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Why big data matters: using smart data and artificial intelligence to enhance FX trading

Why big data matters: using smart data and artificial intelligence to enhance FX trading

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CLS provides its view on the importance of big data and AI to FX and FX trading in an era of digital innovation that is transforming the financial services industry.

The digital revolution has unleashed a wave of innovation in smart data and artificial intelligence (AI) that is transforming the financial services industry.

Over the last ten years there has been a sharp fall in the cost of data storage, better processing capabilities and computing power, as well as more sophisticated analytics. These developments, paired with recent regulation in financial services around data standards for transparency, have contributed to making AI and smart data more accessible to the industry and have presented an opportunity for market participants to enhance their FX trading strategies.

However, until recently, compared to their peers in other asset classes, FX market participants have been slower to realize the benefits of big data and advanced technologies. This is because of the fragmented nature of the FX market which has hindered the availability of sufficient and quality data. As a result, many of the systems developed by fintechs and banking institutions have historically lacked the volume of data necessary to be credible. However, this is now changing with the emergence of new data services offering smart data and AI tools.

Smart data and AI have the potential to enhance FX trading in a number of ways. As a prime example, currency rates are determined by multiple factors, including macro-economic events, geo-political developments, and at times, FX flows, which can all lead to unexpected and short-term volatility. These technologies can be used to analyse the enormous amounts of data produced in the currency market to help traders to spot patterns and correlations despite this volatility. FX trading systems can then be programmed to carry out user-defined algorithms, characterised by a set of rules based on parameters such as pricing, volume, and liquidity, to structure the trades that will be executed.

There are also various stages of the trade lifecycle that are ripe for the application of AI and smart data technologies. The first is pre-trade analysis, in which participants seek to understand when the market is most liquid, who is buying or selling, and the flow of volumes. The other is historical data which can feed into execution algorithms and trading models for scenario testing and cost analysis to ensure best execution.

In addition, by applying smart data-driven tools to FX volume data, it can be aggregated and segregated for analysis purposes on a more frequent basis. Traders can input this data into algorithmic or non-algorithmic trading tools to detect potential price movements and depth of liquidity to determine the best time to trade. They can also use machine learning trading algorithms to exploit volume patterns and generate investment gains.

Lack of transparency in the FX market means that it has historically been difficult for market participants to inform directional trade strategies using order flow data; however, developments in smart data and AI are changing this. Order flow can be an important mechanism for both dealers and individual FX traders to track the flow and volume of trades made by banks and institutions, and to detect or generate trading signals. It can also reveal market participation, giving traders valuable insights on underlying market dynamics and allowing them to gauge the relative predominance of informed and uninformed traders in any given currency price movement.

AI systems can also perform analyses using forecast data. FX forecast data provides a forward-looking view of FX markets over short, medium and long-term horizons to identify times to trade with greater liquidity. Traders can use this information to optimise strategies in liquid and illiquid pairs. Incorporating volume forecasts in either algorithmic or non-algorithmic trading strategies can reduce execution costs and diminish price slippage and market impact. This can be beneficial for corporate treasurers, asset managers and other market participants are sensitive to friction in trade entry or exit, and are always looking for ways to “execute better”. One way to do this is to trade when liquidity is plentiful; however, structural changes in the FX market have reduced the depth of available liquidity and created a fragmented landscape.

Smart data and AI technologies are advancing at an unprecedented pace. Exponential increases in computer processing power, increased storage enabled by cloud computing and rapid expansion in the availability of data and regulation are combining to create a more harmonised marketplace. As such, the time has come for FX market participants to join their peers in other asset classes by realising the transformative benefits brought by these tools or they risk lagging behind the curve.

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