CoT data reveals how different types of traders are positioned in the futures markets. This is a completely free resource provided by Movement Capital


The CFTC releases four variations of the CoT report: 1) aggregated 2) disaggregated 3) futures only and 4) futures & options combined. Aggregated reports are a legacy format where swap dealers were included in the commercial category. Swap dealers can initiate a position in the futures market for a variety of reasons, and for that reason this site excludes analysis of swap dealers. The report below is disaggregated, where each trader type is broken out. The futures and options combined report converts positions in the options market to futures on a delta-equivalent basis. This website bases its analysis off of the disaggregated futures and options combined report.

The image above shows a raw CoT report from the CFTC’s website. The commercials, or producers and users, are long 351,366 contracts and short 778,294 contracts of corn. Each contract is a commitment to buy or sell 5000 bushels. On a net basis, commercials are short 426,928 contracts. This looks like a massive short position, but it’s important to know if producers and users being net short 426,928 contracts is historically extreme.


In a nutshell, analyzing any one CoT report in isolation is pointless. It is crucial to know not if commercials or speculators are net long or net short, but rather how extreme their position is relative to the past. I use percentiles to give context to the current net positioning number.

For example, consider the position of commercial traders in corn over the past five years. If their most bullish position had been net short 12,363 contracts, and their most bearish position had been net short 555,715 contracts, then the current net CoT position of net short 426,928 contracts would translate to a five-year percentile of 24%. A percentile of 0% would mean that commercials are more net short than they’ve ever been in the past five years.

While creating a percentile to give context to net positioning is a step forward, it’s still a flawed approach for one reason: you have to account for markets growing and shrinking over time. In the above example, what if when commercials had their huge net short position of 555,715 contracts the open interest in corn futures was 2,088,225 contracts? Now, the market is smaller since the open interest is only 1,707,744. So the current net short position of 426,928 is “bigger” relative to the total size of the market than it would have been when open interest was +20% higher.

The solution is simple. Rather than calculating percentiles solely based on net position numbers, I calculate percentiles based on net position numbers as a percentage of open interest. Using the exact same data from above, here’s the data adjusted for OI:

Most Bullish Position: -12,363 contracts on 11/1/2013 (when OI was 1,902,900) = -0.65%
Most Bearish Position: -450,253 contracts on 6/17/2016 (when OI was 1,531,416) = -29.40%
Current Position: -426,928 contracts on 3/10/2017 (when OI was 1,707,744) = -24.99%

Using the above three numbers, the 5-year percentile is now 15%. This is much less than the previous reading of 24%. Why? Because open interest in corn has decreased and the current net short position of -426,928 is bigger relative to the size of the market than it would have been a few years ago. Let’s dive into what this metric looks like.

In the majority of contracts I cover, this OI adjustment doesn’t mean much. But in contracts that have significantly grown (VIX, WTI crude oil) and contracted (Nasdaq, palladium) the differences are substantial and worth showing.


CoT data is most meaningful at extremes, when either commercial traders or speculators are super net long or net short. I define five-year percentile extremes as being above 90% or below 10%. Look at the above graph. In late 2013, producers and users were more net long corn futures than they had ever been in the past five years. These traders, the people who know the most about the underlying commodity business, were basically saying a combination of the following two statements: “We don’t want to hedge and sell too much corn production here, we think prices are going up” (commercial producers) and “We want to lock in our future corn inventory needs here, we think prices are cheap” (commercial users). The commercial CoT percentile of 100% was followed by a 20% rise in the price of corn.


It’s easy to understand the concept of a “smart money” category in the commodity markets. Producers and users, like an oil driller or a copper user, have a clear link to the cash commodity business. In financial futures (bonds, currencies, stocks), there are no producers or users. The group of traders that’s analogous to producers and users are called dealers. Dealers are sell-side companies that sell their price risk to offset the products they sell to their institutional and retail clients.

In the commodity markets, speculators are composed of CTAs (commodity trading advisors), CPOs (commodity pool operators), hedge funds, other reportables, and non-reportables. Other reportables are traders other than CTAs, CPOs, and HFs that carry positions above the CFTC’s reporting limits. Non-reportables are typically called small speculators. These are retail traders who own small positions under the reporting limits.

In financial futures, speculators are composed of two main trader types: asset managers and leveraged money. The asset manager category includes mutual funds, endowments, and pension funds. The leveraged money category includes the previously mentioned CTAs, CPOs, and hedge funds. In addition to these main two categories, other reportables and non-reportables are included to form my composite “speculator” category.


Knowing how speculators are positioned is useful. If you come up with a thesis that leads you to believe stocks will fall, but you check this site’s CoT analysis and see that speculators are extremely net short S&P 500 futures, you might want to hold back on your short. Examine the following CoT analysis for the S&P 500 futures contract.

The market was quickly selling off in the fall of 2015. Everybody was offering up a reason to sell stocks. At that point in time, speculators got more net short S&P 500 futures than they had ever been over the past five years. Both positioning and negative sentiment were at an extreme. The S&P went on to rally 9% in a month. When hedge funds and institutions reach an extreme position, long or short, it indicates a potential for less buying or selling pressure. In this example, with speculators so short, who else was there left to sell? They also made the same mistake during the U.S. debt-ceiling crisis in the summer of 2011.

In closing, CoT data doesn’t always work. Sometimes producers and users are on the wrong side of a trade for months. Sometimes speculators are at a 0% five-year CoT percentile and price keeps on going down. With that being said, I consider CoT analysis to be a useful tool when incorporated with other fundamental and technical approaches. I hope you find it useful too.


The CFTC releases the CoT report on Friday afternoons, and the data covers positions as of Tuesday’s close. The site has been updated to reflect the CoT report released on 12/18/2020

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If you have any questions about this site or CoT data in general, feel free to send me an e-mail at [email protected]