The code for the moving average analysis below is taken from: https://www.r-bloggers.com/tidyquant-bringing-quantitative-financial-analysis-to-the-tidyverse/.

## # A tibble: 251 x 7
##    date        open  high   low close   volume adjusted
##    <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 2018-10-22  220.  223.  219.  221. 28792100     217.
##  2 2018-10-23  216.  223.  215.  223. 38767800     219.
##  3 2018-10-24  223.  224.  215.  215. 40925500     212.
##  4 2018-10-25  218.  221.  217.  220. 29855800     216.
##  5 2018-10-26  216.  220.  213.  216. 47258400     213.
##  6 2018-10-29  219.  220.  206.  212. 45935500     209.
##  7 2018-10-30  211.  215.  209.  213. 36660000     210.
##  8 2018-10-31  217.  220.  217.  219. 38358900     216.
##  9 2018-11-01  219.  222.  217.  222. 58323200     219.
## 10 2018-11-02  210.  214.  205.  207. 91328700     204.
## # … with 241 more rows
## # A tibble: 251 x 9
##    date        open  high   low close   volume adjusted SMA.short SMA.long
##    <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>     <dbl>    <dbl>
##  1 2018-10-22  220.  223.  219.  221. 28792100     217.        NA       NA
##  2 2018-10-23  216.  223.  215.  223. 38767800     219.        NA       NA
##  3 2018-10-24  223.  224.  215.  215. 40925500     212.        NA       NA
##  4 2018-10-25  218.  221.  217.  220. 29855800     216.        NA       NA
##  5 2018-10-26  216.  220.  213.  216. 47258400     213.        NA       NA
##  6 2018-10-29  219.  220.  206.  212. 45935500     209.        NA       NA
##  7 2018-10-30  211.  215.  209.  213. 36660000     210.        NA       NA
##  8 2018-10-31  217.  220.  217.  219. 38358900     216.        NA       NA
##  9 2018-11-01  219.  222.  217.  222. 58323200     219.        NA       NA
## 10 2018-11-02  210.  214.  205.  207. 91328700     204.        NA       NA
## # … with 241 more rows
## # A tibble: 753 x 3
##    date       type  price
##    <date>     <chr> <dbl>
##  1 2018-10-22 close  221.
##  2 2018-10-23 close  223.
##  3 2018-10-24 close  215.
##  4 2018-10-25 close  220.
##  5 2018-10-26 close  216.
##  6 2018-10-29 close  212.
##  7 2018-10-30 close  213.
##  8 2018-10-31 close  219.
##  9 2018-11-01 close  222.
## 10 2018-11-02 close  207.
## # … with 743 more rows

Q1 Import S&P500 for the last one year.

Hint: Copy and revise the importing part of the code from above.

## # A tibble: 251 x 7
##    date        open  high   low close     volume adjusted
##    <date>     <dbl> <dbl> <dbl> <dbl>      <dbl>    <dbl>
##  1 2018-10-22 2774. 2779. 2749. 2756. 3307140000    2756.
##  2 2018-10-23 2721. 2754. 2691. 2741. 4348580000    2741.
##  3 2018-10-24 2738. 2743. 2652. 2656. 4709310000    2656.
##  4 2018-10-25 2675. 2723. 2668. 2706. 4634770000    2706.
##  5 2018-10-26 2668. 2692. 2628. 2659. 4803150000    2659.
##  6 2018-10-29 2683. 2707. 2604. 2641. 4673700000    2641.
##  7 2018-10-30 2641. 2685. 2635. 2683. 5106380000    2683.
##  8 2018-10-31 2706. 2737. 2706. 2712. 5112420000    2712.
##  9 2018-11-01 2718. 2742. 2709. 2740. 4708420000    2740.
## 10 2018-11-02 2745. 2757. 2700. 2723. 4237930000    2723.
## # … with 241 more rows
## Q2 Calculate 15-day and 50-day simple moving averages.
Hint: Copy and revise the moving average part of the code from above.

