High-Frequency Trading Friday February 11th, 2022 – Posted in: Forex trading – Tags: hft, high frequency trading
High-Frequency Trading. HFT – High-frequency trading) was first applied to trading in financial markets in 1989. The main advantage of this method of trading is the speed of information processing. It’s no secret that a computer processor can do this in some applications much faster than a human. Not all areas of the processor surpass the human brain, but in trading, in most cases, it happens to be. The main idea for high-frequency trading was to use high-performance computers to make a profit from trading platforms. At present, this way of earning is available to almost everyone who has even a superficial knowledge of the work of trading platforms, processors, computers, price movements. In fact, in high-frequency trading, the one who has got quotes faster and has made transactions on the trading floor the fastest due to the computer characteristics of the equipment used in the work. The process is very fast and takes a fraction of a second. A person in such a contest is better not to participate – doomed in advance. High-frequency trading and algorithmic trading have some differences – with high-frequency one of the main properties affecting profit is the power of the equipment, the speed of the Internet, and proximity to the data center.
The idea of using high-performance computers in commerce was conceived by Stephen Sonson. In his time he worked with the determination of movement of market quotations for a few seconds. Steven Swanson, David Whitcomb, and Jim Hawkes have formed a trading automation company, Automated Trading Desk. The advantage at the time was too obvious, as the speed of trading was one second. At the same time, other bidders carried out their trades with the participation of the telephone. High-frequency trading entered markets en masse in the late nineties of the 20th century. After the SEC (US) Commission permitted electronic trading sites in 1998. At first, high-frequency deals lasted only a few seconds. In 2010, time was already measured in milliseconds, sometimes even faster. Information on the use of high-frequency trading in trading venues was rarely leaked outside the financial circle until the end of the two-thousandth years. One of the first articles to draw public attention to this type of trade was published in July 2009 in the newspaper New York Times.
The CFTC Commission proposed in 2011 the use of core features of HFT trading.
1. Systems implementing extremely fast placement, modification, or cancellation of orders less than five milliseconds with minimum delay.
2. The use of software or special algorithms to automate the decision-making mechanism, in the process of which all operations with orders are determined by the software and does not require the involvement of a person to perform any individual action.
3. Co-location, the location of servers near a trading floor to reduce the time it takes for information to pass, direct access to the trading floor, or a separate data-receiving channel that exchanges or other organizations can offer to reduce network delays.
4. Very limited lifetime deals.
5. Large cash turnover and/or a large proportion of placed applications about the order-to-trade ratios.
6. Placing a large volume of orders that are canceled within milliseconds.
7. The trading day ends with zero volume of positions, for the night large non-hedged positions are not held.
The profits from such superfast trading are derived from the margin between the purchase and sale of the trading instrument. High-frequency trading uses large volumes of transactions since the profit from one transaction is very small and the result can be achieved only by volumes. The basis of high-frequency trading based on the general principles of high-speed trading is trading within the day, without moving positions to the next trading day.
High-frequency trading algorithms
High-frequency trading algorithms. Algorithmic trading has become popular among hedge fund investors who trade their capital to extract speculative profits. Given that hedge funds in their reserves have serious enough capital, one can understand how high-frequency trading has become popular among major market players. High-frequency trading can be divided by the use of algorithms of trading strategies earnings on trading sites. Consider a few such trading algorithms.
Passive market making
Passive market making – high-frequency algorithm of work with liquidity. When using this strategy, a high-frequency algorithm makes a profit from the trading floor by trading inside the spread. The spread reflects the difference between the seller’s price and the buyer’s price of the selected trading instrument at the current time. There are situations when the spread expands – many buyers or sellers are participating in the trade. At the same time, there is a market-maker on the trading floors, whose duties include maintaining liquidity. The market-maker makes sure that the spread does not expand significantly because of the search for buyers or sellers who are not enough at the moment in the market. If a market maker cannot find enough buyers or sellers, it has to cover the supply or demand for the trading instrument. It is quite obvious that the market-maker does not always want to maintain the liquidity of the trading instrument on the trading floor by its means. To do this, it attracts third-party traders through commissions and liquidity rewards. Having understood this mechanism, owners of high-frequency trading systems are involved in maintaining the liquidity of trading instruments on trading platforms. Holders of high-frequency trading systems, with the help of their capacities, partially perform the function of market-maker to maintain liquidity. The market-maker drops some of its functions, not wanting to plug holes of the trading instrument once again, owners of high-frequency trading systems earn on narrowing the spread, buyers with sellers can easily make transactions with the trading instrument. In this case, it turns out that owners of high-frequency trading systems benefit from the trading floor and at the same time earn money.
