TradeVision
A high-performance AI trading intelligence platform with a multi-agent analysis system and sub-20ms latency WebSocket streams.
Case study
TradeVision
Problem
What gap does this fill?
Financial market analysis requires processing real-time data streams at high throughput while running computationally expensive risk models — a combination that typically requires either compromising on latency or on analytical depth.
Solution
What was built
TradeVision is built on a scalable FastAPI backend with a 3-layer Redis cache and async I/O with connection pooling to process WebSocket streams at sub-20ms latency for 5,000+ concurrent users. The analysis layer is an autonomous multi-agent system with 4 specialist agents coordinated via LangGraph DAGs for stateful routing, with Monte Carlo risk analysis evaluating 10,000+ market scenarios per trade.
My Contribution
What I specifically built
I architected the FastAPI backend with the 3-layer Redis caching strategy, implemented async I/O and connection pooling for the WebSocket stream processing, and designed the 4-agent LangGraph system with stateful DAG routing. The Monte Carlo risk simulation pipeline and the sub-20ms latency target were the core engineering objectives I built around.
Technology
Stack used
Backend
AI / Agents
Infrastructure
Engineering Decisions
Notable technical choices
3-layer Redis cache architecture
separates hot data (real-time quotes), warm data (recent analysis), and cold data (historical scenarios) to maximize throughput while minimizing compute.
LangGraph DAGs for stateful agent routing
4 specialist agents (market analysis, risk assessment, scenario modeling, execution planning) coordinate via directed acyclic graphs, enabling conditional branching without state loss.
Monte Carlo simulation at 10,000+ scenarios per trade
probabilistic risk assessment rather than point estimates, giving a full distribution of outcomes rather than a single predicted result.
Sub-20ms WebSocket latency via async I/O + connection pooling
allows real-time streaming to 5,000+ concurrent users without blocking the event loop.