Practical Data Science: Analyzing Stock Market Data with R
What you'll learn
- Use R on stock market data for insight and ideas
- Download free, daily stock market data from Yahoo
- Plot great looking financial charts
- Apply basic technical analysis on stock market data
- Explore trading ideas and display entries and exits
- Gain additional insights by comparing similar stocks
Course content
- Preview01:36
- Preview06:35
Requirements
- Basic understanding of R
- Access to R Console or RStudio
- Interest in stock-market data
Description
In this class, we will explore various technical and quantitative analysis techniques using the R programming language. I will code as I go and explain what I am doing. All the code is included in PDFs attached to each lecture. I encourage you to code along to not only better understand the concepts but realize how easy they are.
What We'll Cover
- Easily access free, stock-market data using R and the quantmod package
- Build great looking stock charts with quantmod
- Use R to manipulate time-series data
- Create a moving average from scratch
- Access technical indicators with the TTR package
- Create a simple trading systems by shifting time series using the binhf package
- A look at trend-following trading systems using moving averages
- A look at counter-trend trading systems using moving averages
- Using more sophisticated indicators (ROC, RSI, CCI, VWAP, Chaikin Volatility)
- Grouping stocks by theme to better understand them
- Finding coupling and decoupling stocks within an index
What This Class Isn't
This class isn't about telling you how to trade or revealing secret trading methods, but to show how easy it is to explore the stock market using R so you can come up with your own ideas.
Who this course is for:
- Those looking to expand their R skills on stock market data
- Those looking to come up with their own conclusions about the markets
- NOT for those seeking easy stock tips or secret trading systems
- NOT a solicitation to trade - trading is difficult, learn as much as you can before risking real money
- NO guarantee that past historical strategies will work on future events
Instructor
![Manuel Amunategui](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.4 Instructor Rating
- 1,150 Reviews
- 32,030 Students
- 12 Courses
Data scientist with over 20-years experience in the tech industry, MAs in Predictive Analytics and International Administration, author of Monetizing Machine Learning and The Little Book of Fundamental Indicators, founder of FastML, reached top 1% on Kaggle and awarded "Competitions Expert" title, taught over 20,000 students on Udemy and VP of Data Science at SpringML.
From consulting in machine learning, healthcare modeling, 6 years on Wall Street in the financial industry, and 4 years at Microsoft, I feel like I’ve seen it all. And this has opened my eyes to the huge gap in educational material on applied data science. Like I say:
"It just ain’t real 'til it reaches your customer’s plate"
I am a startup advisor and available for speaking engagements with companies and schools on topics around building and motivating data science teams, and all things applied to machine learning.
Reach me at amunategui@gmail.com