Vps On Google Cloud – 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 been leaning on Google Cloud Platform (GCP) to run my trading algorithms (and much more) and it’s quickly become an important tool in my workflow!
In this post, I’m going to show you how to set up a Google Cloud Platform compute instance to act as a server for hosting a trading algorithm. We will also see why such a setup can be a good option and when it can pay to consider alternatives. Cloud compute instances are only a small part of the entire GCP ecosystem, so before we continue, let’s take a high-level overview of the various components that make up the Google Cloud Platform.
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The GCP cloud includes a suite of storage, compute, analytics and development infrastructure and services. Google says that GCP runs on the same infrastructure that Google uses for its own products like Google Search. This suite of services and infrastructure goes beyond simple cloud storage and compute resources, providing some of the most convenient and affordable machine learning, big data, and analytics tools.
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Services and infrastructure generally play well with each other and with standard open source tools for development and analysis. For example, DataLab integrates with BigQuery and Cloud Machine Learning and runs Python code. Google strives to make GCP a self-contained, one-stop-shop for development, analytics, 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 VM is an emulation of a computer system that provides the functionality of a physical computer). You can essentially use a VM like you would a normal computer, without actually owning the necessary hardware. In the example below, I used a VM instance:
GCE allows you to quickly launch an instance using default CPU, RAM and storage specifications, as well as create your own custom machine. You can also select from several pre-defined ‘images’, which include the operating system (both Linux and Windows options are available), its configuration and some standard software. What’s really cool is that GCE enables you to create your own custom image that includes software and tools specific to your use case. This means that you don’t have 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’ve previously saved.
Before we get into a walk-through of setting up an example and running the trading algorithm, I’ll touch on 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 your business infrastructure. But of course, there will always be edge cases where other solutions would be more appropriate. I can only think of one (see below), but if you come up with more, I’d love to hear about them in the comments.
I was almost going to list security as a disadvantage, because it can be easy to assume that if security isn’t handled in-house, it’s a potential issue. However, one would think that Google does security better than anyone can (at least, that’s what you think after reading Google’s spiel on security) and so it makes sense to include security outsourcing as a benefit. . This issue may be a bit more complicated for a commercial firm that prefers to keep security in-house, but for most people, it is possible to outsource it to an expert.
GCE is surprisingly affordable. The cost of hosting and running my algorithm is about 7.6 cents per hour, which works out to about $55 per month (if I leave the instance running 24/7), including the continued usage discount, which is automatically applied. Factoring in the $300 of free credit I received for signing up for GCP, the first year of operation will cost me about $360.
I used the n1-standard-1 machine from GCE’s list of standard machine types. This machine type uses a single CPU and allocates 3.75 GB of memory, and I have attached a 50GB persistent disk. It was enough to run my trading algorithm through Zoro Trading Automation software (which requires Windows), interactive brokers through IB Gateway. The algorithm in question generates trading signals (for a portfolio of three futures markets) by processing hourly data with callouts to a feedforward neural network written as an R script, and it monitors tick-wise price data for micro-managing individual trades. The machine type I chose handled the job well, despite recommendations from Google’s automatic monitoring that I assign some additional resources. These recommendations are usually generated as a result of retraining my neural network, a task that proved to be more resource intensive than actual trading. Thankfully, this only happens periodically and I’ve chosen to ignore Google’s recommendations so far without obvious negative consequences.
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I used a Windows Server 2016 image (my trading application runs on Windows only) and a 50GB persistent disk, which is the minimum required to run such an image. The Windows Server image accounts for the lion’s share of the cost—about $29 per month.
A scaled-down version running Linux (Ubuntu 17.04) with a small persistent disk runs for less than half this cost: 3.4 cents per hour or $24.67 per month with a continuous usage discount. Obviously there are huge savings if you can move away from Windows-based applications for your business infrastructure.
It is also worth mentioning that you are 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 your instance is actually running. Most niche providers of private trading servers will charge you at best for a full month even if you stop running your algorithm.
As you can see from the previous descriptions, the Google Cloud Platform includes many different services. Finding your way around the first time can be a little tricky. This part of the article is aimed at new GCP users and includes a walk-through for setting up and running trading algorithms in GCE.
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Go to https://cloud.google.com/ and log in to your Google account (or sign up if you don’t have one). Note the $300 in free credits you receive for use within the first 12 months.
Then fill the glasses for your new instance. The specs I used look like this (you can see the cost estimate on the right):
Since IB’s servers are located in the New York area, I used one of Google Cloud Platform’s US East-coast data centers. My algorithm is not latency-sensitive, but every little bit helps.
, the instance will take a few moments to spin up, then it will appear on your VM dashboard:
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Follow the prompts, and then copy and save the password. Now you are ready to connect your instance!
Once connected to the instance, it should look like a normal (albeit somewhat spartan) Windows desktop. To test that it can connect to Interactive Brokers (IB), we’re going to connect to IB’s test page. But first, we need to adjust some default internet settings. To do this, open Internet Explorer. select
Button and add https://www.interactivebrokers.com to the list of trusted sites. Then save the changes. Here is a scene from my example:
Now, connect to IB’s test page to check that your instance can communicate with IB’s servers. Simply navigate to https://www.interactivebrokers.com/cgi-bin/conn_test.pl in Internet Explorer. If the instance is connecting properly, you should see a page that looks like this:
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Now you can copy and paste your trading software and algorithm from your home computer or download any necessary software from the net and upload it to your instance. Note that to copy and paste from your home computer, you need to access the instance using Windows RDP, not Chrome RDP (this may change with future updates to Chrome RDP).
I was able to install R packages from a script due to restrictions on accessing certain parts of the Windows file structure. To fix 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 compute resources you’re not using. Do so from the VM dashboard:
As long as you don’t delete the instance, you can always resume it from where it stopped, meaning you don’t have to re-upload your software and scripts. You will not be billed for blocked instances.
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On the other hand, if you delete your instance and want to restart later, you’ll have to create a whole new instance and re-upload all your business infrastructure. That’s where pictures come in handy:
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