80%
Reduction in Processing Time
Automated Data Pipeline and Orchestration for ML-Based Algorithmic Trading
Executive Snapshot
A FinTech startup partnered with Transcloud to design and implement a fully automated, cloud-native data pipeline to power its ML-based algorithmic trading platform.
The objective was to enable real-time data ingestion, transformation, and model execution and leveraging external APIs such as Refinitiv and Interactive Brokers, while ensuring scalability, security, and cost efficiency.
Through this engagement, the client achieved:
- Automated end-to-end data ingestion and orchestration
- Seamless integration with key trading APIs
- Secure, scalable, and cloud-native architecture
- Simplified monitoring and operational visibility
The Challenge: Building a Reliable Data Foundation for Trading
As a startup building a new machine learning-based trading platform, the client faced multiple technical and architectural challenges. Managing financial data from multiple third-party sources demanded precision, consistency, and real-time responsiveness — all while maintaining a cost-effective, cloud-first infrastructure.
Key Challenges:
- Complex Data Sources: Integrating and normalizing real-time data from Refinitiv and Interactive Brokers APIs.
- Scalability Requirements: Handling fluctuating workloads during peak trading hours without downtime.
- Operational Complexity: Managing data pipelines manually led to inefficiencies and visibility gaps.
- Security Concerns: Ensuring all communication and data transfers remained compliant and secure within a private network.
- Rapid Experimentation Needs: The platform required a flexible architecture to test and deploy evolving ML models efficiently.
Without automation and proper orchestration, scaling the ML pipeline and ensuring reliability across trading cycles was becoming increasingly difficult.
The Solution: Cloud-Native Data & ML Pipeline Engineering
Transcloud developed a robust and modular data engineering architecture that automated the entire lifecycle — from ingestion to ML model execution — all built on Google Cloud Platform (GCP).
What We Did:
- Airflow-Based Orchestration: Implemented Cloud Composer (Airflow) to automate and monitor every stage of data ingestion and processing.
- ML Pipeline Automation: Designed scalable pipelines to train and deploy machine learning models efficiently.
- Secure Network Design: Established a protected private network ensuring data confidentiality and API communication security.
- External API Integration: Seamlessly connected with Refinitiv and Interactive Brokers APIs for real-time financial data retrieval.
- Centralized Logging & Monitoring: Enabled unified visibility into data operations and performance through GCP-native tools.
- Modular Framework: Built the pipeline as a flexible framework, capable of supporting future data sources and ML enhancements.
- Cost Efficiency: Optimized infrastructure provisioning to balance performance and operational costs.
This solution provided the client with a cloud-native foundation capable of scaling and adapting as their trading models and data needs evolved.
The Results: Automation, Agility & Scalability Delivered
The engagement resulted in a future-ready, automated ecosystem that accelerated the client’s journey toward production-grade ML trading operations — achieving measurable improvements in reliability, performance, and transparency.
Business Impact:
- End-to-End Automation: Data flows from ingestion to analysis now operate autonomously, reducing manual intervention by over 80%.
- High-Performance Data Ingestion: The system now handles up to 100,000 ticker events per minute, ensuring uninterrupted processing during volatile trading sessions.
- Improved Scalability: The cloud-native architecture consistently delivers 99% uptime SLA compliance, maintaining stable performance even under heavy loads.
- Enhanced Security: All communications occur within secure, isolated environments with built-in encryption and access control.
- Operational Transparency: Centralized logging and monitoring provide real-time visibility into every pipeline component, accelerating issue resolution and performance optimization.
- Future Flexibility: The modular design enables rapid integration of new APIs and ML models, supporting ongoing innovation and expansion.
By modernizing their data and ML foundation, the client positioned themselves for rapid innovation, operational resilience, and sustainable growth in the FinTech landscape.
Why Transcloud
The client selected Transcloud for its deep expertise in cloud-native engineering and MLOps — and its ability to align technology outcomes with strategic business goals.
Why They Partnered With Us:
- End-to-End Cloud Competence: Proficiency across GCP services, including Composer, BigQuery, and Cloud Functions.
- Strong MLOps Capability: Experience in automating and scaling ML workflows for real-time decision systems.
- Security-First Architecture: Every layer of the solution was designed for compliance and data protection.
- Scalable and Cost-Efficient Design: Our frameworks ensure long-term flexibility and optimized operational spend.
Transcloud’s technical precision and cloud-native approach empowered the client to launch a resilient, scalable trading platform that’s ready for the future of intelligent finance.
