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>
2026-08-22 23:28:05 +00:00 · 2026-03-21 02:10:10 +00:00
parent 56a9561f71
commit b2acda81ee
111 changed files with 13883 additions and 270 deletions
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+"""Discounted Cash Flow (DCF) valuation engine.
+
+Implements multiple DCF model variants:
+- Simple 5-year single-stage DCF
+- 10-year two-stage DCF (growth fades from Stage 1 to terminal)
+- Excel-style full DCF (EV -> Equity -> per-share value)
+
+Also includes Damodaran sector WACC reference data and smart-default
+assumption generation from CAPM beta and analyst growth estimates.
+"""
+
+from typing import Dict, List, Optional
+
+from server.utils.safe_float import _safe_float
+
+try:
+    from scipy.optimize import brentq
+except ImportError:
+    brentq = None  # type: ignore[assignment]
+
+try:
+    import yfinance as yf
+except ImportError:
+    yf = None  # type: ignore[assignment]
+
+# ---------------------------------------------------------------------------
+# Damodaran sector WACC reference (approx. 2024/2025 baseline)
+# ---------------------------------------------------------------------------
+
+DAMODARAN_WACC: Dict[str, float] = {
+    "Software": 8.5,
+    "Retail": 7.5,
+    "Hardware": 9.0,
+    "Financials": 8.0,
+    "Healthcare": 7.2,
+    "Consumer": 7.5,
+    "Technology": 8.5,
+    "Industrial": 7.8,
+    "Energy": 8.2,
+    "Utilities": 6.5,
+}
+
+DAMODARAN_ERP_PCT: float = 4.6
+"""US Equity Risk Premium (Damodaran estimate)."""
+
+DAMODARAN_RF_PCT: float = 4.2
+"""10-year risk-free rate (Damodaran estimate)."""
+
+
+# ---------------------------------------------------------------------------
+# DCF models
+# ---------------------------------------------------------------------------
+
+def dcf_intrinsic_value(
+    fcf: float,
+    wacc: float,
+    terminal_growth: float,
+    fcf_growth: float,
+    years: int = 5,
+) -> float:
+    """5-year single-stage DCF returning enterprise value.
+
+    Projects FCF at *fcf_growth* for *years* periods, then computes a
+    Gordon Growth terminal value discounted at *wacc*.
+    """
+    if fcf is None or fcf <= 0:
+        return 0.0
+    if wacc <= terminal_growth or wacc <= 0:
+        return 0.0
+    pv = 0.0
+    fcft = float(fcf)
+    for t in range(1, years + 1):
+        pv += fcft / ((1 + wacc) ** t)
+        fcft *= (1 + fcf_growth)
+    terminal_fcf = fcft
+    tv = terminal_fcf * (1 + terminal_growth) / (wacc - terminal_growth)
+    pv += tv / ((1 + wacc) ** years)
+    return pv
+
+
+def dcf_10y_2stage(
+    fcf: float,
+    wacc: float,
+    term_growth: float,
+    fcf_growth: float,
+) -> float:
+    """10-year two-stage DCF.
+
+    Stage 1 (Y1-5): FCF grows at *fcf_growth*.
+    Stage 2 (Y6-10): growth linearly fades to *term_growth*.
+    Terminal value at Y10 using Gordon Growth.
+    """
+    if fcf is None or fcf <= 0:
+        return 0.0
+    if wacc <= term_growth or wacc <= 0:
+        return 0.0
+    pv = 0.0
+    fcft = float(fcf)
+    for t in range(1, 6):
+        pv += fcft / ((1 + wacc) ** t)
+        fcft *= (1 + fcf_growth)
+    for t in range(6, 11):
+        fade = (t - 6) / 4.0
+        g_t = fcf_growth + fade * (term_growth - fcf_growth)
+        fcft *= (1 + g_t)
+        pv += fcft / ((1 + wacc) ** t)
+    tv = fcft * (1 + term_growth) / (wacc - term_growth)
+    pv += tv / ((1 + wacc) ** 10)
+    return pv
+
+
+def excel_style_dcf(
+    fcf_base: float,
+    wacc: float,
+    term_growth: float,
+    fcf_growth: float,
+    total_debt: float,
+    cash: float,
+    shares: float,
+) -> Dict[str, Optional[float]]:
+    """Full DCF: EV -> Equity Value -> Value per Share.
+
+    Returns
+    -------
+    dict
+        Keys: ``ev``, ``equity_value``, ``value_per_share``, ``shares``.
