Quiz 4

Q1 Calculate 20-day moving averages and 20-day running standard deviation. Save the result under stock and print it.
Q2 Calculate the Bollinger Bands. Save the result under stock and print it.
Q3 Select variables to build the Bollinger Bands. Save the result under stock_selected and print it.
Q4 Transform data to long form from wide form for graphing.Save the result under stock_long and print it.
Q5 Visualize data.
Q6 If you had invested $1 million on the day of the first buying point and sold all the shares on the following selling point, how much would you have won or lost?
Q7 An analyst may tinker with the width of the Bollinger Bands depending the volatility of the stock. Which of the following scenarios would have yield the largest profit? Elaborate your answer.
Q8 Hide the messages, the code, and its results on the webpage.
Q9 Display the title and your name correctly at the top of the webpage.
Q10 Use the correct slug.

Witht the given stock, conduct the Bollinger Bands analysis by answering the questions below.

## Loading required package: lubridate

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## Loading required package: PerformanceAnalytics

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## Attaching package: 'PerformanceAnalytics'

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## Loading required package: quantmod

## Loading required package: TTR

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

## == Need to Learn tidyquant? ====================================================
## Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
## </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>

## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --

## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.0.6     v dplyr   1.0.4
## v tidyr   1.1.2     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1

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## x lubridate::setdiff()     masks base::setdiff()
## x lubridate::union()       masks base::union()

## # A tibble: 313 x 8
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 MSFT   2020-01-02  159.  161.  158.  161. 22622100     159.
##  2 MSFT   2020-01-03  158.  160.  158.  159. 21116200     157.
##  3 MSFT   2020-01-06  157.  159.  157.  159. 20813700     157.
##  4 MSFT   2020-01-07  159.  160.  157.  158. 21634100     156.
##  5 MSFT   2020-01-08  159.  161.  158.  160. 27746500     158.
##  6 MSFT   2020-01-09  162.  162.  161.  162. 21385000     160.
##  7 MSFT   2020-01-10  163.  163.  161.  161. 20725900     159.
##  8 MSFT   2020-01-13  162.  163.  161.  163. 21626500     161.
##  9 MSFT   2020-01-14  163.  164.  162.  162. 23477400     160.
## 10 MSFT   2020-01-15  163.  164.  163.  163. 21417900     161.
## # ... with 303 more rows

Q1 Calculate 20-day moving averages and 20-day running standard deviation. Save the result under stock and print it.

Hint: Take stock, pipe it to tidyquant::tq_mutate to calculate 20-day moving averages, pipe it to tidyquant::tq_mutate to calculate 20-day running standard deviation, pipe it to rename() to rename value to SD, and assign the result to stock. To calculate the running standard deviation, use runSD in place of SMA. You can see all the available functions in tidyquant::tq_mutate using tq_mutate_fun_options().

## # A tibble: 313 x 10
##    symbol date        open  high   low close   volume adjusted   SMA    SD
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl> <dbl> <dbl>
##  1 MSFT   2020-01-02  159.  161.  158.  161. 22622100     159.    NA    NA
##  2 MSFT   2020-01-03  158.  160.  158.  159. 21116200     157.    NA    NA
##  3 MSFT   2020-01-06  157.  159.  157.  159. 20813700     157.    NA    NA
##  4 MSFT   2020-01-07  159.  160.  157.  158. 21634100     156.    NA    NA
##  5 MSFT   2020-01-08  159.  161.  158.  160. 27746500     158.    NA    NA
##  6 MSFT   2020-01-09  162.  162.  161.  162. 21385000     160.    NA    NA
##  7 MSFT   2020-01-10  163.  163.  161.  161. 20725900     159.    NA    NA
##  8 MSFT   2020-01-13  162.  163.  161.  163. 21626500     161.    NA    NA
##  9 MSFT   2020-01-14  163.  164.  162.  162. 23477400     160.    NA    NA
## 10 MSFT   2020-01-15  163.  164.  163.  163. 21417900     161.    NA    NA
## # ... with 303 more rows

Q2 Calculate the Bollinger Bands. Save the result under stock and print it.

Hint: Take stock, pipe it to mutate(sd2up = SMA + 2 * SD, sd2down = SMA - 2 * SD), and assign the result to stock.

