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>

atlas-terminal/server/models/db.py

@@ -0,0 +1,22 @@
+"""Supabase database client for ATLAS Terminal."""
+import os
+from typing import Optional
+
+# Supabase client (lazy init)
+_supabase_client = None
+
+def get_supabase():
+    """Get or create Supabase client. Returns None if not configured."""
+    global _supabase_client
+    if _supabase_client is not None:
+        return _supabase_client
+    url = os.environ.get("SUPABASE_URL")
+    key = os.environ.get("SUPABASE_KEY")
+    if not url or not key:
+        return None
+    try:
+        from supabase import create_client
+        _supabase_client = create_client(url, key)
+        return _supabase_client
+    except ImportError:
+        return None

atlas-terminal/server/models/schemas.py

@@ -0,0 +1,257 @@
+"""Pydantic request/response schemas for ATLAS Terminal API."""
+
+from pydantic import BaseModel, Field
+from typing import Optional, List, Dict, Any
+
+
+# ---------------------------------------------------------------------------
+# Request models
+# ---------------------------------------------------------------------------
+
+class TickerRequest(BaseModel):
+    """Generic request carrying a ticker and optional market selector."""
+    ticker: str = Field(..., description="Stock ticker symbol, e.g. AAPL, 005930.KS")
+    market: str = Field(
+        default="US (S&P/Dow/Nasdaq)",
+        description="Market selector: US, South Korea (KOSPI/KOSDAQ), Japan (Nikkei), UK (LSE)",
+    )
+
+
+class EdgarRequest(BaseModel):
+    """Request to download / fetch SEC EDGAR 10-K filings."""
+    ticker: str = Field(..., description="Stock ticker symbol")
+    email: str = Field(..., description="Email address required by SEC EDGAR fair-access policy")
+
+
+class AnalysisRequest(BaseModel):
+    """Request for AI-powered 10-K analysis (Gemini)."""
+    ticker: str
+    api_key: str = Field(..., description="Google Gemini API key")
+    sector: str = ""
+    industry: str = ""
+
+
+class DCFInputs(BaseModel):
+    """Inputs for discounted cash-flow valuation."""
+    fcf: float = Field(..., description="Base free cash flow (trailing)")
+    wacc: float = Field(..., description="Weighted-average cost of capital (decimal, e.g. 0.10)")
+    terminal_growth: float = Field(..., description="Terminal growth rate (decimal, e.g. 0.025)")
+    fcf_growth: float = Field(..., description="Near-term FCF growth rate (decimal, e.g. 0.12)")
+    total_debt: float = Field(default=0, description="Total debt for bridge to equity value")
+    cash: float = Field(default=0, description="Cash & equivalents for bridge to equity value")
+    shares: float = Field(default=1, description="Shares outstanding for per-share value")
+
+
+class CompanySearch(BaseModel):
+    """Search for a company by name or partial ticker."""
+    query: str = Field(..., description="Search term, e.g. 'Apple', 'Samsung'")
+    market: str = ""
+
+
+class CompsRequest(BaseModel):
+    """Request for industry comparable companies data."""
+    tickers: List[str] = Field(..., description="List of ticker symbols to compare")
+
+
+class ForensicRequest(BaseModel):
+    """Request for forensic audit analysis (Item 3 & 9A)."""
+    ticker: str
+    api_key: str
+    item3: str = ""
+    item9a: str = ""
+
+
+class FinancialsLLMRequest(BaseModel):
+    """Request to extract financials from Item 8 via LLM."""
+    ticker: str
+    api_key: str
+    item8_text: str = ""
+
+
+class PortfolioPositionCreate(BaseModel):
+    """Create a new portfolio position."""
+    ticker: str
+    company_name: str = ""
+    quantity: float
+    avg_price: float
+    currency: str = "USD"
+    source: str = "manual"
+
+
+# ---------------------------------------------------------------------------
+# Response models
+# ---------------------------------------------------------------------------
+
+class DCFResult(BaseModel):
+    """Result of a DCF valuation calculation."""
+    enterprise_value: float = 0
+    equity_value: float = 0
+    value_per_share: Optional[float] = None
+    shares: Optional[float] = None
+    scenarios: Dict[str, Any] = {}
+
+
+class DCFInputsResponse(BaseModel):
