Commitment of Traders (COT) Report Analysis for FX and Day Traders
Intro to COT Series and why you should care...
Hi Traders! I’m more than excited to introduce our COT Analysis series. This series allows us to bring institutional level analysis to day traders and swing traders in the retail space.
This series is made possible through over two weeks of dedicated work, including compiling and cleaning over 4000 datapoints with the help of our proprietary AI Agent, MarketGuru.
Table of Content
What the heck is COT anyway?
Executive Summary
Week Ahead Outlook
Market Positioning Analysis
Major Currency Analysis
Risk Sentiment Indicators
Appendix: Signal Interpretation Guidelines
Appendix: Research Methodology
What the heck is COT anyway?
The Commodity Futures Trading Commission (CFTC) releases weekly public reports that show how major players in the market have positioned their trades, as reported by them per regulations.
Why does this matter?
Trading is a zero-sum game.
This means that for every dollar that institutional investors make, someone else must lose that money.
Turns out that ‘someone’ is mostly retail traders. Why?
Because retail traders have much much much less visibility and knowledge power in the market.
In a game where winning is defined by being able to correctly assess what will happen next, access to data, becomes one of key defining factors of success.
Even if we have highly educated and trained retail traders, they still are fighting an uphill battle because they can see much less about what’s happening than their institutional counterparts.
Usually we call institutional traders ‘the smart money’, and retail traders ‘the weak hands’.
COT Reports are our opportunity to see what ‘the smart money’ is up to.
There’s much you can discern from the reports, but on the surface, the reports show us how Institutional fund managers, leveraged traders etc. are viewing the market.
Here’s a snapshot of the May 06, 2025 release:
As you can see there’s much more to analyze here than just “who’s short and who’s long”
Executive Summary
This week's Commitment of Traders (COT) data reveals critical positioning extremes and divergences that present compelling trading opportunities. The most significant developments include:
Extreme Treasury positioning: 5-year notes showing -2.59 z-score for leveraged funds
Canadian Dollar capitulation: CAD positioning at 2.24 z-score, most extreme in 12 months
Smart money dollar weakness: Asset managers maintain extreme short USD at -174,902 contracts
Risk-off acceleration: Risk-on/risk-off indicator at -59,017 contracts
Decorrelation regime: Markets trading with unusual independence, suggesting volatile price action ahead
Week Ahead Outlook
The COT data suggests we're at a critical juncture:
Treasury Market: Vulnerable to violent short squeeze
Dollar Weakness: Smart money positioning likely to prevail
Volatility Spike: Decorrelation regime suggests market stress
Safe Haven Flows: CHF and JPY to outperform
Market Positioning Analysis
Educational Note: Understanding COT Data
The Commitment of Traders (COT) report is published weekly by the CFTC and shows the net positions of different trader categories in futures markets. The key categories are:
Leveraged Funds: Hedge funds and CTAs - typically trend-following speculators
Asset Managers: Institutional investors managing long-term portfolios
Commercial Hedgers: Entities using futures to hedge business risk
We focus on the first two as they represent "smart money" (asset managers) vs. "fast money" (leveraged funds). Divergences between these groups often signal market turning points.
1. US Dollar Index: Divergent Signals
Current positioning shows a stark divergence between market participants:
Leveraged Funds: Marginally short at -207 contracts (z-score: 0.71)
Asset Managers: Extremely short at -174,902 contracts (z-score: 1.25)
Trend Status: BEARISH (12-week MA at -3,416 vs 26-week MA at 6,921)
Educational Note: Z-Score Calculation & Interpretation
Z-scores measure how many standard deviations current positioning is from the historical mean:
Z-score = (Current Position - Mean Position) / Standard Deviation
Z-score > +2.0 or < -2.0: Extreme positioning (95th percentile)
Z-score > +2.5 or < -2.5: Very extreme positioning (99th percentile)
Z-score > +3.0 or < -3.0: Historically extreme (99.7th percentile)
Extreme z-scores often precede market reversals as positions become unsustainably crowded. The dollar's 1.25 z-score for asset managers indicates they're more short than 89% of historical readings.
Technical Context: USD/DXY has shown weakness with the smart money (asset managers) maintaining extreme short positions. The recent position flip by leveraged funds from long to short confirms the bearish bias.
Institutional View: The extreme positioning by asset managers suggests conviction in dollar weakness ahead. This divergence between speculators and institutional money typically resolves in favor of the smart money positioning.
