Analysing Closing Prices of TATAMOTORS and NIFTY50 using Simple Moving Average
Amritanshu
2023-11-16
Introduction
Short run performance analysis using Technical Analysis(SMA) on the selected STOCK and INDEX
We will be fetching the data(historical prices) from the website(Yahoo) on selected stock and Index. this is called Web Scrapping.
Selected Stock- TATAMOTORS
Selected Index- NIFTY50
Data Duration- April 1, 2021 to March 31, 2023
List of packages
library(quantmod)## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## 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 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ 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'
##
## The following object is masked from 'package:ggplot2':
##
## last_plot
##
## The following object is masked from 'package:stats':
##
## filter
##
## The following object is masked from 'package:graphics':
##
## layoutlibrary(dplyr)
library(PerformanceAnalytics)##
## Attaching package: 'PerformanceAnalytics'
##
## The following object is masked from 'package:graphics':
##
## legendLet us set Start Date and start date to fetch the data from yahoo
start <- as.Date("2021-04-01")
end <- as.Date("2023-03-31")The First dataset- Closing Prices of TATAMOTORS.NS
getSymbols("TATAMOTORS.NS",src="yahoo",from = start, to = end) # SYNATAX is universal --important to do## [1] "TATAMOTORS.NS"The Second dataset- Closing Prices of ‘^NSEI- NIFTY50’
getSymbols("^NSEI",src="yahoo",from = start, to = end)## [1] "NSEI"Exploring the DatasetS
FIRST DATASET
head(TATAMOTORS.NS)## TATAMOTORS.NS.Open TATAMOTORS.NS.High TATAMOTORS.NS.Low
## 2021-04-01 306.75 309.85 303.05
## 2021-04-05 306.80 311.70 297.20
## 2021-04-06 306.15 313.80 304.80
## 2021-04-07 306.75 310.65 305.10
## 2021-04-08 307.90 319.80 307.50
## 2021-04-09 313.20 325.00 312.50
## TATAMOTORS.NS.Close TATAMOTORS.NS.Volume TATAMOTORS.NS.Adjusted
## 2021-04-01 307.75 44088352 306.7938
## 2021-04-05 305.05 66178755 304.1022
## 2021-04-06 307.75 63031783 306.7938
## 2021-04-07 307.80 39073986 306.8437
## 2021-04-08 313.95 62459774 312.9746
## 2021-04-09 318.20 75462572 317.2114tail(TATAMOTORS.NS) ## TATAMOTORS.NS.Open TATAMOTORS.NS.High TATAMOTORS.NS.Low
## 2023-03-22 416.00 418.40 413.75
## 2023-03-23 416.00 422.50 416.00
## 2023-03-24 419.00 420.55 414.80
## 2023-03-27 416.00 418.50 410.25
## 2023-03-28 414.15 414.50 400.45
## 2023-03-29 401.60 410.60 401.60
## TATAMOTORS.NS.Close TATAMOTORS.NS.Volume TATAMOTORS.NS.Adjusted
## 2023-03-22 416.10 5728054 414.8072
## 2023-03-23 419.15 9817460 417.8477
## 2023-03-24 416.50 6877960 415.2059
## 2023-03-27 412.05 7433933 410.7697
## 2023-03-28 401.60 9553659 400.3522
## 2023-03-29 409.20 10478506 407.9286SECOND DATASET
head(NSEI)## NSEI.Open NSEI.High NSEI.Low NSEI.Close NSEI.Volume NSEI.Adjusted
## 2021-04-01 14798.40 14883.20 14692.45 14867.35 445000 14867.35
## 2021-04-05 14837.70 14849.85 14459.50 14637.80 509700 14637.80
## 2021-04-06 14737.00 14779.10 14573.90 14683.50 475300 14683.50
## 2021-04-07 14716.45 14879.80 14649.85 14819.05 0 14819.05
## 2021-04-08 14875.65 14984.15 14821.10 14873.80 514800 14873.80
## 2021-04-09 14882.65 14918.45 14785.65 14834.85 504100 14834.85tail(NSEI)## NSEI.Open NSEI.High NSEI.Low NSEI.Close NSEI.Volume NSEI.Adjusted
## 2023-03-22 17177.45 17207.25 17107.85 17151.90 0 17151.90
## 2023-03-23 17097.40 17205.40 17045.30 17076.90 219200 17076.90
## 2023-03-24 17076.20 17109.45 16917.35 16945.05 228000 16945.05
## 2023-03-27 16984.30 17091.00 16918.55 16985.70 218400 16985.70
## 2023-03-28 17031.75 17061.75 16913.75 16951.70 238800 16951.70
## 2023-03-29 16977.30 17126.15 16940.60 17080.70 345900 17080.70Let us get a plot and a candlestick chart FOR EACH DATASET
FIRST DATASET
plot(TATAMOTORS.NS[,"TATAMOTORS.NS.Close"],main = "TATAMOTORS Closing Prices",col="brown")
candleChart(TATAMOTORS.NS, up.col="green",dn.col="red",theme ="white") # A CANDLE HAS 'TOP' AND 'BOTTOM' 
#Point to remember: Technical analysis- short run- less than a year to
visualise candles properly
SECOND DATASET
plot(NSEI[,"NSEI.Close"],main = "NIFTY50 Closing Prices",col="black")
candleChart(NSEI, up.col="green",dn.col="red",theme ="white")
### LET US SHOW THE DATA TOGETHER
head(list(TATAMOTORS.NS,NSEI))## [[1]]
