Perdagangan Bitcoin itu sangat berbeda dengan trading forex karena dari istilah cara tradingnya sekalipun sudah berbeda. Kalau dari forex kita mengenal 2 istilah: Long dan Short. Long yaitu menargetkan harga naik untuk mendapatkan profit dan Short yaitu menargetkan harga turun untuk mendapatkan profit. Bitcoin adalah sistem kas transaksi global yang terdesentralisasi. Berdasarkan jurnal yang ditulis oleh penemunya (dilansir dalam 24crypto.de), sebenarnya Bitcoin adalah: A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. The best free 🚀 cryptocurrency and bitcoin API. Programmatically access current and historical price, markets, and exchange rate data from exchanges like Binance, Gemini, GDAX, and Poloniex. Quickly create mobile apps, charts, and pricing websites with our lightning fast RESTful JSON API.
Trade api bitcoin adalahApa Perbedaan Trading Forex dan Trading Bitcoin?
We built the API before we built anything else. If you do go to Nomics that entire website was built with our API so anything you see on the website we have available but we also have a lot of data available that is not on our website. So I think perhaps the way to start this out is by talking about data and data quality. So, our service and most of what we do is based around raw trade data, right. So, for the majority of the exchanges that we have data from we have literally every trade on every trading pair on that exchange.
So, we have essentially the entire trading history of that exchange and from those trades we construct candles and from those candles we construct tickers. Here we have on this chart, trades. As you can see, this is fairly high fidelity. As you can see, there's just a lot that's left out—you can actually hide a lot of fake volume in candles. And then you have tickers—which a lot of our competitors are gathering ticker data rather than candles or trades.
And ticker data is pretty bad You essentially get tickers whenever they're computed, you don't necessarily get them at a specific time, so if you want to find out what an asset was priced at the end of a given time period you can't do that with tickers.
There's just a lot of problems So probably a good way to think about data and how we do data is around this idea of a data pyramid. So at the bottom kind of underlying everything that we do is gapless historical raw data.
So let's say, for example, that you wanted to price Ethereum. We start out by gathering every—let's start with a trading pair on an exchange, right, because there's a lot of trading activity on Ethereum that isn't with USD or fiat pair.
We'd start off with all the trades on all the Ethereum pairs—and this is an example of one. Then we would move to creating exchange candles based on this pair. So there's a lot that goes into this and a lot of our competitors just are ingesting tickers or candles and we normalized the way that we compute candles based on the raw trade. So, what we found in some cases is that exchanges are reporting candle data that is, in fact, inaccurate, right.
They'll pump up the volume by just adding volume numbers to their candles and when you actually count—when you have gapless historical raw trade data—you can actually like count each individual trade and add it up and get to the volume and see if the math checks out, and often it doesn't.
So, because we have the trades, we can compute the candles ourselves. So trade data is better than candle data, is better than ticker data, which is the worst and this is what our data set looks like: We have raw trade data and from those raw trades we can construct candles and from those candles we can construct tickers and that's for exchanges that do have raw trade data from.
If an exchange only provides candle data then we will get the candle data and will calculate tickers but we won't use their tickers—we'll calculate them ourselves. And then the worst case scenario is you're in exchange that only provides tickers. I think the beauty of our data approach is that we have a database that allows raw trade data to coexist with candle data to coexist from ticker data as the primary source data from exchanges and we inform you about what kind of data you're getting and how the numbers that you're asking for are derived from these data points.
So if an exchange has great data we'll get it and if they have terrible data we'll get that too because people often do want data from these crappy exchanges.
So we'll log it all—whereas others often only have tickers from exchanges. In other words, they're ingesting tickers and then constructing candles from those tickers and that's something that I think is pretty important to talk about. A lot of our competitors, what they're doing is they're ingesting tickers like ticker feed data in real time and they're constructing candles from that.
So let's say you want to construct a 1-minute candle and then you've got hour tickers coming in so a ticker is basically like a hour candle that you get whenever you get it—whenever it's computed—it isn't computed on specific time intervals that you can rely on. So let's say you're ingesting data from an exchange that only provides ticker data that's all that they do and you want to construct a 1-minute candle.
