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41 changes: 25 additions & 16 deletions docs/build/_modules/famafrench/famafrench.html
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253 changes: 130 additions & 123 deletions docs/build/_sources/applications/applications.rst.txt
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10 changes: 1 addition & 9 deletions docs/build/_sources/changes/changes.rst.txt
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Expand Up @@ -11,18 +11,10 @@ Change Log
Version 0 (Beta)
################

Release 0.1.1
=============
Initial release as of April 25, 2020.

Release 0.1.2
=============
Current release as of May 1, 2020.
Initial release as of May 1, 2020.

* Resolved ``FutureWarning`` for deprecated ``pandas.np`` module used to fill missing values (i.e. ``nan`` or ``None``): replaced the use of ``value=pd.np.nan`` w/ ``value=np.nan`` in :obj:`pandas.DataFrame.fillna`.
* Fixed a bug that used the wrong instance method on a :class:`pandas.DatetimeIndex`. To check if a :class:`pandas.DatetimeIndex` value landed on a Saturday or Sunday, incorrect use of ``pandas.DatetimeIndex.weekday_name`` is replaced w/ :meth:`pandas.DatetimeIndex.day_name()`. The aforementioned is required when comparing datasets constructed at a weekly frequency to those from Ken French's online library (e.g. `Weekly Fama-French 3 Factors <https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html>`_).
* Fixed a bug that **did not** exclude data for an `incomplete` final calendar year in the construction of annual datasets. For example, annual datasets constructed from a given start date to the present should end in 2019, not 2020.
* Added options to construct portfolios based on sorts not included in Ken French's online data library. A single anomaly characteristic or multiple anomaly characteristics can now be split into 4 (i.e. quartiles), 6, 8, 20, 25, or 100 (i.e. percentiles) buckets.



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104 changes: 55 additions & 49 deletions docs/build/_sources/gettingstarted/gettingstarted.rst.txt
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Expand Up @@ -71,25 +71,31 @@ Instances of the :class:`FamaFrench<FamaFrench>` object will vary depending on w

For both types of instances, the frequency of portfolios :attr:`freqType` as well as the starting and end dates must be specified. Both starting and ending dates must be in :class:`datetime.date` format. In addition, attribute :attr:`runQuery` is set to ``True`` or ``False`` depending on whether the user prefers to query all datafiles from `wrds-cloud` from scratch or whether previously queried and locally-saved datafiles are pickled in constructing the instance. The latter choice is particularly useful when updating data following a new set of observation points released by WRDS. Making use of previously queried and locally-saved datafiles significantly speeds up run-time and execution of code.

A required attribute is the absolute path directory where pickled datafiles will be saved. Starting from the current working directory, we will create a folder ``pickled_db`` and save all pickled files there. To do that, let's define the string variable ``pickled_dir`` as follows:

.. code-block:: ipython
In [1]: import os
In [2]: pickled_dir = os.getcwd() + '/pickled_db/'
For example, to construct the **Fama-French 3 factors**: the `Market Premium` ``MKT-RF``, `Small Minus Big` ``SMB``, and `High Minus Low` ``HML``, at the monthly frequency (from 1960 to the present, or the most recent date for which there is stock returns data available in CRPS and fundamentals data in Compustat), we execute the following lines of Python code:

