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https://github.com/shawnkim1997/All-in-one-Financial-Analysis.git
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shawnkim1997
ef429e93c4
- /daily-news route fetches FT ePaper headlines with Gemini-backed Korean translation, plus calendar selector and cached translation TTL - /api/search and use-ticker-search wire async sidebar search through a unified backend that merges static aliases with the local KOSPI/KOSDAQ universe (162 names) and yfinance metadata - ticker-alias gains Korean display names, currency and market hints; PeerComparison formats KRW/JPY with locale-aware zero decimals - EquityOverview surfaces HQ city/country, with Korean Naver snapshot fallback when yfinance info is empty - market_data adds /korean-universe/search for autocomplete - New tests for FT ingestion, Korean universe lookups, and updated smoke prefixes
242 lines
9.9 KiB
Python
242 lines
9.9 KiB
Python
"""ETF and equity-like overview helpers."""
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from __future__ import annotations
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import math
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from typing import Any
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def _safe_num(v: Any) -> float | None:
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try:
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f = float(v)
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if math.isnan(f) or math.isinf(f):
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return None
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return f
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except Exception:
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return None
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def _compute_sharpe(returns) -> float | None:
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if returns is None or len(returns) < 2:
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return None
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std = returns.std()
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if not std:
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return None
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return round(float((returns.mean() / std) * (252**0.5)), 2)
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def _compute_sortino(returns) -> float | None:
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if returns is None or len(returns) < 2:
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return None
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downside = returns[returns < 0]
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if downside is None or len(downside) < 2:
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return None
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std = downside.std()
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if not std:
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return None
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return round(float((returns.mean() / std) * (252**0.5)), 2)
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def _max_drawdown(returns) -> float | None:
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if returns is None or len(returns) < 2:
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return None
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curve = (1 + returns).cumprod()
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dd = (curve / curve.cummax()) - 1
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return round(float(dd.min()) * 100, 2)
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async def get_benchmark_comparison(ticker: str, benchmark: str = "SPY", period: str = "1y") -> dict:
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import yfinance as yf
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data = yf.download([ticker.upper(), benchmark.upper()], period=period, auto_adjust=True, progress=False)
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if data is None or data.empty:
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return {}
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close = data["Close"] if "Close" in data else data
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if close is None or close.empty:
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return {}
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t_col = ticker.upper()
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b_col = benchmark.upper()
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if t_col not in close.columns or b_col not in close.columns:
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return {}
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close = close[[t_col, b_col]].dropna()
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if close.empty:
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return {}
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normalized = close / close.iloc[0] * 100
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return {
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"dates": normalized.index.strftime("%Y-%m-%d").tolist(),
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"ticker_values": [float(x) for x in normalized[t_col].tolist()],
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"benchmark_values": [float(x) for x in normalized[b_col].tolist()],
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"benchmark": benchmark.upper(),
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}
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async def get_etf_holdings(ticker: str, top_n: int = 10) -> list[dict]:
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import yfinance as yf
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t = yf.Ticker(ticker.upper())
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out = []
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try:
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holdings = getattr(t, "fund_top_holdings", None)
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if holdings is not None and not holdings.empty:
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for _, row in holdings.head(top_n).iterrows():
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out.append(
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{
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"symbol": row.get("symbol") or row.get("holdingName") or "",
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"name": row.get("holdingName") or row.get("symbol") or "",
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"weight_pct": _safe_num(row.get("holdingPercent")),
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}
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)
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except Exception:
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pass
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return out
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async def get_etf_overview(ticker: str) -> dict:
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import yfinance as yf
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t = yf.Ticker(ticker.upper())
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info = t.info or {}
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hist = t.history(period="5y", auto_adjust=True)
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def period_return(days: int) -> float | None:
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if hist is None or hist.empty or len(hist) <= days:
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return None
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cur = _safe_num(hist["Close"].iloc[-1])
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prev = _safe_num(hist["Close"].iloc[-days])
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if cur is None or prev is None or prev == 0:
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return None
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return round((cur / prev - 1) * 100, 2)
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ytd_days = 0
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if hist is not None and not hist.empty:
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ytd_days = int((hist.index.year == hist.index[-1].year).sum())
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returns = hist["Close"].pct_change().dropna() if hist is not None and not hist.empty else None
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return {
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"name": info.get("longName") or info.get("shortName", ticker.upper()),
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"category": info.get("category") or info.get("fundFamily") or "N/A",
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"aum": _safe_num(info.get("totalAssets")),
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"expense_ratio": _safe_num(info.get("annualReportExpenseRatio")),
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"nav": _safe_num(info.get("navPrice")),
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"inception": info.get("fundInceptionDate"),
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"price": _safe_num(info.get("currentPrice") or info.get("regularMarketPrice")),
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"high_52w": _safe_num(info.get("fiftyTwoWeekHigh")),
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"low_52w": _safe_num(info.get("fiftyTwoWeekLow")),
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"returns": {
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"1m": period_return(21),
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"3m": period_return(63),
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"6m": period_return(126),
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"ytd": period_return(ytd_days) if ytd_days else None,
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"1y": period_return(252),
