library(tidyquant)

## Loading required package: lubridate

## 
## Attaching package: 'lubridate'

## The following object is masked from 'package:base':
## 
##     date

## Loading required package: PerformanceAnalytics

## Loading required package: xts

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## Registered S3 method overwritten by 'xts':
##   method     from
##   as.zoo.xts zoo

## 
## Attaching package: 'PerformanceAnalytics'

## The following object is masked from 'package:graphics':
## 
##     legend

## Loading required package: quantmod

## Loading required package: TTR

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

## Version 0.4-0 included new data defaults. See ?getSymbols.

## Loading required package: tidyverse

## Registered S3 methods overwritten by 'ggplot2':
##   method         from 
##   [.quosures     rlang
##   c.quosures     rlang
##   print.quosures rlang

## Registered S3 method overwritten by 'rvest':
##   method            from
##   read_xml.response xml2

## -- Attaching packages --------------------------------------------------------- tidyverse 1.2.1 --

## √ ggplot2 3.1.1       √ purrr   0.3.2  
## √ tibble  2.1.1       √ dplyr   0.8.0.1
## √ tidyr   0.8.3       √ stringr 1.4.0  
## √ readr   1.3.1       √ forcats 0.4.0

## -- Conflicts ------------------------------------------------------------ tidyverse_conflicts() --
## x lubridate::as.difftime() masks base::as.difftime()
## x lubridate::date()        masks base::date()
## x dplyr::filter()          masks stats::filter()
## x dplyr::first()           masks xts::first()
## x lubridate::intersect()   masks base::intersect()
## x dplyr::lag()             masks stats::lag()
## x dplyr::last()            masks xts::last()
## x lubridate::setdiff()     masks base::setdiff()
## x lubridate::union()       masks base::union()

FANG_daily_returns <- FANG %>%
  dplyr::group_by(symbol) %>%
  nest() %>%
  dplyr::rename(data_train = data) %>%
  dplyr::mutate(returns = map(data_train, ~ (.$adjusted / lag(.$adjusted) - 1))) %>%
  dplyr::mutate(ranking = map(returns, ~ dense_rank(.))) %>%
  unnest() %>%
  select(-open, -high, -low, -close, -volume, -adjusted) %>%
  dplyr::arrange(symbol, ranking) %>%
  dplyr::filter(ranking <= 8)
FANG_daily_returns

## # A tibble: 32 x 4
##    symbol returns ranking date      
##    <chr>    <dbl>   <int> <date>    
##  1 AMZN   -0.110        1 2014-01-31
##  2 AMZN   -0.0988       2 2014-04-25
##  3 AMZN   -0.0965       3 2014-07-25
##  4 AMZN   -0.0834       4 2014-10-24
##  5 AMZN   -0.0761       5 2016-01-29
##  6 AMZN   -0.0724       6 2013-04-26
##  7 AMZN   -0.0636       7 2016-02-05
##  8 AMZN   -0.0629       8 2015-08-24
##  9 FB     -0.0693       1 2014-03-26
## 10 FB     -0.0669       2 2013-10-08
## # ... with 22 more rows