<a name="readme-top"></a>
# getfactormodels
 
Reliably retrieve data for various multi-factor asset pricing models.
## Models
- The 3-factor, 5-factor, and 6-factor models of Fama & French <sup>[[1]](#1) [[3]](#3) [[4]](#4)</sup>
- Mark Carhart's 4-factor model <sup>[[2]](#2)</sup>
- Pastor and Stambaugh's liquidity factors <sup>[[5]](#5)</sup>
- Mispricing factors of Stambaugh and Yuan<sup>[[6]](#6)</sup>
- The $q$*-factor* model of Hou, Mo, Xue and Zhang<sup>[[7]](#7)</sup>
- The augmented $q^5$*-factor* model of Hou, Mo, Xue and Zhang<sup>[[8]](#8)</sup>
- *Intermediary Capital Ratio* (ICR) of He, Kelly & Manela<sup>[[9]](#9)</sup>
- The *DHS behavioural factors* of Daniel, Hirshleifer & Sun<sup>[[10]](#10)</sup>
- The *HML* $^{DEVIL}$ factor of Asness & Frazzini<sup>[[11]](#11)</sup>
- The 6-factor model of Barillas and Shanken<sup>[[12]](#12)</sup>
_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
`getfactormodels` requires Python ``>=3.7``
* The easiest way to install getfactormodels is via pip:
```shell
$ pip install getfactormodels
```
## Usage
>[!IMPORTANT]
>
>
>``getfactormodels`` is new. It was released on December 20, 2023. Don't rely on it for anything.
After installation, import and call the ``get_factors()`` function with the ``model`` and ``frequency`` params:
* For example, retrieving the monthly ${q}^{5}$ factor model:
```python
import getfactormodels
data = getfactormodels.get_factors(model='q', frequency='m')
```
> _Trimmed output:_
```txt
> print(data)
Mkt-RF R_ME R_IA R_ROE R_EG RF
date
1967-01-03 0.000778 0.004944 0.001437 -0.007118 -0.008563 0.000187
1967-01-04 0.001667 -0.003487 -0.000631 -0.002044 -0.000295 0.000187
1967-01-05 0.012990 0.004412 -0.005688 0.000838 -0.003075 0.000187
1967-01-06 0.007230 0.006669 0.008897 0.003603 0.002669 0.000187
1967-01-09 0.008439 0.006315 0.000331 0.004949 0.002979 0.000187
... ... ... ... ... ... ...
2022-12-23 0.005113 -0.001045 0.004000 0.010484 0.003852 0.000161
2022-12-27 -0.005076 -0.001407 0.010190 0.009206 0.003908 0.000161
2022-12-28 -0.012344 -0.004354 0.000133 -0.010457 -0.004953 0.000161
2022-12-29 0.018699 0.008568 -0.008801 -0.012686 -0.002162 0.000161
2022-12-30 -0.002169 0.001840 0.001011 -0.004151 -0.003282 0.000161
[14096 rows x 6 columns]
```
* Retrieving the daily data for the Fama-French 3-factor model, since `start_date`:
```python
import getfactormodels as gfm
df = gfm.get_factors(model='ff3', frequency='d', start_date=`2006-01-01`)
```
* Retrieving data for Stambaugh and Yuan's monthly *Mispricing* factors, between `start_date` and `end_date`, and saving the data to a file:
```python
import getfactormodels as gfm
df = gfm.get_factors(model='mispricing', start_date='1970-01-01', end_date=1999-12-31, output='mispricing_factors.csv')
```
>``output`` can be a filename, directory, or path. If no extension is specified, defaults to .csv (can be one of: .xlsx, .csv, .txt, .pkl, .md)
You can import only the models that you need:
* For example, to import only the *ICR* and *q-factor* models:
```python
from getfactormodels import icr_factors, q_factors
# Passing a model function without params defaults to monthly data.
df = icr_factors()
# The 'q' models, and the 3-factor model of Fama-French have weekly data available:
df = q_factors(frequency="W", start_date="1992-01-01, output='.xlsx')
```
>``output`` allows just a file extension (with the `.`, else it'll be passed as a filename).
