Leveraging deep learning for the creation of a smarter generation of FX algos

FX algo usage has increased dramatically over the last half decade. As a result, there is significant pressure on FX dealers to successfully meet complex client expectations – especially regarding liquidity during volatile times.

Daniele Grassi
Daniele Grassi

FX dealers and aggregators have an extremely challenging task to make sure they optimally match incoming orders with liquidity providers and achieve high fill rates, while at the same time providing low slippage, positive yield curve for the liquidity providers themselves, without any material market impact. As trading volumes and client expectations rise, so to does the need to improve execution performance. The need for a smarter generation of FX algos is something that is being felt industry wide.

AI and Machine Learning (ML), and more specifically, a subset of AI known as deep learning, are finding numerous applications in the FX algo trading space. Deep learning is increasingly valuable in the development of a smarter generation of FX algos. Algos that are based on this type of machine learning can improve the quality of order fills no matter how complex or volatile the markets are and do so irrespective of the high volume of data or the data’s structure. This is something that was driven home to everyone involved with algos, either through building them or through utilising them, during the pandemic. The use of deep learning and AI/ML-driven applications can help algos respond to real-time market dynamics and associated areas like data and liquidity management, creating substantial improvements in execution results, even during times of high market volatility.

Deep Learning can effectively emulate and enhance the way that humans understand and decipher knowledge

What is deep learning?

Deep learning is a type of AI technology that allows predictive analytics to be automated, using even the most complex and abstract source data. As a result, it can effectively emulate and enhance the way that humans understand and decipher knowledge. In terms of trading, its value comes from its ability to recognise more intricate and non-linear patterns in behaviour in the markets, including rapidly changing market conditions.

Whatever the application, deep learning has the edge over human involvement in dealing with vast swathes of data. There’s no way a human could cope with the speed of the market and the volume of data, even using the simplest rule-based models.

But the question for some is: how can technology go beyond the intelligence, experience, and knowledge of a human? Deep learning bridges that gap, as a technology that can process and extract the necessary insights from even the largest volume of data, in a way that an intelligent person would.

As such, it equips electronic trading platforms with the tools they need to quickly interpret data and execute orders through enhanced FX algos that are automated to a much higher level, smarter, and reactive to changes in market conditions.

How deep learning can advance FX algos

The value of Deep Learning in trading comes from its ability to recognise more intricate and non-linear patterns in behaviour in the markets

Traditionally, order routing has been performed with static routing tables or, more recently, algorithmically, with the aim of increasing trading efficiency and striving for better execution. But there are multiple factors that come into play, and so many ever-changing variables that could potentially have an impact on results. Things like market conditions (both live and historical) and the impact of trades, order flow, trading profiles, and taker and maker behaviours can all have a serious effect on order routing.

These more traditional order routing approaches struggle to achieve optimum execution and profitability, mainly because they are not able to fully grasp the sheer volume of data that needs to be analysed. The enormous amount of data involved in the process requires more than statistical or algorithmic approaches can handle, and that’s where deep learning comes in. Deep learning technology can include extensive amounts of data in its analysis, as well as successfully identifying patterns and behaviours that are not immediately apparent with the traditional statistical methods.

AI tools that use deep learning not only allow algos to produce decisions that are more intelligent than simple algos based on benchmarks can typically generate, they can also shrewdly employ predictive analytics to calculate future market movements. Based on a combination of complex rules and historical data, they offer real-time analysis and long-term predictions that allow platforms and traders to make better informed and more successful trading decisions.

Liquidity management is key to FX trading, and the better liquidity is managed, the better the order fills are. However, it’s important to be able to balance data science with maintaining relationships, and this is something that the use of AI-powered FX algos with deep learning technology can support.

Anomaly detection is another process made easier by leveraging deep learning in FX algos

The evolution of algos for smart order routing

FX algos were initially used as buy-side risk management tools, aiming to increasing the efficiency of trades – for example, slicing orders across either time or volume, or even both. Then came the emergence of other types of algorithmic models.

Models like alpha generation models were designed to generate profit, for example, while quantitative models were generally obscure and often based on mechanical heuristics. One of the main drawbacks of these models was, and continues to be, that they fail when the dynamics of the market change. So how can market dynamics and conditions be considered? The answer is AI and machine learning.

Deep learning and smart order routing

Without a doubt, smart order routing makes achieving optimised results easier, but there are several performance issues involved that can be addressed and improved by using deep learning.

For example, in addition to maximising order fill rates and improving the overall quality of order fills, deep learning can examine historical fill rates. When used in this way, it can help to predict future order fill rates, according to market conditions and different liquidity providers.

Understanding the potential market price reaction to an order is vital in terms of understanding the overall quality of execution, but the factors involved in assessing it are variable to say the least. The advanced analytics of deep learning technology makes predicting an order’s market impact much more effective.

Anomaly detection is another process made easier by leveraging deep learning in FX algos, which is valuable when it comes to supporting automated compliance processes, such as the automatic detection of anomalous or irregular trades.
Deep learning also makes it easier and quicker to detect patterns in large volumes of data, so if there are trading or market maker trends, FX algos can use them to make smarter decisions.

Leveraging deep learning to create FX algos leads to smarter algos

Important considerations for leveraging deep learning

While applying deep learning to dynamically create and manage FX algos leads to better execution, getting to this level of complexity is not immediate. There are several requirements needed for a high success rate in the development of these AI models.

First and foremost, an extensive domain knowledge is crucial to be able to accurately understand how the order routing process works, as well as the exact meaning and the variability of the data that is generated during the routing process.

Some of the common impairments to the capabilities of AI tools and systems within the trading and asset management industry are data leakage, overfit, and bias problems, which can be effectively managed with knowledge and experience in financial time series modelling. At the same time, a complex feature engineering process is needed to reduce data noise to a minimum, as well as handling potential data gaps and high variability.

It’s also worth noting that training and selecting the best performing (and the most generalising) predictive deep learning models is extremely intensive, computationally, and will generally require access to more than one computing system. Having enough computational distribution capability to efficiently allocate the training and evolution of the models across several nodes is key.

Finally, the fact that market patterns change means there’s a requirement to refresh the deep learning models that are used for prediction. This means performance monitoring is a vital part of the process, along with the ability to update models and retrain as needed.

Developing algos for a smarter future

Automation is key for a smarter generation of FX algos, with client order volumes increasing and traders under pressure to handle them with more speed and more efficiency. That’s why the obvious next step in algorithmic trading and liquidity optimization is deep learning, with its ability to generate insights from huge amounts of unique data in a fraction of the time it would take statistical or human-based approaches.

By going one step further than just analyzing historical data and truly learning from the patterns and behavior, deep learning technology can power the next generation of FX algos to dynamically manage liquidity and improve execution performance. Leveraging deep learning to create FX algos leads to smarter algos that are specifically designed to alleviate market impact, improve liquidity, and enhance execution performance, by understanding and reacting rapidly to the ever-changing market dynamics.

 

Related Posts

Scroll to Top