**Fama-French 3 Factors:**

.. code-block:: ipython
In [1]: import datetime as dt
In [2]: import famafrench.famafrench as ff
In [3]: import datetime as dt
In [4]: import famafrench.famafrench as ff
In [3]: startDate = dt.date(1960, 1, 1)
In [4]: endDate = dt.date.today()
In [5]: runQuery = True
In [6]: ffFreq = 'M'
In [7]: ffsortCharac = ['ME', 'BM']
In [8]: ffFactors = ['MKT-RF', 'SMB', 'HML']
In [9]: ff3 = ff.FamaFrench(runQuery, ffFreq, ffsortCharac, ffFactors)
In [10]: factorsTableM = ff3.getFFfactors(startDate, endDate)
In [5]: startDate = dt.date(1960, 1, 1)
In [6]: endDate = dt.date.today()
In [7]: runQuery = True
In [8]: ffFreq = 'M'
In [9]: ffsortCharac = ['ME', 'BM']
In [10]: ffFactors = ['MKT-RF', 'SMB', 'HML']
In [11]: ff3 = ff.FamaFrench(pickled_dir, runQuery, ffFreq, ffsortCharac, ffFactors)
In [12]: factorsTableM = ff3.getFFfactors(startDate, endDate)
CRSP (monthly) dataset currently NOT saved locally w/ required dates. Querying from wrds-cloud...
CRSP delisted returns (monthly) dataset currently NOT saved locally. Querying from wrds-cloud...
Expand All @@ -98,8 +104,8 @@ For example, to construct the **Fama-French 3 factors**: the `Market Premium` ``
Constructing Fama-French return factor(s): 100%|██████████| 2/2 [00:03<00:00, 1.73s/it]
Historical risk-free interest rate (monthly) dataset currently NOT saved locally. Querying from wrds-cloud...
In [11]: factorsTableM.head()
Out[11]:
In [13]: factorsTableM.head()
Out[13]:
mkt mkt-rf smb hml
date
1960-01-31 -0.066497 -0.069797 0.017755 0.025267
Expand All @@ -117,17 +123,17 @@ We can compare the constructed factors to those provided by `Ken French <https:/

.. code-block:: ipython
In [12]: kffactorsTableM = ff3.getkfFFfactors(ffFreq, startDate, endDate)
In [13]: kffactorsTableM.head()
Out[13]:
In [14]: kffactorsTableM = ff3.getkfFFfactors(ffFreq, startDate, endDate)
In [15]: kffactorsTableM.head()
Out[15]:
mkt mkt-rf smb hml
1960-01-31 -0.0665 -0.0698 0.0209 0.0273
1960-02-29 0.0146 0.0117 0.0051 -0.0199
1960-03-31 -0.0128 -0.0163 -0.0051 -0.0285
1960-04-30 -0.0152 -0.0171 0.0031 -0.0223
1960-05-31 0.0339 0.0312 0.0121 -0.0376
In [14]: _, _, _, = ff3.comparePortfolios('Factors', ffFreq, startDate, endDate)
In [16]: _, _, _, = ff3.comparePortfolios('Factors', ffFreq, startDate, endDate)
CRSP (monthly) dataset currently NOT saved locally w/ required dates. Querying from wrds-cloud...
CRSP delisted returns (monthly) dataset currently NOT saved locally. Querying from wrds-cloud...
Expand Down Expand Up @@ -161,28 +167,28 @@ We can compare the constructed factors to those provided by `Ken French <https:/

