Automated Trading
Platform Modernization
How we rescued a failing trading system by rebuilding the backend with tick-level data accuracy, modernizing the UI, and enabling concurrent multi-symbol trading strategies.
Challenge & Objectives
An algorithmic trader needed to rescue a failing trading platform that previous developers couldn't get working. Critical issues with data accuracy and architecture blocked production deployment.
Key Challenges
Previous system couldn't reliably source tick-level market data, causing missed trades and execution errors
Could only trade one symbol at a time, severely limiting strategy effectiveness and scalability
Multiple orders for same action (close + open separately) and poor error handling led to excessive costs and failed trades
Legacy UI made it difficult to monitor positions, strategies, and system health in real-time
Project Objectives
- 1Integrate reliable tick-level data source (Databento) for execution accuracy
- 2Rebuild backend architecture to support concurrent multi-symbol trading
- 3Implement robust error recovery and state management
- 4Modernize UI with real-time position tracking and strategy controls
- 5Enable scalability for additional strategies and broker integrations
- 6Deliver production-ready system previous developers couldn't build
Development Process
A rapid rebuild approach that addressed core architectural issues while modernizing the entire platform for production reliability in just one week.
Project Timeline
Total Duration: 1 week- Audited existing codebase to identify critical failures
- Documented tick data integration gaps and data quality issues
- Analyzed single-threaded bottlenecks preventing multi-symbol trading
- Defined requirements for broker integrations and strategy execution
- Integrated Databento for reliable tick-level market data
- Rebuilt architecture with OOP and strategy pattern for scalability
- Implemented Redis Streams for concurrent multi-symbol data distribution
- Built robust state management with error recovery capabilities
- Developed modular strategy framework (EMA, Supertrend, 0DTE)
- Implemented tick-level execution engine for precise order timing
- Built multi-threaded orchestration for concurrent symbol processing
- Redesigned dashboard with Next.js 15 and real-time updates
- Added broker integrations (TastyTrade, Schwab) with OAuth2
- Conducted paper trading validation with live market data
- Tested multi-symbol concurrent execution under load
- Deployed to production with monitoring and alerts
Results & Impact
The modernized platform delivered what previous developers couldn't: a production-ready automated trading system with reliable data, concurrent execution, and professional UI.
Key Achievements
Technical Implementation
A complete architectural rebuild using modern technologies, scalable patterns, and production-grade infrastructure for reliable automated trading.
backend
data
frontend
integrations
Tick-Level Data
Databento integration provides microsecond-precision market data for accurate execution
Multi-Symbol Trading
Concurrent processing of unlimited symbols via multi-threaded architecture
Robust Recovery
State persistence and error recovery prevent position loss during failures
Modular Strategies
Strategy pattern enables easy addition of new trading algorithms
Client Testimonial
"After months of frustration with developers who couldn't solve the tick data problem, this team delivered a production-ready system in just one week. The multi-symbol execution is exactly what I needed, and the modern UI makes monitoring positions effortless. This is the platform I've been trying to build for over a year."
Need to Rescue or Build a Trading Platform?
Whether your current system is failing or you're starting fresh, we deliver production-ready automated trading platforms with reliable data, modern architecture, and professional UI.
Next Steps: Free consultation → System audit → Implementation roadmap