Observation :

All the graphs show exponential or at least near exponential rise in two companies.

library(dplyr)

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googlAllStock <- filter(stockdata, stockdata$Name == "GOOGL")
googlAllStock$AvgOC <- ( googlAllStock$Open + googlAllStock$Close ) / 2

amznAllStock <- filter(stockdata, stockdata$Name == "AMZN")
amznAllStock$AvgOC <- ( amznAllStock$Open + amznAllStock$Close ) / 2

a1 <- ggplot(aes(x = as.Date(Date), y = AvgOC), data = googlAllStock) +
  geom_line(colour = 'blue') +
  xlab("GOOGL : Year")

a2 <- ggplot(aes(x = as.Date(Date), y = AvgOC), data = amznAllStock) +
  geom_line(colour = 'red') +
  xlab("AMZN : Year")

grid.arrange(a1, a2, ncol = 2)

Candlestick chart :

The candlestick chart is used to describe the price movement of stocks. Each candlestick represents one day. It is a combination of line graph and bar graph. Each bar represents all four important pieces of information for that day: The Open, the Close, the High and the Low. I have used conventional coloring for the candles. The green color signifies that the Close price was greater than the Open price of the stock for that day. The red color signifies that the Open price was greater than the Close price for that day. In an attempt to enhance the readability of graph, I have used hover text indicating all the details - Date, Open, High, Low, Close, Volume and the name of the company as well. The hover can be used for comparison of two consecutive trading days by clicking on the ‘Compare data on hover’ icon of the candlestick graph.

library(plotly)

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library(tidyr)

aaplStock <- filter(stockdata, stockdata$Name == "AAPL")

aaplStock$Date <- as.Date(aaplStock$Date)

hovertxt <- Map(function(x, y)paste0(x, ":", y), names(aaplStock), aaplStock)
hovertxt <- Reduce(function(x, y)paste0(x, "<br&gt;", y), hovertxt)

plot_ly(data = aaplStock, x = ~Date, xend = ~Date, color = ~Close > Open, colors = c("red","green")) %>%
  
  add_segments(y = ~Low, yend = ~High, line = list(width = 1, color = "black")) %>%
  
  add_segments(y = ~Open, yend = ~Close, line = list(width = 3)) %>%
  
  add_markers(y = ~(Low + High)/2, hoverinfo = "text",
              text = hovertxt, marker = list(color = "transparent")) %>% 
  
  layout(title = "Basic Candlestick Chart") %>%

  layout(showlegend = FALSE, 
         yaxis = list(title = "Price", domain = c(0, 0.9)),
         annotations = list(
           list(xref = "paper", yref = "paper", 
                x = 0, y = 1, showarrow = F, 
                xanchor = "left", yanchor = "top",
                align = "left",
                text = paste0("<b>AAPL</b>")),
           
           list(xref = "paper", yref = "paper", 
                x = 0.75, y = 1, showarrow = F, 
                xanchor = "left", yanchor = "top",
                align = "left",
                text = paste(range(aaplStock$Date), collapse = " : "),
                font = list(size = 8))),
         plot_bgcolor = "#f2f2f2")

1. Yearly Returns:

The motivation behind investing in stocks is the returns they give over time. The dataset consists of stock data for twelve years and hence annual returns can be calculated along with other time-specific intervals. The typical time intervals for calculating the returns are yearly, quarterly, monthly, weekly and daily. The following graphs depict the returns over these intervals either for all companies and a group of up to four companies showing irregular spikes. The first graphs gives the yearly returns. It is interesting to observe how the scales of the Y-axis i.e. returns change for different time intervals.

library(lubridate)

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library(quantmod)

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library(tidyquant)

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stockData_yearly_returns <- stockdata %>%
    group_by(Name) %>%
    tq_transmute(select     = Close, 
                 mutate_fun = periodReturn, 
                 period     = "yearly", 
                 type       = "arithmetic")
#stockData_yearly_returns

ggplot(data = stockData_yearly_returns, aes(x = Date, y = yearly.returns)) + 
  geom_line(aes(color = Name)) +
  xlab("Years") +
  ylab("Yearly Returns") + 
  ggtitle(" Yearly Returns of all companies: 2006 - 2017")

