Technical analysis in R

Introduction

Alvin Tan created interesting Noteboook, which is partly used in this presentation. The Investopedia information source is also used. In addition, a Bookdown of Technical Analysis with R (second edition) from Ko Chiu Yu and ChatGPT were helpful with the coding in R.

Technical analysis is a method to predict price movement in the financial markets. Unlike the usual balance sheet analysis (fundamental investing), technical analysis estimates the value of a given stock using underlying trends of the price movement. The core assumption behind this method is that the fundamentals (information from financial statements) were factored into the price fluctuation, hence, detecting the patterns and signals from the fluctuation should provide sufficient indicator for future performance.

For this project, we are going to explore some of the common technical indicators using the TQQQ Exchange trade fund (ETF) stock. Among leveraged ETFs, ProShares UltraPro QQQ (TQQQ) is one of the largest with assets under management of \(\$18.56\) billion as of July 2022. TQQQ is also one of the more heavily traded leveraged ETFs in the U.S. with an average daily volume of \(\$5.29\) billion (compared with QQQ’s \(\$21\) billion).

TQQQ carries an expense ratio of 0.95%.

#install.packages("quantmod")
#install.packages("data.table")
#install.packages("ggplot2")
#install.packages("tidyquant")
library(quantmod)
library(data.table)
library(ggplot2)
library(tidyquant)  

Data Extraction & Manipulation, Data structure

TQQQ data is extracted using the getSymbols function from Yahoo Finance. Alternatively, the source can be replaced with FRED, MySQL, Google, or others. The data extracted should be in an ‘xts’ object - a format commonly used in time series analysis where observations are recorded in a matrix structure with an index of corresponding time.

Technically, analysis can be done in xts format alone, but for the sake of simplicity, data is converted into a standard dataframe and index column is renamed as “Date’. The end result is shown as below:

getSymbols('MSFT',src = 'yahoo')       ## change TQQQ ticker to Your ticker symbol

[1] "MSFT"

structure of the stock data

stock <<- as.data.table(MSFT)        ###### change TQQQ ticker to Your ticker symbol  #########
names(stock)[1] <- c('Date')

tail(stock)

Interpretation of the HLCO graph

In finance, HLCO graph is a type of financial chart that plots the high, low, close, and open prices for a given period of time. The HLCO chart is similar to a candlestick chart, but it includes the open price in addition to the high, low, and close prices.

The interpretation of an HLCO chart can provide insights into the price action of a security during a given period of time. Here is a brief explanation of how to interpret each component of an HLCO chart:

1. High: The top of the vertical line represents the highest price reached during the given period. This shows the highest price that buyers were willing to pay for the security during the period.

2. Low: The bottom of the vertical line represents the lowest price reached during the given period. This shows the lowest price that sellers were willing to accept for the security during the period.

3. Close: The horizontal line to the right of the vertical line represents the closing price for the period. This shows the final price at which the security traded during the period.

4. Open: The horizontal line to the left of the vertical line represents the opening price for the period. This shows the initial price at which the security traded during the period.

By looking at the patterns of the high, low, close, and open prices in an HLCO chart, traders and investors can gain insight into the overall trend and sentiment of the market for the given security. For example, a long green candlestick with a high close and low open may indicate bullish sentiment, while a long red candlestick with a low close and high open may indicate bearish sentiment.

# feel free to change "HLCO" to "candlestick" or to some other types ("lines", "auto"). 
chartSeries(stock, subset='2020-01-01::2020-09-23', type = "candlesticks", bar.type = "ohlc",theme=chartTheme('white'))

My analysis is based on Microsoft. The code of this company is MSFT. I used two different charts to carry out the analysis: the first is the “HLCO chart” and the second is the last chart of the ” Price - Moving Average “.

The HLCO chart is a chart that shows the high, low, close and open prices for the trading periods (usually on a daily time frame). These prices represent the fundamental characteristics of the price movement of a particular security (e.g. a stock). The trading period is observed in the chart from 2 January 2020 to 23 September 2020. After the open, the value of Microsoft shares was around $160million. In the following periods, it started to rise slightly, reaching roughly 165 million at the end of January. Then a tiny drop was seen in early February to 160 million. After that, we see a sharp rise until around the beginning of March, when it reached $190 million. From the beginning of March, the share price started to fluctuate continuously, with the result that by about mid-April the share price had reached its low point, i.e. it had fallen to around 125 million. Thereafter, a steady rise was seen until the beginning of July, after which a slight decline was seen. By the beginning of September, the share reaches the high point of almost 240 million dollars. Then it starts to fall again.

Volume

One of the most common basic indicators traders examine is the trading volume. Trading volume is an indication for the ‘activeness’ of a financial instrument. Depending on the financial instruments, trading volume can be measured either using the number of stocks traded or number of contracts with changed ownerships. To put this in practice, if an increase in volume is observed with a steady increase in price, the instrument can be viewed as steady and strong. However, if volume and price are changing in different directions, a reversal might be happened.

You can analyze it in the previous picture.

Price - Moving Average

chartSeries(stock,
            subset='2020-01-01::2020-09-23', type = "candlestick",
            theme=chartTheme('white'))

addSMA(n = 12, on = 1, with.col = Cl, overlay = TRUE, col = "brown")

addSMA(n = 24, on = 1, with.col = Cl, overlay = TRUE, col = "blue")

NA
NA

Here you can see my next analysis, which is the Moving Avarage. The period shown in the figure is also from 2 January 2020 to 23 August 2020. After the opening, an increase in the market can be seen. The blue line crosses the red line around the beginning of March. Then a decline is seen until mid April, where the two lines, red and blue, cross again. Then the market starts a sharp rise until mid-July. Then a small decline is seen until around the beginning of August. Then it starts to rise again and reaches a peak in early September and then starts to fall again.

Moving Average Convergence Divergence (MACD)

chartSeries(stock,
            subset='2020-01-01::2020-09-23', type = "bars",
            theme=chartTheme('white'))

addMACD(fast=12,slow=26,signal=9,type="EMA")

Bollinger Band

chartSeries(stock,
            subset='2020-01-01::2020-09-23', type = "bars",
            theme=chartTheme('white'))

addBBands(n = 20, sd = 2, maType = "SMA", draw = 'bands', on = -1)

Relative Strength Index (RSI)

chartSeries(stock,
            subset='2020-01-01::2020-09-23', type = "bars",
            theme=chartTheme('white'))

addRSI(n = 14, maType = "EMA", wilder = TRUE)

Parabolic stop and reversal

chartSeries(stock,
            subset='2020-01-01::2020-09-23', type = "bars",
            theme=chartTheme('white'))

addSAR(accel = c(0.02, 0.2), col = "blue")

NA
NA

Summary graphs

Apart from the few that was mentioned above, other indicators include rate of change, money flow index (MFI), MACD and etc.

Chart Series

ChartSeries provides an alternative way to summarise the plot. Instead of one plot for each indicator, all of them can now be compiled into a single figure, where indicators can be added to the bottom of the existing plot.

chartSeries(stock,
            subset='2020-01-01::2020-09-23', type = "bars",
            theme=chartTheme('white'))

addBBands(n=20,sd=2)

addRSI(n=14,maType="EMA")

addMACD(fast=12,slow=26,signal=9,type="EMA")