install.packages("tidyfinance")We are happy to annouce the release of version 0.4.0 of the tidyfinance R package on CRAN. The package contains a set of helper functions for empirical research in financial economics, addressing a variety of topics covered in Tidy Finance with R (TFWR). We designed the package to provide easy shortcuts for the applications that we discuss in the book. If you want to inspect the details of the package or propose new features, feel free to visit the package repository on Github.
As the new release brings many new features, we split them up into two blog posts. In this blog post, we discuss the new data downloads that tidyfinance now supports.
Install the package
You can install the released version of tidyfinance from CRAN via:
You can install the development version of tidyfinance from GitHub using the pak package:
pak::pak("tidy-finance/r-tidyfinance")You then load the package via:
library(tidyfinance)Download stock data
You can download stock prices from Yahoo Finance using the type stock_prices and provide symbols:
download_data("stock_prices", symbols = c("AAPL", "MSFT"))No `start_date` or `end_date` provided.
Using the range 2022-08-30 to 2023-08-30 to avoid downloading large
amounts of data.# A tibble: 502 × 8
symbol date volume open low high close adjusted_close
<chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 AAPL 2022-08-30 77906200 162. 158. 163. 159. 157.
2 AAPL 2022-08-31 87991100 160. 157. 161. 157. 155.
3 AAPL 2022-09-01 74229900 157. 155. 158. 158. 156.
4 AAPL 2022-09-02 76957800 160. 155. 160. 156. 154.
5 AAPL 2022-09-06 73714800 156. 154. 157. 155. 153.
# ℹ 497 more rowsAs you can see, we included defaults for start_date and end_date that you can of course overwrite. We introduced default dates across all datasets to allow for fast initial data analysis for your convenience.
download_data("stock_prices", symbols = c("AAPL", "MSFT"),
start_date = "2020-01-01", end_date = "2020-12-31")# A tibble: 504 × 8
symbol date volume open low high close adjusted_close
<chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 AAPL 2020-01-02 135480400 74.1 73.8 75.2 75.1 72.9
2 AAPL 2020-01-03 146322800 74.3 74.1 75.1 74.4 72.2
3 AAPL 2020-01-06 118387200 73.4 73.2 75.0 74.9 72.7
4 AAPL 2020-01-07 108872000 75.0 74.4 75.2 74.6 72.4
5 AAPL 2020-01-08 132079200 74.3 74.3 76.1 75.8 73.6
# ℹ 499 more rowsTo inspect details about the download approach, you can call the documentation via ?download_data_stock_prices.
The above function takes stock symbols as inputs (i.e., stock-specific identifiers). To get symbols you have now the following option: you can download index constituents from selected iShares ETFs that physically replicate the corresponding index:
download_data("constituents", index = "DAX")# A tibble: 40 × 5
symbol name location exchange currency
<chr> <chr> <chr> <chr> <chr>
1 SAP.DE SAP Deutschland Xetra EUR
2 SIE.DE SIEMENS N AG Deutschland Xetra EUR
3 ALV.DE ALLIANZ Deutschland Xetra EUR
4 DTE.DE DEUTSCHE TELEKOM N AG Deutschland Xetra EUR
5 AIR.BE AIRBUS Frankreich Boerse Berlin EUR
# ℹ 35 more rowsThe documentation ?download_data_constituents provides details. The list of supported indexes for download_data_constituents() can be inspected via:
list_supported_indexes()# A tibble: 13 × 3
index url skip
<chr> <chr> <dbl>
1 DAX https://www.ishares.com/de/priv… 2
2 EURO STOXX 50 https://www.ishares.com/de/priv… 2
3 Dow Jones Industrial Average https://www.ishares.com/de/priv… 2
4 Russell 1000 https://www.ishares.com/ch/prof… 9
5 Russell 2000 https://www.ishares.com/ch/prof… 9
# ℹ 8 more rowsYou can easily use the index or constituents data to download stock prices:
constituents_dax <- download_data("constituents", index = "DAX")
download_data("stock_prices", symbols = constituents_dax$symbol)Download macro data
We also added support for the Federal Reserve Economic Databa (FRED), where you can easily download multiple series (see ?download_data_fred for details):
download_data("fred", series = c("GDP", "CPIAUCNS"))No `start_date` or `end_date` provided. Returning the full data set.# A tibble: 1,649 × 3
date value series
<date> <dbl> <chr>
1 1947-01-01 243. GDP
2 1947-04-01 246. GDP
3 1947-07-01 250. GDP
4 1947-10-01 260. GDP
5 1948-01-01 266. GDP
# ℹ 1,644 more rowsOur approach is a simple wrapper around the FRED download data site. If you want to systematically download FRED data via API, please consider using fredr (Boysel and Vaughan 2021) package.
