QuantGrid — AI-Powered Trading
Enterprise-grade energy trading platform using Kafka & Visual Knowledge Graphs
Overview
QuantGrid is a high-performance trading platform designed for the volatile energy market. By combining real-time Kafka streams with React Flows visual knowledge graphs, it allows traders to visualize complex bidding strategies and automate execution using ML models.
The Challenge
Energy trading involves massive datasets, millisecond-latency requirements, and complex regulatory constraints. Existing tools were static dashboards that couldn't visualize the "logic" behind automated bids, leading to trust issues with black-box AI models.
The Solution
We built a "Glass Box" AI system. Instead of just showing the output, QuantGrid visualizes the decision tree using interactive node graphs. It ingests market data via Kafka, processes it through Python ML models, and streams recommendations to the React frontend in real-time via WebSockets.
Key Features
Visual Knowledge Graphs
Interactive React Flow graphs that visualize the AI's decision path, allowing traders to audit and tweak logic on the fly.
Real-Time Data Streaming
Kafka + WebSocket pipeline ensuring sub-50ms latency for market data updates and order execution.
Automated Bidding Agents
Python-based ML agents that execute complex arbitrage strategies across multiple grid zones autonomously.
Containerized Architecture
Fully dockerized microservices architecture orchestrated by Kubernetes for high availability and scaling.
Impact & Outcomes
- Reduced trade execution latency by 85%.
- Increased trader trust in AI automation, leading to a 40% increase in automated volume.
- Selected as the primary platform for a regional energy cooperative.
Tech Stack
Implementation Hurdles
- • Synchronizing high-frequency state updates across the visual graph without freezing the UI.
- • Handling backpressure from Kafka streams during market volatility spikes.
- • Designing a "Dark Mode" UI that remains readable under bright trading floor lighting.