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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
10
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
32
1
8
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
Six libraries, one workflow
Six public libraries now cover the reusable parts of the ML4T workflow: data, features, domain-specific models, diagnostics, backtesting, and live…
May 12
•
Stefan Jansen
19
4
Nine case studies, one end-to-end workflow
Setup, labels, features, models, costs, risk — what the ML4T strategy research workflow does at every stage.
May 5
•
Stefan Jansen
9
Inside the Agent Lab
A live implementation of the AIA Forecaster paper, and what each pipeline stage actually does.
May 1
•
Stefan Jansen
8
April 2026
More than 27 chapters
How the five libraries, 112 primers, and 56 agent skills complement the 27 chapters.
Apr 28
•
Stefan Jansen
16
9
2
What changed in six years, and what didn't
A survey of what's new in the 3rd ed of ML for Trading — generative AI, autonomous agents, causal ML, nine case studies, five libraries — and what…
Apr 24
•
Stefan Jansen
17
1
2
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