AI in Algorithmic Trading and Asset Management

Explain the components and strategies of algorithmic trading systems.

Describe how time-series and alternative data (e.g., news sentiment) are used in ML models for market prediction.

Analyze the challenges of using ML for stock prediction, including market efficiency and data non-stationarity.

Describe the concept of robo-advisors and how they automate portfolio management.

Explain the core concepts of reinforcement learning (agent, environment, reward) and its application in dynamic trading strategies.