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 15:18:04 +00:00 · 2026-03-21 02:10:10 +00:00
parent 56a9561f71
commit b2acda81ee
111 changed files with 13883 additions and 270 deletions
@@ -0,0 +1,88 @@
+"""Numeric safety utilities for the ATLAS Terminal backend.
+
+Provides safe type-coercion helpers used across all services to handle
+None, NaN, and non-numeric values gracefully without raising exceptions.
+"""
+
+from typing import Optional
+
+import pandas as pd
+
+
+def _safe_float(x: object) -> Optional[float]:
+    """Convert *x* to ``float``, returning ``None`` for unconvertible values.
+
+    Handles ``None``, ``NaN`` (both Python ``float('nan')`` and pandas
+    ``pd.NA``), and arbitrary objects whose ``float()`` conversion fails.
+
+    Parameters
+    ----------
+    x:
+        Any value that might be numeric.
+
+    Returns
+    -------
+    Optional[float]
+        The float representation, or ``None`` if conversion is impossible.
+    """
+    if x is None or (isinstance(x, float) and (x != x or pd.isna(x))):
+        return None
+    try:
+        return float(x)
+    except (TypeError, ValueError):
+        return None
+
+
+def _na(x: object) -> object:
+    """Return the string ``'N/A'`` for ``None``/``NaN``, otherwise *x* unchanged.
+
+    Useful when building display-ready dictionaries or DataFrames where
+    missing numeric values should appear as a human-readable sentinel.
+
+    Parameters
+    ----------
+    x:
+        Any value.
+
+    Returns
+    -------
+    object
+        ``'N/A'`` when *x* is ``None`` or ``NaN``; *x* otherwise.
+    """
+    if x is None or (isinstance(x, float) and (pd.isna(x) or x != x)):
+        return "N/A"
+    return x
+
+
+def _format_shares_display(shares: Optional[float]) -> str:
+    """Format a share count for human-friendly display.
+
+    Examples
+    --------
+    >>> _format_shares_display(15_420_000_000)
+    '15.42B Shares'
+    >>> _format_shares_display(1_200_000)
+    '1.20M Shares'
+    >>> _format_shares_display(None)
+    'N/A'
+
+    Parameters
+    ----------
+    shares:
+        Raw share count (absolute number, not in millions/billions).
+
+    Returns
+    -------
+    str
+        A concise string such as ``'15.42B Shares'`` or ``'N/A'``.
+    """
+    if shares is None or shares <= 0:
+        return "N/A"
+    s = float(shares)
+    if s >= 1e9:
+        return f"{s / 1e9:.2f}B Shares"
+    if s >= 1e6:
+        return f"{s / 1e6:.2f}M Shares"
+    if s >= 1e3:
+        return f"{s / 1e3:.2f}K Shares"
+    return f"{s:.0f} Shares"
@@ -0,0 +1,122 @@
+"""Ticker formatting, market inference, and company/sector reference data.
+
+Centralises the mapping logic that converts bare ticker symbols into
+Yahoo Finance-compatible identifiers with the correct market suffix,
+and provides the static lookup tables for companies and sectors.
+"""
+
+from typing import List, Tuple
+
+# ---------------------------------------------------------------------------
+# Company reference data
+# ---------------------------------------------------------------------------
+
+COMPANY_LIST: List[Tuple[str, str]] = [
+    ("NVIDIA Corporation", "NVDA"), ("Apple Inc.", "AAPL"), ("Microsoft Corporation", "MSFT"),
+    ("Amazon.com Inc.", "AMZN"), ("Alphabet Inc.", "GOOGL"), ("Meta Platforms Inc.", "META"),
+    ("AMD", "AMD"), ("Intel Corporation", "INTC"), ("Qualcomm Inc.", "QCOM"), ("Tesla Inc.", "TSLA"),
+    ("Berkshire Hathaway", "BRK.B"), ("JPMorgan Chase", "JPM"), ("Visa Inc.", "V"),
+    ("UnitedHealth", "UNH"), ("Procter & Gamble", "PG"), ("Exxon Mobil", "XOM"),
+    ("Johnson & Johnson", "JNJ"), ("Mastercard", "MA"), ("Chevron", "CVX"),
+    ("Home Depot", "HD"), ("Merck", "MRK"), ("AbbVie", "ABBV"), ("Costco", "COST"),
+    ("PepsiCo", "PEP"), ("Coca-Cola", "KO"), ("Pfizer", "PFE"), ("Walmart", "WMT"),
+    ("Netflix", "NFLX"), ("Adobe", "ADBE"), ("Salesforce", "CRM"), ("Comcast", "CMCSA"),
+    ("Cisco", "CSCO"), ("Oracle", "ORCL"), ("American Express", "AXP"),
+    ("Bank of America", "BAC"), ("Wells Fargo", "WFC"), ("Verizon", "VZ"),
+    ("AT&T", "T"), ("Walt Disney", "DIS"), ("Nike", "NKE"), ("McDonald's", "MCD"),
+    ("Starbucks", "SBUX"), ("Goldman Sachs", "GS"), ("Morgan Stanley", "MS"),
+    ("Target", "TGT"), ("Boeing", "BA"), ("IBM", "IBM"),
+]
+
+COMPANY_OPTIONS: List[str] = [f"{t} - {n}" for n, t in COMPANY_LIST]
+"""Pre-formatted ``'TICKER - Company Name'`` strings for dropdowns."""
