使用 R 对数据框和过滤结果进行连续的正或负计算

Question

I have the following dataset and looking to write a code that can help pull out which stocks have been positive or negative consecutively. The data would have first 3 column. last 2 columns are manually calculated in excel to depict expected results.

This is only sample, i would have data for 200+ stocks and few years of data with all stocks not trading every day.

In the end, i want to extract which stocks have say 3 or 4 or 5 consecutive positive or negative change for the day.

`    Stocks Date    Close Price Change for day  Positive/Negative Count
A   11/11/2020         11       
B   11/11/2020         50       
C   11/11/2020        164       
A   11/12/2020         19         8                 1
B   11/12/2020         62        12                 1
C   11/12/2020        125        -39               -1
A   11/13/2020          7        -12               -1
B   11/13/2020         63         1                 2
C   11/13/2020        165        40                 1
A   11/16/2020         17        10                 1
B   11/16/2020         70         7                 3
C   11/16/2020        170         5                 2
A   11/17/2020         24         7                 2
B   11/17/2020         52        -18               -1
C   11/17/2020        165         -5               -1
A   11/18/2020         31          7                3
B   11/18/2020         61          9                1
C   11/18/2020        157         -8               -2

Answer 1

The difficulty is to have a function that makes the cumulative sum, both positive and negative, resetting the count when the sign changes, and starting the count with the first value. I managed to make one, but it is not terribly efficient and will probably get slow on a bigger dataset. I suspect there is a way to do better, if only with a simple for loop in C or C++.

library(tidyverse)


df <- read.table(text="Stocks Date    Close_Price Change_for_day  Positive/Negative_Count
A   11/11/2020         11       NA                 0
B   11/11/2020         50       NA                 0
C   11/11/2020        164       NA                 0
A   11/12/2020         19         8                 1
B   11/12/2020         62        12                 1
C   11/12/2020        125        -39               -1
A   11/13/2020          7        -12               -1
B   11/13/2020         63         1                 2
C   11/13/2020        165        40                 1
A   11/16/2020         17        10                 1
B   11/16/2020         70         7                 3
C   11/16/2020        170         5                 2
A   11/17/2020         24         7                 2
B   11/17/2020         52        -18               -1
C   11/17/2020        165         -5               -1
A   11/18/2020         31          7                3
B   11/18/2020         61          9                1
C   11/18/2020        157         -8               -2",
           header = TRUE) %>%
  select(1:3) %>%
  as_tibble()


# this formulation could be faster on data with longer stretches
nb_days_cons2 <- function(x){
  n <- length(x)
  if(n < 2) x
  out <- integer(n)
  y <- rle(x)
  cur_pos <- 1
  for(i in seq_len(length(y$lengths))){
    out[(cur_pos):(cur_pos+y$lengths[i]-1)] <- cumsum(rep(y$values[i], y$lengths[i]))
    cur_pos <- cur_pos + y$lengths[i]
  }
  out
}

# this formulation was faster on some tests, and would be easier to rewrite in C
nb_days_cons <- function(x){
  n <- length(x)
  if(n < 2) x
  out <- integer(n)
  out[1] <- x[1]
  for(i in 2:n){
    if(x[i] == x[i-1]){
      out[i] <- out[i-1] + x[i]
    } else{
      out[i] <- x[i]
    }
  }
  out
}

Once we have that function, the dplyr part is quite classic.

df %>%
  group_by(Stocks) %>%
  arrange(Date) %>%   # make sure of order
  mutate(change = c(0, diff(Close_Price)),
         stretch_duration = nb_days_cons(sign(change))) %>%
  arrange(Stocks)
#> # A tibble: 18 x 5
#> # Groups:   Stocks [3]
#>    Stocks Date       Close_Price change stretch_duration
#>    <chr>  <chr>            <int>  <dbl>            <dbl>
#>  1 A      11/11/2020          11      0                0
#>  2 A      11/12/2020          19      8                1
#>  3 A      11/13/2020           7    -12               -1
#>  4 A      11/16/2020          17     10                1
#>  5 A      11/17/2020          24      7                2
#>  6 A      11/18/2020          31      7                3
#>  7 B      11/11/2020          50      0                0
#>  8 B      11/12/2020          62     12                1
#>  9 B      11/13/2020          63      1                2
#> 10 B      11/16/2020          70      7                3
#> 11 B      11/17/2020          52    -18               -1
#> 12 B      11/18/2020          61      9                1
#> 13 C      11/11/2020         164      0                0
#> 14 C      11/12/2020         125    -39               -1
#> 15 C      11/13/2020         165     40                1
#> 16 C      11/16/2020         170      5                2
#> 17 C      11/17/2020         165     -5               -1
#> 18 C      11/18/2020         157     -8               -2
Created on 2020-11-19 by the reprex package (v0.3.0)

Of course, the final arrange() is just for easy visualization, and you can remove the columns you don't need anymore with select() .