While LLMs are generally deficient at creating production-grade algorithmic trading systems, they are adept at a number of discrete tasks and components that go into building automated trading strategies or larger distributed systems in finance. Here are the top few: Order entry protocols: All trading operations that connect directly to exchanges involve a time-consuming, developer-intensive task of adapting each exchange’s proprietary order protocol to their algorithmic trading system. Most venues provide detailed specification documents for FIX, binary, and JSON-based messaging that can be read by LLMs, saving tens of hours of protocol normalization work. If the exchange provides a sandbox environment, an LLM can test its own adaptation in a REPL-style loop. Market data: Similar to order entry, LLMs are great at normalizing L1, L2, and L3 feeds for consumption by strategies and modeling processes. LLMs are also able to build the rote connectivity and authentication stack required to retrieve the data streams. LLMs lack the level of taste required to develop the right abstraction for feed normalization that preserves venue-specific details while also enabling developers to write venue-agnostic strategies. However, once such abstractions are established, there is little need to manually adapt market data protocols. Research database management: It’s common practice to record every event that passes through a trading system in databases: book updates, orders, cancels, trades, fair value updates, error messages, etc. General-purpose time-series databases are the common solution, and form the foundation of research, monitoring, and debugging stacks. Assuming that the trading system uses a well-known database technology such as Postgres or ClickHouse, LLMs can handle schema generation, table compaction, version migrations, backups, and automated reports. User interfaces: Whether an algorithmic trading system is black-box (minimal human input during the order lifecycle) or gray-box (automated order placement with manual human intervention on parametrization), all traders leverage some form of user interface for trade, risk, and P&L monitoring. Trading engineers tend not to be the most skilled GUI developers, but internal interfaces do not need to be pretty to be highly functional. Out of all areas of software development, frontier coding models have reached expert level in building terminal- and web-based applications. In building our derivatives exchanges, we’ve designed our APIs and external-facing components with both institutional traders and agentic developers in mind. This should be the financial industry norm. Most of our institutional customers have made use of our Claude skill for protocol integration, and we expect other brokerages and exchanges to support these emerging methods of trading system development in addition to agentic trading.