London Multi-Commodity Rotation

While the top momentum commodity might have the 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.

Because UK retail traders usually access these commodities through FCA-regulated spread bets or CFDs with flexible GBP-per-point sizing, you can run a 3-4 commodity rotation with roughly GBP 10,000-20,000 while keeping proper risk management, and you can start smaller (around GBP 5,000) with tight sizing. Full exchange futures on the LME and ICE are a different matter - contracts are large (LME copper is 25 tonnes, ICE Brent is 1,000 barrels) and exchange margins are high, so direct futures typically require GBP 50,000 or more. The key is having enough to maintain proper risk management on each position.

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 the 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%, Brent vol=3%, inverse vols are 100, 50, 33.3 (sum=183.3). Weights: Gold 54.5%, Silver 27.3%, Brent 18.2%. Lower vol = higher weight. For an unhedged sterling account, use the GBP-denominated return series so the volatility reflects the FX move as well.

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, Brent, 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 the 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 the first opportunity rather than hoping for recovery. Consider reducing position sizes in highly gap-prone commodities (UK Natural Gas, Brent around inventory reports). Diversification across commodities also helps - a gap in one won't destroy the entire portfolio. The LME nickel halt of March 2022 is a reminder that, in extreme cases, a market can stop trading altogether.

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 a cleaner price series for trend and momentum analysis. For LME metals there is no calendar-month expiry in the usual sense - build the continuous series from the rolling 3-month price. For actual trading, use the actively traded contract (or the spread bet/CFD that tracks it), but base your selection signals on continuous data.

Calculate cross-sectional volatility (standard deviation of all commodities' returns). Compare to the historical distribution. If in the top quartile (high vol), use a 15-day lookback and consider bi-weekly rebalancing. If in the bottom quartile (low vol), use a 30-day lookback. For the holding period, calculate the 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.

Positioning data shows the stance of commercial hedgers vs speculators. For London commodities use the ICE/CFTC Commitments of Traders reports and the LME's own Commitments of Traders Report (COTR). Extreme speculator long positioning (>90th percentile historically) can precede reversals. Use positioning as a filter/warning, not a 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 are also weak. Positioning is a contrarian indicator best used at extremes.

Build a regression of portfolio returns against factor returns: Portfolio = alpha + b1xMomentum_Factor + b2xTrend_Factor + b3xCarry_Factor + error. The momentum factor = return of the top momentum quintile minus the 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. For an unhedged sterling book, add a GBP/USD term so currency does not masquerade as commodity alpha. Alternatively, use Brinson-style attribution decomposing into selection, allocation and interaction effects.

Create a macro regime indicator: Score = w1xFTSE_Momentum + w2x(-VIX_level) + w3x(-DXY) + w4xPMI_momentum. A 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 the precious metals cap (to 45-50%), reduce industrial sectors (to 25%). Apply these caps to sector allocation before individual commodity selection. Review the macro regime weekly, sector caps monthly. Track GBP/USD separately so you can decide whether to hedge the currency leg.

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 (0.03-0.05% for futures, wider dealer spreads for spread bets/CFDs plus overnight financing). (4) Over-optimisation - limit parameters tested, require out-of-sample validation. (5) Ignoring roll costs - continuous and LME 3-month series 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. (8) Ignoring FX - for a sterling account, back-test in GBP so the GBP/USD effect is included rather than assumed away.