“Performance Analysis of Asian Paints”
Pranav Nambiar
2023-11-26
Short-run performance analysis using technical on this selected stock.
We will be fetching the data(Historic data) from the website(yahoo) on selected stock.
Selected stock- Asian Paints Limited
List of packages
library(quantmod)## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
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## as.Date, as.Date.numeric## Loading required package: TTR## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoolibrary(xts)
library(rvest)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::first() masks xts::first()
## ✖ readr::guess_encoding() masks rvest::guess_encoding()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::last() masks xts::last()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(stringr)
library(forcats)
library(lubridate)
library(plotly)##
## Attaching package: 'plotly'
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## last_plot
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## filter
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## The following object is masked from 'package:graphics':
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## layoutlibrary(dplyr)
library(PerformanceAnalytics)##
## Attaching package: 'PerformanceAnalytics'
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## The following object is masked from 'package:graphics':
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## legendLet us set start date and end date to fetch the data from yahoo
start <- as.Date("2023-01-01")
end <- as.Date("2023-11-24")
getSymbols("ASIANPAINT.NS", src = "yahoo", from =start, to=end)## [1] "ASIANPAINT.NS"Exploring the Dataset
head(ASIANPAINT.NS)## ASIANPAINT.NS.Open ASIANPAINT.NS.High ASIANPAINT.NS.Low
## 2023-01-02 3087.90 3087.90 3021.00
## 2023-01-03 3047.00 3059.95 3025.00
## 2023-01-04 3035.00 3050.00 3001.55
## 2023-01-05 3039.55 3046.60 2995.40
## 2023-01-06 3010.00 3023.50 2952.00
## 2023-01-09 2993.30 3017.65 2951.00
## ASIANPAINT.NS.Close ASIANPAINT.NS.Volume ASIANPAINT.NS.Adjusted
## 2023-01-02 3047.25 1015990 3021.817
## 2023-01-03 3028.25 860367 3002.975
## 2023-01-04 3016.85 836382 2991.670
## 2023-01-05 3004.35 1113920 2979.275
## 2023-01-06 2978.40 1045526 2953.541
## 2023-01-09 2984.15 808263 2959.243tail(ASIANPAINT.NS)## ASIANPAINT.NS.Open ASIANPAINT.NS.High ASIANPAINT.NS.Low
## 2023-11-16 3119.0 3140.90 3097.60
## 2023-11-17 3150.0 3228.25 3145.65
## 2023-11-20 3168.9 3171.90 3132.00
## 2023-11-21 3156.9 3156.90 3127.00
## 2023-11-22 3140.0 3157.00 3121.20
## 2023-11-23 3148.0 3171.40 3115.15
## ASIANPAINT.NS.Close ASIANPAINT.NS.Volume ASIANPAINT.NS.Adjusted
## 2023-11-16 3130.30 675454 3130.30
## 2023-11-17 3168.90 1430175 3168.90
## 2023-11-20 3140.35 406431 3140.35
## 2023-11-21 3133.25 409069 3133.25
## 2023-11-22 3133.00 489889 3133.00
## 2023-11-23 3122.95 792460 3122.95plot(ASIANPAINT.NS[,"ASIANPAINT.NS.Close"], main = "ASIANPAINT.NS")
2nd way to fetch required data
plot(ASIANPAINT.NS$ASIANPAINT.NS.Close)
Candle chart
candleChart(ASIANPAINT.NS, up.col="green", dn.col="red", theme = "white")
Let us get Nifty index adding the ASIANPAINT.NS.
start <- as.Date("2023-01-01")
end <- as.Date("2023-11-24")
getSymbols("^NSEI", src = "yahoo", from =start, to=end)## [1] "NSEI"Exploring the Dataset
head(NSEI)## NSEI.Open NSEI.High NSEI.Low NSEI.Close NSEI.Volume NSEI.Adjusted
## 2023-01-02 18131.70 18215.15 18086.50 18197.45 256100 18197.45
## 2023-01-03 18163.20 18251.95 18149.80 18232.55 208700 18232.55
## 2023-01-04 18230.65 18243.00 18020.60 18042.95 235200 18042.95
## 2023-01-05 18101.95 18120.30 17892.60 17992.15 269900 17992.15
## 2023-01-06 18008.05 18047.40 17795.55 17859.45 238200 17859.45
## 2023-01-09 17952.55 18141.40 17936.15 18101.20 257200 18101.20tail(NSEI)## NSEI.Open NSEI.High NSEI.Low NSEI.Close NSEI.Volume NSEI.Adjusted
## 2023-11-16 19674.70 19875.25 19627.00 19765.20 282700 19765.20
## 2023-11-17 19674.75 19806.00 19667.45 19731.80 236800 19731.80
## 2023-11-20 19731.15 19756.45 19670.50 19694.00 173800 19694.00
## 2023-11-21 19770.90 19829.10 19754.05 19783.40 195000 19783.40
## 2023-11-22 19784.00 19825.55 19703.85 19811.85 184500 19811.85
## 2023-11-23 19828.45 19875.15 19786.75 19802.00 170000 19802.00plot(NSEI[,"NSEI.Close"], main = "NSEI")
plot(NSEI$NSEI.Close)
Candle chart
candleChart(NSEI, up.col="green", dn.col="red", theme = "white")
To check top 10 and bottom stock prices.
