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Complete guide for Quants Trading using Machine Learning

Created by Stanford and IIT alumni with work experience in Google and Microsoft, this course teaches techniques of Machine Learning that can be combined with Quantitative Trading to design powerful models.

10h:56m
Lifetime access
624 learners
Introduction to the Course

Quantitative Trading consists of trading strategies that are formed after quantitatively analysing data, while Machine Learning is a data analysis methodology that automates analytical model building. This course explains how the approaches of Machine Learning can be combined with Quantitative Trading to design powerful Quant Trading models.

This course on Quants Trading in Machine Learning introduces you to the basics and the traditional ways to develop Quants Trading, like Momentum Strategy and Mean Revision. It consists of a variety of Machine Learning Techniques right from the basics ones like Decision Trees and K-Nearest Neighbors to advanced techniques like Gradient Boosted Classifiers and Random Forests.

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Course Objectives

What will you gain from this course?

  • An understanding on developing Quants Trading models using advanced Machine Learning Techniques
  • Knowledge on how to compare and evaluate strategies using Sharpe Ratios
  • The ability to use approaches like Random Forests and K-Nearest Neighbors in developing Quant trading models
  • Techniques to use Gradient Boosted trees and tune them to achieve a high performance

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Prerequisites and Target Audience

Having basic knowledge of Machine Learning will make this course easier for you to understand. If you want to run the source code at the end of this course, you will require the basic knowledge of Python. This course is for:

  • Analytics professionals big data professionals, and modelers who require/are looking to get a hands on experience with Machine Learning
  • Quants traders who want to use Machine Learning techniques to develop trading strategies
  • Anyone who wants to learn Machine Learning through a practical approach

Course Plan
Certificate of completion

1. Introduction to Quant Trading
4 videos
Financial Markets - Who are the players? 16:38

What is a Stock Market Index? 03:13

The Mechanics of Trading - Long vs Short positions 11:56

Futures Contracts 14:25
2. Developing Trading Strategies in Excel
7 videos
The 2 Step process - Modeling and Backtesting 03:48

Developing a Trading Strategy in Excel 11:42
3. Setting up your Development Environment
5 videos
MySQL Server Configuration and MySQL Workbench (Mac OS X) 17:32

MySQL Installation (Windows) 06:31
4. Setting up a Price Database
15 videos
Programmatically Downloading Historical Price Data 06:23

CodeAlong - Dowloading Price data from Yahoo Finance 14:39

CodeAlong - Downloading a URL in Python 07:38

CodeAlong - Downloading Price data from the NSE 13:55

CodeAlong - Unzip and process the downloaded files 05:21

Manually download data for 10 years

CodeAlong - Download Historical Data for 10 years 06:26

Inserting the Downloaded files into a Database 10:10

CodeAlong - Bulk loading downloaded files into MySQL tables 15:12

Data Preparation 04:16

CodeAlong - Data Preparation 12:43

Adjusting for Corporate Actions 08:41

CodeAlong - Adjusting for Corporate Actions 1 15:29

CodeAlong - Adjusting for Corporate Actions 2 08:47

CodeAlong - Inserting Index prices into MySQL 05:40

CodeAlong - Constructing a Calendar Features table in MySQL 06:53
5. Decision Trees, Ensemble Learning and Random Forests
11 videos
Planting the seed - What are Decision Trees? 17:00

Growing the Tree - Decision Tree Learning 18:03

Branching out - Information Gain 18:51

Decision Tree Algorithms 07:50

Overfitting - The Bane of Machine Learning 19:03

Overfitting Continued 11:19

Cross Validation 18:55

Regularization 07:18

The Wisdom Of Crowds - Ensemble Learning 16:39

Ensemble Learning continued - Bagging, Boosting and Stacking 18:02

Random Forests - Much more than trees 12:28
6. A Trading Strategy as Machine Learning Classification
1 video
Defining the problem - Machine Learning Classification 15:51
7. Feature Engineering
8 videos
Know the basics - A Pandas tutorial 11:41

CodeAlong - Fetching Data from MySQL 18:34

CodeAlong - Constructing some simple features 07:27

CodeAlong - Constructing a Momentum Feature 08:42

CodeAlong - Constructing a Jump Features 05:52

CodeAlong - Assigning Labels 03:12

CodeAlong - Putting it all together 18:08

CodeAlong - Include support features from other tickers 06:34
8. Engineering a Complex Feature - A Categorical Variable with Past Trends
2 videos
Engineering a Categorical Variable 03:49

CodeAlong - Engineering a Categorical Variable 06:46
9. Building a Machine Learning Classifier in Python
5 videos
Introducing Scikit-Learn 03:33

Introducing RandomForestClassifier 09:25

Training and Testing a Machine Learning Classifier 15:01

Compare Results from different Strategies 05:44

Using Class probabilities for predictions 03:11
10. Nearest Neighbors Classifier
2 videos
A Nearest Neighbors Classifier 06:49

CodeAlong - A nearest neighbors Classifier 04:16
11. Gradient Boosted Trees
3 videos
What are Gradient Boosted Trees? 12:38

Introducing XGBoost - A python library for GBT 11:51

CodeAlong - Parameter Tuning for Gradient Boosted Classifiers 09:21

Meet the Author


Loonycorn
4 Alumni of Stanford, IIM-A, IITs and Google, Microsoft, Flipkart

Loonycorn is a team of 4 people who graduated from reputed top universities. Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh have spent years (decades, actually) working in the Tech sector across the world.

  • Janani: Graduated from Stanford and has worked for 7 years at Google (New York, Singapore). She also worked at Flipkart and Microsoft.
  • Vitthal: Studied at Stanford; worked at Google (Singapore), Flipkart, Credit Suisse, and INSEAD.
  • Swetha: An IIM Ahmedabad and IIT Madras alumnus having experience of working in Flipkart.
  • Navdeep: An IIT Guwahati alumnus and Longtime Flipkart employee.
  • More from Loonycorn
    Ratings and Reviews     4.3/5

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