Trading Exchange Forecasting

As early as the 1990s, AI researchers had speculated that AI would change the forex market. A groundbreaking article called Managing Foreign Exchange for Competitive Advantage published in the MIT Sloan Management Review underlined the importance of computerized models, how they’d become indispensable in the foreign exchange market, and by extension, trading exchanges. Traders are increasingly relying on predictive analytics, and sophisticated big-data AI algorithms are capable of real-time data collection and eventually making accurate forecasts.

The algorithmic trading market was valued at $11.1 billion in 2019 and is projected to grow by over 10% annually, reaching $18 billion by 2024. It highlights that the forex and trading market has transformed over time, giving rise to predictive analytics models and machine learning that have given a significant advantage. It was making forecasts that were unavailable earlier.

There are three traditional methods used to analyze and predict the stock market: financial, technical, and sentiment. Financial analysis involves evaluating past statements, reports, and balance sheets. You can then compare it to prospects, the market, and changes in government policy. Technical analysis relies on the idea that all factors which can influence the price are included in the current price of the stock. Therefore, no fundamental information analysis is required. This comes with the precondition that prices move in trends and have the same historical patterns. Sentiment analysis relied on taking advice from experts and going through newspapers to monitor the stocks and what existing investors would like to invest in.  All of these methods can be handled easily, as AI-driven systems can effectively process millions of data points in real-time.


Although AI systems have proved worthy for scalping trading, their veracity is yet to be established over sustained periods. As the number of variables increases, it becomes more difficult to study, analyze and forecast pricing changes.

This becomes potent while considering the ‘chaos’ prevalent in the trading markets that influence stock fluctuations. These include ‘self-fulfilling’ prophecies, unquantifiable factors that constitute human emotions and sentiment and are almost impossible to predict. Chaos can be unpredictable. Political turmoil, public protests, social unrest,  and other factors can influence pricing fluctuations and predictions from investors, traders, and advisors. On the other hand, the weather is a ‘chaos’ that usually doesn’t impact the stock market. In this context, it is important for investors, traders, and advisors to recognize these meticulously and understand them before making predictions because these are generally overlooked.

Since humans are prone to strong cognitive biases, an AI system seems the best-fit solution to predict market movements based on the easy availability of vast amounts of data and algorithm-based trading. Although massive amounts of data are now more readily available, analytics and models created off this data have been overwhelmed with irrelevant data. Bad data can lead forecasts astray.

Mere historical data is inadequate to forecast outperforming investment strategies. Investors who try to predict the market and rely on naive AI approaches end up incurring financial losses. It is primarily due to the lack of enough data to meaningfully train algorithms.

Solution Approach platform provides out-of-the-box development frameworks.  The project was started with the relevant environments, which were then created automatically. Development images configured based on pre-defined templates were installed on-premises or in a development VM within the infrastructure. This enabled authentication using LDAP, seamless project setup using Bitbucket, Jenkins, and Docker (ensuring build and deployment without software compatibility issues). 

The platform made available by leverages the latest ML and DL tools while preparing models. It includes Pachyderm-based data versioning, Kubernetes, Kubeflow, and Spark-based ML and DL. It also includes an Istio-based service mesh-enabled microservice architecture, and ELK-based monitoring capability, contributing to a reduction in latency time.

The platform can potentially be fed with input data in the form of historical trading performance of securities in a fund and data on risk factors to predict future performance under different economic test conditions. These can be the latest announcements about an organization on various social media channels and traditional sources such as newspapers, their annual and quarterly revenue results, and other ‘chaotic’ elements that are likely to influence stock prices. was utilized to collect data from this extensive data repository and analyze details.’s MLOps platform allows establishing high-end Alluxio and Presto-based efficient data connectivity and collecting data from diverse sources. A major part of the data transformation journey while creating models involved setting up the required infrastructure and establishing high-end data connections to collect raw, continuous, unformatted, unparsed data. 

The details collected were added as exploratory variables by using libraries and analyzed. By using data versioning and connectivity libraries, data versions were easily controlled, stored into xpresso Data Model (XDM)-enabled data store. This enabled easy retrieval and storage of datasets/ files into internal XDM. The attributes obtained were used for categorization (employing Pachyderm-based data versioning) and then performing univariate, bi-variate, and Bag of Words analysis — for both structured and unstructured datasets through xpresso Exploratory Data Analysis (Data and Statistical Analysis).  Different datasets and their different versions were easily controlled and stored into an xpresso Data Model (XDM)-enabled data store that enabled easy retrieval and storage of datasets/ files into internal XDM. This was achieved using two excellent features of

  1. Data Connectivity Marketplace libraries
  2. Data Versioning

The xpresso Data Pipeline Management (Rapid Model Training and Experimentation) uses Kubeflow-enabled pipelines. Thus, multiple experiments using different models and datasets could be created, tested, paused, and restarted to gain better insight.

The results gathered from this analysis and training models provide investment managers in financial institutions with better risk analysis and portfolio management skills. Individual investors and asset managers can assess risk levels or returns in a particular investment portfolio. They do this by easily monitoring numerous risk-related factors each day (like interest rates or currency rates) and test portfolio performance under different economic conditions. By giving investment managers the capability to predict portfolios’ performance in real-time, it is possible to help investment managers make better decisions.

How can help Finance Organizations transform their journey to cognitive AI solutions is an AI/ML Application Lifecycle Management Platform. enables complete lifecycle management of AI/ML solutions, addressing the AI transformation journey of enterprises on any cloud platform of choice. offers functionality essential for building AI/ML solutions – primarily enabling data scientists to rapidly build predictive and prescriptive models. The platform provides a user-friendly interface to develop, deploy, and manage AI/ML solutions at scale. In addition, supports the incorporation of these solutions into business processes, surrounding infrastructure, products and applications.

Key benefits of include:

  • Empowers data scientists to transform AI/ML research into solutions 
  • Improves the productivity of data scientists by enabling them to focus on the business problem, developing algorithms and rapid experimentation of models 
  • Addresses the shortage of skilled data science resources with automated workflows, toolkits and frameworks 
  • Manages AI transformation journey costs without any wastage of R&D efforts  
  • Provides an enterprise-ready and secure environment for complete lifecycle management of AI/ML applications
  • Enables at-scale deployment of enterprise AI/ML applications on-premise, cloud (AWS, GCP, Azure), or hybrid environments

Additional details on can be found at: We can schedule a demo of the platform for anyone interested in learning more.

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