Python Finance and Math Tutorials: Gopalakrishnan Range Index (GAPO)

Key Takeaways

what's going on guys welcome to another python mathematics and finance indicators video this video we're going to be talking about the gopala krishnan range index otherwise known as gapo and that's what i'm going to call it for the rest of this video the gapo is intended to measure the volatility in a stock and it doesn't incorporate volume so you can think what you want about that the only reason i throw that in there is that sometimes either your volume information isn't included or you might not want to use volume or you might want to maybe add volume to the equation and make it that much better but it's kind of neat when you can have an indicator that mimics volume yet does not use uh volume the calculation for the gapo indicators is basically going to be the logarithm of the highest high for whatever time frame you're considering minus the lowest low for whatever time frame you're considering then you're going to do that divided by the logarithm of that time frame so with that let's go ahead and look at an example of the scapo indicator before we get into actually programming it so here's our charting application if you want to know more about it uh check out our python charting application tutorial video series what so we're going to look at ebay and so here we have ebay and down here we've got the gappo 14 for ebay um actually uh nevermind i thought i had changed it but no this is indeed the gap of 14. uh so here's an example of the gap out here and as you can see so as you know the volatility here in theory is going up you can not only see this in the chart price but you can see in the volume where volume really spikes up there and price falls sure enough the gap is pretty high again here as it's very volatile falling it goes up and then as it's rising you can see the gapo goes up and then uh where you've got more of like a controlled like here's a spike rise and here's a spike rise both times you see the gap spike up uh then you've got more of a controlled steady rise without too much change and it's not so crazy and then here you you could say that price is going up down up down up down up down but for the most part it's relatively stable and it's a little up down a little process and as you can see here the gappo kind of like levels out so with that let's look at another uh gap indicator let's look at something like uh tesla would be a good one where it like kind of flat line and then it went way up and then it's been going way down uh yeah it's a pretty good one so here's tesla and as you can see i mean it's relatively stable here and then the price starts really rising and so the gappo indicator goes way up right and then it starts really falling and sure enough the gap stays up right because the idea of the gapo isn't really for trend at all it's volatility and here's a great example of it matching volume pretty well where you know trade volume is pretty low it's stable it's low and stable here and then it spikes up here with trade volume and price as it you know gains popularity or whatever uh and really the gambo indicator is like pretty much right on par with the volume here and then as you know the actual stock starts trending down the rsi starts trending down but the gap is still there because it's obvious it's volatility only so it's pretty interesting indicator actually um it's just another one of those i actually quite like the indicators that measure volatility without using volume i think it's pretty cool um obviously it's it's fairly simple i mean the stock price itself measures volatility usually without volume but still i like the indicators that do it's just kind of like a neat out of the or like out of the box method uh for doing it so anyways that's the gapo indicator in the next video what we're going to be doing is actually programming the gapo indicator in python and then the video after that we'll plot it up just like this in this charting application so if that sounds interesting to you guys stay tuned to the next video as always thanks for watching