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"3y": period_return(756),
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"5y": period_return(1260),
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},
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"holdings": await get_etf_holdings(ticker, top_n=10),
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"risk": {
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"sharpe": _compute_sharpe(returns),
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"sortino": _compute_sortino(returns),
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"max_drawdown": _max_drawdown(returns),
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"volatility": round(float(returns.std()) * (252**0.5) * 100, 2) if returns is not None and len(returns) > 1 else None,
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},
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"benchmark_comparison": await get_benchmark_comparison(ticker, "SPY", "1y"),
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}
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async def get_equity_overview(ticker: str) -> dict:
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import yfinance as yf
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from server.services.korean_market import get_naver_stock_snapshot, is_korean_equity_ticker
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normalized = ticker.upper()
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t = yf.Ticker(normalized)
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info = t.info or {}
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# Fallback: derive 52W low/high from history when yfinance returns 0 or None
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# (common for some Asian tickers like 005930.KS where info.fiftyTwoWeekLow == 0)
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hi52 = _safe_num(info.get("fiftyTwoWeekHigh"))
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lo52 = _safe_num(info.get("fiftyTwoWeekLow"))
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if not hi52 or not lo52 or lo52 == 0:
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try:
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hist = t.history(period="1y")
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if hist is not None and len(hist) > 0:
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h_max = float(hist["High"].max())
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l_min = float(hist["Low"].min())
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if not hi52:
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hi52 = h_max
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if not lo52 or lo52 == 0:
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lo52 = l_min
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except Exception:
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pass
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city = (info.get("city") or "").strip()
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state = (info.get("state") or "").strip()
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country = (info.get("country") or "").strip()
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hq_parts = [part for part in [city, state, country] if part]
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hq = ", ".join(hq_parts) if hq_parts else None
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naver = await get_naver_stock_snapshot(normalized) if is_korean_equity_ticker(normalized) else None
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price = _safe_num(info.get("currentPrice") or info.get("regularMarketPrice"))
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market_cap = _safe_num(info.get("marketCap"))
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currency = info.get("currency") or info.get("financialCurrency") or "USD"
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exchange = info.get("exchange")
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country_name = info.get("country")
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description = info.get("longBusinessSummary")
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if naver:
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price = _safe_num(naver.get("current_price")) or price
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market_cap = _safe_num(naver.get("market_cap")) or market_cap
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hi52 = _safe_num(naver.get("fifty_two_week_high")) or hi52
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lo52 = _safe_num(naver.get("fifty_two_week_low")) or lo52
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currency = str(naver.get("currency") or currency or "KRW")
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exchange = str(naver.get("exchange") or exchange or "")
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country_name = str(naver.get("country") or country_name or "KR")
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description = str(naver.get("description") or description or "")
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return {
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"name": naver.get("name") if naver else info.get("longName") or info.get("shortName", normalized),
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"name_ko": naver.get("name_ko") if naver else None,
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"sector": info.get("sector"),
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"industry": info.get("industry"),
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"market_cap": market_cap,
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"pe_ratio": _safe_num(info.get("trailingPE")) or _safe_num(info.get("forwardPE")),
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"dividend_yield": _safe_num(info.get("dividendYield")) or _safe_num(naver.get("dividend_yield") / 100.0 if naver and naver.get("dividend_yield") is not None else None),
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"beta": _safe_num(info.get("beta")),
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"high_52w": hi52,
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"low_52w": lo52,
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"price": price,
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"description": description,
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"currency": currency,
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"exchange": exchange,
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"country": country_name,
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"market": naver.get("market") if naver else None,
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"stock_code": naver.get("stock_code") if naver else None,
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"full_time_employees": info.get("fullTimeEmployees"),
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"average_volume": _safe_num(info.get("averageVolume")),
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"trailing_pe": _safe_num(info.get("trailingPE")),
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"forward_pe": _safe_num(naver.get("forward_pe") if naver else None) or _safe_num(info.get("forwardPE")),
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"enterprise_to_ebitda": _safe_num(info.get("enterpriseToEbitda")),
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"debt_to_equity": _safe_num(info.get("debtToEquity")),
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"return_on_equity": _safe_num(info.get("returnOnEquity")),
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"return_on_assets": _safe_num(info.get("returnOnAssets")),
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"free_cashflow": _safe_num(info.get("freeCashflow")),
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"revenue_growth": _safe_num(info.get("revenueGrowth")),
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"profit_margins": _safe_num(info.get("profitMargins")),
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"target_mean_price": _safe_num(naver.get("target_mean_price") if naver else None) or _safe_num(info.get("targetMeanPrice")),
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"target_high_price": _safe_num(info.get("targetHighPrice")),
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"target_low_price": _safe_num(info.get("targetLowPrice")),
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"recommendation": naver.get("recommendation") if naver else info.get("recommendationKey"),
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"num_analysts": info.get("numberOfAnalystOpinions"),
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"change": _safe_num(naver.get("change") if naver else None),
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"change_pct": _safe_num(naver.get("change_pct") if naver else None),
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"forward_eps": _safe_num(naver.get("forward_eps") if naver else None) or _safe_num(info.get("forwardEps")),
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"trailing_eps": _safe_num(naver.get("trailing_eps") if naver else None) or _safe_num(info.get("trailingEps")),
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"peg_ratio": _safe_num(info.get("pegRatio")),
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"ceo": info.get("companyOfficers", [{}])[0].get("name") if isinstance(info.get("companyOfficers"), list) and info.get("companyOfficers") else None,
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"founded": info.get("founded"),
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"hq": hq,
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"website": info.get("website"),
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"ipo_date": info.get("ipoExpectedDate") or info.get("firstTradeDateEpochUtc"),
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"source": naver.get("source") if naver else "yfinance",
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}
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