## TATAMOTORS.NS.Open TATAMOTORS.NS.High TATAMOTORS.NS.Low
## 2021-04-01 306.75 309.85 303.05
## 2021-04-05 306.80 311.70 297.20
## 2021-04-06 306.15 313.80 304.80
## 2021-04-07 306.75 310.65 305.10
## 2021-04-08 307.90 319.80 307.50
## 2021-04-09 313.20 325.00 312.50
## 2021-04-12 307.85 307.85 282.90
## 2021-04-13 287.25 304.55 280.55
## 2021-04-15 303.00 307.55 296.50
## 2021-04-16 302.00 315.00 300.15
## ...
## 2023-03-16 413.30 418.50 405.00
## 2023-03-17 419.00 423.45 414.55
## 2023-03-20 415.60 416.95 405.50
## 2023-03-21 412.85 413.90 407.70
## 2023-03-22 416.00 418.40 413.75
## 2023-03-23 416.00 422.50 416.00
## 2023-03-24 419.00 420.55 414.80
## 2023-03-27 416.00 418.50 410.25
## 2023-03-28 414.15 414.50 400.45
## 2023-03-29 401.60 410.60 401.60
## TATAMOTORS.NS.Close TATAMOTORS.NS.Volume TATAMOTORS.NS.Adjusted
## 2021-04-01 307.75 44088352 306.7938
## 2021-04-05 305.05 66178755 304.1022
## 2021-04-06 307.75 63031783 306.7938
## 2021-04-07 307.80 39073986 306.8437
## 2021-04-08 313.95 62459774 312.9746
## 2021-04-09 318.20 75462572 317.2114
## 2021-04-12 286.55 105342538 285.6597
## 2021-04-13 302.85 116874853 301.9091
## 2021-04-15 303.50 75262749 302.5570
## 2021-04-16 310.00 75330709 309.0368
## ...
## 2023-03-16 415.70 11854885 414.4084
## 2023-03-17 419.00 8658701 417.6982
## 2023-03-20 410.75 10075022 409.4738
## 2023-03-21 412.55 7391950 411.2682
## 2023-03-22 416.10 5728054 414.8072
## 2023-03-23 419.15 9817460 417.8477
## 2023-03-24 416.50 6877960 415.2059
## 2023-03-27 412.05 7433933 410.7697
## 2023-03-28 401.60 9553659 400.3522
## 2023-03-29 409.20 10478506 407.9286
##
## [[2]]
## NSEI.Open NSEI.High NSEI.Low NSEI.Close NSEI.Volume NSEI.Adjusted
## 2021-04-01 14798.40 14883.20 14692.45 14867.35 445000 14867.35
## 2021-04-05 14837.70 14849.85 14459.50 14637.80 509700 14637.80
## 2021-04-06 14737.00 14779.10 14573.90 14683.50 475300 14683.50
## 2021-04-07 14716.45 14879.80 14649.85 14819.05 0 14819.05
## 2021-04-08 14875.65 14984.15 14821.10 14873.80 514800 14873.80
## 2021-04-09 14882.65 14918.45 14785.65 14834.85 504100 14834.85
## 2021-04-12 14644.65 14652.50 14248.70 14310.80 650200 14310.80
## 2021-04-13 14364.90 14528.90 14274.90 14504.80 605100 14504.80
## 2021-04-15 14522.40 14597.55 14353.20 14581.45 568000 14581.45
## 2021-04-16 14599.60 14697.70 14559.00 14617.85 577200 14617.85
## ...
## 2023-03-16 16994.65 17062.45 16850.15 16985.60 349800 16985.60
## 2023-03-17 17111.80 17145.80 16958.15 17100.05 408100 17100.05
## 2023-03-20 17066.60 17066.60 16828.35 16988.40 241800 16988.40
## 2023-03-21 17060.40 17127.70 17016.00 17107.50 246700 17107.50
## 2023-03-22 17177.45 17207.25 17107.85 17151.90 0 17151.90
## 2023-03-23 17097.40 17205.40 17045.30 17076.90 219200 17076.90
## 2023-03-24 17076.20 17109.45 16917.35 16945.05 228000 16945.05
## 2023-03-27 16984.30 17091.00 16918.55 16985.70 218400 16985.70
## 2023-03-28 17031.75 17061.75 16913.75 16951.70 238800 16951.70
## 2023-03-29 16977.30 17126.15 16940.60 17080.70 345900 17080.70Let us get the stock AND Index closing prices in one tabular form
stocks<-as.xts(data.frame(TATAMOTORS.NS=TATAMOTORS.NS[,"TATAMOTORS.NS.Close"],NSEI=NSEI[,"NSEI.Close"]))