High-frequency statistical arbitrage algorithm
The trading strategy of high-frequency statistical arbitrage is based on finding a correlation between the underlying asset and the derivatives. For example, algorithmic trading allows a high-frequency algorithm to look for a relationship between the futures of a trading instrument and the price of its underlying asset. The arbitrage high-frequency trading is mainly conducted by investment commercial banks. For this they have enough money, the software for high-frequency trading can afford to install a unique, specialists for work can hire quite qualified. In short, nothing is denied to achieve the objectives of profit-making from trading platforms.
Policy strategy or strategy for finding liquidity in high-frequency trading. This trading strategy is based on finding large bids in a glass of prices. The liquidity search is performed as follows: The holder of the high-frequency trading system launches an order to buy or sell at prices from a glass of orders. The purpose of this operation is to detect transactions with large volumes. When the price level present on the trading floor from a large player is revealed, the liquidity search algorithm starts working towards the found deal. The probability that a major player will discover a high-frequency liquidity search algorithm and want to get rid of it is extremely low. Such trading is non-hedged, meaning that risks from price movements in the opposite direction are assumed.
The structural strategy is aimed at exploiting possible vulnerabilities in the structure of the trading floor and getting the most needed data from the trading floor faster. In this approach, their servers are located as close as possible to the center of the trading floor or directly connected to the information. With such a scheme of work, players will earn on the trade if others will have a longer time to deliver information.
All strategies of high-frequency trading are possible due to the application of high-speed execution of trade orders. In modern conditions, the speed of trade is milliseconds.
Let’s look at how deals are concluded on trading floors. A trader or an algorithmic expert of the trader gives an electronic application for making a transaction on a chosen trading instrument to his broker via a trading platform. Orders for the purchase and sale of a trading instrument are compared at the trading floor. If the demand coincides with the supply, the deal is concluded. Trading applications can be seen by all bidders through feed technology. Feed is the information about a price cup bid that a special organization, such as Options Price Reporting Authority (OPRA), provides to bidders. A feed carries information about a standard set of data about a trading instrument and is delivered to a trading participant’s terminal via a special protocol, usually via Ethernet using UDP. In order to keep up with the competition and gain some advantage in high-frequency trading, it is essential to optimize data transmission to reduce time. The outlet becomes placing their servers next to the data center of the trading floor. In such a case, the time for taking the feed is reduced. At present, even this approach to the placement of its server is no longer a special advantage in high-frequency trading. Those wishing to obtain a clear advantage, negotiate with the trading floor itself, as a result, have the opportunity to get data a few milliseconds faster than the rest. Of course, such a service costs not little, at the expense of small traders are at a disadvantage. This can be critical for high-frequency trading.
Recently, there has been a decrease in the growth of high-frequency trading. There are reasons to believe that such trading could potentially lead to large losses due to malfunctions in a well-functioning system. Such cases have already been encountered in practice. Each trading algorithm, including high-frequency, has both its pros and cons. Even taking into account the modern possibilities of technology, this method of trade still has much to develop – every millisecond on the account. Programs for high-frequency trading. Also in some languages, such concepts as a robot for high-frequency trading are used.
There are two ways to do high-frequency trading. The first is with the help of a broker specialized in such trade. The second is to buy the necessary high-power equipment and install specialized software on it. There are several developers on the market. First of all, it is necessary to calculate in advance all the costs associated with this type of trade. Mandatory expenses include expenses for software and trading system. Developers provide a program without an algorithm, signal, or strategy. High-frequency trading has high costs, often prohibitive for the average trader – payment of broker services on connection to the server, high-speed Internet, expenses on the placement of trading server. Hedge funds typically use such services. Programs using high-frequency trading require fine-tuning to start trading.
