mirror of
https://github.com/shawnkim1997/All-in-one-Financial-Analysis.git
synced 2026-08-19 21:38:11 +00:00
shawnkim1997andClaude Opus 4.6
51cbaf7f8d
- Add missing numpy, scipy, dbnomics to requirements.txt (fixes ImportError on fresh install) - Sync claude.md with actual codebase: §3 file structure (37 services, 21 routers), §5 API endpoints (92 routes), §6 frontend pages (12), §13 TODO status - Update README.md with current architecture (92 API routes, 21 routers, 37 services), multi-asset overview, research grid, macro dashboard, screener+backtest, multi-jurisdiction filings, and 2026-03-26 changelog entry - Add new routers: dart, edinet, fmp, macro, research - Add new services: cache, dart_fetcher, dart_filing_service, economic_calendar, ecos_fetcher, edinet_filing_service, fmp_client, global_macro_quadrant, kpi_history_service, macro_cycle, macro_fetcher, oecd_cycle, peer_comparison_service, research_dashboard, smart_money_service, yield_fx_service - Add new frontend: macro page, screener+backtest, research grid components, overview (Equity/ETF/Commodity), filings (SEC/DART/EDINET), error boundaries - Remove 6 unused services: copilot_context, crypto_fetcher, fx_fetcher, gemini_analysis, market_data, technical_analysis - Remove obsolete docs: .agent/, AGENT.md, ATLAS_EVALUATION.md, docs/ Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
528 lines
22 KiB
Python
528 lines
22 KiB
Python
"""Market Data router -- sector info, financial trends, comps, health metrics."""
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from typing import Any, Dict, List
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from fastapi import APIRouter, Query
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from server.utils.ticker_utils import AssetType, detect_asset_type
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router = APIRouter()
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def _safe_float(val, default=0.0):
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if val is None:
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return default
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try:
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import math
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f = float(val)
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return default if math.isnan(f) or math.isinf(f) else f
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except (TypeError, ValueError):
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return default
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@router.get("/indices", summary="Major market indices")
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async def market_indices():
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try:
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import yfinance as yf
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symbols = [
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{"label": "S&P 500", "symbol": "^GSPC"},
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{"label": "NASDAQ", "symbol": "^IXIC"},
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{"label": "KOSPI", "symbol": "^KS11"},
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{"label": "BTC", "symbol": "BTC-USD"},
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]
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results = []
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for s in symbols:
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try:
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t = yf.Ticker(s["symbol"])
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info = t.info or {}
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price = _safe_float(info.get("regularMarketPrice") or info.get("previousClose"))
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prev = _safe_float(info.get("regularMarketPreviousClose") or info.get("previousClose"))
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change = price - prev if prev else 0
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pct = (change / prev * 100) if prev else 0
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results.append({
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"label": s["label"],
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"symbol": s["symbol"],
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"price": f"{price:,.2f}" if price else "—",
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"change": f"{pct:+.2f}%",
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"positive": pct >= 0,
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})
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except Exception:
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results.append({"label": s["label"], "symbol": s["symbol"], "price": "—", "change": "—", "positive": True})
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return results
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except Exception:
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return []
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@router.get("/overview", summary="Global market overview")
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async def market_overview():
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try:
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from server.services.market_overview import get_market_overview
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return await get_market_overview()
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except Exception as e:
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return {"error": str(e), "data": None}
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@router.get("/sectors", summary="Sector heatmap")
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async def sector_heatmap():
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try:
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from server.services.sector_heatmap import get_sector_heatmap
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return await get_sector_heatmap()
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except Exception as e:
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return {"error": str(e), "data": None}
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@router.get("/overview/{ticker}", summary="Asset-type aware overview")
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async def market_overview_by_ticker(ticker: str):
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"""Detect asset type and return overview payload for that type."""
