Personalized Financial Analytics & “Year‑In‑Review” AI Tools for Traders covers the technologies and workflows that convert heterogeneous market and portfolio data into individualized, audit‑ready end‑of‑year summaries and ongoing trading intelligence. As of early 2026, the space is defined by automated data integration and real‑time analytics, embeddable business intelligence, and enterprise LLMs that enable narrative, attribution, and client‑facing reports. Key capabilities include continuous ingestion and normalization of market, execution, and reference data (Domo), embedded dashboards and SDKs for tailored visualizations inside trading apps (Sisense), and managed model infrastructure for training, evaluation, and deployment of both forecasting and generative models (Vertex AI). Enterprise LLM providers and model services—Cohere for private, customizable LLMs and embeddings, and Google’s multimodal Gemini family for richer text, chart and multimodal summaries—power automated trade attribution, natural‑language performance narratives, and retrieval‑augmented analysis. Notion and Microsoft 365 Copilot represent workflow layers that capture knowledge, automate report generation, and integrate AI summaries into documents and client communications. Trends shaping relevance: increasing demand for explainability and regulatory audit trails; vector search and retrieval‑augmented generation for personalized insights; fine‑tuning and private hosting to protect sensitive portfolio data; and the need for low‑latency, real‑time signals alongside periodic ‘year‑in‑review’ narratives for performance review, tax preparation, and client reporting. This topic spans Data Analytics Tools and Market Intelligence Tools and emphasizes practical implementation choices—data pipeline, model governance, embedding/search layer, visualization/embedding, and document/workflow integration—necessary to deliver reliable, compliant, and personalized trader analytics.