* When using `ff_factors()`, specify an additional `model` parameter (**this might be changed**):
```python
# To get annual data for the 5-factor model:
data = ff_factors(model="5", frequency="Y", output=".xlsx")
# Daily 3-factor model data, since 1970 (not specifying an end date
# will return data up until today):
data = ff_factors(model="3", frequency="D", start_date="1970-01-01")
```
There's also a ``FactorExtractor`` class (which doesn't do much yet, it's mainly used by the CLI; lots to do):
```python
from getfactormodels import FactorExtractor
fe = FactorExtractor(model='carhart', start_date='1980-01-01', end_date='1980-05-01)
fe.get_factors()
fe.drop_rf()
fe.to_file('~/carhart_factors.md')
```
* _The resulting ``carhart_factors.md`` file will look like this:_
| date | Mkt-RF | SMB | HML | MOM |
|:--------------------|---------:|--------:|--------:|--------:|
| 1980-01-31 00:00:00 | 0.0551 | 0.0162 | 0.0175 | 0.0755 |
| 1980-02-29 00:00:00 | -0.0122 | -0.0185 | 0.0061 | 0.0788 |
| 1980-03-31 00:00:00 | -0.129 | -0.0664 | -0.0101 | -0.0955 |
| 1980-04-30 00:00:00 | 0.0397 | 0.0105 | 0.0106 | -0.0043 |
>``.drop_rf()`` will return the DataFrame without the `RF` column. You can also drop the "Mkt-RF" column with ``.drop_mkt()``
### CLI
``bash >=4.2``
* You can also use getfactormodels from the command line. It's very barebones, here's the `-h`:
```shell
$ getfactormodels -h
usage: getfactormodels [-h] -m MODEL [-f FREQ] [-s START] [-e END] [-o OUTPUT] [--no_rf] [--no_mkt]
```
* An example of how to use the CLI to retrieve the Fama-French 3-factor model data:
```shell
$ getfactormodels --model ff3 --frequency M --start-date 1960-01-01 --end-date 2020-12-31 --output ".csv"
```
* Here's another example that retrieves the annual Fama-French 5-factor data without the RF column (using ``--no_rf``)
```shell
$ getfactormodels -m ff5 -f Y -s 1960-01-01 -e 2020-12-31 --no_rf -o ~/some_dir/filename.xlsx
```
* To return the factors without the risk-free rate `RF`, or the excess market return `Mkt-RF`, columns:
## Data Availability
>[TODO]
## References
1. <a id="1"></a> 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](https://people.duke.edu/~charvey/Teaching/BA453_2006/FF_Common_risk.pdf)
2. <a id="2"></a> M. Carhart, ‘On Persistence in Mutual Fund Performance’, *Journal of Finance*, vol. 52, no. 1, pp. 57–82, 1997. [PDF](https://onlinelibrary.wiley.com/doi/full/10.1111/j.1540-6261.1997.tb03808.x)
3. <a id="3"></a> 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](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2287202)
4. <a id="4"></a> E. F. Fama and K. R. French, ‘Choosing factors’, *Journal of Financial Economics*, vol. 128, no. 2, pp. 234–252, 2018. [PDF](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2668236)
5. <a id="5"></a>L. Pastor and R. Stambaugh, ‘Liquidity Risk and Expected Stock Returns’, *Journal of Political Economy*, vol. 111, no. 3, pp. 642–685, 2003. [PDF](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=279804)
6. <a id="6"></a>R. F. Stambaugh and Y. Yuan, ‘Mispricing Factors’, *The Review of Financial Studies*, vol. 30, no. 4, pp. 1270–1315, 12 2016. [PDF](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2626701)
7. <a id="7"></a>K. Hou, H. Mo, C. Xue, and L. Zhang, ‘Which Factors?’, *National Bureau of Economic Research, Inc*, 2014. [PDF](https://academic.oup.com/rof/article/23/1/1/5133564)
8. <a id="8"></a>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](https://academic.oup.com/rof/article/25/1/1/5727769)
9. <a id="9"></a>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](https://cpb-us-w2.wpmucdn.com/voices.uchicago.edu/dist/6/2325/files/2019/12/jfepublishedversion.pdf)
10. <a id="10"></a>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](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3086063)
11. <a id="11"></a>C. Asness and A. Frazzini, ‘The Devil in HML’s Details’, *The Journal of Portfolio Management*, vol. 39, pp. 49–68, 2013. [PDF](https://stockmarketmba.com/docs/Asness_Frazzini_AdjustHML.pdf)
12. <a id="12"></a>F. Barillas and J. Shanken, ‘Comparing Asset Pricing Models’, *Journal of Finance*, vol. 73, no. 2, pp. 715–754, 2018. [PDF](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2700000)
**Data sources:**
* K. French, "Data Library," Tuck School of Business at Dartmouth.
[Link](https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html)
* R. Stambaugh, "Liquidity" and "Mispricing" factor datasets, Wharton School, University of Pennsylvania.
[Link](https://finance.wharton.upenn.edu/~stambaug/)
* Z. He, "Intermediary Capital Ratio and Risk Factor" dataset, University of Chicago.
