US Multi-Commodity Rotation

While the top momentum commodity might have highest return potential, it also carries concentration risk. If that commodity reverses, your entire portfolio suffers. Selecting 2-4 commodities provides diversification - some protection if one pick underperforms. Additionally, momentum leadership can change quickly, and having multiple positions reduces the impact of being slightly late on rotations.

For meaningful rotation across 3-4 commodities using micro and E-mini contracts, approximately $25,000-$50,000 is recommended. This provides enough buffer to size each position by ATR-based risk without over-leveraging margin. With a smaller account ($15,000-$20,000), consider rotating among just 2-3 commodities using micro contracts (MGC, SIL, MCL, MHG). Because futures are margin-based, the constraint is risk buffer and stop placement, not the full notional value.

Monthly rotation reviews don't mean mandatory changes. If your current holdings still rank in the top 3-4, keep them. Rotation only occurs when rankings change significantly - a commodity drops out of top tier or a new leader emerges. Forced rotation regardless of rankings increases unnecessary turnover costs.

If the entire commodity complex is falling, rotation becomes less effective as you're picking the 'best of the bad.' In such environments, the trend filter (50 EMA) will likely exclude most or all commodities. This is when holding cash is appropriate - wait until clear leaders emerge with uptrends before deploying capital.

Rotation is systematic and rules-based. You calculate momentum for all commodities, rank them objectively, and select top performers following predefined criteria. Regular multi-commodity trading might involve picking commodities based on subjective analysis or news. Rotation removes emotion by following the momentum signal consistently, rebalancing at fixed intervals.

Calculate each commodity's volatility (20-day standard deviation of daily returns). Invert each volatility: 1/Vol. Sum all inverted values. Weight for each = (1/Its Vol) / (Sum of all 1/Vols). Example: If Gold vol=1%, Silver vol=2%, Crude vol=3%, inverse vols are 100, 50, 33.3 (sum=183.3). Weights: Gold 54.5%, Silver 27.3%, Crude 18.2%. Lower vol = higher weight.

First, calculate sector momentum (weighted average of constituent commodities' returns). Rank sectors. Allocate across sectors based on rank: Top sector 40-50%, Second 25-35%, Third 15-25%, Weakest 0-10%. Then within each allocated sector, select the top momentum commodity. This two-tier approach captures both sector and individual commodity momentum.

Target average pairwise correlation below 0.5 among your selections. This provides meaningful diversification benefit. Cross-sector selections (e.g., Gold, Crude, Copper) naturally achieve this. Same-sector selections (Gold + Silver) will have high correlation (0.8+). If forced to select same-sector commodities due to momentum, understand diversification benefit is reduced.

Overnight gaps are a reality in commodity trading. Individual stops should be set at 2x ATR which typically accommodates normal gaps. For extreme gaps beyond stops, exit at first opportunity rather than hoping for recovery. Consider reducing position sizes in highly gap-prone commodities (Natural Gas, and Crude around the weekly EIA petroleum status report). Diversification across commodities also helps - a gap in one won't destroy the entire portfolio.

Use continuous contracts (back-adjusted or ratio-adjusted) for momentum calculation. Near-month contracts can have distortions near expiry due to roll effects. Continuous contracts provide cleaner price series for trend and momentum analysis. For actual trading, use the actively traded contract (usually near-month or first far-month), but base your selection signals on continuous data.

Calculate cross-sectional volatility (standard deviation of all commodities' returns). Compare to historical distribution. If in top quartile (high vol), use 15-day lookback and consider bi-weekly rebalancing. If in bottom quartile (low vol), use 30-day lookback. For holding period, calculate month-over-month autocorrelation of momentum rankings. High autocorrelation (>0.7) = extend holding, low (<0.3) = shorten. Implement as rules with thresholds in your algorithm.

COT (Commitment of Traders) data, published weekly by the CFTC, shows positioning of commercial hedgers vs speculators. Extreme speculator long positioning (>90th percentile historically) can precede reversals. Use COT as a filter/warning, not primary signal. If a commodity has top momentum but extreme speculator positioning, consider: reducing its weight by 20-30%, setting tighter stops, or skipping if other factors also weak. COT is contrarian indicator best used at extremes.

Build regression of portfolio returns against factor returns: Portfolio = alpha + b1 x Momentum_Factor + b2 x Trend_Factor + b3 x Carry_Factor + e. Momentum factor = return of top momentum quintile minus bottom quintile. Similarly for other factors. Coefficients show factor exposures. Run rolling 12-month regressions to track how exposures change. Attribution = Factor Return x Factor Exposure. Alternatively, use Brinson-style attribution decomposing into selection, allocation, and interaction effects.

Create macro regime indicator: Score = w1 x EquityMomentum + w2 x (-VIX_level) + w3 x (-DXY) + w4 x PMI_momentum. Positive score = risk-on regime, negative = risk-off. In risk-on: increase energy and base metals sector caps (to 45-50%), reduce precious metals cap (to 20-25%). In risk-off: increase precious metals cap (to 45-50%), reduce industrial sectors (to 25%). Apply these caps to sector allocation before individual commodity selection. Review macro regime weekly, sector caps monthly.

Key pitfalls: (1) Survivorship bias - include commodities that delisted or became illiquid. (2) Look-ahead bias - ensure rankings use only data available at decision time. (3) Unrealistic execution - add slippage (0.1-0.3%) and transaction costs (commissions plus exchange/NFA fees). (4) Over-optimization - limit parameters tested, require out-of-sample validation. (5) Ignoring roll costs - continuous contracts may not reflect actual roll costs. (6) Short history - need 5+ years covering different regimes. (7) Ignoring capacity - ensure selected commodities have enough liquidity for your size.