fagan2888 / portfolioopt

Financial Portfolio Optimization Routines in Python

Financial Portfolio Optimization

This module provides a set of functions for financial portfolio optimization, such as construction of Markowitz portfolios, minimum variance portfolios and tangency portfolios (i.e. maximum Sharpe ratio portfolios) in Python. The construction of long-only, long/short and market neutral portfolios is supported. Please read the docstring of the function you intend to use by typing e.g. help(portfolioopt.markowitz_portfolio) in the interactive interpreter. You can also find some documentation here.

Installation

To manually install the library, clone the repository via git clone https://github.com/czielinski/portfolioopt.git and install the module with python setup.py install. To install the requirements by hand you can also use pip install -r requirements.txt. You can run the tests with python setup.py test or with python -m unittest discover in the module directory. If everything is right, all tests should pass.

The portfolioopt module provides the optimization routines, the file example.py provides a simple usage example. Please also read the LICENSE.txt file.

Example

The example output looks like the following:

$ python example.py 

Example returns
---------------
                            asset_a   asset_b   asset_c   asset_d   asset_e
2000-01-01 00:00:00+00:00  0.025836 -0.005913  0.033384  0.077151 -0.010708
2000-01-02 00:00:00+00:00 -0.010707  0.079961  0.039372 -0.022474  0.028128
2000-01-03 00:00:00+00:00 -0.022171 -0.022286  0.013098 -0.094664 -0.085246
2000-01-04 00:00:00+00:00 -0.027114 -0.049642  0.016712 -0.044401 -0.069615
2000-01-05 00:00:00+00:00  0.074282 -0.010289  0.004376 -0.070237 -0.026219
2000-01-06 00:00:00+00:00  0.006546 -0.056550  0.019785 -0.029032 -0.013585
2000-01-07 00:00:00+00:00 -0.029085  0.093614  0.000325 -0.051886  0.042127
2000-01-08 00:00:00+00:00 -0.060042  0.011443 -0.096984 -0.065409  0.010843
2000-01-09 00:00:00+00:00  0.037923  0.009568 -0.004782 -0.014055 -0.072926
2000-01-10 00:00:00+00:00 -0.034992 -0.022032  0.053856  0.018181 -0.087152
...


Average returns
---------------
asset_a   -0.001237
asset_b    0.004848
asset_c   -0.003694
asset_d    0.007403
asset_e   -0.000610
dtype: float64


Covariance matrix
-----------------
          asset_a   asset_b   asset_c   asset_d   asset_e
asset_a  0.002027 -0.000362  0.000099 -0.000220 -0.000305
asset_b -0.000362  0.002421  0.000297  0.000090  0.000151
asset_c  0.000099  0.000297  0.002420  0.000020  0.000113
asset_d -0.000220  0.000090  0.000020  0.002302  0.000047
asset_e -0.000305  0.000151  0.000113  0.000047  0.002877


Minimum variance portfolio (long only)
--------------------------------------
Optimal weights:
asset_a    0.294283
asset_b    0.192216
asset_c    0.138206
asset_d    0.208794
asset_e    0.166501
dtype: float64

Expected return:   0.00150128915014
Expected variance: 0.000443881332631
Expected Sharpe:   0.0712575531382


Minimum variance portfolio (long/short)
---------------------------------------
Optimal weights:
asset_a    0.294284
asset_b    0.192217
asset_c    0.138202
asset_d    0.208795
asset_e    0.166502
dtype: float64

Expected return:   0.0015013136255
Expected variance: 0.000443881332596
Expected Sharpe:   0.0712587148452


Markowitz portfolio (long only, target return: 0.00376)
-------------------------------------------------------
Optimal weights:
asset_a    0.235067
asset_b    0.286836
asset_c    0.001546
asset_d    0.368534
asset_e    0.108017
dtype: float64

Expected return:   0.00375625399053
Expected variance: 0.000587574392946
Expected Sharpe:   0.154961396104


Markowitz portfolio (long/short, target return: 0.00376)
--------------------------------------------------------
Optimal weights:
asset_a    0.241321
asset_b    0.287506
asset_c   -0.006595
asset_d    0.365424
asset_e    0.112344
dtype: float64

Expected return:   0.00375616820372
Expected variance: 0.000587278581077
Expected Sharpe:   0.154996878211


Markowitz portfolio (market neutral, target return: 0.00376)
------------------------------------------------------------
Optimal weights:
asset_a   -0.088226
asset_b    0.158734
asset_c   -0.241207
asset_d    0.260916
asset_e   -0.090217
dtype: float64

Expected return:   0.00375618451738
Expected variance: 0.000397921658527
Expected Sharpe:   0.188299050118


Tangency portfolio (long only)
------------------------------
Optimal weights:
asset_a    0.013638
asset_b    0.370651
asset_c    0.000000
asset_d    0.615711
asset_e    0.000000
dtype: float64

Expected return:   0.00633799771227
Expected variance: 0.00123946655115
Expected Sharpe:   0.180025768076


Tangency portfolio (long/short)
-------------------------------
Optimal weights:
asset_a    0.048052
asset_b    0.635228
asset_c   -0.534982
asset_d    0.936986
asset_e   -0.085284
dtype: float64

Expected return:   0.0119844615356
Expected variance: 0.00354334941516
Expected Sharpe:   0.201331410159