stocks## TATAMOTORS.NS.Close NSEI.Close
## 2021-04-01 307.75 14867.35
## 2021-04-05 305.05 14637.80
## 2021-04-06 307.75 14683.50
## 2021-04-07 307.80 14819.05
## 2021-04-08 313.95 14873.80
## 2021-04-09 318.20 14834.85
## 2021-04-12 286.55 14310.80
## 2021-04-13 302.85 14504.80
## 2021-04-15 303.50 14581.45
## 2021-04-16 310.00 14617.85
## ...
## 2023-03-16 415.70 16985.60
## 2023-03-17 419.00 17100.05
## 2023-03-20 410.75 16988.40
## 2023-03-21 412.55 17107.50
## 2023-03-22 416.10 17151.90
## 2023-03-23 419.15 17076.90
## 2023-03-24 416.50 16945.05
## 2023-03-27 412.05 16985.70
## 2023-03-28 401.60 16951.70
## 2023-03-29 409.20 17080.70head(stocks)## TATAMOTORS.NS.Close NSEI.Close
## 2021-04-01 307.75 14867.35
## 2021-04-05 305.05 14637.80
## 2021-04-06 307.75 14683.50
## 2021-04-07 307.80 14819.05
## 2021-04-08 313.95 14873.80
## 2021-04-09 318.20 14834.85plot(as.zoo(stocks),screens=1,lty=1:2,xlab="Date",ylab="Price")
Let us calculate the returns
library(magrittr)##
## Attaching package: 'magrittr'## The following object is masked from 'package:purrr':
##
## set_names## The following object is masked from 'package:tidyr':
##
## extractstock_return <- apply(stocks, 1, function(x) {x / stocks[1,]}) %>% t %>% as.xts
stock_return## TATAMOTORS.NS.Close NSEI.Close
## 2021-04-01 1.0000000 1.0000000
## 2021-04-05 0.9912266 0.9845601
## 2021-04-06 1.0000000 0.9876340
## 2021-04-07 1.0001624 0.9967513
## 2021-04-08 1.0201463 1.0004338
## 2021-04-09 1.0339562 0.9978140
## 2021-04-12 0.9311129 0.9625656
## 2021-04-13 0.9840780 0.9756144
## 2021-04-15 0.9861901 0.9807700
## 2021-04-16 1.0073111 0.9832183
## ...
## 2023-03-16 1.3507718 1.1424766
## 2023-03-17 1.3614947 1.1501748
## 2023-03-20 1.3346872 1.1426650
## 2023-03-21 1.3405361 1.1506758
## 2023-03-22 1.3520715 1.1536623
## 2023-03-23 1.3619821 1.1486177
## 2023-03-24 1.3533712 1.1397493
## 2023-03-27 1.3389114 1.1424833
## 2023-03-28 1.3049553 1.1401964
## 2023-03-29 1.3296507 1.1488732head(stock_return)## TATAMOTORS.NS.Close NSEI.Close
## 2021-04-01 1.0000000 1.0000000
## 2021-04-05 0.9912266 0.9845601
## 2021-04-06 1.0000000 0.9876340
## 2021-04-07 1.0001624 0.9967513
## 2021-04-08 1.0201463 1.0004338
## 2021-04-09 1.0339562 0.9978140plot(as.zoo(stock_return),screens=1,lty=1:2,xlab="Date",ylab="Price")
legend("topleft",c("TATAMOTORS.NS","NSEI"),lty=1:2, cex=0.5)
Here, it can be observed that the the volatility is more for TATAMOTORS returns as compared to NIFTY50 returns.
