The COT Report: Smart Money Signals for Algo Trading

The CFTC's weekly Commitment of Traders Report reveals how commercial hedgers, large speculators, and retail traders are positioned in futures markets. Learn how to turn this data into algorithmic trading signals.

By David Bergstrom··10 min read

What Is the COT Report?

The Commitment of Traders Report is a weekly report released by the Commodity Futures Trading Commission (CFTC) that shows open interest for futures and options on futures markets where 20 or more traders hold positions at or above CFTC reporting levels. The data is as of Tuesday but released Friday afternoon.Position data comes from reporting firms, FCMs, clearing members, foreign brokers and exchanges, and is categorized by each trader's dominant business purpose as self-reported on CFTC Form 40.
CFTC Commitment of Traders COT Report showing open interest breakdown by commercial hedgers non-commercial large speculators and small traders in futures markets
The COT Report breaks down open interest by three trader categories

Three Categories of Traders

The COT Report classifies all traders into three groups. Understanding what each group represents is key to interpreting the data:
SMART $
🏛️
Commercial
Hedgers

Producers, processors, merchants, and swap dealers who use futures to hedge against price changes. They have direct commercial interest in the underlying commodity.

SMART $
🏦
Non-Commercial
Large Speculators

Hedge funds, CTAs, individual traders, and investment firms trading for financial purposes, not hedging. Institutional-grade positioning.

RETAIL
👤
Nonreportable
Small Traders

Positions below CFTC reporting levels. Retail and small speculators. Valuable insight into market sentiment and crowd behavior.

Smart Money vs. Dumb Money

Commercials and large speculators are often considered "smart money". They have direct market knowledge or institutional resources. Nonreportable (small) traders get called "dumb money". Not because they're unintelligent, but because as a group their positioning tends to be contrarian to the eventual move.
🏛️
Smart Money

Commercials + Large Specs. Direct market knowledge, institutional resources, hedging needs.

VS
👤
Dumb Money

Small speculators / retail. Crowd positioning often contrarian to the eventual move.

🔑 The Key Signal

Large divergences between smart money and dumb money typically precede significant market moves. When these two categories diverge to extremes, someone has to be someone's liquidity. Watching this divergence is the most popular COT trading approach.

The COT Index

Raw COT data, a number like 10,374 contracts long, is meaningless without context. Is that a lot or a little? It depends on total open interest and historical norms. The COT Index solves this by normalizing positioning relative to the past 36 months.

COT Index

Where current net positioning ranks vs. the past 36 months for a given trader group.
0 = Most Bearish50100 = Most Bullish
S&P 500 price chart with non-commercial large speculators COT Index overlay showing extreme positioning at market turning points over 36 month lookback period
S&P 500 with non-commercial COT Index, extreme readings often mark turning points
In Build Alpha, traders can test any COT Index value or range from the point-and-click interface. For example, a simple strategy going long QQQ when Nasdaq non-commercial COT Index is ≤ 50 (large speculators less bullish) has drastically outperformed QQQ buy and hold over the past 20 years.
Build Alpha strategy equity curve showing QQQ long only when Nasdaq non-commercial COT Index is below 50 outperforming buy and hold over 20 years
Long QQQ when large specs are less bullish (blue) vs. buy and hold (red), 20 years

Deriving Signals from COT Data

Simply using raw COT positions as a trade signal is not a great idea. The values need normalization. There are two primary approaches:

Normalized Net Position

Subtract shorts from longs, then divide by total open interest. A value of -0.20 means 20% of open interest is positioned net short. This creates an apples-to-apples comparison over time and across markets.
COT Report commercial traders versus non-commercial traders normalized net positioning chart showing divergences between hedger and speculator positioning in futures markets
Commercial (hedger) vs. non-commercial (speculator) normalized positioning, divergences are key

COT Index

Shows where the current normalized position ranks vs. the past 36 months. A reading of 100 = most bullish in 3 years; 0 = most bearish. More responsive to recent extremes than raw positioning.

⚠️ Common Mistake

Using raw COT values like "10,374 contracts long" as a signal. This number is meaningless without knowing total open interest. Always normalize. Either as a percentage of open interest or via the COT Index.

Is There Free COT Data?

Yes. COT data is freely available from the CFTC at cftc.gov. However, the historical data is not formatted for easy use and needs modification to create useful futures market signals. Build Alpha has pre-processed COT data back to 2000 included in the 5,000+ built-in signal library, already normalized and ready to use.

COT Signals in Build Alpha

Build Alpha includes COT data for 15+ futures markets. Search "COT" in the signal filter to find all available signals, usable as entries, exits, or strategy filters, no coding required.
Build Alpha signal library search showing COT commitment of traders signals for algorithmic trading strategy development with net position and COT Index options
Search "COT" in Build Alpha's signal library, entries, exits, or filters

Available Markets

S&P 500
Nasdaq
Dow Jones
VIX Futures
AUDUSD
Dollar Index
EURUSD
2-Year Note
5-Year Note
10-Year Note
30-Year Bond
Natural Gas
WTI Oil
Silver

