feat: add Atlas Terminal — Next.js 14 + FastAPI full-stack migration

Complete migration from Streamlit to Next.js 14 App Router + FastAPI backend.

Frontend (Next.js 14):
- 10 pages: Overview, Research, Valuation, Technical, Markets, Earnings, News, Portfolio, Filings, Settings
- Terminal Noir dark theme with custom Tailwind config
- TradingView Lightweight Charts for candlestick/volume
- Valuation: DCF, Sensitivity Matrix, Monte Carlo, Tornado, Reverse DCF
- Financial Statements table with YoY growth badges and margin rows
- SEC EDGAR inline filing viewer with section tabs
- News split-view with iframe article embedding
- Technical Analysis with RSI, MACD, Bollinger, Fibonacci, Moving Averages
- Earnings beat/miss visualization
- AI Copilot chat panel with Gemini integration

Backend (FastAPI):
- 13 routers: market_data, financials, valuation, technical, earnings, insider, edgar, news, portfolio, analysis, chat, estimates, fx
- Services: DCF engine, Monte Carlo simulation, sensitivity analysis, risk metrics, SEC parser, technical indicators
- yfinance + yahooquery data sources with fallback pattern
- SQLite caching layer

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

views/tab10_kpi.py

@@ -0,0 +1,66 @@
+"""
+KPI section — AI-powered company-specific KPI analysis via Gemini.
+Generates key performance indicators relevant to the company's industry.
+"""
+import streamlit as st
+
+
+def render_kpi_section(ticker: str, sector: str, industry: str):
+    """Render AI-generated KPI analysis for a company."""
+    st.markdown("---")
+    st.markdown("#### Company KPI Analysis (AI-Powered)")
+    st.caption(
+        "AI identifies and analyzes the most important KPIs for this company's "
+        "business model and industry."
+    )
+
+    google_api_key = (st.session_state.get("google_api_key") or "").strip()
+    if not google_api_key:
+        st.info("Enter your Google API Key in the sidebar to enable KPI analysis.")
+        return
+
+    kpi_key = f"kpi_analysis_{ticker}"
+
+    if st.button("Generate KPI Analysis", key=f"btn_kpi_{ticker}"):
+        with st.spinner("Analyzing company KPIs with Gemini..."):
+            try:
+                import google.generativeai as genai
+                from config.constants import GEMINI_MODEL
+
+                genai.configure(api_key=google_api_key)
+                model = genai.GenerativeModel(GEMINI_MODEL)
+
+                prompt = f"""You are a senior equity research analyst. For **{ticker}** (Sector: {sector}, Industry: {industry}), identify and analyze the **top 5 most critical KPIs** that investors should track.
+
+For each KPI:
+1. **KPI Name** — what it measures and why it matters for this specific company
+2. **Current Context** — what investors should know about this metric's recent trajectory
+3. **Industry Benchmark** — how to interpret good vs bad values
+
+Format as a clean markdown list. Focus on KPIs that are:
+- Specific to this company's business model (not generic financial ratios)
+- Forward-looking indicators of growth or risk
+- Examples: For NVIDIA → CUDA developer adoption, data center revenue mix, AI training chip market share
+- Examples: For Starbucks → same-store sales growth, store count, average ticket size
+
+Also include a brief section: "**Key Risks to Watch**" with 2-3 forward-looking risk indicators.
+Keep under 500 words. Be specific and actionable."""
+
+                r = model.generate_content(
+                    prompt,
+                    generation_config={"temperature": 0.3, "max_output_tokens": 2048},
+                )
+                text = (r.text or "").strip()
+                if text:
+                    st.session_state[kpi_key] = text
+                else:
+                    st.error("Empty response from Gemini.")
+            except Exception as e:
+                err = str(e).lower()
+                if "429" in err or "resource" in err:
+                    st.error("Rate limit. Please wait and retry.")
+                else:
+                    st.error(f"Error: {e}")
+
+    if st.session_state.get(kpi_key):
+        st.markdown(st.session_state[kpi_key])