# OpenAP Download Provides access to data from the Open Source Asset Pricing (OpenAP) project directly in R. The package enables users to access two primary types of data: 1. Predictor Portfolio Returns : * Access 212 cross-sectional predictors. * Download portfolio returns using various portfolio construction methods, including original paper methods, deciles, quintiles, equal-weighted, value-weighted, price filters and more. 2. Firm Characteristics : * Access 209 firm characteristics from OpenAP (+ 3 additional characteristics from CRSP (Price, Size, Short-term Reversal)). Learn more about OpenAP: [Data website](https://www.openassetpricing.com) | [GitHub code](https://github.com/OpenSourceAP/CrossSection) | [Publication](https://www.nowpublishers.com/article/Details/CFR-0112) ## Examples ```r ## ------------------------------------------------ ## Method `OpenAP$new` ## ------------------------------------------------ # Offline example obj <- OpenAP$new(mock = TRUE) # Real initialization (requires internet connection) ## Not run: openap <- OpenAP$new() openap_24 <- OpenAP$new(release_year = "2024_10") ## End(Not run) ## ------------------------------------------------ ## Method `OpenAP$list_port` ## ------------------------------------------------ # List available portfolios (offline example) openap <- OpenAP$new(mock = TRUE) openap$list_port() # Real usage (internet connection required) ## Not run: openap <- OpenAP$new() openap$list_port() ## End(Not run) ## ------------------------------------------------ ## Method `OpenAP$dl_port` ## ------------------------------------------------ # Download entire portfolio file (offline example) openap <- OpenAP$new(mock = TRUE) df <- openap$dl_port("op") # Download with specific predictor(s) only df <- openap$dl_port("op", predictor = "Accruals") # Real usage (internet connection required) ## Not run: openap <- OpenAP$new() df <- openap$dl_port("op", predictor = "Accruals") df <- openap$dl_port("op", predictor = c("Accruals", "Mom12m")) ## End(Not run) ## ------------------------------------------------ ## Method `OpenAP$dl_signal` ## ------------------------------------------------ # Download firm characteristics (offline example) openap <- OpenAP$new(mock = TRUE) # Single signal (OpenAP or WRDS) df <- openap$dl_signal("BM") # Multiple signals df <- openap$dl_signal(c("BM", "STreversal")) # Real usage (requires internet connection) ## Not run: openap <- OpenAP$new() df <- openap$dl_signal("BM") df <- openap$dl_signal(c("BM", "STreversal")) df <- openap$dl_signal("BM", signed = FALSE) ## End(Not run) ## ------------------------------------------------ ## Method `OpenAP$dl_all_signals` ## ------------------------------------------------ # Download all signals (offline example) openap <- OpenAP$new(mock = TRUE) signals_data <- openap$dl_all_signals() # Real usage (requires internet connection) ## Not run: openap <- OpenAP$new() signals_data <- openap$dl_all_signals() ## End(Not run) ## ------------------------------------------------ ## Method `OpenAP$dl_signal_doc` ## ------------------------------------------------ # Load signal documentation (offline example) openap <- OpenAP$new(mock = TRUE) signal_doc <- openap$dl_signal_doc() # Real usage (requires internet connection) ## Not run: openap <- OpenAP$new() signal_doc <- openap$dl_signal_doc() ## End(Not run) ``` ## Public fields - **`name_id_map`**: A mapping between names and their corresponding IDs in the OpenAP database. - **`individual_signal_id_map`**: A mapping of unique identifiers for individual signals. - **`signal_sign`**: The direction or "sign" of the signal (i.e. positive or negative). - **`url`**: The base URL for downloading OpenAP data. - **`mock`**: Logical; if TRUE, network calls are skipped and mock data is returned for all download functions ## Methods ### Public methods * [list("OpenAP$new()")](#method-OpenAP-new) * [list("OpenAP$list_port()")](#method-OpenAP-list_port) * [list("OpenAP$get_url()")](#method-OpenAP-get_url) * [list("OpenAP$dl_port()")](#method-OpenAP-dl_port) * [list("OpenAP$get_individual_signal_url()")](#method-OpenAP-get_individual_signal_url) * [list("OpenAP$dl_signal_crsp3()")](#method-OpenAP-dl_signal_crsp3) * [list("OpenAP$apply_sign_logic()")](#method-OpenAP-apply_sign_logic) * [list("OpenAP$merge_crsp_with_signals()")](#method-OpenAP-merge_crsp_with_signals) * [list("OpenAP$dl_signal()")](#method-OpenAP-dl_signal) * [list("OpenAP$dl_all_signals()")](#method-OpenAP-dl_all_signals) * [list("OpenAP$dl_signal_doc()")](#method-OpenAP-dl_signal_doc) * [list("OpenAP$clone()")](#method-OpenAP-clone) ### Method `new()` Initializes the OpenAP class instance with data for the specified release year (or per default with the latest data). Loads mappings, individual signal IDs and signal documentation. #### Usage ``` OpenAP$new(release_year = NULL, mock = FALSE) ``` #### Arguments - **`release_year`**: Optional release identifier, e.g. `"2024_08"`. - **`mock`**: Logical; if TRUE, network initialization is skipped and small mock datasets are returned instead of downloading data. This is intended for