The Bank of China and Refinitiv have partnered to address the biggest challenges to artificial intelligence application in FX trading. The collaboration will help launch an AI application through Eikon for FX trading signal prediction, utilizing machine power and human knowledge.
Eikon’s open platform allows customers to build their own apps and quickly share FinTech innovations to the global community. Refinitiv’s API also opens access to seamless content integration with the customer’s system, which supports their innovations.
AI innovation in FX trading has been a goal in the field for some time now but it has become a more practical option with progress in big data and machine learning (ML). FX traders are using this progress more and more for predictive analysis.
The Bank of China has over 70 years of experience in FX trading. The bank has utilized deep-learning algorithms to predict the FX price movement for only a couple of years forward, however the Bank’s Digital Asset Management Department has made significant progress.
Here are some of the major challenges in using AI innovation with FX trading:
- Data: Figuring out what kind of data and data combinations to use in building a FX trading AI model, as well as acquiring high quality data source, also known as ‘feature engineering’.
- Algorithms: Picking effective ML frameworks and algorithms for different business cases works.
- Platforms: a platform that can integrate different datasets used in the ML training process, as well as providing enough computation power (GPUs/CPUs) to handle big data.
- Domain knowledge: FX trader who have deep domain knowledge are necessary to provide market insight and help the machine to use their market experiences in a programmatic way. The data scientist takes the critical role of bridging the gap between technology and business.
