I run a financial-news and analytics platform and want to move beyond basic headlines toward truly data-driven stock picks. The immediate objective is to sharpen our investment recommendations, with a clear focus on stock-market predictions powered by AI. Here is what I need from you: • A practical, step-by-step strategy detailing how to ingest and combine historical price data, real-time market news, and social-media sentiment. • Model architecture suggestions—whether that points us toward gradient-boosting ensembles, LSTM/Transformer time-series networks, or a hybrid that fuses NLP sentiment scores with numerical features. Justify each choice in terms of accuracy, latency, and maintainability. • A data-engineering outline covering collection, cleansing, feature engineering, and continual retraining, keeping latency low for intraday updates. • A deployment roadmap that plugs neatly into our existing Python micro-service stack (FastAPI, Docker, AWS), including monitoring metrics and fallback logic for model drift. • Milestones we can realistically hit inside a phased roll-out—prototype, back-test, sandbox trading, and production cut-over. Acceptance criteria 1. The plan lets us reproduce all steps end-to-end on our AWS dev environment. 2. Back-testing methodology is defined with clear KPIs (hit ratio, Sharpe, draw-down). 3. Decision points are backed by citations—research papers, Kaggle benchmarks, or industry case studies—so stakeholders can sign off quickly. If you can translate AI buzzwords into an actionable roadmap, I’m ready to dive in together.