Hi Huapu, To address your question directly, visually it may appear that the volatility of the MVP is higher than that of the buy and hold strategy but a quantitative measure would be more interesting. If you record the returns of both portfolios in a list then take the standard deviation of the list that could prove to be a nice one number comparison. I would not be surprised with either algorithm having the higher volatility but volatility can be deceiving visually. Another point to note is that in the second algorithm you have chosen to use an equal weighted portfolio. It is my feeling that comparing the MVP to a cap weighted buy and hold strategy could be more of what you meant to do. Equal weighted is actually really quite good and there has been quite a bit of research done on this. Edit: I found the reference I was looking for here
One pitfall of this Markowitz type of analysis is the curse of dimensionality. You have 43 stocks which means that you are estimating the covariance matrix containing 43*42/2+43 = 9,073 parameters utilizing just 252 observations. This estimator is going to have a high degree of variability. You are going to want to increase the length of your observation window to tighten the standard errors on the covariance estimates. 90,000 days would probably be sufficient for nice tight estimation with 43 assets. Obviously that size of estimation window is probably unreasonable which is why the next step would be to implement dimension reduction techniques. An exogenous factor model could work nicely, the principal components approach is another methodology or, the factors on demand methodology of Meucci. Asymptotic principal components by Connor and Korajczyk could be an excellent solution with a very large number of assets selected using the universe feature of quantopian.
Hope this helps.
Short version: Use dimension reduction techniques and compare to a capitalization weighted portfolio