Case Study / QuantEdge Capital
AI-PoweredPredictiveTradingAnalyticsPlatform
Built a real-time predictive analytics platform processing 2M+ data points per second for quantitative trading strategies.
8ms avgInference Latency
2M events/secData Throughput
94.7%Model Accuracy
99.99%Uptime
The Challenge
What Needed Solving
The client needed sub-10ms inference latency for ML models analyzing market microstructure data across 50+ exchanges simultaneously.
Our Solution
What We Engineered
We architected a distributed ML pipeline using PyTorch models served via ONNX Runtime, with Redis Streams for real-time data ingestion and Kubernetes for elastic scaling during market hours.
"CoreKod delivered an ML infrastructure that outperforms systems built by teams five times their size."
Marcus Chen
CTO, QuantEdge Capital
Technology Stack
PythonPyTorchRedisKubernetesAWSPostgreSQL
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