Similarly, let's say you want to create a 1-hour candle and you've got the steady stream of tickers coming in. You know whenever they send them to you, well, you can't use. So, let's just go all the way down.
So let's say you do luck out, you hit the lottery and you do get a ticker that gives you a data point at the exact time of this candle opening and let's say you get some additional points that you are going to believe are the high and low.
The low at least they're the highest and lowest prices of the ticker points that you have—which are not a lot—during this period and let's say the last ticker you get before the close of this candle is at Well, you have to just taken this price that you got at , just assume that it's close if you are constructing tickers from candles , which is generally a bad idea.
This isn't how we do things. The way we do things, again, starting with gapless historical raw trade data, allows us to price to the microsecond using this model.
So, anyway, there's a lot I can talk about here. I think it's probably worth discussing a little bit our transparency ratings. They did not look at exchanges that did not have Bitcoin to USD in Bitcoin to other markets and we were looking at this data and we found something interesting We found that of the 10 exchanges that were deemed to be trusted by Bitwise, that 8 out of these 10 exchanges provided historical gapless raw trade data.
And why would that be, right? I think the reason this would be the case is that just like the IRS if you provide a lot of data and you're doing something wrong you're likely to be caught. So we have found that providing historical gapless raw trade data is correlated with being a good exchange. And then of the exchanges that Bitwise identified as being suspect, that they explicitly called out as being suspect, all but two of those did not provide historical gapless raw trade data.
We care quite a bit about how we approach data. I can tell you a little bit about our data services. Basically, we can create customized endpoints for you. Often, there's analysis that people want that requires them to download a whole lot of data and then analyze that data and often—because we have all the data in our database—we can just give you an API endpoint that just outputs the number that you're looking for that just sort of does the analysis for you.
So, that's one of the things that we do. Let's start off with the first one. I'm not going to go through all these slides but we do custom asset pricing so what we found is that a lot of hedge funds and funds that calculate nav for investors, that they want to calculate prices according to a specified methodology.
So they might say, "We want to calculate prices based on only these ten exchanges and even just in and only based on Fiat pairs on these ten exchanges," and so they specify and they want to "calculate end-of-day prices based on the end of the day" in their time zone.
Let's say they're in California Then they would calculate these based on end of day prices in the Pacific time zone Another thing that we do is we provide low latency data. So if you need super low latency order book snapshots and trading data, that's something we can do. We can get order book snapshots down to milliseconds. Another thing that we do—and this is more for exchanges—but we can power white label market data API.
So if you're an exchange and you do have a data API, we can run that for you. And, finally, we can stand up market data websites for you. So let's say you have an investor portal and you want to give your investors like, you know, real-time access to what's happening with the price of a whole bunch of different cryptocurrencies and you want to give them real-time access to maybe an index or prices on the exchanges that you guys or gals are trading on, then we can do that for you. For more information, please see our docs.
Trades and orders on top cryptocurrency exchanges including historical trade data behind one API. Historical aggregate cryptocurrency market cap since January of Price, crypto market cap , supply, and all-time high data.
Uptime and response time guarantees through Service level agreements SLAs. Rapid customer support turnaround times. Brian Krogsgard: Hello and welcome to Ledger Cast. This is an all encompassing API project where he's really looking to be the data layer for crypto and for maintaining the history of the price of any crypto asset previously and going forward.
He believes that there will be thousands and thousands of these assets that need to be tracked and they're looking to create a hardened layer of data to maintain that price history and integrity.
We talk all about this project. Clay is a seasoned entrepreneur and this is his latest project. He was part of Leadpages. I think you'll really enjoy it. This episode is brought to you by Delta. Go to ledgerstatus. They have some really great stuff going on right now because they just released live order books and depth charts. It's all in the latest version of Delta. This is one of the most requested features they've had.