High-frequency crypto trading.
HFT can be applied to crypto trading instruments. High-frequency trading opportunities for crypto trading are identical to those used in other trading venues. It should be borne in mind that the volatility of crypto instruments is higher compared to traditional ones. This creates new opportunities for trade and risks with it. High-frequency trading algorithms are primarily used for short-term trading and arbitrage in the crypto market. Currently, not much information on high-frequency crypto trading is presented, which can be concluded that this segment is just beginning to develop. In modern conditions, given the high cost of preparation for trade, individual software, the small number of suppliers of such software, expectations of the results greatly exceed the results. The returns are small if any. There are many ways of trading superior to high-frequency crypto trading in price-quality ratio.
Market making and arbitrage in different variations are most often used in the application of HFT. Over time, the number of strategies and trading systems increases, new developments have been identified that try not to advertise. There is no single definition of high-frequency transactions as positive or negative for the market. On the one hand, HFT provides liquidity and reduces trading costs, keeps markets stable, and on the other, high-frequency trading takes profits away from honest investors.
What is high-frequency trading for a simple trader?
It is obvious that without very powerful financial support, it is not necessary to start doing this. Comparing the pros and cons of this method of trading suggests that it can be applied by units from a circle of hedge funds or large banks. For the average trader, this method of trading looks initially too costly and unprofitable. There is a sufficient choice of methods to be used in trading venues with significantly lower costs for both setting up and servicing the trading process.
Compare high-frequency trading with trend scalping. HFT trading requires software investments, trading algorithms, installation of own servers close to the data center. When using trend scalping, you can use trade manually, create your expert or purchase a ready. Prices for the acquisition of a ready expert vary from a hundred dollars to ten thousand dollars depending on the success of the algorithm and marketing tactics of the seller. The proximity of the trading robot to the trading floor is not critical – you can choose a VPS server for fifteen dollars a month. The costs in the first case are disproportionately greater than in the second.
The use of HFT for market-makers brings income to the owner of this type of trade through bonuses and commissions, which are profit at trade. At the same time, the trader with a robot based on trend scalping bears losses on commissions and spreads. From this point of view, high-frequency trading is preferred. Using HFT requires large capital in trading, as the profit from single transactions is very small and can only be reached by the number of transactions and large volumes of trade, for trend scalping investment in a deposit for trading can be much more modest and as a result, a commensurate profit. One can infer the pros and cons for each method of trade, the decision on what to use in the trade is left to the person making the choice. In my view, for people without hedge funds or investment banks, the choice is clear on a less costly method of trading.
With shortcomings such as heavy investment in the pre-trade and trading stages, and long-standing, loss-making disruptions, high-frequency trading is not a thing of the past. It is logical to assume that professionals and investors using such trading see the future in HFT. Let us assess where the development of such trade technology can go. Based on the strengths of this trading method – the development of the speed of the “hardware” used, the software gives an advantage to those who use the most advanced technology to date. At the moment, based on the current HFT publications, the system of forecasting the change of quotations based on the data centers is not yet used. In other words, at the outlet of the server of the trading floor, there are servers of traders who try their best before others to catch precious information and on its basis to make transactions. And at the entrance to the server of the trading floor like no one stands. This may only seem so, but there is no public coverage of such a method of trading yet. Of course, there is a possibility that the incoming information will not affect the quotes or at all, will reflect the sign directly opposite the expected movement. But these points should be taken into account by the trading system and the established rules of wealth management.
Short-term forecast strategy using high-frequency trading
In this way, a strategy for the immediate future can be formulated. Short-term forecast strategy using high-frequency trading. The essence of the algorithm consists in intercepting the information received by the data centers of trading platforms, processing it with artificial intelligence outside the trading platform, transmitting the forecast of quotations to the trading server. The indisputable advantage in using such a trading scheme will be that when it is used, the time of receipt of the information on quotes from the trading platform can be equal to zero or even have quoted before they are processed and issued by the trading platform. In the race to speed, such a scheme leaves far behind existing, sitting at the exit of the trading floor. For high-frequency trading, time is a critical factor, so if short-term forecasting strategies become practical, the current well-heeled and well-heeled will take a back seat. Perhaps even quite off the mark, supplanted by more advanced technology.