Observation (Yearly Returns):

The graph shows a very clear dip in returns in the year 2009. This seems quite natural given the financial scenario during that period. It is obvious that the impact of the financial crisis of 2008 would be visible thereafter. The crisis had long lasting impact after bouncing back from the crisis as can be seen by the linear returns on stocks for majority of the companies. The prominent spikes of four companies are plotted separately.

spike_stockdata_yly_ret <- subset(stockdata, 
                                  stockdata$Name == "AABA" | stockdata$Name == "AAPL" | stockdata$Name == "AMZN" | stockdata$Name == "BA")

spike_stockdata_yly_ret <- spike_stockdata_yly_ret %>%
    group_by(Name) %>%
    tq_transmute(select     = Close, 
                 mutate_fun = periodReturn, 
                 period     = "yearly", 
                 type       = "arithmetic")
#spike_stockdata_yly_ret

ggplot(data = spike_stockdata_yly_ret, aes(x = Date, y = yearly.returns)) + 
  geom_line(aes(color = Name)) +
  xlab("Years") +
  ylab("Yearly Returns") + 
  ggtitle(" Yearly Returns of visible Spikes: 2006 - 2017")

2. Quarterly Returns:

The second graph of the set series is the quarterly returns. The time period considered is 2008 -2009. The objective of choosing different time periods and companies is to facilitate in subsetting the data by Name and Date in the hope of finding patterns and irregularities in those periods. These should be considered as only representational.

stockData_qly_ret_sub <- subset(stockdata,stockdata$Date >= "2008-01-01" & stockdata$Date <= "2009-12-31")

stockData_qly_ret_sub <- stockData_qly_ret_sub %>%
    group_by(Name) %>%
    tq_transmute(select     = Close, 
                 mutate_fun = periodReturn, 
                 period     = "quarterly", 
                 type       = "arithmetic")
#stockData_qly_ret_sub

ggplot(data = stockData_qly_ret_sub, aes(x = Date, y = quarterly.returns)) + 
  geom_line(aes(color = Name)) +
  xlab("Years") +
  ylab("Quarterly Returns") + 
  ggtitle(" Quarterly Returns of all companies: 2008-09")

### Observation (Quarterly Returns): The returns are fairly linear from Jan. 2008 to October 2008. Thereafter the returns diminish till February 2009 approximately. Then there is a overall increase in returns of all companies till December 2009. The four companies with highest spikes are separately shown below combined in a single graph.

spike_stockData_qly_ret_sub <- subset(stockdata, 
                                        (stockdata$Name == "AMZN" | stockdata$Name == "AXP" | stockdata$Name == "CAT" | stockdata$Name == "JPM") 
                                        & (stockdata$Date >= "2008-01-01" & stockdata$Date <= "2009-12-31"))

spike_stockData_qly_ret_sub <- spike_stockData_qly_ret_sub %>%
    group_by(Name) %>%
    tq_transmute(select     = Close, 
                 mutate_fun = periodReturn, 
                 period     = "quarterly", 
                 type       = "arithmetic")
#spike_stockData_qly_ret_sub

ggplot(data = spike_stockData_qly_ret_sub, aes(x = Date, y = quarterly.returns)) + 
  geom_line(aes(color = Name)) +
  xlab("Years") +
  ylab("Quarterly Returns") + 
  ggtitle(" Quarterly Returns of visible spikes: 2008-09")

3. Monthly returns:

The monthly returns of all the companies for the year 2010 - January through December, are shown in the following graph. The graph is more of a sinusoidal wave with ups and downs. Most of the companies follow this behavior but there are one or two incidences where there is an observable deviation but that turns out to be temporary.