So I'm really excited to be able to share with my listeners that that's now available because I know a lot of technical traders want to be able to check out the order books, get an idea of depth on the price a while they're looking at their portfolio. They've got that and so much more. Thanks to Delta for being a Ledger Status partner. Now, here's the show.
Brian Krogsgard: Hello and welcome to the Ledger Cast. He's the co-founder of Nomics and nomics. Clay and I've been talking a good bit over the past several weeks, ever since I pinged him on Twitter looking for information about their API. Hey Clay, welcome to the show.
Brian Krogsgard: Yeah. So I was a stalking what y'all were building for a bit, between listening to your podcast and then just kind of checking out your blog posts and your newsletter and all that kind of stuff. And then I was actually looking to potentially use your API and we're gonna dig into this about what Nomics is, why you're building what you're building. And you responded to me in like record time and it required y'all to potentially build a new feature and you're like, "Yeah.
We'll have that like tomorrow. And I'd like you to fill in for everyone else, like what the heck is Nomics at a thousand foot view? Clay Collins: Yeah. So, great question. There's two components of Nomics. The front end, which is at nomics. We're gonna eventually open source completely the front end as well as iOS and Android apps. And not only do we have ticker data, but we have multiple candlestick links on the back end for aggregate market, so all Bitcoin markets, all Ethereum markets, et cetera.
But we have candlestick data for individual markets for example, like the [inaudible ] market on Poloniex for example. So we've got aggregate candlestick data and we have data for individual markets on individual exchanges, and we have every single trade on all of those markets, on all of those exchanges going back to the inception of those markets. It's fast, it's free and you can sign up and get an API really quickly and be in business.
And something that I think is worth noting is that everything you see on nomics. So there's no back doors, there's no hidden in points.
We're consuming this exactly like a customer is. So we're a big believer in dog fooding and being a customer of our own products. And that was one of the rules that we put in place from day one, is that we couldn't do anything with our app that our customers couldn't do with the free version of our product.
Brian Krogsgard: Nice. So at a baseline you are providing data specifically around coin data at a high level and then very specific data in terms of pricing on a daily basis, and I think an hourly basis at a core.
I think what I actually asked you all about in that thing was whether y'all could do So that was something else that y'all were looking to add and now people can use this to build something just like nomics.
Brian Krogsgard: This is essentially just a massive data feed, but instead of me going and saying, "Hey, I want this data from a Poloniex. Your dealing with all the hassles of getting data off an exchange, so that I don't have to integrate with every single exchange in the world and instead I integrate with Nomics and I'm good to go.
So I think you summarized that correctly. I think kind of accompany that were similar to is a company called You know what actually, I won't get too much into that. So basically, one of the The problem that we're solving for is a problem that kind of came up a lot in conversations when we were talking to hedge funds and family offices and institutional investors, which was, they'd hire a pretty fancy developer to do data science work, to find edge and opportunities in the data sets.
And their developer that they'd hired for that purpose would end up spending much of their time rather than finding opportunities in the data set, just maintaining those data sets. So if you spend much time at all ingesting data from these exchanges, you'll find that ticker symbols change from exchange to exchange, and then the exchanges themselves will change a ticker symbols. They'll change their data schemas without telling you, their data feeds will turn off and then they'll come back on again, there's lots of downtime.
Clay Collins: And so if you're just ingesting data from one of these exchanges and you're okay with dealing with just a bunch of friction, then I think it's probably okay. The second you want to ingest data from multiple exchanges, things get a lot trickier. And when we started in this business, we just Clay Collins: Exactly, exactly. So you're having to integrate with more and more of these exchanges to get an accurate picture of what's happening-.
Brian Krogsgard: So the long tail The long tail of a global trading is getting larger basically. Clay Collins: Yep, exactly. So there's lots of just real oddities when integrating with these exchanges. For example, some exchanges when their APIs go down because of the way they're cashing works, they just persist the last candle. So they'll give Other exchanges do things like We were looking at an exchange the other day that had a market called USD Like what the hell is going on here?
