---
title: "Lab 1 - Dashboards & Dashboarding Theory [Finance]"
author: "Adil Abdul Habeeb"
date: "`r Sys.Date()`"

output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
    vertical_layout: fill
  html_document:
    df_print: paged
---
# Conclusion {.sidebar}

**Table of Contents:**

* Agenda                                                
  Overview                 
  Objective                              
  The Decision & Rules                        
  Methods Help 
  
* Key Indicator Analysis                          
  Metrics                                 
  Metric Analysis          
  
* Individual Stock Analysis
  Companys Stocks Performances
  
* Monthly Return Comparision

* Conclusion

# **Agenda**

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### **Overview** 
Due to the inherent growth in the electronic production and storage of information, there is often a feeling of “information overload” or inundation when facing the process of quantitative decision making. As an analyst your job will often be to conduct analyses or create tools to support quantitative decision making.

A principle tool used in industry, government, non-profits, and academic fields to compensate for the information overload is the information dashboard. Functionally, a dashboard is meant to provide a user with a central resource to present in a clear and concise manner all the information necessary to support day-to-day decision making and support operations.

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### **Objective** 

The objective of this laboratory is to plan, design, and create an information dashboard to support quantitative decision making. To accomplish this task, you will have to complete a number of steps:

1.Delineate the necessary decision (I will do that below).
2.Identify what information will be relevant to decision making.
3.Find and collect the data necessary to create your visualization plan.
4.Organize and summarize the collected data.
5.Design and create the best visualizations to present that information.
6.Finally organize the layout of those visualizations in a way that conforms to the theory of dashboarding.
7.Write a summary about what decisions you made based on the visualizations that you developed.

### **The Decision & Rules**

You make investments for an organization; your objective is to purchase securities/commodities for the key objective of maximizing profits. You want to make an investment in securities/commodities to make some short-term gains. You are considering investing in one of any four companies, for example: Twitter (TWTR), Microsoft (MSFT), or Apple (AAPL) (don’t use these). Choose 4 companies or commodities and determine which one of the four will produce the most short-term gains. Use your imagination.

### **Methods Help**
##### *Getting data*
There are lots of places we can get financial data to support this decision. The simplest would be to go to for instance to the Yahoo Finance (https://finance.yahoo.com/) for data on the Hershey Company (HSY) the URL would be: (https://finance.yahoo.com/quote/HSY/history?p=HSY) and collect historical price data, and other financial and company information.


# **Key Indicator Analysis**
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-------------------------
### **Financial Indicators/Metrics**

+ `r kableExtra::text_spec("**P-E Ratio**", color = "#5c5c5c")` - It is the ratio of a company's share price to the company's earnings per share. This ratio is used for valuing companies and to find out whether they are overvalued or undervalued.
+ `r kableExtra::text_spec("**EPS**", color = "#5c5c5c")` - It is the portion of a company's profit that is allocated to every individual share of the stock and helps in understanding the financial strength of a company
+ `r kableExtra::text_spec("**Dividend Yield Ratio**", color = "#5c5c5c")` - is a financial ratio that shows how much a company pays out in dividends each year relative to its stock price.
+ `r kableExtra::text_spec("**Market Cap**", color = "#5c5c5c")` - the total value of a company's shares.

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### **Key Indicator Analysis at a glance**
The table provides financial metrics for 4 big tech companies - META, TESLA, NETFLIX, GOOGLE. We can have a quick analysis based on the information we get: There are also indicators like the symbol, P-E ratio, price EPS estimate for the following year, division yields, and market Cap.Since all of the companies being evaluated are large-cap, the ones we are evaluating may not necessarily generate significant returns quickly. 

```{r, echo = TRUE, include = FALSE, message = FALSE}
library(xts) 
library(pdfetch)
library(DT)
library(lubridate)
library(dygraphs)
library(quantmod) 
library(dplyr)
library(knitr)
library(ggplot2)
library(tidyr)
library(plyr)
library(PerformanceAnalytics)
library(stocks)
library(kableExtra)
installed.packages("dplyr")
```


