COT Analysis

What is COT Analysis?

COT analysis reads the Commitments of Traders reports the U.S. Commodity Futures Trading Commission publishes each week: a snapshot of open futures positions (with a combined futures-and-options version) as of Tuesday's close, released Friday afternoon. The legacy format splits positioning into commercials (entities hedging an underlying business), non-commercial large speculators, and small non-reportable traders; the newer disaggregated and financial-futures formats break these into finer groups such as producers, swap dealers, managed money, and leveraged funds.

The report has a long federal lineage. Its ancestor was the grain-market position data the U.S. Department of Agriculture began publishing in the 1920s; the modern Commitments of Traders series began in 1962 under the CFTC's USDA-era predecessor, passed to the CFTC when Congress created the commission in 1974, and moved from monthly to biweekly and then, in 2000, to weekly publication. The disaggregated format arrived in 2009 and the Traders in Financial Futures report in 2010. Authors such as Larry Williams and Steve Briese did much to popularize trading applications, including the COT Index normalization most charting implementations use.

The working assumptions: commercials hedge, so they tend to sell into rallies and buy into declines, while large speculators tend to be trend-followers whose positioning peaks near turns. Analysts therefore look for positioning extremes relative to each market's own history rather than raw contract counts. The limits are the three-day publication lag, weekly frequency, and the fact that extremes can persist for months while a trend keeps running, so COT works as context for swing and position trades, not as a timing tool.

COT sits inside a wider family of positioning and sentiment reads. Options-derived gauges such as the VIX and implied volatility skew price fear directly; dealer gamma exposure infers hedging pressure from the options surface; crypto markets lean on open interest, funding rates, and exchange and stablecoin flows instead. What COT uniquely offers is regulator-collected data on who actually holds futures positions, rather than an inference from prices.

How to read COT data on a chart

COT indicators plot net positioning by trader group beneath price on a weekly chart. The workflow:

1. 1Plot net positions (longs minus shorts) for commercials, large speculators, and small traders for the specific market; each futures market has its own report, and raw contract counts are not comparable across markets.
2. 2Normalize with a COT Index: rescale each group's net position to a 0-100 range over a lookback of six months to three years, so extremes are defined relative to that market's own history.
3. 3Mark the stretched configurations: commercials near one end of their range with large speculators at the other is the classic setup worth noting; readings in the middle carry little information.
4. 4Compare current positioning against price behavior at prior extremes on the same chart before treating the reading as meaningful, since some markets respect their extremes far better than others.
5. 5Respect the lag: positions are measured Tuesday and published Friday, so pair extremes with price structure on the traded timeframe for timing rather than acting on the release itself.

How it's calculated

Summarizes weekly CFTC Commitments of Traders positioning by trader group as net exposure, then normalizes it to flag extremes.

Data is the weekly CFTC report: positions as of Tuesday, published Friday.

The COT Index is Larry Williams' normalization, with readings above 80 and below 20 as the conventional extremes; Briese's movement index takes the 6 week change in the index.

Group nets roughly offset across the market, so commercial and large speculator indexes tend to mirror each other.

How traders use it

• Positioning extremes as contrarian context: when speculators hold a historically stretched net position and commercials the opposite, trend-continuation trades get less benefit of the doubt. Extremes are usually measured with a COT Index that rescales net positioning over a lookback window.
• Confirmation of participation: rising speculator net positioning alongside rising open interest during a breakout suggests fresh money behind the move rather than short-covering.
• Divergence watching: price making new highs while large speculators quietly reduce longs is read as fading conviction, similar in spirit to positioning reads in crypto derivatives.
• Adjusting by market character: the commercial signal reads cleanest in physical commodities, where hedgers dominate; in financial futures the TFF categories matter more, since swap dealers and leveraged funds behave differently from classic producers.
• Cross-market confirmation: forex traders map futures positioning onto spot pairs, checking whether a currency strength reading or an intermarket relationship agrees with how speculators are positioned in the corresponding futures.

COT Analysis vs related concepts