantConnect | Open-source LEAN engine powering 300,000+ quants; platform-scale thinking |
| Vivek Singh | Product Leader, AWS | LLM architectures, GenAI evaluation, plus hedge fund fundamental analysis background |
| Philip Sun | CEO, Adaptive Investment Solutions; Adjunct Professor, BU | 27+ years in quant trading, hedge fund management, and teaching the next generation |
The range matters. Jared knows what breaks at platform scale. Vivek bridges the gap between frontier AI research and financial applications. Philip brings decades of hedge fund experience and understands what students actually need to hear. And I’ve been obsessed with the intersection of cloud infrastructure and trading systems for years — building, breaking, and rebuilding them at RocketEdge.
Why Does This Matter Beyond the Classroom?
I think about this a lot: the best quant finance education in 2026 looks nothing like it did five years ago. The students logging into this Zoom session aren’t just learning Black-Scholes and Monte Carlo simulations. They need to understand transformer architectures, feature stores, cloud-native backtesting infrastructure, and how to evaluate whether an LLM-generated trading signal is alpha or noise.
The fact that a top programme like BU’s MSMF is bringing in practitioners — a startup founder from Singapore, a platform CEO from Miami, an AWS AI leader, and a hedge fund manager — via Zoom tells you two things. First, finance education is catching up to how the industry actually operates. Second, geography no longer gates access to expertise. By 2028, I predict every serious quant finance programme will have a mandatory “AI systems in production” course, not just an elective guest lecture. The programmes that move first will produce the traders and portfolio managers who actually thrive.
How to Get the Book
If you want to go deeper before (or after) the lecture, grab a copy of Hands-On AI Trading with Python, QuantConnect, and AWS. It covers everything from setting up your first QuantConnect algorithm to deploying deep learning models on AWS infrastructure — with working code throughout. This isn’t a theory book. It’s the manual I wish I’d had when I started building trading systems.
Event Details at a Glance
- Date: Friday, March 27, 2026
- Time: 1:30 PM – 4:15 PM EDT
- Location: Via Zoom (guest speakers joining remotely); students at HAR406, Questrom Business School Building
- Format: Lecture + interactive Q&A
- Registration: None required — open to all Questrom students and faculty, first-come first-served
FAQ
Is this lecture open to the public?
The event is open to all Questrom School of Business students and faculty. Seats are limited and available on a first-come, first-served basis — no registration required.
Are the guest speakers presenting in person?
The guest speakers — myself included — are joining via Zoom from different locations globally. Students attend in person at HAR406 or via the course Zoom link.
What level of technical knowledge is expected?
The lecture is designed for master’s-level students in mathematical finance and financial technology, so we’ll assume comfort with Python, basic ML concepts, and financial fundamentals. That said, we’re focused on practical insights, not proofs.
Will the lecture be recorded?
The announcement doesn’t confirm a recording. If you can’t make it, follow me on LinkedIn or X — I’ll share key takeaways afterward.
Where can I get the book?
Hands-On AI Trading with Python, QuantConnect, and AWS is available on Amazon. It covers Python-based algo trading, deep learning for finance, and cloud deployment on AWS.
Jiri Pik is the founder of RocketEdge, an AI fintech company based in Singapore. Follow him on LinkedIn and X for more.