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https://github.com/shawnkim1997/All-in-one-Financial-Analysis.git
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shawnkim1997
38c56a5a43
Add asset-type aware market/overview flows, portfolio OCR reverse-engineering with exchange overrides, and interactive index heatmap features. Update README with recent updates and wire backend/frontend APIs for FX matrix, exchange options, and improved portfolio editing flows. Made-with: Cursor
100 lines
3.6 KiB
Python
100 lines
3.6 KiB
Python
"""Commodity future analysis helpers."""
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from __future__ import annotations
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from typing import Any
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from server.utils.ticker_utils import COMMODITY_FUTURES
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COMMODITY_RELATED: dict[str, list[str]] = {
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"GC=F": ["GLD", "SI=F", "DX-Y.NYB", "^TNX"],
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"CL=F": ["USO", "BZ=F", "XLE", "^GSPC"],
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"SI=F": ["SLV", "GC=F", "HG=F", "^GSPC"],
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"NG=F": ["UNG", "CL=F", "XLE"],
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}
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def _get_related_assets(ticker: str) -> list[str]:
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return COMMODITY_RELATED.get(ticker.upper(), [])
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async def compute_commodity_correlations(ticker: str, period: str = "1y") -> dict:
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import yfinance as yf
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t = ticker.upper()
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related = _get_related_assets(t)
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if not related:
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return {}
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all_tickers = [t] + related
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data = yf.download(all_tickers, 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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returns = close.pct_change().dropna()
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if returns is None or returns.empty or t not in returns.columns:
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return {}
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corr = returns.corr()
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result = {}
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for r in related:
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if r in corr.columns:
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result[r] = round(float(corr.loc[t, r]), 2)
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return result
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async def get_commodity_overview(ticker: str) -> dict:
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import yfinance as yf
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t = ticker.upper()
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y = yf.Ticker(t)
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info = y.info or {}
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hist_1y = y.history(period="1y", auto_adjust=True)
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hist_10y = y.history(period="10y", auto_adjust=True)
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seasonal = {}
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if hist_10y is not None and not hist_10y.empty:
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monthly = hist_10y["Close"].resample("ME").last().pct_change().dropna()
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for month in range(1, 13):
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m = monthly[monthly.index.month == month]
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seasonal[month] = round(float(m.mean()) * 100, 2) if len(m) > 0 else 0
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related = _get_related_assets(t)
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related_cards = []
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if related:
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data = yf.download(related, period="5d", auto_adjust=True, progress=False)
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close = data["Close"] if hasattr(data, "columns") and "Close" in data.columns else data
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if close is not None:
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try:
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if hasattr(close, "columns"):
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for sym in related:
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if sym not in close.columns:
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continue
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s = close[sym].dropna()
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if len(s) < 1:
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continue
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cur = float(s.iloc[-1])
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prev = float(s.iloc[-2]) if len(s) > 1 else cur
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pct = ((cur - prev) / prev * 100) if prev else 0
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related_cards.append({"symbol": sym, "price": round(cur, 2), "change_pct": round(pct, 2)})
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else:
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s = close.dropna()
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if len(s) >= 1:
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cur = float(s.iloc[-1])
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prev = float(s.iloc[-2]) if len(s) > 1 else cur
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pct = ((cur - prev) / prev * 100) if prev else 0
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related_cards.append({"symbol": related[0], "price": round(cur, 2), "change_pct": round(pct, 2)})
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except Exception:
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pass
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return {
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"name": COMMODITY_FUTURES.get(t, info.get("shortName", t)),
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"price": info.get("regularMarketPrice") or info.get("currentPrice"),
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"open_interest": info.get("openInterest"),
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"volume": info.get("volume"),
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"high_52w": info.get("fiftyTwoWeekHigh"),
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"low_52w": info.get("fiftyTwoWeekLow"),
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"seasonal_pattern": seasonal,
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"related_assets": related_cards,
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"correlation_matrix": await compute_commodity_correlations(t),
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"asset_class": "commodity_future",
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}
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