| name | commodity-analysis |
| description | Commodity analysis (oil supply-demand balance / gold pricing / copper as an economic predictor / inventory cycles / futures premium-discount structure / seasonality), generating directional commodity signals. |
| category | analysis |
Commodity Analysis
Overview
Analyze commodities from four dimensions — supply-demand balance, pricing model, inventory cycle, and futures structure — and output directional signals suitable for backtesting. Focuses on crude oil (global pricing anchor), gold (safe haven + inflation hedge), and copper (economic barometer).
Core Concepts
1. Crude Oil Supply-Demand Balance
Key supply-side variables:
| Variable | Data Source | Frequency | Direction of Impact |
|---|
| OPEC production | OPEC monthly report | Monthly | Production cuts → oil price ↑ |
| US shale output | EIA weekly report | Weekly | Higher output → oil price ↓ |
| Rig count (Baker Hughes) | Baker Hughes | Weekly | Leads production by 3-6 months |
| Strategic Petroleum Reserve (SPR) | EIA | Weekly | SPR release → short-term oil price ↓ |
Key demand-side variables:
- IEA global oil demand forecast (quarterly)
- China crude imports (customs monthly data)
- US gasoline demand (EIA weekly report, implied demand)
- Global PMI (leads demand by 1-2 months)
Supply-demand balance signals:
if opec_compliance > 90% and us_rig_count_declining:
supply_signal = "tight"
elif opec_compliance < 80% and us_production_rising:
supply_signal = "loose"
if global_pmi > 50 and china_import_yoy > 5%:
demand_signal = "strong"
elif global_pmi < 48 and china_import_yoy < 0:
demand_signal = "weak"
2. Gold Pricing Framework
Four-factor model:
| Factor | Weight | Logic | Indicator |
|---|
| Real rates | 40% | Real rates ↓ → lower opportunity cost of holding gold → gold ↑ | 10Y TIPS yield |
| US dollar index | 25% | USD ↓ → gold becomes cheaper in pricing terms → gold ↑ | DXY |
| Safe-haven demand | 20% | Risk ↑ → safe-haven buying → gold ↑ | VIX + geopolitical risk index |
| Central-bank buying | 15% | Central-bank purchases → structural demand support | WGC quarterly report |
Practical rules:
- 10Y TIPS < 0%: strong support for gold (negative real rates mean negative holding cost)
- 10Y TIPS > 2%: pressure on gold (positive real rates reduce attractiveness)
- Correlation between DXY and gold is around -0.6, but not absolute (they both rose in 2022 due to safe-haven demand)
- Central-bank purchases >1000 tons / year (2022-2023 level): long-term structural bullish support
3. Dr. Copper as an Economic Predictor
Copper as a leading indicator:
- YoY copper-price change leads industrial production by about 2-3 months
- Copper / gold ratio is highly positively correlated with the US 10Y Treasury yield (
r > 0.7)
- Copper breakout above the prior high confirms economic recovery
Copper fundamental tracking:
| Indicator | Data Source | Threshold |
|---|
| LME copper inventory | LME daily report | <150k tons = tight |
| SHFE copper inventory | SHFE weekly report | WoW decline >10% = tight |
| Copper concentrate TC/RC | SMM | TC < $30/ton = tight mining supply |
| China copper imports | Customs monthly report | YoY growth >10% = strong demand |
4. Inventory Cycle Analysis
Visible inventory vs hidden inventory:
- Visible inventory: published by exchanges (LME / SHFE / COMEX), transparent and trackable
- Hidden inventory: bonded areas / trader warehouses, opaque but potentially larger
- The true turning point in prices is the turning point in total inventory
Four inventory-cycle stages (using copper as example):
Active restocking (price↑ volume↑) -> Passive restocking (price↓ volume↑) -> Active destocking (price↓ volume↓) -> Passive destocking (price↑ volume↓)
mid bull market late bull market mid bear market late bear / early bull market
Signal mapping:
| Stage | Inventory Direction | Price Direction | Trading Signal |
|---|
| Passive destocking | ↓ | ↑ | Long (best buying point) |
| Active restocking | ↑ | ↑ | Keep long positions |
| Passive restocking | ↑ | ↓ | Close longs (warning) |
