The Ultimate Guide To Numerical Optimization For Portfolio Management

In this video I am going over implementing advanced Portfolio Analysis using Numerical Optimization Algorithms. Use-case here is constructing the Efficient Frontier of the Dow Jones Industrial Average. (30 assets).
I am recapping the previous (iterative) approach, going over the optimization conditions such as constraints and bounds and then coding the optimization in Python.
There are a ton of more interesting things to check out here so do your part and like, comment and share the video.

Highly recommended is my in depth Python for Finance course:

Get access to the Notebooks as well as support the channel by becoming a member here:
Get the Notebook/Source code by becoming a Tier-3 Channel member:

#python #finance #optimization

[jean-francoislebroch]

Fascinating, your code concepts take a step up, less beginners friendly but very powerful

[prajwaltuladhar]

Great video as always. Waiting for your videos on double sorted factor (value, momentum, size, low vol, etc) portfolios!

[anilmm]

Great one . Waiting for follow up video on optimising sharpe ratio and global minimum

[kawabiker]

wieder ein grossartiges video. über w_mvp = np.dot(Sinv, one) / np.dot(one.T, np.dot(Sinv, one)) lassen sich bequem die weights des minimum variance portfolios erhalten, wobei one ein einservektor und Sinv die Inverse der Kovarianzmatrix sind. dann kann man bei diesem Punkt starten und nur den oberen ast (die eigentliche ef) zeichnen. danke sehr.

[bryan-]

Great Work! Is that an efficient frontier or a minimum variance portfolio?