Final Project.Rmd

---
title: "TSX Composite Price Prediction"
author: "Chetan Kumar"
date: "12/07/2021"
output:
  html_document: default
  word_document: default
---

```{r package loading, results='hide', warning=FALSE, message=FALSE, echo=FALSE}

rm(list = ls())
# Package names added in vector packages
packages <- c("ggplot2","quantmod","dplyr","ggplot2","rmarkdown","psych","tseries","timeSeries","data.table","fpp2","forecast","stats","lubridate")
# Install packages which are not yet installed
installed_packages <- packages %in% rownames(installed.packages())

if(any(installed_packages == FALSE)){
        install.packages(packages[!installed_packages])
}
# Package Loading
invisible(lapply(packages, library, character.only = TRUE))
```



```{r data loading and summary, warning=FALSE, message=FALSE}
# Download symbol data
TSX_Composite_Data_gs <- getSymbols("^GSPTSE", auto.assign=FALSE, from="2016-01-01", to="2019-12-31")

TSX_Composite_Data <- as.data.frame(TSX_Composite_Data_gs)
TSX_Composite_Data$Date <- time(TSX_Composite_Data_gs)

# Chart series bar graph
chartSeries(TSX_Composite_Data_gs, type = "bars", name = "TSX Composite Price 2016-2019")



```

 

```{r plot close price with day number}
describe(TSX_Composite_Data_gs)

# Close price of stock with day number
plot(~TSX_Composite_Data$Date + TSX_Composite_Data$GSPTSE.Close , main = "Closing price of stock with day number" , pch =1)

```

```{r Corelation}
# Correlation to check linearity component
cor(TSX_Composite_Data_gs[,c(1:5)])

```
```{r T-Test}
# Welch's t-test on Open Price and Close Price
t.test(TSX_Composite_Data_gs$GSPTSE.Open, TSX_Composite_Data_gs$GSPTSE.Close)
# Welch's t-test on Volumne and Close Price
t.test(TSX_Composite_Data_gs$GSPTSE.Volume, TSX_Composite_Data_gs$GSPTSE.Close)



```

```{r linear regression}
#Creating linear model of Date and Volume on Close Price
model <- lm(formula = TSX_Composite_Data$GSPTSE.Close ~ TSX_Composite_Data$Date + TSX_Composite_Data$GSPTSE.Volume)
summary(model)


# Plot of linear model
plot(model)
# Plot Date and Close Price
plot(TSX_Composite_Data$Date,fitted(model), xlab = "Date", ylab = "Close Price", main = "Close Price VS Time Scatterplot")
# Calculate RSS
RSS <- c(crossprod(model$residuals))
# Claculate MSE
MSE <- RSS / length(model$residuals)
# Calculate RMSE
RMSE <- sqrt(MSE)
sprintf("MAPE : %s", MSE)
sprintf("RMSE : %s", RMSE)

# Check residuals of linear model
checkresiduals(model)
# Accuracy level of linear model
accuracy(model)

```
```{r Decomposing and splitting data}
# Take only the closing price
closing_pr <- Cl(to.monthly(TSX_Composite_Data_gs))

# Decompose it
dc <- decompose(as.ts(closing_pr, start=c(2016,1)))
plot(dc)

# Seasonal component 
dc$seasonal

# Number of period we want to forecast
n <- 100

# Splitting the data
train <- head(Cl(TSX_Composite_Data_gs), length(Cl(TSX_Composite_Data_gs))-n)
test <- tail(Cl(TSX_Composite_Data_gs), n)
```


```{r Creating Arima Model}
# Creating Seasonal ARIMA model

# Create the Model
model_s <- auto.arima(train)

# Forecast n periods of the data
fc_s <- forecast(model_s, h=n)

# Plot the result
autoplot(fc_s)+
  autolayer(ts(test, start= length(train)), series="Test Data")


```


```{r Residuals ana Accuracy}

# Residual of ARIMA Model
checkresiduals(fc_s)

#Accuracy Metrics of ARIMA
accuracy(fc_s)

# Summary of ARIMA model
summary(model_s)
```

# Linkedin Link

https://www.linkedin.com/in/chetan-kumar-937a67114/