The implementation program where you build the system that closes the gap: from your first working AI agent in 60 minutes to a 9-agent pipeline that researches, backtests, validates, and deploys trading strategies autonomously.
Built by a Managing Director of AI who uses this exact system daily on Fortune 500 client engagements and his own trading strategies. 20 years of quant experience. 1,500+ students. 4.97/5 rating.
You’ve been coding Python for years. You can build strategies, run backtests, wrangle data. You’re not a beginner.
But something shifted in the last 18 months.
You see developers on Twitter building complete trading systems with AI agents in an afternoon. Things that would take you 2–3 weeks. You’ve tried copying code from ChatGPT. Some of it ran. You don’t know how much of it was wrong.
You’ve spent 20 minutes crafting a prompt only to get output that missed transaction costs, had look-ahead bias in the signal, or aligned dates to midnight UTC instead of market close. You rewrote it by hand and wondered why you bothered.
But here’s the part that should worry you more than the speed gap.
Developers who adopted AI coding tools without a system are shipping unreliable code faster. They’re building on output they don’t fully understand. They’re accumulating technical debt at the speed of generation instead of the speed of typing.
The gap isn’t between “uses AI” and “doesn’t use AI.” The gap is between “uses AI with a system” and “uses AI without one.”
Doing it wrong is worse than not doing it at all. And doing nothing isn't an option either, because the way quant code gets written is changing structurally. Agentic workflows are replacing manual line-by-line coding across the industry. Not in 10 years. Now.
The question isn’t whether to learn this. It’s whether you learn it right.
Bridgewater publicly discussed restructuring their research workflow around AI agent pipelines. Citadel has been hiring for “AI-augmented strategy development” roles since late 2025. Two Sigma’s quant research postings now list “agent orchestration” as a preferred skill.
The developers who master agentic workflows now are building a compounding advantage. Every strategy they build with agents makes the next one faster. Every prompt template they refine makes the output more reliable. The advantage compounds weekly.
The skill set that defines “a good quant developer” is shifting. Python proficiency is now table stakes. The new differentiator: can you architect and operate AI agent systems that produce reliable, verifiable output on complex quant tasks?
Time the average quant developer spends on data plumbing an agent handles in 90 minutes
Ideas that never make it past the notebook because implementation overhead is too high
Quant developers working without a systematic code review process for AI-generated output
“This was the best course I have taken in my 28 year career in finance.”
“The course is structured really well with a nice level of progression and focus on the fundamentals.”
“It is clear to me that Jason is very skilled in math/stats and its applications. I like that I can ask him questions about the math and he knows exactly how that applies to the finance.”
Jason Strimpel has spent 20+ years in quantitative finance: trading derivatives at J.P. Morgan, managing risk for a $7 billion derivatives trading business at BP Trading, and building analytics infrastructure for a $60 billion metals trading firm at Rio Tinto.
Today, he's Managing Director and Global Head of AI & Advanced Analytics at a top-tier consulting firm, where he leads AI strategy and implementation for Fortune 500 financial services, insurance, and reinsurance clients. He uses Claude Code, the Claude Agent SDK, multi-agent architectures, and agentic workflows daily, in production, at enterprise scale.
The Trading Strategy Development Agent you build in this program is the same system Jason uses on his own trading strategies and on million-dollar client engagements. He didn’t design it as a teaching exercise. He built it because he needed it. Then he structured the build process into a curriculum so you can construct it yourself.
Jason sits at the intersection of three things that almost never overlap: deep quant finance expertise, enterprise-grade AI implementation experience, and a trusted education platform with a proven track record. That's why this program exists, and why nobody else can teach it.
The Trading Strategy Development Agent is a 9-agent pipeline built on the Claude Agent SDK. You provide a strategy description in plain English. The system does the rest.
Here’s what happens when you type “Develop a momentum strategy with 12-month lookback and volatility scaling, trading US large-cap equities”:
Output: 9 files. Academic research, web research, code research, strategy synthesis, backtest plan, backtest report, IB implementation plan, runnable Zipline backtest, runnable IB live strategy.
You build every component of this system across the program. Level 1 teaches you to run each phase manually. Level 2 assembles the agents. The capstone wires them together. The end product is yours: real software you own, operate, and extend.
You can write Python and you understand markets, but every strategy you build still takes weeks of manual coding, debugging, and iteration. The system gives you 8 modules that systematically transform how you work: you'll build agent-augmented workflows that compress weeks of development into hours, with production-quality output. 8 modules. 8 working deliverables. ~15–19 hours. Every example is quant finance.
The setup that most developers skip, and the reason their agent output is unreliable from day one. You'll have a fully configured Claude Code environment with a project-specific CLAUDE.md running a real agent task on market data before you close your laptop. Time to first deliverable: under 60 minutes.
Why your ChatGPT code "runs but isn't correct," and the context engineering framework that fixes it. You'll build a reusable template that produces domain-correct output without manual correction.
The "spec-first" pattern that eliminates 73 hours per quarter of data plumbing. You write a pipeline specification. The agent builds the full implementation (download, clean, validate, compute derived features) with automated quality checks.
Those strategy ideas sitting in your notebook for months? The Backtest Scaffold Pattern goes from a plain-English strategy description to a complete, verified backtest with a professional tearsheet in a single session.
