CrudeQ
Methodology
CrudeQ combines market data, supply-demand indicators, volatility, positioning, and systematic signal frameworks to assess weekly crude market direction — identifying when physical conditions confirm or contradict headline narratives. Every output is reproducible from structured data workflows, not manual interpretation.
How the Model Works
INPUTS
EIA inventory, curve, cracks, COT positioning, OVX/vol, Brent–WTI spread, and macro/geopolitical overlays ingested and normalized.
SIGNAL ENGINE
Weighted composite score across six input factors. Divergence between signals triggers elevated analytical priority and flags regime uncertainty.
REGIME OVERLAY
Active regime classified: Normal Tight, Transitional, Divergent, or Event Override. Regime context dynamically re-weights the composite signal.
RISK CONTROLS
Conviction is downgraded to Low when signals conflict. Event override flags (geopolitical, OPEC) suppress quantitative outputs and force qualitative framing.
TRADE OUTPUT
Directional bias, trade ideas with entry/target/stop, scenario probabilities, and catalyst watchlist generated from signal outputs and regime context.
CALL TRACKING
Every directional call is logged to call_tracker.csv with outcome, WTI price at publish, and 1-week return. Win rate by regime tracked continuously.
Signal Weighting
Base weights reflect each factor's contribution to the composite directional score under a normal market regime. Weights are dynamic — regime overlays re-allocate conviction toward whichever inputs carry the highest signal-to-noise ratio in the active environment.
| FACTOR | BASE WEIGHT | REGIME SENSITIVITY |
|---|---|---|
| Curve Structure (CL1–CL2) | 30% | Elevated in Tightening / Crisis regimes |
| Inventory Surprise | 20% | Elevated in Normal / Seasonal regimes |
| Crack Spread | 20% | Elevated when product demand diverges from crude |
| Product Draws | 15% | High weighting when gasoline/distillates diverge |
| COT Positioning | 10% | Elevated in positioning-driven trend regimes |
| Macro / DXY / OVX | 5% | Elevated during Event Override regimes |
What Predicts What
Core Signal Inputs
EIA Weekly Petroleum Status Report
Crude, gasoline, distillates, Cushing inventories, and refinery utilization — processed within minutes of the Wednesday 10:30 AM ET release for same-day analysis.
Term Structure — CL1–CL2 Spread
Front-to-second-month futures spread as a real-time proxy for prompt tightness. Backwardation depth and trend direction are the single most reliable indicator of physical scarcity or surplus.
Refining Economics — 3-2-1 Crack Spread
Downstream demand indicator. Sustained elevation confirms end-product pull through to crude; compression signals softening demand before it appears in crude inventory data.
OVX / Volatility Layer
CBOE Oil VIX (OVX), 20-day realized vol, and the Vol Risk Premium (VRP = OVX − RV) measure market fear, tail-risk pricing, and whether options are rich or cheap relative to actual moves.
CFTC COT — Managed Money Positioning
Weekly CFTC Commitment of Traders report tracks managed money net length, WoW change, and 1-year percentile rank. Crowded longs compress upside; washed-out positioning improves risk/reward.
Brent–WTI Spread
Tracks Brent premium over WTI as a proxy for Cushing delivery-point dynamics, transatlantic supply differentials, and crude-specific geopolitical risk premiums embedded in either leg.
Macro & Geopolitical Overlay
OPEC+ policy, Middle East supply risk, USD strength (DXY), equity conditions, and macro growth signals are incorporated as scenario stress-tests and conviction modifiers. Event overrides can dominate all quantitative inputs.
Weekly Report Outputs
Each brief moves from raw data to actionable intelligence across structured analytical sections:
Failure Modes
Known conditions under which the framework underperforms or produces unreliable signals.
Sudden Geopolitical Shocks
A Hormuz closure, OPEC emergency cut, or conflict escalation can invalidate all quantitative signals within hours. The model has no forward-looking geopolitical input — it reacts, not predicts.
Macro Recession Repricing
During demand-destruction cycles, crude decouples from physical fundamentals and trades on macro sentiment. Inventory signals become lagging indicators; spread and crack data lose predictive power.
OPEC+ Surprise Decisions
Unscheduled production cuts or quota changes override all fundamental signals. The framework assigns no probability weight to OPEC policy surprise — this is a known structural gap.
EIA Data Revisions
Initial EIA WPSR releases are frequently revised in subsequent weeks. Signals generated from first-release data may be invalidated by revisions, especially for gasoline and distillate categories.
Liquidity Gaps & Thin Markets
Holiday-shortened weeks, expiry periods, or low-liquidity environments produce misleading spread and vol signals. CL1–CL2 spread can gap sharply on roll dynamics unrelated to physical conditions.
Refinery Outages & Weather Events
Unplanned refinery shutdowns or hurricane-season disruptions create temporary product supply shocks outside the model's normal parametric range. Crack spreads and utilization signals become distorted.
Research Pipeline
The data ingestion, signal generation, chart production, and brief drafting process is powered by a modular Python research pipeline built for repeatable energy market analysis. Each publication is generated from structured data workflows — not manual headline interpretation. Executed weekly following the EIA WPSR release at 10:30 AM ET.
Execution Sequence
Primary Data Sources
Fundamental Data
EIA Open Data API
- Crude stocks (PET.WCRSTUS1.W)
- Cushing inventories (PET.WCUOK_3.W)
- Gasoline & distillates
- Refinery utilization (PET.WPULEUS2.W)
- Production & imports/exports
Market Data
NYMEX / Yahoo Finance
- WTI & Brent front-month settlements
- CL1–CL2 prompt spread
- RBOB & heating oil futures
- OVX (CBOE Oil VIX)
- Historical curve structure
Positioning Data
CFTC / Reuters
- Managed money net length (COT)
- Gross longs & shorts
- WoW positioning change
- 1-year percentile rank
- Consensus inventory survey estimates
Research Infrastructure
Python · GitHub
- Pandas, NumPy, Plotly, Jinja / Markdown
- Local eia_cache/ for reproducibility
- GitHub Actions automated execution
- Version-controlled call tracker (CSV)
AdityaManojMenon / weekly-crude-market-brief
Pipeline source · EIA ingestion · Signal models · COT + Vol layers · Call tracker · Initiated March 2026
Important Note
This research is informational only and does not constitute investment advice. All analysis reflects the author's independent views based on publicly available data. Past signal accuracy is not indicative of future results. Known failure modes are documented above — readers should apply independent judgment and risk management.