Downloads and processes data based on the specified domain (e.g., Fama-French factors, Global Q factors, or macro predictors), dataset, and date range. This function checks if the specified domain is supported and then delegates to the appropriate function for downloading and processing the data.
Usage
download_data(
domain = NULL,
dataset = NULL,
start_date = NULL,
end_date = NULL,
type = deprecated(),
...
)Arguments
- domain
The domain of the dataset to download, using the names returned by
list_supported_datasets():"Fama-French","Global Q","Goyal-Welch","WRDS","Pseudo Data","Index Constituents","FRED","Stock Prices","Open Source Asset Pricing","Global Factor Data","Pastor-Stambaugh","Stambaugh-Yuan", or"Tidy Finance". Use"Pseudo Data"to obtain pseudo data with the same schema as"WRDS"for testing or rendering without a WRDS subscription. The previous machine-readable names (e.g.,"famafrench","wrds","pseudo","tidyfinance") are soft-deprecated but still accepted.- dataset
Optional. The specific dataset to download within the domain.
- start_date
Optional. A character string or Date object in "YYYY-MM-DD" format specifying the start date for the data. If not provided, the full dataset or a subset is returned, depending on the dataset type.
- end_date
Optional. A character string or Date object in "YYYY-MM-DD" format specifying the end date for the data. If not provided, the full dataset or a subset is returned, depending on the dataset type.
- type
![[Deprecated]](figures/lifecycle-deprecated.svg)
Use domainanddatasetinstead.- ...
Additional arguments passed to specific download functions depending on the
domain. For instance, ifdomainis"Index Constituents", arguments are passed todownload_data_constituents(). Ifdomainis"Global Factor Data", thedatasetargument and arguments such asregion,factors,classification,frequency, andweightingare passed todownload_data_jkp(). Ifdomainis"Tidy Finance"anddatasetis"factor_library", arguments are either filter inputs (e.g.,sorting_variable,rebalancing,fill_all) or an explicitidsvector that bypasses the grid filter and downloads the specified portfolios directly viadownload_factor_library_ids(); seedownload_data_huggingface()for details.
Value
A tibble with processed data, including dates and the relevant financial metrics, filtered by the specified date range.
See also
Other download functions:
download_data_constituents(),
download_data_factors_ff(),
download_data_factors_q(),
download_data_fred(),
download_data_fred_md(),
download_data_huggingface(),
download_data_jkp(),
download_data_macro_predictors(),
download_data_osap(),
download_data_pastor_stambaugh(),
download_data_risk_free(),
download_data_stambaugh_yuan(),
download_data_stock_prices(),
download_factor_library_grid(),
download_factor_library_ids()
Examples
# \donttest{
download_data(
"Fama-French",
