Calculate average returns in daily time series over 5 days prior to event date

General
1

I have time series date (daily returns) and which to compute the average of the returns 5 days prior each event (all data is in the same file).

The following code does not work:

Blockquote

  final_returns_data5$mean_returns <- ave(final_returns_data5$returns, final_returns_data5$coin_name, final_returns_data5$event_date, FUN = function(x) {
    idx <- which(final_returns_data5$date == final_returns_data5$event_date)
    if (length(idx) > 0 && idx > 5)
      mean(x[(idx - 5): (idx - 1)])
    else
      NA
  })```
2

We probably need to see the rest of your code, any error messages and some sample data.

FAQ

A handy way to supply some sample data is the dput() function. In the case of a large dataset something like dput(head(mydata, 100)) should supply the data we need. Just do dput(mydata) where mydata is your data. Copy the output and paste it here.data

3

I agree with @jrkrideau that we need to see your data in order to offer a solution.

The only thing I can say from looking at your code is that I don't think idx is being calculated per group since it is based on the entire final_returns_data object. This means for every group that ave() is running your function on, the same idx value is being used.

5

Sure, my bad. I attached a screen shot of the raw data here. I am still trying to find a shorter was to calculate the mean of the returns that only includes the 5 days prior to the event date.
Many thanks!

image
image840×483 15 KB

6

I think something like the following could work using dplyr and tidyr. It could be done in base R too, but this is how I would do it.

I am making a few assumptions since you still haven't given us your data, only a screenshot.

  1. dates after an event_date are for a different event
  2. the event_date column is empty everywhere except where it matches date
  3. the data is ordered by date 
library(dplyr)
test <- final_returns_data5 %>%
  tidyr::fill(event_date, .direction = "up") %>%
  group_by(coin_name, event_date) %>%
  filter(difftime(event_date, date, units = "days") <= 5) %>%
  summarise(mean_returns = mean(returns),
                          .groups = "drop")

I haven't tested this code since we still don't have a usuable sample of your data. The link @jrkrideau provided has instructions for sharing data to help us help you.

7

I tried to supply some sample data below; not sure if this will do the trick. The test data code only outputs 3 variables where the event date remains not identifiable.

In the meanwhile, I tried the following code which yields the average return over the past 5 days prior to the event date. But it does not fill up the 5 days prior and past the event_date with the same number (the average over 5 days). I am trying that now.

Blockquote
final_returns_data6 <- final_returns_data5 %>%
arrange(coin_name, date) %>%
group_by(coin_name) %>%
mutate(average_return_m5 = if_else(event_marker == 1, {
prior_returns <- lag(returns, default = NA_real_)
if (sum(!is.na(prior_returns)) >= 5) {
rollapplyr(prior_returns, width = 5, FUN = mean, fill = NA)
} else {
rep(NA_real_, n())
}
}, NA_real_, missing = NA_real_)) %>%
ungroup()