# A tibble: 251 x 9

date open high low close volume adjusted SMA.short SMA.long

1 2018-10-22 2774. 2779. 2749. 2756. 3.31e9 2756. NA NA

2 2018-10-23 2721. 2754. 2691. 2741. 4.35e9 2741. NA NA

3 2018-10-24 2738. 2743. 2652. 2656. 4.71e9 2656. NA NA

4 2018-10-25 2675. 2723. 2668. 2706. 4.63e9 2706. NA NA

5 2018-10-26 2668. 2692. 2628. 2659. 4.80e9 2659. NA NA

6 2018-10-29 2683. 2707. 2604. 2641. 4.67e9 2641. NA NA

7 2018-10-30 2641. 2685. 2635. 2683. 5.11e9 2683. NA NA

8 2018-10-31 2706. 2737. 2706. 2712. 5.11e9 2712. NA NA

9 2018-11-01 2718. 2742. 2709. 2740. 4.71e9 2740. NA NA

10 2018-11-02 2745. 2757. 2700. 2723. 4.24e9 2723. NA NA

# … with 241 more rows



## Q3 Transform data to long form from wide form for graphing.
Hint: Copy and revise the transformation part of the code from above.

# A tibble: 753 x 3

date type price

1 2018-10-22 close 2756.

2 2018-10-23 close 2741.

3 2018-10-24 close 2656.

4 2018-10-25 close 2706.

5 2018-10-26 close 2659.

6 2018-10-29 close 2641.

7 2018-10-30 close 2683.

8 2018-10-31 close 2712.

9 2018-11-01 close 2740.

10 2018-11-02 close 2723.

# … with 743 more rows


## Q4 Visualize data.
Hint: Copy and revise the visualization part of the code from above.
<img src="Quiz4-a_files/figure-html/unnamed-chunk-8-1.png" width="672" />

## Q5 List all bullish or bearish crossovers with dates and closing prices. 

* A ***bullish*** crossover if the 100-day moving average cross ***above*** the 200-day moving average.
* A ***bearish*** crossover if the 100-day moving average cross ***below*** the 200-day moving average.

## Q6 If you had invested $1 million on the day of the first bullish crossover and sold your shares on the following bearish crosover, how much would you have won or lost?

## Q7 Try 50-day and 100-day simple moving average instead. Does your answer in Q6 change?

# A tibble: 251 x 7

date open high low close volume adjusted

1 2018-10-22 2774. 2779. 2749. 2756. 3307140000 2756.

2 2018-10-23 2721. 2754. 2691. 2741. 4348580000 2741.

3 2018-10-24 2738. 2743. 2652. 2656. 4709310000 2656.

4 2018-10-25 2675. 2723. 2668. 2706. 4634770000 2706.

5 2018-10-26 2668. 2692. 2628. 2659. 4803150000 2659.

6 2018-10-29 2683. 2707. 2604. 2641. 4673700000 2641.

7 2018-10-30 2641. 2685. 2635. 2683. 5106380000 2683.

8 2018-10-31 2706. 2737. 2706. 2712. 5112420000 2712.

9 2018-11-01 2718. 2742. 2709. 2740. 4708420000 2740.

10 2018-11-02 2745. 2757. 2700. 2723. 4237930000 2723.

# … with 241 more rows

# A tibble: 251 x 9

date open high low close volume adjusted SMA.short SMA.long

1 2018-10-22 2774. 2779. 2749. 2756. 3.31e9 2756. NA NA

2 2018-10-23 2721. 2754. 2691. 2741. 4.35e9 2741. NA NA

3 2018-10-24 2738. 2743. 2652. 2656. 4.71e9 2656. NA NA

4 2018-10-25 2675. 2723. 2668. 2706. 4.63e9 2706. NA NA

5 2018-10-26 2668. 2692. 2628. 2659. 4.80e9 2659. NA NA

6 2018-10-29 2683. 2707. 2604. 2641. 4.67e9 2641. NA NA

7 2018-10-30 2641. 2685. 2635. 2683. 5.11e9 2683. NA NA

8 2018-10-31 2706. 2737. 2706. 2712. 5.11e9 2712. NA NA

9 2018-11-01 2718. 2742. 2709. 2740. 4.71e9 2740. NA NA

10 2018-11-02 2745. 2757. 2700. 2723. 4.24e9 2723. NA NA

# … with 241 more rows

# A tibble: 753 x 3

date type price

1 2018-10-22 close 2756.

2 2018-10-23 close 2741.

3 2018-10-24 close 2656.

4 2018-10-25 close 2706.

5 2018-10-26 close 2659.

6 2018-10-29 close 2641.

7 2018-10-30 close 2683.

8 2018-10-31 close 2712.

9 2018-11-01 close 2740.

10 2018-11-02 close 2723.

# … with 743 more rows

```

Q8 Hide the messages and the code, but display results of the code from the webpage.

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

Q9 Display the title and your name correctly at the top of the webpage.

Q10 Use the correct slug.