Sentiment Analysis of a Stock Data on News

We built an end-to-end AI stock signal pipeline that turns live market data and news headlines into explainable trade signals — fully accelerated on AMD Instinct MI300X via ROCm + PyTorch. The system runs three cooperating agents in an agentic workflow: 1. Signal Agent — pulls live OHLCV and headlines via yfinance, computes rolling-volatility regimes (LOW/MED/HIGH), and runs batched market inference on GPU. 2. Sentiment Agent — a fine-tuned DistilBERT classifier from Hugging Face Hub (POS/NEU/NEG with signed scores) running batched ROCm inference for thousands of headlines per second. 3. Reasoning Agent — Qwen3-8B generates a natural-language explanation for each BUY / SELL / HOLD decision, with entry, stop-loss, and target levels. A simulated execution engine then runs an OPEN-to-CLOSED trade lifecycle with slippage and P&L, surfacing win-rate, average return, and a cumulative P&L curve. The Streamlit dashboard exposes a live GPU diagnostics banner, KPI tiles, signal table, sector heatmap, sentiment analytics, and CPU-vs-GPU throughput charts. Verified on AMD Developer Cloud (ROCm 6.2 + PyTorch 2.5.1+rocm6.2): 17.0x speedup on 100-batch market pipeline, 208.0x on 1000-batch market pipeline, 14.49x on sentiment batch — all from the exact same code path that runs on CPU. AMD-first, IP-safe, judge-friendly, and built to scale to multi-GPU MI300X clusters.