Quiz on Bollinger Bands by Tom Merritt
- Q1 Load tidyquant and tidyverse packages.
- Q2 Import Target, a retail giant, for the last one year. Save the result under stock and print it.
- Q3.a Calculate 20-day moving averages and 20-day running standard deviation. Save the result under stock and print it.
- Q3.b Calculate the Bollinger Bands. Save the result under stock and print it.
- Q3.c Keep variables to build the Bollinger Bands. Save the result under stock_selected and print it.
- Q3.d Transform data to long form from wide form for graphing.Save the result under stock_long and print it.
- Q3.e Visualize data.
- Q4 Add a subtitle to the chart that states “Target during the last one year”
- Q5 Identify the first buying opportunity with the date and closing price.
- Q6 If you had invested $1 million on the day of the first buying point and followed the Bollinger Bands strategy, when would you have sold the stocks and at what price? Consider only one selling point immediately after the buying point in Q5.
- Q7 How much would you have made or lost?
- Q8 Hide the code but display the results of the code on the webpage.
- Q9 Display the title and your name correctly at the top of the webpage.
- Q10 Use the correct slug.
Q1 Load tidyquant and tidyverse packages.
Hint: Use library().
Q2 Import Target, a retail giant, for the last one year. Save the result under stock and print it.
Hint: Use tq_get() from the tidyquant package.
## # A tibble: 251 x 7
## date open high low close volume adjusted
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2018-03-28 68.7 71.8 68.7 69.2 8052400 66.9
## 2 2018-03-29 69.2 69.9 68.9 69.4 7594300 67.1
## 3 2018-04-02 69.0 69.4 67.4 69.0 5903000 66.7
## 4 2018-04-03 69.4 69.9 69.0 69.5 3484200 67.2
## 5 2018-04-04 68.9 71.9 68.8 71.9 5200200 69.5
## 6 2018-04-05 72.0 72.6 70.7 72.4 4525100 70.1
## 7 2018-04-06 71.8 73.4 71.5 72.3 6360100 69.9
## 8 2018-04-09 72.4 72.7 71.4 71.5 3093300 69.1
## 9 2018-04-10 72.9 73.0 71.8 72.8 4653900 70.4
## 10 2018-04-11 72.5 73.5 72.1 72.8 3545700 70.4
## # ... with 241 more rowsQ3.a 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: 251 x 9
## date open high low close volume adjusted SMA SD
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2018-03-28 68.7 71.8 68.7 69.2 8052400 66.9 NA NA
## 2 2018-03-29 69.2 69.9 68.9 69.4 7594300 67.1 NA NA
## 3 2018-04-02 69.0 69.4 67.4 69.0 5903000 66.7 NA NA
## 4 2018-04-03 69.4 69.9 69.0 69.5 3484200 67.2 NA NA
## 5 2018-04-04 68.9 71.9 68.8 71.9 5200200 69.5 NA NA
## 6 2018-04-05 72.0 72.6 70.7 72.4 4525100 70.1 NA NA
## 7 2018-04-06 71.8 73.4 71.5 72.3 6360100 69.9 NA NA
## 8 2018-04-09 72.4 72.7 71.4 71.5 3093300 69.1 NA NA
## 9 2018-04-10 72.9 73.0 71.8 72.8 4653900 70.4 NA NA
## 10 2018-04-11 72.5 73.5 72.1 72.8 3545700 70.4 NA NA
## # ... with 241 more rowsQ3.b 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: 251 x 11
## date open high low close volume adjusted SMA SD sd2up
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2018-03-28 68.7 71.8 68.7 69.2 8.05e6 66.9 NA NA NA
