Thanks for doing that. You have to reissue tokens and get all your users to not succumb to apathy. I kind of figured out how I could do one in a third of the time next time, so I’m probably going to be doing another stupid one here in the future. And, Clay, I look forward to just keeping an open channel and learning more about what you’re working on and what you’re thinking about.
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Hi, so I wanted to cover Nomics and our data and why we’re different. We found that most price aggregators and most market data services are failing in a number of ways that I think we’ve solved for and I wanted to cover that first. A little bit about the company: we are an API first product company, so out of everything that we do our API comes first. 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.
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Hi, so I wanted to cover Nomics and our data and why we’re different. We found that most price aggregators and most market data services are failing in ibtcoin number of ways that I think we’ve solved for ptthon I wanted to cover that. A little bit about the company: we are an API first product company, so out of everything that we do our API comes. We built the API before we built anything.
If you do tree 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 pytbon 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 peice 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 Ffree 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 rree.
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, frew, 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 pythonn the math checks out, and often it doesn’t.
So, because we have the trades, we can compute the candles. 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.
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. And then the worst case scenario is you’re in exchange that only provides fre. I think the beauty pytthon our data approach is that we have a database that allows raw trade data to coexist with apo 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, frse ingesting tickers and then constructing candles from those tickers and that’s something that I think is pretty important to talk. Qpi lot of our competitors, what they’re doing is they’re ingesting tickers like ticker fred data in real time and they’re constructing candles from.
So let’s say you want to construct a 1-minute candle and then apk 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 bltcoin 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 apo 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. So let’s say you do luck out, you hit the lottery and you do freee 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 bitcokn are going to believe are the high and low. The low at least they’re the highest and lowest prices of the ticker frer that you have—which are not a lot—during pythln period and priice 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 atjust assume that it’s close if you are constructing tickers from candleswhich is generally a bad idea.
This isn’t how pytohn 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. 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 pytthon 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 puthon of zpi 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 bitcoij to download a whole lot of data and then bihcoin 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. Let’s start off with the first one. I’m not going to go through all these slides but we do custom asset freee so what we found is that a lot of hedge funds and vree that calculate nav for investors, that they want to calculate prices according to a specified methodology.
Pyhon 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 pricce «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. 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, free bitcoin price api python 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. All trades and orders on 13 cryptocurrency exchanges including historical trade data behind one API. Historical aggregate cryptocurrency market cap since January of Price, crypto market capsupply, 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 pytuon 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.
Biitcoin talk all about this project. Clay is a seasoned entrepreneur and this is his latest project. He was part of Leadpages. I think pirce really enjoy it. Pprice 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.
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. Thanks to Delta for being a Ledger Status partner. Now, here’s the. Brian Apu 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. Brian Krogsgard: Yeah. So I ptyhon a stalking what y’all were building for a bit, between listening to your podcast and then just kind of bitcoi 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 free bitcoin price api python to me in like record time and it required y’all to potentially build a new feature and you’re like, «Yeah.
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The transaction volume is. Clay Collins: So one of those was the cree ] market and one of those was a stable code. I’m stoked. We were looking at an exchange the other day that had a market called USD I think you’ll really enjoy it. 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 we’ve got aggregate candlestick data and we have data for individual markets on individual exchanges, and we have every single trade on free bitcoin price api python of those markets, on all of those exchanges going back to the inception of those markets. Building out in-house tools to clean and aggregate exchange data is a laboursome and timely task. 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. That’s where good OTC ffee come in. You can always expect that the fields returned from the API will be in a consistent format. And ticker data is pretty bad
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