| Active destocking | ↓ | ↓ | Short or stay neutral |
5. Futures Premium / Discount Structure
Contango (futures > spot, normal market):
- Supply is abundant, and the market prices in carrying costs (storage + funding)
- Roll yield is negative (
roll yield < 0), unfavorable for long holders
- Deep contango (
far month - near month > 5%) = severe oversupply
Backwardation (futures < spot, inverted market):
- Supply is tight, and spot premium reflects strong immediate demand
- Roll yield is positive (
roll yield > 0), favorable for long holders
- Deep backwardation (
near month - far month > 3%) = squeeze or extreme shortage
Term-structure signal:
spread_ratio = (front_month - second_month) / front_month
if spread_ratio > 0.02:
signal = "strongly bullish"
elif spread_ratio < -0.03:
signal = "bearish"
else:
signal = "neutral"
6. Seasonality
Oil seasonality:
- March-May: refinery maintenance ends + summer inventory build → seasonal rise (ahead of the "driving season")
- September-October: hurricane season (Gulf of Mexico) → supply disruption → higher volatility
- November-December: heating-oil demand → stronger diesel crack spread
Gold seasonality:
- January-February: Lunar New Year + Indian wedding-season physical demand → relatively strong
- July-August: traditional soft season → relatively weak
- October-November: Diwali + Christmas restocking → relatively strong
Copper seasonality:
- March-April: China construction season starts → demand recovery
- June-July: off-season inventory buildup → pressure
- September-October: "Golden September, Silver October" → demand recovery
Analysis Framework
Five-Step Commodity Analysis
- Supply-demand sets direction: is the balance in surplus or shortage? Which way are marginal variables moving?
- Inventory sets rhythm: which inventory-cycle stage are we in? Is a turning point close?
- Term structure confirms: contango or backwardation? Does it confirm the supply-demand judgment?
- Seasonality overlay: is seasonality currently a tailwind or a headwind?
- Macro validation: do the dollar / rates / risk appetite support the directional judgment?
Composite Scoring Template
commodity_score = {
"supply_demand": +1,
"inventory_cycle": +2,
"term_structure": +1,
"seasonality": 0,
"macro_env": -1,
}
Output Format
## Commodity Analysis Report — [Commodity Name]
### Supply-Demand Structure
- Supply side: [surplus / balanced / shortage] — [specific data]
- Demand side: [strong / stable / weak] — [specific data]
- Balance table: [inventory build X tons / drawdown X tons]
### Inventory Cycle
- Current stage: [active restocking / passive restocking / active destocking / passive destocking]
- Visible inventory: [LME X tons, SHFE X tons, WoW change]
### Term Structure
- Front-back spread: [contango X% / backwardation X%]
- Roll yield: [positive / negative]
### Composite Score
| Dimension | Score(-2~+2) | Basis |
|------|------------|------|
| Supply-demand | +1 | OPEC compliance rate 92% |
| Inventory | +2 | LME inventory hit 18-month low |
### Trading Direction
- Direction: [bullish / bearish / neutral]
- Confidence: [high / medium / low]
- Risk points: [specific risks]
Notes
- Commodity data sources are fragmented (EIA / OPEC / LME / SHFE, etc.). This skill provides the analytical framework; data should be retrieved through
web-reader or entered manually
- Futures prices include roll costs, so direct comparison across different contracts must account for expiry-roll effects
- Seasonal patterns are statistical averages and may be completely overwhelmed by fundamentals in a given year
- Gold has both commodity and financial attributes, and the financial side (rates / dollar) usually dominates short-term pricing
- Copper’s financial characteristics have strengthened since 2020 (copper futures are used as a macro hedge), so pure fundamental analysis may be insufficient
- Inventory data is lagged (hidden inventories cannot be tracked in real time), so cross-check with price and basis behavior
- This framework is for research backtesting only and does not constitute investment advice