mnthly_StockData <- subset(stockdata, stockdata$Date >= "2010-01-01" & stockdata$Date <= "2010-12-31")

mnthly_StockData <- mnthly_StockData %>%
    group_by(Name) %>%
    tq_transmute(select     = Close, 
                 mutate_fun = periodReturn, 
                 period     = "monthly", 
                 type       = "arithmetic")
#mnthly_StockData

ggplot(data = mnthly_StockData, aes(x = Date, y = monthly.returns)) + 
  geom_line(aes(color = Name)) +
  xlab("Years") +
  ylab("Monthly Returns") +
  ggtitle("Monthly Returns : 2010")

Observation (Monthly Returns):

The graph is more of a sinusoidal wave with ups and downs. Most of the companies follow this behavior but there are one or two incidences where there is an observable deviation but that turns out to be temporary.

mnthly_StockData_ret <- subset(stockdata, 
                               (stockdata$Name == "HD" | stockdata$Name == "CSCO" | stockdata$Name == "GS" | stockdata$Name == "MSFT") 
                               & (stockdata$Date >= "2010-01-01" & stockdata$Date <= "2010-12-31"))

mnthly_StockData_ret <- mnthly_StockData_ret %>%
    group_by(Name) %>%
    tq_transmute(select     = Close, 
                 mutate_fun = periodReturn, 
                 period     = "monthly", 
                 type       = "arithmetic")
#mnthly_StockData_ret

ggplot(data = mnthly_StockData_ret, aes(x = Date, y = monthly.returns)) + 
  geom_line(aes(color = Name)) +
  xlab("Years") +
  ylab("Monthly Returns") +
  ggtitle("Monthly Returns : 2010")

4. Weekly Returns

The fourth graph of the returns series is the weekly graph where the returns are calculated on weekly basis. I have chosen a time period of one year which approximately 50 weeks. The randomly chosen time period is from June 2008 to June 2009.

wkly_Stock <- subset(stockdata, (stockdata$Date >= "2008-06-01" & stockdata$Date <= "2009-06-30"))

wkly_Stock <- wkly_Stock %>%
    group_by(Name) %>%
    tq_transmute(select     = Close, 
                 mutate_fun = periodReturn, 
                 period     = "weekly", 
                 type       = "arithmetic")
#wkly_Stock

ggplot(data = wkly_Stock, aes(x = Date, y = weekly.returns)) +
  geom_line(aes(color = Name)) +
  xlab("Years") +
  ylab("Weekly Returns") +
  ggtitle("Weekly Returns : June 2008 - June 2009")

Observation (Weekly Returns) :

The returns are confined mostly within the range -0.2 to 0.2 but there are certain spikes which go well beyond this range and such spikes are again plotted separately. The major focus is on the returns that at least touch the mark of 0.4. There are three observations that hit the mark of which two go beyond significantly beyond the mark.

wkly_Stock_ret <- subset(stockdata, 
                         (stockdata$Name == "JPM" | stockdata$Name == "UNH" | stockdata$Name == "GS" | stockdata$Name == "CVX") 
                         & (stockdata$Date >= "2008-06-01" & stockdata$Date <= "2009-06-30"))

wkly_Stock_ret <- wkly_Stock_ret %>%
    group_by(Name) %>%
    tq_transmute(select     = Close, 
                 mutate_fun = periodReturn, 
                 period     = "weekly", 
                 type       = "arithmetic")
#wkly_Stock_ret

ggplot(data = wkly_Stock_ret, aes(x = Date, y = weekly.returns)) +
  geom_line(aes(color = Name)) +
  xlab("Years") +
  ylab("Weekly Returns") +
  ggtitle("Weekly Returns : June 2008 - June 2009")

5. Daily Returns :

The last of the series is the daily returns. The randomly chosen time period is of one year from January 2017 to December 2017. The spikes in the plot are observed and highlighted in the next graph.

