Role Of Co-Location Servers In Algorithmic Trading

The software landscape for algorithmic trading has now been surveyed. It is now time to turn attention towards implementation of the hardware that will execute our strategies.

A retail trader will likely be executing their strategy from home during market hours, turning on their PC, connecting to the brokerage, updating their market software and then allowing the algorithm to execute automatically during the day. Conversely, a professional quant fund with significant assets under management (AUM) will have a dedicated exchange-colocated server infrastructure in order to reduce latency as far as possible to execute their high speed strategies.

1.) Home Desktop:

The simplest approach to hardware deployment is simply to carry out an algorithmic strategy with a home desktop computer connected to the brokerage via a broadband (or similar) connection.

While this approach is straightforward to get started it does suffers from many drawbacks. Primarily, the desktop machine is subject to power failure, unless backed up by a UPS. In addition, a home internet connection is also at the mercy of the ISP. Power loss or internet connectivity failure could occur at a crucial moment in trading, leaving the algorithmic trader with open positions that are unable to be closed.

Secondly, a desktop machine must occasionally be restarted, often due to the reliability of the operating system. This means that the strategy suffers from a degree of indirect manual intervention. If this occurs outside of trading hours the problem is mitigated. However, if a computer needs a restart during trading hours the problem is similar to a power loss. Unclosed positions may still be subject to risk.

Component failure also leads to the same set of “downtime” problems. A failure in the hard disk, monitor or motherboard often occurs at precisely the wrong time. For all of these reasons I hesitate to recommend a home desktop approach to algorithmic trading. If you do decide to pursue this approach, make sure to have both a backup computer AND a backup internet connection (e.g. a 3G dongle) that you can use to close out positions under a downtime situation.

2.) VPS:

The next level up from a home desktop is to make use of a virtual private server (VPS). A VPS is a remote server system often marketed as a “cloud” service. They are far cheaper than a corresponding dedicated server, since a VPS is actually a partition of a much larger server, with a virtual isolated operating system environment solely available to you. CPU load is shared between multiple servers and a portion of the systems RAM is allocated to the VPS.

Common VPS providers include Amazon EC2 and Rackspace Cloud. They provide entrylevel systems with low RAM and basic CPU usage, through to enterprise-ready high RAM, high CPU servers. For the majority of algorithmic retail traders, the entry level systems suffice for low-frequency intraday or interday strategies and smaller historical data databases.

The benefits of a VPS-based system include 24/7 availability (with a certain realistic downtime!), more robust monitoring capabilities, easy “plugins” for additional services, like file storage or managed databases and a flexible architecture. The drawbacks include expense as the system grows, since dedicated hardware becomes far cheaper per performance, assuming colocation away from an exchange, as well as handling failure scenarios (i.e. by creating a second identical VPS, for instance).

In addition, latency is not always improved by choosing a VPS/cloud provider. Your home location may be closer to a particular financial exchange than the data centres of your cloud provider. This is somewhat mitigated by choosing a firm that provide VPS geared specifically for algorithmic trading which are located at or near exchanges, however these will likely cost more than a “traditional” VPS provider such as Amazon or Rackspace.

3.) Exchange:

In order to get the best latency minimisation and fastest systems, it is necessary to colocate a dedicated server (or set of servers) directly at the exchange data centre. This is a prohibitively expensive option for nearly all retail algorithmic traders (unless they’re very well capitalised). It is really the domain of the professional quantitative fund or brokerage.

Algorithmic trading co-location is the practice of putting trading servers in close proximity to the exchange’s data centers. For High-frequency trading (HFT) and latency-sensitive methods, this greatly lowers network latency, enabling faster order execution.