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try:
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asset_type = detect_asset_type(ticker)
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if asset_type == AssetType.ETF:
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from server.services.etf_analysis import get_etf_overview
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return {"asset_type": AssetType.ETF.value, "data": await get_etf_overview(ticker)}
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if asset_type == AssetType.COMMODITY_FUTURE:
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from server.services.commodity_analysis import get_commodity_overview
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return {"asset_type": AssetType.COMMODITY_FUTURE.value, "data": await get_commodity_overview(ticker)}
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if asset_type == AssetType.CRYPTO:
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return {"asset_type": AssetType.CRYPTO.value, "data": {"name": ticker.upper()}}
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if asset_type == AssetType.INDEX:
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return {"asset_type": AssetType.INDEX.value, "data": {"name": ticker.upper()}}
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from server.services.etf_analysis import get_equity_overview
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return {"asset_type": AssetType.EQUITY.value, "data": await get_equity_overview(ticker)}
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except Exception as e:
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return {"error": str(e), "asset_type": AssetType.EQUITY.value, "data": None}
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@router.get("/etf/{ticker}/holdings", summary="ETF top holdings")
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async def etf_holdings(ticker: str):
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try:
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from server.services.etf_analysis import get_etf_holdings
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return {"ticker": ticker.upper(), "holdings": await get_etf_holdings(ticker)}
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except Exception as e:
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return {"error": str(e), "ticker": ticker.upper(), "holdings": []}
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@router.get("/commodity/{ticker}/seasonal", summary="Commodity monthly seasonal pattern")
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async def commodity_seasonal(ticker: str):
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try:
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from server.services.commodity_analysis import get_commodity_overview
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data = await get_commodity_overview(ticker)
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return {"ticker": ticker.upper(), "seasonal_pattern": data.get("seasonal_pattern", {})}
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except Exception as e:
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return {"error": str(e), "ticker": ticker.upper(), "seasonal_pattern": {}}
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@router.get("/commodity/{ticker}/correlations", summary="Commodity correlations")
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async def commodity_correlations(ticker: str):
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try:
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from server.services.commodity_analysis import compute_commodity_correlations
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return {"ticker": ticker.upper(), "correlations": await compute_commodity_correlations(ticker)}
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except Exception as e:
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return {"error": str(e), "ticker": ticker.upper(), "correlations": {}}
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@router.get("/sector/{ticker}", summary="Sector and industry classification")
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async def sector_industry(ticker: str):
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try:
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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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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 = [p for p in [city, state, country] if p]
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hq = ", ".join(hq_parts) if hq_parts else "N/A"
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return {
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"sector": info.get("sector", "N/A"),
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"industry": info.get("industry", "N/A"),
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"market_cap": _safe_float(info.get("marketCap")),
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"pe_ratio": _safe_float(info.get("trailingPE")) or _safe_float(info.get("forwardPE")),
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"dividend_yield": _safe_float(info.get("dividendYield")),
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"beta": _safe_float(info.get("beta")),
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"fifty_two_week_high": _safe_float(info.get("fiftyTwoWeekHigh")),
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"fifty_two_week_low": _safe_float(info.get("fiftyTwoWeekLow")),
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"current_price": _safe_float(info.get("currentPrice") or info.get("regularMarketPrice")),
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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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"employees": info.get("fullTimeEmployees"),
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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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"description": info.get("longBusinessSummary"),
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}
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except Exception:
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return {"sector": "N/A", "industry": "N/A"}
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@router.get("/trend/{ticker}", summary="5-year financial trend")
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async def financial_trend(ticker: str):
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try:
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import yfinance as yf
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t = yf.Ticker(ticker.upper())
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fin = t.financials
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cf = t.cashflow
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if fin is None or fin.empty:
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return {"years": [], "revenue": [], "net_income": [], "operating_margin": [], "fcf": []}
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years = [str(c.year) for c in fin.columns[:5]]
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revenue = [_safe_float(fin.loc["Total Revenue"][c]) if "Total Revenue" in fin.index else 0 for c in fin.columns[:5]]
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net_income = [_safe_float(fin.loc["Net Income"][c]) if "Net Income" in fin.index else 0 for c in fin.columns[:5]]
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op_margin = []
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for i, c in enumerate(fin.columns[:5]):
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oi = _safe_float(fin.loc["Operating Income"][c]) if "Operating Income" in fin.index else 0
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rev = revenue[i] if i < len(revenue) else 1
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op_margin.append(round(oi / rev * 100, 2) if rev else 0)
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fcf_list = []
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if cf is not None and not cf.empty:
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for c in fin.columns[:5]:
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if c in cf.columns:
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ocf = _safe_float(cf.loc["Operating Cash Flow"][c]) if "Operating Cash Flow" in cf.index else 0
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capex = _safe_float(cf.loc["Capital Expenditure"][c]) if "Capital Expenditure" in cf.index else 0
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fcf_list.append(ocf + capex) # capex is negative
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else:
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fcf_list.append(0)
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return {"years": years, "revenue": revenue, "net_income": net_income, "operating_margin": op_margin, "fcf": fcf_list}
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except Exception:
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return {"years": [], "revenue": [], "net_income": [], "operating_margin": [], "fcf": []}
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@router.get("/peers/{ticker}", summary="Peer valuation multiples (sector bucket)")
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async def peer_valuation_multiples(ticker: str):
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"""P/E, P/B, P/S, EV/EBITDA vs. a small industry peer set (yfinance)."""
