Algorithmic Trading : Future Of Financial Markets

Due to high volatility currents in the financial markets, the increase of speed matters, and algorithmic trading has changed the nature of trading. Algorithmic trading also known as algo-trading tracks deals automatically through computer programs at velocity and frequency, which could only be dreamed by manual traders. To get to the bottom of the wonders, possibilities, and pitfalls of algorithmic trading, this blog starts with the origins of the trend, its advantages and disadvantages and the role it could yet play in the future of the world’s financial markets.

Understanding Algorithmic Trading

Algorithmic trading is the process of utilizing trading software that is able to launch a large number of trades in fractions of a second with specific criteria as guidelines. Such criteria can be simple such as moving averages, price levels among others or involve complicated instructions based on ML, statistical analysis among other indicators. It is used to make a few points repeatedly in the market price in an arduous activity that can never be done by a human.

A Brief History

The use of algorithms in trading began in 1974 when stock exchanges began to adopt computer-based trading facilities. Nonetheless, it was not until the late 1990 and early 2000, with the development of high frequency trading (HFT) and increased availability of computing resources, that algo trading was to capture the markets. The improvement of the trading algorithms, the globalization of markets and the development of the systems of the electronic communication networks have enabled the algo trading to expand.

Types of Algorithmic Trading

Actually, algo trading includes many different types of strategies and all these types have been intended for operations in different types of markets. Some of the most common strategies include:

Market Making: This is a technique of making money whereby an individual engulfs both sides of trade with an intention of making a profit through the spread between the bid and ask price. They effectively get involved in trades in the market and get a little revenue in the process which is the main role of acting as market makers.

Statistical Arbitrage: It has been using the split modeling strategy whereby it looks at the pattern in the odd setting in price of demutualized products. It can use Pairs trading in which the algorithm buys a particular security and at the same time sells another security because the algorithm is sure that the two securities will perform similarly.

Trend Following: This strategy as the name may suggest is meant for generating profits from the purchased securities that are in an up or down trend. Such an approach mainly uses moving averages and/or momentum indicators or any other related technical indicators.

Mean Reversion: This strategy works under the premise that prices have a mean reversion hence it is possible to sell high priced stocks and purchase cheaper ones. Cohort is applied to find out when a security is overbought or sold so as to enter trades assuming that the mean reversion will occur.

Event-Driven Trading: This strategy entails dealing with events that influence the market and some of these include reports on earnings and other economical factors, political events, among others. These are events that require algorithms that allow for the interpretation of the impact that these have on prices and the necessary trades.

High-Frequency Trading (HFT): It could be noted that HFT is a subset of algorithmic trading as its primary attribute is its ability to make tens of millions of trades per millisecond. This speed is utilized by the HFT firms to grab opportunities in the market that a normal and slow firm cannot.

How Algorithmic Trading Works

The adoption of algorithmic trading has brought several significant benefits to financial markets:

Algorithmic trading has introduced the following benefits in the financial markets;

Increased Efficiency: Algoware has been instrumental in the functioning of financial markets given that it helped to do away with the middleman in trading. Using algorithms it is possible to look at lot of data within a short span of time, decisions can be made and trades can be made and all this within a few milliseconds and this is less costly and time consuming than having a human being do it.

Liquidity Provision: These algorithms hence improve the market liquidity since they are constantly submitting bid and offer prices in the market. This assists in narrowing of bid-ask spreads and consequently the lowering of volatility of markets thus contributing to the emergence of efficient markets.

Reduced Human Error: On the other hand human traders are dealt with emotions, stress, and bias and all these many times lead to costly mistakes. However, algorithms trade using some particular coupled rules whereby they cannot be subjected to emotions or any possibly related circumstances.

Access to Advanced Strategies: Motivational quotes beginning particularly as well as other particles of investment institutions can now deploy various superior trading techniques which were out of its reach of giants. It has made trading strategies available to everyone making competition in the financial markets irrelevant.

Scalability: One algorithm is capable of making thousands of trades in a single pass and across various markets that you can only imagine cannot be done by an ordinary human being. This scalability therefore allows traders to exploit opportunities in different markets since they’re not constricted by issues to do with humanity.

Disadvantages and Risk in Algorithmic Trading

While the benefits of algorithmic trading are substantial, it also comes with its share of challenges and risks:Though the advantages of algorithmic trading are significant, it does not go without its drawbacks and opportunities from one’s side:

Market Volatility: In general, algorithmic trading is known to have certain issues like the rising occurrence frequency of flash crashes and other instability factors for specific market. For instance, the flash crash on May 6 2010 meaning that the loss of nearly 1000 points in the Dow Jones Industrial Average was confirmed by the high frequency trading.

