Financial Portfolio Optimization
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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