finds.readers.ffreader

Wraps around pandas_datareader to retrieve French data library

MIT License

Copyright 2022-2023 Terence Lim

class finds.readers.ffreader.FFReader(symbol)[source]

Bases: object

Wraps over pandas_datareader to load FamaFrench factors from website

Hints for using pandas datareader to read a single series

from pandas_datareader.famafrench import FamaFrenchReader as FFR
mkt = FFReader('F-F_Research_Data_Factors', start=1900, end=2099).read()
__getitem__(key)[source]
static fetch(name: str, item: int = 0, suffix: str = '', date_formatter: ~typing.Callable = <function FFReader.<lambda>>) → DataFrame[source]

Retrieve item and return as DataFrame

Parameters:

  • name – Name of research factor in Ken French website

  • item – Index of item to research (e.g. 0 is usually value-weighted)

  • suffix – Suffix to append to name (e.g. to distinguish monthly from daily)

  • date_formatter – to reformat dates, e.g. bd.offset or bd.endmo

Returns:

DataFrame of asset returns (converted to decimal, not percentages)

static keys() → List[str][source]

Return names of all available datasets

static sectoring(scheme: str, source: str = '') → DataFrame | None[source]

Load FamaFrench sectoring based on sic-4, from website or zipfile

Parameters:

scheme – in {codes5, codes10, 12, 17, 30, 38, 48, 49}

Notes:

Retrieved from “https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/” + “ftp/Siccodes5.zip”

For example, the industry definitions file for Siccodes49 looks like:

1 Agric  Agriculture
0100-0199 Agricultural production - crops
0200-0299 Agricultural production - livestock
0700-0799 Agricultural services
0910-0919 Commercial fishing
2048-2048 Prepared feeds for animals
2 Food   Food Products
2000-2009 Food and kindred products
2010-2019 Meat products
2020-2029 Dairy products
daily = [('F-F_Research_Data_5_Factors_2x3_daily', 0, ''), ('F-F_Research_Data_Factors_daily', 0, ''), ('F-F_Momentum_Factor_daily', 0, ''), ('F-F_LT_Reversal_Factor_daily', 0, ''), ('F-F_ST_Reversal_Factor_daily', 0, ''), ('49_Industry_Portfolios_daily', 0, '49vw'), ('48_Industry_Portfolios_daily', 0, '48vw'), ('49_Industry_Portfolios_daily', 1, '49ew'), ('48_Industry_Portfolios_daily', 1, '48ew')]

Common daily FF series, with subset index and a suggested suffix

monthly = [('F-F_Research_Data_5_Factors_2x3', 0, '(mo)'), ('F-F_Research_Data_Factors', 0, '(mo)'), ('F-F_Momentum_Factor', 0, '(mo)'), ('F-F_LT_Reversal_Factor', 0, '(mo)'), ('F-F_ST_Reversal_Factor', 0, '(mo)')]

Common monthly FF series, with subset index and a suggested suffix