"Fama/French 5 Factors (2x3) [Daily]",
"2000-01-01",
"2020-12-31"
)
#> # A tibble: 5,284 × 7
#> date mkt_excess smb hml rmw cma risk_free
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2000-01-03 -0.0071 -0.0009 -0.0131 -0.0146 -0.0071 0.0002
#> 2 2000-01-04 -0.0406 0.0034 0.0207 0.0053 0.0136 0.0002
#> 3 2000-01-05 -0.0009 0.0035 -0.0005 0.0044 0.0114 0.0002
#> 4 2000-01-06 -0.0074 -0.0003 0.0124 0.0064 0.0122 0.0002
#> 5 2000-01-07 0.0321 -0.0089 -0.0157 -0.0083 -0.0099 0.0002
#> 6 2000-01-10 0.0175 0.0043 -0.0135 -0.022 -0.0024 0.0002
#> 7 2000-01-11 -0.0171 0.0034 0.0092 0.009 0.012 0.0002
#> 8 2000-01-12 -0.0069 -0.0022 0.0074 0.0041 0.0085 0.0002
#> 9 2000-01-13 0.0159 0.0048 -0.0084 -0.0172 -0.0102 0.0002
#> 10 2000-01-14 0.0114 0.0022 -0.0048 -0.0035 -0.0059 0.0002
#> # ℹ 5,274 more rows
download_data("Goyal-Welch", "monthly", "2000-01-01", "2020-12-31")
#> # A tibble: 252 × 15
#> date rp_div dp dy ep de svar bm ntis tbl
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2000-01-01 -0.0244 -4.42 -4.48 -3.35 -1.08 0.00521 0.155 0.0254 0.0532
#> 2 2000-02-01 0.0866 -4.40 -4.42 -3.31 -1.09 0.00300 0.167 0.0274 0.0555
#> 3 2000-03-01 -0.0355 -4.49 -4.40 -3.38 -1.11 0.00668 0.150 0.0183 0.0569
#> 4 2000-04-01 -0.0269 -4.46 -4.49 -3.34 -1.12 0.00794 0.153 0.0119 0.0566
#> 5 2000-05-01 0.0194 -4.44 -4.46 -3.32 -1.13 0.00519 0.156 0.00918 0.0579
#> 6 2000-06-01 -0.0212 -4.47 -4.44 -3.33 -1.13 0.00236 0.157 0.00712 0.0569
#> 7 2000-07-01 0.0532 -4.46 -4.47 -3.30 -1.15 0.00207 0.156 0.00471 0.0596
#> 8 2000-08-01 -0.0595 -4.52 -4.46 -3.35 -1.17 0.00104 0.146 0.00467 0.0609
#> 9 2000-09-01 -0.0105 -4.48 -4.53 -3.29 -1.19 0.00160 0.154 0.00447 0.06
#> 10 2000-10-01 -0.0876 -4.47 -4.48 -3.30 -1.17 0.00557 0.149 0.00374 0.0611
#> # ℹ 242 more rows
#> # ℹ 5 more variables: lty <dbl>, ltr <dbl>, tms <dbl>, dfy <dbl>, infl <dbl>
download_data("Index Constituents", index = "DAX")
#> Error in download_data_constituents(...): Failed to download data for index `index`. Please check the index name
#> or try again later.
download_data("FRED", series = c("GDP", "CPIAUCNS"))
#> No `start_date` or `end_date` provided. Returning the full data set.
#> # A tibble: 1,682 × 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
#> 6 1948-04-01 273. GDP
#> 7 1948-07-01 279. GDP
#> 8 1948-10-01 280. GDP
#> 9 1949-01-01 275. GDP
#> 10 1949-04-01 271. GDP
#> # ℹ 1,672 more rows
download_data("FRED", "FRED-MD")
#> # A tibble: 801 × 127
#> date RPI W875RX1 DPCERA3M086SBEA CMRMTSPLx RETAILx INDPRO IPFPNSS
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1959-01-01 2584. 2426 15.2 276677. 17689. 22.0 23.4
#> 2 1959-02-01 2594. 2435. 15.3 278714. 17819. 22.4 23.7
#> 3 1959-03-01 2610. 2453. 15.5 277775. 17968. 22.7 23.8
#> 4 1959-04-01 2627. 2470 15.4 283363. 17979. 23.2 24.2
#> 5 1959-05-01 2643. 2486. 15.6 285307. 18120. 23.5 24.4
