The use of High-Frequency and Algorithmic trading in finance, also known as algo-trading, is the application of automated electronic systems for trading strategy execution. They are slightly different terms but similar and go together.

The core difference between them is that algorithmic trading is designed for long-term trading, while high-frequency trading (HFT) allows to buy and sell at a very fast rate. The use of these methods became very common since they beat the human trading capacity making it a far superior option.

US Stock Trading

The electronic style trading first surfaced in the seventies with the creation of Nasdaq. It was a system that used electronic bulletin board without computer commands.

Later in 1987, the Chicago Mercantile Exchange implemented a more widespread platform, Globex, that became fully established in 1992. This system traded several assets such as treasuries, foreign exchange, and commodities.

Lower prices and faster execution time drove other exchanges to become electronic.

Humans can’t compute giant volumes of trading data like a computer can. This lead to the inspiration for high-frequency and algorithmic trading, just like every other drive for automation such as improved service, cost effect, and speed in execution time.

It made the whole trading process to be cheaper and less cumbersome. Today, even people who are not trained professionals can be traders.

The Difference between HFT and Algorithmic Trading

The Difference between HFT and Algorithmic Trading

High-Frequency Trading

High-Frequency Trading is a subset of algorithmic trading. Its major characteristics are high speed, a huge turnover rate, co-location, and high order-to-order ratios. It operates by using complex algorithms and sophisticated technological tools to trade securities.

HFT solutions manage small scale trade orders sending them to a market or exchange at great speed. It benefits from bid-ask spreads. The height of the speed involved in the transaction process makes high-frequency trading a market maker.

Real-time data feeds are needed to reduce microseconds delay and avoid profit loss. Usually, the latency should be between 300 – 800 nanoseconds. This is achieved with a high-performance software, low-latency networks, and FPGA-based hardware acceleration.

Opportunities are noted through sensing large size orders that are pending by placing small-sized multiple orders and analyzing the pending and execution time. The successfully noted opportunities in the form of pending orders are then capitalized by adjusting prices to cover them and make profits.

Learn more: In Pursuit of Ultra-Low Latency: FPGA in High-Frequency Trading

Algorithmic Trading

Algorithm trading is also known as automated trading or black box trading. It’s a trading solution that uses coded sets of algorithms and execution strategies to submit orders to a market or exchange automatically after a technical analysis.

In other words, algorithm trading involves the use of predefined sets of variables such as price, time, and volume by automated pre-programmed trading instructions. These instructions, known as an execution algorithm, send child orders (small slices) to make up for larger orders too big to send at once.

Slicing into small orders helps to attain good pricing within a specified time. Reduction in the size of orders is good for an aggressive market. Thus, making algorithmic trading widely applicable to trading with high market volumes such as mutual funds, investment banks, hedge funds, etc.

The main objective of algorithmic trading is not just to profit by trading but to save cost, minimize market impact and the execution risk of a trading order. Traders don’t need to watch stocks or send slices manually. Algorithmic trading enables the execution strategies of a seller side to get a good order and monitor the trade chart simultaneously.

Potentials of High-Frequency and Algorithmic Trading

Potentials of High-Frequency and Algorithmic Trading

To be able to transact assets with the time of possession narrowed to one microsecond is a great task for a human, even via the command of a button. The human neurons are not designed to navigate signals at such speed and yet process the information, decide, and act. The need for such a speedy process of a transaction is the hand in glove relationship between high-frequency and algorithmic trading.

The projection of algorithmic trading was by sequential processing, but the application of parallel computing and neural networking is a promising step. Processing via several nodes, as it is in machine learning, with several inputs and outputs perfectly aligns with parallel computing. Thus, algorithmic trading is more likely to be parallel instead of sequential.

Another factor of importance is using Big Data. With the volume of transactions there are tons of information for analysis to decide a good or bad deal.

The efficiency of the trading solutions will naturally increase with more data and, as a result, create a more efficient market. The data harvested will also be an advantage for machine learning.

Both high-frequency and algorithmic trading are fit for automation by AI. Most trading platforms are autonomous already, but there is always room for improvement. For instance, systems that analyze business information in the form of news will be a great trading tool.

AI will consolidate trading by harnessing and analyzing market patterns and behavior from the past. It will also be able to learn and adapt itself as the market conditions change, thereby creating a perfect tool for trading.

Case study: Intelligent Trading Platform

The Reality of Today’s Trading Markets

Estimated US stock trade placed by computers are about that 75% and on the increase constantly. The use of high-frequency and algorithmic trading are making the market more liquid. This is helping investors to make quick investments and earn enough to execute their trades fully.

Be it high-frequency or algorithmic trading you cannot take away the use of algorithms. It’s an established factor in today’s market. The use of trading solutions have replaced the stress of noisy trading, use of paper documents and the old trading pits at stock exchanges.

Humans are the creators of algorithms and since these instructions have a defined purpose they have to be constantly perfected. Therefore, there is an increased urge to use compliance solutions to monitor trading algorithms.

The use of high-frequency and algorithmic trading has also given birth to robo-advisers. Robo-advisory solutions are substituting for the human-curated data. They autonomously generate different investment strategies and send them as a data feed to investors.

Assets under management in the Robo-Advisors segment amounts to US$980,541m in 2019.


High-frequency and algorithmic trading have unlimited possibilities and rapid progress for the success of any trading firm, hence full automation should be a special consideration.

As a Chicago’s top development firm specialized in capital markets and financial services sectors, Velvetech has a strong expertise in trading algorithms, financial models, trading platforms, FPGA-based solutions for HFT, and more. Contact us today for professional opinions and consultations from our experts.

Get the conversation started!

Discover how Velvetech can help your project take off today.