Python Trading Backend Architect
Budget: ₹12,500 – ₹37,500 INR
I need a seasoned back-end developer who has already shipped robust algorithmic trading platforms and understands the pressures of live markets. The heart of the job is designing a highly available architecture—one that keeps running even when market activity surges or a node drops—while staying lean enough for millisecond-level execution.
Here is what I’m looking to have built and refined:
• A FastAPI-driven service layer (asyncio throughout) that exposes REST endpoints and maintains long-lived WebSocket streams for both live market data and order routing.
• Tight integrations with proprietary broker APIs; these feeds will be our single source of truth for quotes, fills, and account events.
• A real-time execution engine able to host multiple trading strategies, enforce risk limits, and persist state in a PostgreSQL store for speed and flexibility.
• Observability baked in from day one—metrics, structured logs, and graceful fail-over so the entire stack meets the high-availability bar.
You’ll own design discussions, schema decisions, and the implementation itself. If you’ve automated strategies before, even better; I’m keen to swap notes on position sizing, portfolio aggregation.
Deliverables are straightforward: a working, containerised back-end with unit tests, brief technical docs, and a short run-book that shows me how to deploy, monitor, and scale the service.
If architecting fault-tolerant, low-latency trading infrastructure excites you, let’s talk specifics and timelines.
Here is what I’m looking to have built and refined:
• A FastAPI-driven service layer (asyncio throughout) that exposes REST endpoints and maintains long-lived WebSocket streams for both live market data and order routing.
• Tight integrations with proprietary broker APIs; these feeds will be our single source of truth for quotes, fills, and account events.
• A real-time execution engine able to host multiple trading strategies, enforce risk limits, and persist state in a PostgreSQL store for speed and flexibility.
• Observability baked in from day one—metrics, structured logs, and graceful fail-over so the entire stack meets the high-availability bar.
You’ll own design discussions, schema decisions, and the implementation itself. If you’ve automated strategies before, even better; I’m keen to swap notes on position sizing, portfolio aggregation.
Deliverables are straightforward: a working, containerised back-end with unit tests, brief technical docs, and a short run-book that shows me how to deploy, monitor, and scale the service.
If architecting fault-tolerant, low-latency trading infrastructure excites you, let’s talk specifics and timelines.