We teach applied AI for investing and wealth workflows — market data literacy, portfolio analytics, risk signals and research copilots — with compliance, disclosure and “AI does not place unsupervised trades” made explicit.
Domain Knowledge We Teach
- Capital markets, instruments, and portfolio basics
- Fundamental vs technical vs alternative data
- Risk, drawdown, and performance metrics
- KYC / AML and suitability awareness for advisors
- Where models assist vs where humans decide
AI Skills You Will Learn
- Market data wrangling and feature pipelines
- Factor, screening, and ranking assistants
- Volatility, VaR, and stress-test orientation
- Research note and earnings-summary copilots
- Fraud / anomaly signals in transaction-style data
Hands-on Outcomes
- Build a portfolio analytics dashboard prototype
- Create a screening / ranking workflow with explainability
- Document risk limits and model failure modes
- Draft a responsible-AI investing checklist
- Present an investment AI case study to non-quants