#> 6 1959-06-01 2651. 2494. 15.7 285280. 18285. 23.6 24.6
#> 7 1959-07-01 2649. 2492 15.6 288768. 18279. 23.0 24.6
#> 8 1959-08-01 2634. 2478. 15.7 273993. 18395. 22.2 24.4
#> 9 1959-09-01 2636. 2478. 15.9 278039. 18155. 22.2 24.3
#> 10 1959-10-01 2640. 2481. 15.8 278490. 18288. 22.0 24.2
#> # ℹ 791 more rows
#> # ℹ 119 more variables: IPFINAL <dbl>, IPCONGD <dbl>, IPDCONGD <dbl>,
#> # IPNCONGD <dbl>, IPBUSEQ <dbl>, IPMAT <dbl>, IPDMAT <dbl>, IPNMAT <dbl>,
#> # IPMANSICS <dbl>, IPB51222S <dbl>, IPFUELS <dbl>, CUMFNS <dbl>, HWI <dbl>,
#> # HWIURATIO <dbl>, CLF16OV <dbl>, CE16OV <dbl>, UNRATE <dbl>, UEMPMEAN <dbl>,
#> # UEMPLT5 <dbl>, UEMP5TO14 <dbl>, UEMP15OV <dbl>, UEMP15T26 <dbl>,
#> # UEMP27OV <dbl>, CLAIMSx <dbl>, PAYEMS <dbl>, USGOOD <dbl>, …
download_data("FRED", "FRED-MD", transform = TRUE)
#> # A tibble: 801 × 127
#> date RPI W875RX1 DPCERA3M086SBEA CMRMTSPLx RETAILx INDPRO
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1959-01-01 NA NA NA NA NA NA
#> 2 1959-02-01 0.00388 0.00362 0.0103 0.00734 0.00731 0.0194
#> 3 1959-03-01 0.00646 0.00732 0.00940 -0.00337 0.00832 0.0143
#> 4 1959-04-01 0.00651 0.00703 -0.00362 0.0199 0.000616 0.0211
#> 5 1959-05-01 0.00580 0.00662 0.0120 0.00684 0.00780 0.0150
#> 6 1959-06-01 0.00307 0.00301 0.00364 -0.0000968 0.00906 0.00114
#> 7 1959-07-01 -0.000580 -0.000762 -0.00339 0.0122 -0.000330 -0.0242
#> 8 1959-08-01 -0.00565 -0.00575 0.00600 -0.0525 0.00636 -0.0345
#> 9 1959-09-01 0.000763 0 0.0100 0.0147 -0.0132 -0.00121
#> 10 1959-10-01 0.00127 0.00117 -0.00683 0.00162 0.00729 -0.00729
#> # ℹ 791 more rows
#> # ℹ 120 more variables: IPFPNSS <dbl>, IPFINAL <dbl>, IPCONGD <dbl>,
#> # IPDCONGD <dbl>, IPNCONGD <dbl>, IPBUSEQ <dbl>, IPMAT <dbl>, IPDMAT <dbl>,
#> # IPNMAT <dbl>, IPMANSICS <dbl>, IPB51222S <dbl>, IPFUELS <dbl>,
#> # CUMFNS <dbl>, HWI <dbl>, HWIURATIO <dbl>, CLF16OV <dbl>, CE16OV <dbl>,
#> # UNRATE <dbl>, UEMPMEAN <dbl>, UEMPLT5 <dbl>, UEMP5TO14 <dbl>,
#> # UEMP15OV <dbl>, UEMP15T26 <dbl>, UEMP27OV <dbl>, CLAIMSx <dbl>, …
download_data("FRED", "FRED-QD", vintage = "2020-03")
#> # A tibble: 244 × 249
#> date vintage GDPC1 PCECC96 PCDGx PCESVx PCNDx GPDIC1 FPIx
#> <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1959-03-01 2020-03 3122. 1924. 76.6 1228. 709. 341. 341.
#> 2 1959-06-01 2020-03 3192. 1953. 79.5 1247. 714. 367. 351.
#> 3 1959-09-01 2020-03 3195. 1974. 81.0 1264. 717. 343. 355.
#> 4 1959-12-01 2020-03 3204. 1976. 77.2 1279. 722. 355. 351.
#> 5 1960-03-01 2020-03 3276. 1995. 79.7 1290. 723. 390. 362.
#> 6 1960-06-01 2020-03 3258. 2020. 81.5 1305. 731. 354. 356.
#> 7 1960-09-01 2020-03 3274. 2012. 80.8 1303. 727. 353. 348.
#> 8 1960-12-01 2020-03 3232. 2015. 78.8 1316. 728. 314. 347.
#> 9 1961-03-01 2020-03 3254. 2014. 74.5 1329. 732. 322. 344.
#> 10 1961-06-01 2020-03 3309. 2044. 76.0 1348. 742. 345. 350.
