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 have similarities and tend to go together.
The core difference between them is that algorithmic trading is designed for the long-term, while high-frequency trading (HFT) allows one to buy and sell at a very fast rate. The use of these methods became very common since they beat the human capacity making it a far superior option.
The electronic style of trading first surfaced in the seventies with the creation of Nasdaq. It was a system that used an 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 data like a computer can. This served as an inspiration for automated trading hardware and software tools development.
The innovative technology made the whole trading process cheaper and less cumbersome. Today, even people who are not trained professionals can be traders.
The Difference between HFT and Algorithmic Trading
Algorithmic, high-frequency, algo-, or automated trading are terms that often show up in trading-related articles. It’s easy to get confused and find yourself lost in all the definitions. This happens because both — the technological and financial landscapes have many nuances, and slight differences in operations create new terms.
Moreover, in some cases, certain phrases end up being used interchangeably and adding to the disorientation. Hence, to help you better understand this area of finance, we’re going to discuss each trading method in more detail.
Algorithm trading is also known as algo-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 pre-programmed trading instructions. These instructions, known as an execution algorithm, send child orders (small slices) to make up for larger orders that are too big to send at once.
Slicing into small orders helps 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 algo-trading is not just to profit by trading but to save costs, minimize market impact, and the execution risk of a trading order. Traders don’t need to watch stocks or send slices manually.
Benefits of Algorithmic Trading
Now that you know what algorithmic stock trading entails, you’re likely already guessing the advantages it can bring. Just to be sure, let’s go over some of the main ones.
Better prices. With algo-trading, trades are executed at the best possible price thanks to being instantly timed to avoid large price fluctuations.
Improved accuracy. If a computer executes a trade instead of a person, there’s a lower likelihood of a mistake being made. The human factor often affects the levels of accuracy. Hence, when a computer algorithm is involved, precision is increased.
Increased speed. Since algorithms are pre-written and executed automatically, the speed at which trades are carried out is significantly boosted.
Lower transaction costs. Thanks to traders not needing to monitor the markets as closely and execution being carried out without their involvement, their time is freed up. Thus, transaction costs are reduced and traders can engage in other activities.
Algorithmic Trading Strategies
Algorithmic trading can be approached in a few ways. Everything depends on the end goal and preferences. These are the nine most common algo-trading strategies:
- Arbitrage leveraging
- Index fund rebalancing
- Mathematical model-based
- Volume-weighted average pricing
- Time-weighted average pricing
- Mean reversion
- Percentage of volume
- Implementation shortfall
Difference Between Algorithmic Trading and Automated Trading
Before we move on to high-frequency trading, we have to talk about one more term that you’ll likely see come up.
At the beginning of today’s piece, we mentioned that some trading-related terms are used interchangeably. In the case of algorithmic and automated trading this is also true. Some traders consider the two as the same, but we believe there is one key difference.
As discussed, algorithmic trading is used to buy and sell large amounts of assets while minimizing transaction costs and increasing speed. These systems only execute the provided orders. Conversely, automated trading software involves the complete automation of the trading process. This means that even the buying and selling decisions are automatic.
So, make sure you remember this detail when diving deeper into the subject of financial trading.
High-Frequency Trading is a subset of algo-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 this trading approach a market maker.
Learn more: In Pursuit of Ultra-Low Latency: FPGA in High-Frequency Trading
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.
Benefits of High-Frequency Trading
High-frequency trading uses complex algorithms to spot emerging trends in milliseconds. As you can imagine, this has some specific benefits.
Large wins on small price changes. It helps traders earn significant profits even from minor price fluctuations. With these tools, financial institutions can attain significant bid-ask spread returns.
Expanded opportunities. Since algorithms are able to scan multiple markets and stock exchanges, traders are able to discover more opportunities. For example, arbitraging the same asset with minor price differences on two separate exchanges.
Higher market liquidity. By increasing competition on the market due to larger volumes and speed of execution, high-frequency trading makes markets more price-efficient. Consequently, market risk declines since there’s always someone to buy what you’re selling and vice versa.
High-Frequency Trading Strategies
High-frequency trading is a very complex process which is why it’s usually only leveraged by large institutions like proprietary firms, investment banks, and hedge funds. These organizations rely on various money-earning strategies, some more controversial than others.
Either way, it’s worth quickly mentioning the most popular ones:
- Liquidity provision
- Statistical arbitrage
- Price movement ignition
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 quickly process the information, make a decision, and take action. The need for such a speedy process of a transaction is the hand-in-glove relationship between these approaches.
The projection of algo-trading was by sequential processing, but the application of parallel computing and neural networking is a promising step. Processing via several nodes, as is in machine learning, with several inputs and outputs perfectly aligns with parallel computing. Thus, it is more likely to be parallel instead of sequential.
Another factor of importance is using Big Data and data science solutions. With the volume of transactions there is tons of market data 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 solutions.
Both trading approaches 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
Software for Algorithmic Trading
You now know the basics of algorithmic trading and might be getting curious about leveraging it for your business. You can do that with the help of algorithmic trading software. It is a tool that will automatically execute your trades once you’ve programmed it in a way that suits your goals.
Overall, there are two ways to get your hands on these types of solutions. Either buy a ready-made product or build your own.
Currently, various trading solutions exist. All of them automate your trades through the use of a computer program that identifies a profitable opportunity and places an order in a much faster way than any human could.
The good thing about pre-built trading products and software tools is that you get quick access and can begin using them almost immediately. Before starting your hunt for the most fitting out-of-the-box system, remember that such solutions can be hard to customize.
Cost of Custom Algorithmic Trading System Development
Should you choose to go the custom software development route, you’ll naturally wonder about its associated costs. However, first, you have to understand that bespoke solutions expand your capabilities and can be built to cater to your unique business goals. Sure, they take time, effort, and deep technical expertise to be created. It doesn’t mean you shouldn’t look into them.
If your company doesn’t have enough in-house expertise to develop the algorithmic software that you need — consider looking for experienced partners. We’ll be honest. Custom development can seem costly at first. Especially, when you see the price ranging between $5,000 to $1,000,000. However, when you begin calculating all the future benefits it can bring down the line, you might be less skeptical.
Bespoke trading tools do have a wide budget range, but only because there are a lot of factors affecting custom software development costs. All of them should be considered when estimating how much you might have to invest.
If you want to get more accurate approximations — reach out to the vendors you’re considering and ask them for a quote. That way, you’ll be able to plan your spending better.
The Reality of Today’s Trading Markets
Estimated US stock trade placed by computers is at about 75% and on the increase constantly. The use of trading solutions is making the market more liquid. This is helping investors make quick investments and earn enough to execute their trades fully.
Despite the type of trading you choose you cannot take away the use of algorithms. It’s an established factor in today’s market. The application of trading solutions has replaced the stress of noisy trading, the 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.
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 are projected to reach $1,426,993m in 2021.
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 Chicago’s top development firm specializing in capital markets and financial services sectors, Velvetech has 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.