3b7fa35456
- COT Positioning: CFTC disaggregated + financial futures (19 markets) via Socrata free API net MM position % OI + weekly change stored in cot_data table - Forward Curves: yfinance front-month vs +3M slope (8 commodities) contango/backwardation/flat stored in forward_curve_data table - Surprise Index: consensus_estimate + actual_value on specialist_reports auto-computes surprise_score = actual - consensus on save - Hawk/Dove Text Scorer: GPT-4o-mini endpoint for CB statements score -1..+1, label, summary, key_phrases (forex/bonds: hawk/dove; commodities: bull/bear) - AI context injection: COT net positioning, forward curve structure, surprise scores, upcoming consensus estimates injected into all desk blocks - Frontend: COT panel (net% bars), Forward Curves panel, SurpriseInput on report cards, Hawk/Dove scorer in forex/bonds config tab - auto_cycle.py: non-blocking COT + curve refresh before each cycle Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
96 lines
3.2 KiB
Python
96 lines
3.2 KiB
Python
"""
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Forward Curve Service — contango / backwardation per commodity.
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Uses yfinance to compare front-month vs near-dated futures contracts.
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"""
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import logging
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from datetime import datetime, timedelta
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from typing import List, Dict, Any, Optional
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logger = logging.getLogger(__name__)
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# Month codes for futures tickers (standard CME/NYMEX/CBOT convention)
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_MONTH_CODES = {1:'F',2:'G',3:'H',4:'J',5:'K',6:'M',7:'N',8:'Q',9:'U',10:'V',11:'X',12:'Z'}
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# (base_ticker, label, asset_class, months_spread)
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_CURVE_SPECS = [
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("CL", "WTI Crude", "energy", 3),
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("NG", "Natural Gas", "energy", 3),
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("GC", "Gold", "metals", 3),
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("SI", "Silver", "metals", 3),
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("HG", "Copper", "metals", 3),
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("ZC", "Corn", "agri", 3),
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("ZW", "Wheat", "agri", 3),
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("ZS", "Soybeans", "agri", 3),
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]
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def _ticker_for_month(base: str, months_ahead: int) -> str:
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target = datetime.now() + timedelta(days=months_ahead * 30)
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code = _MONTH_CODES[target.month]
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year = str(target.year)[-2:]
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return f"{base}{code}{year}"
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def _get_price(ticker: str) -> Optional[float]:
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try:
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import yfinance as yf
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data = yf.download(ticker, period="3d", progress=False, auto_adjust=True)
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if data.empty:
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return None
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close = data["Close"].dropna()
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if close.empty:
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return None
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val = float(close.iloc[-1])
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# Handle multi-index DataFrames
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if hasattr(val, '__iter__'):
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val = list(val)[0]
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return round(val, 4) if val and val > 0 else None
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except Exception:
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return None
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def fetch_forward_curves() -> List[Dict[str, Any]]:
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"""Fetch front-month and far-month prices to compute curve slope."""
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results = []
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for base, label, ac, spread in _CURVE_SPECS:
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front_ticker = f"{base}=F"
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front_price = _get_price(front_ticker)
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if not front_price:
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logger.warning(f"Forward curve: no front-month price for {label} ({front_ticker})")
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continue
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# Try M+spread, M+spread-1, M+spread+1 (some contract months are illiquid)
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far_price = None
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for offset in [0, -1, 1, -2, 2]:
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far_ticker = _ticker_for_month(base, spread + offset)
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far_price = _get_price(far_ticker)
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if far_price:
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break
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slope_pct = None
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structure = "unknown"
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if far_price:
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slope_pct = round((far_price - front_price) / front_price * 100, 2)
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if slope_pct > 0.15:
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structure = "contango"
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elif slope_pct < -0.15:
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structure = "backwardation"
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else:
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structure = "flat"
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results.append({
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"asset": label,
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"asset_class": ac,
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"front_price": round(front_price, 2),
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"far_price": round(far_price, 2) if far_price else None,
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"slope_pct": slope_pct,
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"structure": structure,
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"months_spread": spread,
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})
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logger.info(
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f"Forward curve {label}: front={front_price:.2f} "
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f"far={far_price} slope={slope_pct}% -> {structure}"
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)
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return results
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