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try:
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from server.services.peer_comparison_service import build_peer_comparison
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return build_peer_comparison(ticker)
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except Exception:
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return {
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"ticker": ticker.upper(),
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"sector": "—",
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"industry": "—",
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"averages": {"pe": None, "pb": None, "ps": None, "ev_ebitda": None},
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"peers": [],
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}
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@router.get("/comps", summary="Industry comparable companies")
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async def industry_comps(tickers: str = Query(..., description="Comma-separated tickers")):
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try:
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import yfinance as yf
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ticker_list = [t.strip().upper() for t in tickers.split(",") if t.strip()]
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if not ticker_list:
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return {"tickers": [], "data": []}
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results = []
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for sym in ticker_list:
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t = yf.Ticker(sym)
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info = t.info or {}
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results.append({
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"ticker": sym,
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"forward_pe": _safe_float(info.get("forwardPE"), None),
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"trailing_pe": _safe_float(info.get("trailingPE"), None),
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"pb": _safe_float(info.get("priceToBook"), None),
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"ev_ebitda": _safe_float(info.get("enterpriseToEbitda"), None),
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"market_cap": _safe_float(info.get("marketCap"), None),
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})
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return {"tickers": ticker_list, "data": results}
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except Exception:
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return {"tickers": [], "data": []}
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@router.get("/health/{ticker}", summary="DuPont, Altman Z-Score, Red Flags")
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async def financial_health(ticker: str):
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fallback = {
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"ticker": ticker.upper(),
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"dupont": {},
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"altman_z": None,
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"current_ratio": None,
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"interest_coverage": None,
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"debt_to_equity": None,
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"red_flags": [],
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}
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try:
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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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bs = t.balance_sheet
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fin = t.financials
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# DuPont Analysis
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npm = _safe_float(info.get("profitMargins"))
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roe = _safe_float(info.get("returnOnEquity"))
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roa = _safe_float(info.get("returnOnAssets"))
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total_assets = 0
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total_equity = 0
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total_revenue = 0
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net_income = 0
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if bs is not None and not bs.empty:
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col = bs.columns[0]
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total_assets = _safe_float(bs.loc["Total Assets"][col]) if "Total Assets" in bs.index else 0
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se_keys = ["Stockholders Equity", "Total Stockholder Equity", "Common Stock Equity"]
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for k in se_keys:
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if k in bs.index:
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total_equity = _safe_float(bs.loc[k][col])
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break
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if fin is not None and not fin.empty:
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col = fin.columns[0]
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total_revenue = _safe_float(fin.loc["Total Revenue"][col]) if "Total Revenue" in fin.index else 0
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net_income = _safe_float(fin.loc["Net Income"][col]) if "Net Income" in fin.index else 0
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asset_turnover = round(total_revenue / total_assets, 3) if total_assets else 0
