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Introduction to Trading, Machine Learning & GCP

Approx. 9 hours to complete

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Course Summary

This course is an introduction to trading with machine learning on the Google Cloud Platform. It covers the basics of machine learning, trading, and GCP, and how they can be combined to create trading models.

Key Learning Points

Learn the basics of machine learning and trading
Discover how to use Google Cloud Platform for trading
Learn how to build and evaluate trading models using machine learning

Learning Outcomes

Understand the basics of machine learning and trading
Learn how to use Google Cloud Platform for trading
Be able to build and evaluate trading models using machine learning

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python programming language
Familiarity with machine learning concepts

Course Difficulty Level

Intermediate

Course Format

Online
Self-Paced

Similar Courses

Algorithmic Trading Strategies
Machine Learning for Finance
Quantitative Methods

Related Education Paths

Related Books

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).

Knowledge

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.

Outline

Introduction to Trading with Machine Learning on Google Cloud
Class Overview - Who these courses are for
Course Overview Introduction to Trading with Machine Learning on Google Cloud
What is AI and ML ? What is the difference between AI and ML?
Applications of ML in the Real World
What is ML?
Game: The importance of good data
Brief History of ML in Quantitative Finance
Why Google?
Why Google Cloud Platform?
What are AI Platform Notebooks
Using Notebooks
Benefits of AI Platform Notebooks
What do we want to model? Let's start simple
Demo: Building a model with BigQuery ML
How to use Qwiklabs for your Labs
Lab Intro: Building a Regression Model
Lab Walkthrough: Building a Regression Model
Trading vs Investing
The Quant Universe
Quant Strategies
Quant Trading Advantages and Disadvantages
Exchange and Statistical Arbitrage
Index Arbitrage
Statistical Arbitrage Opportunities and Challenges
Introduction to Backtesting
Backtesting Design
Supervised Learning and Regression
Welcome to Introduction to Trading, Machine Learning and GCP
Case Study: Capital Markets in the Cloud
AI and Machine Learning
Trading Concepts Review

Supervised Learning with BigQuery ML
What is forecasting? - part 1
What is forecasting? - part 2
Choosing the right model and BQML - part 1
Choosing the right model and BQML - part 2
Lab Intro: Forecasting Stock Prices using Regression in BQML
Lab Walkthrough: Forecasting Stock Prices using Regression in BQML
Staying current with BigQuery ML model types

Time Series and ARIMA Modeling
What is a time series?
AR - Auto Regressive
MA - Moving Average
The Complete ARIMA Model
ARIMA compared to linear regression
How can you get a variety of models from just a single series?
How to choose ARIMA parameters for your trading model
Time Series Terminology: Auto Correlation
Sensitivity of Trading Strategy
Lab Intro: Forecasting Stock Prices Using ARIMA
Lab Walkthrough: Forecasting Stock Prices using ARIMA

Introduction to Neural Networks and Deep Learning
Short history of ML: Neural Networks
Short history of ML: Modern Neural Networks
Overfitting and Underfitting
Validation and Training Data Splits
Course Recap + Preview of next course
Example BigQuery ML DNN code
Recap Quiz

Summary of User Reviews

Discover the basics of trading using machine learning on the Google Cloud Platform with this comprehensive course. Many users found the course to be informative and engaging, with a clear and concise presentation style. One key aspect that users appreciated was the hands-on practice exercises that allowed them to apply the concepts they learned in a practical setting.

Pros from User Reviews

The course covers a wide range of topics related to machine learning and trading, providing a solid foundation for beginners.
The course is well-structured and easy to follow, with clear explanations of complex concepts.
The hands-on practice exercises are challenging but rewarding, allowing users to apply what they've learned in a practical setting.
The course materials are high-quality and professionally produced, with clear audio and video.
The course instructors are knowledgeable and engaging, providing helpful feedback and insights throughout the course.

Cons from User Reviews

Some users found the course to be too basic, lacking in-depth coverage of more advanced topics.
The course can be time-consuming, with many hours of video lectures and practice exercises.
Some users found the course to be too focused on theory, with limited practical applications.
The course may not be suitable for users without a background in programming or statistics.
The course may not be as relevant for users outside of the finance industry.

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