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Finance

System Optimization and Real-Time Analytics for a Financial Trading Platform

Visualization of real-time analytics and system performance optimization

Focus Areas

Advanced Data Analytics

Cloud Infrastructure

Machine Learning (ML)

Data Monitoring and System Optimization in Finance

Business Problem

A financial trading platform’s outdated infrastructure could not handle large transaction volumes efficiently and had slow trade execution speeds, resulting in operational inefficiencies and hindered trades. Additionally, the system did not have real-time analytics, which led to loss of opportunities for traders. The business was looking for a scalable, high-performance solution to these challenges.

Key challenges:

  • Delayed Trade Executions: The outdated system led to slow trade executions because it was not optimized for high-frequency trading, causing a loss of opportunities for traders who needed to work with real-time transactions.

  • Managing Transactions: With the growth in trading volumes but the absence of real-time analytics and system optimization, the platform faced challenges with processing large volumes of transactions quickly and accurately, creating operational bottlenecks.

  • Scaling: The platform’s outdated infrastructure and inability to adapt to the increasing number of users and trades limited its scalability, prompting traders to seek more reliable and faster systems.

  • Operational Downtime: The system faced frequent crashes and downtime, especially during peak hours, because of the strain on the infrastructure. This limited reliability and user experience.

The Approach

Curate’s consultants collaborated with the firm’s internal teams, conducted an in-depth analysis of the platform’s existing infrastructure and operations, and designed a solution that’d perform the necessary and complete overhaul of the platform’s trading infrastructure to make it scalable, real-time, and efficient. This involved focusing on cloud migration and real-time analytics integration.

Key components of the solution:

  1. Discovery and Requirements Gathering: The platform’s present architecture, trading procedures, and operational workflows were thoroughly examined by the Curate team Curate determined important areas for development and designed a solution to upgrade the trading platform by working with the client’s external vendors and internal IT team, including:

    • Aiding scalability with a cloud-based infrastructure

    • Optimizing trade execution with real-time data analytics

    • Redesigning the system architecture for high availability and performance

    • Minimizing latency by ensuring that the system could handle increased transaction volume

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  2. Implementing a High-Performance Trading System: To implement the solution a complete overhaul of the system was necessary with a focus on cloud migration and real-time analytics integration.

    • Cloud-Based Infrastructure: AWS and Azure, which offered the flexibility and scalability required to manage high trade volumes, were used to migrate the platform to a cloud-based infrastructure. This improved the platform’s availability and decreased the possibility of outages during busy trading hours.

    • Real-Time Processing System Optimisation: A real-time data pipeline was introduced with Redis and Apache Kafka, enabling the platform to handle high trade volumes with low latency. The real-time processing capabilities helped conduct trades instantly and increased accuracy and speed.

    • API and Microservices Architecture: Curate improved performance and maintainability by restructuring the platform’s architecture into a microservices-based solution. Redundant services were eliminated to streamline operations and APIs were optimised to lower latency.

  3. Trading Insights with Advanced Analytics: Advanced Analytics tools were integrated to manage large volumes of transactions and provide actionable insights.

    • Real-Time Data Analytics: Curate implemented real-time analytics solutions like Google BigQuery and Apache Flink which gave the client instant access to market data analysis. This helped the platform optimize transaction execution based on real-time data analytics databases and gave traders instant insights into market movements.

    • Predictive analytics for trade optimization: To forecast optimum trade execution times and market conditions, machine learning models were integrated into the platform’s decision-making processes to ensure quicker and more intelligent trading decisions.

  4. Infrastructure and System Scalability:  Curate’s solution design accounted for scalability and future growth. 

    • Auto-Scaling Infrastructure: AWS Auto Scaling was set up to dynamically adjust the platform’s resources based on the trading volume. This allowed the platform to manage high loads without seeing performance deterioration.

    • Load Balancing and Redundancy: To reduce the danger of system overload, Curate used load balancing techniques to divide trading workloads among several servers. Redundancies were incorporated into the system to guarantee high availability even in the case of a network or hardware failure.

