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Dakota Wixom – Become a Quantitative Analyst

Original price was: ₹74,100.00.Current price is: ₹8,300.00.

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What is this Course About?
Wall Street needs more quants and data scientists.
Dakota Wixom – Become a Quantitative Analyst
This course will allow you to build the essential initial programming skills and tool belt of statistical techniques required for quantitative analysis.
First, we’ll teach you how to program with financial timeseries before diving deep into multivariate regressions using factor analysis to explain Berkshire Hathaway’s performance.
Next, we’ll examine the performance of 9 different hedge fund strategies and compare the risk and return characteristics of each type of fund.
Finally, we’ll construct our own fund strategy using quadratic optimization to track a benchmark on a rolling basis, and we’ll build our own backtesting engine in R to analyze our strategy.

Am I Ready for this Course?
Whether you’re a hedge fund manager or a business student, this course is for you if you’re looking to upgrade your game and begin investing intelligently.
We’ll provide you with commented source code, guided video tutorials and high quality animations to help you understand every line of code and concept.
Become a Quant.

Course Curriculum

Getting up to Speed with Financial Programming in R

Start
Getting Started With R

Start
R Financial Programming Bootcamp (1:05:29)

Analyzing Hedge Fund Strategy Performance

Start
Hedge Fund Strategy Indices | Downloading Data From Quandl (12:11)

Start
Beating the Market or Not | Analyzing Hedge Fund Performance (20:48)

Multivariate Rolling Regressions | How Does Warren Buffett Do It?

Start
Market Factors | Setting Up the Multivariate Rolling Regression (25:30)

Start
Warren Buffett vs. The Fama-French Factor Model (19:23)

Start
Analyzing Warren Buffett’s Sector Exposure (11:01)

 

Construct Your Own Index Fund Strategy and Backtesting Engine

Preview
FREE: Example Custom Index Strategy Reports

Start
Picking a Benchmark | Dynamically Downloading the Datasets (16:43)

Start
Quadratic Optimization | Building a Rolling Backtesting Engine (24:37)

Start
Visualizing the Results | Tracking New Benchmarks (23:55)

Start
Calculating Portfolio Turnover | Implementing a Transaction Cost Model (12:05)

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