dly_Stock <- subset(stockdata, stockdata$Date >= "2017-01-01")

dly_Stock <- dly_Stock %>%
    group_by(Name) %>%
    tq_transmute(select     = Close, 
                 mutate_fun = periodReturn, 
                 period     = "daily", 
                 type       = "arithmetic")
#dly_Stock

ggplot(data = dly_Stock, aes(x = Date, y = daily.returns)) +
  geom_line(aes(color = Name)) +
  xlab("Years") +
  ylab("Daily Returns") +
  ggtitle("Daily Returns : 2017")

Observation (Daily Returns):

The spikes in daily returns are significant for three to four companies between the months of October and December. The spikes are towards the end of October to be more precise. Two graphs are plotted as a part to detailed view of the daily analysis. First part zooms in on the last week of October i.e 23 - 31 October, 2017. The daily returns of all the companies are plotted. The second graph is the observed spikes plotted separately.

spike_dly_Stock <- subset(stockdata, (stockdata$Date >= "2017-10-23" & stockdata$Date <= "2017-10-31"))

spike_dly_Stock <- spike_dly_Stock %>%
    group_by(Name) %>%
    tq_transmute(select     = Close, 
                 mutate_fun = periodReturn, 
                 period     = "daily", 
                 type       = "arithmetic")
#spike_dly_Stock

#d1 <- 
ggplot(data = spike_dly_Stock, aes(x = Date, y = daily.returns)) +
  geom_line(aes(color = Name)) +
  xlab("Years") +
  ylab("Daily Returns") +
  ggtitle("Daily Returns for Oct 23 - 31, 2017")

spike_dly_Stock_4 <- subset(stockdata, 
                            (stockdata$Name == "CVX" | stockdata$Name == "MRK" | stockdata$Name == "INTC" | stockdata$Name == "MMM")
                            & (stockdata$Date >= "2017-10-23" & stockdata$Date <= "2017-10-31"))

spike_dly_Stock_4 <- spike_dly_Stock_4 %>%
    group_by(Name) %>%
    tq_transmute(select     = Close, 
                 mutate_fun = periodReturn, 
                 period     = "daily", 
                 type       = "arithmetic")
#spike_dly_Stock_4

#d2 <- 
ggplot(data = spike_dly_Stock_4, aes(x = Date, y = daily.returns)) +
  geom_line(aes(color = Name)) +
  xlab("Days") +
  ylab("Daily Returns") +
  ggtitle("Sampling Daily Returns")

#grid.arrange(d1, d2, ncol = 2)

NOTE :

The time periods and detailed spikes observations are meant to showcase the ability to zoom in on a particular time scale and thereafter selectively choosing the data that deviates from the normal behavior. This is done as a precursor to call attention to such deviations which serve as a stepping stone in further investigations attempting to explain the oddities in the behavior. The investigations and explanations are not explored in this analysis.

1. Trend : MACD

The Moving Average Convergence Divergence (MACD) graph is plotted as an example for ‘Trend’. The MACD graph for company maned DIS for the year 2011 is plotted below. The company names are chosen in such a way that so as to cover most of the dataset.

disStock <- subset(stockdata, Name == "DIS")

disStock_macd <- disStock %>%
    group_by(Name) %>%
    tq_mutate(select     = Close, 
              mutate_fun = MACD, 
              nFast      = 12, 
              nSlow      = 26, 
              nSig       = 9, 
              maType     = SMA) %>%
    mutate(diff = macd - signal) %>%
    select(-(Open:Volume))
#disStock_macd

disStock_macd %>%
    filter(Date >= "2011-01-01" & Date <= "2011-12-31") %>%
    ggplot(aes(x = Date)) + 
    geom_hline(yintercept = 0, color = palette_light()[[1]]) +
    geom_line(aes(y = macd, col = Name)) +
    geom_line(aes(y = signal), color = "blue", linetype = 2) +
    geom_bar(aes(y = diff), stat = "identity", color = palette_light()[[1]]) +
    labs(title = "DIS: Moving Average Convergence Divergence - 2011",
         y = "MACD", x = "", color = "") +
    theme_tq() +
    scale_color_tq()