There's just a lot of bizarre stuff happening in this space. So we wanted to create a super professional lightening fast API and that's what we're solving-. Brian Krogsgard: Out of of curiosity on that exact pair, were they basically seeking to provide a trading pair between to different stable coins in order to smooth the market on their own platform? Clay Collins: So one of those was the [inaudible ] market and one of those was a stable code.
You just didn't know which-. Clay Collins: Because The blend of stable coins is super interesting to me, like the way And trying to find out like what's gonna be supported, how do we measure stuff like that. I even saw one the other day where So they are creating kinda index funds on the go and one of their funds is actually a stable coin blend.
So if you buy their stable coin blend, I guess their whole point is like you're buying the average of all the stable coins so that it will be stabilized to stable coin mix to be even closer to a dollar. Brian Krogsgard: There's just a lot of effort going into people trying to call a dollar a dollar in crypto, which I And I think it's perhaps just a bit of a signal for how difficult data is in not only this space, but pretty much any space.
And I'm fascinated by this play because there's so much opportunity I think as the ecosystem grows and I never had heard what you said earlier about just how much trading is going on on the long tail. Because when you think about like, "Hey, where are people trading crypto?
You hear that they're on Binance and that they're on Coinbase and to a lesser degree, Bittrex and Poloniex, and then you've got some Asian exchanges that are doing a lot of trading, but you don't actually know if it's real for some of them. Brian Krogsgard: And keeping track of all of it is really difficult. I come from a development background. You come from a web background. I actually knew who you were in your prior company, which is Leadpages by the way, for anyone listening from the web space.
So how did you transition from building a big company So that's a great question. So to speak to my previous history or what I was doing before this, my first software company was a company called Leadpages that was started in January of From to , we grew that to about 50, paying customers.
We raised 38 million in venture capital, hired hundreds of people, had a really good go there. Something I realized about myself is that I think I cap out at around people in terms of company size and my ability to manage at scale. At some point you're managing people and then you're managing people who manage people and then you're managing the people who manage people who manage people.
And I really liked that spot of like between 80 people to people. So perhaps I can scale beyond that with my second software company, but at some point I just kinda went to the board and said, "Hey. I think we should hire a CEO and I can stay on the board. So I started-. Brian Krogsgard: So you're on the board of Leadpages today and Clay Collins: Exactly, yeah. I'm not going into the office and I mean I'm officially chairman of the board, but that's kind of a nice honorary title.
I asked for it. Clay Collins: They were nice enough to me. So one of the things that I saw in the marketing tech space, which was really fascinating, was just how a data got So when you first started using marketing tech in the space, someone would use something like Infusionsoft or HubSpot or Salesforce and everything would be in one place.
But then as the space exploded about every single year, the number of martech companies doubled. So folks found themselves sort of originating a place where everything was in their CRM or everything was in their email service provider, to a space where they had to open And then they had information about who attended what webinars in a place like Zoom or GoToWebinar. And then they had They had payment data in something like Stripe and they had information about what webpages people are visiting in a place like Google Analytics.
Clay Collins: And over time, the data just got more and more distributed and it became harder to know what was actually happening in terms of the view of the customer and what they were doing across all these different SAS products that you were using to run your business.
And as that happened, there became a real desire to integrate all these different systems and that became a real challenge. And at that time, I got really interested in data platforms and customer data platforms where Brian Krogsgard: So essentially, you have your customers in all these different places and then the hard part is saying, "Well this singular customer data over here and this singular customer data over here, we want to bring those together so we can get the profile of who this customer was, both in terms of what they've bought.
But also how they've interacted with our website or app. And then also like how they treat our emails and stuff. Clay Collins: Yeah, yeah.
What pages they visited, what emails they've opened, what webinars they've attended, what You know. Clay Collins: All that stuff and tracking their behavior before you even have an email address or some sort of identifier. So while they're anonymous users onto So all this sort of post-purchase information and stitching together a unified customer timeline of everything they did across this timeline. And I saw the same thing happening in the crypto space, again with lots of consolidation in data.
