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Changelog

Unreleased

  • Polars backend returns WRDS date columns as Date (#66): With set_backend("polars"), the calendar-date columns of the WRDS downloads (trd_exctn_dt from trace_enhanced, datadate from Compustat, linkdt/linkenddt from CCM links, calculation_date from monthly CRSP, and the FISD date columns) are now cast to polars.Date instead of surfacing as polars.Datetime, matching the R package and the existing normalization of the date column. This fixes SchemaErrors when joining or vertically stacking TRACE output against Date-typed frames. True time-of-day columns (e.g. trd_exctn_tm) and timezone-aware datetimes are unaffected.
  • Added FRED-MD and FRED-QD macroeconomic databases: download_data("FRED", "FRED-MD") and download_data("FRED", "FRED-QD") download the McCracken and Ng (2016, 2021) curated monthly / quarterly macro panels as wide tables (one column per series). transform=True applies each series' stationarity transform code (tcode). vintage selects the current release (default), a specific "YYYY-MM" release, or "all" — the full real-time panel across every archived vintage (recent vintages are hosted individually; older ones are read from the St. Louis Fed vintage archive zips), enabling leak-free point-in-time analysis.
  • Added Global Factor Data, Pastor-Stambaugh, and Stambaugh-Yuan downloads: download_data("Global Factor Data") downloads characteristic-managed portfolio returns, the underlying long-short portfolios, industry returns, or reference cutoff files from Global Factor Data (Jensen, Kelly, and Pedersen, 2023); the requested selection is validated against the library's live availability manifest, and list_supported_jkp_factors() lists the available regions and factors. download_data("Pastor-Stambaugh") downloads the liquidity factors of Pastor and Stambaugh (2003). download_data("Stambaugh-Yuan") downloads the mispricing factors of Stambaugh and Yuan (2017), with a dataset argument selecting "monthly" (default) or "daily" data.
  • OSAP download aligned with beginning-of-month and scaled returns: download_data("Open Source Asset Pricing") now aligns the date column to the beginning of the month (the dataset previously returned end-of-month dates), matching the convention used by the other download functions. All predictor columns are monthly long-short returns expressed in percent and are now divided by 100 to return plain numeric (decimal) returns.
  • sorting_variable is now optional for factor_library: Calling download_data("Tidy Finance", "factor_library") without a sorting_variable now returns the default portfolio construction for all sorting variables instead of raising an error. Other defaults (e.g. rebalancing, weighting_scheme) still apply; pass fill_all=True to leave every column unrestricted. Passing None for any filter column now removes that filter entirely and returns all values for that column (e.g. min_size_quantile=None includes all size groups), matching the R package's NULL behavior.
  • Added detail parameter to estimate_fama_macbeth: matching the R package, passing detail=True returns a dict with coefficients (the usual risk premium estimates) and summary_statistics (a one-row data frame with the mean cross-sectional r_squared, adj_r_squared, and n_obs across all per-period regressions). The default (detail=False) behavior — returning only the coefficients data frame — is unchanged.
  • Dependencies (replaced pyfixest with formulaic): The pyfixest dependency was dropped in favor of formulaic plus a small internal numpy OLS helper (_fit_ols). pyfixest was used only for plain OLS with classical (IID) standard errors in estimate_model and the cross-sectional / IID-variance steps of estimate_fama_macbeth, but it pulled in great-tables → multimark, a cffi C-extension that ships no Python 3.14 wheels and therefore required a C/C++ toolchain (e.g. MSVC Build Tools on Windows) to install on unsupported interpreters. _fit_ols builds the design matrix via formulaic and reproduces feols coefficients, standard errors, t-statistics, and residuals to ~1e-9 for models without fixed effects, so results are unchanged. Note that estimate_model and estimate_fama_macbeth now perform classical OLS only (the fixed-effects / clustered-SE features of pyfixest were never used and are no longer available).

v0.1.0

  • Development version

v0.1.1 (2025-03-21)

  • Initial PyPI release.

v0.1.2 (2025-07-03)

  • Added new FRED url for data download
  • added curl_cffi package to handle HTTP 429 Too Many Requests client error on Yahoo finance

v0.2.2 (2025-10-19)

  • removed pandas_datareader package requirements, replaced with requests from curl_cffi package
  • bug fix fred download
  • added "_" to internal data download function names

v0.2.3 (2025-11-17)

  • removed internal __download_data_factors function

v0.2.4 (2025-11-17)

  • added get_available_famafrench_datasets as a public function

v0.2.5 (2025-11-20)

  • fix date for monthly data in _download_data_factors_ff

v0.2.6 (2026-04-02)

  • Added support for Hugging Face datasets via domain="tidyfinance", including high_frequency_sp500 and factor_library

v0.3.0 (2026-06-28)