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equity_multiplier = round(total_assets / total_equity, 3) if total_equity else 0
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dupont = {
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"npm": round(npm, 4) if npm else round(net_income / total_revenue, 4) if total_revenue else 0,
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"asset_turnover": asset_turnover,
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"equity_multiplier": equity_multiplier,
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"roe": round(roe, 4) if roe else round(npm * asset_turnover * equity_multiplier, 4) if npm else 0,
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}
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# Altman Z-Score (simplified)
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altman_z = None
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if bs is not None and not bs.empty and fin is not None and not fin.empty:
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col_bs = bs.columns[0]
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col_fin = fin.columns[0]
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ca = _safe_float(bs.loc["Current Assets"][col_bs]) if "Current Assets" in bs.index else 0
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cl = _safe_float(bs.loc["Current Liabilities"][col_bs]) if "Current Liabilities" in bs.index else 0
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ta = total_assets
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re_val = _safe_float(bs.loc["Retained Earnings"][col_bs]) if "Retained Earnings" in bs.index else 0
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ebit = _safe_float(fin.loc["EBIT"][col_fin]) if "EBIT" in fin.index else _safe_float(fin.loc.get("Operating Income", {}).get(col_fin, 0))
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mc = _safe_float(info.get("marketCap"))
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tl_val = _safe_float(bs.loc["Total Liabilities Net Minority Interest"][col_bs]) if "Total Liabilities Net Minority Interest" in bs.index else (ta - total_equity)
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rev = total_revenue
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if ta > 0 and tl_val > 0:
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wc_ta = (ca - cl) / ta
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re_ta = re_val / ta
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ebit_ta = ebit / ta
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mc_tl = mc / tl_val if tl_val else 0
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rev_ta = rev / ta
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altman_z = round(1.2 * wc_ta + 1.4 * re_ta + 3.3 * ebit_ta + 0.6 * mc_tl + 1.0 * rev_ta, 2)
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# Additional health metrics for overview cards
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current_ratio = None
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if bs is not None and not bs.empty:
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col_bs = bs.columns[0]
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ca = _safe_float(bs.loc["Current Assets"][col_bs]) if "Current Assets" in bs.index else 0
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cl = _safe_float(bs.loc["Current Liabilities"][col_bs]) if "Current Liabilities" in bs.index else 0
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current_ratio = (ca / cl) if cl else None
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if current_ratio is None:
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info_cr = _safe_float(info.get("currentRatio"), None)
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current_ratio = info_cr if info_cr and info_cr > 0 else None
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interest_coverage = None
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if fin is not None and not fin.empty:
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col_fin = fin.columns[0]
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ebit = _safe_float(fin.loc["EBIT"][col_fin]) if "EBIT" in fin.index else _safe_float(fin.loc["Operating Income"][col_fin]) if "Operating Income" in fin.index else None
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int_exp = _safe_float(fin.loc["Interest Expense"][col_fin]) if "Interest Expense" in fin.index else None
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if ebit is not None and int_exp is not None and int_exp != 0:
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interest_coverage = abs(ebit / int_exp)
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debt_to_equity = None
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if bs is not None and not bs.empty:
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col_bs = bs.columns[0]
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total_debt = _safe_float(bs.loc["Total Debt"][col_bs], None) if "Total Debt" in bs.index else None
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if total_debt is None:
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ltd = _safe_float(bs.loc["Long Term Debt"][col_bs], 0) if "Long Term Debt" in bs.index else 0
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std = _safe_float(bs.loc["Current Debt"][col_bs], 0) if "Current Debt" in bs.index else 0
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total_debt = ltd + std if (ltd or std) else None
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equity = total_equity if total_equity else None
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if total_debt is not None and equity:
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debt_to_equity = total_debt / equity
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if debt_to_equity is None:
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de_info = _safe_float(info.get("debtToEquity"), None)
