tirthajyoti / optimization-python Goto Github PK

Hello,

I am trying to maximize Sharpe with this code adding this line:

sharpe = ret / risk

just before the problem definition and changing it by:

prob = Problem(Maximize(sharpe), [sum(x)==1, x >= 0])

obtaining this problem;

DCPError: Problem does not follow DCP rules. Specifically:
The objective is not DCP. Its following subexpressions are not:
[-2.16695988e-03 -9.60588203e-04 -6.80243265e-04 -9.10794378e-04
2.84821499e-04 -1.84951106e-03 -2.41240531e-03 -2.39710967e-03
-1.59690065e-03 -1.96844915e-03 2.78261359e-03 1.32043210e-04
3.10924999e-05 -2.04406783e-03 -3.59892133e-04 7.34013179e-04
1.08369348e-03 -2.05024607e-03 1.14489241e-03 -4.87174427e-04
-9.55131222e-05 -1.19424145e-03 -2.19036230e-03 -1.10589192e-03
-2.40868247e-03 -1.45145088e-03 -3.10006405e-04 -1.55078960e-03
-2.62811048e-03 -2.54809462e-03 -6.85525210e-05 -1.73200801e-03
-2.35653381e-03 -1.32429376e-04] * var446 / QuadForm(var446, [[0.00092668 0.00036417 0.00045662 ... 0.0002323 0.00041562 0.00010376]
[0.00036417 0.00046147 0.00021776 ... 0.00024083 0.00027291 0.0001139 ]
[0.00045662 0.00021776 0.00077415 ... 0.00028546 0.00028192 0.00015766]
...
[0.0002323 0.00024083 0.00028546 ... 0.00051062 0.00027009 0.00016667]
[0.00041562 0.00027291 0.00028192 ... 0.00027009 0.00059854 0.00011856]
[0.00010376 0.0001139 0.00015766 ... 0.00016667 0.00011856 0.00044083]])

Do you have any idea to solve it?

Thanks in advance and regards

Page: Portfolio_optimization.ipynb

Please change this line:
mr.set_value(date,s,ret)

To this:
mr.at[date, s] = ret

thanks for
https://towardsdatascience.com/linear-programming-and-discrete-optimization-with-python-using-pulp-449f3c5f6e99
only you mentioned
It is noteworthy that even the widely-used SciPy has a linear optimization method built-in.

https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linprog.html#scipy.optimize.linprog

but scipy.optimize.linprog can not deal with integer optimization problem

Reference to other optimizer repos:

• https://github.com/Zuriich/Pyomo-Diet
• https://github.com/DerekGloudemans/Nutrition-Optimization-New

Reference on Datasets of USDA:

• SQLite & JSON https://github.com/schirinos/nutrient-db
• SQLite https://github.com/alyssaq/usda-sqlite
• SQLite https://github.com/dunhamsteve/nutrition-data
• CSV of Version 28 (without the long-form Descriptors) https://github.com/buswedg/dataquest/blob/master/Data%20Analysis%20with%20Pandas/Dataframe%20Basics/data/food_info.csv
• https://www.kaggle.com/haithemhermessi/usda-national-nutrient-database

Reference on Datasets of Satiety ("Fullness"), Nutrition Density and Insulin Sensitivity (with matching Version 28 Long-Form Descriptors): https://www.kaggle.com/bradkmlee/satiety

I am trying to reimplement code in your notebooks. I get the error "PulpSolverError: Pulp: Error while executing /usr/local/lib/python3.6/dist-packages/pulp/solverdir/cbc/linux/64/cbc"
I tried checking the installations which seems to be fine. Can you suggest what might be the issue?