head(list(ASIANPAINT.NS,NSEI))## [[1]]
## ASIANPAINT.NS.Open ASIANPAINT.NS.High ASIANPAINT.NS.Low
## 2023-01-02 3087.90 3087.90 3021.00
## 2023-01-03 3047.00 3059.95 3025.00
## 2023-01-04 3035.00 3050.00 3001.55
## 2023-01-05 3039.55 3046.60 2995.40
## 2023-01-06 3010.00 3023.50 2952.00
## 2023-01-09 2993.30 3017.65 2951.00
## 2023-01-10 2984.15 2985.00 2952.20
## 2023-01-11 2974.90 2974.90 2935.00
## 2023-01-12 2939.00 2939.85 2896.00
## 2023-01-13 2925.00 2925.00 2871.75
## ...
## 2023-11-09 3100.00 3111.40 3074.05
## 2023-11-10 3070.00 3085.00 3040.10
## 2023-11-13 3098.00 3098.00 3065.00
## 2023-11-15 3085.70 3118.85 3078.70
## 2023-11-16 3119.00 3140.90 3097.60
## 2023-11-17 3150.00 3228.25 3145.65
## 2023-11-20 3168.90 3171.90 3132.00
## 2023-11-21 3156.90 3156.90 3127.00
## 2023-11-22 3140.00 3157.00 3121.20
## 2023-11-23 3148.00 3171.40 3115.15
## ASIANPAINT.NS.Close ASIANPAINT.NS.Volume ASIANPAINT.NS.Adjusted
## 2023-01-02 3047.25 1015990 3021.817
## 2023-01-03 3028.25 860367 3002.975
## 2023-01-04 3016.85 836382 2991.670
## 2023-01-05 3004.35 1113920 2979.275
## 2023-01-06 2978.40 1045526 2953.541
## 2023-01-09 2984.15 808263 2959.243
## 2023-01-10 2960.35 864325 2935.642
## 2023-01-11 2940.40 890426 2915.858
## 2023-01-12 2915.95 1558620 2891.613
## 2023-01-13 2909.15 1507864 2884.869
## ...
## 2023-11-09 3080.20 849702 3080.200
## 2023-11-10 3076.45 364232 3076.450
## 2023-11-13 3085.60 405941 3085.600
## 2023-11-15 3116.20 700017 3116.200
## 2023-11-16 3130.30 675454 3130.300
## 2023-11-17 3168.90 1430175 3168.900
## 2023-11-20 3140.35 406431 3140.350
## 2023-11-21 3133.25 409069 3133.250
## 2023-11-22 3133.00 489889 3133.000
## 2023-11-23 3122.95 792460 3122.950
##
## [[2]]
## NSEI.Open NSEI.High NSEI.Low NSEI.Close NSEI.Volume NSEI.Adjusted
## 2023-01-02 18131.70 18215.15 18086.50 18197.45 256100 18197.45
## 2023-01-03 18163.20 18251.95 18149.80 18232.55 208700 18232.55
## 2023-01-04 18230.65 18243.00 18020.60 18042.95 235200 18042.95
## 2023-01-05 18101.95 18120.30 17892.60 17992.15 269900 17992.15
## 2023-01-06 18008.05 18047.40 17795.55 17859.45 238200 17859.45
## 2023-01-09 17952.55 18141.40 17936.15 18101.20 257200 18101.20
## 2023-01-10 18121.30 18127.60 17856.00 17914.15 283300 17914.15
## 2023-01-11 17924.25 17976.35 17824.35 17895.70 259900 17895.70
## 2023-01-12 17920.85 17945.80 17761.65 17858.20 227800 17858.20
## 2023-01-13 17867.50 17999.35 17774.25 17956.60 256700 17956.60
## ...
## 2023-11-09 19457.40 19463.90 19378.35 19395.30 208400 19395.30
## 2023-11-10 19351.85 19451.30 19329.45 19425.35 152200 19425.35
## 2023-11-13 19486.75 19494.40 19414.75 19443.55 189300 19443.55
## 2023-11-15 19651.40 19693.20 19579.65 19675.45 291500 19675.45
## 2023-11-16 19674.70 19875.25 19627.00 19765.20 282700 19765.20
## 2023-11-17 19674.75 19806.00 19667.45 19731.80 236800 19731.80
## 2023-11-20 19731.15 19756.45 19670.50 19694.00 173800 19694.00
## 2023-11-21 19770.90 19829.10 19754.05 19783.40 195000 19783.40
## 2023-11-22 19784.00 19825.55 19703.85 19811.85 184500 19811.85
## 2023-11-23 19828.45 19875.15 19786.75 19802.00 170000 19802.00tail(list(ASIANPAINT.NS,NSEI))## [[1]]
## ASIANPAINT.NS.Open ASIANPAINT.NS.High ASIANPAINT.NS.Low
## 2023-01-02 3087.90 3087.90 3021.00
## 2023-01-03 3047.00 3059.95 3025.00
## 2023-01-04 3035.00 3050.00 3001.55
## 2023-01-05 3039.55 3046.60 2995.40
## 2023-01-06 3010.00 3023.50 2952.00
## 2023-01-09 2993.30 3017.65 2951.00
## 2023-01-10 2984.15 2985.00 2952.20
## 2023-01-11 2974.90 2974.90 2935.00
## 2023-01-12 2939.00 2939.85 2896.00
## 2023-01-13 2925.00 2925.00 2871.75
## ...
