Commodities have been a group which has moved around quite frequently in 2026. We review the space as we close out October and then provide a hypothetical model solution you could explore.

Amid emerging volatility within the domestic and international equity space, it can be easy to allow your attention to be drawn from other areas of your portfolio. Despite this, it remains mission critical for you to be able to quickly digest movement across the investable landscape to keep track of budding winners and losers. The commodity space remains a volatile space due to its large reliance on the Middle East (energy) or in some parts rates (precious metals). With so many moving parts in the space, we will take today’s feature to recap some of the recent shifts within the asset class. Finally, we will discuss a possible tactical solution you can slide into your client’s broader allocation.

While an admittedly cherrypicked datapoint, the broad commodities group has picked up a good chunk of strength within NDW’s DALI rankings since the end of June. At a recent low water mark around 147 (as of 6/26), the current 238 signal count represents a 60%+ gain in the total tally. Since that 6/27 date, no other group has managed to pick up additional signals, seeing the equity groups (intl. & domestic) shed a combined 46 signals to other areas of the rankings. It is worth noting here that those two groups maintain their position at the top of the rankings and your overall allocation shouldn’t shift too much… however, the swift uptick in signal count is certainly worth noting. While the exact matrix behind the NDW DALI rankings is proprietary, you can keep track of a similar type of matrix by using the Asset Class Matrix to identify points of strength/weakness across various asset classes. Included below are a screenshot of the majority of the rankings as of 6/28 (left) and 10/20 (right). You’ll notice that four of the top ten members of the matrix are representatives from the commodity space (including both of the top two). Compare this to the 6/26 date, where commodities had no representation within the top 5 of the rankings and overall X counts amongst the space were depleted as near-term strength was at a low. The major mover has been DBO, which has quickly picked up steam over the course of the last few months.

While you might imagine that the magnitude of upside participation for the fund has been intense recently, overall technical posture for the fund remains quite productive as of 10/21. Earning a near-perfect 5.86 fund score, DBO sits on a pair of buy signals on its default chart. The bullish support line below at $19.50 has ushered the ETF higher throughout 2026 despite plenty of volatility in the space. All that to say, a trip back towards the top of the trading band at $27.50 would not be out of the question heading into the close of 2026. To the right of the chart for DBO below is the 2-point chart for crude oil (CL/) which has pushed higher over the course of the year. Previous analyst commentary has discussed the important of the upper $70’s/$80 mark, a point that has seemingly corresponded to markets opinion about the continuation/end of the Iran war. While the energy space remains extremely headline dependent, we have gone ahead and marked off some important points of support and resistance below. 

We will close today’s feature with a hypothetical model that isn’t far from one you might be able to deploy within your own commodities exposure. Behind the scenes, we will run a 3.25% matrix comprised of DBO (oil), DBB (base metals), DBA (agriculture), DBP (precious metals), and DBC (broad commodities). With each of the major segments of the commodities space included, we can then run a model that owns the top ranked asset within the matrix, selling that asset once it falls out of the top ranked position on a monthly basis. Keep in mind that within your portfolio, the exact strategy could take on many different forms (buying two assets for example, or selling lower in the matrix instead of the second position, etc.). Point being, running said test produces the below model. While precious metals rapid fall from grace left the model behind the 8-ball in 2026, overall long-run results are extremely favorable when compared to a broader benchmark like DBC. While owning solely one asset for an entire asset class’s exposure isn’t always the right path, the model below shows how valuable identifying high relative strength assets can be over the course of several decades.

We will end today’s feature by introducing one final wrinkle into the equation. This time, we will replicate our previous model exactly (1 holding, sell at 2, eval monthly, etc.) except for the fact that we will include SPY in the universe. In essence, this answers the question: “What if we don’t want to own commodities right now, and instead prefer large cap equities?” The results are included below, as well as an area graph that displays the “on/off” switches between commodities and US stocks over time. From a high level, this lone addition increases your yearly CAGR from 11.93% to 14.54% since 1994 (5.22% for benchmark DBC. There are certainly instances where the strategy underperforms but the idea remains the same: rotate to points of RS over time, replacing the asset class with US equities when strength dictates. For any questions about the strategy or a free trial of the modeler to replicate such a strategy, email miles.clark@nasdaq.com.