Stock Price

-From stock price, we find that: + FaceBook’s and Apple’s stock price steadily rised in the past 4 years. + The most volatile stock is AliNVDA. + SBAC has the lowest price, but it has a steady growing trend.

Column {.tabset data-width=600}

Stock Return

Column {.tabset data-width=600}

Key Factors

Column {.tabset data-width=600}

Stock Volume

Row

NVDA

NTES

Row

EQIX

SBAC

Technical Analysis:Candlestick Chart with Bollinger Bands

###NTES

Column {.tabset data-width=300}

###NVDA

Summary

  1. From stock price, we find that : -FaceBook’s and Apple’s stock price steadily rised in the past 4 years.
  1. From stock return, we find that :
  1. From key factors:
  1. From stock volume:
  1. From bollinger bands:

To sum up, our decision is to choose FaceBook. + It has the second highest stock return. + It has a very high trading volume, which indicate the high liquidity of the stock. + Its PE ratio is not too high as AliNVDA, which mean investors paying less to get the earnings from FaceBook comparing with AliNVDA.

---
title: "Stock Selection"
output: 
  flexdashboard::flex_dashboard:
    theme: united
    storyboard: true
    orientation: rows
    vertical_layout: fill
    source_code: embed
    social: menu
---

```{r setup, include=FALSE}
library(flexdashboard)
library(quantmod)
library(ggplot2)
library(tidyr)
library(plyr)
library(dygraphs)
library(DT)
library(tidyquant)
library(lubridate)
tickers <- c("NVDA","NTES","EQIX","SBAC")

```

```{r,message=FALSE, include=FALSE}
#choose three companies: AliNVDA, FaceBook, Apple, and SBAC
getSymbols(tickers, src='yahoo', from='2004-12-31', to= '2021-03-2021')
```


-----------------------------------------------------------------------


Stock Price {data-orientation=rows}
=====================================  

----------------------------------------

```{r}
StockPrices <- do.call(merge, lapply(tickers, function(x) Cl(get(x))))
dateperiod<-c("2004-12-31", "2021-03-2021")
dygraph(StockPrices, main="Stock Price", group="Stock") %>%
    dyAxis("y", label="Stock Price(USD)") %>%
  dyAxis("x", label="Time") %>%
  dyOptions(axisLineWidth = 1.0,  colors = RColorBrewer::brewer.pal(6, "Set1")) %>%
  dyHighlight(highlightSeriesBackgroundAlpha = 2.0,
              highlightSeriesOpts = list(strokeWidth = 3)) %>%
  dyRangeSelector(height = 60)
```

***

-From stock price, we find that:
  + FaceBook's and Apple's stock price steadily rised in the past 4 years. 
  + The most volatile stock is AliNVDA. 
  + SBAC has the lowest price, but it has a steady growing trend.

Column {.tabset data-width=600}


Stock Return {data-orientation=rows}
=====================================  

```{r}
stocks <- cbind(NVDA$BA.Adjusted,NTES$NTES.Adjusted, EQIX$EQIX.Adjusted, SBAC$SBAC.Adjusted)
stock_return <- apply(stocks, 1, function(x) {x / stocks[1,]}) %>%
  t %>% as.xts

plot(as.zoo(stock_return), screens = 4, col=10:12, lty = 18:20, xlab = "Time", ylab = "Return",main='Return for Selected Stocks')
legend("topleft", c("NVDA", "NTES", "EQIX","SBAC"), lty = 18:20, col=10:12, cex = 0.8)
```

Column {.tabset data-width=600}


Key Factors {data-orientation=rows}
=====================================  

----------------------------------------

```{r}
what_metrics <- yahooQF(c("Price/Sales", 
                          "P/E Ratio",
                          "Price/EPS Estimate Next Year",
                          "PEG Ratio",
                          "Dividend Yield", 
                          "Market Capitalization"))

metrics <- getQuote(paste(tickers, sep = "",collapse = ";"),what = what_metrics)
metrics <- data.frame(Symbol=tickers, metrics[,2:length(metrics)])
colnames(metrics) <- c("Symbol", "P-E Ratio", "Price EPS Estimate Next Year", "Div Yield", "Market Cap")
DT::datatable(metrics)
```
Column {.tabset data-width=600}




Stock Volume {data-orientation=rows}
=====================================     
   

Row
-------------------------------------
### NVDA   

```{r}
invisible(getSymbols("NVDA", src = "yahoo", from='2004-12-31', to= '2021-03-2021'))
candleChart(NVDA, up.col = "blue", dn.col = "blue", theme = "white")
```   
 
### NTES

```{r}
invisible(getSymbols("NTES", src = "yahoo", from='2004-12-31', to= '2021-03-2021'))
candleChart(NTES, up.col = "yellow", dn.col = "red", theme = "white")
```


