Fama French Factors Downloader

FamaFrenchDownloader is a Python library that allows you to easily download and work with Fama-French factor data (3-factor, 5-factor, and Momentum models) across multiple global regions.

Supported Regions

• US
• North America
• Europe
• Japan
• Asia Pacific ex Japan
• Developed
• Developed ex US

Supported Factor Models

• Fama-French 3-Factors
• Fama-French 5-Factors
• Momentum (WML / MOM)

Installation

To install the package using pip:

pip install FamaFrenchDownloader

Usage

from FamaFrenchDownloader import FamaFrenchFactor

df_five_fac_monthly = FamaFrenchFactor.get_data(
    annual=False,
    region=["US", "Japan"],
    factors= 5)


df_mom_annual = FamaFrenchFactor.get_data(
    annual=True,
    region=["Europe", "North_America"],
    factors="MOM")

Parameters

region (str or list of str)

The region parameter defines which geographical region(s) of Fama-French factor data to download.

You can pass:

• A single region (as a string)
• Multiple regions (as a list of strings)

Supported values:

Region name	Description
"US"	United States only
"North_America"	US + Canada
"Europe"	European developed countries
"Japan"	Japan only
"Asia_Pacific_ex_Japan"	Asia Pacific excluding Japan
"Developed"	All developed markets
"Developed_ex_US"	Developed markets excluding the US

Examples:

region = "Europe"

region = ["Europe", "Japan"]

factors (str or int)

Specifies which Fama-French factor model to download.

You can pass:

• 3 for the 3-Factor Model
• 5 for the 5-Factor Model
• "MOM" for the Momentum Factor

Available values:

Value	Model	Factors Included
3	3-Factor Model	Mkt-RF, SMB, HML
5	5-Factor Model	Mkt-RF, SMB, HML, RMW, CMA
"MOM"	Momentum Factor	WML (also appears as Mom, auto-renamed)

Examples:

factors = 3       # 3-Factor model
factors = 5       # 5-Factor model
factors = "MOM"   # Momentum model

annual (bool)

Defines the frequency of the data to be downloaded.

• True → Download annual data (one observation per year)
• False → Download monthly data (one observation per month)

Effect on the date index:

Value	Description	Index format
True	Annual data	YYYY-12-31
False	Monthly data	YYYY-MM-01

Examples:

annual = True    # Get one row per year (e.g. 2020-12-31, 2021-12-31, ...)
annual = False   # Get one row per month (e.g. 2020-01-01, 2020-02-01, ...)