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>

ai/gemini_portfolio.py

@@ -0,0 +1,87 @@
+"""
+Gemini Vision — extract portfolio holdings from Trading 212 / IBKR screenshots.
+Uses multimodal Gemini to OCR brokerage screenshots and return structured data.
+"""
+import json
+import streamlit as st
+
+
+def extract_portfolio_from_image(api_key: str, image_bytes: bytes, broker: str = "auto") -> list:
+    """
+    Send a brokerage screenshot to Gemini Vision and extract holdings.
+    Returns list of dicts: [{"ticker": "AAPL", "name": "Apple Inc", "shares": 10, "avg_cost": 150.0}, ...]
+    """
+    import google.generativeai as genai
+    from config.constants import GEMINI_MODEL
+
+    genai.configure(api_key=api_key)
+    model = genai.GenerativeModel(GEMINI_MODEL)
+
+    prompt = f"""You are a financial data extraction expert. The user has uploaded a screenshot from their **{broker}** brokerage account (Trading 212, IBKR, or similar).
+
+Extract ALL stock/ETF holdings visible in the screenshot. For each holding, extract:
+1. **ticker** — the stock ticker symbol (e.g., "AAPL", "MSFT"). If only the company name is visible, infer the most likely US ticker.
+2. **name** — the full company/ETF name as shown
+3. **shares** — number of shares held (decimal OK)
+4. **avg_cost** — average purchase price per share (if visible, otherwise null)
+5. **current_price** — current market price per share (if visible, otherwise null)
+
+Return ONLY a valid JSON array. No explanation, no markdown. Example:
+[
+  {{"ticker": "AAPL", "name": "Apple Inc", "shares": 10.5, "avg_cost": 150.25, "current_price": 178.50}},
+  {{"ticker": "MSFT", "name": "Microsoft Corp", "shares": 5, "avg_cost": 380.00, "current_price": 415.20}}
+]
+
+If you cannot extract any holdings, return an empty array: []
+Important: Extract ALL visible rows, do not skip any."""
+
+    import PIL.Image
+    import io
+    img = PIL.Image.open(io.BytesIO(image_bytes))
+
+    try:
+        response = model.generate_content(
+            [prompt, img],
+            generation_config={"temperature": 0.1, "max_output_tokens": 4096},
+        )
+        text = (response.text or "").strip()
+        # Clean markdown code fences if present
+        if text.startswith("```"):
+            text = text.split("\n", 1)[-1]
+        if text.endswith("```"):
+            text = text.rsplit("```", 1)[0]
+        text = text.strip()
+        holdings = json.loads(text)
+        if not isinstance(holdings, list):
+            return []
+        # Normalize each holding
+        cleaned = []
+        for h in holdings:
+            cleaned.append({
+                "ticker": str(h.get("ticker", "")).upper().strip(),
+                "name": str(h.get("name", "")),
+                "shares": _safe_num(h.get("shares")),
+                "avg_cost": _safe_num(h.get("avg_cost")),
+                "current_price": _safe_num(h.get("current_price")),
+            })
+        return [c for c in cleaned if c["ticker"]]
+    except json.JSONDecodeError:
+        st.error("AI could not parse the screenshot. Please try a clearer image.")
+        return []
+    except Exception as e:
+        err = str(e).lower()
+        if "429" in err or "resource" in err:
+            st.error("Gemini API rate limit. Please wait and retry.")
+        else:
+            st.error(f"Error extracting portfolio: {e}")
+        return []
+
+
+def _safe_num(val):
+    """Convert to float safely, return None on failure."""
+    if val is None:
+        return None
+    try:
+        return float(val)
+    except (ValueError, TypeError):
+        return None