SaaS Commodity Trading Software Development Platform
- Commodity Trading Software
- SaaS Development
- .NET Development
- Artificial Intelligence
- Real-Time Data Processing
- Microservices
- Trading Analytics
- .NET
Overview of Our Client
Our client was a mid-sized commodity trading company operating in fast-moving commodity markets. The organization needed a modern trading platform capable of processing live market data, supporting data-driven decision-making, and automating trading operations.
Its existing tools were unable to properly process growing data volumes, fragmented market information, and sophisticated forecasting requirements.
The client engaged SCAND to develop a SaaS platform that would combine real-time analytics, automated workflows, and AI-powered market forecasting. The solution also had to incorporate commodity risk management capabilities to help traders monitor exposure alongside daily trading operations.
- Region: Global
- Industry: Commodity Trading
- Timeline: ~12 months
Challenge
Commodity trading organizations normally function in highly dynamic markets where early access to accurate information directly impacts business outcomes. This way, the main challenges of this project included:
- Processing massive volumes of market and transactional data through a real-time market data pipeline
- Supporting low-latency data processing for automated trading workflows and decision-making
- Integrating multiple trading venues and external data providers using standards such as the FIX protocol
- Providing predictive analytics capabilities without expensive enterprise infrastructure
- Automating reporting and compliance processes
- Maintaining scalability and reliability under continuously growing workloads
Main Goals
To create a corresponding commodity trading software platform, we determined the following objectives:
- Deliver SaaS trading platform development with cloud-native architecture and advanced analytics
- Process high-frequency market data with minimal latency
- Integrate multiple external trading systems and market data providers
- Implement AI/ML forecasting and predictive analytics trading capabilities
- Automate trading processes and operational reporting
- Build a resilient microservices architecture for future growth
Project Overview
SCAND engineers contributed to the backend development, real-time data processing pipelines, and advanced analytics functionality of the commodity trading platform.
To support scalability and flexibility, we built the platform using a microservices trading architecture that simplified deployment, integrations, and future expansion. Such an approach simplified deployment, made it easier to integrate with external trading systems and market data providers, and allowed the platform to grow as business needs changed.
In addition, we developed high-performance data pipelines capable of ingesting, normalizing, and processing high-frequency market data in real time. At the same time, we integrated predictive analytics models to support market forecasting, automated decision-making, and trading workflow optimization.
Solution
The completed solution was a cloud-based commodity trading and analytics platform that combined real-time data processing, AI-driven forecasting, and automated operational workflows.
Its event-driven architecture supported continuous ingestion of market data, while predictive models helped organizations spot market opportunities and optimize trading strategies.
Core Platform Capabilities
- Real-time market data ingestion and processing
- Automated trading workflow orchestration
- AI-powered forecasting and predictive analytics
- Data aggregation, normalization, and transformation
- Integration with external trading systems and market data providers
- Automated reporting and regulatory data exchange
- Built-in risk monitoring and anomaly detection
- Support for multiple commodity markets and configurable trading domains
User Workflow
- Market Data Collection: The platform ingests market data and trading information from external providers.
- Data Processing: Incoming data is normalized, aggregated, and distributed through event-driven pipelines.
- Analytics & Forecasting: AI models generate market forecasts and operational insights to support trading decisions.
- Trading Operations: Automated workflows execute predefined business processes and assist traders with decision support.
- Monitoring & Reporting: Performance metrics, trading activity, and operational reports are available through centralized dashboards.
Technology Stack
To support CTRM software development, live trading, predictive analytics, and large-scale data processing, we selected the following suite of technologies:
Backend
- .NET Core
- C#
- Dapper
- Microsoft SQL Server
- RabbitMQ
- FIX Protocol
- SoupBinTCP
- Microservices architecture
Frontend/UI
- Angular
- TypeScript
- WebSockets
AI & Analytics
- Python
- AI/ML models for forecasting and predictive analytics
Infrastructure & DevOps
- Grafana
- Prometheus, Serilog
- Azure DevOps
- Jenkins
- Octopus Deploy
Core Team
- Product Owner: Defined the product vision and trading platform roadmap
- Solution Architect: Designed the microservices architecture and data processing pipelines
- .NET Engineers: Developed trading services, APIs, integrations, and backend infrastructure
- Frontend Engineers: Built analytics dashboards and trading interfaces using Angular
- Data & AI Engineers: Implemented forecasting models, data pipelines, and predictive analytics
- DevOps Engineers: Managed cloud infrastructure, monitoring, CI/CD, and deployment automation
- QA Engineers: Validated trading workflows, analytics accuracy, system reliability, and platform performance
Related Cases
- React.js
- Spring Boot
- Node.js
- Spring Boot
- Node.js
- Java Spring
- System Integration
- Data Orchestration
- .NET
- MS SQL
- RabbitMQ
- C++
Results
The commodity trading platform enabled trading organizations to modernize their operations through automation and real-time analytics. The major results include:
- Centralized processing of high-volume market data
- Low-latency trading workflows and decision support
- AI-backed forecasting for improved market analysis and better decision-making for mid-market trading companies.
- Automated reporting and regulatory data exchange
- Scalable microservices architecture for multiplying trading operations
- Improved visibility through real-time monitoring
- Flexible SaaS platform supporting multiple commodity markets