Real-Time Financial Microstructure Ingestion and Sequence Modeling for Stock Market Prediction

Abstract

Modern quantitative trading infrastructure and financial asset management demand highly resilient, low-
latency system architectures capable of processing massive volumes of tick-by-tick market data while executing rapid
forward-pass predictions. This paper presents an end-to-end, event-driven AI architecture engineered for a high-
throughput Stock Market Index Prediction System, focusing on real-time market trend classification via Long Short-Term
Memory (LSTM) recurrent networks. The proposed platform decouples compute workloads across a synchronized four-
layer pipeline consisting of an edge-enabled Ingestion Layer, a concurrent Streaming and Storage Layer, a Contextual
Feature Assembly Pipeline, and a hardware-accelerated Predictive Inference Engine. The transaction lifecycle transitions
continuously from high-frequency raw market feeds (capturing order book updates, trade executions, instantaneous
volumes, and bid-ask spreads) ingested via non-blocking proxies into distributed Apache Kafka partitions. A stateful
stream processor aggregates these streaming inputs with dynamically shifting macro-indicators, executing microsecond-
scale parallel MGET queries against an in-memory Redis cluster to stitch rolling technical indicators, historical volatility
metrics, global market sentiments, and cross-asset correlations into a single, dense numerical tensor. The assembled array
is passed over high-performance binary gRPC transport channels to an optimized LSTM model hosted on a Triton
Inference Server running a TensorRT-accelerated backend. By utilizing specialized sequential input framing and native
missing data mask routing to handle intermittent feed dropouts, the model classifies next-interval market direction
parameters within a strict P95 latency ceiling of 11.6 milliseconds under a sustained load of 25,000 requests per second.
When a transaction breaches designated probability thresholds for structural trend reversal, an automated classification
interrupt activates an intelligent execution routing override, modifying dynamic portfolio vectors and dispatching
automated orders to the exchange. Empirical validation confirms that the proposed architecture achieves a robust
Precision-Recall AUC of 0.864 and a target direction classification accuracy of 82.4%, effectively balancing sub-15ms
runtime mechanics with stable quantitative returns.