Making our returns more accurate and insightful perspective on investment returns
Using log returns –to get the real impact
stock_change<- stocks %>% log %>% diff # This gives the relative closing price change.
stock_change## TATAMOTORS.NS.Close NSEI.Close
## 2021-04-01 NA NA
## 2021-04-05 -0.0088121075 -0.0155602965
## 2021-04-06 0.0088121075 0.0031172037
## 2021-04-07 0.0001624167 0.0091890877
## 2021-04-08 0.0197835947 0.0036877607
## 2021-04-09 0.0134463778 -0.0026221466
## 2021-04-12 -0.1047671559 -0.0359646328
## 2021-04-13 0.0553246552 0.0134651323
## 2021-04-15 0.0021439569 0.0052705707
## 2021-04-16 0.0211906870 0.0024931716
## ...
## 2023-03-16 0.0105194272 0.0007921149
## 2023-03-17 0.0079070443 0.0067155299
## 2023-03-20 -0.0198861630 -0.0065506520
## 2023-03-21 0.0043726240 0.0069861826
## 2023-03-22 0.0085682497 0.0025920136
## 2023-03-23 0.0073032059 -0.0043822815
## 2023-03-24 -0.0063423749 -0.0077508941
## 2023-03-27 -0.0107417900 0.0023959657
## 2023-03-28 -0.0256880880 -0.0020036899
## 2023-03-29 0.0187474803 0.0075810477head(stock_change)## TATAMOTORS.NS.Close NSEI.Close
## 2021-04-01 NA NA
## 2021-04-05 -0.0088121075 -0.015560296
## 2021-04-06 0.0088121075 0.003117204
## 2021-04-07 0.0001624167 0.009189088
## 2021-04-08 0.0197835947 0.003687761
## 2021-04-09 0.0134463778 -0.002622147plot(as.zoo(stock_change),screens=1,lty=1:2,xlab="Date",ylab="Price",col= c("red","blue"))
legend("topleft", c("TATAMOTORS.NS","NSEI"),lty=1:2, cex=0.5)
Simple Moving Averages
For TATAMOTORS
candleChart(TATAMOTORS.NS, up.col="green",dn.col="red",theme ="beige")
addSMA(n=c(20,50,200),col=c("green","red","blue"))
Let us see the visuals for ‘6 months at a time’ for TATMOTORS in chronological order in the given duration of study.
candleChart(TATAMOTORS.NS, up.col="green",dn.col="red",theme ="beige",subset= "2021-04-01/2021-09-30")
addSMA(n=c(20,50,200),col=c("green","magenta","blue"))
candleChart(TATAMOTORS.NS, up.col="green",dn.col="red",theme ="beige",subset= "2021-10-01/2022-03-31")
addSMA(n=c(20,50,200),col=c("green","magenta","blue"))
candleChart(TATAMOTORS.NS, up.col="green",dn.col="red",theme ="beige",subset= "2022-04-01/2022-09-30")
addSMA(n=c(20,50,200),col=c("green","magenta","blue"))
candleChart(TATAMOTORS.NS, up.col="green",dn.col="red",theme ="beige",subset= "2022-10-01/2023-03-31")
addSMA(n=c(20,50,200),col=c("green","magenta","blue"))
FOR NIFTY50
candleChart(NSEI, up.col="green",dn.col="red",theme ="white")
addSMA(n=c(20,50,200),col=c("green","magenta","blue"))
Let us see the visuals for ‘6 months at a time’ for NIFTY50 in chronological order in the given duration of study.
candleChart(NSEI, up.col="green",dn.col="red",theme ="white",subset= "2021-04-01/2021-09-30")
addSMA(n=c(20,50,200),col=c("green","magenta","blue"))
candleChart(NSEI, up.col="green",dn.col="red",theme ="white",subset= "2021-10-01/2022-03-31")
addSMA(n=c(20,50,200),col=c("green","magenta","blue"))
candleChart(NSEI, up.col="green",dn.col="red",theme ="white",subset= "2022-04-01/2022-09-30")
addSMA(n=c(20,50,200),col=c("green","magenta","blue"))
candleChart(NSEI, up.col="green",dn.col="red",theme ="white",subset= "2022-10-01/2023-03-31")
addSMA(n=c(20,50,200),col=c("green","magenta","blue"))
Conclusion
- The historical daily closing prices for NIFTY50 and TATAMOTORS are plotted separately using the JAPANESE Candle Sticks.
- Three SIMPLE MOVING AVERAGE LINES for 20,50,200 days are plotted on the graph.
- Analyzing the TataMotors candlestick chart, I can see that the in the year 2021, the fast moving average (green line) was above the slow moving average(red line) during October- December, thus indicating the BULLISH trend.
- And the trend analysis is getting validated by the historical price movement.
- Analyzing the NIFTY50 candlestick chart, it can be seen that in the year 2021, during June- December the fast moving average(green line) was above the slow moving average line (magenta line), thus validating the actual price movement of BULLISH trend.