The Research Sprint pattern that lets you test 10 strategy ideas in the time it used to take to test 1. Agent-generated factor construction, signal evaluation, and comparative analysis, all from a structured hypothesis, not random exploration.
A 12-point Financial Code Review Checklist built for the bugs AI introduces: the ones that don't throw errors but silently corrupt your backtest results. Look-ahead bias, survivorship bias, transaction cost omissions, date alignment errors, NA propagation.
The difference between “using AI tools” and “having an AI-augmented system.” You build a personal Agent Workflow Playbook covering 5–7 recurring tasks with documented prompts, context requirements, and verification steps. Systems compound. Ad hoc doesn’t.
Everything applied to a single project: from strategy description to documented, verified backtest with a professional tearsheet. You'll run the full pipeline end-to-end and write a retrospective that becomes the blueprint for the 9-agent system you build in Level 2.
The Agentic Quant System is self-paced with lifetime access. Level 1 is ~15–19 hours across 8 modules.
Most students work through 1–2 modules per week on weeknights.
The first module takes 60 minutes and delivers a working agent.
By Module 3, you have a data pipeline that eliminates 73 hours per quarter of manual data work.
The program pays for itself in time savings within the first 3 modules.
You can.
And you've probably tried.
The problem isn't access to information.
It's that generic AI content teaches "how to prompt ChatGPT" with generic examples.
No YouTube video teaches you to build a 9-agent pipeline using the Claude Agent SDK, because the people who know how to do that are building systems, not filming tutorials.
This program compresses 18 months of trial-and-error into ~32 hours of structured implementation.
Good.
Then you've already experienced the problem this program solves.
Casual AI tool use is Level 0.
The Agentic Quant System ends at a multi-agent pipeline that runs autonomously.
It is.
The 30-day implementation addresses that directly.
A comparable AI/ML bootcamp runs $5,000–$15,000.
The CQF costs $23,000.
This isn't a course.
It's an implementation program.
The deliverable isn't comprehension, it's a working system.
✅ You’ve completed Getting Started With Python for Quant Finance or have equivalent Python proficiency
✅ You use ChatGPT, Claude, or Copilot casually but don’t have a systematic agent workflow
✅ You want to build, backtest, and automate trading strategies faster
✅ You want one stack, one system, every decision made
✅ You have 45 minutes on weeknights and want every session to produce a working deliverable
✅ You want to build a real, working system, not just learn concepts
❌ You don’t have Python proficiency yet (start with Python Foundations)
❌ You want a general-purpose AI course (this is quant finance only)
❌ You’re looking for theory without implementation
❌ You want someone to build the system for you
❌ You’re already operating multi-agent systems in production
A comparable AI/ML bootcamp runs $5,000–$15,000. The CQF program costs $23,000. The Agentic Quant System delivers more targeted, more practical implementation, focused entirely on the quant finance work you already do, at a fraction of the cost.
Yes. You should be comfortable with Python fundamentals: variables, functions, loops, and basic data structures. You don't need to be an expert, but this isn't a beginner Python course. If you can write a script that pulls data from an API and processes it into a DataFrame, you're ready.
Great. You'll move faster through the theory sections and spend more time on the implementation side. Most of our alumni with quant backgrounds say the agent-based architecture and production deployment modules alone were worth the investment. If you've built models but never deployed them as autonomous systems, this fills that gap.
Lifetime. You get permanent access to all course materials, including any future updates. The curriculum evolves as the field does; when new tools, libraries, or techniques emerge, we add them. Your founding member price locks in access to everything, forever.
No. This is an engineering course, not a trading signals service. You'll learn to build systematic, risk-managed quantitative systems, the same kind used by institutional firms. We focus on robust architecture, proper backtesting, and production-grade deployment. If you're looking for "10 hot stock picks," this isn't for you.
Level 1 covers single-agent quantitative systems: data pipelines, backtesting, risk management, and deploying one autonomous trading agent. The Bundle adds Level 2: multi-agent orchestration, advanced validation gates, cross-strategy coordination, and production monitoring at scale. If you want to build a single strategy, Level 1 is sufficient. If you want to run a portfolio of coordinated agents, get the Bundle.
Yes. While we use Interactive Brokers and specific data providers in the examples, the architecture is broker-agnostic. You'll learn to build adapter layers so you can plug in any broker API or data source. Several alumni run their systems on Alpaca, TD Ameritrade, and custom crypto exchange APIs.
You can paper trade (simulated) with zero capital while learning. When you're ready to go live, that's entirely up to your risk tolerance and financial situation. The course teaches you to build the systems; how much capital you deploy is a personal decision we don't make for you.
You'll have access to the alumni community where you can ask questions and get help from peers and mentors. Group implementation sessions (included with both tiers) provide live support for working through challenges. You're not doing this alone.
Yes. We offer a 3-month payment plan for both tiers. Details are available on the checkout page. The founding member pricing applies to payment plans as well, so you lock in the discounted rate.
30 days, no questions asked. If you go through the material and decide it's not for you, email us and we'll refund every penny. We've been doing this long enough to know that the people who enroll and do the work get results. We're confident enough in the curriculum to make this guarantee simple.