Practical Data Science: Analyzing Stock Market Data with R

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

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

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.

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.

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.

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

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

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.

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.

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.

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.

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