rm(list=ls())
library(ggplot2)
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':
## 
##     layout
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
Raut <- read.csv("C:/Users/CHOME/OneDrive/Desktop/R/Raut.csv")

   View(Raut)
   
colnames(Raut) <- c("Date", "Price", "Return","Open", "High","low")

Raut$Date <- as.Date(Raut$Date, format="%m/%d/%Y")

Raut$Price <- as.numeric(gsub(",", "", Raut$Price))

Raut$Return <- as.numeric(gsub("%", "", Raut$Return)) / 100

Raut$Open <- as.numeric(gsub(",", "", Raut$Open))

Raut$High <- as.numeric(gsub(",", "", Raut$High))

Raut$low <- as.numeric(gsub(",", "", Raut$low))


str(Raut)
## 'data.frame':    60 obs. of  6 variables:
##  $ Date  : Date, format: "2020-01-01" "2020-02-01" ...
##  $ Price : num  4470 4480 4008 4008 4060 ...
##  $ Return: num  NA 0.0024 -0.1053 0 0.013 ...
##  $ Open  : num  4453 4470 4480 4008 4008 ...
##  $ High  : num  4550 4827 4511 4008 4060 ...
##  $ low   : num  4010 4375 3593 4008 3998 ...
summary(Raut)
##       Date                Price          Return               Open     
##  Min.   :2020-01-01   Min.   :3989   Min.   :-0.105300   Min.   :3989  
##  1st Qu.:2021-03-24   1st Qu.:5272   1st Qu.:-0.020500   1st Qu.:5272  
##  Median :2022-06-16   Median :6180   Median :-0.000900   Median :6196  
##  Mean   :2022-06-16   Mean   :5816   Mean   : 0.003607   Mean   :5808  
##  3rd Qu.:2023-09-08   3rd Qu.:6341   3rd Qu.: 0.015500   3rd Qu.:6354  
##  Max.   :2024-12-01   Max.   :7329   Max.   : 0.157700   Max.   :7329  
##                                      NA's   :1                         
##       High           low      
##  Min.   :4008   Min.   :3593  
##  1st Qu.:5339   1st Qu.:5248  
##  Median :6214   Median :6177  
##  Mean   :5926   Mean   :5723  
##  3rd Qu.:6367   3rd Qu.:6322  
##  Max.   :7411   Max.   :6869  
##

Graph_1

  ggplot(Raut, aes(x = Date, y = Price)) +
  geom_line(color = "blue", size = 1) +
  labs(title = "DSEX Index Price Over Time", x = "Date", y = "Price") +
  scale_x_date(date_breaks = "6 month", date_labels = "%Y") +  
  theme_minimal()

Yearly movement of DSEX from 2020 to 2024 is depicted in the file where we can see how the DSEX goes up after Covid 19 and how 2024 August movement impact the stock market.From 2021 to 2023 the movement was a bit stable but then it decline.

Graph_2

ggplot(Raut, aes(x = Date, y = Return)) +
  geom_line(color = "red", size = 1) +
  labs(title = " DSEX Index Return Over Time", x = "Date", y = "Return") +
   scale_x_date(date_breaks = "6 month", date_labels = "%Y")

The graph above depicts the evolution of average return of DSEX year wise from 2020 to 2024. The return fluctuation appears unpredictable because there are loots of ups and downs. The two highest ups and downs were occurred in 2020 and 2024 which could be due to the COVID 19 and movement happened in 2024 August.

Graph_3

ggplot(Raut, aes(x = Return)) +
  geom_density(fill = "pink", alpha = 0.5) +
  geom_vline(aes(xintercept = mean(Return, na.rm = TRUE)), 
             color = "blue", linetype = "dashed", size = 1) +
  labs(title = "Bell Curve of Returns", x = "Return", y = "Density") +
  theme_minimal()

From the Bell curve of monthly return of DSEX we can say that its a leptokurtic and positively skewed. The blue line represent the mean return in the above graph

Graph_4

ggplot(Raut, aes(x = Return, y = Price)) +
  geom_point(color = "blue", size = 2, alpha = 0.6) +
  geom_smooth(method = "lm", color = "red", linetype = "solid", se = FALSE) + 
  labs(title = "Scatter Plot of Price vs Return with Trend Line", x = "Return (%)", y = "Price") +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'

From the given above scatter plot of DSEX with return trend line, the relation is not correlated because the behavior of DSEX Index and return looks very fragile or the dots are away from the red line which is showing the average return.

Graph_5

Raut$Price <- as.numeric(gsub(",", "", Raut$Price))

summary(Raut$Price)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    3989    5272    6180    5816    6341    7329
summary(Raut$Open)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    3989    5272    6196    5808    6354    7329
summary(Raut$High)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    4008    5339    6214    5926    6367    7411
summary(Raut$Low)
## Length  Class   Mode 
##      0   NULL   NULL
fig <- Raut %>%
  plot_ly(x = ~Date, type = "candlestick",
          open = ~Open, high = ~High, low = ~low, close = ~Price) %>%
  layout(title = "DSEX Candlestick Chart 2020 to 2024",
         xaxis = list(title = "Date"),
         yaxis = list(title = "Price"))


fig

Figure above shows the candlestick chart of DSEX index and their high low price based on the DSEX index data.

Thank you