The transaction of assets, with the time of possession, crunched up to a microsecond is a close to impossible task for a human, but this became possible with high-frequency and algorithmic trading. In the course of the last decade, algorithmic trading (AT) and high-frequency trading (HFT) have come to dominate the trading world. 

Electronic trading first surfaced in the 1970s with the creation of NASDAQ and then in the early 90s with the implementation of Globex by the Chicago Mercantile Exchange. This system traded assets such as treasuries, foreign exchange, and commodities. It's efficiency in execution and lower prices compelled other exchanges to become electronic.

The trading process becoming cheaper and less cumbersome was the inspiration for high frequency and algorithmic trading. Thus the use of these methods became rampant leaving human trading behind as the traditional method of trading, thereby proving to be a superior option.

What is HFT? 

High frequency and algorithmic trading in finance, is a method of trading that uses automated electronic systems and computer programs to place several trades or orders in a fraction of a second. High-frequency trading (HFT) is an automated trading platform that large institutions like investment banks, hedge funds, and institutional investors use to transact a large number of orders at extremely high speeds. 

HFT systems operate using algorithms (a defined set of instructions based on a mathematical model) to scan markets and exchanges in seconds thus allowing traders to execute millions of orders. Algorithmic trading (AT), also known as automated trading, black-box trading, or algo-trading is designed for long-term trading, whereas HFT is a subset of AT and enables buying and selling at a very fast rate.

How does it work?

A computer program monitors the stock price and other indicators necessary to determine trade orders. It places the buy and sell orders automatically when the defined conditions are met. In such cases, the trader does not need to monitor live prices and graphs or place orders manually. The algorithmic trading system does this by correctly identifying trading opportunities.

HFT Structure

Algorithms work as middlemen between buyers and sellers with HFT and Ultra HFT as tools for traders to capitalize on the price discrepancies that might appear or exist only for a minuscule period. Computer-assisted AT manages large-sized orders, usually placed by pension funds or insurance companies that can have a severe impact on stock price levels. AT splits large-sized orders into small-sized ones to reduce the price impact and offer traders some price advantage. These algorithms based on pricing, quantity, and timing, detect and identify trading signals, and appropriate price levels to place trade orders, in suitable situations. 

HFT is an extension of AT. It helps traders in executing and sending small-sized orders to the market at high speeds, often in milliseconds or microseconds. Therefore, it operates by using complex algorithms and technological tools to manage small scale trade orders by sending them to markets or exchanges at a great speed.

HFT algorithms consist of two-sided order placements (buy-low and sell-high) and aim to benefit from bid-ask spreads. These algorithms send multiple small-sized orders, analyze the patterns and time taken in trade execution in an attempt to sense any pending large-sized orders. In case they sense any opportunity, HFT algorithms try to capitalize on large pending orders by adjusting prices to cover them and make profits.

Benefits

The major characteristics of HFT are high speed, a huge turnover rate, co-location, and high order-to-order ratios. Bid-ask spreads reduce significantly due to HFT trading, which makes markets more efficient. Other than this HFT algorithm has massive profit potential as it exploits market conditions that can't be detected by the human eye in a short duration.

Other than profit opportunities for the trader, it also renders markets more liquid and makes trading more systematic by ruling out the impact of human emotions on trading activities. HFT creates high liquidity and eases the effects of market fragmentation. It also aids in determining the price level as it is based on a large number of orders. 

Another important point is using Big Data. Since there are numerous transactions that take place, there is enough data and information for analyzing if a deal is a good bet or not. The efficiency of trading solutions will naturally increase with more data and will thus create a more efficient market. The data collected will also aid in machine learning.

HFT Infrastructure Needs

HFT participants will need the following infrastructure:

1. High-speed computers with expensive hardware upgrades
2. Co-location, a facility that places trading computers as close as possible to the exchange servers, to reduce time delays
3. Real-time data feeds, which are required to prevent any type of delays with regards to time (even a microsecond) that may impact profits
4. Computer algorithms, which are the heart of AT and HFT

Critiques of HFT

HFT has met with some harsh criticism as it replaced a lot of broker-dealers, it used mathematical models and algorithms to make key decisions, removing human intuition, interaction, and decision out of the equation. Decisions happen within seconds with such technology and this could result in big market moves which could turn out to be disastrous.

An example of the same is the DJIA, Dow Jones Industrial Average drop in 2010 when it declined 1000 points and dropped by 10% in 20 minutes accruing to a massive order that triggered a sell-off for the crash. Another critique of HFT is that large firms and institutions use this technology to their advantage in the open market against smaller investors. Lastly, the liquidity provided by HFT is "ghost liquidity" as it lasts for seconds and is gone within no time, preventing traders from being able to trade this liquidity.

Conclusion

Despite its shortcomings, the estimated US stock trade placed by computers is about 75% and on the increase constantly. High-frequency and algorithmic trading have massive potential and have led to progressive and successful trading methods in trading firms. AT and HFT are classic examples of the growth and development in computer algorithms that have outdone regulatory regimes.