getfactormodels 0.0.10

Download datasets for various multifactor asset pricing models.

getfactormodels

Reliably retrieve data for various multi-factor asset pricing models.

Models

• The 3-factor, 5-factor, and 6-factor models of Fama & French ^{[1] [3] [4]}
• Mark Carhart's 4-factor model ^[2]
• Pastor and Stambaugh's liquidity factors ^[5]
• Mispricing factors of Stambaugh and Yuan^[6]
• The $q$-factor model of Hou, Mo, Xue and Zhang^[7]
• The augmented $q^5$-factor model of Hou, Mo, Xue and Zhang^[8]
• Intermediary Capital Ratio (ICR) of He, Kelly & Manela^[9]
• The DHS behavioural factors of Daniel, Hirshleifer & Sun^[10]
• The HML $^{DEVIL}$ factor of Asness & Frazzini^[11]
• The 6-factor model of Barillas and Shanken^[12]

Thanks to: Kenneth French, Robert Stambaugh, Lin Sun, Zhiguo He, AQR Capital Management (AQR.com) and Hou, Xue and Zhang (global-q.org), for their research and for the datasets they provide.

Installation

[!IMPORTANT] getfactormodels is pre-alpha (until version 0.1.0), don't rely on it for anything.

But a huge thanks to anyone who has tried it!

Requires:

• Python >=3.10

The easiest way to install (and update) getfactormodels is with pip:

pip install -U getfactormodels

You can also download the latest release and install using pip.

linux/macOS

 curl -LO https://github.com/x512/getfactormodels/archive/latest.tar.gz
 tar -xzf latest.tar.gz
 cd getfactormodels-*
 pip install .

Quick start

Basic usage:

• Import getfactormodels and use the get_factors function, with a model parameter:

 import getfactormodels

 data = getfactormodels.get_factors(model='q', frequency='d')

• All other parameters are optional. By default monthly data is returned.

# monthly Fama-French 3-factors since start_date
df = getfactormodels.get_factors(model='ff3', start_date='2006-01-01')

# Daily Mispricing factors saved to file:
df = getfactormodels.get_factors(
    model='mispricing',
    start_date='1970-01-01',
    end_date='1999-12-31',
    output='~/mispricing_factors.csv'  #.csv, .pkl, .parquet, .txt
)

• Using the model classes, you can import only the models you want:

from getfactormodels import ICRFactors, QFactors

model = ICRFactors(frequency='m', start_date='2000-01-01')

# use the download module to get the data
df = model.download()

# use the extract module to get a factor
factor = model.extract("IC_RATIO")

• Fama-French 3-Factors and the q-factors have weekly data available:

df = QFactors(frequency='w',
              start_date='1992-05-22',
              end_date='2019-01-05').download() # chained! Wow!

• For more examples see the notebook: here

CLI

You can use getfactormodels from the command line. Just call getfactormodels with the --model -m flag.

• Frequency -f defaults to monthly and all other parameters are optional:

#monthly Fama-French 3 factor model
getfactormodels --model ff3

# daily mispricing factors since start
getfactormodels -m mis --frequency d --start 2000-01-01

Note: all data is cached for 1 day, re-running commands isn't wasteful.

• Save data to a file with --output -o:

#save annual Fama-French 5-Factors to file:
getfactormodels -m 5 - f y --output "~/dir/filename.csv" # can be csv, pkl, parquet, txt.

getfactormodels -m liq -f m -o somefile # will be a csv, will be in users current directory.

Note: Fama French models can be a string ("ff3") or int (3, 4, 5, 6, where 4 = carhart).

• Extract a factor from a model with the --extract -x flag:

getfactormodels -m carhart -f m --extract MOM
# extract multiple factors to a file 
getfactormodels -m ff3 -f m -x SMB HML -o "dir/filename.pkl"

• Access Fama-French Emerging and Developed/International markets using the --region -r flag:

# 3 factor model for developed markets
getfactormodels -m ff3 --region developed

# 5-Factor model for Europe saved to file 
getfactormodels -m 5 -r europe -o euro_factors

# extract the SMB and WML factors from the 4 Factor model
getfactormodels -m 4 --region emerging --extract SMB WML

• See more in the example notebook: here

Or try it for yourself:

Classes

A list of model classes available:

• FamaFrenchFactors
• CarhartFactors
• QFactors
• ICRFactors
• DHSFactors
• LiquidityFactors
• MispricingFactors
• HMLDevilFactors
• BarillasShankenFactors

For a list of parameters, see the example notebook. (Docs are coming)

