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>

data/portfolio.py

@@ -0,0 +1,164 @@
+"""
+Portfolio data layer — fetch current prices, earnings calendar, dividends, sector info, news for portfolio holdings.
+"""
+import streamlit as st
+import pandas as pd
+from datetime import datetime, timedelta
+
+try:
+    import yfinance as yf
+except ImportError:
+    yf = None
+
+
+@st.cache_data(ttl=120)
+def get_portfolio_prices(tickers: tuple) -> dict:
+    """Fetch current price, previous close, and day change for each ticker.
+    Returns {ticker: {"price": float, "prev_close": float, "change_pct": float}}
+    """
+    if not yf or not tickers:
+        return {}
+    result = {}
+    for sym in tickers:
+        try:
+            t = yf.Ticker(sym)
+            info = t.info or {}
+            price = info.get("regularMarketPrice") or info.get("currentPrice")
+            prev = info.get("regularMarketPreviousClose") or info.get("previousClose")
+            if price and prev and prev != 0:
+                change = (price - prev) / prev * 100
+            else:
+                change = 0.0
+            result[sym] = {"price": price, "prev_close": prev, "change_pct": round(change, 2)}
+        except Exception:
+            result[sym] = {"price": None, "prev_close": None, "change_pct": 0.0}
+    return result
+
+
+@st.cache_data(ttl=300)
+def get_sector_allocation(tickers: tuple) -> dict:
+    """Return {ticker: sector} for pie chart. Uses yfinance .info."""
+    if not yf or not tickers:
+        return {}
+    result = {}
+    for sym in tickers:
+        try:
+            info = yf.Ticker(sym).info or {}
+            result[sym] = info.get("sector") or info.get("sectorDisp") or "Other"
+        except Exception:
+            result[sym] = "Other"
+    return result
+
+
+@st.cache_data(ttl=600)
+def get_earnings_calendar(tickers: tuple) -> list:
+    """Return list of upcoming earnings: [{"ticker", "name", "date", "days_until"}]."""
+    if not yf or not tickers:
+        return []
+    events = []
+    now = datetime.now()
+    for sym in tickers:
+        try:
+            t = yf.Ticker(sym)
+            cal = t.calendar
+            if cal is None:
+                continue
+            # yfinance returns dict or DataFrame
+            if isinstance(cal, pd.DataFrame):
+                if "Earnings Date" in cal.index:
+                    dates = cal.loc["Earnings Date"]
+                    ed = pd.Timestamp(dates.iloc[0]) if len(dates) > 0 else None
+                else:
+                    continue
+            elif isinstance(cal, dict):
+                ed_val = cal.get("Earnings Date")
+                if isinstance(ed_val, list) and ed_val:
+                    ed = pd.Timestamp(ed_val[0])
+                elif ed_val:
+                    ed = pd.Timestamp(ed_val)
+                else:
+                    continue
+            else:
+                continue
+            if ed and ed >= pd.Timestamp(now):
+                delta = (ed - pd.Timestamp(now)).days
+                name = (t.info or {}).get("shortName", sym)
+                events.append({
+                    "ticker": sym, "name": name,
+                    "date": ed.strftime("%m/%d"), "days_until": delta,
+                })
+        except Exception:
+            continue
+    events.sort(key=lambda x: x["days_until"])
+    return events
+
+
+@st.cache_data(ttl=600)
+def get_dividend_schedule(tickers: tuple) -> list:
+    """Return upcoming dividend info: [{"ticker", "name", "ex_date", "amount", "yield_pct"}]."""
+    if not yf or not tickers:
+        return []
+    divs = []
+    for sym in tickers:
+        try:
+            t = yf.Ticker(sym)
+            info = t.info or {}
+            div_rate = info.get("dividendRate")
+            div_yield = info.get("dividendYield")
+            ex_date = info.get("exDividendDate")
+            if not div_rate and not div_yield:
+                continue
+            name = info.get("shortName", sym)
+            ex_str = ""
+            if ex_date:
+                try:
+                    ex_dt = datetime.fromtimestamp(ex_date) if isinstance(ex_date, (int, float)) else ex_date
+                    ex_str = ex_dt.strftime("%m/%d/%Y") if hasattr(ex_dt, "strftime") else str(ex_date)
+                except Exception:
+                    ex_str = str(ex_date)
+            divs.append({
+                "ticker": sym, "name": name, "ex_date": ex_str,
+                "amount": f"${div_rate:.2f}" if div_rate else "N/A",
+                "yield_pct": f"{div_yield * 100:.2f}%" if div_yield else "N/A",
+            })
+        except Exception:
+            continue
+    return divs
+
+
+@st.cache_data(ttl=300)
+def get_portfolio_news(tickers: tuple, max_per_ticker: int = 3) -> list:
+    """Fetch recent news for portfolio tickers via yfinance."""
+    if not yf or not tickers:
+        return []
+    all_news = []
+    for sym in tickers:
+        try:
+            t = yf.Ticker(sym)
+            news_list = t.news or []
+            for n in news_list[:max_per_ticker]:
+                all_news.append({
+                    "ticker": sym,
+                    "title": n.get("title", ""),
+                    "publisher": n.get("publisher", ""),
+                    "link": n.get("link", ""),
+                    "published": n.get("providerPublishTime", 0),
+                })
+        except Exception:
+            continue
+    all_news.sort(key=lambda x: x.get("published", 0), reverse=True)
+    return all_news[:20]
+
+
+@st.cache_data(ttl=300)
+def get_sparkline_data(ticker: str, period: str = "1y") -> list:
+    """Return list of close prices for sparkline chart."""
+    if not yf or not ticker:
+        return []
+    try:
+        hist = yf.Ticker(ticker).history(period=period)
+        if hist is not None and not hist.empty and "Close" in hist.columns:
+            return hist["Close"].tolist()
+    except Exception:
+        pass
+    return []