Analyzing Stock Market Data with R – Practical Data Science

You are currently accessing the institutional-grade blueprint for <b>Analyzing Stock Market Data with R – Practical Data Science</b>. Instant digital deployment and lifetime encrypted access are guaranteed immediately upon transaction clearance.

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 this course 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

What you will 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

Develop all 18 sessions04:03:29

–Introduction

08:11

What is covered in this class

Preview1:36

Optional: R Console or RStudio?

Preview06:35

+Downloading Free Stock Market Data with R

1 session12:47

+Creating Amazing Stock Charts with quantmod

3 sessions30:59

+Applying technical analysis Indicators

6 sessions01:48:13

+Tracking Profit and Loss for Fun!

1 session17:33

+Analyzing Stocks in Groups

4 sessions01:05:17

+Conclusions

1 session00:30

Prerequisites

• Basic understanding of R
• Access to R Console or RStudio
• Interest in stock-market data

Stock trading course: Learn about Stock trading

A stock trader or equity trader or share trader is a person or company involved in trading equity securities.

Stock traders may be an agent, hedger, arbitrageur, speculator, stockbroker.

Such equity trading in large publicly traded companies may be through a stock exchange.

Stock shares in smaller public companies may be bought and sold in over-the-counter (OTC) markets.

Stock traders can trade on their own account, called proprietary trading, or through an agent authorized to buy and sell on the owner’s behalf.

Trading through an agent is usually through a stockbroker. Agents are paid a commission for performing the trade.

Major stock exchanges have market makers who help limit price variation (volatility) by buying and selling a particular company’s shares on their own behalf and also on behalf of other clients.

Get Analyzing Stock Market Data with R – Practical Data Science , Only Price $15

---

Tag: Analyzing Stock Market Data with R – Practical Data Science  Review. Analyzing Stock Market Data with R – Practical Data Science download. Analyzing Stock Market Data with R – Practical Data Science  discount.

Trading foreign exchange, cryptocurrencies, and algorithmic assets on margin carries a high level of risk and may not be suitable for all investors. Past performance of any trading system or quantitative blueprint does not guarantee future results.<br><br><b>Asset Classification:</b>