Optimization using Quadratic programming (2021)

Data science is part of investing. Application of advanced investment strategies require some coding skills. Here you will find out the idea of quadratic approach to portfolio optimization and then you can check on our website how to actually apply it in R:

Synopsis:
It is very convenient to look at the mean-variance optimization as quadratic programming problem. Traditional approach to portfolio optimization is to maximize the investor’s utility function. In this function risk (corrected with risk-aversion coefficient) is subtracted from the expected portfolio return.
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[abiranalytics]

Do you think portfolio optimization will help you earn more on the markets?

[alecmunnur]

Its a great video. thanks so much sir!
I'm not fully understanding the part with the min an max weights yet: In your example you have 3 stocks and therefore 3 weights. the 3 weights must add up to 1, which is defined by the equality constraint . Now I don't understand why you can restrict the weights of all 3 stocks to max 0.15. As far as I understand, this would mean that the total weights could not add up to 1, since 3*0.15 < 1. Where do I make a thinking error?

[soldman]

Nice illustration! The weight vector in the constraint term should be horizontal, or am I wrong?

[muriloafurtado]

Where is the second video? Have you already uploaded it? Could you please share the link? I could not find it on your channel. Thank you very much.

[javiereguez]

Soñé example wifi Codina soul de he nice

[SirPep]

hey, im having trouble with my constraints: the only inequality one i have is that no short selling is allowed (so wi >= 0). But i keep getting some negative weights in my solutions. this is my bvec vector. bvec <- c(1, R, rep(0, N), rep(-1, N)); so for this case all weights must add up to 1, under a certain R return and no-short selling allowed. Any help is really appreciated