Custom COT Signals

The built-in signals above use each group's positioning on its own. The real information is often in the relationship between groups: how far apart hedgers and the crowd are, whether that gap is widening, and whether an extreme is starting to turn. I built a set of custom signals that combine the COT Index readings for commercials, non-commercials, and non-reportables into spread, extreme, and regime conditions. Each one is usable as an entry, exit, or filter, and every threshold (50, 70, 80, X) is a parameter you can optimize in Build Alpha.
  • Hedgers − Crowd SpreadCommercialIndex - NonreportIndex > 50Hedgers are far more bullish than the small-trader crowd. A wide gap in favor of the smart money.
  • Crowd − Hedgers SpreadNonreportIndex - CommercialIndex > 50The mirror image. Small traders are far more bullish than hedgers, a classic contrarian warning.
  • Extreme Disagreementabs(CommercialIndex - NonreportIndex) > 70Hedgers and the crowd are at opposite extremes, in either direction. Flags the most polarized readings.
  • Hedgers Bullish / Crowd BearishCommercialIndex > 80 AND NonreportIndex < 20Both groups at extremes at the same time, with the smart money long and the crowd short.
  • Hedgers Bearish / Crowd BullishCommercialIndex < 20 AND NonreportIndex > 80The opposite pairing. Smart money short into a crowd that is heavily long.
  • Hedgers vs Large Specs DivergenceCommercialIndex - NonCommercialIndex > XCompares hedgers to the large speculators (funds) instead of the crowd. X is a threshold you can optimize.
  • Positioning AccelerationCommercialNet - CommercialNet[1] > XWeek-over-week change in commercial net positioning. Catches hedgers building a position quickly, not just holding one.
  • Spread Widening(CommercialIndex - NonreportIndex) > (CommercialIndex[1] - NonreportIndex[1])The gap between hedgers and the crowd grew this week. Direction of the disagreement, not just its size.
  • Extreme + ReversalCommercialIndex[1] < 10 AND CommercialIndex > CommercialIndex[1]Tests hedgers turning up from an extreme. The prior reading was near the floor and this week's reading is higher.
  • Consensus / Compression Regimeabs(CommercialIndex - NonreportIndex) < 10Tests periods where hedgers and crowd are unusually aligned. Low disagreement often marks a regime with little positioning edge, or the calm before one.
Download the Custom COT Signals
Place the file in your BuildAlpha folder. Rename your existing CustomIndicators.xml first so you don't overwrite your own signals!
Download ↓
Want to keep your existing custom signals too? Upload both XML files (these signals and your current CustomIndicators.xml) to an AI assistant and ask it to merge them. Merging two XML files is a simple task for AI. Then place the merged file, named exactly CustomIndicators.xml, back into your BuildAlpha folder.

COT Trading Strategy Example

Here's a pure COT strategy on S&P 500 futures, no price, volume, or technical signals used. Only COT positioning data as entry signals:

📋 Strategy Rules (Entry)

  • Nasdaq Commercials net positioning ≤ 15% of open interest
  • Five-Year Note Commercials net positioning ≤ 15% of open interest
  • AUDUSD Commercials net positioning ≤ 10% of open interest
Exit: after one bar. Period: 2007-2023. Size: 1 contract per trade.
Build Alpha COT trading strategy equity curve on S&P 500 futures using commercial hedger net positioning from Nasdaq five year note and AUDUSD as intermarket COT signals
Pure COT strategy on S&P 500 futures, intermarket commercial positioning as signals

💡 Best Practice: COT as Filter, Not Signal

A better use of COT data is as a contextual filter layered on top of price-based strategies. Price strategies often lack context about the market environment. COT data provides that context. Use Build Alpha's "required signal" functionality to force COT conditions before any price-based entry fires. Pair with economic news events for even richer context, or use as a filter for intraday trading systems.

Takeaways

✓ Key Takeaways

  1. The COT Report reveals how three trader categories, commercials, large speculators, and retail, are positioned in futures markets
  2. Smart money vs. dumb money divergences at extremes often precede significant market moves
  3. Always normalize raw COT data, use either the COT Index (36-month ranking) or net position as % of open interest

💡 Need to Know

  1. COT data is most powerful as a contextual filter, not a standalone entry signal
  2. Build Alpha includes pre-processed COT data for 15+ futures markets back to 2000
  3. Pair COT with price, volume, or news signals. Build Alpha searches thousands of complimentary filters automatically

COT Report FAQ

The COT Report is a weekly report from the CFTC showing open interest for futures and options on futures. It breaks down positioning by commercials (hedgers), non-commercials (large speculators), and nonreportable (small traders). Data is as of Tuesday but released Friday afternoon.
The COT Index ranks current net positioning against the past 36 months. A reading of 100 means the most bullish in 3 years; 0 means the most bearish. It normalizes raw positioning data into a comparable, actionable scale.
Yes. COT data is freely available from the CFTC at cftc.gov. However, the raw data needs normalization to be useful as trading signals. Build Alpha includes pre-processed, normalized COT data for 15+ markets back to 2000.
COT data is most powerful as a contextual filter on top of price-based strategies. For example, only take long signals when commercial hedgers are net bullish. Build Alpha includes COT signals usable as entries, exits, or filters with no coding required.
Commercials and large speculators are considered "smart money" due to direct commercial interest or institutional resources. Small/nonreportable traders (retail) are sometimes called "dumb money." Large divergences between these groups often precede significant market moves.

Related Reading

David Bergstrom Build Alpha founder and quantitative strategy developer specializing in COT report and alternative data integration for algorithmic trading

David Bergstrom

Founder, Build Alpha
A decade-plus in professional trading as a market maker and quantitative strategy developer at a high-frequency trading firm with a CME seat. Self-taught C++, C#, and Python programmer specializing in data science, machine learning, and trading strategy development.

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