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Getting Started with Azure Functions and their extensions Superpower

This is a part of my “Journey with Azure” seriesI have always been fascinated by managed services and serverless computing. It started with my first startup PureMetics, where we used AppEngine and BigQuery to built an Analytics product. Because both AppEngine and BigQuery were fully managed services, A single person (Abhishek Nandi) was able to build the entire tech for it. Later on when Abhishek & I were building Odiocast, we used other managed services to keep things simple. FireBase, AWS Lambdas and other services allowed us to worry more about the product rather than worry about Devops as a 2 person startup.The logic is simple when you have the best technology companies willing to be your Devops team, at a cost, why would you say no? You would then have time to focus on what makes your product different. Also since all of the providers are running at scale, they get to reap scale benefits which they pass on to you. Today I am practically running a side project for almost nothing on GCP and my next project is also going to be close to free on Azure.Abstraction of OperationManaged services abstract out service management and scaling. They vary from just abstracting out the basics like infrastructure i.e Infrastructure as a Service (IaaS) providers like AWS EC2, to abstracting out all details i.e Functions as a Service (FaaS) like Azure FunctionsLevels of AbstractionDepending on your needs you can pick services between the 3 abstraction types: IaaS, PaaS & FaasAdditional AbstractionAzure Functions take abstraction to a whole new level with Bindings. With Bindings, Azure Functions allow you to connect a bunch of services without writing additional code for it. It achieves this by abstracting out the service like Table Storage, Queue Storage etc and providing you objects to refer to the service. With Bindings, you are actually writing the least amount of code to connect 2 services. More on this later.Azure FunctionsWhile AWS Lambda was the pioneer in FaaS, almost all service providers now have equivalents. Personally, Azure Functions are the most interesting service for me because of the additional abstraction layer it provides via Extensions and Logic Apps.Getting Started with Azure FunctionsThere are many ways to build an Azure Function, you can write an Azure Function in various languages (C#, JS, Java, Python), which then provide you the option of using Visual Studio, Visual Studio Code, the Azure Portal or Any other editor to write it. For this tutorial, I am going to be using VSCode and JavaScript to build my function.Step 1. Create a new Functions ResourceHead over to the Azure Portal and select Create a Resource (1) and then select Serverless Function App (2)Next the next Blade you can name your app (3), select subscription (the billing account) and the hosting plan and OS amongst other things. These are important choices. For example, Python is only available on Linux, and Linux is only available in a few regions. The important option here is the hosting plan (4). You have 2 options here:Consumption plan: Here you only pay for the number of times your function is runApp Service Plan: Here your function run on a dedicated VM run on an App Service, which will cost extra but will be useful in a few cases which are documented here.If you are just starting up with Azure Functions, Consumption plan is the way to go.Lastly, select the Runtime stack (5), I selected Javascript for this tutorial.Press create, this will take a while. The portal will first validate things and then close the blade. Don’t worry this is normal.You will need a notification pop up which would state that the deployment is in progress.Side note: One thing you start noticing is the options/flexibility Azure Functions provides. You will see in the next step too. Once the deployment is done head over to the Functions Resource, you will see the following screenGo ahead and click on new functions. Depending on the options you selected you will see different options. For my selected options I see the followingOnce again for this tutorial, I will select VS Code, post which I am shown two options, publish using VS code or via the Deployment Centre. I will select Direct publishStep 2 Install various dependenciesThis then gives you the steps to install the following dependenciesVisual Studio CodeNodeJS & NPMAzure Functions Core Tools which needs .NET Core 2.1Azure Functions for VS Code, once this is installed you also need to sign into Azure.Step 3: Getting started on VS CodeOnce all of them are installed, we need to move over to VS Code to get started. Open up the Azure panel on VS Code (1), You will see a Functions section, with your subscription plan and your functions plan, select it (2)Lastly, click the new Functions button (3) . Now VSCode will first ask you for a location and then will ask you to initialise the project, yes is the obvious selection. VScode will take you through a wizard to create your functions app,Select the language — JavascriptSelect the function template — Since we are just getting started, Select the simplest, HttpTriggerSelect the default name provided HttpTriggerAuthorisation Level will be AnonymousLastly, add it to a workspace of your choiceFor now, just remember triggers are events which cause the function to run, we will take a deeper dive into trigger later Next VSCode will open up the function file index.js, if you don’t see a folder like I do below, open the folder you created during the steps aboveNow you should see your folder like thisTwo files which are important are the function code in index.js and the function.json file which defines the bindings for this function.Index.jsThis is the code of your function. The arguments of the function vary based on the type of trigger you are using. The HTTP trigger will have a context object and a request object. Azure Functions use the context object to communicate with your function. For example, if you had an output binding you would refer it via the context object using context.bindings.<name of binding>.In case of the HTTP Trigger we also get the http request as an argument.Function.jsonThis Json file defines the input and output bindings of your function. In case of our example, both of them are going to be HTTP bindings. More on bindings later.Running the FunctionTo run your function press F5, Your console in VSCode will show something like thisIf you call the URL without any request parameters you will get the following messageJust pass a name as a get parameter, like this: http://localhost:7071/api/HttpTrigger?name=RaviThe tutorial works, what next?Function are great at doing stuff when something happens, a trigger. They are ideal for microservices or workers which do a particular job. Here are a few examplesUse a timer trigger to hit an API ever 24 hours and store the response in a table, for example you can hit the Nasa Near Earth Object Web Service to get the count of near earth objects dailyUse a HTTP trigger to hit an API with some parameters and store the response in queue & process it with another functionRead a log file and pass error events into a queue to be processedUse a HTTP trigger to pass an upvote to a queue and then use a function to increment the vote up in an asynchronous mannerStore a profile picture in Storage and use a storage triggered function to generate a thumbnail image and store itDiving Deeper into Triggers & BindingsTriggers at the simplest are events which trigger your function to start. They may also have information with them. A HTTP trigger, as we saw above, has the http request data passed to your function, a queue trigger will have the object from the queue. They are passed as arguments to your function. As such a function can only have one trigger but can have multiple input bindings.What are bindings?Bindings allow your function to connect to various resources without the need of writing a lot of code for the service. Without bindings, you would need to add the SDK of the service you want to use, write all the boiler plate for it and then start using it, with bindings, Azure Functions do the heavy lifting for you. As mentioned above, bindings are available for you to use via the context objects.Declaring Triggers and BindingsDepending on how you are writing your functions. Since we are using Javascript we need to update the functions.json file, if you were using C# you would need to decorate methods and parameters with C# attributes. The Azure Portal has a Wizard/GUI mechanism.When you declare the binding you will need to define the following:NameDirection — Can be in, out, there is also a special inout directionTypeThe rest of the parameters depend on the type of binding you are using. If you are using Tables or Queues, for example, you need to provide the storage account, and the name of the Queue or Table you want to use.Here is an example of a function.json file. Here I am reading a queue which has a list of JSON objects, the function then reads in the individual objects and pushes them to the output-queue-individual queue and it’s metadata to output-queue-meta.{ "disabled": false, "bindings": [ { "name": "myQueueItem", "type": "queueTrigger", "direction": "in", "queueName": "queue-input-list", "connection": "AzureWebJobsStorage" }, { "type": "queue", "direction": "out", "name": "outputqueuemeta", "queueName": "output-queue-meta", "connection": "AzureWebJobsStorage" }, { "type": "queue", "direction": "out", "name": "outputqueueindividual", "queueName": "output-queue-individual", "connection": "AzureWebJobsStorage" } ]}When you are developing locally, the connection string is best defined in the local.settings.json file.For a complete list of possible triggers and bindings refer this document.You now know enough to get started with Azure functions.Follow Ravi Vyas on WordPress.comOriginally published at on March 23, 2019.Getting Started with Azure Functions and their extensions Superpower was originally published in Hacker Noon on Medium, where people are continuing the conversation by highlighting and responding to this story.