dput(head(final_returns_data5, 100))
structure(list(date = structure(c(17167, 17168, 17169, 17170,
17171, 17172, 17173, 17174, 17175, 17176, 17177, 17178, 17179,
17180, 17181, 17182, 17183, 17184, 17185, 17186, 17187, 17188,
17189, 17190, 17191, 17192, 17193, 17194, 17195, 17196, 17197,
17198, 17199, 17200, 17201, 17202, 17203, 17204, 17205, 17206,
17207, 17208, 17209, 17210, 17211, 17212, 17213, 17214, 17215,
17216, 17217, 17218, 17219, 17220, 17221, 17222, 17223, 17224,
17225, 17226, 17227, 17228, 17229, 17230, 17231, 17232, 17233,
17234, 17235, 17236, 17237, 17238, 17239, 17240, 17241, 17242,
17243, 17244, 17245, 17246, 17247, 17248, 17249, 17250, 17251,
17252, 17253, 17254, 17255, 17256, 17257, 17258, 17259, 17260,
17261, 17262, 17263, 17264, 17265, 17266), class = "Date"), name_to_match_on = c("0x protocol",
"0x protocol", "0x protocol", "0x protocol", "0x protocol", "0x protocol",
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"0x protocol", "0x protocol", "0x protocol", "0x protocol", "0x protocol",
"0x protocol", "0x protocol", "0x protocol", "0x protocol"),
project.x = c("0x protocol", "0x protocol", "0x protocol",
"0x protocol", "0x protocol", "0x protocol", "0x protocol",
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"0x protocol", "0x protocol", "0x protocol", "0x protocol",
"0x protocol", "0x protocol", "0x protocol", "0x protocol",
"0x protocol"), coin_name = c("0x protocol", "0x protocol",
"0x protocol", "0x protocol", "0x protocol", "0x protocol",
"0x protocol", "0x protocol", "0x protocol", "0x protocol",
"0x protocol", "0x protocol", "0x protocol", "0x protocol",
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"0x protocol", "0x protocol", "0x protocol", "0x protocol",
"0x protocol", "0x protocol", "0x protocol", "0x protocol",
"0x protocol", "0x protocol"), price = c(NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
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NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_), market_cap.x = c(NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_), volume = c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_), returns = c(NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_
), benchmark_returns_bitcoin = c(0.0325698735169436, 0.0211901207354343,
0.0160248422584485, 0.0920476438720172, -0.123961689241214,
-0.0975453419033123, 0.00676295324834372, 0.00889499897120173,
-0.00683428529370787, 0.0043027669356001, -0.134121660000675,
0.0193171422105055, 0.0313615681221878, -0.00809437199908602,
0.00499801302234527, 0.0077836752695695, 0.091562941410825,
-0.0334208001415698, 0.0262662554995513, -0.0060555921015955,
0.0316545195358443, -0.000977177309812367, 0.000140064163062896,
-0.0360413861025187, 0.00867588300958417, 0.0244956259489646,
0.00319819524558657, 0.00213168533819744, -0.00663138620693423,
0.00749187996648477, 0.0513307368144681, 0.0189065656270754,
0.0221275367398984, 0.00698052222721937, 0.0179109107605797,
-0.0161603682212873, 0.00745868315687106, 0.0263432106238947,
0.00361512125534915, -0.0697396089628622, 0.0106002312303595,
0.0189847651165749, -0.010309678067854, -0.00441751947308793,
0.0140529772903671, -0.00120865186268665, 0.0257934208910408,
0.0209239651579011, 0.00191455596508169, -0.00479739479677653,
0.0270662134127254, 0.040102880470583, -0.00186937831650143,
0.0555056593612316, -0.00403471967603454, -0.0266475523830813,
0.0272048214426729, 0.0105503513987003, -0.00234552494233746,
0.0312065710849258, 0.0281910348091259, 0.0217574082956222,
-0.0160724520168303, 0.00584677098005296, 0.00372424157657691,
-0.037416093221943, -0.0676574900169876, 0.0361905015479767,
-0.0765810277604402, 0.0738115919256144, 0.0417843760424607,
0.0103245046078149, 0.00264771975086471, 0.00954650529922478,
-0.0326927809151384, -0.117652970269338, -0.104396713071076,
0.0639610513468044, 0.0125471770437356, 0.0736921362915158,
-0.0654814948230614, -0.0102907727420192, -0.0865954466361974,
0.0268551937997058, -0.0111766219524708, 0.0923316581158031,
0.00203042114007204, -0.00584347068765247, -0.00685940112145814,
0.0453534921940581, 0.00647171712144923, 0.0118083064502011,
0.037557123461894, 0.00104389212318188, -0.00715274232598168,
0.0546400376198155, -0.00371448286507783, -0.00630717818326486,
0.00459116395479538, 0.0192102544499913), HODL5 = c(102.22,
104.68, 107.38, 115.92, 105.88, 102.83, 101.63, 101.76, 102.64,
104.21, 95.63, 95.16, 96.52, 96.82, 96.62, 96.75, 101.26,
100.74, 101.83, 103.53, 105.57, 104.89, 104.98, 103.91, 102.27,
103.78, 103.31, 103.08, 103.06, 103.87, 106.62, 106.61, 109.65,
109.95, 110.9, 110.74, 111.99, 113.92, 113.88, 106.92, 107.37,
109.81, 109.51, 108.51, 113.25, 113.6, 115.26, 115.74, 115.08,
115.03, 115.28, 117.53, 119.01, 120.77, 124.13, 121.86, 125.12,
128.3, 129.28, 132.42, 143.62, 146.49, 146.23, 146.07, 148.81,
146.27, 138.9, 139.17, 143, 144.55, 154.12, 176.41, 172.56,
181.47, 206.15, 215.51, 187.38, 196.96, 211.54, 209.05, 197.08,
206.03, 226.3, 230.2, 226.68, 232.31, 232.22, 239.5, 248.21,
247.73, 259.93, 282.94, 288.2, 271.55, 280.61, 278.9, 279.2,
285.08, 278.25, 280.49), benchmark_returns_hodl5 = c(102.22,
104.68, 107.38, 115.92, 105.88, 102.83, 101.63, 101.76, 102.64,
104.21, 95.63, 95.16, 96.52, 96.82, 96.62, 96.75, 101.26,
100.74, 101.83, 103.53, 105.57, 104.89, 104.98, 103.91, 102.27,
103.78, 103.31, 103.08, 103.06, 103.87, 106.62, 106.61, 109.65,
109.95, 110.9, 110.74, 111.99, 113.92, 113.88, 106.92, 107.37,
109.81, 109.51, 108.51, 113.25, 113.6, 115.26, 115.74, 115.08,
115.03, 115.28, 117.53, 119.01, 120.77, 124.13, 121.86, 125.12,
128.3, 129.28, 132.42, 143.62, 146.49, 146.23, 146.07, 148.81,
146.27, 138.9, 139.17, 143, 144.55, 154.12, 176.41, 172.56,
181.47, 206.15, 215.51, 187.38, 196.96, 211.54, 209.05, 197.08,
206.03, 226.3, 230.2, 226.68, 232.31, 232.22, 239.5, 248.21,
247.73, 259.93, 282.94, 288.2, 271.55, 280.61, 278.9, 279.2,
285.08, 278.25, 280.49), abnormal_returns_hodl5 = c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_), project_lower = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_), auditor = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_), project.y = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_,
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NA_character_, NA_character_, NA_character_), index_number = c(NA_real_,
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8

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