+    """
+    ev = dcf_10y_2stage(fcf_base, wacc, term_growth, fcf_growth)
+    equity = ev - total_debt + cash
+    shares_safe = float(shares) if (shares is not None and float(shares) > 0) else None
+    value_per_share = (equity / shares_safe) if shares_safe else None
+    return {
+        "ev": ev,
+        "equity_value": equity,
+        "value_per_share": value_per_share,
+        "shares": shares_safe,
+    }
+
+
+# ---------------------------------------------------------------------------
+# WACC helpers
+# ---------------------------------------------------------------------------
+
+def reverse_dcf(
+    current_price: float,
+    shares: float,
+    total_debt: float,
+    cash: float,
+    wacc: float,
+    term_growth: float,
+    fcf_base: float,
+    projection_years: int = 10,
+) -> Optional[float]:
+    """Solve for the implied FCF growth rate that produces the current market price.
+
+    Uses Brent's root-finding method (scipy.optimize.brentq) to find the
+    growth rate *g* such that ``excel_style_dcf(..., g)["value_per_share"] == current_price``.
+
+    Returns
+    -------
+    float | None
+        Implied annual FCF growth rate (decimal), or None if no solution is found.
+    """
+    if brentq is None:
+        return None
+    if shares <= 0 or current_price <= 0 or wacc <= term_growth:
+        return None
+
+    def _objective(g: float) -> float:
+        result = excel_style_dcf(fcf_base, wacc, term_growth, g, total_debt, cash, shares)
+        vps = result.get("value_per_share")
+        if vps is None:
+            return -current_price
+        return vps - current_price
+
+    try:
+        implied_growth = brentq(_objective, -0.50, 1.00, xtol=1e-6, maxiter=200)
+        return round(implied_growth, 6)
+    except (ValueError, RuntimeError):
+        return None
+
+
+def _damodaran_wacc_for_sector(sector: str) -> float:
+    """Map a yfinance sector string to closest Damodaran WACC (default 8.0%)."""
+    if not sector:
+        return 8.0
+    s = (sector or "").lower()
+    if "software" in s or "technology" in s or "internet" in s:
+        return DAMODARAN_WACC.get("Software", 8.5)
+    if "hardware" in s or "semiconductor" in s:
+        return DAMODARAN_WACC.get("Hardware", 9.0)
+    if "retail" in s or "consumer" in s or "cyclical" in s:
+        return DAMODARAN_WACC.get("Retail", 7.5)
+    if "financial" in s or "bank" in s or "insurance" in s:
+        return DAMODARAN_WACC.get("Financials", 8.0)
+    if "health" in s or "pharma" in s:
+        return DAMODARAN_WACC.get("Healthcare", 7.2)
+    if "industrial" in s:
+        return DAMODARAN_WACC.get("Industrial", 7.8)
+    if "energy" in s or "oil" in s:
+        return DAMODARAN_WACC.get("Energy", 8.2)
+    if "utilities" in s:
+        return DAMODARAN_WACC.get("Utilities", 6.5)
+    return 8.0
+
+
+# ---------------------------------------------------------------------------
+# Smart defaults
+# ---------------------------------------------------------------------------
+
+def get_dcf_smart_defaults(ticker: str) -> Dict[str, float]:
+    """Auto-generate WACC, Terminal Growth, and FCF Growth from CAPM beta and analyst estimates.
+
+    Returns
+    -------
+    dict
+        Keys: ``wacc_pct``, ``term_growth_pct``, ``fcf_growth_pct``.
+    """
+    out: Dict[str, float] = {"wacc_pct": 10.0, "term_growth_pct": 2.5, "fcf_growth_pct": 8.0}
+    if not yf or not ticker:
+        return out
+    try:
+        t = yf.Ticker(ticker.upper())
+        info = t.info or {}
+        beta = info.get("beta")
+        if beta is None:
+            beta = 1.0
+        else:
+            try:
+                beta = float(beta)
+            except (TypeError, ValueError):
+                beta = 1.0
+        risk_free = 4.0
+        market_risk_premium = 5.0
+        calculated_wacc = risk_free + (beta * market_risk_premium)
+        out["wacc_pct"] = round(min(20.0, max(4.0, calculated_wacc)), 1)
+        out["term_growth_pct"] = 2.5
+        rev_growth = info.get("revenueGrowth") or info.get("earningsGrowth")
+        if rev_growth is not None:
+            try:
+                g = float(rev_growth)
+                out["fcf_growth_pct"] = round(min(30.0, max(-10.0, g * 100)), 1)
+            except (TypeError, ValueError):
+                pass
+        return out
+    except Exception:
+        return out