Great work, but why have your viewer type up the source over again instead of posting the source in Github?
This guy sounds like Naval. Insightful video, thank you.
the best py tutorial i ve found on youtube, thanks for the rich content
Erm, your "optimized" portfolio has a Sharpe ratio of 1.35. The unoptimized one had 32%/23% = 1.391. Most likely this is because you use arithmetic annual returns instead of compounded returns: for each stock product(1 + daily returns) - 1.
Really didactic! Greetings from Brazil
Excellent. You forgot to plot Efficient Frontier.
This is a 10/10 tutorial please make more
This was an awesome video. Thank you so much for the instructions. I am just starting Python and love the support. I hope you will do more! You are very good. Thanks again.
At 23:02, you are printing the "Simple Annual Return" as "Expected Annual Return". They are different isn't it? For computing Expected Annual return, you should use historical returns, with a model such as Black-Scholes model? I am confused.
I'm wondering if there is a function that lets you optimize for short selling. Is there for example a Monte Carlo method that allows you to do it, maybe with some constraints? That would help a lot because it would give back a market-neutral portfolio, which is always a plus.
very well narrated. and loved working on colab. thanks mate
Thanks for sharing this education , i have try to practice the video but i have got a problem with this line of code is giving me an remotedata error:. can you please help
hey, can you visualize the data? i mean the sharpe ratio, the efficient frontier and all the probable portfolios from different combinations of this 5 stocks.
How would you add constraints such as min and max weights for each stock
why did you calculate the simple return (in 13:55) instead of log return
really helpful. thank you!
WHere can we find your colab? Amazing video btw
how do I plot the efficient frontier graph?
Can you make a discord group or telegram ?
@matthewrowe821