Azure Table Storage vs GCP DataStore

This is a part of my “Journey with Azure” seriesSo as a part of playing around with Azure, I decided to clone AirMeet’s codebase with Azure as a backend service. Airmeet is currently running on AppEngine with Datastore as a database. The equivalents on the Azure side are AppService with TableStore as the database. Based on that experience here are some of the differences between the two NoSQL key-value stores.Structuring your StorageTable Storage needs a partition key and a row key, whereas data store only needs a key. Now you may look at it as extra work, but this also allows you to plan around your data structure and also for possible performance benefits in the future.Web interface & Azure Storage explorerGCP has a slightly better interface. For example, I can do a multi-select in the interface to delete multiple entities, I was not able to get the same working with Table Storage.Azure, on the other hand, has a big win in the Storage Explorer App, which allows you to view your tables, queues and blobs via an App.Update entityThis is my favourite feature of TableStorage, While an update is possible in Datastore, it is more of reading the entity, modifying it on the client side and write the entire object.Reading partial entitiesTable Storage has an SQL like query model, thus you can actually read only a part of an entity, super useful when you have large entities and want to save on cost on bandwidth.Cost comparisonDoing a cost comparison would be unfair, more important it would be inaccurate because of the way pricing is structured on both the products. Azure has more billing options based on how you want your data to be replicated. GCP does not provide that flexibility (or limits complexity based on how you are looking at it). Azure also treats your reads, writes, and delete equally when it comes to cost, whereas GCP has different pricing for each operation, where reads cost 1/3rd of writes. Overall operation costs are lower on Azure at $0.00036 per 10,000 transactions for a table stored in East US with Locally Redundant Replication. GCP $0.036 per 100,000 entities reads. Writes are 3X more expensive as mentioned before. In most cases, Azure will be cheaper unless you can fit your workload into GCPs free monthly tiers.NitpickingOne issue I had initially when moving from DataStore to Table Storage is the fact that I had to worry about “creating tables”DataStore simply creates the entity group if does not exits. The same logic did not work on the TableStore side, in fact my insertEntity method failed until I wrapped it around the createTableIfNotExists method. If there is something in the documentation, I certainly missed it.#Originally published at on March 19, 2019.Azure Table Storage vs GCP DataStore was originally published in Hacker Noon on Medium, where people are continuing the conversation by highlighting and responding to this story.

Kaleido’s Blockchain Business Cloud now available on Microsoft Azure

CryptoNinjas Kaleido, a ConsenSys venture project, today announced its Blockchain Business Cloud is now available on Microsoft Azure’s Marketplace. This new support for Azure, in tandem with Kaleido’s earlier collaboration with Amazon Web Services (AWS), allows Kaleido to offer a seamless blockchain solution spanning multiple... Kaleido’s Blockchain Business Cloud now available on Microsoft Azure

Kaleido’s Blockchain SaaS Integrates To Microsoft Azure And Amazon Web Services (AWS)

Kaleido’s SaaS Integrates To Azure And AWS Kaleido’s Software as a service blockchain solution has been given a huge boost as it now works on both Microsoft’s Azure and Amazon Web Services. As a result of this integration, Kaleido now has access to over 80 percent of cloud infrastructure clients as well as support from […]
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Commit, push, deploy — Git in the Microsoft Azure Cloud