## 2023-11-09 3100.00 3111.40 3074.05
## 2023-11-10 3070.00 3085.00 3040.10
## 2023-11-13 3098.00 3098.00 3065.00
## 2023-11-15 3085.70 3118.85 3078.70
## 2023-11-16 3119.00 3140.90 3097.60
## 2023-11-17 3150.00 3228.25 3145.65
## 2023-11-20 3168.90 3171.90 3132.00
## 2023-11-21 3156.90 3156.90 3127.00
## 2023-11-22 3140.00 3157.00 3121.20
## 2023-11-23 3148.00 3171.40 3115.15
## ASIANPAINT.NS.Close ASIANPAINT.NS.Volume ASIANPAINT.NS.Adjusted
## 2023-01-02 3047.25 1015990 3021.817
## 2023-01-03 3028.25 860367 3002.975
## 2023-01-04 3016.85 836382 2991.670
## 2023-01-05 3004.35 1113920 2979.275
## 2023-01-06 2978.40 1045526 2953.541
## 2023-01-09 2984.15 808263 2959.243
## 2023-01-10 2960.35 864325 2935.642
## 2023-01-11 2940.40 890426 2915.858
## 2023-01-12 2915.95 1558620 2891.613
## 2023-01-13 2909.15 1507864 2884.869
## ...
## 2023-11-09 3080.20 849702 3080.200
## 2023-11-10 3076.45 364232 3076.450
## 2023-11-13 3085.60 405941 3085.600
## 2023-11-15 3116.20 700017 3116.200
## 2023-11-16 3130.30 675454 3130.300
## 2023-11-17 3168.90 1430175 3168.900
## 2023-11-20 3140.35 406431 3140.350
## 2023-11-21 3133.25 409069 3133.250
## 2023-11-22 3133.00 489889 3133.000
## 2023-11-23 3122.95 792460 3122.950
##
## [[2]]
## NSEI.Open NSEI.High NSEI.Low NSEI.Close NSEI.Volume NSEI.Adjusted
## 2023-01-02 18131.70 18215.15 18086.50 18197.45 256100 18197.45
## 2023-01-03 18163.20 18251.95 18149.80 18232.55 208700 18232.55
## 2023-01-04 18230.65 18243.00 18020.60 18042.95 235200 18042.95
## 2023-01-05 18101.95 18120.30 17892.60 17992.15 269900 17992.15
## 2023-01-06 18008.05 18047.40 17795.55 17859.45 238200 17859.45
## 2023-01-09 17952.55 18141.40 17936.15 18101.20 257200 18101.20
## 2023-01-10 18121.30 18127.60 17856.00 17914.15 283300 17914.15
## 2023-01-11 17924.25 17976.35 17824.35 17895.70 259900 17895.70
## 2023-01-12 17920.85 17945.80 17761.65 17858.20 227800 17858.20
## 2023-01-13 17867.50 17999.35 17774.25 17956.60 256700 17956.60
## ...
## 2023-11-09 19457.40 19463.90 19378.35 19395.30 208400 19395.30
## 2023-11-10 19351.85 19451.30 19329.45 19425.35 152200 19425.35
## 2023-11-13 19486.75 19494.40 19414.75 19443.55 189300 19443.55
## 2023-11-15 19651.40 19693.20 19579.65 19675.45 291500 19675.45
## 2023-11-16 19674.70 19875.25 19627.00 19765.20 282700 19765.20
## 2023-11-17 19674.75 19806.00 19667.45 19731.80 236800 19731.80
## 2023-11-20 19731.15 19756.45 19670.50 19694.00 173800 19694.00
## 2023-11-21 19770.90 19829.10 19754.05 19783.40 195000 19783.40
## 2023-11-22 19784.00 19825.55 19703.85 19811.85 184500 19811.85
## 2023-11-23 19828.45 19875.15 19786.75 19802.00 170000 19802.00Let us get the multiple stocks into our study
getSymbols(c("ASIANPAINT.NS","^NSEI"), src = "yahoo", from =start,to=end)## [1] "ASIANPAINT.NS" "NSEI"stock <- as.xts(data.frame(NSEI=NSEI[,"NSEI.Close"], ASIANPAINT.NS=ASIANPAINT.NS[,"ASIANPAINT.NS.Close"]))
