Subscribe
Sign in
Home
Book
Chapters
Libraries
Primer
Agent Lab
Archive
About
Latest
Top
Discussions
The Coding-Agent Toolkit: A Workflow Loop You Can Install
Seven host-neutral steps to improve agent productivity, now public, that run the same on Claude Code and OpenAI Codex.
Jul 7
•
Stefan Jansen
5
1
A Coding-Agent Reading List: Behind the Loops
Loop engineering is only the surface. A reading path through the older control problems underneath — and the line between what a coding agent may change…
Jul 2
•
Stefan Jansen
6
4
From Research to Production with Nine Case Studies
A hands-on ML4T course built on nine case studies: one research workflow from feasibility through deployment — equities, options, futures, FX, and…
Jul 1
•
Stefan Jansen
3
2
June 2026
New Release: From Data to Model-Ready Evidence
The ML4T code release for Chapters 6-10 covers the research framework, labels, feature engineering, model-based features, and text features.
Jun 29
•
Stefan Jansen
1
How to build a Multi-Agent Forecasting System
They can turn current unstructured evidence into scorable probabilities, but the output has to compete with a model, market, or human baseline.
Jun 25
•
Stefan Jansen
1
1
New Release: The ML4T Data Layer Is Now Public
This release makes the Chapters 1-5 code public and focuses on the data foundation: data loaders, market microstructure, as well as alternative and…
Jun 22
•
Stefan Jansen
4
1
The ML4T Third-Edition Code Rollout Starts Today
Between now and launch, the public repo will fill in stages: workflow, case studies, libraries, and the checks that turn model forecasts into trading…
Jun 19
•
Stefan Jansen
3
3
2
Watching the World Cup With a Forecasting Agent
A live World Cup lab for model priors, noisy match news, market comparisons, and scoreable agent calls.
Jun 16
•
Stefan Jansen
4
1
When multi-agent systems are worth the overhead
A multi-agent panel only helps when the agents disagree for real reasons. Forecasting is where you can check.
Jun 10
•
Stefan Jansen
8
2
May 2026
Deep Learning for End-to-End Portfolio Construction
Neural allocators can train directly on portfolio objectives such as Sharpe, costs, and drawdowns. That makes the model more aligned with trading…
May 26
•
Stefan Jansen
13
1
2
AI Agents in Finance: A Reading List
What to read before building read-only financial agents that retrieve evidence, use tools, make forecasts, and leave audit trails.
May 22
•
Stefan Jansen
47
2
14
How ML4T uses case studies to test strategies across markets
Nine case studies across seven asset classes, run under one research protocol.
May 19
•
Stefan Jansen
12
3
1
This site requires JavaScript to run correctly. Please
turn on JavaScript
or unblock scripts
