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/valuation_metrics.py

@@ -0,0 +1,141 @@
+"""
+Valuation metrics — PER, PBR, PSR, P/OCF, EV/EBITDA with historical & industry comparison.
+"""
+import streamlit as st
+import pandas as pd
+
+try:
+    import yfinance as yf
+except ImportError:
+    yf = None
+
+try:
+    from yahooquery import Ticker as YQTicker
+except ImportError:
+    YQTicker = None
+
+
+@st.cache_data(ttl=300)
+def get_valuation_multiples(ticker: str) -> dict:
+    """Fetch current valuation multiples for a ticker."""
+    if not yf or not ticker:
+        return {}
+    try:
+        t = yf.Ticker(ticker)
+        info = t.info or {}
+        price = info.get("regularMarketPrice") or info.get("currentPrice") or 0
+        mcap = info.get("marketCap") or 0
+        # P/OCF calculation
+        ocf = info.get("operatingCashflow")
+        shares = info.get("sharesOutstanding") or 1
+        p_ocf = (price / (ocf / shares)) if ocf and shares and ocf > 0 else None
+
+        return {
+            "PER": info.get("trailingPE"),
+            "Forward PER": info.get("forwardPE"),
+            "PBR": info.get("priceToBook"),
+            "PSR": info.get("priceToSalesTrailing12Months"),
+            "P/OCF": round(p_ocf, 2) if p_ocf else None,
+            "EV/EBITDA": info.get("enterpriseToEbitda"),
+            "EV/Revenue": info.get("enterpriseToRevenue"),
+            "PEG": info.get("pegRatio"),
+            "Dividend Yield": info.get("dividendYield"),
+            "Price": price,
+            "Market Cap": mcap,
+            "52W High": info.get("fiftyTwoWeekHigh"),
+            "52W Low": info.get("fiftyTwoWeekLow"),
+            "Beta": info.get("beta"),
+        }
+    except Exception:
+        return {}
+
+
+@st.cache_data(ttl=600)
+def get_historical_multiples(ticker: str) -> dict:
+    """Calculate 5Y average PE, PB, PS from historical data."""
+    if not yf or not ticker:
+        return {}
+    try:
+        t = yf.Ticker(ticker)
+        info = t.info or {}
+        hist = t.history(period="5y", interval="1mo")
+        if hist is None or hist.empty:
+            return {}
+        eps = info.get("trailingEps")
+        bvps = info.get("bookValue")
+
+        result = {}
+        if eps and eps > 0:
+            pe_series = hist["Close"] / eps
+            result["5Y Avg PER"] = round(pe_series.mean(), 2)
+            result["5Y High PER"] = round(pe_series.max(), 2)
+            result["5Y Low PER"] = round(pe_series.min(), 2)
+        if bvps and bvps > 0:
+            pb_series = hist["Close"] / bvps
+            result["5Y Avg PBR"] = round(pb_series.mean(), 2)
+        return result
+    except Exception:
+        return {}
+
+
+@st.cache_data(ttl=600)
+def get_industry_avg_multiples(ticker: str) -> dict:
+    """Get industry peer average multiples for comparison."""
+    if not yf or not ticker:
+        return {}
+    try:
+        t = yf.Ticker(ticker)
+        info = t.info or {}
+        industry = info.get("industry", "")
+        sector = info.get("sector", "")
+        if not industry:
+            return {"industry": "N/A"}
+
+        # Use sector-based peer mapping
+        from config.constants import SECTORS
+        peers = []
+        sector_lower = sector.lower() if sector else ""
+        for sec_name, tickers_list in SECTORS.items():
+            if any(k in sector_lower for k in sec_name.lower().split()):
+                peers = [p for p in tickers_list if p != ticker.upper()][:4]
+                break
+        if not peers:
+            return {"industry": industry}
+
+        pe_vals, pb_vals, ps_vals = [], [], []
+        for p in peers:
+            try:
+                pi = yf.Ticker(p).info or {}
+                if pi.get("trailingPE") and pi["trailingPE"] > 0:
+                    pe_vals.append(pi["trailingPE"])
+                if pi.get("priceToBook") and pi["priceToBook"] > 0:
+                    pb_vals.append(pi["priceToBook"])
+                if pi.get("priceToSalesTrailing12Months"):
+                    ps_vals.append(pi["priceToSalesTrailing12Months"])
+            except Exception:
+                continue
+
+        result = {"industry": industry, "peers": peers}
+        if pe_vals:
+            result["Industry Avg PER"] = round(sum(pe_vals) / len(pe_vals), 2)
+        if pb_vals:
+            result["Industry Avg PBR"] = round(sum(pb_vals) / len(pb_vals), 2)
+        if ps_vals:
+            result["Industry Avg PSR"] = round(sum(ps_vals) / len(ps_vals), 2)
+        return result
+    except Exception:
+        return {}
+
+
+@st.cache_data(ttl=300)
+def get_pe_history_chart_data(ticker: str) -> pd.DataFrame:
+    """Return monthly close prices for 5Y PE chart overlay."""
+    if not yf or not ticker:
+        return pd.DataFrame()
+    try:
+        hist = yf.Ticker(ticker).history(period="5y", interval="1mo")
+        if hist is not None and not hist.empty:
+            return hist[["Close"]].reset_index()
+    except Exception:
+        pass
+    return pd.DataFrame()