Introduction to Trading, Machine Learning & GCP

Introduction to Trading, Machine Learning & GCP

This course is part of Machine Learning for Trading Specialization

Instructor: Jack Farmer

Access provided by Universidad Austral

67,752 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

897 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.

897 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.

Skills you'll gain

Tools you'll learn

Model Deployment

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.

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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).

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 videos Total 127 minutes

Class Overview - Who these courses are for 2 minutes
Course Overview Introduction to Trading with Machine Learning on Google Cloud 6 minutes
What is AI and ML ? What is the difference between AI and ML? 1 minute
Applications of ML in the Real World 1 minute
What is ML? 4 minutes
Game: The importance of good data 5 minutes
Brief History of ML in Quantitative Finance 12 minutes
Why Google? 2 minutes
Why Google Cloud Platform? 2 minutes
What are AI Platform Notebooks 1 minute
Using Notebooks 2 minutes
Benefits of AI Platform Notebooks 2 minutes
What do we want to model? Let's start simple 6 minutes
Demo: Building a model with BigQuery ML 26 minutes
Lab Intro: Building a Regression Model 1 minute
Lab Walkthrough: Building a Regression Model 9 minutes
Trading vs Investing 6 minutes
The Quant Universe 2 minutes
Quant Strategies 7 minutes
Quant Trading Advantages and Disadvantages 4 minutes
Exchange and Statistical Arbitrage 9 minutes
Index Arbitrage 2 minutes
Statistical Arbitrage Opportunities and Challenges 5 minutes
Introduction to Backtesting 5 minutes
Backtesting Design 6 minutes

3 readings Total 30 minutes

Supervised Learning and Regression 10 minutes
Welcome to Introduction to Trading, Machine Learning and GCP 10 minutes
Case Study: Capital Markets in the Cloud 10 minutes

4 assignments Total 20 minutes

Python Skills Assessment Quiz 0 minutes
Google Cloud 0 minutes
AI and Machine Learning 5 minutes
Trading Concepts Review 15 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 videos Total 29 minutes

What is forecasting? - part 1 6 minutes
What is forecasting? - part 2 4 minutes
Choosing the right model and BQML - part 1 4 minutes
Choosing the right model and BQML - part 2 2 minutes
Lab Intro: Forecasting Stock Prices using Regression in BQML 1 minute
Lab Walkthrough: Forecasting Stock Prices using Regression in BQML 12 minutes

1 reading Total 10 minutes

Staying current with BigQuery ML model types 10 minutes

1 assignment

Forecasting 0 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 videos Total 52 minutes

What is a time series? 8 minutes
AR - Auto Regressive 7 minutes
MA - Moving Average 3 minutes
The Complete ARIMA Model 4 minutes
ARIMA compared to linear regression 8 minutes
How can you get a variety of models from just a single series? 2 minutes
How to choose ARIMA parameters for your trading model 4 minutes
Time Series Terminology: Auto Correlation 4 minutes
Sensitivity of Trading Strategy 5 minutes
Lab Intro: Forecasting Stock Prices Using ARIMA 1 minute
Lab Walkthrough: Forecasting Stock Prices using ARIMA 8 minutes

1 assignment

Time Series 0 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 videos Total 29 minutes

Short history of ML: Neural Networks 8 minutes
Short history of ML: Modern Neural Networks 9 minutes
Overfitting and Underfitting 6 minutes
Validation and Training Data Splits 5 minutes
Course Recap + Preview of next course 2 minutes

1 reading Total 10 minutes

Example BigQuery ML DNN code 10 minutes

2 assignments Total 8 minutes

Model generalization 0 minutes
Recap Quiz 8 minutes

1 discussion prompt Total 10 minutes

Applying ML to Winter Ski Resort Problem 10 minutes

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Instructor

Instructor ratings

(194 ratings)

Jack Farmer

New York Institute of Finance

13 Courses 176,539 learners

Offered by

Google Cloud

New York Institute of Finance

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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 18, 2022

Good material... Assignment are very helpful. Flow is bit choppy specially for ML parts. It switches from simple to advance topic rather randomly.

Reviewed on Jun 2, 2020

Good introduction to quant theory and ML, labs could be a lot better though, they lack proper explanations and don't cover some of the basics necessary to complete them.

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