5.     Stakeholder Engagement and Change Management: Curate worked closely with the client’s internal teams, external vendors, and key      stakeholders throughout the project to ensure smooth implementation and minimal disruption to ongoing operations.

  • Collaboration with Internal Teams: Curate met often with the platform’s operations and IT departments to align technical requirements and project milestones. This ensured the smooth integration of the new system with the client’s current processes.

  • Vendor Coordination: Curate oversaw the connections with third-party analytics vendors and cloud providers (AWS, Azure) to ensure that every element of the new system was implemented effectively and fulfilled the client’s performance requirements.

  • Training and Support: Curate trained the client’s internal teams on using the new cloud-based infrastructure and analytics capabilities following the system’s deployment. Additionally, Curate also created thorough documentation to facilitate continuous system optimization and maintenance.

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Business Outcomes

Faster Trade Execution


Traders were able to complete transactions nearly instantaneously thanks to the optimized infrastructure, which drastically shortened trade execution time from 2 seconds to 250 milliseconds. This resulted in better market positioning and a competitive advantage for the platform

Enhanced Scalability


The platform was able to grow easily in response to increased user demand and trading volumes thanks to the cloud-based architecture. With no performance deterioration, the platform could now manage twice as many trades as it did before.

Enhanced Operational Efficiency


The platform experienced a 30% increase in operational efficiency after implementing predictive models and real-time data analytics. Real-time transaction processing and instantaneous trader insights eased the burden on internal systems and enhanced overall service delivery.

High Availability and Reliability


Cloud migration, load balancing, and system redundancy together ensured the platform achieved 99.9% uptime even during peak trading hours.

Sample KPIs

Here’s a quick summary of the kinds of KPI’s and goals teams were working towards**:

Metric Before After Improvement
Trade execution time 2 seconds 250 milliseconds 87.5% reduction
Platform uptime 95% 99.9% 4.9% improvement
Transaction volume (trades/day) 500,000 1,000,000 100% improvement
Operational efficiency Moderate High 30% improvement
Downtime during peak hours 10 hours/month 0.5 hours/month 95% reduction
**Disclaimer: The set of KPI’s are for illustration only and do not reference any specific client data or actual results – they have been modified and anonymized to protect confidentiality and avoid disclosing client data.

List of skills, tools, and technologies

The following set of skills, resources, tools, and technologies were used:

  • Cloud engineers with knowledge in infrastructure scalability, AWS, and Azure cloud migration.

  • Data engineers skilled in stream processing technologies, Apache Kafka, and real-time data pipelines.

  • DevOps engineers with expertise in load balancing, auto-scaling system deployment, and high-availability architecture monitoring.

  • Data scientists with expertise in real-time data processing, financial modeling, and predictive analytics.

  • Software architects with expertise in high-performance system design, API optimization, and microservices architecture.

  • Project managers having experience in vendor management, project delivery, and coordinating cross-functional teams.

Tools & Technologies

  • Cloud Platforms: Azure, Azure Data Lake, Azure SQL

  • Data Integration & Automation: Azure Data Factory, Apache Kafka

  • Real-Time Analytics & Reporting: Power BI, SQL Server Reporting Services (SSRS), Apache Spark

  • Security & Compliance: Azure Security Center, encryption protocols, multi-factor authentication (MFA)

  • Collaboration & Project Management: Jira, Confluence, Microsoft Teams

Optimized backend system architecture supporting real-time analytics for financial trading platforms

Conclusion

With an emphasis on cloud-based architecture, real-time analytics, and machine learning models, Curate successfully grew the platform’s capabilities and optimized its infrastructure for high-frequency trading. The platform was now able to provide reliable, high-availability services by handling massive trade volumes and providing quicker transaction processing.

The project exemplifies Curate’s ability to meet the complex needs of the financial services industry by offering innovative, data-driven solutions that help businesses maintain their scalability and competitiveness in a dynamic market.

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