.. code-block:: ipython
In [1]: import datetime as dt
In [2]: import famafrench.famafrench as ff
In [3]: import datetime as dt
In [4]: import famafrench.famafrench as ff
In [3]: startDate = dt.date(1960, 1, 1)
In [4]: endDate = dt.date.today()
In [5]: runQuery = True
In [6]: ffFreq = 'M'
In [7]: sortingDim = [2, 3]
In [8]: retType = 'vw'
In [9]: ffsortCharac = ['ME', 'BM']
In [10]: ffFactors = []
In [11]: me_bm_2x3 = ff.FamaFrench(runQuery, ffFreq, ffsortCharac, ffFactors)
In [12]: returnsTableM = me_bm_2x3.getPortfolioReturns(False, startDate, endDate, sortingDim, retType)
In [5]: startDate = dt.date(1960, 1, 1)
In [6]: endDate = dt.date.today()
In [7]: runQuery = True
In [8]: ffFreq = 'M'
In [9]: sortingDim = [2, 3]
In [10]: retType = 'vw'
In [11]: ffsortCharac = ['ME', 'BM']
In [12]: ffFactors = []
In [13]: me_bm_2x3 = ff.FamaFrench(pickled_dir, runQuery, ffFreq, ffsortCharac, ffFactors)
In [14]: returnsTableM = me_bm_2x3.getPortfolioReturns(False, startDate, endDate, sortingDim, retType)
CRSP (monthly) dataset currently NOT saved locally w/ required dates. Querying from wrds-cloud...
CRSP delisted returns (monthly) dataset currently NOT saved locally w/ required dates. Querying from wrds-cloud...
Compustat (annual) dataset currently NOT saved locally w/ required dates. Querying from wrds-cloud...
In [13]: returnsTableM.head()
Out [13]:
In [15]: returnsTableM.head()
Out[15]:
me0-50_bm0-30 me0-50_bm30-70 ... me50-100_bm30-70 me50-100_bm70-100
date
1960-01-31 -0.065363 -0.053950 ... -0.045400 -0.071003
Expand All @@ -191,17 +197,17 @@ We can compare the constructed factors to those provided by `Ken French <https:/
1960-04-30 -0.010691 -0.016785 ... -0.020938 -0.014375
1960-05-31 0.043435 0.035750 ... 0.010110 -0.017830
In [14]: kfreturnsTableM = me_bm_2x3.getkfPortfolioReturns(ffFreq, startDate, endDate, sortingDim, retType)
In [15]: kfreturnsTableM.head()
Out [15]:
In [16]: kfreturnsTableM = me_bm_2x3.getkfPortfolioReturns(ffFreq, startDate, endDate, sortingDim, retType)
In [17]: kfreturnsTableM.head()
Out[17]:
small lobm me1 bm2 small hibm big lobm me2 bm2 big hibm
1960-01-31 -0.057876 -0.031988 -0.029368 -0.082071 -0.043931 -0.055931
1960-02-29 0.020772 0.014530 0.005015 0.013139 0.022903 -0.010929
1960-03-31 -0.023385 -0.024016 -0.038293 -0.008789 -0.010768 -0.050967
1960-04-30 0.000545 -0.021162 -0.029614 -0.015721 -0.013525 -0.030143
1960-05-31 0.053034 0.018239 0.023730 0.043100 0.018242 -0.002716
In [16]: _, _, _, = me_bm_2x3.comparePortfolios('Returns', ffFreq, startDate, endDate, sortingDim, retType)
In [18]: _, _, _, = me_bm_2x3.comparePortfolios('Returns', ffFreq, startDate, endDate, sortingDim, retType)
CRSP (monthly) dataset currently NOT saved locally w/ required dates. Querying from wrds-cloud...
CRSP delisted returns (monthly) dataset currently NOT saved locally w/ required dates. Querying from wrds-cloud...
Expand Down Expand Up @@ -232,9 +238,9 @@ We can compare the constructed factors to those provided by `Ken French <https:/

.. code-block:: ipython
In [16]: firmsTableM = me_bm_2x3.getNumFirms(False, startDate, endDate, sortingDim)
In [17]: firmsTableM.head()
Out [17]:
In [19]: firmsTableM = me_bm_2x3.getNumFirms(False, startDate, endDate, sortingDim)
In [20]: firmsTableM.head()
Out[20]:
me0-50_bm0-30 me0-50_bm30-70 ... me50-100_bm30-70 me50-100_bm70-100
date ...
1960-01-31 21 49 ... 69 27
Expand All @@ -243,17 +249,17 @@ We can compare the constructed factors to those provided by `Ken French <https:/
1960-04-30 21 49 ... 69 27
1960-05-31 21 49 ... 69 27
In [18]: kffirmsTableM = me_bm_2x3.getkfNumFirms(ffFreq, startDate, endDate, sortingDim)
In [19]: kffirmsTableM.head()
Out [19]:
In [21]: kffirmsTableM = me_bm_2x3.getkfNumFirms(ffFreq, startDate, endDate, sortingDim)
In [22]: kffirmsTableM.head()
Out[22]:
small lobm me1 bm2 small hibm big lobm me2 bm2 big hibm
1960-01-31 66 193 223 228 199 72
1960-02-29 66 193 222 228 199 72
1960-03-31 66 192 222 228 199 72
1960-04-30 66 190 221 228 199 72
1960-05-31 66 187 221 228 199 72
In [20]: _, _, _, = me_bm_2x3.comparePortfolios('NumFirms', ffFreq, startDate, endDate, sortingDim)
In [23]: _, _, _, = me_bm_2x3.comparePortfolios('NumFirms', ffFreq, startDate, endDate, sortingDim)
*********************************** ME x BM (2 x 3) ************************************
*********************** Observation frequency: M ************************
Expand Down Expand Up @@ -281,8 +287,8 @@ We can compare the constructed factors to those provided by `Ken French <https:/

