-
Top Three Posts
Free updates by email
-
Recent Posts
Categories
- Agile
- API
- Architecture
- Automation
- AWS
- Booking Practice
- Business Rules
- Cloud
- Conferences
- Connectivity
- Continuous Integration
- Data Flow
- Data Mapping
- Databases
- Derivatives Industry
- DevOps
- Docker
- Domain Model
- EMIR
- Enterprise Integration
- Enterprise Wide Data
- Eurex 13
- FpML
- High Frequency Trading
- Home Computing
- Humour
- ISDA
- Jenkins
- JMeter
- Kanban
- MariaDB
- Message Queue Software
- Messaging
- Messaging Standards
- MiFID
- Models
- MTFs
- Music Production
- Oracle
- Orchestration
- Project Management
- Rapid Application Development
- Regression
- Regulatory Reporting
- Replay
- Risk Management
- Rogue Trader
- Routing Rules
- Rule Engines
- Scrum
- Security Exchanges
- Smoke Testing
- Static Data
- STP
- Test Automation
- Test Data
- Testing
- TICK
- Time Recording
- Trade Flow
- Trade Organisations
- Travel
- Uncategorized
- Vendors
- VirtualBox
- Virtualisation
- WaveMaker
Tags
- adapter framework
- agile
- Asynchronous
- AWS
- break
- CCP
- ClearVision
- Cloud computing
- Consolidated Tape
- Continuous Deployment
- Continuous Integration
- devops
- Docker
- EC2
- edge case
- Enterprise Integration
- Enterprise Integration Patterns
- eurex 13
- family
- FFastFill
- fpml
- Google App Engine
- HTTP API
- IDX
- infrastructure
- Jenkins
- logging
- Messaging Systems
- Middle Office
- OTC
- Post-Trade
- Project Scope
- regression test packs
- rules
- Rules engine
- screen time
- SEALS
- security
- sequencing
- static data
- STP
- SunGard
- testing
- use case
- workflow
Tag Archives: rework
Success with Matching Engines – what does that look like?
Implementing a Matching Engine application presents a host of challenges. If you’re responsible for such a project then you need to give serious consideration to a number of critical system components. Here are just a few of the questions … Continue reading
Posted in Business Rules, Messaging, Models, Project Management, Regression, Risk Management, Routing Rules, Rule Engines, Test Automation, Testing
Tagged automated, complexity, data set, downstream, engine, false positives, freeze, implementation, legacy design, matching, overlap, overlapping, problem space, regression, release, requirements, result set, rework, rule management, rules, slice and dice
Comments Off on Success with Matching Engines – what does that look like?