Learning to use “Financial R Packages” including QuantMod
and TTR in FINA 4931: Topics in FinTech at Marquette University
While Martin Luther King Jr. Holiday and some significant winter
weather limited the class meeting time during the second week of the semester for FINA 4931: Topics in Financial
Technologies, the students were introduced to two financial R packages: QuantMod
and TTR. They have several R coding assignments due at the end of the week utilizing these packages.
The QuantMod package was originally designed to assist the
quantitative securities trader in the development, testing, and deployment of
statistically based trading models.
Getting data:
The most useful function in QuantMod is getSymbol(), which allows
to conveniently load data from several websites like YahooFinance,
GoogleFinance, FRED, etc. While Apple (AAPL) is used as the example below, any security with a ticker recognized by YahooFinance could be used. The basic R code follows:
> library(QuantMod)
>
getSymbols(c("AAPL", "GOOG"), from = "2018-07-01",
to = "2019-1-26")
#[1] "AAPL"
"GOOG"
> str(AAPL)
#An ‘xts’ object on 2018-07-02/2019-01-25 containing:
# Data: num [1:139, 1:6] 184 188 185 185 190 ...
# - attr(*,
"dimnames")=List of 2
# ..$ : NULL
# ..$ : chr [1:6] "AAPL.Open" "AAPL.High"
"AAPL.Low" "AAPL.Close" ...
# Indexed by objects of class: [Date] TZ: UTC
# xts Attributes:
# List of 2
# $ src : chr "yahoo"
# $ updated: POSIXct[1:1], format: "2019-01-25 18:11:21"
> head(AAPL)
# AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted
#2018-07-02 183.82 187.30 183.42 187.18 17731300 185.8773
#2018-07-03 187.79 187.95 183.54 183.92 13954800 182.6400
#2018-07-05 185.26 186.41 184.28 185.40 16604200 184.1096
#2018-07-06 185.42 188.43 185.20 187.97 17485200 186.6618
#2018-07-09 189.50 190.68 189.30 190.58 19756600 189.2536
#2018-07-10 190.71 191.28 190.18 190.35 15939100 189.0252
Charting with QuantMod: The function
chartSeries() is a nice tool to visualize financial time series in a
way that many practicioners are familiar with–line charts, as well as OHLC bar
and candle charts. There are convenience wrappers to these different styles (lineChart(), barChart(), and candleChart()), though chartSeries() does quite a bit to automatically handle data in the
most appropriate way.> chartSeries(AAPL["2018-8/2019-1"], name = "AAPL")
# Add multi-coloring and change background to white
> candleChart(AAPL["2018-8/2019-1"], multi.col = TRUE, theme = "white", name = "AAPL")
# Now weekly with custom color candles using the QuantMod function to.weekly
> chartSeries(to.weekly(AAPL), up.col = "green", dn.col = "red", theme = "white",name = "AAPL")
#Technical analysis charting tools: One can add technical analysis studies from package TTR to the above charts:
> chartSeries(AAPL["2018-8/2019-1"], name = "AAPL",theme = "white",
TA = "addMACD(); addBBands()")
> chartSeries(AAPL["2018-8/2019-1"], name = "AAPL", theme = "white",
TA = "addMomentum(); addEMA(); addRSI()")
> reChart(subset = "2018-11/2019-1", theme = "white", type = "candles")
The package TTR
(Technical Trading Rules) was
designed for traditional technical analysis and charting.
> library(TTR)
# simple moving average
> sma10 <- l="" n="10)<o:p" sma="">
> head(sma10, 20)
SMA
2018-07-11 NA
2018-07-12 NA
2018-07-13 NA
2018-07-16 188.655
2018-07-17 189.082
2018-07-18 189.730
2018-07-19 190.378
2018-07-20 190.725
2018-07-23 190.828
2018-07-24 191.093
2018-07-25 191.787
2018-07-26 192.105
2018-07-27 192.070
2018-07-30 191.970
#Bollinger Bands:
> bb20 <- bbands="" sd="2.0)<o:p">
str(bb20)
#An ‘xts’ object on 2018-07-02/2019-01-18 containing:
# Data: num [1:139, 1:4] NA NA NA NA NA NA NA NA NA NA ...
# - attr(*, "dimnames")=List of 2
# ..$ : NULL
# ..$ : chr [1:4] "dn" "mavg" "up" "pctB"
# Indexed by objects of class: [POSIXct,POSIXt] TZ: UTC
# xts Attributes:
#List of 2
# $ src : chr "yahoo"
# $ updated: POSIXct[1:1], format: "2019-01-21 18:11:21"
> plot(bb20)
#RSI – Relative Strength Indicator:
> rsi14 <- l="" n="14)<o:p" rsi="">
> plot(cbind(Cl(AAPL), rsi14), legend.loc = "topleft")
#MACD: moving average convergence/divergence
> macd = MACD(Cl(AAPL), nFast = 12, nSlow = 26, nSig = 9, maType = SMA)
> str(macd)
#An ‘xts’ object on 2018-07-02/2019-01-18 containing:
# Data: num [1:139, 1:2] NA NA NA NA NA NA NA NA NA NA ...
# - attr(*, "dimnames")=List of 2
# ..$ : NULL
# ..$ : chr [1:2] "macd" "signal"
# Indexed by objects of class: [POSIXct,POSIXt] TZ: UTC
# xts Attributes:
#List of 2
# $ src : chr "yahoo"
# $ updated: POSIXct[1:1], format: "2019-01-21 18:11:21"
> plot(cbind(Cl(AAPL), macd), legend.loc = "topleft")
