Algorithmic Trading with Python

I spent 12 years at Goldman Sachs building trading algorithms that generated hundreds of millions in profits. Now I'm teaching retail traders the same methods. We start with Python fundamentals for finance - pandas, numpy, and data visualization. You'll learn to fetch market data, clean it, and prepare it for analysis. These are the same tools institutional quants use. Backtesting is where most retail algos fail. You'll learn to build robust backtesting frameworks that account for slippage, commissions, and market impact. We cover walk-forward analysis and out-of-sample testing to avoid overfitting. The machine learning section teaches you to build predictive models. Random forests, gradient boosting, and neural networks applied to market data. You'll understand feature engineering and how to avoid the pitfalls of ML in trading. Finally, we deploy strategies live. You'll connect to broker APIs, manage orders, and monitor performance. We cover risk controls, position limits, and what to do when things go wrong. This is production-grade algo trading.

Alex Krieger

Former Goldman Sachs Quant

4.97 (7200 students)