The Complete Machine Learning Bundle

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$99.00 Value

Quant Trading Using Machine Learning

Loonycorn

64 Lessons (11h)

Lifetime

Certification included
Experience level required: All levels
Access 64 lectures & 11 hours of content 24/7
Length of time users can access this course: Lifetime

Course Curriculum

64 Lessons (11h)

You, This Course and Us
You, This Course and Us2:00
Setting up your Development Environment
Installing Anaconda for Python9:00
Installing Pycharm - a Python IDE3:55
MySQL Introduced and Installed (Mac OS X)7:03
MySQL Server Configuration and MySQL Workbench (Mac OS X)17:32
MySQL Installation (Windows)6:31
Introduction to Quant Trading
Financial Markets - Who are the players?16:38
What is a Stock Market Index?3:13
The Mechanics of Trading - Long vs Short positions11:56
Futures Contracts14:25
Evaluating Trading Strategies - Risk And Return16:22
Evaluating Trading Strategies - The Sharpe Ratio10:16
The 2 Step process - Modeling and Backtesting3:48
Developing Trading Strategies in Excel
Are markets efficient or inefficient?10:27
Momentum Investing11:31
Mean Reversion6:30
Developing a Trading Strategy in Excel11:42
Setting up a Price Database
Programmatically Downloading Historical Price Data6:24
CodeAlong - Dowloading Price data from Yahoo Finance14:39
CodeAlong - Downloading a URL in Python7:38
CodeAlong - Downloading Price data from the NSE13:55
CodeAlong - Unzip and process the downloaded files5:22
CodeAlong - Download Historical Data for 10 years6:26
Inserting the Downloaded files into a Database10:10
CodeAlong - Bulk loading downloaded files into MySQL tables15:12
Data Preparation4:16
CodeAlong - Data Preparation12:43
Adjusting for Corporate Actions8:41
CodeAlong - Adjusting for Corporate Actions 115:29
CodeAlong - Adjusting for Corporate Actions 28:47
CodeAlong - Inserting Index prices into MySQL5:40
CodeAlong = Constructing a Calendar Features table in MySQL6:53
Decision Trees, Ensemble Learning and Random Forests
Planting the seed - What are Decision Trees?17:00
Growing the Tree - Decision Tree Learning18:03
Branching out - Information Gain18:51
Decision Tree Algorithms7:50
Overfitting - The Bane of Machine Learning19:03
Overfitting Continued1:42
Cross Validation18:55
Regularization7:18
The Wisdom Of Crowds - Ensemble Learning16:39
Ensemble Learning continued - Bagging, Boosting and Stacking18:03
Random Forests - Much more than trees12:28
A Trading Strategy as Machine Learning Classification
Defining the problem - Machine Learning Classification15:51
Feature Engineering
Know the basics - A Pandas tutorial11:42
CodeAlong - Fetching Data from MySQL18:34
CodeAlong - Constructing some simple features7:27
CodeAlong - Constructing a Momentum Feature8:42
CodeAlong - Constructing a Jump Feature5:52
CodeAlong - Assigning Labels3:12
CodeAlong - Putting it all together18:08
CodeAlong - Include support features from other tickers6:34
Engineering a Complex Feature - A Categorical Variable with Past Trends
Engineering a Categorical Variable3:49
CodeAlong - Engineering a Categorical Variable6:46
Building a Machine Learning Classifier in Python
Introducing Scikit-Learn3:33
Introducing RandomForestClassifier9:25
Training and Testing a Machine Learning Classifier15:01
Compare Results from different Strategies5:44
Using Class probabilities for predictions3:11
Nearest Neighbors Classifier
A Nearest Neighbors Classifier6:49
CodeAlong - A nearest neighbors Classifier4:16
Gradient Boosted Trees
What are Gradient Boosted Trees?12:38
Introducing XGBoost - A python library for GBT11:51
CodeAlong - Parameter Tuning for Gradient Boosted Classifiers9:21

Quant Trading Using Machine Learning

Loonycorn

Loonycorn is comprised of four individuals--Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh--who have honed their tech expertises at Google and Flipkart. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students. For more details on the course and instructor, click here.

Description

Financial markets are fickle beasts that can be extremely difficult to navigate for the average investor. This 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!

Access 64 lectures & 11 hours of content 24/7
Get a crash course in quantitative trading from stocks & indices to momentum investing & backtesting
Discover machine learning principles like decision trees, ensemble learning, random forests & more
Set up a historical price database in MySQL using Python
Learn Python libraries like Pandas, Scikit-Learn, XGBoost & Hyperopt
Access source code any time as a continuing resource

Specs

Details & Requirements

Length of time users can access this course: lifetime access
Access options: web streaming, mobile streaming
Certification of completion not included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels, but working knowledge of Python would be helpful

Compatibility