Introduction to Quantitative Trading with Machine Learning and GCP

Bridging the gap between financial markets and data science can feel overwhelming without a clear starting point. This course guides you through the foundational concepts of quantitative trading, machine learning, and cloud deployment. You will transition from understanding basic market mechanics to designing and evaluating predictive models. By studying structured written explanations and practical code examples, you will learn how to leverage cloud resources to build robust backtesting environments and automate your trading strategies. What you'll learn: - Understand core trading concepts including volatility, returns, trends, and risk management. - Build machine learning models to forecast market movements and identify profitable patterns. - Configure cloud pipelines on GCP to ingest, process, and store financial market data. - Apply modern data libraries to clean datasets and engineer predictive features. - Design and implement backtesting workflows to evaluate model generalization and performance. - Deploy automated trading workflows using cloud-native services and basic MLOps principles. The journey begins with essential trading terminology and financial theory before moving into data preparation and predictive modeling. You will then explore how to scale these models using cloud infrastructure, culminating in the creation of a systematic backtesting framework. This course is designed for beginners in finance, data science, or software development who want to explore quantitative trading. No prior experience with trading algorithms or cloud platforms is required. Start reading today to build your first cloud-powered algorithmic trading model.