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shawnkim1997andClaude Opus 4.6
b2acda81ee
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>
123 lines
4.5 KiB
Python
123 lines
4.5 KiB
Python
"""Ticker formatting, market inference, and company/sector reference data.
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Centralises the mapping logic that converts bare ticker symbols into
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Yahoo Finance-compatible identifiers with the correct market suffix,
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and provides the static lookup tables for companies and sectors.
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"""
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from typing import List, Tuple
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# ---------------------------------------------------------------------------
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# Company reference data
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# ---------------------------------------------------------------------------
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COMPANY_LIST: List[Tuple[str, str]] = [
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("NVIDIA Corporation", "NVDA"), ("Apple Inc.", "AAPL"), ("Microsoft Corporation", "MSFT"),
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("Amazon.com Inc.", "AMZN"), ("Alphabet Inc.", "GOOGL"), ("Meta Platforms Inc.", "META"),
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("AMD", "AMD"), ("Intel Corporation", "INTC"), ("Qualcomm Inc.", "QCOM"), ("Tesla Inc.", "TSLA"),
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("Berkshire Hathaway", "BRK.B"), ("JPMorgan Chase", "JPM"), ("Visa Inc.", "V"),
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("UnitedHealth", "UNH"), ("Procter & Gamble", "PG"), ("Exxon Mobil", "XOM"),
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("Johnson & Johnson", "JNJ"), ("Mastercard", "MA"), ("Chevron", "CVX"),
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("Home Depot", "HD"), ("Merck", "MRK"), ("AbbVie", "ABBV"), ("Costco", "COST"),
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("PepsiCo", "PEP"), ("Coca-Cola", "KO"), ("Pfizer", "PFE"), ("Walmart", "WMT"),
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("Netflix", "NFLX"), ("Adobe", "ADBE"), ("Salesforce", "CRM"), ("Comcast", "CMCSA"),
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("Cisco", "CSCO"), ("Oracle", "ORCL"), ("American Express", "AXP"),
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("Bank of America", "BAC"), ("Wells Fargo", "WFC"), ("Verizon", "VZ"),
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("AT&T", "T"), ("Walt Disney", "DIS"), ("Nike", "NKE"), ("McDonald's", "MCD"),
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("Starbucks", "SBUX"), ("Goldman Sachs", "GS"), ("Morgan Stanley", "MS"),
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("Target", "TGT"), ("Boeing", "BA"), ("IBM", "IBM"),
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]
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COMPANY_OPTIONS: List[str] = [f"{t} - {n}" for n, t in COMPANY_LIST]
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"""Pre-formatted ``'TICKER - Company Name'`` strings for dropdowns."""
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COMPANY_TICKER_MAP: dict[str, str] = {t: n for n, t in COMPANY_LIST}
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"""Mapping from ticker symbol to full company name."""
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MARKET_OPTIONS: List[str] = [
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"US (S&P/Dow/Nasdaq)",
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"South Korea (KOSPI/KOSDAQ)",
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"Japan (Nikkei)",
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"UK (LSE)",
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]
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# ---------------------------------------------------------------------------
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# Sector / industry peer groups (top-down analysis)
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# ---------------------------------------------------------------------------
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SECTORS: dict[str, List[str]] = {
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"Semiconductors & Hardware": ["NVDA", "AMD", "INTC", "TSM", "AVGO"],
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"Software & Cloud": ["MSFT", "ADBE", "CRM", "PANW", "CRWD"],
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"Consumer Retail": ["AMZN", "SBUX", "MCD", "WMT", "HD"],
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"Financial Services": ["JPM", "BAC", "GS", "MS", "V"],
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"Healthcare": ["LLY", "UNH", "JNJ", "ABBV", "MRK"],
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}
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# ---------------------------------------------------------------------------
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# Ticker helpers
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# ---------------------------------------------------------------------------
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def get_global_ticker(ticker: str, market: str) -> str:
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"""Append the correct Yahoo Finance suffix based on the selected market.
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US tickers are returned as-is. If the ticker already carries a known
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suffix (``.KS``, ``.KQ``, ``.T``, ``.L``) it is returned unchanged
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regardless of the *market* argument.
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Parameters
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----------
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ticker:
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Raw ticker string entered by the user.
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market:
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One of the values in :data:`MARKET_OPTIONS`.
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Returns
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-------
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str
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The ticker with an appropriate suffix (or unchanged for US).
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"""
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if not (ticker or "").strip():
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return (ticker or "").strip()
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t = (ticker or "").strip()
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if t.upper().endswith((".KS", ".KQ", ".T", ".L")):
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return t
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m = (market or "").strip()
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if "US" in m or not m:
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return t
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if "Korea" in m or "KOSPI" in m or "KOSDAQ" in m:
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return t + ".KS"
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if "Japan" in m or "Nikkei" in m:
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return t + ".T"
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if "UK" in m or "LSE" in m:
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return t + ".L"
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return t
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def infer_market_from_ticker(ticker: str) -> str:
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"""Guess the market label from a ticker's suffix.
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Useful when the caller has a fully-qualified ticker (e.g. ``005930.KS``)
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but no explicit market selection.
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Parameters
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----------
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ticker:
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A ticker string that may include a market suffix.
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Returns
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-------
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str
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The best-matching entry from :data:`MARKET_OPTIONS`.
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"""
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if not (ticker or "").strip():
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return MARKET_OPTIONS[0]
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t = (ticker or "").strip().upper()
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if t.endswith(".KS") or t.endswith(".KQ"):
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return "South Korea (KOSPI/KOSDAQ)"
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if t.endswith(".T"):
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return "Japan (Nikkei)"
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if t.endswith(".L"):
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return "UK (LSE)"
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return "US (S&P/Dow/Nasdaq)"
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