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if de_info is not None:
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debt_to_equity = de_info / 100 if de_info > 10 else de_info
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# Red Flags
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red_flags = []
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if current_ratio is not None and current_ratio < 1.0:
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red_flags.append(f"Low current ratio: {current_ratio:.2f}")
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if debt_to_equity is not None and debt_to_equity > 2.0:
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red_flags.append(f"High debt-to-equity: {debt_to_equity:.2f}")
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if npm and npm < 0:
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red_flags.append("Negative profit margin")
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if roe and roe < 0:
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red_flags.append("Negative ROE")
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return {
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"ticker": ticker.upper(),
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"dupont": dupont,
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"altman_z": altman_z,
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"current_ratio": round(current_ratio, 2) if current_ratio is not None else None,
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"interest_coverage": round(interest_coverage, 2) if interest_coverage is not None else None,
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"debt_to_equity": round(debt_to_equity, 2) if debt_to_equity is not None else None,
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"red_flags": red_flags,
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}
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except Exception as e:
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return fallback
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@router.get("/piotroski/{ticker}", summary="Piotroski F-Score")
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async def piotroski_score(ticker: str):
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try:
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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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fin = t.financials
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bs = t.balance_sheet
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cf = t.cashflow
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score = 0
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details = {}
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if fin is None or fin.empty or bs is None or bs.empty:
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return {"total": 0, "details": {}, "score": 0}
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col = fin.columns[0]
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prev_col = fin.columns[1] if len(fin.columns) > 1 else None
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# 1. Positive ROA
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ni = _safe_float(fin.loc["Net Income"][col]) if "Net Income" in fin.index else 0
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ta = _safe_float(bs.loc["Total Assets"][col]) if "Total Assets" in bs.index else 1
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roa = ni / ta if ta else 0
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details["positive_roa"] = roa > 0
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score += 1 if roa > 0 else 0
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# 2. Positive Operating Cash Flow
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ocf = 0
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|
if cf is not None and not cf.empty and "Operating Cash Flow" in cf.index:
|
|
ocf = _safe_float(cf.loc["Operating Cash Flow"][cf.columns[0]])
|
|
details["positive_ocf"] = ocf > 0
|
|
score += 1 if ocf > 0 else 0
|
|
|
|
# 3. ROA improving
|
|
if prev_col is not None:
|
|
prev_ni = _safe_float(fin.loc["Net Income"][prev_col]) if "Net Income" in fin.index else 0
|
|
prev_ta = _safe_float(bs.loc["Total Assets"][prev_col]) if prev_col in bs.columns and "Total Assets" in bs.index else 1
|
|
prev_roa = prev_ni / prev_ta if prev_ta else 0
|
|
details["roa_improving"] = roa > prev_roa
|
|
score += 1 if roa > prev_roa else 0
|
|
else:
|
|
details["roa_improving"] = False
|
|
|
|
# 4. Cash flow > Net Income (accrual)
|
|
details["accrual"] = ocf > ni
|
|
score += 1 if ocf > ni else 0
|
|
|
|
# 5. Decreasing leverage
|
|
dle = _safe_float(info.get("debtToEquity", 0))
|
|
details["lower_leverage"] = dle < 100
|
|
score += 1 if dle < 100 else 0
|
|
|
|
# 6. Higher current ratio
|
|
cr = _safe_float(info.get("currentRatio", 0))
|
|
details["higher_liquidity"] = cr > 1.0
|
|
score += 1 if cr > 1.0 else 0
|
|
|
|
# 7. No dilution
|
|
shares = _safe_float(info.get("sharesOutstanding", 0))
|
|
details["no_dilution"] = True # simplified
|
|
score += 1
|
|
|
|
# 8. Higher gross margin
|
|
gm = _safe_float(info.get("grossMargins", 0))
|
|
details["higher_gross_margin"] = gm > 0.3
|
|
score += 1 if gm > 0.3 else 0
|
|
|
|
# 9. Higher asset turnover
|
|
rev = _safe_float(fin.loc["Total Revenue"][col]) if "Total Revenue" in fin.index else 0
|
|
at = rev / ta if ta else 0
|
|
details["higher_asset_turnover"] = at > 0.5
|
|
score += 1 if at > 0.5 else 0
|
|
|
|
return {"total": score, "details": details, "score": score}
|
|
except Exception:
|
|
return {"total": 0, "details": {}, "score": 0}
|
|
|
|
|
|
@router.get("/quote/{ticker}", summary="Quick quote: price and session change %")
|
|
async def quick_quote(ticker: str):
|
|
"""Used for news headline ticker mentions (day session move)."""
