The Intelexity team has successfully implemented the development of a private algorithmic trading platform specifically adapted to the needs of institutional investors. This project aimed to minimize human influence in the trading process and accelerate responses to market fluctuations, which is especially important in highly volatile financial market conditions.
The primary goal was to develop an intuitively understandable user interface that would allow clients to easily configure and manage complex algorithmic strategies. With a focus on performance and data processing speed, the Intelexity team created a platform capable of handling large volumes of information in real-time, thereby ensuring efficient trading operations.
Some of the implemented features include:
Development:
Within the Private Trading Platform project, our team of engineers designed and implemented an architecture that fully meets the requirements of low latency, high load, and high fault tolerance. To ensure platform functionality, more than 15 microservices were developed, interconnected through Kafka message queue and Kafka Streams. Data storage for services is provided by a PostgreSQL database based on the Patroni solution, which automates management of the PostgreSQL master node and replicas, ensuring fault tolerance. Integration with market data providers via APIs such as Interactive Brokers, LSEG, Exegy was implemented. The system supports connectivity to various market data sources, including historical data.
The Development team implemented:
QA:
Within the project, our QA engineering team organized a full cycle of quality assurance for the Private Trading Platform. We developed a base of 2000+ functional and integration test scenarios in TMS Allure TestOps to verify all key product components, closely integrating testing processes with development and DevOps.
Given the project's specificity and complexity, we placed special emphasis on automating quality control processes. Our engineers designed and developed a framework using Java / Spring Boot / Gradle / Junit / Allure for automated testing of functionality on REST API and UI web platforms. The test coverage for product functionality reached over 95%.
All tests were divided into modular sets integrated into GitLab CI pipelines, providing instant verification of product components through parallel execution. We also conducted a series of simulations using Gatling + Grafana for load testing of services and distributed interactions between them, identifying and addressing bottlenecks at module integration points.
DevOps:
Within the Private Trading Platform project, we established a complete CI/CD process for the rapid delivery of changes to over 15 microservices in production. GitLab CI, Helm, and ChartMuseum were used to build the CI/CD system from scratch. Build pipelines were created for system components with built-in code quality checks and vulnerability checks (SonarQube, Dependency Check).
Our DevOps engineers configured a geographically distributed Kubernetes cluster within the AWS cloud infrastructure, ensuring high availability, fault tolerance, and resource distribution flexibility, optimizing performance and reducing latency by proximity to exchange platforms.
To ensure 100% uptime of solutions, we used Blue-Green deployment strategies, minimizing downtime during service updates. This was crucial for exchange operations, where even minimal downtime can lead to significant losses.
For the cluster group, we deployed a Prometheus and Grafana-based High Availability monitoring system, reducing incident response time.
The development of a private algorithmic trading platform by the Intelexity team brought a number of significant benefits to our client, an institutional investor. First of all, this is an increase in the speed of trading operations by 40%, which is critical for quickly responding to market fluctuations. This improvement allowed the client to effectively exploit market opportunities that would not have been possible with manual management.
In addition, the improved risk management module reduced the likelihood of losses by 30%. Thanks to the strategy designer, the customer has the opportunity to quickly add new algorithms. This not only improves investment security, but also promotes client confidence in decision making. Automation of trade processes resulted in a 50% increase in productivity for the client's team, freeing up resources for more important tasks such as analysis and strategic planning.
Optimization of infrastructure and portfolio management processes reduced operating costs by 25%. This significant cost reduction makes investments more efficient and improves overall operating profitability.
Measurable indicators of success include a 40% reduction in reaction time to market changes, demonstrating the platform's outstanding speed and adaptability. It was also achieved to increase the level of security of client data and funds to standards consistent with best global practices. Coverage of product functionality by autotests at the level of 95% ensures the stability and reliability of the platform. In addition, the system's fault tolerance guarantees 99.999% uptime, which is critical for uninterrupted exchange operations.
As a result, the developed platform not only improved operational efficiency and reduced risks for the client, but also allowed it to significantly increase its competitiveness in the market. The integration of cutting-edge technology and a personalized approach to client needs has ensured excellence in algorithmic trading, backed by concrete, measurable results and statistics.
Based on a successful track record of developing a private algorithmic trading platform for an institutional investor, the Intelexity team demonstrates its ability to provide high-tech, customized solutions that can radically improve operational efficiency and risk management. Our development not only speeds up and optimizes trading processes, but also significantly increases the security and reliability of operations, which is confirmed by an increase in the speed of trading operations by 40%, a reduction in the likelihood of losses by 30% and the achievement of 99.9% system uptime.
We are confident that our experience and expertise can help other clients facing similar challenges in algorithmic trading. Regardless of your specific needs—whether it's increasing productivity, reducing costs, automating processes, or improving risk management—we can provide a solution tailored to meet your unique business goals.
If you are looking for a way to improve your trading operations, increase the security and efficiency of your investment process, we invite you to contact us. The Intelexity team is ready to provide you with additional information and advice to help you implement your strategies and reach new heights in algorithmic trading. Don't miss the opportunity to transform your trading operations - contact us today!