Row
-------------------------------------
### EQIX

```{r}
invisible(getSymbols("AAP", src = "yahoo", from='2004-12-31', to= '2021-03-2021'))
candleChart(EQIX, up.col = "red", dn.col = "red", theme = "white")
```

### SBAC

```{r}
invisible(getSymbols("SBAC", src = "yahoo", from='2004-12-31', to= '2021-03-2021'))
candleChart(SBAC, up.col = "green", dn.col = "red", theme = "white")
```





```{r}

invisible(getSymbols("NTES", from = "2017-01-01", auto.assign=TRUE))
NTES <- as.data.frame(NTES)


# Simple Moving Averages (SMA)
# 20-day SMA
sma20 <- SMA(NTES$NTES.Close, n=20)
# 50-day SMA
sma50 <- SMA(NTES$NTES.Close, n=50)
# 200-day SMA
sma200 <- SMA(NTES$NTES.Close, n=200)

# Exponential Moving Average (EMA)
# 14-day EMA
ema14 <- EMA(NTES$NTES.Close, n=14)

# Bollinger Bands 
bb20 <- BBands(NTES$NTES.Close, sd=2.0, n=14, maType=EMA)
# Overall data frame
NTESplusBB <- data.frame(NTES,bb20)

# Relative Strength Indicator
rsi14 <- RSI(NTESplusBB$NTES.Close, n=14)

#MACD
macd <- MACD(NTESplusBB$NTES.Close, nFast = 12, nSlow = 26,
             nSig = 9, maType = SMA)

# allData
allData <- data.frame(NTES,sma20,sma50,sma200,ema14,bb20,rsi14,macd)

```


Technical Analysis:Candlestick Chart with Bollinger Bands {data-orientation=rows}
=====================================  

----------------------------------------
###NTES

```{r}

m <- cbind(allData[,1:4], allData[,11], allData[,12], allData[,13])
colnames(m)[5]  <- "dn"
colnames(m)[6]  <- "mavg"
colnames(m)[7]  <- "up"

dygraph(m, main = "Bollinger Bands" ) %>%
  dyCandlestick() %>%
  dyRangeSelector(height = 60)

```


Column {.tabset data-width=300}



```{r}

invisible(getSymbols("NVDA", from = "2017-01-01", auto.assign=TRUE))
NVDA <- as.data.frame(NVDA)


# Simple Moving Averages (SMA)
# 20-day SMA
sma20 <- SMA(NVDA$NVDA.Close, n=20)
# 50-day SMA
sma50 <- SMA(NVDA$NVDA.Close, n=50)
# 200-day SMA
sma200 <- SMA(NVDA$NVDA.Close, n=200)

# Exponential Moving Average (EMA)
# 14-day EMA
ema14 <- EMA(NVDA$NVDA.Close, n=14)

# Bollinger Bands 
bb20 <- BBands(NVDA$NVDA.Close, sd=2.0, n=14, maType=EMA)
# Overall data frame
NVDAplusBB <- data.frame(NVDA,bb20)

# Relative Strength Indicator
rsi14 <- RSI(NVDAplusBB$NVDA.Close, n=14)

#MACD
macd <- MACD(NVDAplusBB$NVDA.Close, nFast = 12, nSlow = 26,
             nSig = 9, maType = SMA)

# allData
allData2 <- data.frame(NVDA,sma20,sma50,sma200,ema14,bb20,rsi14,macd)

```


###NVDA

```{r}

m <- cbind(allData2[,1:4], allData2[,11], allData2[,12], allData2[,13])
colnames(m)[5]  <- "dn"
colnames(m)[6]  <- "mavg"
colnames(m)[7]  <- "up"

dygraph(m, main = "Bollinger Bands" ) %>%
  dyCandlestick() %>%
  dyRangeSelector(height = 50)

```




Summary {data-orientation=rows}
=====================================  

----------------------------------------


***
1. From stock price, we find that :
  -FaceBook's and Apple's stock price steadily rised in the past 4 years. 
  + The most volatile stock is AliNVDA. 
  + SBAC has the lowest price, but it has a steady growing trend.
  
2. From stock return, we find that :
  + The return of Apple is lower than the return of FackBook. 
  + As a relative new company, AliNVDA's returns are more fluctuate, and getting higher in recent period
  
3. From key factors:
  + Apple has the largest market cap while SBAC has the smaller market cap
  + Apple has the lowest EPS estimated next year
  + AliNVDA has the highest PE ratio while Apple has the lowest PE ratio.

4. From stock volume:
  + SBAC has the lowest trading volume among four stocks.
 
5. From bollinger bands:
  + FaceBook has a big drop in April while AliNVDA is relative stable recently. 
 
To sum up, our decision is to choose FaceBook. 
  + It has the second highest stock return.
  + It has a very high trading volume, which indicate the high liquidity of the stock.
  + Its PE ratio is not too high as AliNVDA, which mean investors paying less to get the earnings from FaceBook comparing with AliNVDA.