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Machine Learning Quant Trading

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366 STUDENTS
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Course Description: 

During this excellent Machine Learning – Quant Trading learners will focus on practically applying ML techniques to develop sophisticated Quant Trading models. This Quant Trading course will give you an introduction to machine learning, a subject which gives computers the ability to learn without being programmed. For those interested in Quant Trading, this course teaches you to apply machine learning to Quant Trading. Discover how to build sophisticated financial models that will better inform your investing decisions.

Our learning material is available to students 24/7 anywhere in the world, so it’s extremely convenient. These intensive online courses are open to everyone, as long as you have an interest in the topic! We provide world-class learning led by IAP, so you can be assured that the material is high quality, accurate and up-to-date.

What skills will I gain?

  • Develop Quant Trading models using advanced Machine Learning techniques
  • Compare and evaluate strategies using Sharpe Ratios
  • Use techniques like Random Forests and K-Nearest Neighbours to develop Quant Trading models
  • Use Gradient Boosted trees and tune them for high performance
  • Use techniques like Feature engineering, parameter tuning and avoiding overfitting
  • Build an end-to-end application from data collection and preparation to model selection

What are the requirements?

  • You must be 16 or over
  • You should have a basic understanding of English, Maths and ICT
  • You will need a computer or tablet with internet connection (or access to one)

Meet the Instructor:

Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.

  • Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft
  • Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too
  • Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum
  • Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum

Course outline:

  • Module 01: You, This Course & Us
  • Module 02: Developing Trading Strategies in Excel
  • Module 03: Setting up your Development Environment
  • Module 04: Setting up a Price Database
  • Module 05: Decision Trees, Ensemble Learning & Random Forests
  • Module 06: A Trading Strategy as Machine Learning Classification
  • Module 07: Feature Engineering
  • Module 08: Engineering a Complex Feature – A Categorical Variable with Past Trends
  • Module 09: Building a Machine Learning Classifier in Python
  • Module 10: Nearest Neighbors Classifier
  • Module 11: Gradient Boosted Trees
  • Module 12: Introduction to Quant Trading

How will I be assessed?

  • You will have one assignment. Pass mark is 65%.
  • You will only need to pay £19 for assessment.
  • You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days.

What Certification am I going to receive?

Those who successfully pass this course will be awarded a Machine Learning – Quant Trading certificate. Anyone eligible for certification will receive a free e-certificate, and printed certificate.

What careers can I get with this qualification?

Once you have completed this Machine Learning – Quant Trading course you will have desirable skills. You could go on to further study of Quant Trading and machine learning, or could gain entry level employment in this area. These roles often command a high salary, for example, the average salary of a Quantitative Developer in the UK is £66,963 (payscale.com). When you complete this Machine Learning – Quant Trading, you could fulfil any of the following roles:

  • Quantitative Developer
  • Software Developer
  • Python Developer
  • Quant Strategist
  • Quant Analyst

Key Features

Gain an accredited UK qualification

Access to excellent quality study materials

Learners will be eligible for TOTUM Discount Card

Personalized learning experience

One year’s access to the course

Support by phone, live chat, and email

Course Curriculum Total Units : 66
1: You, This Course & Us
1. Introduction – You, This Course & Us!
2: Developing Trading Strategies in Excel
1. Are markets efficient or inefficient?
2. Momentum Investing
3. Mean Reversion
4. Evaluating Trading Strategies – Risk & Return
5. Evaluating Trading Strategies – The Sharpe Ratio
6. The 2 Step process – Modeling & Backtesting
7. Developing a Trading Strategy in Excel
3: Setting up your Development Environment
1. Installing Anaconda for Python
2. Installing Pycharm – a Python IDE
3. MySQL Introduced & Installed (Mac OS X)
4. MySQL Server Configuration & MySQL Workbench (Mac OS X)
5. MySQL Installation (Windows)
6. [For Linux/Mac OS Shell Newbies] Path & other Environment Variables
4: Setting up a Price Database
1. Programmatically Downloading Historical Price Data
2. Code Along – Downloading Price data from Yahoo Finance
3. Code Along – Downloading a URL in Python
4. Code Along – Downloading Price data from the NSE
5. Code Along – Unzip & process the downloaded files
6. Manually download data for 10 years
7. Code Along – Download Historical Data for 10 years
8. Inserting the Downloaded files into a Database
9. Code Along – Bulk loading downloaded files into MySQL tables
10. Data Preparation
11. Code Along – Data Preparation
12. Adjusting for Corporate Actions
13. Code Along – Adjusting for Corporate Actions 1
14. Code Along – Adjusting for Corporate Actions 2
15. Code Along – Inserting Index prices into MySQL
16. Code Along – Constructing a Calendar Features table in MySQL
5: Decision Trees, Ensemble Learning & Random Forests
1. Planting the seed – What are Decision Trees?
2. Growing the Tree – Decision Tree Learning
3. Branching out – Information Gain
4. Decision Tree Algorithms
5. Overfitting – The Bane of Machine Learning
6. Overfitting Continued
7. Cross-Validation
8. Regularization
9. The Wisdom Of Crowds – Ensemble Learning
10. Ensemble Learning continued – Bagging, Boosting & Stacking
11. Random Forests – Much more than trees
6: A Trading Strategy as Machine Learning Classification
1. Defining the problem – Machine Learning Classification
7: Feature Engineering
1. Know the basics – A Pandas tutorial
2. Code Along – Fetching Data from MySQL
3. Code Along – Constructing some simple features
4. Code Along – Constructing a Momentum Feature
5. Code Along – Constructing a Jump Feature
6. Code Along – Assigning Labels
7. Code Along – Putting it all together
8. Code Along – Include support features from other tickers
8: Engineering a Complex Feature – A Categorical Variable with Past Trends
1. Engineering a Categorical Variable
2. Code Along – Engineering a Categorical Variable
9: Building a Machine Learning Classifier in Python
1. Introducing Scikit-Learn
2. Introducing RandomForestClassifier
3. Training & Testing a Machine Learning Classifier
4. Compare Results from different Strategies
5. Using Class probabilities for predictions
10: Nearest Neighbors Classifier
1. A Nearest Neighbors Classifier
2. Code Along – A nearest neighbors Classifier
11: Gradient Boosted Trees
1. What are Gradient Boosted Trees?
2. Introducing XGBoost – A Python library for GBT
3. Code Along – Parameter Tuning for Gradient Boosted Classifiers
12: Introduction to Quant Trading
1. Financial Markets – Who are the players?
2. What is a Stock Market Index?
3. The Mechanics of Trading – Long Vs Short positions
4. Futures Contracts
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