Reference

Download data

download.download_data(): Download and process data based on domain and dataset.

download_open_source.list_supported_indexes(): Return a DataFrame of supported financial indexes.
download_open_source.get_available_famafrench_datasets(): Get the list of datasets available from the Fama/French data library.

download_wrds.get_wrds_connection(): Establish a connection to Wharton Research Data Services (WRDS).
download_wrds.disconnect_connection(): Close an open WRDS database connection safely.
download_wrds.load_wrds_credentials(): Load WRDS credentials from environment variables or a ‘.env’ file.
download_wrds.process_trace_data(): Clean Enhanced TRACE trade reports.
download_wrds.set_wrds_credentials(): Set WRDS credentials in a ‘.env’ file.

portfolios.data_options(): Create data options for tidyfinance functions.
portfolios.assign_portfolio(): Assign data points to portfolios based on a sorting variable.
portfolios.breakpoint_options(): Create breakpoint options for portfolio sorting.
portfolios.compute_breakpoints(): Compute breakpoints based on a sorting variable.
portfolios.filter_options(): Create filter options for sample construction.
portfolios.portfolio_sort_options(): Create portfolio sort options.
portfolios.compute_portfolio_returns(): Compute portfolio returns.
portfolios.compute_long_short_returns(): Compute long-short returns.
portfolios.filter_sorting_data(): Filter sorting data.
portfolios.implement_portfolio_sort(): Implement a portfolio sort.

lagging.get_backend(): Return the active data frame backend (‘“pandas”’ or ‘“polars”’).
lagging.add_lagged_columns(): Append lagged columns to a data frame via a join-based approach.
lagging.join_lagged_values(): Join lagged values of variables over a date range.
lagging.compute_rolling_value(): Compute a rolling value by period.

supported_datasets.list_supported_datasets(): List all datasets supported by ‘download_data’.

utilities.create_summary_statistics(): Create summary statistics for specified variables.
utilities.list_supported_indexes(): Return a DataFrame of supported financial indexes.
utilities.list_supported_jkp_factors(): Return the regions and factors supported by Global Factor Data.
utilities.list_tidy_finance_chapters(): Return the chapter slugs of the Tidy Finance book.
utilities.open_tidy_finance_website(): Open the Tidy Finance website or a specific chapter.
utilities.winsorize(): Winsorize a numeric vector at symmetric quantiles.
utilities.trim(): Trim a numeric vector by removing extreme observations.

backend.set_backend(): Set the global data frame backend for the tidyfinance API.
backend.get_backend(): Return the active data frame backend (‘“pandas”’ or ‘“polars”’).