HFT in Crypto
What is HFT?
High-frequency trading (HFT) was initially developed in 1983 after NASDAQ introduced a purely electronic form of trading. By the advancements of computer processing power in the 21 century, the market witnessed more competition on the development of HFT strategies. More and more HFT traders started executing their trades based on microseconds differences.
HFT started to draw massive public attention after the U.S. May 6, 2010 flash crash and HFT played an important role in that market event. The flash crash and the discussions on flash orders relate to the U.S. equity markets and the National Market System in there. In Europe, there is a more flexible best execution regime and share-by-share volatility. A safeguards regime has been in place for two decades where there have been no market quality problems related to HFT documented so far.
HFT is basically a technical means to implement established trading strategies. HFT is not a trading strategy as such but applies the latest technological advances in market access, market data access and order routing to maximize the returns of established trading strategies. Most of HFT based strategies contributes to market liquidity (market making strategies) or to price discovery and market efficiency (arbitrage strategies). We will cover some of these strategies later in this article.
What are the characteristics of HFT?
Most of HFT operations but not all of them have some mutually shared characteristics with algorithmic trading. Most of them have a pre-defined trading decision programmed in them. Additionally, HFT is known to be used by professional traders who have a comprehensive understanding of the market’s behaviours and appropriately respond to them. Moreover, HFTs are mainly using real-time data feed through API integrations and automate the process of order submission and order management. We could add the use of direct to market access and no human intervention.
Furthermore, there are more attributes to HFTs that are more exclusive to it, such as a high number of orders with rapid order cancellations in order to adjust to best-suited market positions. Low latency execution and a sophisticated high-speed connection to the relevant marketplaces are also an important factor for HFTs.
HFT in Crypto
Due to maturity and infrastructural improvements in the crypto trading assets in cryptocurrency exchanges, the emergence of HFT in crypto was inevitable. According to Financial Times, a few leading high-frequency trading houses, including DRW, Jump Trading, DV Trading, and Hehmeyer are now trading in the crypto asset markets. That also caused a few newly opened hedge funds specializing in crypto trading which utilize algorithmic trading to make profits in the crypto markets.
Considering that now around 150 hedge funds are actively trading across different cryptocurrencies, Algorithmic Trading (AT) and HFT are becoming more and more relevant.
Furthermore, the access to high-frequency trading strategies in the crypto world is unprecedented, individual and institutional investors both have a range of Crypto trading bots with varying degrees of sophistication. Cryptocurrency trading bots allow traders to build and customize trading strategies based on a specific predefined set of parameters and then with full automation, execute their trades.
What are the pros of HFT?
By far, the most appealing advantage of HFT is that it is an effective way to add value to the market liquidity. For instance, trade exchanges and institutions offer incentivising programs to add liquidity to the market, which is performed by electronic liquidity capitalized by the use of HFT.
HFT takes advantage of both the computing power in order to move as fast as possible and benefit from arbitrage, as well as supply and demand. Let’s name some of the well-known pros of HFTs:
While the types of HFT strategies are too diverse and opaque to name them all, some of these strategies are well known and not necessarily new to the markets. The notion of HFT often relates to traditional trading strategies that use the possibilities provided by state-of-the-art IT. HFT is a means to employ specific trading strategies rather than a trading strategy in itself. Therefore, instead of trying to assess HFT as such, it is necessary to have a close look at the individual strategies that use HFT technologies.
The term market making refers to the strategy of quoting a simultaneous and constant buy and sell limit order (also known as quoting). This operation is performed for a financial instrument in order to provide liquidity for that instrument but also make some profit from the bid-ask spread. Market making can be either imposed by mandatory requirements set by market operators/regulators for entities covering that role or some may work these algorithms out with different motivations. Some of the HFT liquidity providers could have two basic sources of revenues: (i) They provide markets with liquidity and earn the spread between the bid and ask limits and (ii) trading venues incentivize these liquidity providers by granting rebates or reduced transaction fees in order to increase market quality and attractiveness.
Opportunities to conduct arbitrage strategies frequently exist only for very brief periods (fractions of a second). In order to catch those opportunities robust and fast processing computers are needed to scan the markets for such short-lived possibilities, arbitrage has become a major strategy applied by HFTs.
These HFT strategies conduct arbitrage in the same way as their traditional counterparts. They leverage state of the art technology to profit from small and short-lived discrepancies between securities. The following types of arbitrage are not limited to the HFT, but are conducted by non-automated market participants as well. Since arbitrageurs react to existing inefficiencies, they are mainly takers of liquidity.
In fragmented markets, real-time investigation of different accessible order execution venues and of available order limits and quotes can improve execution final results. Smart order routing (SOR) systems enable traders to access multiple liquidity pools simultaneously to identify the best order routing destination and to optimize order execution. They scan pre-defined markets in real-time to determine the best bid and offer quotes for a specific order, thereby achieving the best price.
The smart order router selects the appropriate execution venue on a dynamic basis, i.e. real-time market data feeds. Such provisions support dynamically allocated orders to the execution venue offering the best conditions at the time of order entry including or excluding explicit transaction costs and/or other factors.
Controversial or fraudulent trading activities using HFT
Let’s address the elephant in the room, HFTs profits are derived from a limited set of activities. These include market making activities, collecting liquidity rebates, successfully performing statistical pattern detection and potentially manipulating markets. Let’s name some of the ways market manipulators and how with the use of HFT could make profits in the market.
Spoofing is the use of HFT algorithms in order to create a false presence of high or low demand in the market. Since a big number of orders can be placed in a short timeframe, traders using the technology can create a sense of false demand and use it to their advantage. For example, a spoofer could place one large order and cause a change in the prices. Then, the spoofer places a different order taking advantage of the price change. The original order which caused the false impression of demand is subsequently cancelled, though not before the spoofer has made a profit.
Like spoofing, quote stuffing is also considered to be a type of market manipulation. Quote stuffing happens when traders flood the market with a large number of buy and sell orders. This overwhelms the market and slows down rival traders.
In an event back in 2016, a British futures trader, Navinder Sarao was convicted for spoofing charges in U.S. federal court in Chicago after losing an extradition battle. In January 2020 he was sentenced to a year of home detention after providing what the judge called “extraordinary cooperation” to prosecutors. Sarao had been arrested in suburban London after the U.S. authorities said his activities had contributed to the flash crash of May 2010, when almost $1 trillion was temporarily wiped out in the U.S. stock market.
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