Chapter 15: Applying Design Patterns in Financial Applications

In this section

• 15.1 Unique Challenges in Financial Software
  • Performance and Latency Requirements in Financial Applications: Leveraging Clojure for Low-Latency and High-Throughput Systems
    Explore the critical performance and latency requirements in financial applications and how Clojure's performance characteristics and optimization techniques can meet these demands.
  • Regulatory Compliance and Auditability in Clojure: Ensuring Compliance with GDPR, PCI DSS, and SOX
    Explore how Clojure's functional programming paradigm aids in achieving regulatory compliance and auditability, focusing on GDPR, PCI DSS, and SOX.
• 15.2 Real-Time Trading System Implementation
  • Architectural Overview of a Real-Time Trading System
    Explore the high-level architecture of a real-time trading system, focusing on market data ingestion, order management, and execution engines, with an emphasis on data flow and decision-making processes.
  • Managing Market Data Streams: High-Velocity Data Handling with Clojure
    Explore techniques for managing high-velocity market data streams using Clojure, focusing on core.async and Apache Kafka for efficient data processing and model updates.
  • Order Execution Pipelines: Implementing Order Validation, Routing, and Execution in Clojure
    Explore the intricacies of building robust order execution pipelines in Clojure, focusing on order validation, routing, execution, risk checks, and compliance validations.
• 15.3 Functional Event Processing
  • Event Sourcing in Clojure: A Comprehensive Guide for Java Professionals
    Explore the intricacies of implementing Event Sourcing in Clojure, providing a robust audit trail and facilitating temporal queries, with practical examples and best practices.
  • CQRS: Command Query Responsibility Segregation in Clojure
    Explore the CQRS pattern in Clojure, a powerful approach to separating read and write models for enhanced scalability and maintainability.
• 15.4 Risk Calculation and Analysis Pipelines
  • Batch vs. Real-Time Calculations: Optimizing Risk Assessment in Clojure
    Explore the intricacies of batch and real-time calculations in financial risk assessment using Clojure. Learn how to design systems that efficiently handle both processing modes.
  • Distributed Computing with Clojure: Leveraging Apache Spark for Large-Scale Calculations
    Explore distributed computing with Clojure and Apache Spark, focusing on parallelizing computationally intensive tasks for large-scale data processing.
• 15.5 Ensuring Precision and Correctness
  • Floating-Point Arithmetic in Financial Calculations: Challenges and Solutions
    Explore the intricacies of floating-point arithmetic in financial applications, understand its pitfalls, and learn how to use arbitrary-precision libraries like Clojure's math.numeric-tower and Java's BigDecimal for precise calculations.
  • Ensuring Numerical Accuracy in Clojure: Testing Strategies and Best Practices
    Explore comprehensive strategies for testing numerical accuracy in Clojure applications, including comparison against known values, sensitivity analysis, and property-based testing.
• Lessons Learned and Best Practices in Clojure Financial Systems
  Explore key takeaways, challenges, solutions, and recommendations from implementing financial systems using Clojure, offering insights for Java professionals transitioning to functional programming.