Introduction to Trading, Machine Learning & GCP

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Course overview

Provider
Coursera
Course type
Free online course
Level
Intermediate
Deadline
Flexible
Duration
9 hours
Certificate
Paid Certificate Available
Course author
Jack Farmer
  • Understand the fundamentals of trading, including the concepts of trend, returns, stop-loss, and volatility.

  • Define quantitative trading and the main types of quantitative trading strategies.

  • Understand the basic steps in exchange arbitrage, statistical arbitrage, and index arbitrage.

  • Understand the application of machine learning to financial use cases.

Description

In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).

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