AI for Finance: Machine Learning & Deep Learning for Trading

Turn market ideas into working AI trading systems. In this hands-on course you’ll build a full pipeline in Python—from pulling real market and macro data to engineering features, training ML/DL models, validating with leakage-safe, walk-forward tests, and backtesting with realistic costs, slippage, and risk controls. You’ll implement multiple strategies (event/earnings & news, sentiment/NLP, trend/momentum, and pairs/stat-arb), compare models like XGBoost, Random Forests, LSTMs, and Transformers, and deploy a paper-trading bot with position sizing, volatility targeting, and clear monitoring dashboards. We work step-by-step in VS Code/Jupyter using pandas, scikit-learn, PyTorch, yfinance, vectorbt/Backtrader, and matplotlib—providing reusable notebooks, templates, and checklists so you can adapt everything to your own tickers and ideas. By the end, you’ll have reproducible workflow, a portfolio-ready project, and the confidence to iterate ethically and safely before going live.Expect practical extras: a capstone project with template repo, model explainability (feature importance and SHAP-style reasoning), error analysis checklists, and hyperparameter tuning playbooks. We’ll cover data sourcing trade-offs, free alternatives to paid feeds, and pitfalls like survivorship bias. You’ll practice version control, experiment tracking, and reproducible runs, then stress-test results with regime changes. Optional extensions include crypto, options, and portfolio optimization. Support includes code reviews, troubleshooting tips, and a community.(Educational use only—no performance guarantees.)