Entry 10 - Algorithmic Trading

http://www.businessinsider.com/heres-how-you-set-up-your-own-high-frequency-trading-operation-2010-6?op=1/#-test-your-setup-and-make-sure-everything-is-functioning-properly-both-on-your-end-and-any-third-party-softwarehardware-vendors-end-10

Topic:

Since the financial crisis in 2008, new technology has changed the way many trades (see terminology for specific definition) are executed. Rather than having human beings responsible for executing each trade, complex algorithms have been created that are able to execute trades much more quickly. These programs are able to identify opportunities that may only be available for a fraction of a second and complete trades in time to capitalize on these opportunities and potentially make millions in a matter of seconds. However, it is incredibly important that the companies and individuals using these algorithms have a proper understanding of computer science because while there is the potential to make lots of money, there is also the possibility of losing everything in a matter of seconds. A specific example of this happening was when a program at Knight Capital that was supposedly deactivated "took off on it's own" and sent the entire New York Stock Exchange spiraling out of control[4]. Knight was losing more than $10,000,000 a minute, which if left unresolved would leave the firm with no funds in under an hour[5]. Fortunately they were able to find the code responsible for the problem and remove it within 45 minutes. 

Terminology:

Algorithmic Trading - the process of using computers programmed to follow a defined set of instructions for placing a trade in order to generate profits at a speed and frequency that is impossible for a human trader. The defined sets of rules are based on timing, price, quantity or any mathematical model[1].

High Frequency Trading (HFT) - High-frequency trading (HFT) is a program trading platform that uses powerful computers to transact a large number of orders at very fast speeds. It uses complex algorithms to analyze multiple markets and execute orders based on market conditions. Typically, the traders with the fastest execution speeds are more profitable than traders with slower execution speeds[2].

Trade - In financial markets, trading refers to the buying and selling of securities, such as the purchase of stock on the floor of the New York Stock Exchange[3].

Relation to Computer Science:

This topic relates to computer science in several different ways. One of the most obvious being the actual algorithms used in the trading process. Another important relation to computer science has to do with the hardware and software involved. Because this entire process happens so quickly, it is important to have your server as close as possible to the exchange. One of the ways that this is generally done is by co-location of servers within data centers[6]. In doing this, one can greatly increase the speed of orders being executed. Related to what we have learned in class, proper testing is needed to ensure all algorithms are functioning properly before they can be put into us. Many firms typically apply the algorithms they are testing to historical market data in order to see how the program deals with different situations. 

Works Cited:

[1] - Seth, Shobhit. "Basics of Algorithmic Trading: Concepts and Examples." Investopedia. N.p., 26 Jan. 2016. Web. 14 Apr. 2017.

[2] - Staff, Investopedia. "High-Frequency Trading - HFT." Investopedia. N.p., 23 July 2009. Web. 14 Apr. 2017.

[3] - Staff, Investopedia. "Trade." Investopedia. N.p., 02 Mar. 2016. Web. 14 Apr. 2017.
[4] - Baumann, Nick, Bryan Schatz, Pema Levy, and Tim Murphy. "Too Fast to Fail: How High-speed Trading Makes Wall Street Disasters Worse." Mother Jones. N.p., Jan. 2013. Web. 14 Apr. 2017. 
[5] - Ibid.
[6] - Veneziani, Vince. "Here's How You Set Up Your Own High-Frequency Trading Operation." Business Insider. Business Insider, 08 June 2010. Web. 14 Apr. 2017.