Python Matplotlib: Visualize Returns, Volatility Scatter, & Relative Performance | Part 12 📈

Okay, this is part 12 in my series on Python for stock analysis and uh you can find links for the code and the other videos in the description. All right, so when we left off in part 11, I had just sort of completed some basic data transformations in my stock class. All right, so we added some columns to a data frame that we uh collected there and then uh I plotted the distribution of returns. All right, so in this video I'm going to continue along with some more plotting that we may want to do. All right, and uh you may want to somehow plot volatility. Okay, so some of this is similar, right? So I'm going to have this title, right? I'm going to have uh the the super title again and uh I'm going to show at the end. So I'm just going to copy that and make a couple of changes. All right, so this will be volatility. All right, of the of the symbol. All right, or the or the returns of the symbol, right? Okay. And then uh how am I going to plot this? Uh I need this start and end again. Okay, I just need those for the title cuz I'm going to make a scatter plot uh of the of the absolute magnitude of that change in standard deviations uh against against the return. All right, so that's just going to be a plot scatter. All right, and then against that uh absolute magnitude, like I said. All right, and then I'll just add a couple of lines into the plot so we can interpret it a little bit. So I'll add a horizontal line. I'll add that at zero. All right, and I'm going to make it red. All right, and I'll make it a dashed line. Okay, and I'll do something similar for Y. Okay, let's take a look at that. And uh so I'll just switch this up and we'll plot volatility now. All right, and then uh yeah, I'll just comment that out so we don't download the data again. And uh let's see what that gets us. All right, so we get sort of a nice scatter plot and we can see, okay, where the the sort of magnitudes fall down here, right? So uh this is a percent change, right? And this is number of standard deviations. All right, so we can see that, oh, uh in the last year we actually haven't had anything beyond, say, three and a half standard deviations. All right, and yeah, we can see everything is pretty much clustered down here uh under one. All right, if you wanted, you could, I guess, put the horizontal line up here and so you can see uh where where you have uh days uh beyond uh two standard deviations, something like that. Okay? All right, so there's our first plot uh and then I'm going to uh plot performance. All right, so the actual price performance. All right, and again, most of the stuff is about the same. I'm going to copy everything here and then just change what I need to change. All right, so the only thing that's changing really is what's getting plotted, so then the title uh and that kind of stuff. All right, so instead of a scatter, I am going to make a line plot here and let me get rid of all this. All right, so what I'm going to do is look at the relative performance here, right? So starting from the first day in the data set, I want to see, okay, how did we do over the life, right? So I'm going to look at the the close. All right, and I'm going to divide that by the close on the first day. Okay, and then I'm going to subtract one so we can turn that into a percentage. All right, and then once I'm done with all that, let's see. I'm going to multiply it by 100 and then reformat it uh on that axis as a percentage. Okay, I don't need a vertical line anymore, but I'll I'll leave the horizontal line in there as a reference. All right, so we can see where we are above or below, right, that starting price. Okay, and then I will add in the formatting to to change it into a percentage from a decimal. All right, so let's get current axis and it's going to be Y axis set major formatter. All right, and and this is where I'm going to use that that percentage ticker that I imported at the beginning. Okay, let's see what that does. Okay, so there is our performance of Apple, right? So we can see about a year ago here we were doing great. All right, and then yeah, in the last few months, not so much. All right, so again, yeah, you you might want to, you know, do things like, you know, set the figure size, make it a little bigger, something like that. All right. All right, but that is essentially the plots that I wanted to show you. All right, it's not an exhaustive list of plots, obviously, uh but at least that's what I want to add for now and I'll leave it up to you to sort of expand on this. All right, but that's going to do it for part 12 and then in part 13, uh we're going to look at some more data transformations. So I hope to see you there.