Next-Generation Energy Trading Platform
- С++
- RabbitMQ
- Custom Software Development
- Cloud Services (AWS)
- FinTech
Overview of Our Client
Our client was a leading German company working in the energy trading market. As the business grew, the volume of daily transactions increased much faster than expected, and their current trading system failed to cope with such loads. Moreover, it required constant manual intervention from the team, which eventually led to delays, extra workload, and difficulties reacting to market changes. In order to help the company move ahead, we needed to develop a new solution that would speed up everyday operations, reduce manual activities, and provide traders with tools for algorithmic strategies.
Challenge
The client's legacy platform limited the processing of approximately 5,000 trades per day and relied heavily on manual data entry. This configuration limited both operational productivity and the ability to respond to market fluctuations. On top of that, the platform was unable to support complex electricity trading activities, such as interconnection reporting, capacity reservations, and automated risk calculations, creating bottlenecks for growth.
Primary Objectives
In order to assist the client in getting over their challenge and to deliver a solution that really works, we made a decision to break down the project into smaller parts that are easier to handle and set the following goals:
- Automate routine trading operations and reduce manual intervention.
- Enable financial and physical settlement of energy trades.
- Support real-time processing of large volumes of transactions.
- Design a system that would allow algorithmic trading and sophisticated reporting.
Project Overview
To meet the scalability requirements, we architected and built a contemporary trading system based on microservices. The system was event-driven and trades were processed in real-time from the pipelines. At the same time, background services took care of heavy calculations and risk management. The platform combined financial logic, hardwired delivery limitations, and various market rules, thus enabling traders to concentrate on making the right call instead of the tedious task of manual processing.
- Region: Europe
- Industry: Energy Trading
- Timeline: 2020–2024
Summary of the Project
As a result, the platform became a fully integrated trading environment that combined automation, algorithmic support, and flexible monitoring. Trades, financial calculations, and physical delivery operations all benefited from real-time processing, while manual override capabilities allowed traders to intervene without disrupting automated workflows. The system handled complex calculations for power losses, capacity reservations, and market adjustments, and provided interconnector reporting to ensure compliance and accurate cross-border trading.
Platform Features:
- Microservices Architecture: Each service handled a specific function, enabling independent scaling and faster updates.
- Event-Driven Trade Processing: Apache Kafka and Azure Event Hubs streamed trades in real time.
- Manual Override Capabilities: Traders could intervene when needed without disrupting automated processes.
- Financial and Physical Trade Logic: Included calculations for power losses, capacity reservations, and external market adjustments.
- Interconnector Reporting: Supported compliance and reporting for cross-border energy flows.
Technology Stack
To support the platform’s high-performance requirements, we selected a modern technology stack combining proven frameworks, cloud services, and messaging systems
Languages & Frameworks
- C++
- Java
Messaging & Streaming
- Apache Kafka
- Amazon MSK
- RabbitMQ
Architecture
- Microservices
- REST APIs
Databases
- Amazon Aurora PostgreSQL
- Amazon DynamoDB
Cloud & Hosting
- Amazon EKS,
- Amazon S3
- Amazon EBS
- Amazon EFS
DevOps & IaC
- AWS services for CI/CD,
- Terraform
- Helm
Business Impact
The platform delivered measurable business benefits by improving trade processing, operational efficiency, and market responsiveness. Key results included:
- A capability to process over 1 million deals per day, with capacity continuing to grow to meet market demand.
- A dramatic reduction in manual processing overhead
- Increased speed, turnover, and trade value.
Core Team
- Project Manager: Oversaw delivery milestones and client coordination.
- Solution Architect: Designed microservices and event-driven workflows.
- Full-Stack Developers: Built backend processing pipelines and integration layers.
- QA & DevOps Engineers: Implemented testing, CI/CD pipelines, and Kubernetes deployments.