Quant trading system architecture: a practical blueprint
Cross-post. Original: stellarbytecapital.com/blog/quant-trading-system-architecture Almost every quant system starts the same way: one script that pulls data, runs a strategy, and places orders. It works — until it doesn't. Add a second strategy, a live account next to the backtest, a third exchange, a teammate, and the single script becomes the bottleneck. The architecture, not the alpha, is now…
A practical blueprint for designing a scalable and secure quant trading system is presented. Most quantitative trading systems begin with a single script that retrieves data, executes a strategy, and submits orders. However, this approach becomes problematic as systems grow. To avoid bottlenecks, the recommended architecture employs a three-tier split that separates strategies, keys, and accounts.
The first tier, known as the control plane, operates as a SaaS platform. It manages user authentication, strategy definitions, parameters, instance scheduling, and system monitoring. Crucially, the control plane does not handle exchange connections or store API keys. Instead, it orchestrates the trading process without directly trading.
The second tier comprises the execution agent, a lightweight process running on the trader's own infrastructure. This agent stores the exchange API keys, maintains connections to the exchange, and executes orders. It communicates with the control plane through a persistent channel, such as WebSocket, to receive assignments and report state. By keeping the API keys within the agent's environment, the risk of exposure is minimized, as a breach of the central system will not compromise trading credentials.
The third tier is the pure strategy, responsible for decision logic alone. It consists of a single pure function called Step() that takes market state as input and returns a decision. This function should contain no network connections, database access, clocks, file I/O, or any other side effects. The same Step() implementation is used by both backtest and live agents, ensuring that backtesting results accurately reflect live performance.
By adhering to this architecture, several benefits become apparent. API keys are physically isolated, as they reside solely on the execution agent's environment rather than the control plane's database. A breach of the central system will not result in the exposure of trading credentials, protecting the platform's security.
Strategies remain portable since they are pure functions with no I/O, allowing the same code to run in backtesting, paper trading, and live environments without modification. Accounts are isolated, with each agent running its own instances, preventing issues from one account from cascading to others. The architecture also enables horizontal scaling, as the tiers can scale independently.
To maintain consistency between backtesting and live trading, strategies must adhere to strategy isomorphism. This means that backtest and live must call the exact same Step() implementation without any conditional branches based on whether they are in a backtest or live environment. This guarantees that a backtested edge behaves identically in production.
Additionally, the architecture promotes instance lifecycle management driven by a periodic clock provided by the control plane. Strategies should not manage their own timers, injecting time as data instead. By keeping the strategy sandbox clean—free from network connections, database access, and time-dependent functions—strategies become safe for automatic optimization.
In summary, this blueprint emphasizes the importance of separating keys, strategies, and accounts to ensure a scalable, secure, and maintainable quant trading system. By adhering to these principles, developers can build systems capable of accommodating multiple strategies and growing as the user base expands.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — it may contain errors, so check the original before relying on it.
