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Introduction to Trading, Machine Learning & GCP

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Introduction to Trading, Machine Learning & GCP

This course is part of Machine Learning for Trading Specialization

Instructor: Jack Farmer

69,058 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

898 reviews

Intermediate level

Recommended experience

Flexible schedule

5 hours to complete

Learn at your own pace

85%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

898 reviews

Intermediate level

Recommended experience

Flexible schedule

5 hours to complete

Learn at your own pace

85%

Most learners liked this course

What you'll 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.

Details to know

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Assessments

8 assignments

Taught in English

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This course is part of the Machine Learning for Trading Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

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).

Module details

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.

What's included

25 videos3 readings4 assignments

25 videosTotal 127 minutes

Class Overview - Who these courses are for2 minutes
Course Overview Introduction to Trading with Machine Learning on Google Cloud6 minutes
What is AI and ML ? What is the difference between AI and ML?1 minute
Applications of ML in the Real World1 minute
What is ML?4 minutes
Game: The importance of good data5 minutes
Brief History of ML in Quantitative Finance12 minutes
Why Google?2 minutes
Why Google Cloud Platform?2 minutes
What are AI Platform Notebooks1 minute
Using Notebooks2 minutes
Benefits of AI Platform Notebooks2 minutes
What do we want to model? Let's start simple6 minutes
Demo: Building a model with BigQuery ML26 minutes
Lab Intro: Building a Regression Model1 minute
Lab Walkthrough: Building a Regression Model9 minutes
Trading vs Investing6 minutes
The Quant Universe2 minutes
Quant Strategies7 minutes
Quant Trading Advantages and Disadvantages4 minutes
Exchange and Statistical Arbitrage9 minutes
Index Arbitrage2 minutes
Statistical Arbitrage Opportunities and Challenges5 minutes
Introduction to Backtesting5 minutes
Backtesting Design6 minutes

3 readingsTotal 30 minutes

Supervised Learning and Regression10 minutes
Welcome to Introduction to Trading, Machine Learning and GCP10 minutes
Case Study: Capital Markets in the Cloud10 minutes

4 assignmentsTotal 20 minutes

AI and Machine Learning5 minutes
Trading Concepts Review15 minutes
Python Skills Assessment Quiz0 minutes
Google Cloud0 minutes

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

What's included

6 videos1 reading1 assignment

6 videosTotal 29 minutes

What is forecasting? - part 16 minutes
What is forecasting? - part 24 minutes
Choosing the right model and BQML - part 14 minutes
Choosing the right model and BQML - part 22 minutes
Lab Intro: Forecasting Stock Prices using Regression in BQML1 minute
Lab Walkthrough: Forecasting Stock Prices using Regression in BQML12 minutes

1 readingTotal 10 minutes

Staying current with BigQuery ML model types10 minutes

1 assignment

Forecasting0 minutes

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.

What's included

11 videos1 assignment

11 videosTotal 52 minutes

What is a time series?8 minutes
AR - Auto Regressive7 minutes
MA - Moving Average3 minutes
The Complete ARIMA Model4 minutes
ARIMA compared to linear regression8 minutes
How can you get a variety of models from just a single series?2 minutes
How to choose ARIMA parameters for your trading model4 minutes
Time Series Terminology: Auto Correlation4 minutes
Sensitivity of Trading Strategy5 minutes
Lab Intro: Forecasting Stock Prices Using ARIMA1 minute
Lab Walkthrough: Forecasting Stock Prices using ARIMA8 minutes

1 assignment

Time Series0 minutes

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.

What's included

5 videos1 reading2 assignments1 discussion prompt

5 videosTotal 29 minutes

Short history of ML: Neural Networks8 minutes
Short history of ML: Modern Neural Networks9 minutes
Overfitting and Underfitting6 minutes
Validation and Training Data Splits5 minutes
Course Recap + Preview of next course 2 minutes

1 readingTotal 10 minutes

Example BigQuery ML DNN code10 minutes

2 assignmentsTotal 8 minutes

Recap Quiz8 minutes
Model generalization0 minutes

1 discussion promptTotal 10 minutes

Applying ML to Winter Ski Resort Problem10 minutes

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Instructor

Instructor ratings

(194 ratings)

Jack Farmer

New York Institute of Finance

13 Courses182,732 learners

Offered by

Google Cloud

New York Institute of Finance

Learner reviews

5 stars
44.20%
4 stars
28.61%
3 stars
13.91%
2 stars
4.67%
1 star
8.57%

Showing 3 of 898

Reviewed on Nov 20, 2020

I thought this was excellent. Some familiarity with standard SQL is needed to get the most benefit from the materials, and the course is clearly aimed at GCP users.

Reviewed on Jan 14, 2020

Some of the content in Week 4, might be better placed earlier in the course. Other than that it was a great learning experience.

Reviewed on Jan 29, 2020

Excellent! But, I am missing some of the prerequisites since I just wanted to take a chance and try things out, but feel like proceeding further might lead to some stumbling blocks.

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