Row
-------------------------

```{r}
library(quantmod)
library(plyr)
what_metrics <- yahooQF(c("Price/Sales", 
                          "P/E Ratio",
                          "Price/EPS Estimate Next Year",
                          "PEG Ratio",
                          "Dividend Yield", 
                          "Market Capitalization"))
tickers <- c("META", "TSLA","NFLX",  "GOOG")
metrics <- getQuote(paste(tickers, sep = ",", collapse = ";"), what = what_metrics)
metrics <- data.frame(Symbol = tickers, metrics[,2:length(metrics)]) 
DT::datatable(metrics)

write.csv(metrics, "FinancialMetrics_new.csv", row.names=FALSE)
```

# **Individual Stock Analysis**

#### **Stocks**

```{r,echo=FALSE}
getSymbols("META", from = "2019-01-01",to = "2023-01-1")
METAlog <- META %>% Ad() %>% dailyReturn( type = 'log')
getSymbols("TSLA", from = "2019-01-01",to = "2023-01-1")
TSLAlog <- TSLA  %>% Ad() %>% dailyReturn(type = 'log')
getSymbols("NFLX", from = "2019-01-01", to = "2023-01-1")
NFLXlog <- NFLX %>% Ad() %>% dailyReturn(type = 'log')
getSymbols("GOOG", from = "2019-01-01",to = "2023-01-1")
GOOGlog <- GOOG %>% Ad() %>% dailyReturn(type = 'log')
```


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-------------------------------------
### META

```{r}
META %>% Ad() %>% chartSeries()
META %>% chartSeries(TA = 'addBBands();addVo();addMACD()',subset='2019')
```

### TSLA
```{r}
TSLA %>% Ad() %>% chartSeries()
TSLA %>% chartSeries(TA = 'addBBands();addVo();addMACD()',subset='2019')
```

### NETFLIX
```{r}
NFLX %>% Ad() %>% chartSeries()
NFLX %>% chartSeries(TA = 'addBBands();addVo();addMACD()',subset='2019') 
```

### GOOGLE
```{r}
GOOG %>% Ad() %>% chartSeries()
GOOG %>% chartSeries(TA = 'addBBands();addVo();addMACD()',subset='2019')
```

Monthly Return Comparison & Conclusion
===================================== 
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-------------------------------------
### **Monthly Return Comparison**  

In addition to the individual stock analysis, below is the comparison of their Monthly return in one combined plot.

```{r}
METAmr <- monthlyReturn(META)
TSLAmr <- monthlyReturn(TSLA)
NFLXmr <- monthlyReturn(NFLX)
GOOGmr <- monthlyReturn(GOOG)

mg.return <- merge.xts(METAmr, TSLAmr, NFLXmr, GOOGmr)
colnames(mg.return) <- c( "META", "TESLA", "NETFLIX","GOOGLE")

dygraph(mg.return, main = "Monthly Return") %>%
  dyAxis("y", label = "Return") %>%
  dyOptions(colors = RColorBrewer::brewer.pal(4, "Dark2")) %>%
  dyHighlight(highlightSeriesBackgroundAlpha = 0.3,
              highlightSeriesOpts = list(strokeWidth = 3)) %>%
  dyRangeSelector(height = 30)

```


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-------------------------------------
### **Conclusion**   
In this assignment, the stock prices of four companies ( Meta [META],TESLA [TSLA],Netflix [NFLX] and GOOGLE [GOOG]) are visually analyzed.Yahoo Finance data is used for the time period January 2019 till December 2022.

Different Key Indicators are visualized for four companies in question.Individual Stock analysis is also analyzed from 2019 to end of 2022 year.

From a short-term investment perspective, Meta (META) and Netflix (NFLX) stock prices have increased significantly over the past six months.The price of Google (GOOG) has fluctuated several times but has largely remained the same.Tesla stock has seen decline in the monthly return analysis.The dividend yield value for all four companies is 0 ,indicating this Key Indicator is not taken into consideration for comparing the short term profits.

Based on EPS estimated for next year , I would select META to invest for obtaining short term profits, since the P/E ratio is low compared to others.