First, I will find a dataset that includes daily time series data for at least two items, such as stock prices or cryptocurrency prices starting from January 1, 2022. The dataset will include the item name, date, and closing price.
Next, I will clean and organize the data. This includes making sure the dates are formatted correctly and that the data is sorted in order from earliest to latest. Window functions only work properly when the data is ordered correctly.
Then, I will calculate the year-to-date (YTD) average for each item. This means I will group the data by item and year and compute the running average from the start of each year to each date.
After that, I will calculate the six-day moving average for each item. This will take the current day and the previous five days to compute a rolling average. This helps smooth out short-term changes in the data.
Finally, I will review the results and compare the original values to the calculated averages.
One challenge may be missing dates, such as weekends or holidays in stock data. While this does not stop the calculations, it can affect how the moving average looks.
Another challenge is making sure the data is correctly grouped and ordered. If the data is not sorted by date, the calculations may be incorrect.
If the dataset covers multiple years, I will need to make sure the year-to-date average resets at the beginning of each year.
This analysis looks at the daily stock prices of Apple (AAPL) from January 3, 2022 to January 10, 2022 obtained from using Yahoo Finance in R. Using time series window functions, we calculate the Year-to-Date (YTD) average and the 6-day moving average to better understand short-term trends. These measures help smooth daily price changes and make it easier to see the overall direction of the stock during this period.
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsstocks <- tq_get(c("AAPL", "MSFT", "TSLA"),
from = "2022-01-01",
to = Sys.Date())
head(stocks)## # A tibble: 6 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL 2022-01-03 178. 183. 178. 182. 104487900 178.
## 2 AAPL 2022-01-04 183. 183. 179. 180. 99310400 176.
## 3 AAPL 2022-01-05 180. 180. 175. 175. 94537600 171.
## 4 AAPL 2022-01-06 173. 175. 172. 172 96904000 168.
## 5 AAPL 2022-01-07 173. 174. 171. 172. 86709100 168.
## 6 AAPL 2022-01-10 169. 172. 168. 172. 106765600 168.library(dplyr)##
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## intersect, setdiff, setequal, unionstocks_calculated <- stocks %>%
arrange(symbol, date) %>%
group_by(symbol, year = format(date, "%Y")) %>%
mutate(
YTD_Avg = cummean(adjusted), # Year-to-date average
MA_6 = rollmean(adjusted, 6, fill = NA, align = "right") # 6-day moving average
) %>%
ungroup()head(stocks_calculated)## # A tibble: 6 × 11
## symbol date open high low close volume adjusted year YTD_Avg MA_6
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 AAPL 2022-01-03 178. 183. 178. 182. 1.04e8 178. 2022 178. NA
## 2 AAPL 2022-01-04 183. 183. 179. 180. 9.93e7 176. 2022 177. NA
## 3 AAPL 2022-01-05 180. 180. 175. 175. 9.45e7 171. 2022 175. NA
## 4 AAPL 2022-01-06 173. 175. 172. 172 9.69e7 168. 2022 173. NA
## 5 AAPL 2022-01-07 173. 174. 171. 172. 8.67e7 168. 2022 172. NA
## 6 AAPL 2022-01-10 169. 172. 168. 172. 1.07e8 168. 2022 172. 172.Between January 3, 2022 and January 10, 2022, Apple’s Year-to-Date (YTD) average decreased from 178.10 to 171.73. This means that the stock price generally moved downward during that first week of the year.
Because the YTD average is a running average starting from January 1, each new day’s price affects the overall average. If the daily closing prices are lower than earlier days, the YTD average will decrease. That is what happened during this period — the stock experienced short-term declines, which pulled the average down.
This suggests that Apple had a negative short-term trend during the first week of January 2022.