Quantamental Commodity Trading 101: The What and How

Commodity trading in the futures markets has long been defined by one of two paths. 

1. Discretionary Fundamental Trading 
2. Systematic Trend Following 

But lately, we’re seeing more and more CTAs take a newer third path to commodity trading: quantamental trading.

What is Quantamental Commodity Trading?

Quantamental commodity trading is a portmanteau that combines the fundamental factors used by many discretionary traders with the quantitative, systematic approach used by many trend followers.

Unlike trend-following models that primarily depend on the price movements of the commodity futures market and the technical indicators built on derivatives of those prices (think moving averages),  quantamental commodity trading relies on market fundamentals without trend-following. 

These “fundamentals” are the core factors driving commodity prices. We’re talking inputs like:

  • supply and demand dynamics, 
  • production and storage costs, 
  • interest rate differences, 
  • and other economic factors directly influencing commodity prices.


That said, while discretionary commodity traders have long since researched and analyzed these factors, quantamental trading involves the computerization — or quantitative modeling — of these inputs. 

A simple example could be a discretionary trader researching the cost of production of crude oil, finding it to be above the current price of oil futures, and buying those futures in anticipation of the price reverting towards its “cost.” 

A trend trader, on the other hand, would have a model that says to buy crude oil futures when they are above their 100-day moving average of prices. 

Meanwhile, the quantamental trader may code up a model that says to buy crude oil when the price is 10% below the price of production (for which they will need to somehow get a digital input).

Here are a few podcasts we’ve done recently with these types of quantamental traders:





The Importance of Domain Knowledge

You’ll see in these videos that this isn’t just a game of modeling up simple commodity-related inputs like cost of production. No, deep domain knowledge in commodities is crucial so as not to arrive at spurious correlations. Before the inputs are modeled, it requires an understanding of physical properties, geopolitical factors, and industry-specific supply and demand dynamics. This expertise helps interpret fundamental data accurately and apply it effectively in trading strategies.

Challenges and Rewards

The primary challenge in systematic commodity trading without trend-following is relying on accurate and timely fundamental data. For example, the USDA reports outlining crop yields are notoriously inaccurate. Factors that define success in the quantamental space are answers to questions like:

  • What are the inputs?
  • How are they adjusted for reality? 
  • How are they getting fed into the system?”


However, the rewards can be significant: by uncovering and exploiting inefficiencies that trend-following models don’t just miss, but that they aren’t even looking for – such strategies can achieve consistent returns and reduce correlation with broader market movements and other commodity strategies. 

To get a full list of CTAs and hedge funds exploring the quantamental space, connect with an RCM team member today – click to call!

 (855) 726-0060


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