2. Momentum: RSI

The Relative Strength Index (RSI) is plotted as an example for ‘Momentum’. The RSI graph for the company WMT for the year 2014 is plotted below.

library(TTR)

wmt_Stock <- subset(stockdata, (stockdata$Name == "WMT")
                    & (stockdata$Date >= "2014-01-01" & stockdata$Date <= "2014-12-31"))

# Calculate the RSI indicator

rsi <- RSI(Cl(wmt_Stock),2) 
sigup <- ifelse(rsi < 10, 1, 0)
sigdn <- ifelse(rsi > 90, -1, 0)


sigup <- lag(sigup,1) 
sigdn <- lag(sigdn,1) 

sigup[is.na(sigup)] <- 0
sigdn[is.na(sigdn)] <- 0 
sig <- sigup + sigdn 
ret <- ROC(Cl(wmt_Stock))
ret[1] <- 0 
eq_up <- cumprod(1+ret*sigup)
eq_dn <- cumprod(1+ret*sigdn*-1)
eq_all <- cumprod(1+ret*sig) 
plot.zoo( cbind(eq_up, eq_dn), ylab=c("Long","Short"), col=c("green", "red"), main="Simple RSI(2) Strategy: WMT: 2014")

3. Volatality : Bollinger Bands

The Bollinger Bands (BB) is plotted as an example for ‘Volatility’. The BB graph for the company JNJ for the year 2015 is plotted below.

jnj_Stock <- subset(stockdata, Name == "JNJ" & (Date >= "2015-01-01" & Date <= "2015-12-31"))
jnj_Stock[is.na(jnj_Stock)] <- 0

jnj_Stock$Date <- strptime(jnj_Stock$Date, format = "%Y-%m-%d")
JNJ <- xts(jnj_Stock$Close, order.by = jnj_Stock$Date)

chartSeries(JNJ, subset = jnj_Stock)

addBBands()

4. Volume : Rate of Change - Volume

The Rate of Change (ROCV) is plotted as an example for ‘Volume’ type. The ROCV graph for the company IBM for the year 2010 is plotted below.

ibm_Stock <- subset(stockdata, Name == "MSFT" & (Date >= "2010-01-01" & Date <= "2010-12-31"))
ibm_Stock[is.na(ibm_Stock)] <- 0

ibm_Stock$Date <- strptime(ibm_Stock$Date, format = "%Y-%m-%d")
IBM <- xts(ibm_Stock$Close, order.by = ibm_Stock$Date)

chartSeries(IBM, subset = ibm_Stock)

addROC()

Simple Moving Average : SMA

The Simple Moving Average (SMA) for MCD for the year 2017 is plotted below.

mcdStock <- subset(stockdata, Name == "MCD" & (Date >= "2017-01-01" & Date <= "2017-12-31"))
mcdStock[is.na(mcdStock)] <- 0

mcdStock$Date <- strptime(mcdStock$Date, format = "%Y-%m-%d")
mcdStock <- xts(mcdStock$Close, order.by = mcdStock$Date)

candleChart(mcdStock, up.col = "black", dn.col = "red", theme = "white")

addSMA(n = c(20,50,100))

1. Candlestick Chart

The candlestick chart is used to describe the price movement of stocks. Each candlestick represents one day. It is a combination of line graph and bar graph. Each bar represents all four important pieces of information for that day: The Open, the Close, the High and the Low. I have used conventional coloring for the candles. The green color signifies that the Close price was greater than the Open price of the stock for that day. The red color signifies that the Open price was greater than the Close price for that day. In an attempt to enhance the readability of graph, I have used hover text indicating all the details - Date, Open, High, Low, Close, Volume and the name of the company as well. The hover can be used for comparison of two consecutive trading days by clicking on the ‘Compare data on hover’ icon of the candlestick graph.

