Introduction to Trading, Machine Learning & GCP

This course will cover the fundamentals of trading, including concepts such as trend, volatility, and return. This course will help you identify the profit source, structure, and profitability of basic quantitative strategies. This course will explain how the model generalizes and the differences between regression and forecasting. It also explains the steps needed to create backtesters that can be used for implementation and development. This course will teach you how to use Google Cloud Platform to create machine learning models in Jupyter Notebooks.

This course requires advanced Python programming skills as well as knowledge of relevant libraries for machine-learning such Scikit-Learn or StatsModels. Experience with SQL is strongly recommended. It is highly recommended that you have a background in statistics (expected values, standard deviations, Gaussian distributions, higher moments, probabilities and linear regressions), as well as foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).