At first there was just a handful of exchanges that had most of the volume and then over time, that data being more and more distributed. And hearing from developers that every time they add a new integration to the system, it made the system exponentially more complex because they had to deal with these different systems going up and down in the interaction between systems and maintaining the integrations and all that.
Clay Collins: So we are not a blockchain company. We're not issuing a token. This is an API business. This is a really kind of "boring business", but I think that's kind of in my DNA. I'm a product person and I'm in this for the long haul. And it's kind of these companies that other people find boring, I find immensely interesting. Almost these online utility companies that charge on a metered basis, that's kind of my sweet spot and where I derive the most amount of interest.
And I think the opportunity for us is that these are often things that most people just aren't interested in because they find them to boring.
And there's a lot of What do you You probably know the term for this, but like where degradation and data over time. So I like to use the example of metal just 'cause it's one I remember of being listed on Bittrex and then listed on Binance later and then de-listed on Bittrex, but it's still on Binance. And this is only over the course of whatever the last year that it's existed. We have no idea how this data might happen for an open source Brian Krogsgard: So I've seen people I say metal because that's the example I know where it has this history of Bittrex and it was way higher than it ever showed on Binance and I've seen people show a chart of metal on Binance and they're like, "Wow.
This thing is so destroyed, like it's so far off the top. But people are essentially lacking information to then make a decision because they don't have all of that aggregated. So one of the things that y'all do, because you're pulling it from Bittrex and Binance, you're piling that into your global average over time and you're essentially providing data security for this asset and every other. For as long as you exist, you have that central source of truth if someone can use for making decisions.
Clay Collins: Yep. And you know, one question we get from folks who don't spend a lot of time looking at data is, "Doesn't QuidMarket cap have this data?
Don't other sites like maybe Live Coin Watch have this data? They don't have candlestick data. For the most part, those services are just ingesting live tickers as the data comes in. They don't have historical trade data. They don't have the kind of data that a real trader would want to observe if they're going to create a bot for example.
They may have I've actually poked around several of the APIs that are out there. CoinMarketCap in particular, if you're building something really baseline where you're okay being somewhat right limited and you're gonna go cash all that, you can get stuff like 24 hour volume on a coin or you can get like current price or the percentage of the supply that's out, stuff like that. But getting detailed data of everything that's happened over the past or lifetime of the coin, like several years sometimes, it gets a lot more challenging with anything.
And then also just the quirks between all the dIfferent exchanges and everything that they support, and that seems to be kind of where y'all are attacking this.
So I'm super interested in this, but what I am What is hard to figure out is where the heck are you gonna make money and why are you doing this 'cause the Everything you do on nomics. I'm not necessarily trading based on what you have there.
So where do you start to make money? Who do What kind of people do you charge if I can build something like nomics. Clay Collins They're mostly institutional traders, quantitative hedge funds. Folks like that. What they're paying for is the raw trade data. When you want every individual trade, then you have to pay us or if you want some custom integrations or if you want SLAs and high level support or you want us to do some custom development work for you-. You're saying you'll be up We don't persist the last candle if their API is down even though they're doing it.
We'll just mark it as a zero and then we'll backfill it. If you're just consuming the live data feeds, they don't repair their data. We go back in and we get after the fact. Those are the folks that pay us. What that allows you to do is it allows you to create your own candles. If you decide you want 38 second candles, you can do it because you have the raw trades.
You can construct everything. Something that some folks want are like volume candles. They don't want candles based on like every hour or every four hours. They want million dollar candles. Actually they're doing a lot of the stuff that they won't even tell us. Clay Collins: I'm a product person, so when someone buys our product, I'll go in and ask them, "What are you doing with this data? Sometimes we get a little insight when we do onsite visits and stuff like that.
Pretty cheap in the scheme of things given the size of folk's data budgets. We'll probably move to a metered plan in the future. Brian Krogsgard: Okay. What would a metered plan look like?