.. code-block:: ipython
In [16]: characsTableM = me_bm_2x3.getCharacs(False, startDate, endDate, sortingDim)
In [17]: for charac in list(me_bm_2x3.mainCharacsId):
In [24]: characsTableM = me_bm_2x3.getCharacs(False, startDate, endDate, sortingDim)
In [25]: for charac in list(me_bm_2x3.mainCharacsId):
print(charac, '\n', characsTableM[charac].head())
ME
Expand All @@ -308,8 +314,8 @@ We can compare the constructed factors to those provided by `Ken French <https:/
[5 rows x 6 columns]
In [18]: kfcharacsTableM = me_bm_2x3.getkfCharacs(ffFreq, startDate, endDate, sortingDim)
In [19]: for charac in list(me_bm_2x3.mainCharacsId):
In [26]: kfcharacsTableM = me_bm_2x3.getkfCharacs(ffFreq, startDate, endDate, sortingDim)
In [27]: for charac in list(me_bm_2x3.mainCharacsId):
print(charac, '\n', kfcharacsTableM[charac].head())
ME
Expand All @@ -328,7 +334,7 @@ We can compare the constructed factors to those provided by `Ken French <https:/
1960-04-30 0.4181 0.8440 1.7532 0.3412 0.7209 1.6762
1960-05-31 0.4175 0.8428 1.7422 0.3395 0.7201 1.6721
In [20]: _, _, _, = me_bm_2x3.comparePortfolios('Characs', ffFreq, startDate, endDate, sortingDim)
In [28]: _, _, _, = me_bm_2x3.comparePortfolios('Characs', ffFreq, startDate, endDate, sortingDim)
CRSP (monthly) dataset currently NOT saved locally w/ required dates. Querying from wrds-cloud...
CRSP delisted returns (monthly) dataset currently NOT saved locally w/ required dates. Querying from wrds-cloud...
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6 changes: 3 additions & 3 deletions docs/build/_sources/index.rst.txt
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Expand Up @@ -78,10 +78,10 @@ How to Cite
************
This package (and its release as of May 1, 2020) should be cited using Zenodo. For example, for the 0.1.2 release,

[*] Christian Jauregui. (2020, May 1). christianjauregui/famafrench: Release 0.1.2. Zenodo. https://sandbox.zenodo.org/record/535156#.XqyCqRNKhTY
[*] Christian Jauregui. (2020, May 1). christianjauregui/famafrench: Release 0.1.2. Zenodo. https://sandbox.zenodo.org/record/535216#.XqyCqRNKhTY

.. image:: https://sandbox.zenodo.org/badge/doi/10.5072/zenodo.535156.svg
:target: https://sandbox.zenodo.org/record/535156#.XqyCqRNKhTY
.. image:: https://sandbox.zenodo.org/badge/doi/10.5072/zenodo.535216.svg
:target: https://sandbox.zenodo.org/record/535216#.XqyCqRNKhTY

*******************
`Todo`
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