Building a Trading Engine in Java: Beyond BUY and SELL Signals

The development of the NERV Trade trading engine in Java presented numerous challenges beyond simply generating BUY or SELL signals. The system needed to become a durable, broker-neutral trading engine capable of handling complex real-world scenarios.

One key insight was that a trading signal and a trade are distinct concepts. A signal represents a strategy's decision, while a trade encompasses the actual execution, order lifecycle, broker interactions, and accounting. This distinction is crucial when building a robust system.

The architecture evolved to separate market data analysis, trading decisions, execution, and persistence into distinct responsibilities. Market analysis should not concern itself with broker-specific details, while a broker adapter shouldn't calculate technical indicators. Each component has its own clear purpose, ensuring the system remains maintainable and scalable.

Another important realization was that a position is not the same as a trade. Multiple strategies can generate exposure in the same instrument without affecting each other's lifecycle. A trade intent owns its exposure, and the aggregate position is simply a derived view. This principle helps avoid unintended consequences when strategies exit or reverse exposure.

Partial fills added another layer of complexity. Realistic execution scenarios involve multiple partial executions, requiring the engine to track individual executions, update relevant state, and maintain consistency. Acknowledgment of an order doesn't guarantee execution, and the system must handle asynchronous updates and reconciliation scenarios carefully.

Handling duplicate executions, which can occur in distributed systems or during restarts, is another critical aspect. The engine must correctly process the execution only once, preventing incorrect position updates. These issues necessitated careful design and robust error handling mechanisms.

As the system grew in complexity, each architectural decision became increasingly important. The separation of concerns, clear ownership of lifecycle stages, and careful tracking of asynchronous updates all contributed to a more reliable and maintainable trading engine. This journey from simple signal generation to a comprehensive trading system demonstrates the engineering challenges inherent in building robust financial infrastructure.