Would that be from there and higher or lower the bar? Clay Collins: It would be like it's just sort of pay as you go. Ala carte. If you want to make a lot more calls, then you'll pay for those additional exposure to data. Brian Krogsgard: How do you bring exchanges on to participate to this? We've got about a dozen. The reason why we only have a dozen right now versus having a lot more is for kicking things off, we only wanted to work with exchanges that give us raw trade data.
That allows us to calculate our own candles versus us believing their candles. We've just found fraud. I can talk about that for a second. I'm not going to name an exchange, but the kind of fraud that we see most frequently occurring is when trades happen like far above the spot price. You've got the bid ask spread. You've got the spot price, which would be a market order.
It would be the bid jump way across the ask and purchase something like way over here. Clay Collins: So if you see those charts it's like jumping across the gap. So they'll be really paying some absurd amount for bitcoin, or whatever the crypto asset is, but buying a tiny amount of it at some insane price, and we're like there's no way an order book should let this happen. So that's what we see most frequently.
Brian Krogsgard: I've seen that specifically when people list a coin. They do that weird stuff and you see the massive first bar for some unknown reason. Then two other scenarios I've seen, one was when Binance had the Syscoin hack and shenanigans that they did recently, someone stole 11 Syscoin for 96 BTC each. I don't know if they skipped through the entire order book, like if it was just thin so that they spiked it to that level or what.
But then the other scenario that I've heard that's fascinating to me is sometimes you can do that through exchange APIs because a lot of times the way you show an order book in a RESTful API is actually it shows every single one and then you can pluck the individual order.
Brian Krogsgard: So it allows you to essentially skip the order book, whereas typically a limit order's going to choose the lowest one or a market order is going to pull from the bottom or whatever. But I've heard there's some exchanges where they have funkiness in their API that would also allow something like that.
If you have really thin markets and you put in a market order then it could just be that it blew past all the sell orders and jumped to some super high price. But I can see what you're talking about with the APIs. You can pluck a specific order, although I don't know why someone would do that. That would just make no sense. So it could just be a crappy programmer somewhere.
But I don't know why a crappy programmer at a hedge fund is buying Syscoin for several bitcoin each. I just can't see any-. Brian Krogsgard: Yeah, I think in that example it was something related to the hack that they had and it was just a hot mess.
Brian Krogsgard: I am curious. Y'all have a ton of data between the pricing data, candle data, exchange rates I'm just looking through some of your documentation right now.
Since you came from a marketing background, how did you even know like here's the data that we need to put into this API? How do you know what to provide and how to build it? Clay Collins: Yeah, so I'm a product person first and foremost. So we get it by talking with customers. But we're also traders ourselves. So we know it from that perspective and we create stuff that we want to dog food ourselves. It's really about talking to customers a lot, doing stuff like we did with you on Twitter where you asked for a feature and like okay, we're going to build it.
Or when we're talking to customers sometimes they'll say "Hey, we want this, but in order for this to really work for us we need you to add this additional thing. Clay Collins: So it's just about talking with the customers all the time and I'm on the phone multiple times per week with institutional traders, developers and trying to learn everything I can about making a solid product.
I think kind of the DNA you have to have to make this kind of product is very different from the average product in this space. There's a lot of hackathon developers. There's a lot of kind of young dudes in their 20s spitting stuff up over the weekend. And to create a data product and a data platform I think it requires a certain level of discipline. So every single line of code has a unit test that covers that code. Brian Krogsgard: Which means, for non-programmers, that means that what he says is going to happen has been tested via a whole nother slate of programming tools to verify that that's what happens, because he said it was going to happen.
I don't know if that described that well. Clay Collins: Yeah, we have just as much code testing the app, as the app itself. Which means that myself as a non-developer, my CTO or someone else on the team will often send me a version of the app and I'll log into GitHub and deploy to production without anyone manually testing it.
So it's just a certain level of rigor. It's not something that most people have the stomach for because it's slower at first, but it pays off in spades down the road. Brian Krogsgard: Let's take a break, say thank you to our partner for this episode, Delta. Go ledgerstatus. This is the best way to track your portfolio in crypto, bar none, guaranteed. And you know they've got some great new features. The last two releases have just been chock full of stuff.
