What is algorithmic trading and what will be the future of algorithmic trading? The answers to the questions are in our blog post.

 

When we say algorithmic trading, automated trading, black-box trading, or algo, it is understood that computers are trading securities for profit according to a set of predefined criteria and commands. An algorithmic transaction can be as simple as “order a buy if the price goes above this moving average”, or it can be in a more complex way, such as entering buy or sell orders at different prices and canceling them before they happen.

 

In high-frequency trading, orders are sent to many markets very quickly according to predetermined criteria. Latency arbitrage, which tries to take advantage of the millisecond delays in orders transmitted to the exchange by having a server closer to the exchange, is also considered within this scope. Although algorithmic trading is a method used in short-term speculations, algorithmic trading methods can also be used in long-term investments.

 

Large funds with long-term investments do not want the prices to fluctuate while they buy large amounts of securities. By following the market conditions, algorithmic trading methods are used for large purchases made without moving prices upwards. With Expert Advisor it is possible to use simple algorithmic trading methods. The point to note here is that the algorithm belongs to you. If someone else has created an algorithm on your behalf, and you accept that someone else decides on your investment in this way, this is within the scope of investment consultancy.

 

Future of Algo Trading

As transaction volumes increase and customer expectations become more complex, the pressure on transaction desks to improve application performance is increasing. Traders are now turning to algorithmic trading and automation more often to handle their various flows. We examine the key themes and trends identified to shape the future of algorithmic trading in 2021 and beyond.

1. Trading systems will use benchmarking to provide intelligence on which algorithm to use in EMS/OMS in real-time.

2. Increased emphasis on real-time tools to translate TCA results into practical system configurations

3. Core algorithms will become smarter and more responsive to market conditions

4. Algos will expand across asset classes, including cross-entity automation

5. Rules will evolve from simple routing instructions to contextual rulebooks that measure market conditions

 

The algo trading environment has not improved significantly for traders over the past few years. The broker also offerings have been relatively sluggish and access to certain algos may be limited by the Order Management System (OMS) / Execution Management System (EMS) technology used by the firm. Algo trading not only provides execution intelligence and automated trading logic for clients but also traders forward orders to intermediaries from liquidity pools that would otherwise not be available to them (e.g. LIS, dark pools, non-member markets, MTFs, and auctions).

 

The challenge most trading desks face is that daily traders use only a small subset of usable algos and a limited number of available parameters within the algo itself. Using algo trading strategies in production has the minimum knowledge required for the trader to choose the best algo based on execution goals and prevailing market conditions. Without confidence in the algo results, traders will often use them as a mechanism to 'deliver' orders to other brokers and steer risk away from their desks with Best Practice.

The market requires a new set of tools to address the complexity of algorithms based on Artificial Intelligence (AI) and Machine Learning (ML). Adoption of ML enables systems to support execution processes by suggesting which Algos to use and specific parameters best suited for a given goal. Algos will continue to play an important role in the future of trading as market participants seek to find new ways to automate their workflows.

 

The future of algorithmic trading: trends for 2021 and beyond

 

1. AI/ML-based selection of dynamic parameters

The problem that desks face today is how to better leverage existing trading strategies, execution processes, and also capabilities that a trader may not be familiar with. Algo trading requires consistent adjustment and regular changes to the trading infrastructure which makes it very difficult to stay up to date. To date, this responsibility rests with the algo providers themselves, whose sales teams market the benefits of certain algos to traders, in the hope that the trading desk will choose the right algo at the right time. This methodology has proven to be unscalable and impractical as a negative result pushes a trader to choose algo behavior that is familiar and easy to explain for months.

Getting out of this loop is the use of AI/ML. In particular, the focus will be on trading systems that use benchmarking to provide intelligence on which algorithm to use in real-time within OMS/EMS. We see algos making smarter decisions beyond a simple benchmark-based algo wheel that will help recommend specific settings and parameters using historical data, thus helping to achieve the best results for traders. As algos become very specific to the instrument, market conditions, and credit available at the time of trading, more confidence will be built among traders and customers to open up a world of opportunities to improve performance and encourage algo providers to invest in their systems. By leveraging automation and machine learning, one can envision a world where order management and execution management flow seamlessly.

 

2. Real-time integration between Algos and Transaction Cost Analysis (TCA)

As there is a shift in the incorporation of TCA into both sell-side and buy-side processes and workflows, traders face similar challenges in determining the way they use TCA at their desks. Orders are regularly evaluated for certain performance metrics and metrics. However, TCA is only a measurement of an outcome. It does not give the trader any insight on how to resolve any poor trading results. As trading technologies and algorithms have become more complex, it has become nearly impossible for a trader to correlate poor TCA results with what settings can affect trading outcomes across a range of complex systems. Learn more about how to use ML-driven TCA to improve trading results and decisions in our latest webinar with Refinitiv, an LSEG business.

We will see an increased emphasis on real-time tools that will translate TCA results into practical system configurations. This feedback loop will be the essential missing ingredient in any trading desk that transforms TCA from a reassuring backslash when something goes wrong with a key tool that influences trading results.

 

This will include a world where pre-trade market impact models inform the choice of broker and algorithm strategy, as well as route-fit orders based on pre-defined rule sets, helping traders wisely decide on the optimal execution algorithm for the required purpose and for which venue.

 

3. Pre-operation recommendations for better performance information

As the menu of algos and the range of configurable settings available to traders continues to increase, traders are suffering from information overload and simply sticking to their favorite top 4-5 algos. However, asset managers have become less willing to submit larger volumes and are increasingly opting for smaller conceptual amounts using Lack of Application (IS) or Auctions. Indeed, in current trading, market participants have opted to break up large orders into smaller digestible chunks that can be distributed across different venues and executed against multiple liquidity pools to improve execution quality and help their clients reduce their trading costs.

Michael Rouillere's words, "The trends we've seen throughout 2020 have been no change in the diversity or complexity of algo usage, with core volumes still focusing on the VWAP execution algorithm and the TWAP algorithm. In this scenario, the subslice of any other algo type could be a fixed point using a dynamic balance from a given benchmark driven by AI/ML predictions of macro volatility and market events. The fixation itself naturally increases the effectiveness of passive trading in any algorithm, combining it with forecasting mechanisms to anticipate minor market changes that will allow traders to score a few additional base points (BP) in all use cases. This also helps brokers introduce new features of algo trading without going through the grueling process of convincing end customers to trust more exotic algo trading behavior.

 

We expect traders to use a wider range of strategies suited to a particular stock and market condition, as pre-trade advice gives a better idea of ​​the performance of all available algos. For traders, this feedback loop of AI/ML-driven recommendations helps to provide more pre-trade confidence as to which broker algorithm or customized algorithm to use in any given situation. The third generation of algos will be adaptive and deploy ML-driven technology and real-time insights so that algos can self-adjust as they trade.

 

Conclusion 

The creation of these complex rules allows for full automation of the flow and making recommendations on specific routes and strategies. In this way, traders can speed up their decision-making at the trading point. This may include cross-entity behavior such as the automatic creation of FX legs for unlisted currency orders. We hope that this blog post will be beneficial for you. We will continue to create useful works to get inspired by everyone. We are sure that we will achieve splendid things altogether. Keep on following Finage for the best and more.  

 

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