## 2 2018-03-29 69.2 69.9 68.9 69.4 7.59e6 67.1 NA NA NA
## 3 2018-04-02 69.0 69.4 67.4 69.0 5.90e6 66.7 NA NA NA
## 4 2018-04-03 69.4 69.9 69.0 69.5 3.48e6 67.2 NA NA NA
## 5 2018-04-04 68.9 71.9 68.8 71.9 5.20e6 69.5 NA NA NA
## 6 2018-04-05 72.0 72.6 70.7 72.4 4.53e6 70.1 NA NA NA
## 7 2018-04-06 71.8 73.4 71.5 72.3 6.36e6 69.9 NA NA NA
## 8 2018-04-09 72.4 72.7 71.4 71.5 3.09e6 69.1 NA NA NA
## 9 2018-04-10 72.9 73.0 71.8 72.8 4.65e6 70.4 NA NA NA
## 10 2018-04-11 72.5 73.5 72.1 72.8 3.55e6 70.4 NA NA NA
## # ... with 241 more rows, and 1 more variable: sd2down <dbl>Q3.c Keep 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: 251 x 5
## date close SMA sd2up sd2down
## <date> <dbl> <dbl> <dbl> <dbl>
## 1 2018-03-28 69.2 NA NA NA
## 2 2018-03-29 69.4 NA NA NA
## 3 2018-04-02 69.0 NA NA NA
## 4 2018-04-03 69.5 NA NA NA
## 5 2018-04-04 71.9 NA NA NA
## 6 2018-04-05 72.4 NA NA NA
## 7 2018-04-06 72.3 NA NA NA
## 8 2018-04-09 71.5 NA NA NA
## 9 2018-04-10 72.8 NA NA NA
## 10 2018-04-11 72.8 NA NA NA
## # ... with 241 more rowsQ3.d 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: 1,004 x 3
## date type price
## <date> <chr> <dbl>
## 1 2018-03-28 close 69.2
## 2 2018-03-29 close 69.4
## 3 2018-04-02 close 69.0
## 4 2018-04-03 close 69.5
## 5 2018-04-04 close 71.9
## 6 2018-04-05 close 72.4
## 7 2018-04-06 close 72.3
## 8 2018-04-09 close 71.5
## 9 2018-04-10 close 72.8
## 10 2018-04-11 close 72.8
## # ... with 994 more rowsQ3.e 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. 
Q4 Add a subtitle to the chart that states “Target during the last one year”
Hint: There are many resources on the Web. For example, Google something like “ggplot2 adding subtitle”. 
Q5 Identify the first buying opportunity with the date and closing price.
Hint: Take stock, pipe it to dplyr::select to keep only three variables (date, close, and sd2down), and pipe it to dplyr::filter to select the rows where closing prices are smaller than the lower band.
- If the stock price shoots above the upper band, the stock may be considered overbought.
If the stock price falls below the lower band, the stock may be considered oversold.
When the bands narrow, it may be used as an indication that volatility is about to rise.
## # A tibble: 12 x 3
## date close sd2down
## <date> <dbl> <dbl>
## 1 2018-05-07 69.3 69.7
## 2 2018-10-03 86.0 86.2
## 3 2018-10-04 84.8 85.6
## 4 2018-10-05 84.5 85.0
## 5 2018-10-11 82.8 83.7
## 6 2018-11-16 79.7 80.0
## 7 2018-11-19 77.8 78.9
## 8 2018-11-20 69.0 74.9
## 9 2018-11-21 69.3 72.1
## 10 2018-11-23 67.3 69.3
## 11 2018-12-19 62.8 63.4
## 12 2018-12-20 61.7 62.2Q6 If you had invested $1 million on the day of the first buying point and followed the Bollinger Bands strategy, when would you have sold the stocks and at what price? Consider only one selling point immediately after the buying point in Q5.
Hint: Take stock, pipe it to dplyr::select to keep only three variables (date, close, and sd2up), and pipe it to dplyr::filter to select the rows where closing prices are greater than the upper band.
## # A tibble: 219 x 3
## date close sd2up
## <date> <dbl> <dbl>
## 1 2018-04-25 71.1 73.8
## 2 2018-04-26 71.9 73.7
## 3 2018-04-27 72.8 73.8
## 4 2018-04-30 72.6 73.6
## 5 2018-05-01 71.1 73.4
## 6 2018-05-02 71.4 73.4
## 7 2018-05-03 71.2 73.4
## 8 2018-05-04 71.1 73.3
## 9 2018-05-07 69.3 73.5
## 10 2018-05-08 69.4 73.5
## # ... with 209 more rowsQ7 How much would you have made or lost?
you would have made 85,000
Q8 Hide the code but display the results of the code on the webpage.
Hint: Change echo and results in the chunk options. The published webpage should display charts.