Quant Trading Using Machine Learning

Quant Trading Using Machine Learning

1 Review
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11 Hours
Deal Price$15.00
Suggested Price
$99.00
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Quant Trading Using Machine Learning
1 Review
$15.00$99.0084% OFF
Quant Trading Using Machine Learning

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
Quant Trading Using Machine Learning
1 Review
$15.00$99.0084% OFF
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Play the Markets Like a Pro After 11 Hours of Integrating Machine Learning into Your Investment Strategies

L
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.
RW

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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