Google Cloud Platform Vps

Google Cloud Platform Vps – Earlier this year, I attended the Google Next conference in San Francisco and got a first-hand perspective on what’s possible with Google’s cloud infrastructure. Since then, I’ve relied on Google Cloud Platform (GCP) to run my trading algorithms (and more) and it’s quickly become an important tool in my workflow!

In this post, I’ll show you how to configure a Google Cloud Platform compute instance to act as a server to host a trading algorithm. We’ll also look at why this setup might be a good choice and when it might pay to consider alternatives. Cloud computing instances are only a small fraction of the entire GCP ecosystem, so before we go any further, let’s take a high-level overview of the different components that make up Google Cloud Platform.

Google Cloud Platform Vps

GCP consists of a set of cloud storage, compute, analytics and development infrastructure and services. Google says that GCP runs on the same infrastructure that Google uses for its own products, such as Google Search. This set of services and infrastructure goes far beyond simple computing and cloud storage resources, offering highly practical and affordable machine learning, big data and analytics tools.

Google Cloud Platform 搭建高速vps

Services and infrastructure generally play well with each other and with standard open source development and analysis tools. For example, DataLab integrates with BigQuery and Cloud Machine Learning and runs Python code. Google has tried to make GCP a single, self-contained window for development, analysis, and hosting. And from what I’ve seen, they’re succeeding!

Google Compute Engine (GCE) provides virtual machines (VMs) that run on hardware located in Google’s global network of data centers (a virtual machine is simply an emulation of a computer system that provides the functionality of a computer physical). Basically, you can use a virtual machine like you would a regular computer, without having the necessary hardware. In the example below, I used a VM instance to:

GCE allows you to quickly launch an instance using predefined CPU, RAM and storage specifications, as well as create your own custom machine. You can also select from several predefined “images”, which consist of the operating system (Linux and Windows options are available), its configuration, and some standard software. What’s really cool is that GCE allows you to build your own custom image that includes the software and tools specific to your use case. This means you don’t need to upload your software and business infrastructure every time you want to launch a new instance; you can simply create an instance from an image you saved earlier.

Before I jump into a walkthrough of setting up an instance and running a business algorithm, I’ll discuss the pros and cons of GCE for this use case, as well as the cost.

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There’s a lot to like about using GCE to manage business infrastructure. But of course there will always be extreme cases where other solutions will be more appropriate. I can only think of one (see below), but if you find more, I’d love to hear about them in the comments.

I was almost going to list security as a downside, as it can be easy to assume that if security isn’t managed at home, then it’s a potential problem. However, you might think that Google would do security much better than any individual (at least, that’s what you’d think after reading Google’s ethos on security) and that it therefore makes sense to include outsourcing the security as an advantage. . This issue can be a bit trickier for a commercial company that may prefer to keep security in-house, but for most people, it probably makes sense to outsource it to an expert.

GCE is surprisingly affordable. The cost to host and run my algorithm is about 7.6 cents per hour, which equates to about $55 per month (if I leave the instance running 24/7), including a sustained usage discount, which is automatically applied. Considering the $300 in free credit I got for signing up for GCP, the first year’s operation will cost me about $360.

I used an n1-standard-1 machine from the GCE standard machine type list. This type of machine uses a single CPU and allocates 3.75 GB of memory and I have attached a 50 GB persistent disk. This was enough to run my trading algorithm using the Zorro trade automation software (which requires Windows), running through Interactive Brokers via the IB Gateway. The algorithm in question generates trade signals (for a portfolio of three futures markets) by processing hourly data with calls to a feedforward neural network written as an R script and monitors price data for micro-management of individual trades. The type of machine I chose handled this job reasonably well, despite recommendations from Google’s automated monitoring that I allocate some additional resources. These recommendations generally emerge as a result of retraining my neural network, a task that turned out to be more resource intensive than actual trading. Fortunately, this only happens periodically and so far I’ve chosen to ignore Google’s recommendations with no apparent negative consequence.

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I used a Windows Server 2016 image (since my business application only runs on Windows) and a 50GB persistent disk, which is the minimum required to run this image. The Windows Server image represents the lion’s share of the cost: approximately $29 per month.

A scaled-down version running Linux (Ubuntu 17.04) with a smaller persistent disk runs at less than half that cost: 3.4 cents per hour or $24.67 per month with a sustained-use discount. Clearly, there are big savings to be made if you can move away from Windows-based applications for your business infrastructure.

It’s also worth mentioning that you’re only charged for what you use. If you need to stop your algorithm in the middle of the month, you will only be charged for the time the instance has been running. Most private commercial server niche providers will charge you at most for the whole month regardless of when you stop running your algorithm.

As you can see from the descriptions above, Google Cloud Platform consists of MANY different services. Meeting for the first time can be a bit tricky. This part of the article is an explanation of setting up and running a business algorithm in GCE, aimed at the new GCP user.

Vpsとgoogle Cloud Platform。gcp?gce?両者の違いや利用方法とは?

Go to https://cloud.google.com/ and sign in to your Google Account (or sign up for an account if you don’t have one). Consider the $300 in free credits you get to use in the first 12 months.

Next, fill in the specifications for your new instance. The specs I used are like this (you can see the cost estimate on the right):

I used one of Google Cloud Platform’s US East Coast data centers, as IB’s servers are located in the New York area. My algorithm is not latency sensitive, but every little bit helps.

, the instance will take a few moments to spin up and then appear in your virtual machines dashboard as follows:

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Follow the instructions, then copy the password and keep it safe. Now you are ready to connect to your instance!

Once connected to the instance, it should look like a normal (albeit somewhat spartan) Windows desktop. To verify that you can connect to Interactive Brokers (IB), we will connect to the IB test page. But first, we need to adjust some default Internet settings. To do this, open Internet Explorer. Select the

And add https://www.interactivebrokers.com to the list of trusted sites. Then save the changes. Here is a visual of my instance:

Now, connect to the IB test page to verify that your instance can communicate with the IB servers. Simply navigate to https://www.interactivebrokers.com/cgi-bin/conn_test.pl in Internet Explorer. If the instance is connecting successfully, you should see a page like this:

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You can now load your software and trading algorithm into your instance by simply copying and pasting from your home computer, or by downloading any necessary software from the network. Note that to copy and paste from your home computer, you’ll need to access the instance using Windows RDP, not Chrome RDP (this may change with future Chrome RDP updates).

I found that I could not install R packages from a script due to access restrictions to certain parts of the Windows file structure. To resolve this, I followed these steps:

If you need to stop trading your algorithm, it’s usually a good idea to stop the instance so you don’t get charged for computing resources you’re not using. Do this from the VM dashboard:

As long as you don’t delete the instance, you can always restart it from the same state it was stopped in, which means you don’t have to reload software and scripts. You are not billed for an instance that has stopped.

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On the other hand, if you delete your instance and later want to restart, you’ll need to create a completely new instance and reload your entire business infrastructure. This is where images come in handy:

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