Machine Learning – Quant Trading

$35.00

Play the Markets Like a Pro by Integrating Machine Learning into Your Investment Strategies.

This ’Quant Trading Using Machine Learning’ online training course takes a completely practical approach to applying Machine Learning techniques to Quant Trading. The focus is on practically applying ML techniques to develop sophisticated Quant Trading models. From setting up your own historical price database in MySQL, to writing hundreds of lines of Python code, the focus is on doing from the get-go.   Supplemental Material included!

Length: 11 hrs

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Description

Financial markets are fickle beasts that can be extremely difficult to navigate for the average investor. This Quant Trading Using Machine Learning course will introduce you to machine learning, a field of study that gives computers the ability to learn without being explicitly programmed, while teaching you how to apply these techniques to quantitative trading. Using Python libraries, you’ll discover how to build sophisticated financial models that will better inform your investing decisions. Ideally, this one will buy itself back and then some!

What am I going to get from this course?

  • Develop Quant Trading models using advanced Machine Learning techniques
  • Compare and evaluate strategies using Sharpe Ratios
  • Use techniques like Random Forests and K-Nearest Neighbors 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

Course Requirements:

  • Working knowledge of Python is necessary if you want to run the source code that is provided.
  • Basic knowledge of machine learning, especially Machine Learning classification techniques, would be helpful but it’s not mandatory.

 

What is the target audience?

  • Quant traders who have not used Machine learning techniques before to develop trading strategies
  • Analytics professionals, modelers, big data professionals who want to get hands-on experience with Machine Learning
  • Anyone who is interested in Machine Learning and wants to learn through a practical, project-based approach

 

Sample clip

 

Chapter 01: You, This Course & Us

Lesson 01: Introduction – You, This Course & Us!

Chapter 02: Developing Trading Strategies in Excel

Lesson 01: Are markets efficient or inefficient?

Lesson 02: Momentum Investing

Lesson 03: Mean Reversion

Lesson 04: Evaluating Trading Strategies – Risk & Return

Lesson 05: Evaluating Trading Strategies – The Sharpe Ratio

Lesson 06: The 2 Step process – Modeling & Backtesting

Lesson 07: Developing a Trading Strategy in Excel

Chapter 03: Setting up your Development Environment

Lesson 01: Installing Anaconda for Python

Lesson 02: Installing Pycharm – a Python IDE

Lesson 03: MySQL Introduced & Installed (Mac OS X)

Lesson 04: MySQL Server Configuration & MySQL Workbench (Mac OS X)

Lesson 05: MySQL Installation (Windows)

Lesson 06: [For Linux/Mac OS Shell Newbies] Path & other Environment Variables

Chapter 04: Setting up a Price Database

Lesson 01: Programmatically Downloading Historical Price Data

Lesson 02: Code Along – Downloading Price data from Yahoo Finance

Lesson 03: Code Along – Downloading a URL in Python

Lesson 04: Code Along – Downloading Price data from the NSE

Lesson 05: Code Along – Unzip & process the downloaded files

Lesson 06: Manually download data for 10 years

Lesson 07: Code Along – Download Historical Data for 10 years

Lesson 08: Inserting the Downloaded files into a Database

Lesson 09: Code Along – Bulk loading downloaded files into MySQL tables

Lesson 10: Data Preparation

Lesson 11: Code Along – Data Preparation

Lesson 12: Adjusting for Corporate Actions

Lesson 13: Code Along – Adjusting for Corporate Actions 1

Lesson 14: Code Along – Adjusting for Corporate Actions 2

Lesson 15: Code Along – Inserting Index prices into MySQL

Lesson 16: Code Along – Constructing a Calendar Features table in MySQL

Chapter 05: Decision Trees, Ensemble Learning & Random Forests

Lesson 01: Planting the seed – What are Decision Trees?

Lesson 02: Growing the Tree – Decision Tree Learning

Lesson 03: Branching out – Information Gain

Lesson 04: Decision Tree Algorithms

Lesson 05: Overfitting – The Bane of Machine Learning

Lesson 06: Overfitting Continued

Lesson 07: Cross-Validation

Lesson 08: Regularization

Lesson 09: The Wisdom Of Crowds – Ensemble Learning

Lesson 10: Ensemble Learning continued – Bagging, Boosting & Stacking

Lesson 11: Random Forests – Much more than trees

Chapter 06: A Trading Strategy as Machine Learning Classification

Lesson 01: Defining the problem – Machine Learning Classification

Chapter 07: Feature Engineering

Lesson 01: Know the basics – A Pandas tutorial

Lesson 02: Code Along – Fetching Data from MySQL

Lesson 03: Code Along – Constructing some simple features

Lesson 04: Code Along – Constructing a Momentum Feature

Lesson 05: Code Along – Constructing a Jump Feature

Lesson 06: Code Along – Assigning Labels

Lesson 07: Code Along – Putting it all together

Lesson 08: Code Along – Include support features from other tickers

Chapter 08: Engineering a Complex Feature – A Categorical Variable with Past Trends

Lesson 01: Engineering a Categorical Variable

Lesson 02: Code Along – Engineering a Categorical Variable

Chapter 09: Building a Machine Learning Classifier in Python

Lesson 01: Introducing Scikit-Learn

Lesson 02: Introducing RandomForestClassifier

Lesson 03: Training & Testing a Machine Learning Classifier

Lesson 04: Compare Results from different Strategies

Lesson 05: Using Class probabilities for predictions

Chapter 10: Nearest Neighbors Classifier

Lesson 01: A Nearest Neighbors Classifier

Lesson 02: Code Along – A nearest neighbors Classifier

Chapter 11: Gradient Boosted Trees

Lesson 01: What are Gradient Boosted Trees?

Lesson 02: Introducing XGBoost – A Python library for GBT

Lesson 03: Code Along – Parameter Tuning for Gradient Boosted Classifiers

Chapter 12: Introduction to Quant Trading

Lesson 01: Financial Markets – Who are the players?

Lesson 02: What is a Stock Market Index?

Lesson 03: The Mechanics of Trading – Long Vs Short positions

Lesson 04: Futures Contracts

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

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