Strategy Research
The commodity edges that actually hold up
Most "winning strategies" you find online are overfit backtests. These are different: fundamental edges (positioning, inventories, the futures curve, weather) drawn from peer-reviewed research, each adversarially fact-checked — with the hype stripped out and the caveats left in.
24 sources reviewed25 claims verified · 6 killedNBER · AQR · J. Futures Markets
1
Carry / Term-Structure
Strongest, most buildable edge
- The rule
- Favor commodities in backwardation (a downward-sloping futures curve); avoid or short those in contango. The slope of the curve — the roll yield — is the signal.
- Why it works
- Backwardation reflects scarcity: buyers pay a premium for prompt delivery, and you collect the roll as each contract converges to spot. It's model-free and observable in advance.
- The evidence
- High-basis minus low-basis ≈ +10.2%/yr (t=3.73). Over 139 years, commodities returned 7.9% in backwardation vs 1.5% in contango.
- Both figures survived adversarial fact-checking.
- The catch
- Carry, momentum and inventory are ~0.87 correlated — they're the same underlying bet. Don't stack them as if they diversify.
- On your data
- Computed from your 40 years of futures term-structure data.
2
Hedging-Pressure Positioning (COT)
Genuine diversifier — and it's not 'fade the speculators'
- The rule
- Go long where commercial hedgers are net short, and short where they're net long. The signal lives in the commercial (producer/user) category — not managed money.
- Why it works
- Hedgers pay a premium to offload price risk; speculators earn it for taking the other side. This 'hedging pressure' is grounded in Keynes/Cootner/Hirshleifer theory.
- The evidence
- Significant abnormal returns with a Sharpe that exceeds long-only commodities — and statistically distinct from carry and momentum, so it adds real diversification.
- Direction and significance verified. Specific performance numbers were refuted in fact-checking, so we don't quote them.
- The catch
- The popular 'fade the crowd' / speculator-extreme contrarian rule is weak — the financialization-distortion premise was tested and largely unsupported (Sanders-Irwin).
- On your data
- Directly usable from your weekly CFTC COT z-scores per commodity.
3
Inventory / Theory of Storage
Scarcity premium
- The rule
- Favor commodities with low inventories (relative to their own history); avoid those flush with supply. Low stocks → high convenience yield → higher expected return.
- Why it works
- When inventories are scarce, holders of the physical good earn a convenience yield, which shows up as backwardation and a higher futures risk premium.
- The evidence
- Low-inventory minus high-inventory ≈ +8.1%/yr (t=3.19), positive in 56% of months (1969–2006).
- Figure verified. Note it partly overlaps the carry/momentum factor.
- The catch
- It's the same family as carry — and a clean cross-commodity inventory panel (days-of-supply) needs harmonizing across the full set.
- On your data
- EIA crude (since 1982) + nat-gas storage; use a 5-year-average baseline since you don't hold analyst consensus.
4
Nat-Gas Storage + Weather
Fundamentals that move the tape
- The rule
- Trade natural gas off storage deviations from the 5-year average and temperature shocks — heating/cooling degree days (HDD/CDD) drive demand.
- Why it works
- Nat-gas returns and volatility are explained by storage announcements and temperature surprises, not price-only signals — a rare clean fundamental link.
- The evidence
- Storage and HDD/CDD significantly explain weekly storage changes and daily futures returns and variance (Chen, 2023).
- Verified. Caveat: your storage history starts in 2010, limiting sample length.
- The catch
- Short backtest window (2010+); weather is noisy and forecasts decay fast.
- On your data
- EIA Lower-48 storage (2010+) + your US population-weighted degree-day series.
5
Crude Inventory Surprise
Event reaction
- The rule
- React to the EIA weekly crude report relative to expectations: larger-than-expected builds push price down, draws push it up. It's the surprise, not the level.
- Why it works
- Unexpected inventory changes hit returns inversely and raise volatility immediately — and the EIA report carries more signal than the API number.
- The evidence
- Significant immediate inverse price reaction to the surprise; EIA shocks are larger and longer-lived than API (Ye & Karali, 2016).
- Verified. Part of the move is pre-empted by the prior-day API leak, which complicates timing.
- The catch
- Needs an expectations baseline you don't have — we'd proxy consensus with a 5-year-average or model.
- On your data
- EIA crude inventory (since 1982); surprise computed against a 5-year-average proxy.
What the research is honest about
- The flashy numbers don't survive. We threw out the specific Sharpe/return stats from several papers when fact-checking refuted them — only direction and statistical significance held up. We quote a figure only where it was verified verbatim.
- Carry, momentum and inventory are the same bet (≈0.87 correlated). Combining carry with hedging-pressure diversifies; stacking carry + momentum + inventory does not.
- "Fade the crowd" is mostly a myth. The idea that speculator/index positioning distorts prices was tested and largely unsupported. Commercial hedging-pressure is the part with theory and evidence behind it.
- All of this is in-sample and gross of costs, and these premia have visibly decayed since 2010 with financialization. Historical returns overstate what's live today — which is exactly why we re-test everything on our own data.
See these run on real data
Tara can pull the live readings, or build and backtest a rule yourself.
Educational research, not investment advice. Past performance does not guarantee future results.