High-frequency trading and Algorithmic trading (HFT & AT) have dominated the trading world in the recent decade, particularly HFT. HFT was responsible for moreover 60% of U.S. trade in 2009-2010.
Overview of High Frequency Algo Trading
HFT is a subset of algorithmic trading, while Ultra HFT trading is a subset of HFT. HFT and Ultra HFT are ways for traders to profit on tiny price disparities that may only exist for a short period of time.
In finance, high-frequency trading (HFT) is the most common type of algorithmic trading. High-frequency trading (HFT) is a sort of algorithmic financial trading that uses fast-frequency financial data and electronic trading tools to trade at high speeds, high turnover rates, and high order-to-trade ratios. While there is no single description of HFT, very advanced algorithms, co-location, and very short-term investment horizons are significant characteristics.
Dedicated programmes that make automatic trading judgments are used to place orders in computer-assisted rule based, algorithmic trading. AT divides huge orders and places them multiple times, and manages trading orders once they’ve been placed.
How does High Frequency Algo Trading work?
High-Frequency Trading is mostly a latency (Tick-To-Trade) game, which refers to how quickly your strategy reacts to incoming market data. With modern, sophisticated, and specialised hardware, the cutting edge technology claims single-digit microsecond or perhaps sub-microsecond latency (Ultra-High-Frequency Trading).
To summarise, the gap between what buyers want to pay and what sellers want to be paid has narrowed considerably over the last 20 years. The rise in accuracy is one of the causes behind this. High-Frequency Trading has also increased market liquidity, lowering bid-ask spreads.
Benefits of High Frequency Algo Trading
Better value for money – Trades are made at the best possible price with algo trading since they are quickly timed to avoid excessive price changes.
Improved accuracy – When a machine conducts a deal instead of a human, the chances of a mistake are reduced. The human aspect has a significant impact on accuracy. As a result, when a computer algorithm is used, precision improves.
Increase in speed – The speed at which trades are carried out is significantly increased because algorithms are pre-written and executed automatically.
Risks to consider for High Frequency Algo Trading
Systemic Risk – In finance, systemic risk refers to the danger of a financial system or market collapsing as a whole, as opposed to the risk of a single firm, group, or component of a system collapsing without hurting the whole.
The financial system is one of the significant hazards associated with algorithmic HFT. Because of the strong interlinkages between financial markets, algorithms operating across markets can transmit shocks quickly from one market to the next, amplifying systemic risk, according to a July 2011 report by the (IOSCO) International Organization of Securities Commissions Technical Committee.
What Causes Algorithmic High-Frequency Trading to Increase Systemic Risk?
Effects of Ripples
Because of the rising degree of connectivity between markets and asset classes in the global economy, a market or asset class meltdown typically causes a chain reaction that affects other markets and asset classes.
Volatility is increasing
Because there is so much algorithmic High Frequency Trading activity in today’s markets, most algorithms are designed to outsmart competitors. Algorithms can react to market conditions in real time. As a result, algorithms may widen their bid-ask spreads during volatile markets (avoid being compelled to take trading positions) or temporarily halt trading, reducing liquidity and increasing volatility.
Excessive market volatility is a significant contributor to algorithmic HFT, which can raise trader and investor concerns in the short term and impair the consumer’s confidence in the long run. When a market crashes unexpectedly, investors are left perplexed as to why such a drastic change occurred. Large traders (including HFT firms) will lower their trading positions to reduce risk during the news vacuum that often occurs at such times, putting extra downward pressure on the markets.
Other crucial Risks
High-frequency trading is still a controversial activity, with regulators, finance experts, and academics disagreeing on its merits.
High-frequency traders rarely retain their portfolios overnight, only invest a small amount of money, and only hold their trades for a short period before liquidating them.
As a result, the risk-reward ratio is relatively high. The ratio is significantly higher than that of a traditional long-term investor. A high-frequency trader may only make a fraction of a penny profit, which is all they need to make gains throughout the day and increases the risk of a considerable loss.
Huge Losses for Investors
Volatility fluctuations exacerbated by algorithmic HFT can result in significant losses for investors. Many investors put stop-loss orders on their stock holdings at levels that are 5% below current trading prices on a regular basis. These stop losses will be initiated if the market gap goes down for no apparent reason (or even for an excellent reason). To add insult to injury, if markets quickly recover, investors will have unnecessarily lost money on their trades and their investments.
Because most algorithmic HFT trading is done at breakneck speed, a single incorrect or flawed algorithm can cause millions in losses in a matter of minutes.
Loss of Confidence in Market Integrity
Traders and Investors trade in financial markets because they have complete faith in their honesty. Repeated occurrences of exceptional market volatility could erode this trust, prompting some cautious investors to exit the markets entirely.
The most significant risk of algorithmic HFT is that it can amplify systemic risk. Its propensity for growing market volatility has the potential to spread to other markets, fueling investor anxiety. Unusual market volatility on a regular basis could erode many investors’ faith in the market’s integrity.
Disclaimer – The article is only for educational purposes and all the information given in the articles is available in the public domain.