Assistant system for traders
#Fintech #.NET #Kafka #PostgreSQL #Trading
Project definition
Location
Client
Project type
Industry
Service list
Back-end Development
Business Analysis
DevOps
Service Integration
AQA/QA
Team size
M-Size (4–8 engineers)
Budget
$250,000+
Task
The client is a fintech company from Europe, engaged in trading and investments, that commissioned the development of a new project for evaluating and supporting traders on their platforms. The system must collect and store historical data on supported markets. All data should be stored using minimal memory while ensuring an acceptable read speed
Solution
The first phase of the project has been successfully completed. To achieve this, our team developed and implemented algorithms that significantly reduce memory usage, both for RAM and storage. Currently, the system supports three types of tradable products (crypto, forex, options) and uses data from eight different providers. The client highly appreciated the results achieved by our team, and the project roadmap is planned until 2026
Impact
Traders gained access to real-time market data, advanced screeners, and seamless order handling. The platform now processes millions of market updates daily with minimal latency, supporting three major asset classes — crypto, forex, and options — from 8 integrated providers. By optimizing storage and memory use, infrastructure costs are kept relatively low, while fault-tolerant architecture enables uninterrupted trading and a roadmap of continuous growth for the next few years
💡 This is an AI-CORE project
💡 This is an AI-powered project
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Level of integration
We created a service for collecting, storing, and analyzing data on cryptocurrency markets, foreign exchange transactions, and securities
Data management
The collection and storage of large volumes of market data was implemented, with the ability to read it relatively quickly while using minimal memory
Development roadmap until 2026
The highest evaluation of the work done by the client is their desire to continue developing their products with us. The project roadmap is planned for the next one and a half years
Development Process
We built a high-load backend optimized for fault tolerance and memory efficiency. Our team implemented core features such as real-time market updates, a customizable screener, simulated trading environments, and a full market data store designed for fast analysis with minimal resource consumption. The architecture supports all major tradable products and integrates with multiple liquidity and data providers. SignalR ensures instant delivery of live updates, Kafka and Redis handle event streaming and caching at scale, and QuickFIX powers connectivity to trading venues. With PostgreSQL and Apache Parquet managing structured and historical data, the system provides both speed and long-term resilience. This scalable foundation allows the client to continuously expand functionality while maintaining stable performance under heavy load
Technologies
Assistant system for traders
Web platform for analyzing cryptocurrency market data, transactions, and securities
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