#> # ℹ 234 more rows
#> # ℹ 240 more variables: Y033RC1Q027SBEAx <dbl>, PNFIx <dbl>, PRFIx <dbl>,
#> # A014RE1Q156NBEA <dbl>, GCEC1 <dbl>, A823RL1Q225SBEA <dbl>, FGRECPTx <dbl>,
#> # SLCEx <dbl>, EXPGSC1 <dbl>, IMPGSC1 <dbl>, DPIC96 <dbl>, OUTNFB <dbl>,
#> # OUTBS <dbl>, OUTMS <dbl>, INDPRO <dbl>, IPFINAL <dbl>, IPCONGD <dbl>,
#> # IPMAT <dbl>, IPDMAT <dbl>, IPNMAT <dbl>, IPDCONGD <dbl>, IPB51110SQ <dbl>,
#> # IPNCONGD <dbl>, IPBUSEQ <dbl>, IPB51220SQ <dbl>, TCU <dbl>, CUMFNS <dbl>, …
download_data("Stock Prices", symbols = c("AAPL", "MSFT"))
#> No `start_date` or `end_date` provided. Using the range 2024-09-24 to
#> 2025-09-24 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 2024-09-24 43556100 229. 226. 229. 227. 225.
#> 2 AAPL 2024-09-25 42308700 225. 224. 227. 226. 224.
#> 3 AAPL 2024-09-26 36636700 227. 225. 228. 228. 226.
#> 4 AAPL 2024-09-27 34026000 228. 227. 230. 228. 226.
#> 5 AAPL 2024-09-30 54541900 230. 230. 233 233 231.
#> 6 AAPL 2024-10-01 63285000 230. 224. 230. 226. 224.
#> 7 AAPL 2024-10-02 32880600 226. 223. 227. 227. 225.
#> 8 AAPL 2024-10-03 34044200 225. 223. 227. 226. 224.
#> 9 AAPL 2024-10-04 37245100 228. 224. 228 227. 225.
#> 10 AAPL 2024-10-07 39505400 224. 221. 226. 222. 220.
#> # ℹ 492 more rows
download_data(
"Tidy Finance",
"risk_free",
"2020-01-01",
"2020-12-31"
)
#> # A data frame: 12 × 2
#> date risk_free
#> <date> <dbl>
#> 1 2020-01-01 0.00129
#> 2 2020-02-01 0.00121
#> 3 2020-03-01 0.0000338
#> 4 2020-04-01 0.0000845
#> 5 2020-05-01 0.000110
#> 6 2020-06-01 0.000110
#> 7 2020-07-01 0.0000760
#> 8 2020-08-01 0.0000676
#> 9 2020-09-01 0.0000676
#> 10 2020-10-01 0.0000676
#> 11 2020-11-01 0.0000676
#> 12 2020-12-01 0.0000676
download_data(
"Tidy Finance",
"high_frequency_sp500",
"2007-07-26",
"2007-07-27"
)
#> # A tibble: 9,360 × 9
#> ts midquote signed_volume trading_volume depth0_ask
#> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 2007-07-26 09:30:05 150. 0 11565577. 13841.
#> 2 2007-07-26 09:30:10 150. 0 3017263. 10795.
#> 3 2007-07-26 09:30:15 150. 0 6108290. 12371.
#> 4 2007-07-26 09:30:20 150. 98630 21820258. 14069.
#> 5 2007-07-26 09:30:25 150. 0 931698 15283.
#> 6 2007-07-26 09:30:30 150. -4400 661077 5307.
#> 7 2007-07-26 09:30:35 150. -12000 2102820 3192.
#> 8 2007-07-26 09:30:40 150. 39927 12384621. 6234.
#> 9 2007-07-26 09:30:45 150. -14671 3105370. 9255.
#> 10 2007-07-26 09:30:50 150. 5347 5518370. 5708.
#> # ℹ 9,350 more rows
#> # ℹ 4 more variables: depth0_bid <dbl>, depth5_ask <dbl>, depth5_bid <dbl>,
#> # spread <dbl>
download_data(
"Tidy Finance",
"factor_library",
sorting_variable = "high52",
rebalancing = "annual"
)
#> No `start_date` or `end_date` provided. Returning the full data set.
