Practical Data Science: Analyzing Stock Market Data with R
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Practical Data Science: Analyzing Stock Market Data with R

Learn basic financial technical analysis technics using R (quantmod, TTR) to better understand your favorites stocks.
Bestselling
4.3 (111 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
795 students enrolled
Last updated 11/2015
English
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Includes:
  • 4 hours on-demand video
  • 1 Article
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What Will I 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
View Curriculum
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

  1. Easily access free, stock-market data using R and the quantmod package
  2. Build great looking stock charts with quantmod
  3. Use R to manipulate time-series data
  4. Create a moving average from scratch
  5. Access technical indicators with the TTR package
  6. Create a simple trading systems by shifting time series using the binhf package
  7. A look at trend-following trading systems using moving averages
  8. A look at counter-trend trading systems using moving averages
  9. Using more sophisticated indicators (ROC, RSI, CCI, VWAP, Chaikin Volatility)
  10. Grouping stocks by theme to better understand them
  11. 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 is the target audience?
  • 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
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Curriculum For This Course
Expand All 18 Lectures Collapse All 18 Lectures 04:03:30
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Introduction
2 Lectures 08:11

This is an optional video explaining where to find the binaries for R and RStudio needed to follow this course.

Preview 06:35
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Downloading Free Stock Market Data with R
1 Lecture 12:47
  • Installing quantmod
  • Downloading stock market data
  • Downloading multiple symbols at once
  • Merging multiple symbols into one data frame
Preview 12:47
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Creating Amazing Stock Charts with quantmod
3 Lectures 30:59

We'll see how in one line of code we can create professional-looking chart stocks

Creating great charts with quantmod
11:23

Add complex indicators from the TTR package on a chart.. Get familiar with creating custom indicators and adding them to charts.

Adding Indicators to quantmod charts
14:03

An easy way to create documents of stock market charts in HTML.You can use these for printing, sharing, and saving in a PDF format.

Creating an R Markdown file to display all your charts in one document
05:33
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Applying Technical Analysis Indicators
6 Lectures 01:48:13

We'll create a simple moving average (SMA) from scratch to better understand how technical indicators work. We'll then look and analyze the equivalent indicator available from the TTR package.

Creating a simple moving average (SMA) from scratch
19:52

Use multiple moving averages to analyze different time frames and look for potential entries and exits. De-trend two moving averages to quantify bullish and bearish periods.

Following the trend with multiple moving averages
19:53

We experiment with a few setting changes on our previous multiple moving average systems and analyze their effects.

More insights from multiple moving averages
19:13

A look at trend-following systems and at both the Welles Wilder's Directional Movement Indicator (ADX) and the Volume-Weighted Average Price (VWAP)

Insight from Common indicators - ADX & VWAP
19:49

A look at counter-trend systems, by going against the short-term trend when contrary to the long-term trend. We'll also look at the look at the Relative Strength Index (RSI), the Commodity Channel Index (CCI), the rate of change (ROC), and the Chaikin Volatility indicators.

Counter-trend systems - ROC, RSI, CCI, Chaikin Volatility
18:48

Optional: Counter-trend systems - tweaks
10:38
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Tracking Profit and Loss for Fun!
1 Lecture 17:33

We will add exits to our earlier systems and create a function to track profits and losses. We will also visualize our entries and exits on charts.

Evaluating our trend-following systems
17:33
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Analyzing Stocks in Groups
4 Lectures 01:05:17

We will add exits to our earlier systems and create a function to track profits and losses. We will also visualize our entries and exits on charts.

Evaluating counter-trend systems
12:05

We'll analyze a small basket of stocks reflected in the QQQ index. We'll also look at percent of times each stock move in the direction of the index.

Safety in Numbers: Basket Analysis
17:13

We'll look at using the correlation function on our market data and splitting by various time periods.

Correlation analysis
18:25

Applying correlations to entries and other experiments.
Applying correlations to entries
17:34
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Conclusions
1 Lecture 00:29
Closing notes
00:29
About the Instructor
Manuel Amunategui
4.5 Average rating
303 Reviews
2,477 Students
4 Courses
Data Scientist & Quantitative Developer

I am data scientist in the healthcare industry. I have been applying machine learning and predictive analytics to better patients lives for the past 3 years. Prior to that I was a developer on a trading desk on Wall Street for 6 years. On the personal side, I love data science competitions and hackathons - people often ask me how can one break into this field, to which I reply: 'join an online competition!'