00:00 Hi guys, in today's video I'll just be showing you briefly how you can connect to the QuiverQuant
00:06 API.
00:07 I will then download some congressional trading data and do just a little bit of brief analysis
00:14 with that data.
00:15 But what you want to start by doing first is, as you can see, pip installing QuiverQuant,
00:19 importing QuiverQuant.
00:20 What you'll need to do next is have access to a key for the API.
00:26 For those of you that are interested, you can go to api.quiverquant.com and sign up
00:31 for a key there.
00:32 If you use the code "twitter" you will get a one month free trial, which is pretty cool.
00:39 So you can read the key in.
00:43 Once you run this cell that I'm writing out here, you should be connected to the API.
00:48 And then I'll show you how you can call some of the different endpoints.
00:51 Like I said, today we'll just be looking at some of the congressional trading data, but
00:55 there's quite a few other endpoints.
00:58 In the future I'll probably be looking to do some videos corresponding to some of these
01:02 other endpoints.
01:03 So let me know, in fact, if there's any of the other data sets that you would like to
01:07 see covered from the perspective of the API, and I'll work on those videos.
01:14 So you should note that the endpoint will only return a thousand rows of data, unless
01:18 you set the parameter "recent" equal to "false".
01:22 And then you can return all of the historical data from this particular endpoint.
01:27 But so yeah, as you can see here, this is what the API will automatically return by
01:32 default, which is just a pandas data frame showing the trade data, pretty much the transaction
01:38 date, the filing date, the stock that was traded, the representative.
01:43 So what I'll be doing next here is just writing a little bit of code, like I said, that will
01:50 download price data for each ticker in the data frame, but I'm going to first limit it
01:57 to trades made only in 2023.
02:01 But what this code here will do is connect to the Y-Finance library, which is a pretty
02:07 cool library.
02:08 It's completely free, and it basically just has a lot of stock data, like stock price
02:14 data and whatnot.
02:16 So yeah, like I said, what I'll do is download price data for each of the stocks in the data
02:22 frame.
02:24 And what I will then do is essentially try to estimate a return on the trade for each
02:30 trade in this data frame, right, again, just from trades made in 2023.
02:34 And what I'll do from there is then just a little bit of analysis, looking at which trades
02:40 have been the most successful, which trades have been the least successful, from both
02:44 a positive and negative return perspective.
02:47 So I'll probably just speed up the video a little bit from here on out while I finish
02:51 up writing up this code.
02:53 I won't go too much into depth here, mostly just because this code is pretty straightforward,
02:58 but also because I imagine that most of you will probably want to write your own back
03:04 testing tools, like I said, or trading algorithms.
03:06 And so yeah, I just won't go too much into depth, but you can always pause the video
03:13 and take a look at the code if you're interested in copying along.
03:16 So go ahead.
03:22 So just to clarify, I'll basically be using each transaction date from each row as a start
03:28 date and then using the most recent date as an end date and returning closing price data
03:35 from the Y Finance library to create a return on trade column, which I'll add to the data
03:43 frame, which you can see here.
03:47 And now you can sort the data frame from the return after trade column and also isolate
03:52 the data frame into both just purchases or just sales.
03:56 From there, you can, of course, recognize representatives who have made well-timed trades,
04:04 and I'll be doing a little bit of that over the next minute or so of the video.
04:08 But for the most part, I hope you guys enjoyed and please do, like I said, let me know in
04:13 the comments if you would like to see some other API tutorials like this from the perspective
04:18 of other datasets or endpoints.
04:21 But yeah, hope you guys have a great day.
04:24 Bye.
04:25 [END]
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