+    """Auto-filled DCF inputs from market data."""
+    fcf: Optional[float] = None
+    total_debt: float = 0
+    cash: float = 0
+    shares: Optional[float] = None
+
+
+class SmartDefaultsResponse(BaseModel):
+    """Smart defaults for DCF with analyst consensus guidance."""
+    wacc: float = 0.10
+    terminal_growth: float = 0.025
+    fcf_growth: float = 0.10
+    sector: str = "N/A"
+    industry: str = "N/A"
+
+
+class ConsensusResponse(BaseModel):
+    """Analyst consensus data for a ticker."""
+    target_mean: Optional[float] = None
+    target_median: Optional[float] = None
+    target_low: Optional[float] = None
+    target_high: Optional[float] = None
+    recommendation: str = ""
+    num_analysts: int = 0
+    data: Dict[str, Any] = {}
+
+
+class SectorIndustryResponse(BaseModel):
+    """Sector and industry classification."""
+    sector: str = "N/A"
+    industry: str = "N/A"
+
+
+class FinancialHealth(BaseModel):
+    """Comprehensive financial health metrics."""
+    dupont: Dict[str, Any] = {}
+    altman_z: Dict[str, Any] = {}
+    red_flags: List[str] = []
+    piotroski: Dict[str, Any] = {}
+
+
+class PiotroskiResponse(BaseModel):
+    """Piotroski F-Score breakdown."""
+    score: int = 0
+    criteria: List[Dict[str, Any]] = []
+    used_ttm: bool = False
+
+
+class SankeyData(BaseModel):
+    """Income statement Sankey diagram data."""
+    labels: List[str] = []
+    sources: List[int] = []
+    targets: List[int] = []
+    values: List[float] = []
+    colors: List[str] = []
+
+
+class RadarMetrics(BaseModel):
+    """Normalised radar chart metrics."""
+    labels: List[str] = []
+    values: List[float] = []
+    raw: Dict[str, Any] = {}
+
+
+class TrendData(BaseModel):
+    """5-year financial trend data."""
+    years: List[int] = []
+    revenue: List[Optional[float]] = []
+    net_income: List[Optional[float]] = []
+    operating_margin: List[Optional[float]] = []
+    fcf: List[Optional[float]] = []
+
+
+class NewsItem(BaseModel):
+    """A single news article."""
+    title: str
+    source: str = ""
+    url: str = ""
+    published_at: str = ""
+    summary: str = ""
+
+
+class PortfolioPosition(BaseModel):
+    """A portfolio position with current market data."""
+    id: Optional[str] = None
+    ticker: str
+    company_name: str = ""
+    quantity: float
+    avg_price: float
+    currency: str = "USD"
+    source: str = "manual"
+    current_price: Optional[float] = None
+    market_value: Optional[float] = None
+    pnl: Optional[float] = None
+    pnl_pct: Optional[float] = None
+
+
+class PortfolioSummary(BaseModel):
+    """Aggregated portfolio summary."""
+    total_value: float = 0
+    total_cost: float = 0
+    total_pnl: float = 0
+    total_pnl_pct: Optional[float] = None
+    positions: List[PortfolioPosition] = []
+
+
+class MarketOverview(BaseModel):
+    """Market overview with indices, FX, and crypto."""
+    indices: Dict[str, Any] = {}
+    fx_rates: Dict[str, float] = {}
+    crypto: List[Dict[str, Any]] = []
+
+
+class FXRateResponse(BaseModel):
+    """Foreign exchange rate response."""
+    pair: str
+    rate: Optional[float] = None
+    rates: Dict[str, float] = {}
+
+
+class FXHistoryResponse(BaseModel):
+    """FX pair historical data."""
+    pair: str
+    dates: List[str] = []
+    rates: List[float] = []
+
+
+class CryptoPrice(BaseModel):
+    """Single cryptocurrency price data."""
+    symbol: str
+    name: str = ""
+    price_usd: Optional[float] = None
+    price_krw: Optional[float] = None
+    change_24h_pct: Optional[float] = None
+
+
+class EdgarSectionsResponse(BaseModel):
+    """Cached or downloaded 10-K section texts."""
+    status: str = ""
+    item1a: str = ""
+    item3: str = ""
+    item7: str = ""
+    item8: str = ""
+    item9a: str = ""
+
+
+class Item7Response(BaseModel):
+    """Item 7 MD&A text."""
+    item7: str = ""
+
+
+class CompareResponse(BaseModel):
+    """Comparison of latest vs 3-year-ago Item 7."""
+    item1a_latest: str = ""
+    item7_latest: str = ""
+    item7_3y_ago: Optional[str] = None
+    has_comparison: bool = False
+
+
+class HealthCheckResponse(BaseModel):
+    """API health check."""
+    status: str = "ok"
+    version: str = "1.0.0"