New alternatives to software for high-frequency trading

High-frequency trading (HFT) appeared to be poised to dominate the market for a while entirely. Approximately “half of U.S. stock market trading activity on an annual basis since the global financial crisis (GFC) a decade ago,” according to the global investment firm Franklin Templeton in 2019, was accounted for by HFT.

After reaching its peak in 2009, when high-frequency traders traded over 3.25 billion shares daily, this may indicate a leveling rate for using high-frequency trading software. 1.6 billion people every day, according to Bloomberg, was the number in 2012.

During this time, the average profit per share decreased from “approximately a tenth of a penny to a twentieth of a penny,” according to the report. This market continues its growth and the trading volumes of stocks, crypto, derivatives, etc. are now enormous.

Strong computers deploy and utilize sophisticated algorithms to assess markets and carry out lightning-fast trades, typically in high volumes, using HFT software. HFT demands sophisticated trading infrastructure, such as powerful computers and expensive hardware, which costs astronomical sums of money and reduces earnings. Success is not assured in the face of heightened competition. This article examines what substitute tactics they are presently employing. 


Finding new ways 


The establishment and upkeep of an HFT program is very expensive, it really costs a good amount of cash. Powerful computer technology and software require regular and expensive upgrades, which reduces earnings. Because of how dynamic markets are, it is only possible to replicate something with computer programs. Trading at extremely high frequencies is also a part of the HFT world. In exchange for access, ultra-high-frequency traders can view price quotes that are displayed earlier than the rest of the market. Also, HFT regulations are likewise becoming more stringent every day. Nowadays high-frequency trading firms’ competitors are moving away from operationally inefficient trading methods and towards less expensive trading methods that don’t require more regulation. Let’s have a look at what is pop now. 


Momentum trading 


One of the well-liked substitutes for HFT is the classic technical analysis indication based on momentum identification. Sensing price movements that are anticipated to continue for a while is a key component of momentum trading (anywhere from a few minutes to a few months).

Once the algorithm on the computer detects a direction, the traders execute one or more staggered trades using large-sized orders. Because there are so many orders, even modest price differences over time provide large gains. Rapid trading within milliseconds or microseconds is not required because momentum trading positions must be held onto for a while. The cost of building infrastructure is greatly reduced. 


high frequency traders


Automated trading on news 


Markets are driven by news. Selling specialized news feeds to traders generates significant revenue for exchanges, news organizations, and data providers. Automated trading that uses automatic news analysis has been picking up steam. Today, computer systems can read news articles and respond to immediate trading movements.

Assume, for instance, that shares of firm ABC are now trading at $25.40 each when the fictitious news stories below are released: ABC announces a 20 cent per share dividend with an ex-date of September 5, 2015. As a result, the stock price will soar up to almost $25.60, or the same as the dividend (20 cents). The computer program recognizes terms like “dividend,” “dividend amount,” and “date” and immediately places a trade order. It should be set up to only buy ABC stock up to the $25.60 (estimated) price increase.

Since HFT orders must be issued in a split second and are frequently based on open market price quotes, they may execute at disadvantageous levels, making this news-based technique more effective. In addition to dividends, news-based automatic trading is programmed for project bidding outcomes, quarterly company results, other corporate activities like stock splits, and changes in FX rates for corporations with large levels of international exposure. 


Trading based on social media feeds 


Another new development in automated trading is the scanning of live social media feeds (or the so-called feed-based trading) from reliable sources and market players. To make trading judgements and place trade orders, predictive analysis of social media information is used.

Assume, for instance, that Paul is a reputable market maker for three well-known stocks. Real-time suggestions for his three equities are posted on his dedicated social media account. Participants in the market who believe in Paul’s trading prowess can pay to subscribe to his exclusive real-time broadcast. His updates are sent to computer algorithms, which analyze and interpret them based on their substance and even the tone of their language. There may be a number of other reliable players who provide suggestions on a specific stock in addition to Paul. The program compiles all the updates from reliable sources, evaluates them for trading decisions, and then automatically executes the trade.

An intricate but reliable method to gauge the market’s sentiment on the movement of a particular stock can be created by combining social media feed analysis with other data sources like news analysis and quarterly results. Such a predictive study is highly well-liked for intraday trading on short time frames.  


Model for firmware development 


High-frequency trading success depends on speed. Speed is determined by the hardware and network settings of the computer as well as the processing capability of the programs (software). Firmware is a novel idea that combines software and hardware, substantially slowing down algorithm processing and decision-making.

The hardware includes such customized firmware that is programmed for quick trading based on identified signals. This eliminates the dependency issues and time delays that arise from running numerous applications on a computer system. These pauses now slow down traditional high-frequency trading.