AI in Algorithmic Trading & Portfolio Optimization
A research guide to supervised, unsupervised, reinforcement, and agentic approaches in trading and portfolio management.
THE DECISION
Where can different AI approaches support investment workflows, and which risks and governance controls matter?
APPROACH
- Compared four AI paradigms and their investment use cases
- Mapped data, validation, and model-risk concerns
- Connected portfolio objectives to analytical techniques
- Emphasized governance and implementation limits
EVIDENCE & DELIVERABLES
- Seven-page completed research article
SELECTED WORK FILES
PDF
AI in Trading Research Guide
Seven-page educational guide on ML approaches, agentic AI, model risk, and governance. Research perspective only, not investment research or trading advice.
108 KBEVIDENCE INTEGRITY
This case is presented as research guide · not a software implementation. The label distinguishes observed work from simulated impact, proxies, historical comparisons, or proposed architecture.