Build practical skills in generative AI and machine learning for trading and learn how AI-powered methods can support disciplined, risk-aware decisions in financial markets. Designed for learners interested in algorithmic trading and quantitative finance, this course explores large language models (LLMs), transformers, financial NLP, sentiment analysis, alternative data, supervised and unsupervised learning, neural networks, and reinforcement learning.

You’ll develop and evaluate financial trading models using feature engineering, stationarity analysis, regularization, model selection, validation, and bias controls. You’ll examine SMA, deep-learning, and intraday strategies through vectorized and event-based backtesting, benchmarks, transaction costs, slippage, liquidity, and out-of-sample testing. You’ll also assess AI infrastructure, governance, compliance, execution risk, and trading performance using volatility, ATR, drawdown, leverage, and cost-adjusted measures. Finally, you’ll design stop-loss, trailing-stop, cooling-period, and take-profit rules. By the end, you’ll be able to evaluate AI trading strategies, interpret model performance, control key risks, and design more reliable trading workflows.