  • Dependencies (removed statsmodels): The statsmodels dependency was dropped; the regression-based functions now use pyfixest instead. estimate_model and the cross-sectional / IID-variance steps of estimate_fama_macbeth call pyfixest.feols, and estimate_betas was rewritten to estimate rolling betas via closed-form OLS on cumulative cross-product sums (the design Gram matrix X'X and moment vector X'y are accumulated and rolled by cumulative-sum differencing, then solved once per window). This follows the fast beta estimation approach, generalized to multiple regressors, and returns coefficients identical to ordinary least squares while avoiding a full refit per window (#49).
  • Docs (Great Docs): Added a Great Docs documentation site configured via great-docs.yml, including LLM-friendly artifacts (llms.txt, llms-full.txt). The API reference is generated from the numpydoc docstrings; build locally with great-docs build (on Windows set PYTHONUTF8=1 to avoid a cp1252 decode error during post-processing). The generated great-docs/ build directory is gitignored. (#29)
  • Breaking (Python version): The minimum supported Python is now 3.11 (was 3.10), as required by the Great Docs toolchain.
  • Docs (R parity): Fixed docstring discrepancies surfaced by the rendered reference, aligning the Python docs with r-tidyfinance: breakpoint_options (removed a duplicated breakpoints_exchanges entry and documented the previously undocumented breakpoints_min_size_threshold), create_summary_statistics (enumerated the reported statistics and detail quantiles), compute_portfolio_returns / implement_portfolio_sort (min_portfolio_size univariate/bivariate semantics and the "set to 0 to deactivate" behavior), estimate_betas (lookback annotated as int to match its use as an observation-count window), and winsorize (corrected the x type to np.ndarray and documented the [0, 0.5] range for cut).
  • Polars support: the public API can now work with polars data frames via a global backend. Call tidyfinance.set_backend("polars") (default "pandas"; get_backend() reports the current setting). When set to "polars", the data-bearing functions (download_data, the estimate_*/compute_* family, add_lagged_columns, assign_portfolio's frame inputs, list_supported_datasets, etc.) return polars data frames, and all of them also accept polars input regardless of the active backend (converted to pandas internally). DataFrame outputs convert; Series/dict/ndarray returns (e.g. assign_portfolio) are left as-is, and date indices are preserved as columns. Internals remain pandas-based for now. Requires the optional polars dependency (pip install tidyfinance[polars]) (#42).
  • Breaking (WRDS credentials): WRDS credentials are now read exclusively from environment variables (e.g. via a .env file). Support for config.yaml has been removed: set_wrds_credentials() now writes a .env file (with WRDS_USER and WRDS_PASSWORD), and get_wrds_connection() no longer accepts a config_path argument. The pyyaml dependency was dropped. Migrate any existing config.yaml credentials into a .env file or environment variables.
  • Breaking (CRSP): the monthly CRSP price column returned by download_data(domain="wrds", dataset="crsp_monthly") is now named prc (was altprc), aligning with r-tidyfinance and both book editions. The value is unchanged — it is mthprc from the CRSP v2 monthly stock file; altprc was the legacy (v1) column name and was semantically stale for v2 downloads. Update any downstream code that referenced altprc (including the dependent mktcap computation).
  • Fix (Fama-MacBeth Newey-West): estimate_fama_macbeth now matches R's sandwich::NeweyWest defaults, so the Python and R editions agree on Newey-West t-statistics. The previous implementation used statsmodels HAC with a fixed maxlags=6 and no prewhitening (textbook Newey-West 1987); the new numpy implementation uses VAR(1) prewhitening plus the automatic Newey & West (1994) bandwidth, Bartlett kernel, recoloring, and no finite-sample adjustment (verified against sandwich 3.1.1 to ~1e-13). vcov_options now mirrors R's interface (lag, prewhite, adjust) and defaults to None; the legacy maxlags key is accepted as a deprecated alias for lag (preserving the old no-prewhitening behavior) and emits a DeprecationWarning (#35).
  • Fix (CRSP column order): download_data(domain="wrds", dataset="crsp_monthly") now orders listing_age before mktcap to match r-tidyfinance's download_data_wrds_crsp() (..., siccd, listing_age, mktcap, mktcap_lag, ...). Values are unchanged; only the column order differed (#36).
  • Fix (TRACE regime cutoff): process_trace_data now uses the correct Dick-Nielsen (2014) enhanced-TRACE regime cutoff of 2012-02-06 (was the transposed 2012-06-02). Samples spanning Feb 6 – Jun 2, 2012 were previously cleaned under the wrong cancellation/correction/reversal regime, producing incorrect output; samples entirely after June 2012 were unaffected. This aligns the Python edition with r-tidyfinance's download_data_wrds_trace_enhanced() (#34).
  • download_data() now uses the human-readable domain names returned by list_supported_datasets() (e.g., "Fama-French", "Global Q", "WRDS", "Tidy Finance"). The "pseudo" and "tidyfinance" domains were renamed to "Pseudo Data" and "Tidy Finance". The previous machine-readable domain names (e.g., "famafrench", "wrds", "pseudo", "tidyfinance") are soft-deprecated but still accepted.
  • Breaking (package API): the dataset-specific _download_data_* helpers (e.g. _download_data_wrds, _download_data_macro_predictors, _download_data_constituents, _download_data_factors_ff, _download_data_factors_q, _download_data_osap, _download_data_risk_free, _download_data_stock_prices) are no longer re-exported from the package root. Public access continues via the dispatcher download_data(domain, dataset, ...). If you need a helper directly, import it from its defining module (e.g. from tidyfinance.data_download import _download_data_wrds).