The volume of trades in the market continues to increase. Client expectations are becoming much more complex. Because of this, there is increased pressure on trading desks to improve performance. With these things in mind, investment expert Daniel Calugar addresses some of the trends in algorithmic trading that 2021 will bring.
Market algorithms have increased the speed and sophistication at which trades can be enacted. Within the realm of algorithmic trading, certain trends are beginning to take hold.
For example, many trading systems will soon begin to implement benchmarking technologies. These tools can provide real-time intelligence about the algorithms to use within an OMS/EMS.
Benchmarking technology generates intelligence based on historical data. In this way, traders can collect suggestions regarding specific trading parameters and settings.
Some traders and clients may be wary of artificial intelligence and machine learning benchmark technology. But algorithms are becoming highly specified to market conditions and available credit.
The success of benchmarking will increase user trust. In 2021 and beyond, this will be a major trend in algorithmic trading.
TCA provides information about the outcome of a trade. But it offers little insight into which factors determine the success or failure of a particular set of trades.
To meet this challenge, there will be an upsurge in the use of real-time tools to interpret TCA information. This will create a feedback loop of data. With these real-time tools, pre-trade impact models will influence algorithm and broker strategies.
Ultimately, this trend will help traders to decide on the smartest execution algorithm to use to achieve their goals.
A third upcoming trend is the use of pre-trade recommendations. Many traders have faced the challenge of information overload.
This leads to traders consistently using the algorithms that have brought success in the past. In addition, more sophisticated ML and AI technology will gradually lead to more reliable insights about anticipated algorithm performance.
Better insights mean that traders can use a more diverse set of strategies for specific market conditions. Over time, this will increase the confidence of both traders and their clients regarding algorithmic trading.
Finally, the next several years will see a significant increase in automation. As a result, trading desks are facing increased pressure to complete client orders more quickly and efficiently.
In addition to the need to move faster, the range of variables that traders have to incorporate has increased. These include latencies, depth, hit ratios, volatility, and client information.
It is untenable for fewer traders to handle an increased volume manually. Because of this, investors will see an upsurge in automation and efficiency across the trading world.
Many of the challenges faced by today’s traders can be addressed through improved technology. Although efficiency expectations are growing, so is the ability of AI and ML to meet this demand.