Commit, push, deploy — Git in the Microsoft Azure CloudFollow me on Twitter, happy to take your suggestions on topics or improvements /ChrisWe have come to rely on Git as our default version control tool ever since it was released, it has become de facto standard even if other options exist. Git helps us manage our source code, divide it in branches and it even helps us working with other developers on places like for example GitHub or GitLab by allowing us to create Pull/Merge requests. Wouldn’t it be great if Git can be there for us when we move our Apps to the Cloud?This article will cover the following:Download a sample app, we want to focus on understanding deployment with Git so we will take some ready-made codeRun the app locally just to ensure it works, this is a must, if it works locally it at least has a sporting chance of working in the CloudConfigure a deployment user, this deployment user is required for FTP and local Git deployment to a web appCreate a resource group, this is needed if you need a new logical grouping for what we are about to do,Create a service plan, we will need this to specify how we will be charged for this and what kind of container we will createCreate the web app, we will need to run a command to create the actual web app and we will need to state here how we deploy it, we will choose git deploy, more on that belowVisit the generated web site, we need to enjoy the fruits of our labor of coursePush the source code to the site, using git deployManage updates, let’s learn how we can update our app and push changes, this deployment, with Git, is not a one-off we can keep changing our code and redeploy for as long as we wantWhy Cloud and why Git?Cloud is almost the default place for developers to deploy new apps today. The reasons are many like it is cost-effective, scalable, elastic and has some great built-in support for security ( even though we can’t rely on the Cloud wholly).Git is a tool we most likely are already using when we code as a way to manage our source code, especially when we are many developers maintaining the same code base. The best way to add a skill like Cloud and deployment to your tool belt is by using technologies you already know. So here it is, take what you already know, Git and add some Cloud to it. Hope you enjoy this article :)ResourcesWe do refer to some docs pages throughout this article so here they are so you can learn more:Installing Azure CLI, for everything we will need the Azure CLI. Azure CLI is quite powerful and will let you perform pretty much anything you can do in the Portal.Local Git deployment to Azure App Service, this page shows how you deploy your app to Azure using Git but also talks a little about Azure DevOps buildGit deployment quickstart, this is an even faster version than this article. It’s faster in the sense that it’s using some smart defaults for resource group, app service plan and it’s also using something called zip deploy, which is another type of deploy than the git deploy we are describing hereDownload and run a sample appLet’s get a sample app first. The easiest way to do that is by installing Git if you haven’t done so already and grab the sample project out of GitHub:git clone cd python-docs-hello-worldThe application above is a Python application using the library Flask to give us a REST API. What does this repository give us? // this is where our application livesrequirements.txt // this is a list of libraries that needs to be installed for our app to work.gitignore // just a file Git reads so it knows what files to NOT include when we push changes to the repoLet’s have a look at and how it sets up some routes for us:// application.pyfrom flask import Flaskapp = Flask(__name__)@app.route("/")def hello(): return "Hello World!"What the above code does is to define a route / and it lets the method hello deal with the mentioned route and it outputs the text Hello World , a pretty standard phrase when it comes to your first application in a new programming language or library.Run the app locallyOk, now we understand a bit more what we have in front of us. Next up is attempting to run this application locally before attempting to deploy it to the Cloud.To get a Python app up and running we need to install Python and the dependent libraries that come with it. The instructions for that is a little bit different depending on what OS we are on, that is Windows, Linux or Mac. If you are on a Mac, you’ve got Python installed already. However, you will need the package manager Pip, which we can get hold of by running:sudo easy_install pipIt’s worth having a look at the upcoming link if you are a serious Python developer. If you are not and you are just here for Git and Azure feel free to skip the below paragraph. short, it’s asking you to install something called virtualenv . It creates different working environments so if you need a separate version of Python or separate version of some libraries this is the way forward.Next thing we need to do is to install the dependent libraries and we do so by invoking the following command in the terminal:sudo pip install -r requirements.txtA quick look at the requirements.txt file reveals we have some depending libraries that our app needs. As we can see below it’s our API framework Flask together with some supporting libraries:click==6.7Flask==1.0.2itsdangerous==0.24Jinja2==2.10MarkupSafe==1.0Werkzeug==0.14.1%Then we just need to set the environment variable FLASK_APP to point to our starter file , like so:FLASK_APP=application.pyfollowed bypython -m flask runThis launches our app at http://localhost:5000 , like so:Great, so we are ready for our next step, deploying to the Cloud.Configure a deployment userTo do any kind of deployment to Azure you will need a so-called deployment user . This deployment user will need to have a name that is globally unique.To create our deployment user we need to use the command az webapp deployment. Then we need to provide a user after the --user-name flag and a password after the --password flag. The password needs to be at least 8 characters long and contain two of the following three elements: letters, numbers, symbols. The full command we need to run