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Data Availability

This table shows each model's start date, available frequencies, and the latest datapoint if not current. The id column contains the shortest identifier for each model. These should all work in python and the CLI.

id	Factor Model	Start	D	W	M	Q	Y	End
3	Fama-French 3	1926-07-01	✓	✓	✓		✓	-
4	Carhart 4	1926-11-03	✓		✓		✓	-
5	Fama-French 5	1963-07-01	✓		✓		✓	-
6	Fama-French 6	1963-07-01	✓		✓		✓	-
hmld	HML $^{DEVIL}$	1990-07-02	✓		✓			-
dhs	DHS	1972-07-03	✓		✓			2023-12-29
icr	ICR	1970-01-31 Daily: 1999-05-03	✓		✓	✓		2025-06-27
mis	Mispricing	1963-01-02	✓		✓			2016-12-30
liq	Liquidity	1962-08-31			✓			2024-12-31
q q4	$q^5$-factors $q$-factors	1967-01-03	✓	✓	✓	$\checkmark$	✓	2022-12-30
bs	Barillas-Shanken	1967-01-03	✓		✓			2024-12-31

References

Publications:

1. E. F. Fama and K. R. French, ‘Common risk factors in the returns on stocks and bonds’, Journal of Financial Economics, vol. 33, no. 1, pp. 3–56, 1993. PDF
2. M. Carhart, ‘On Persistence in Mutual Fund Performance’, Journal of Finance, vol. 52, no. 1, pp. 57–82, 1997. PDF
3. E. F. Fama and K. R. French, ‘A five-factor asset pricing model’, Journal of Financial Economics, vol. 116, no. 1, pp. 1–22, 2015. PDF
4. E. F. Fama and K. R. French, ‘Choosing factors’, Journal of Financial Economics, vol. 128, no. 2, pp. 234–252, 2018. PDF
5. L. Pastor and R. Stambaugh, ‘Liquidity Risk and Expected Stock Returns’, Journal of Political Economy, vol. 111, no. 3, pp. 642–685, 2003. PDF
6. R. F. Stambaugh and Y. Yuan, ‘Mispricing Factors’, The Review of Financial Studies, vol. 30, no. 4, pp. 1270–1315, 12 2016. PDF
7. K. Hou, H. Mo, C. Xue, and L. Zhang, ‘Which Factors?’, National Bureau of Economic Research, Inc, 2014. PDF
8. K. Hou, H. Mo, C. Xue, and L. Zhang, ‘An Augmented q-Factor Model with Expected Growth*’, Review of Finance, vol. 25, no. 1, pp. 1–41, 02 2020. PDF
9. Z. He, B. Kelly, and A. Manela, ‘Intermediary asset pricing: New evidence from many asset classes’, Journal of Financial Economics, vol. 126, no. 1, pp. 1–35, 2017. PDF
10. K. Daniel, D. Hirshleifer, and L. Sun, ‘Short- and Long-Horizon Behavioral Factors’, Review of Financial Studies, vol. 33, no. 4, pp. 1673–1736, 2020. PDF
11. C. Asness and A. Frazzini, ‘The Devil in HML’s Details’, The Journal of Portfolio Management, vol. 39, pp. 49–68, 2013. PDF
12. F. Barillas and J. Shanken, ‘Comparing Asset Pricing Models’, Journal of Finance, vol. 73, no. 2, pp. 715–754, 2018. PDF

Data sources:

• K. French, "Data Library," Tuck School of Business at Dartmouth. Link
• R. Stambaugh, "Liquidity" and "Mispricing" factor datasets, Wharton School, University of Pennsylvania. Link
• Z. He, "Intermediary Capital Ratio and Risk Factor" dataset, zhiguohe.net. Link
• K. Hou, G. Xue, R. Zhang, "The Hou-Xue-Zhang q-factors data library," at global-q.org. Link
• AQR Capital Management's Data Sets.
• Lin Sun, DHS Behavioural factors Link

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License

Known issues

• The first hml_devil_factors() retrieval is slow, because the download from aqr.com is slow. It's the only model implementing a cache—daily data expires at the end of the day, and will only re-download when the requested end_date exceeds the cache's latest index date. Similar for monthly, expiring at at the end of the month, and re-downloaded when next needed.
• Some models aren't downloading. Update: all models should be downloading.

Todo

• Refactor: a complete rewrite, implementing a better interface and design patterns, dropping dependencies.
• Refactor: FF models.
• Docs
• Every model should have an about and author/copyright info, and general disclaimer
• This README
  • Example ipynb
• Tests
• Error handling!
• Types