[Link](https://voices.uchicago.edu/zhiguohe/data-and-empirical-patterns/intermediary-capital-ratio-and-risk-factor/)
* K. Hou, G. Xue, R. Zhang, "The Hou-Xue-Zhang q-factors data library," at global-q.org.
[Link](http://global-q.org/factors.html)
* AQR Capital Management's Data Sets.
* Lin Sun, DHS Behavioural factors [Link](https://sites.google.com/view/linsunhome)
<p align="right">(<a href="#readme-top">back to top</a>)</p>
## License
*The code in this project is released under the [MIT License]().*
[](https://pycqa.github.io/isort/)
[](https://simpleicons.org/?q=ruff)
---
#### Known issues
* The first `hml_devil_factors()` retrieval is slow, because the download from aqr.com is slow. It's the only model, so far, implementing a cache—daily data expires at the end of the day and is only re-downloaded when the requested`end_date` exceeds the file's last index date. Similar for monthly, expiring at EOM and re-downloaded when needed.
#### Todo
- [ ] Docs
- [ ] Examples
- [ ] Tests
- [ ] Error handling
Raw data
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"description": "<a name=\"readme-top\"></a>\n\n# getfactormodels\n\n \n\n\n\nReliably retrieve data for various multi-factor asset pricing models.\n\n## Models\n\n- The 3-factor, 5-factor, and 6-factor models of Fama & French <sup>[[1]](#1) [[3]](#3) [[4]](#4)</sup>\n- Mark Carhart's 4-factor model <sup>[[2]](#2)</sup>\n- Pastor and Stambaugh's liquidity factors <sup>[[5]](#5)</sup>\n- Mispricing factors of Stambaugh and Yuan<sup>[[6]](#6)</sup>\n- The $q$*-factor* model of Hou, Mo, Xue and Zhang<sup>[[7]](#7)</sup>\n- The augmented $q^5$*-factor* model of Hou, Mo, Xue and Zhang<sup>[[8]](#8)</sup>\n- *Intermediary Capital Ratio* (ICR) of He, Kelly & Manela<sup>[[9]](#9)</sup>\n- The *DHS behavioural factors* of Daniel, Hirshleifer & Sun<sup>[[10]](#10)</sup>\n- The *HML* $^{DEVIL}$ factor of Asness & Frazzini<sup>[[11]](#11)</sup>\n- The 6-factor model of Barillas and Shanken<sup>[[12]](#12)</sup>\n\n\n_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._\n\n## Installation\n\n`getfactormodels` requires Python ``>=3.7``\n\n* The easiest way to install getfactormodels is via pip:\n \n ```shell\n $ pip install getfactormodels\n ```\n\n## Usage\n\n>[!IMPORTANT]\n>\n>\n>``getfactormodels`` is new. It was released on December 20, 2023. Don't rely on it for anything.\n\nAfter installation, import and call the ``get_factors()`` function with the ``model`` and ``frequency`` params:\n\n* For example, retrieving the monthly ${q}^{5}$ factor model:\n \n ```python\n import getfactormodels\n \n data = getfactormodels.get_factors(model='q', frequency='m')\n ```\n\n > _Trimmed output:_\n\n ```txt\n > print(data)\n Mkt-RF R_ME R_IA R_ROE R_EG RF\n date \n 1967-01-03 0.000778 0.004944 0.001437 -0.007118 -0.008563 0.000187\n 1967-01-04 0.001667 -0.003487 -0.000631 -0.002044 -0.000295 0.000187\n 1967-01-05 0.012990 0.004412 -0.005688 0.000838 -0.003075 0.000187\n 1967-01-06 0.007230 0.006669 0.008897 0.003603 0.002669 0.000187\n 1967-01-09 0.008439 0.006315 0.000331 0.004949 0.002979 0.000187\n ... ... ... ... ... ... ...\n 2022-12-23 0.005113 -0.001045 0.004000 0.010484 0.003852 0.000161\n 2022-12-27 -0.005076 -0.001407 0.010190 0.009206 0.003908 0.000161\n 2022-12-28 -0.012344 -0.004354 0.000133 -0.010457 -0.004953 0.000161\n 2022-12-29 0.018699 0.008568 -0.008801 -0.012686 -0.002162 0.000161\n 2022-12-30 -0.002169 0.001840 0.001011 -0.004151 -0.003282 0.000161\n\n [14096 rows x 6 columns]\n ```\n\n* Retrieving the daily data for the Fama-French 3-factor model, since `start_date`:\n\n ```python\n import getfactormodels as gfm\n\n df = gfm.get_factors(model='ff3', frequency='d', start_date=`2006-01-01`)\n ```\n\n* Retrieving data for Stambaugh and Yuan's monthly *Mispricing* factors, between `start_date` and `end_date`, and saving the data to a file:\n\n ```python\n import getfactormodels as gfm\n \n df = gfm.get_factors(model='mispricing', start_date='1970-01-01', end_date=1999-12-31, output='mispricing_factors.csv')\n ```\n\n >``output`` can be a filename, directory, or path. If no extension is specified, defaults to .csv (can be one of: .xlsx, .csv, .txt, .pkl, .md)\n\nYou can import only the models that you need:\n\n* For example, to import only the *ICR* and *q-factor* models:\n\n ```python\n from getfactormodels import icr_factors, q_factors\n\n # Passing a model function without params defaults to monthly data.\n df = icr_factors()\n\n # The 'q' models, and the 3-factor model of Fama-French have weekly data available:\n df = q_factors(frequency=\"W\", start_date=\"1992-01-01, output='.xlsx')\n ```\n\n >``output`` allows just a file extension (with the `.`, else it'll be passed as a filename).\n\n* When using `ff_factors()`, specify an additional `model` parameter (**this might be changed**):\n \n ```python\n # To get annual data for the 5-factor model:\n data = ff_factors(model=\"5\", frequency=\"Y\", output=\".xlsx\")\n\n # Daily 3-factor model data, since 1970 (not specifying an end date\n # will return data up until today):\n data = ff_factors(model=\"3\", frequency=\"D\", start_date=\"1970-01-01\")\n ```\n\nThere's also a ``FactorExtractor`` class (which doesn't do much yet, it's mainly used by the CLI; lots to do):\n\n ```python\n from getfactormodels import FactorExtractor\n\n fe = FactorExtractor(model='carhart', start_date='1980-01-01', end_date='1980-05-01)\n fe.get_factors()\n fe.drop_rf() \n fe.to_file('~/carhart_factors.md')\n ```\n\n* _The resulting ``carhart_factors.md`` file will look like this:_\n \n | date | Mkt-RF | SMB | HML | MOM |\n |:--------------------|---------:|--------:|--------:|--------:|\n | 1980-01-31 00:00:00 | 0.0551 | 0.0162 | 0.0175 | 0.0755 |\n | 1980-02-29 00:00:00 | -0.0122 | -0.0185 | 0.0061 | 0.0788 |\n | 1980-03-31 00:00:00 | -0.129 | -0.0664 | -0.0101 | -0.0955 |\n | 1980-04-30 00:00:00 | 0.0397 | 0.0105 | 0.0106 | -0.0043 |\n\n>``.drop_rf()`` will return the DataFrame without the `RF` column. You can also drop the \"Mkt-RF\" column with ``.drop_mkt()``\n\n### CLI\n\n``bash >=4.2``\n\n* You can also use getfactormodels from the command line. It's very barebones, here's the `-h`:\n\n ```shell\n $ getfactormodels -h\n\n usage: getfactormodels [-h] -m MODEL [-f FREQ] [-s START] [-e END] [-o OUTPUT] [--no_rf] [--no_mkt]\n ```\n\n* An example of how to use the CLI to retrieve the Fama-French 3-factor model data:\n\n ```shell\n $ getfactormodels --model ff3 --frequency M --start-date 1960-01-01 --end-date 2020-12-31 --output \".csv\"\n ```\n\n* Here's another example that retrieves the annual Fama-French 5-factor data without the RF column (using ``--no_rf``)\n\n ```shell\n $ getfactormodels -m ff5 -f Y -s 1960-01-01 -e 2020-12-31 --no_rf -o ~/some_dir/filename.xlsx\n ```\n* To return the factors without the risk-free rate `RF`, or the excess market return `Mkt-RF`, columns:\n \n## Data Availability\n\n>[TODO]\n\n## References\n1. <a id=\"1\"></a> E. F. Fama and K. R. French, \u2018Common risk factors in the returns on stocks and bonds\u2019, *Journal of Financial Economics*, vol. 33, no. 1, pp. 3\u201356, 1993. [PDF](https://people.duke.edu/~charvey/Teaching/BA453_2006/FF_Common_risk.pdf)\n2. <a id=\"2\"></a> M. Carhart, \u2018On Persistence in Mutual Fund Performance\u2019, *Journal of Finance*, vol. 52, no. 1, pp. 57\u201382, 1997. [PDF](https://onlinelibrary.wiley.com/doi/full/10.1111/j.1540-6261.1997.tb03808.x)\n3. <a id=\"3\"></a> E. F. Fama and K. R. French, \u2018A five-factor asset pricing model\u2019, *Journal of Financial Economics*, vol. 116, no. 