looks therefore like this:az webapp deployment user set --user-name <username> --password <password>Here is an example of what invoking the above command can look like. Let’s call the user deployment-user-chris and give it the password test1234. Thereby our command now looks like this:az webapp deployment user set --user-name deployment-user-chris --password test1234This should give you an output looking like this:The above output means that our command succeeded and we can keep going to the next step.Create a resource groupA resource group is a logical grouping in which you place everything that goes together like Databases, Service plans, Apps and so on, we need a resource group for almost everything we do.You can either use one of your existing resource groups or create a new one in which case you would type the following:az group create \ --name [your name for a resource group] \ --location westeuropeCreate a service planAn App Service plan in Azure defines a set of compute resources for an App to run on. This corresponds to a service farm in a more traditional Web Hosting. A service plan comes with a pricing tier. The pricing tier decides what App Service features you get and how much you pay for your plan. There is a lot more to know here, the interested reader is urged to have a look at the following link to see how this works more in detail.For the sake of this article though it’s enough to understand that all Apps running on AppService needs a service plan as a prerequisite.The full command for creating a service plan is this:az appservice plan create --name [your name for an app service plan] --resource-group [you name for a resource group] --sku B1 --is-linuxThe value B1 means we are on a basic service plan ( relatively cheap so you dare test this out a bit) and --is-linux means we will get a Linux containerWe are using the appservice command above and the subcommand plan to create our service plan. This should also give us a JSON response back with provisioningState key having value SucceededCreate a web appBelow is the command for creating the web app. We will need to state the resource group , pricing plan the name of the app, the version of Python and lastly how we will deploy it, which is git deploy:az webapp create --resource-group [your name for a resource group] --plan [your name for a service plan] --name [your name for an app name] --runtime "PYTHON|3.7" --deployment-local-gitA comment on the above command is that the app name will need to be globally unique.In the JSON response to this we are looking for two interesting pieces of information:the name of the git repository, look for key called deploymentLocalGitUrl .the public URL, look for a key called defaultHostNameWhat you’ve accomplished is an empty web app with git deployment enabled. The reason is empty is cause we have yet to put any code in there.Visit the web siteEven though we haven’t pushed any code to our site yet it still contains something namely a default landing page that we can visit. To visit the web site go to http://<app name>.azurewebsites.netThis should show you a default site looking like this:That seems to work, great! Ok, so we’ve done most of the heavy lifting, now it’s time to roll up your sleeves, crack those knuckles cause we are about to make our web site come alive.Push the code to the web siteOk, the last step is to set up the git deploy properly and push the code and see the site being updated with your code. We’ve set up the git deploy by typing the following:git remote add azure <deploymentLocalGitUrl-from-create-step>Now it’s time to push the code so we simply type:git push azure masterThis will prompt you for your deployment users password. Just type it and your deployment is underway.This will take a little while so go and have a warm beverage :) It should finally dump out a long list of logs ending with:That means that your app deployed successfully and you can now go back to your app URL and you should see this:Now, that’s how it’s done. Now, celebrate… :)Manage changes and redeployThis is quite easy now that we have git deploy activated. Let’s do the following:make some changes to a filecommit the changespush the code to our remote branchMake some changesLet’s go into and change it to this:Commit changesWe commit as we would normally do in git with:git commit -am "changed text"Push the code to our remoteLet’s now push to our remote branch by typing:git push azure masterThis should generate a bunch of log entries and end with deployment successful. Let's verify that that is the case by going to our app url:That’s looking good :)SummaryWe have learned how to deploy an app to Azure and even update it and redeploy. That’s a lot in one article. Let’s have a brief look at the steps we took to accomplish this:downloaded a sample app. Obviously, in a real scenario, we would have created our own code and not as in this case use a sample app with Python and Flaskcreated a deployment user that we would need for our deployment. That also made us choose deployment type which we chose to be git deployment.created a resource group. This created a logical group for our app. This is a group we can reuse for other things like adding a database or something else the app might need to exist.created a service plan. This meant that we decided how we want to be billed for this as well as what kind of container we wanted the app to run in.created the web site itself, or rather a placeholder where our app could live.pushing our code to the newly created web site using git. We also showed how we could use git to update our web site and redeploy.This taught us the basics of starting with web app development with Azure and prepared us for future adventures. Stay tuned for more fun in the cloud :)And finally almost all you need to remember from this article is using Git and then push and you’ve made it to the Cloud…Commit, push, deploy — Git in the Microsoft Azure Cloud was originally published in Hacker Noon on Medium, where people are continuing the conversation by highlighting and responding to this story.
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Ravencoin Grows 20% And Continues to See RVN Token Surge in the Crypto Market