Live order books and depth charts, number one on the request list for people that I've talked to who said that they like Delta but they want more. That's the biggest thing they've wanted. You've got that now. Brian Krogsgard: I think they support like a dozen exchanges so that you can see the actual order book, the depth charts, recent trades, all that stuff, right there in the app.
It's really great. They've just released portfolio analytics as well and I've thought this was really cool because I can go back and it'll actually tell me, if I'm a pro user it tells me even more, but it tells me stuff like what exchange are my coins on or what wallet is it in and it gives me these really nice graphs with all of that information, with a lot of analytical data. It also even tells me what's a good trade or a bad trade. So if I sold something and it's gone down since then it'll tell me hey that was a good sell because it's gone down since then.
Brian Krogsgard: It gives you some insights on your past decision making to let you know if you've done a good thing or a bad thing with that trade. Just give you a little more information about your trading and so that you can learn more to be a better trader. Brian Krogsgard: Delta's really awesome. They're always working on cool stuff.
Somebody may be listening to this and they might just say okay, so you want to provide data for hedge funds or for traders or people that want to build something like nomics. You have to be dreaming up more that this will be, in terms of the entire market, beyond a whole bunch of weird crypto assets that most should die.
Other than Bitcoin, Ethereum, and some large caps, do we really need this data? What else do you imagine in terms of being able to fit into your ecosystem? You have a grand vision of the future it seems. Clay Collins: Yeah, totally, totally. There's a couple of functions that we want to serve. One is want to be like the internet archive of the new financial system. So archiving all of these dead coins, all of these markets that have expired, we want to tell the story and the history of what was happening when all of this started to come onto the scene.
I think a second thing is that Perhaps this is overkill for what we have right now, but what we're intending to build is the data backbone for the new financial world, for the open financial system. Clay Collins: And we take that very seriously. Also, the think right now there's not a lot of data.
Perhaps there is. We've indexed billions of trades. And multiple versions of local bitcoins that are reporting their data. Then OTC desks. And then add to that security token exchanges. Clay Collins: So imagine someday every single local coffee shop, pizza shop, anyone who wants to fundraise in this way, every single building in your city has a token and that token is perhaps traded on some kind of local exchange, there's just going to be an explosion of exchanges.
And then add to that order book data. So data for orders that haven't been filled or have been canceled or maybe the order's been placed and that order converts to an actual trade and then add to that blockchain data and you have a huge undertaking in terms of-. Brian Krogsgard: And that's all underlying physical product.
That's the asset itself. That doesn't even get into a future where there's derivative products or futures or options. There's a whole nother set of trades and orders and everything. So y'all want to support all of that someday right?
Clay Collins: Oh, no, and we are. And we have specs to handle that right now. So if you're from a blockchain project, if you're from an exchange, if you're from an OTC desk and you want to integrate your data with us, let us know. We have specs for you to write to. If you can stand up three endpoints, pretty simple endpoints, we can give you a heck of a lot of exposure. So yeah, we're doing all of that and then add to that different indexes.
So each of those bots are going to have their own rankings. There's quite a future. Brian Krogsgard: This seems like an exponential explosion of data that's going to be on your ecosystem. How are you looking to be able to scale that? Like is this built on just a regular old database? I mean what's this look like? Clay Collins: So kind of the latest is using Kafka and Cassandra and that's what we're building on. We're not using Microsoft Access. Clay Collins: Kind of these large nonrelationable wide column store databases that can handle trillions upon trillions of data points.
That's how you got to do it. Brian Krogsgard: And then I don't want to get too much in the weeds. There's no rate limiting. So we cache the hell out of our endpoints. So you can hit us as hard as you want. All rights reserved. Risk Disclosure. Investments can fall and rise. You may get back less than you invested. Past performance is no guarantee of future results. You should consider whether you understand how trading works and whether you can afford to take the high risk of losing your money.
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