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
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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
- Model Evaluation
- Artificial Neural Networks
- Statistical Machine Learning
- Machine Learning
- Applied Machine Learning
- Deep Learning
- Financial Trading
- Technical Analysis
- Machine Learning Software
- Finance
- Machine Learning Algorithms
- Machine Learning Methods
- Securities Trading
- Model Training
- Cloud Platforms
- Model Optimization
- Artificial Intelligence and Machine Learning (AI/ML)
- Google Cloud Platform
- Supervised Learning
- Time Series Analysis and Forecasting
Details to know
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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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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.
Reviewed on May 1, 2020
This is a very good course because it tuned my already forecasting knowledge to look more into machine learning
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.
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