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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>
120 lines
3.1 KiB
Python
120 lines
3.1 KiB
Python
"""US 10Y minus peer 10Y spread vs FX pairs (yfinance)."""
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from __future__ import annotations
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from datetime import datetime
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from typing import Any, Dict, List, Optional
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import pandas as pd
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from server.services.macro_fetcher import fetch_fred_series
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_FRED_US10Y = "DGS10"
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_FRED_JP10Y = "IRLTLT01JPM156N"
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_FRED_EZ10Y = "IRLTLT01EZM156N"
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_FRED_KR10Y = "IRLTLT01KRM156N"
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_YF_PAIR = {
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"usdjpy": "USDJPY=X",
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"eurusd": "EURUSD=X",
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"usdkrw": "KRW=X",
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}
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_PEER_FRED = {
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"usdjpy": _FRED_JP10Y,
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"eurusd": _FRED_EZ10Y,
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"usdkrw": _FRED_KR10Y,
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}
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def _fred_to_daily_series(series_id: str) -> pd.Series:
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rows = fetch_fred_series(series_id)
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if not rows:
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return pd.Series(dtype=float)
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df = pd.DataFrame(rows)
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df["dt"] = pd.to_datetime(df["date"])
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df = df.sort_values("dt").drop_duplicates("dt", keep="last")
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s = pd.Series(df["value"].astype(float).values, index=df["dt"])
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return s.sort_index()
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def _fred_to_monthly_end(series_id: str) -> pd.Series:
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s = _fred_to_daily_series(series_id)
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if s.empty:
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return pd.Series(dtype=float)
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# Monthly FRED series: normalize to month-end
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m = s.resample("ME").last().dropna()
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return m
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def _us10y_monthly() -> pd.Series:
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daily = _fred_to_daily_series(_FRED_US10Y)
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if daily.empty:
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return pd.Series(dtype=float)
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return daily.resample("ME").last().dropna()
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def _yf_fx(ticker: str, period: str = "2y") -> pd.Series:
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try:
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import yfinance as yf
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t = yf.Ticker(ticker)
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hist = t.history(period=period, auto_adjust=True)
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if hist is None or hist.empty or "Close" not in hist:
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return pd.Series(dtype=float)
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s = hist["Close"].astype(float)
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s.index = pd.to_datetime(s.index).tz_localize(None)
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return s.resample("ME").last().dropna()
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except Exception:
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return pd.Series(dtype=float)
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def get_yield_fx_pair(pair: str) -> Dict[str, Any]:
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"""Return aligned monthly spread (US10Y - peer10Y) vs FX."""
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key = (pair or "").strip().lower()
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out: Dict[str, Any] = {
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"pair": key,
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"updated_at": datetime.utcnow().isoformat() + "Z",
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"series": [],
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"error": None,
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}
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if key not in _YF_PAIR:
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out["error"] = "invalid_pair"
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return out
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peer_id = _PEER_FRED.get(key)
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if not peer_id:
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out["error"] = "missing_peer"
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return out
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us_m = _us10y_monthly()
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peer_m = _fred_to_monthly_end(peer_id)
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if us_m.empty or peer_m.empty:
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out["error"] = "fred_empty"
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return out
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spread = us_m.sub(peer_m, fill_value=None)
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spread = spread.dropna()
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fx = _yf_fx(_YF_PAIR[key])
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if fx.empty:
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out["error"] = "fx_empty"
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return out
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df = pd.DataFrame({"spread_pct": spread}).join(
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pd.DataFrame({"fx": fx}), how="inner"
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)
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df = df.dropna()
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df = df.sort_index()
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series: List[Dict[str, Any]] = []
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for idx, row in df.iterrows():
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series.append({
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"date": idx.strftime("%Y-%m-%d"),
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"spread_pct": round(float(row["spread_pct"]), 4),
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"fx": round(float(row["fx"]), 6),
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})
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out["series"] = series
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return out
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