Platform Architecture
Modular Trading OS
Microservices foundation that turns heterogeneous financial data into low-latency, actionable signals, built for rapid iteration, fault isolation, and flexible deployment.
The Trading OS is the foundation the rest of the platform runs on. It breaks a trading system into discrete, independently deployable services so data ingestion, processing, inference, and execution can each evolve, scale, and fail in isolation, without taking the whole system down with them.
Overview
A microservices design gives the platform three properties that matter in markets:
- Rapid iteration: individual components can be updated or replaced without disrupting the rest of the system.
- Fault isolation: a failure in one service is contained rather than cascading, which keeps the platform reliable and easier to operate.
- Flexible scaling: services scale horizontally and deploy on-premise, in the cloud, or in hybrid configurations to meet latency and throughput needs.
On top of that foundation, the OS integrates diverse financial data, ranging from real-time tick data to regulatory filings and sentiment feeds, and turns it into a normalized stream that downstream models can act on.
Market data engine
The data engine ingests and normalizes the inputs a trading system reasons over:
| Source | What it provides |
|---|---|
| Tick data | Real-time price and volume from exchanges |
| Technical indicators | Trend and momentum signals derived from price history |
| Fundamental filings | Regulatory documents such as SEC reports |
| News & social | Live feeds with sentiment extraction |
| Insider transactions | Disclosed activity that enriches market context |
Streaming frameworks (such as Kafka and Flink) move this data with minimal latency, and a vectorized representation makes it efficient to retrieve for machine-learning workloads downstream.
Processing and storage
Ingested data passes through transformation pipelines that extract, normalize, and vectorize it into a consistent format across modules. Two storage layers back the system: time-series databases tuned for high-frequency reads, and vector databases (such as FAISS) for fast similarity search over embedded documents and market state. Standardized REST interfaces expose this data to internal modules and external systems alike.
In practice
- Plug-and-play data: streams can be added or removed as markets and sources change, without re-architecting the platform.
- Hybrid deployment: cloud, on-premise, or hybrid configurations meet regulatory and latency constraints.
- Backtesting and simulation: the same data path feeds historical simulation, so strategies are validated before they go live.