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.
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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).
Course 1 of 3 in the Machine Learning for Trading Specialization.
What You Will Learn
- 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.
Syllabus
WEEK 1
Introduction to Trading, Machine Learning and GCP
In this module you will be introduced to the fundamentals of trading. You will also be introduced to machine learning. Machine Learning is both an art that involves knowledge of the right mix of parameters that yields accurate, generalized models and a science that involves knowledge of the theory to solve specific types of problems.
WEEK 2
Supervised Learning and Forecasting
In this module you will be introduced to supervised machine learning and some relevant algorithms commonly applied to trading problems. You will get some hands-on experience building a regression model using BigQuery Machine Learning
WEEK 3
Time Series and ARIMA Modeling
In this module you will learn about ARIMA modeling and how it is applied to time series data. You will get hands-on experience building an ARIMA model for a financial dataset.
WEEK 4
Introduction to Neural Networks and Deep Learning
In this module you'll learn about neural networks and how they relate to deep learning. You'll also learn how to gauge model generalization using regularization, and cross-validation. Also, you'll be introduced to Google Cloud Platform (GCP). Specifically, you'll be shown how to leverage GCP for implementing trading techniques.