## # A tibble: 313 x 12
##    symbol date        open  high   low close volume adjusted   SMA    SD sd2up
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl> <dbl> <dbl> <dbl>
##  1 MSFT   2020-01-02  159.  161.  158.  161. 2.26e7     159.    NA    NA    NA
##  2 MSFT   2020-01-03  158.  160.  158.  159. 2.11e7     157.    NA    NA    NA
##  3 MSFT   2020-01-06  157.  159.  157.  159. 2.08e7     157.    NA    NA    NA
##  4 MSFT   2020-01-07  159.  160.  157.  158. 2.16e7     156.    NA    NA    NA
##  5 MSFT   2020-01-08  159.  161.  158.  160. 2.77e7     158.    NA    NA    NA
##  6 MSFT   2020-01-09  162.  162.  161.  162. 2.14e7     160.    NA    NA    NA
##  7 MSFT   2020-01-10  163.  163.  161.  161. 2.07e7     159.    NA    NA    NA
##  8 MSFT   2020-01-13  162.  163.  161.  163. 2.16e7     161.    NA    NA    NA
##  9 MSFT   2020-01-14  163.  164.  162.  162. 2.35e7     160.    NA    NA    NA
## 10 MSFT   2020-01-15  163.  164.  163.  163. 2.14e7     161.    NA    NA    NA
## # ... with 303 more rows, and 1 more variable: sd2down <dbl>

Q3 Select variables to build the Bollinger Bands. Save the result under stock_selected and print it.

Hint: Take stock, pipe it to dplyr::select to keep date, close, SMA, sd2up, and sd2down, and assign the result to stock_selected.

## # A tibble: 313 x 5
##    date       close   SMA sd2up sd2down
##    <date>     <dbl> <dbl> <dbl>   <dbl>
##  1 2020-01-02  161.    NA    NA      NA
##  2 2020-01-03  159.    NA    NA      NA
##  3 2020-01-06  159.    NA    NA      NA
##  4 2020-01-07  158.    NA    NA      NA
##  5 2020-01-08  160.    NA    NA      NA
##  6 2020-01-09  162.    NA    NA      NA
##  7 2020-01-10  161.    NA    NA      NA
##  8 2020-01-13  163.    NA    NA      NA
##  9 2020-01-14  162.    NA    NA      NA
## 10 2020-01-15  163.    NA    NA      NA
## # ... with 303 more rows

Q4 Transform data to long form from wide form for graphing.Save the result under stock_long and print it.

Hint: Take stock_selected, pipe it to gather(key = type, value = price, close:sd2down), and assign the result to stock_long.

## # A tibble: 1,252 x 3
##    date       type  price
##    <date>     <chr> <dbl>
##  1 2020-01-02 close  161.
##  2 2020-01-03 close  159.
##  3 2020-01-06 close  159.
##  4 2020-01-07 close  158.
##  5 2020-01-08 close  160.
##  6 2020-01-09 close  162.
##  7 2020-01-10 close  161.
##  8 2020-01-13 close  163.
##  9 2020-01-14 close  162.
## 10 2020-01-15 close  163.
## # ... with 1,242 more rows

Q5 Visualize data.

Hint: Take stock_selected and pipe it to ggplot(). Map date to the x-axis, price to the y-axis, and type to color in the line chart.

## Warning: Removed 57 row(s) containing missing values (geom_path).

Q6 If you had invested $1 million on the day of the first buying point and sold all the shares on the following selling point, how much would you have won or lost?

So on the first buying point on Feb 27th, I could have bought 6,321 shares at $158.18 per share for a total of $999,885.18. On the following sell point on June 8th I could have sold all my shares at $188.36 per share, netting a total $1,190,623.56. This would give me a profit of $190,738.38.

Q7 An analyst may tinker with the width of the Bollinger Bands depending the volatility of the stock. Which of the following scenarios would have yield the largest profit? Elaborate your answer.

Hint: A correct answer must show the per-share profit each scenario, the difference between the buying price and the selling price.

the upper band being 2 sd up from the average and the lower band 2 sd below (standard)
the upper band being 2.5 sd up from the average and the lower band 2 sd below

The upper band if from the average than the lower band, there fore there is more room to yield a positive return and statistically a higher chance.

Q8 Hide the messages, the code, and its results on the webpage.

Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.