The macro predictors by Ivo Welch and Amig Goyal, which we prepare for immediate use in our book chapter on Accessing and Managing Financial Data, now also come in different flavors: monthly, quarterly, and annual. For instance, the annual data can be fetched via:
download_data("macro_predictors_annual")No `start_date` or `end_date` provided. Returning the full data set.# A tibble: 97 × 15
date rp_div dp dy ep de svar bm ntis
<date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1926-01-01 0.232 -2.97 -2.89 -2.39 -0.586 0.0165 0.441 0.0509
2 1927-01-01 0.275 -3.13 -2.86 -2.77 -0.366 0.00942 0.375 0.0765
3 1928-01-01 -0.163 -3.36 -3.03 -2.87 -0.485 0.0198 0.260 0.0631
4 1929-01-01 -0.342 -3.10 -3.22 -2.59 -0.507 0.125 0.338 0.164
5 1930-01-01 -0.612 -2.75 -3.09 -2.76 0.0103 0.0666 0.555 0.114
# ℹ 92 more rows
# ℹ 6 more variables: tbl <dbl>, lty <dbl>, ltr <dbl>, tms <dbl>,
# dfy <dbl>, infl <dbl>See ?download_data_macro_predictors for details.
Finally, we also added support to download a collection of replicated time series of factor portfolios from Open Source Asset Pricing (OSAP) compiled by Andrew Y. Chen and Tom Zimmermann:
download_data("osap")No `start_date` or `end_date` provided. Returning the full data set.# A tibble: 1,164 × 213
date am aop abnormal_accruals accruals accruals_bm
<date> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1926-01-30 NA NA NA NA NA
2 1926-02-27 NA NA NA NA NA
3 1926-03-31 NA NA NA NA NA
4 1926-04-30 NA NA NA NA NA
5 1926-05-28 NA NA NA NA NA
# ℹ 1,159 more rows
# ℹ 207 more variables: activism1 <dbl>, activism2 <dbl>,
# ad_exp <dbl>, age_ipo <dbl>, analyst_revision <dbl>,
# analyst_value <dbl>, announcement_return <dbl>,
# asset_growth <dbl>, bm <dbl>, bmdec <dbl>, bpebm <dbl>,
# beta <dbl>, beta_fp <dbl>, beta_liquidity_ps <dbl>,
# beta_tail_risk <dbl>, bid_ask_spread <dbl>, …You can get more information by calling ?download_data_osap.
Concluding remarks
To get a list of supported data types, you can call:
list_supported_types()# A tibble: 317 × 3
type dataset_name domain
<chr> <chr> <chr>
1 factors_q5_daily q5_factors_daily_2023.csv Global Q
2 factors_q5_weekly q5_factors_weekly_2023.csv Global Q
3 factors_q5_weekly_w2w q5_factors_weekly_w2w_2023.csv Global Q
4 factors_q5_monthly q5_factors_monthly_2023.csv Global Q
5 factors_q5_quarterly q5_factors_quarterly_2023.csv Global Q
# ℹ 312 more rowsWe are curious to learn in which direction we should extend the package, so please consider opening an issue in the package repository. For instance, we could support more data sources, add more parameters to the download_* family of functions, or we could put more emphasis on the generality of portfolio assignment or other modeling functions. Moreover, if you discover a bug, we are very grateful if you raise them in the repository.
References
Boysel, Sam, and Davis Vaughan. 2021. fredr: An R client for the ’FRED’ API. https://CRAN.R-project.org/package=fredr.