Advanced Portfolio Analysis using Numerical Optimization Algorithms

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:
www.pythonforfinance.info
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#python #finance #optimization

[@jean-francoislebroch9171]

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

[@Algovibes]

Thank you mate :-)

[@kawabiker2655]

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.

[@Algovibes]

Danke mein Lieber!

[@CrynetOmega]

Great video! Could you make a video on how to use the python-kraken sdk for a live-data trading bot? Or even for trading NFTs?

[@prajwaltuladhar6742]

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

[@Algovibes]

thx mate

[@anilmm2005]

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

[@Algovibes]

Thanks mate! Can't promise, video didn't perform that well unfortunately

[@bryan-9742]

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

[@Algovibes]

Thx mate, doing an EF here.