I manage a stream of real-time stock market data and I’m ready to replace the manual number-crunching with a fully automated, AI-driven workflow. The goal is simple: pull raw market feeds, process and enrich them, run predictive or descriptive models, then deliver clean, actionable results that I can view instantly or schedule for end-of-day summaries. Here’s what the build should cover: data ingestion from the chosen market API, automated cleaning and feature engineering, model selection or fine-tuning (Python, TensorFlow, PyTorch—whichever suits the job), and a light dashboard or CSV/JSON output so I can track signals without opening a Jupyter notebook every time. Speed and reproducibility matter; I’d like to trigger the pipeline with a single command or on a cron schedule and know exactly which model version produced each forecast. Acceptance criteria – End-to-end script or containerised solution that ingests live stock data and stores intermediate files deterministically – At least one trained model with documented performance metrics on a recent historical slice – Configurable report or dashboard summarising key insights (price movement probabilities, anomalies, or whatever the model supports) – Clear README showing install steps, dependencies, and how to rerun training with fresh data If this sounds up your alley, let’s talk timeline and tech stack so we can get the automation humming.