+
+COMPANY_TICKER_MAP: dict[str, str] = {t: n for n, t in COMPANY_LIST}
+"""Mapping from ticker symbol to full company name."""
+
+MARKET_OPTIONS: List[str] = [
+    "US (S&P/Dow/Nasdaq)",
+    "South Korea (KOSPI/KOSDAQ)",
+    "Japan (Nikkei)",
+    "UK (LSE)",
+]
+
+# ---------------------------------------------------------------------------
+# Sector / industry peer groups (top-down analysis)
+# ---------------------------------------------------------------------------
+
+SECTORS: dict[str, List[str]] = {
+    "Semiconductors & Hardware": ["NVDA", "AMD", "INTC", "TSM", "AVGO"],
+    "Software & Cloud": ["MSFT", "ADBE", "CRM", "PANW", "CRWD"],
+    "Consumer Retail": ["AMZN", "SBUX", "MCD", "WMT", "HD"],
+    "Financial Services": ["JPM", "BAC", "GS", "MS", "V"],
+    "Healthcare": ["LLY", "UNH", "JNJ", "ABBV", "MRK"],
+}
+
+
+# ---------------------------------------------------------------------------
+# Ticker helpers
+# ---------------------------------------------------------------------------
+
+def get_global_ticker(ticker: str, market: str) -> str:
+    """Append the correct Yahoo Finance suffix based on the selected market.
+
+    US tickers are returned as-is.  If the ticker already carries a known
+    suffix (``.KS``, ``.KQ``, ``.T``, ``.L``) it is returned unchanged
+    regardless of the *market* argument.
+
+    Parameters
+    ----------
+    ticker:
+        Raw ticker string entered by the user.
+    market:
+        One of the values in :data:`MARKET_OPTIONS`.
+
+    Returns
+    -------
+    str
+        The ticker with an appropriate suffix (or unchanged for US).
+    """
+    if not (ticker or "").strip():
+        return (ticker or "").strip()
+    t = (ticker or "").strip()
+    if t.upper().endswith((".KS", ".KQ", ".T", ".L")):
+        return t
+    m = (market or "").strip()
+    if "US" in m or not m:
+        return t
+    if "Korea" in m or "KOSPI" in m or "KOSDAQ" in m:
+        return t + ".KS"
+    if "Japan" in m or "Nikkei" in m:
+        return t + ".T"
+    if "UK" in m or "LSE" in m:
+        return t + ".L"
+    return t
+
+
+def infer_market_from_ticker(ticker: str) -> str:
+    """Guess the market label from a ticker's suffix.
+
+    Useful when the caller has a fully-qualified ticker (e.g. ``005930.KS``)
+    but no explicit market selection.
+
+    Parameters
+    ----------
+    ticker:
+        A ticker string that may include a market suffix.
+
+    Returns
+    -------
+    str
+        The best-matching entry from :data:`MARKET_OPTIONS`.
+    """
+    if not (ticker or "").strip():
+        return MARKET_OPTIONS[0]
+    t = (ticker or "").strip().upper()
+    if t.endswith(".KS") or t.endswith(".KQ"):
+        return "South Korea (KOSPI/KOSDAQ)"
+    if t.endswith(".T"):
+        return "Japan (Nikkei)"
+    if t.endswith(".L"):
+        return "UK (LSE)"
+    return "US (S&P/Dow/Nasdaq)"