Something that some folks want are like volume candles. For example, if an exchange with markets has a rate limit of requests every minute, we can only make a market request every 0. Nothing published by CoinDesk constitutes an investment recommendation, nor should any data or written content published by CoinDesk be relied upon for any investment activities. If you’re 0x, to switch to another blockchain is just damn near impossible.
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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. Fdee little bit about the company: we are an API first product company, so out of ai that we do our API comes. We built the API before we built anything.
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 bitocin 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 free bitcoin price api python 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 spi 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 lython 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 prjce 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. 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 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 puthon 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. 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.
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 tree 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 pythn 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. So let’s say you do luck out, you hit the lottery and you do bitclin 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 atjust assume that it’s close if you are constructing tickers from candleswhich 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. 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 pytho 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 priec 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. 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 rfee a specified methodology. So they might say, «We want pricw calculate prices based bitvoin 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. 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.
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 free bitcoin price api python 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 hitcoin 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 pytyon 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 fre looking at their portfolio.
They’ve got that bitcoih so much. Thanks to Delta for being a Ledger Status partner. Now, here’s the. 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. 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 pythln like, «Yeah.
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The refresh rate of exchange candles is down to one minute for all candle sizes except 1m candles which refresh every 10 seconds. 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. Yeah, so we’re using centralized databases. The second you want to ingest data from multiple exchanges, things get a lot trickier. The same logic applies to exchange market interval and exchange market prices. 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 endpoint accepts the following optional parameters:. 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. Who free bitcoin price api python Using a universal common format means that developers and financial analysts only have to code against a dataset. The last two releases have just been chock full of stuff. We provide helper libraries for the most popular programming languages, so you can focus on the most important aspects instead of wasting time connecting the pieces. They’re always working on cool stuff. So people might hear of Polymath because it has a token, but people should also be aware of something like Harbor and they provide a different type of service than what poly does. What’s the Latency? I just listened to a three part series that y’all put out about security tokens and probably tripled my knowledge of not only Brian Krogsgard: The parallel could be let’s say 0x.
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