Quant Trading Using Machine Learning

Name: Quant Trading Using Machine Learning
Brand: Loonycorn
Price: 15.00 USD
Availability: Discontinued
Rating: 3 (1 reviews)

1 Review

972 Enrolled

11 Hours

$15.00$99.00

You save 84%

64 Lessons (11h)

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

DescriptionInstructorImportant DetailsReviewsRelated Products

Play the Markets Like a Pro After 11 Hours of Integrating Machine Learning into Your Investment Strategies

Loonycorn

Instructor

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

All featured courses are designed for educational purposes only and do not reflect our views or recommendations. Please note that all course purchasers invest at their own risk.

Specs

Important Details

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

Requirements

Internet required

Terms

Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.

1 Review

3/ 5

All reviews are from verified purchasers collected after purchase.

Reginald Williams

Verified Buyer

Informative. Looking forward to more in depth info. Content is quite teasing and keeps me yearning for more

May 20, 2020

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