examples, testing, and offline use. #### Examples ``` # Offline example obj <- OpenAP$new(mock = TRUE) # Real initialization (requires internet connection) \dontrun{ openap <- OpenAP$new() openap_24 <- OpenAP$new(release_year = "2024_10") } ``` ### Method `list_port()` Returns a list of available portfolio types for the OpenAP dataset, depending on the specified release year. #### Usage ``` OpenAP$list_port() ``` #### Examples ``` # List available portfolios (offline example) openap <- OpenAP$new(mock = TRUE) openap$list_port() # Real usage (internet connection required) \dontrun{ openap <- OpenAP$new() openap$list_port() } ``` ### Method `get_url()` Retrieves the URL for a specific dataset based on its name. #### Usage ``` OpenAP$get_url(data_name) ``` #### Arguments - **`data_name`**: The name of the Portfolio. ### Method `dl_port()` Downloads portfolio data for a specified data set and optionally filters by predictor. #### Usage ``` OpenAP$dl_port(data_name, predictor = NULL) ``` #### Arguments - **`data_name`**: The name of the data set. - **`predictor`**: A vector of predictor names to filter (optional). #### Examples ``` # Download entire portfolio file (offline example) openap <- OpenAP$new(mock = TRUE) df <- openap$dl_port("op") # Download with specific predictor(s) only df <- openap$dl_port("op", predictor = "Accruals") # Real usage (internet connection required) \dontrun{ openap <- OpenAP$new() df <- openap$dl_port("op", predictor = "Accruals") df <- openap$dl_port("op", predictor = c("Accruals", "Mom12m")) } ``` ### Method `get_individual_signal_url()` Retrieves the URL for an individual signal based on its name. #### Usage ``` OpenAP$get_individual_signal_url(signal_name) ``` #### Arguments - **`signal_name`**: The name of the signal to retrieve. #### Returns A string representing the URL of the signal. ### Method `dl_signal_crsp3()` Is being used in "dl_signal()" to download CRSP signals. Requires WRDS credentials. #### Usage ``` OpenAP$dl_signal_crsp3( requested_crsp_signals = c("Price", "Size", "STreversal") ) ``` #### Arguments - **`requested_crsp_signals`**: A vector of CRSP signals to download. ### Method `apply_sign_logic()` applies the sign logic to the data based on the signal documentation. #### Usage ``` OpenAP$apply_sign_logic(data, predictors, signal_sign, signed = TRUE) ``` #### Arguments - **`data`**: The data frame to apply the sign logic to. - **`predictors`**: A vector of predictor names. - **`signal_sign`**: A data frame containing the signal documentation. - **`signed`**: Logical; whether to apply signed transformation based on signal documentation. Default is TRUE. ### Method `merge_crsp_with_signals()` Merges downloaded OpenAP firm-level signals with CRSP-based signals (e.g., Size, Price, STreversal). #### Usage ``` OpenAP$merge_crsp_with_signals(signals, crsp_data) ``` #### Arguments - **`signals`**: Data frame containing OpenAP firm-level signals with columns `permno` and `yyyymm`. - **`crsp_data`**: Data frame containing CRSP signals (e.g., Size, Price) with columns `permno` and `yyyymm`. ### Method `dl_signal()` Downloads specific firm characteristics. #### Usage ``` OpenAP$dl_signal(predictor = NULL, signed = FALSE) ``` #### Arguments - **`predictor`**: A vector of predictor names to download. - **`signed`**: Logical; whether to apply signed transformation based on signal documentation. #### Returns A data frame containing the signal data. #### Examples ``` # Download firm characteristics (offline example) openap <- OpenAP$new(mock = TRUE) # Single signal (OpenAP or WRDS) df <- openap$dl_signal("BM") # Multiple signals df <- openap$dl_signal(c("BM", "STreversal")) # Real usage (requires internet connection) \dontrun{ openap <- OpenAP$new() df <- openap$dl_signal("BM") df <- openap$dl_signal(c("BM", "STreversal")) df <- openap$dl_signal("BM", signed = FALSE) } ``` ### Method `dl_all_signals()` Downloads all firm level characteristics from the release folder. #### Usage ``` OpenAP$dl_all_signals(signed = FALSE) ``` #### Arguments - **`signed`**: Logical; whether to apply signed transformation based on signal documentation. Default is TRUE. #### Returns A data frame containing all firm level characteristics. #### Examples ``` # Download all signals (offline example) openap <- OpenAP$new(mock = TRUE) signals_data <- openap$dl_all_signals() # Real usage (requires internet connection) \dontrun{ openap <- OpenAP$new() signals_data <- openap$dl_all_signals() } ``` ### Method `dl_signal_doc()` Downloads the signal documentation CSV for the release. #### Usage ``` OpenAP$dl_signal_doc() ``` #### Returns A data frame containing the signal documentation. #### Examples ``` # Load signal documentation (offline example) openap <- OpenAP$new(mock = TRUE) signal_doc <- openap$dl_signal_doc() # Real usage (requires internet connection) \dontrun{ openap <- OpenAP$new() signal_doc <- openap$dl_signal_doc() } ``` ### Method `clone()` The objects of this class are cloneable with this method. #### Usage ``` OpenAP$clone(deep = FALSE) ``` #### Arguments - **`deep`**: Whether to make a deep clone.

OpenAP function