Client Overview
Client is a leading global asset-management firm, with over 25 years of experience in financial services. Its deep expertise in investment strategy, risk management, macro‐economics, and market analytics gives it a strong reputation for delivering highly regarded financial reports and advisory services. As part of its commitment to client experience and risk oversight, Client decided to improve how it monitors, evaluates, and supports client trading activities across multiple registered users.
The Challenge
Client needed a system that could track and analyze the real-time trading behavior of many users under different strategies, while maintaining high standards of security, accuracy, and performance.
They wanted to allow clients to define their own strategies and indicators, automate trades, and receive alerts for potentially profitable opportunities or risk exposures.
The platform had to integrate reliably with external data sources (e.g. stock exchanges, commodities and currency feeds), support algorithmic trading automations, and provide advanced tools for market analysis (indicators, charts, etc.). Performance, reliability, and software architecture scalability were critical. Also, because of regulatory and business risk, error margins needed to be very low; delays or inaccuracies could harm trust and lead to financial losses.
Our Approach
To address this, our team adopted a multi-phase, structured approach:
- Architectural Redesign & Scalability : Pirotan Developer built the system using microservices architecture to allow independent component scaling, better fault isolation, and clear delineation of responsibilities.
- Real-Time Data Integration & Automation : Integration of stock exchange APIs and external feeds to pull live data for commodities, currencies, and equities. We also developed modules for user-defined automated trading rules and algorithmic execution (buy/sell automation).
- User-Driven Strategy Engine & AI Alerts : Deployed Developer designed and implemented a strategy engine that allowed users to configure indicators, thresholds, and custom strategies. An AI component monitors market data and user strategies to generate alerts for potential opportunities.
- Quality, Reliability & DevOps Excellence : We ensured strong CI/CD pipelines, containerization (Docker), automated testing, continuous deployment, monitoring, and incident-analysis capabilities. Also, strong focus on database performance (for MongoDB, PostgreSQL), data integrity, and responsive UI (Angular).
- Dedicated Staffing & Continuous Improvement : Pirotan provided senior engineers in staff-augmentation model, including full ownership of certain modules, regular reviews, post-mortem analyses of incidents, and ongoing refinement based on user feedback.
Project Description & Key Deliverables
Platform Features: Multi-user dashboards, user-defined algorithms, custom market indicators, real-time trading feed, and automation of buy/sell orders.
Technologies Used: .NET Core, MVC, Entity Framework, Microservices, Angular v12; data stores with MongoDB & PostgreSQL; hosted on Azure; CI/CD with Docker, Bamboo/Octopus; strong emphasis on scalable, maintainable architecture.
Role & Service Type: Staff augmentation: a senior software engineer worked continuously for ~2 years alongside Client’s internal team.
Major Deliverables
- Designing the system architecture for scalable, multi-tenant, microservices-based platform.
- Building modules for custom strategy definition and automated trading execution.
- Implementing AI-based alerting for trade opportunities.
- Setting up and optimizing CI/CD pipelines to reduce deployment time and maintain stability.
- Ensuring database performance, reliability under high load, and secure integration with external backend APIs.
- Conducting post-mortem reviews for escalations, fixing root cause issues, and implementing preventive strategies.
Results & Client Satisfaction
Performance & Reliability Improved: Client saw substantial reduction in latency, higher system uptime, and fewer incidents than in earlier platforms.
Greater Client Confidence: The new dashboard and alerting features gave Client’s clients more visibility in their trading, improving trust and satisfaction.
Automation Adoption: Many clients adopted automated trading and custom strategy tools, which improved their decision-making responsiveness to market movements.
Strategic Partnership: Because of the high quality, transparent delivery, and ability to address escalations promptly, Client began to see us not merely as a contractor but as a reliable, long-term partner for their trading tools/monitoring needs.
Conclusion
Through careful architecture, dedicated staffing, and continuous improvement, our work enabled Client to meet its goal: monitoring and evaluating multi-user trading activities with speed, accuracy, and client-centric flexibility. The success of the project led to ongoing collaboration and laid foundations for future enhancements.
