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

[1] "MCD"

structure of the stock data

stock <<- as.data.table(MCD)        ###### 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'))

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

Price-Moving average is a technical analysis indicator.There are two types when calculating PMA: SMA ans EMA. We can see datas from january 2020 to september 2020. Volume in millions was 2,777,700.Sells and buys are visible. It’s a bit difficult to interpret because I’m seeing this for the first time, but the short term average intersects the long term average six times during this period. The biggest fluctuation is in March.

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")

The next technical analysis indicator is Moving Average Convergence Divergence. The gray line is the MACD and the red dashed line is the signal. So we know that the buy signal arises when MACD crosses from below to above the signal line, and Sell signal arises when MACD crosses from above to below the signal line. Buy signals are approximately in: March 25th, May 20th and July 1st. Sell signals are approximately in: May 1st, June 10th and July 28th.

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)

We know that the RSI oscillates between 0 and 100, with readings above 70 indicating that the asset is overbought, and readings below 30 indicating that the asset is oversold. The diagram shows that the asset is overbought between January and February, at the beginning of June and after that between August and September. The asset is oversold in this peroid only once- between March and April.

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")

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NA

When the price is moving upwards, the SAR dots are placed below the price, and when the price is moving downwards, the SAR dots are placed above the price.The diagram is constantly changing, once the current increases and once it decreases

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")