#library(plotly)
#library(tidyr)

aaplStock <- filter(stockdata, stockdata$Name == "AAPL")

aaplStock$Date <- as.Date(aaplStock$Date)

hovertxt <- Map(function(x, y)paste0(x, ":", y), names(aaplStock), aaplStock)
hovertxt <- Reduce(function(x, y)paste0(x, "<br&gt;", y), hovertxt)

plot_ly(data = aaplStock, x = ~Date, xend = ~Date, color = ~Close > Open, colors = c("red","green")) %>%
  
  add_segments(y = ~Low, yend = ~High, line = list(width = 1, color = "black")) %>%
  
  add_segments(y = ~Open, yend = ~Close, line = list(width = 3)) %>%
  
  add_markers(y = ~(Low + High)/2, hoverinfo = "text",
              text = hovertxt, marker = list(color = "transparent")) %>% 
  
  layout(title = "Basic Candlestick Chart") %>%

  layout(showlegend = FALSE, 
         yaxis = list(title = "Price", domain = c(0, 0.9)),
         annotations = list(
           list(xref = "paper", yref = "paper", 
                x = 0, y = 1, showarrow = F, 
                xanchor = "left", yanchor = "top",
                align = "left",
                text = paste0("<b>AAPL</b>")),
           
           list(xref = "paper", yref = "paper", 
                x = 0.75, y = 1, showarrow = F, 
                xanchor = "left", yanchor = "top",
                align = "left",
                text = paste(range(aaplStock$Date), collapse = " : "),
                font = list(size = 8))),
         plot_bgcolor = "#f2f2f2")

Observation :

The majority of the graph is green in color implying that the Close value is greater than the Open value. This hints to a bullish trend. There are three major observable bearish trends - first during the year end of 2012 and beginning of 2013, second during the year 2015 to 2016 and the third one during the end of 2017. Other than these three bearish trends the graph projects an overall bullish trend. Of course, this is a generalized behavior over time and not individual days.

2. Bollinger Bands

The Bollinger Bands (BB) is plotted as an example for ‘Volatility’. The BB graph for the company JNJ for the year 2015 is plotted below. Bollinger Bands can be used to measure the “highness” or “lowness” of the price relative to previous trades.The purpose of Bollinger Bands is to provide a relative definition of high and low. By definition, prices are high at the upper band and low at the lower band. This definition can aid in rigorous pattern recognition and is useful in comparing price action to the action of indicators to arrive at systematic trading decisions.

jnj_Stock <- subset(stockdata, Name == "JNJ" & (Date >= "2015-01-01" & Date <= "2015-12-31"))
jnj_Stock[is.na(jnj_Stock)] <- 0

jnj_Stock$Date <- strptime(jnj_Stock$Date, format = "%Y-%m-%d")
JNJ <- xts(jnj_Stock$Close, order.by = jnj_Stock$Date)

chartSeries(JNJ, subset = jnj_Stock)

addBBands()

Observation :

The behavior seems to down-trending and has hit quite low values in the interval from August and September. From October onward, the stocks have shoot up to recover and approximately reach back to the values at the beginning of the year.

3. Simple Moving Averages

The Simple Moving Average (SMA) for MCD for the year 2017 is plotted below. Moving Averages are an important analytical tool used to identify current price trends and the potential for a change in an established trend. The simplest form of using a simple moving average in analysis is using it to quickly identify if a security is in an uptrend or downtrend.

mcdStock <- subset(stockdata, Name == "MCD" & (Date >= "2017-01-01" & Date <= "2017-12-31"))
mcdStock[is.na(mcdStock)] <- 0

mcdStock$Date <- strptime(mcdStock$Date, format = "%Y-%m-%d")
mcdStock <- xts(mcdStock$Close, order.by = mcdStock$Date)

candleChart(mcdStock, up.col = "black", dn.col = "red", theme = "white")

addSMA(n = c(20,50,100))

Observation :

The period over which the SMA is averaged is 20, 50 and 100 days. There is one crossover at the beginning of October. The stocks for MCD in the year 2017 are clearly an uptrend.