Systemic Risk: From the paper it can be argued that the current financial systems are intertwined such that a problem with one part of the system can result in negative consequences. These risks may be amplified when Algorithmic trading is used especially when several of the algorithms are developing their trades based on the same preconditions in the market.

Regulatory Concerns: Algorithmic trading has advanced greatly over the years, and has been of uneasiness to other regulators, who perceive it as a threat to the integrity of the market. Hence, issues relating to impact of algorithmic trading in the market is well articulated and attempts to reign in the practice is improved.

Technical Failures: Algorithmic trading being a technical trade any technical hitches are very costly. In essence, latencies, delays, or inaccuracies of algorithms, the data feed or connectivity can result into trades not being made at all or being done dubiously.

Market Manipulation: The HFT traders’ effect is also worrisome considering the capacity of algorithms in spoofing or, at the very least, layering techniques.

Future of Algo Trading

The adoption of algorithmic trading has brought several significant benefits to financial markets. Here’s a brief description of how algorithmic trading has influenced the financial markets positively. The benefits that have been brought by the application of algorithmic trading are as follows:

Artificial Intelligence and Machine Learning: Combining artificial intelligence and machine learning with trading algorithms is already a concept that is being implemented and predicted to do much within the trading field. These technologies enable the learning from past data and market situation and thus improved results in predictive modeling and improved trade decisions.

Quantum Computing: Although, quantum computing is relatively new in the field of finance, this field can grow exponentially by speeding up the algorithmic trading process. It is also understood that when quantum algorithms reach the same level of complexity as classical ones, they may solve certain problems in seconds that would take other forms many years to crack and this may help in formulating many trading strategies.

Blockchain and Distributed Ledger Technology (DLT): Blockchain and DLT as an instrument in financial markets bring the possibility of making trading mostly a transparent and secure process. What has made these technologies to be developed is that they can help in the reduction of ‘’counterparty risk’, the speeding up of settlement times and the general improvement of the efficiency of the financial market.

Ethical and Responsible Trading: Also, there is increasing concern in daily trading through the use of algorithms with a shift towards ethical and responsible trading. This is as follows; algorithms that help the market collaborate honestly and fairly; and also the integration of ESG (Environmental, Social, and Governance) standards in trading practices.

Globalization of Markets: Algorithmic trading is expected to remain on course and grow further with globalization of the financial markets across the globe. This means that as the markets introduce more linking, the possibility for algorithms to actively link different markets will be of greater importance.

Conclusion

Algorithmic trading undoubtedly brought significant change in the structure of the modern financial markets by bringing speed, efficiency and accessibility of the advanced techniques to trade. But such benefits are associated with high risks and problems, which have to be tackled properly. To extrapolate on existing work on the future trends in algorithmic trading, it is pivotal to look at the possibilities that may dawn with the advance in technology, including the incorporation of artificial intelligence and quantum computing in algorithmic trading, the use of blockchain in the trading process and the principles of responsible trading that are likely to come into existence in the future.

Thus, the further development of algorithmic trading affects the future of financial markets, pushing new opportunities for share traders and investors while posing the questions of stability, regulation, and ethicality. Hence, it is always important for any course of action for the future to make optimum use of technology while maintaining the ethical standards and fairness in the markets.

Also Read : TOP 10 STRONGEST CURRENCIES IN THE WORLD

Conclusion

FAQ

What is Algo Trading?

Algorithmic trading or ‘algo trading’ is the practice of employing an algorithm or a computer program to deal on financial markets. Such algorithms incorporate predetermined formula that may include timing, price, quantity or any mathematical formation and these shall enter orders with speeds and frequencies that are inconceivable by a human trader.

Is Algo Trading Legal in India?

Indeed, Algorithmic trading is legal in India as it does not contravene any of the provisions in the Indian securities laws. Algo trading in India is regulated by the SEBI and the practise has been cleared starting right from the year 2008. However, being independent traders or institutional investors, they have no other option but to operate within the guidelines of SEBI rules and regulations for optimising the performance of capital markets.

Who Can Do Algo Trading in India?

Algorithmic trading is mainly used by the bigger investors in India and they are fundamentally mutual funds, hedge funds or Proprietary Trading Firms. However, if any person wants to trade through algo trading, which in this case means that the name ‘algorithm’ is a trader, then it is incumbent upon him/her to operate with the guidelines provided by SEBI.

What is the role of co-location in Algo Trading?

Co-location has been defined as the physical location of trading servers near the exchange servers for purposes of minimizing the time taken in the execution of trades. The equity trading body of India, SEBI makes sure that co-location services are made fairly available to the nano second advantage which is very important in high-frequency trading.