Algorithmic Trading: Exploring the Latest Trends

 

There was a time when trading in the stock market was done manually. As you can imagine, this was a mentally taxing endeavor that not only took time to execute but was laced with many issues such as being slow and filled with human error. However, for decades, algorithmic trading has grown into a valuable tool that curbs these issues, thus becoming a staple in 2024.

 

If it does interest you, there are some important things that are relevant to a topic and might be of interest to you. This will include naturally what it is and how it works. Another key aspect we'll look at is some of the key trends the trading type will see going forward. With that settled, let's begin!

 

Contents:

- Tracing the development stages

- Improving efficiency through automated processes

- Incoming trends

- AI's use

- Regulations and compliance

- HFT evolution

- Algorithmic risk management

- Quantum computing

- Final thoughts

Tracing the development stages

Algorithmic trading refers to a general strategy where the execution of trades occurs automatically utilizing preset parameters such as volume, time, and price. Via computer technology, under human oversight, trades are made much faster and generally more efficient.

 

Now, the history of algorithmic trading goes back to 1971, when electronic elements were first incorporated into quoting systems. Since then, a host of major developments have occurred including:

- Program trading of the 1980s, which much like the modern algorithms, had pre-programmed trade executions based on set measures and was greatly popular;

- The late 1990s creation of Island ECN, one of the first electronic communications networks, that allow for the trading of financial products outside the confines of the stock market, with the specific example as being able to match buy-sell orders automatically;

- The early 2000s rise of high-frequency trading, a branch of this trading style that in milliseconds, executes a large amount of orders.

Improving efficiency through automated processes

The idea of this trading style is that you create parameters that include the aforementioned time, price, and volume in addition to other metrics like moving averages. Utilizing the limitations you've set, the computer program acts accordingly.

 

To achieve this, it's important to note that various strategies could be employed in algorithmic trading. The most notable o these include:

- Mean reversion, which assumes that an asset’s price will return to its average at some point

- Arbitrage, which sees the simultaneous buying and selling of assets to take advantage of price inefficiencies

- Market making, which sees market makers create a two-way market by continuously quoting bids and asking for a particular security to benefit from the spread

- Trend following, which sees the algorithm follow established trends through technical analysis, exiting, and jumping on trends when necessary

Incoming trends

If you look back at the evolution of algorithmic trading, you'll notice that some of the innovations of modernity haven't been listed. That's because they belong in this segment, so below are a few of the trends we are to see in this space going forward:

 

AI's use

While it has been efficient, the upside brought by this trading style, which includes speed, and general efficiency is relatively lacking. This is because as it stands, our oversight is still a thing, and is thus going to be slower, and likely inaccurate.

 

However, with machine learning, and artificial intelligence, the process speeds up as the tools can not only process the data quicker, it can learn, predict, and act. The resulting achievement is to shave time off and allow for better efficiency.

 

Regulations and compliance

Because the technology is becoming more widespread, and popular within the space, it's only natural that market manipulation can become a thing. As such, the constant development of the technology will be matched by regulations set by regulatory bodies.

 

These in turn will influence how algorithmic trading progresses. It is worth noting that for some, this may be problematic, as such fast-paced moves will require increased compliance.

 

HFT evolution

To piggyback off the last point, high-frequency trading will also see its evolution, to ever-changing regulations, which it will have to adhere to. In addition to this efforts will be made to make things even speedier, and less latent. This is done because of rising competition, which demands that one be faster, even by the last millisecond.

 

Algorithmic risk management

Complex models are being deployed to properly manage and reduce risk. Automatic real-time tracking of the algorithm's results detects abnormalities and ensures reliable operation. Stress testing, which imitates harsh market situations, is used to ensure the durability of these algorithms, and it is considered an important trend.

 

Quantum computing

Financial institutions are looking at quantum algorithms as a way to handle complex optimization challenges more efficiently. The initial adoption of quantum computing research is obvious, with quantum cryptography improving the security of trading algorithms.

 

Final thoughts

Having been here in various forms throughout the years, the question is no longer about algorithmic trading in itself, but rather what form it's going to take from 2024 onwards. What we see is that the approach as always is centered around the evolving technology, and how it's going to be dealt with as it evolves.

 

With the boost that AI and ML could give this trading style, it only makes sense that the regulations set allow for a fair environment with as little market manipulation as possible. Regardless, the principle thread that is automation, will remain as it did decades ago during algorithms trading's inception. You can use some great tools for algorithmic trading with Finage's powerful API and improve your trading strategy. Experience the future of trading with new solutions and get started today by subscribing here.

 

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