stock## NSEI.Close ASIANPAINT.NS.Close
## 2023-01-02 18197.45 3047.25
## 2023-01-03 18232.55 3028.25
## 2023-01-04 18042.95 3016.85
## 2023-01-05 17992.15 3004.35
## 2023-01-06 17859.45 2978.40
## 2023-01-09 18101.20 2984.15
## 2023-01-10 17914.15 2960.35
## 2023-01-11 17895.70 2940.40
## 2023-01-12 17858.20 2915.95
## 2023-01-13 17956.60 2909.15
## ...
## 2023-11-09 19395.30 3080.20
## 2023-11-10 19425.35 3076.45
## 2023-11-13 19443.55 3085.60
## 2023-11-15 19675.45 3116.20
## 2023-11-16 19765.20 3130.30
## 2023-11-17 19731.80 3168.90
## 2023-11-20 19694.00 3140.35
## 2023-11-21 19783.40 3133.25
## 2023-11-22 19811.85 3133.00
## 2023-11-23 19802.00 3122.95plot(as.zoo(stock), screens = 2, lty = 1:2, xlab = "Date", ylab = "Price")
legend("topleft", c("TATASTEEL.NS","JINDALSTEEL.NS","JSWSTEEL.NS"), LTY=1:3, cex = 0.5)
Let us calculate the returns
library(magrittr)##
## Attaching package: 'magrittr'## The following object is masked from 'package:purrr':
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## set_names## The following object is masked from 'package:tidyr':
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## extractstock_return <- apply(stock, 1, function(x) {x / stock[1,]}) %>% t %>% as.xts
stock_return## NSEI.Close ASIANPAINT.NS.Close
## 2023-01-02 1.0000000 1.0000000
## 2023-01-03 1.0019289 0.9937649
## 2023-01-04 0.9915098 0.9900238
## 2023-01-05 0.9887183 0.9859218
## 2023-01-06 0.9814260 0.9774058
## 2023-01-09 0.9947108 0.9792928
## 2023-01-10 0.9844319 0.9714825
## 2023-01-11 0.9834180 0.9649356
## 2023-01-12 0.9813573 0.9569120
## 2023-01-13 0.9867647 0.9546804
## ...
## 2023-11-09 1.0658252 1.0108130
## 2023-11-10 1.0674765 1.0095824
## 2023-11-13 1.0684767 1.0125851
## 2023-11-15 1.0812202 1.0226269
## 2023-11-16 1.0861522 1.0272541
## 2023-11-17 1.0843168 1.0399212
## 2023-11-20 1.0822396 1.0305522
## 2023-11-21 1.0871524 1.0282222
## 2023-11-22 1.0887158 1.0281401
## 2023-11-23 1.0881745 1.0248421plot(as.zoo(stock_return), screens = 2, lty = 1:3, xlab = "Date", ylab = "Price")
legend("topleft", c("ASIANPAINTS.NS","NESI"), lyt = 1:3, cex = 0.5)
making our returns more accurate and insightful perspective on investment
stock_change <- stock %>% log%>% diff
head(stock_change)## NSEI.Close ASIANPAINT.NS.Close
## 2023-01-02 NA NA
## 2023-01-03 0.001927070 -0.006254650
## 2023-01-04 -0.010453518 -0.003771622
## 2023-01-05 -0.002819410 -0.004152002
## 2023-01-06 -0.007402837 -0.008675060
## 2023-01-09 0.013445455 0.001928706plot(as.zoo(stock_change), screens = 2, lty = 1:3, xlab = "Date", ylab = "Price")
legend("topleft", c("ASIANPAINTS.NS","NESI"), lyt = 1:3, cex = 0.5)
Simple Moving Averages
candleChart(ASIANPAINT.NS, up.col="green", dn.col="red", theme = "white")
addSMA(n=20)
addSMA(n=c(20,50,200), col = c("green","red","blue"))
CONCLUSION
The 50-day moving average is above the 200-day moving average, which is a bullish sign. This indicates that the stock is in an uptrend.
The technical analysis suggests that Asian Paints Ltd. is in a good position for continued growth. However, investors should be aware of the overbought RSI and the possibility of a pullback.