1, pp. 1\u201322, 2015. [PDF](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2287202)\n4. <a id=\"4\"></a> E. F. Fama and K. R. French, \u2018Choosing factors\u2019, *Journal of Financial Economics*, vol. 128, no. 2, pp. 234\u2013252, 2018. [PDF](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2668236)\n5. <a id=\"5\"></a>L. Pastor and R. Stambaugh, \u2018Liquidity Risk and Expected Stock Returns\u2019, *Journal of Political Economy*, vol. 111, no. 3, pp. 642\u2013685, 2003. [PDF](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=279804)\n6. <a id=\"6\"></a>R. F. Stambaugh and Y. Yuan, \u2018Mispricing Factors\u2019, *The Review of Financial Studies*, vol. 30, no. 4, pp. 1270\u20131315, 12 2016. [PDF](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2626701)\n7. <a id=\"7\"></a>K. Hou, H. Mo, C. Xue, and L. Zhang, \u2018Which Factors?\u2019, *National Bureau of Economic Research, Inc*, 2014. [PDF](https://academic.oup.com/rof/article/23/1/1/5133564)\n8. <a id=\"8\"></a>K. Hou, H. Mo, C. Xue, and L. Zhang, \u2018An Augmented q-Factor Model with Expected Growth*\u2019, *Review of Finance*, vol. 25, no. 1, pp. 1\u201341, 02 2020. [PDF](https://academic.oup.com/rof/article/25/1/1/5727769)\n9. <a id=\"9\"></a>Z. He, B. Kelly, and A. Manela, \u2018Intermediary asset pricing: New evidence from many asset classes\u2019, *Journal of Financial Economics*, vol. 126, no. 1, pp. 1\u201335, 2017. [PDF](https://cpb-us-w2.wpmucdn.com/voices.uchicago.edu/dist/6/2325/files/2019/12/jfepublishedversion.pdf)\n10. <a id=\"10\"></a>K. Daniel, D. Hirshleifer, and L. Sun, \u2018Short- and Long-Horizon Behavioral Factors\u2019, *Review of Financial Studies*, vol. 33, no. 4, pp. 1673\u20131736, 2020. [PDF](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3086063)\n11. <a id=\"11\"></a>C. Asness and A. Frazzini, \u2018The Devil in HML\u2019s Details\u2019, *The Journal of Portfolio Management*, vol. 39, pp. 49\u201368, 2013. [PDF](https://stockmarketmba.com/docs/Asness_Frazzini_AdjustHML.pdf)\n12. <a id=\"12\"></a>F. Barillas and J. Shanken, \u2018Comparing Asset Pricing Models\u2019, *Journal of Finance*, vol. 73, no. 2, pp. 715\u2013754, 2018. [PDF](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2700000)\n\n**Data sources:**\n\n* K. French, \"Data Library,\" Tuck School of Business at Dartmouth.\n [Link](https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html)\n* R. Stambaugh, \"Liquidity\" and \"Mispricing\" factor datasets, Wharton School, University of Pennsylvania.\n[Link](https://finance.wharton.upenn.edu/~stambaug/)\n* Z. He, \"Intermediary Capital Ratio and Risk Factor\" dataset, University of Chicago. \n[Link](https://voices.uchicago.edu/zhiguohe/data-and-empirical-patterns/intermediary-capital-ratio-and-risk-factor/)\n* K. Hou, G. Xue, R. Zhang, \"The Hou-Xue-Zhang q-factors data library,\" at global-q.org.\n[Link](http://global-q.org/factors.html)\n* AQR Capital Management's Data Sets.\n* Lin Sun, DHS Behavioural factors [Link](https://sites.google.com/view/linsunhome)\n\n<p align=\"right\">(<a href=\"#readme-top\">back to top</a>)</p>\n\n## License\n\n\n\n*The code in this project is released under the [MIT License]().*\n\n[](https://pycqa.github.io/isort/)\n[](https://simpleicons.org/?q=ruff)\n---\n\n#### Known issues\n\n* The first `hml_devil_factors()` retrieval is slow, because the download from aqr.com is slow. It's the only model, so far, implementing a cache\u2014daily data expires at the end of the day and is only re-downloaded when the requested`end_date` exceeds the file's last index date. Similar for monthly, expiring at EOM and re-downloaded when needed.\n\n#### Todo\n\n- [ ] Docs\n - [ ] Examples\n- [ ] Tests\n- [ ] Error handling\n",
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