#> # A data frame: 774 × 19
#> id date ret sorting_variable min_size_quantile min_stock_price
#> <int> <date> <dbl> <chr> <dbl> <dbl>
#> 1 2.53e6 1960-07-01 -0.0186 high52 0.2 NA
#> 2 2.53e6 1960-08-01 0.00310 high52 0.2 NA
#> 3 2.53e6 1960-09-01 0.00178 high52 0.2 NA
#> 4 2.53e6 1960-10-01 0.0264 high52 0.2 NA
#> 5 2.53e6 1960-11-01 -0.00333 high52 0.2 NA
#> 6 2.53e6 1960-12-01 0.0197 high52 0.2 NA
#> 7 2.53e6 1961-01-01 -0.0526 high52 0.2 NA
#> 8 2.53e6 1961-02-01 0.0352 high52 0.2 NA
#> 9 2.53e6 1961-03-01 0.00377 high52 0.2 NA
#> 10 2.53e6 1961-04-01 -0.0138 high52 0.2 NA
#> # ℹ 764 more rows
#> # ℹ 13 more variables: min_listing_age <int>, exclude_financials <lgl>,
#> # exclude_utilities <lgl>, exclude_negative_book_equity <lgl>,
#> # exclude_negative_earnings <lgl>, sorting_variable_lag <chr>,
#> # rebalancing <chr>, n_portfolios_main <dbl>, sorting_method <chr>,
#> # breakpoints_min_size_threshold <dbl>, n_portfolios_secondary <dbl>,
#> # breakpoints_exchanges <chr>, weighting_scheme <chr>
download_data("Tidy Finance", "factor_library", ids = c(1L, 2L, 3L))
#> No `start_date` or `end_date` provided. Returning the full data set.
#> # A data frame: 2,337 × 19
#> id date ret sorting_variable min_size_quantile min_stock_price
#> <int> <date> <dbl> <chr> <dbl> <dbl>
#> 1 1 1960-02-01 0 abnormalaccruals NA NA
#> 2 1 1960-03-01 0 abnormalaccruals NA NA
#> 3 1 1960-04-01 0 abnormalaccruals NA NA
#> 4 1 1960-05-01 0 abnormalaccruals NA NA
#> 5 1 1960-06-01 0 abnormalaccruals NA NA
#> 6 1 1960-07-01 0 abnormalaccruals NA NA
#> 7 1 1960-08-01 0 abnormalaccruals NA NA
#> 8 1 1960-09-01 0 abnormalaccruals NA NA
#> 9 1 1960-10-01 0 abnormalaccruals NA NA
#> 10 1 1960-11-01 0 abnormalaccruals NA NA
#> # ℹ 2,327 more rows
#> # ℹ 13 more variables: min_listing_age <int>, exclude_financials <lgl>,
#> # exclude_utilities <lgl>, exclude_negative_book_equity <lgl>,
#> # exclude_negative_earnings <lgl>, sorting_variable_lag <chr>,
#> # rebalancing <chr>, n_portfolios_main <dbl>, sorting_method <chr>,
#> # breakpoints_min_size_threshold <dbl>, n_portfolios_secondary <dbl>,
#> # breakpoints_exchanges <chr>, weighting_scheme <chr>
download_data("Tidy Finance", "factor_library_grid")
#> # A data frame: 4,105,728 × 17
#> id sorting_variable min_size_quantile min_stock_price min_listing_age
#> <int> <chr> <dbl> <dbl> <int>
#> 1 1 abnormalaccruals NA NA 24
#> 2 2 abnormalaccruals NA NA 24
#> 3 3 abnormalaccruals NA NA 24
#> 4 4 abnormalaccruals NA NA 24
#> 5 5 abnormalaccruals NA NA 24
#> 6 6 abnormalaccruals NA NA 24
#> 7 7 abnormalaccruals NA NA 24
#> 8 8 abnormalaccruals NA NA 24
#> 9 9 abnormalaccruals NA NA 24
#> 10 10 abnormalaccruals NA NA 24
#> # ℹ 4,105,718 more rows
#> # ℹ 12 more variables: exclude_financials <lgl>, exclude_utilities <lgl>,
#> # exclude_negative_book_equity <lgl>, exclude_negative_earnings <lgl>,
#> # sorting_variable_lag <chr>, rebalancing <chr>, n_portfolios_main <dbl>,
#> # sorting_method <chr>, breakpoints_min_size_threshold <dbl>,
#> # n_portfolios_secondary <dbl>, breakpoints_exchanges <chr>,
#> # weighting_scheme <chr>
# }