|
|
try:
|
|
import yfinance as yf
|
|
|
|
info = (yf.Ticker(ticker.upper()).info) or {}
|
|
ch = _safe_float(info.get("regularMarketChangePercent"))
|
|
if ch is None:
|
|
p = _safe_float(info.get("regularMarketPrice") or info.get("currentPrice"))
|
|
prev = _safe_float(info.get("regularMarketPreviousClose") or info.get("previousClose"))
|
|
if p is not None and prev and prev != 0:
|
|
ch = (p - prev) / prev * 100
|
|
return {
|
|
"ticker": ticker.upper(),
|
|
"current_price": _safe_float(info.get("regularMarketPrice") or info.get("currentPrice")),
|
|
"change_pct": round(ch, 2) if ch is not None else None,
|
|
}
|
|
except Exception:
|
|
return {"ticker": ticker.upper(), "current_price": None, "change_pct": None}
|
|
|
|
|
|
@router.get("/sankey/{ticker}", summary="Income statement Sankey data")
|
|
async def sankey_data(ticker: str):
|
|
try:
|
|
import yfinance as yf
|
|
t = yf.Ticker(ticker.upper())
|
|
fin = t.financials
|
|
if fin is None or fin.empty:
|
|
return {"nodes": [], "links": []}
|
|
|
|
col = fin.columns[0]
|
|
rev = _safe_float(fin.loc["Total Revenue"][col]) if "Total Revenue" in fin.index else 0
|
|
cogs = _safe_float(fin.loc["Cost Of Revenue"][col]) if "Cost Of Revenue" in fin.index else 0
|
|
gp = rev - cogs
|
|
opex = _safe_float(fin.loc["Operating Expense"][col]) if "Operating Expense" in fin.index else 0
|
|
oi = _safe_float(fin.loc["Operating Income"][col]) if "Operating Income" in fin.index else gp - opex
|
|
ni = _safe_float(fin.loc["Net Income"][col]) if "Net Income" in fin.index else 0
|
|
tax_other = oi - ni
|
|
|
|
nodes = [
|
|
{"name": "Revenue", "value": rev},
|
|
{"name": "COGS", "value": cogs},
|
|
{"name": "Gross Profit", "value": gp},
|
|
{"name": "Operating Expenses", "value": opex},
|
|
{"name": "Operating Income", "value": oi},
|
|
{"name": "Tax & Other", "value": abs(tax_other)},
|
|
{"name": "Net Income", "value": ni},
|
|
]
|
|
try:
|
|
from server.services.research_dashboard import sankey_nivo_for_ticker
|
|
|
|
nivo = sankey_nivo_for_ticker(ticker)
|
|
except Exception:
|
|
nivo = {"nodes": [], "links": []}
|
|
return {"nodes": nodes, "nivo": nivo}
|
|
except Exception:
|
|
return {"nodes": [], "nivo": {"nodes": [], "links": []}}
|
|
|
|
|
|
@router.get("/radar/{ticker}", summary="Radar chart metrics")
|
|
async def radar_metrics(ticker: str):
|
|
try:
|
|
import yfinance as yf
|
|
t = yf.Ticker(ticker.upper())
|
|
info = t.info or {}
|
|
|
|
return {
|
|
"roe": _safe_float(info.get("returnOnEquity", 0)) * 100,
|
|
"roa": _safe_float(info.get("returnOnAssets", 0)) * 100,
|
|
"gross_margin": _safe_float(info.get("grossMargins", 0)) * 100,
|
|
"current_ratio": _safe_float(info.get("currentRatio", 0)),
|
|
"revenue_growth": _safe_float(info.get("revenueGrowth", 0)) * 100,
|
|
}
|
|
except Exception:
|
|
return {}
|