Consider what is needed for a short-term forecast strategy from a technical perspective. Input information to the shopping center date on which quotes are generated. Do not guess long before the appearance of interesting information and its transformation into quotes. For a more reliable version of trading, it is enough to take it at the entrance to the data center. As an option, it can be ordered from the same sources used by the trading floor. The task is quite logical and executable at the modern level of technology.
Using Trained Artificial Intelligence for Short-Termforecasting of changes of quotations. With the development of artificial intelligence technology, the task for him is also quite possible. The only thing left is to train him on the history, how the incoming information affects the change of quotes. This step of the trading strategy under consideration will become one of the main ones, so it is not worth saving for it. Here it is better to focus the main investments in the algorithm of learning artificial intelligence, to provide it with the best “hardware”. In general, whatever the “brain” of the trading system needs nothing – it will work out the return in the form of profits earned on the trading floor. The task is also feasible today.
A trading robot working on the weekend quotes of the trading floor. A long time has passed, there’s not much to come up with. Perhaps, to implement a faster variant of trade, it is worth combining artificial intelligence, engaged in short-term forecast trading robots. I.e. to put in the duties of artificial intelligence to carry out trading operations on the account. This point is better considered in terms of the performance of the whole system. Perhaps the best time will come when functions are split between artificial intelligence and the merchant robot. This task has already been solved in the past, it remains to optimize it with the addition of an artificial intelligence function.
As a result, it turns out that all the problems can already be solved at the modern stage of technology development. Perhaps someone has already decided, just does not advertise for obvious reasons. We will have to share money with emerging competitors. The competition will inevitably emerge. How strong depends on the “brain” used in trade strategy. It’s no secret that artificial intelligence with the same input can produce different results at the output depending on what data it was learning from. There is a problem of overlearning and under learning artificial intelligence. The company with the most accurate forecast of short-term changes of quotes will be the one with the most profit.
There is a discrepancy depending on the final prices from the incoming data. For example, the so-called “dollar smile”, consists of certain illogical behavior of US dollar quotes from the incoming data from the economy. When very good data comes in – the dollar rises logically when very bad data comes in – the dollar rises illogically. At this point uses its property of “security currency”, a “safe haven”. In the economy’s intermediate values, the US currency could fall against its competitors. The explanation for this behavior is usually that investors awaken interest in risk at such moments. Such claims may be well-founded to criticize, but the fact remains that the “dollar smile” exists and grows on very bad economic data from the US economy.
The more accurately used in a short-term forecast trading strategy, artificial intelligence will predict the behavior of quotes, the greater the profit the owner of such a strategy may have. The dependence of such an algorithm directly on the properties of used AI is not in doubt. Adding artificial intelligence to existing high-frequency trading algorithms will not significantly increase already very large costs. But the economic impact could be greater than expected, and the current stagnation in HFT technology is giving way to rapid advances in artificial intelligence.
The time is ripe for the development of artificial intelligence technologies. The scope is constantly being reviewed and expanded. In addition to the financial sector, this technology has spread into almost every area of daily life and remains there, occupying new niches or displacing obsolete technologies. No exception and trading. The first use of AI in trading was not so successful that one could say, “How did we live without it before?” This may be due to problems with AI itself, such as overtraining or undertraining. As always, the optimal golden mean lies between such extremes, it must be found and based on historical data make the most accurate forecasts for the future. At least, as in high-frequency trading – at intervals calculated by milliseconds. One of the main properties of artificial intelligence from different developers is different output results for the same input. This is absolutely normal. In relation to the market, have the right to the existence of different AI, with different algorithms, from different developers, which in the end trade gives the maximum positive result with minimal deposit slips. All organizations using high-frequency trading will come to this, the sooner it will happen – the less likely it is to remain in the role of laggards. And this option is clearly not suitable for players focusing on HFT – technology.