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Bitcoin [BTC] Futures in good stead against its Spot equivalent: Bitwise Report

Bitcoin [BTC] Futures were thought to be a snippet of the overarching cryptocurrency market, though meager in comparison to the larger spot market. A recent report from Bitwise Asset Management, the crypto-centric investment firm has stated otherwise. In a March 20 report presented to the United States’ Securities and Exchange Commission [SEC], Bitwise analyzed the Chicago Mercantile Exchange [CME], and the Chicago Board Options Exchange, with ten prominent cryptocurrency exchanges’ in terms of their trade volume. Prior to shedding light on their Futures versus Spot findings, it must be noted that the report revealed that 95 percent of the trading volume of unregulated exchanges were seemingly “fake and/or non-economic wash trading”. Taking into account this disparity, the percentage of futures volume to their spot equivalent increases from 1.51 percent to 33.33 percent. Reported Spot volume totaled $6 billion, but after removing the “suspicious exchanges”, the actual volume recorded dropped to $273 million, in comparison to the futures market volume of $91 million. Furthermore, the increase in futures’ volume as a percentage of the spot market has been steadily increasing. From November 2018 to January 2019, the futures market was just over 15 percent, and almost doubled in February 2019 to 33 percent. Since the Futures contracts were approved in December 2017, only on two occasions did the Futures volume, in comparison to the Spot market, shoot above 20 percent; this was in May and August 2018. Futures Volume expressed as a percentage of their Spot Equivalent In terms of their stand-alone trade volume, the CME and the CBOE are in good stead against the world’s top cryptocurrency exchanges. The daily volume the CME, which brings in $84.82 million, ranks second behind Binance’s $110.5 million and ahead of Bitfinex, which records $38.06 million in daily trade volume. The CBOE also fairs well, taking the ninth spot on the ladder, ringing in $6.12 million in daily trade volume. Gemini takes the eight spot with $8.11 million and itBit caps off the top-10 with $5.58 million in daily volume. Notable, among the top-12, eight exchanges are registered within the United States. Despite the CBOE’s comparative success against the spot exchanges’, it has not been performing well against its cross-town rival, the CME. This slump forced the CBOE to delist their Bitcoin Futures [XBT] for March 2019. However, the XBT futures that are yet to expire later in the year will not be off-loaded prematurely. Bitwise also points out that the CME Futures Price tracks the Global Spot Price based on an arbitrage model. Given below is a chart attesting the same: Arbitrage between the CME Futures price and the global Spot price The post Bitcoin [BTC] Futures in good stead against its Spot equivalent: Bitwise Report appeared first on AMBCrypto.

How Cryptocurrency Trading Volume Fiasco Can Lead to Bitcoin ETF Approval

The SEC has held the ETF approval for Bitcoin and Cryptocurrency for a couple of reasons. The most significant reason for the same has been the unregulated marketplace. While decentralization in Bitcoin is an attribute that makes it an ideal asset class, the market places or Exchanges that provide for conversion of FIAT to Cryptocurrency is still controlled by independent entities. A recent report by Bitwise Asset Management published by the SEC inferred that more than 95% of the cryptocurrency volume is being faked. Hence, according to that, the ‘actual spot volume’ on cryptocurrency exchanges is a little above $270 million. Moreover, the reported volume of CME and Cboe Bitcoin Futures is more than one-third of the ‘actual spot volume’ estimated by Bitwise. According to Bitwise Asset Management, This is good news because it means CME— a regulated, surveilled market— is of material size, which important for an ETF. The case of a Bitcoin ETF Approval Now CME Bitcoin Futures reported a spot trading volume of $85 million. Moreover, according to Bitwise Asset Management, the actual trading volume of the Crypto-to-FIAT Exchanges is around $273 million. Hence, according to this statistic the Futures Trading Volume of CME alone accounted for 31.1% of the ‘Actual Exchange Volume.’ Moreover, there are other Bitcoin Futures market active in Europe and Japan as well. Hence, going by the above statistic, it can be said that the institutional investment might be in parity with the unregulated investment in Bitcoin. However, the Exchanges have reported total spot volumes total to the tune of $6 billion. This can necessarily raise doubts on its demand being higher than $100 billion. However, it does not directly affect the total market capitalization of a cryptocurrency.   Parity Between Spot Trading of Bitcoin and Gold The spot trading volume of Gold is 0.55% of its total market capitalization, while according to Bitwise statistics spot ‘actual spot trading on Bitcoin is 0.39%. If the CME Futures volume is included in this data, the percentage will increase to 0.51%. The OTC trading volume on most exchanges is also not added in the Exchange Data. All this suggest that the institutional investment in Bitcoin is considerably more significant than one expects. It is not only healthy in volume but also agrees statistically with the closest relatable asset class, i.e., Gold. Hence, a new form of informational mechanics for the trading of Bitcoin and Cryptocurrency in regulated Exchanges could alleviate the doubts around the Bitcoin ETF approval.   The post How Cryptocurrency Trading Volume Fiasco Can Lead to Bitcoin ETF Approval appeared first on Coingape.

Top 5 Crypto Performers Overview: ONT, ADA, ETC, BCH, IOTA

Top 5 Crypto Performers Overview: ONT, ADA, ETC, BCH, IOTA The views and opinions expressed here are solely those of the author and do not necessarily reflect the views of Every investment and trading move involves risk, you should conduct your own research when making a decision. The market data is provided by the HitBTC exchange. […] Cet article Top 5 Crypto Performers Overview: ONT, ADA, ETC, BCH, IOTA est apparu en premier sur Bitcoin Central.
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