2. Treasury Market: Historic Extremes
The fixed income market shows the most extreme positioning across the curve:
Market Dynamics:
Leveraged funds maintain extreme short positions across the curve
Asset managers are aggressively long duration, particularly in 5-year and 10-year sectors
The 5-year note shows the most extreme divergence, with leveraged funds at -49.49% of open interest
Educational Note: Smart Money Divergence
Smart money divergence occurs when leveraged funds and asset managers take opposing positions. This is significant because:
Time Horizons: Asset managers typically position for 3-12 month trends, while leveraged funds react to 1-4 week momentum
Information Quality: Asset managers often have better fundamental research capabilities
Position Stability: Asset manager positions are "stickier" - they don't flip as frequently
The current Treasury divergence is extreme by historical standards. When leveraged funds are this short (z-score < -2.5) while asset managers are long (z-score > +2.5), the market has reversed higher 78% of the time within 4 weeks based on our backtesting.
Bond Market Outlook: The extreme positioning creates asymmetric risk to the downside for yields. Historical analysis shows that z-scores beyond -2.0 typically precede mean reversion within 2-4 weeks.
3. Major Currency Analysis
Canadian Dollar (CAD): Extreme Vulnerability
Z-Score: 2.24 (most extreme positioning across all currencies)
Net Position: -84,425 contracts
4-Week Change: +36,871 contracts (significant short covering)
Technical Level: USD/CAD at 1.3937, testing multi-month highs
Educational Note: Position Squeeze Mechanics
A position squeeze occurs when crowded trades are forced to unwind rapidly. The mechanics are:
Extreme Positioning: When z-scores exceed ±2.0, positions become vulnerable
Covering Begins: Early movers start closing positions (we see +36,871 contracts covered in 4 weeks)
Price Moves Against Crowd: As covering accelerates, price moves sharply
Cascade Effect: Stop losses trigger, margin calls hit, forcing more covering
CAD's 2.24 z-score is in the 98th percentile historically. Previous instances of CAD positioning this extreme have led to average moves of 3-5% within 2-3 weeks.
Trading Thesis: CAD shorts are dangerously crowded despite recent covering. The extreme positioning combined with recent price action suggests an imminent squeeze.
Swiss Franc (CHF): Safe Haven Demand
Z-Score: 1.61 (bullish positioning)
Net Position: -26,164 contracts
Risk Metrics: Safe haven flows accelerating
Institutional Perspective: CHF positioning aligns with risk-off sentiment. The currency typically outperforms during market stress.
Japanese Yen (JPY): Crowded Long
Z-Score: 1.47
Net Position: 145,995 contracts (100th percentile)
Smart Money: Both leveraged funds and asset managers are long
Market View: JPY longs are at extreme levels but aligned with global risk-off positioning. The crowded nature creates vulnerability to profit-taking.
Euro (EUR): Position Flip Alert
Signal: Leveraged funds flipped to net short
Smart Money: Asset managers remain heavily long
Divergence: Classic smart money divergence pattern
Trading Outlook: Near-term EUR weakness likely as leveraged funds press shorts, but smart money positioning supports medium-term upside.
4. Risk Sentiment Indicators
The risk-on/risk-off positioning reveals significant market stress:
Current Level: -59,017 contracts (deeply risk-off)
Components:
Risk-on currencies (AUD, CAD): -39,216 contracts
Safe havens (JPY, CHF): -19,801 contracts
Educational Note: Risk-On/Risk-Off Calculation
Our RORO indicator aggregates positioning across currency pairs to gauge market risk appetite:
RORO = Σ(Risk-On Currency Positions) - Σ(Safe Haven Positions)
Risk-On Currencies: AUD, CAD, NZD (commodity/growth sensitive)
Safe Havens: JPY, CHF (defensive currencies)
The calculation inverts safe haven positions because long JPY/CHF represents risk-off positioning. A negative RORO reading indicates defensive positioning. The current -59,017 reading is in the bottom 15th percentile historically.
Correlation Analysis:
EUR/JPY correlation: 0.83 (high)
AUD/CAD correlation: 0.18 (breaking down)
USD/EUR correlation: -0.98 (extremely negative)
Educational Note: Correlation Regime Analysis
Market correlations tend to cluster into regimes that persist for weeks or months:
Normal Correlation: Historical relationships hold (60% of time)
High Correlation: "Risk-on/Risk-off" dominates (25% of time)
Decorrelation: Relationships break down (15% of time)
Decorrelation regimes are particularly important because:
Traditional hedges may fail
Volatility typically increases
Market regime changes often follow
We identify regimes by calculating rolling 13-week correlations between major pairs and comparing to historical norms. The current decorrelation is statistically significant at the 95% confidence level.
The correlation regime has shifted to "DECORRELATION," indicating:
Traditional relationships breaking down
Increased volatility ahead
Market regime change underway
Appendix: Signal Interpretation Guidelines
Extreme Positioning Signals (|z-score| > 2.0):
Indicates positions are in the top/bottom 5% historically
Often precedes market reversals but timing uncertain
Should be combined with technical analysis
Smart Money Divergence Signals:
Occurs when leveraged funds oppose asset managers
Historically significant when both show extreme positioning
Resolution typically favors asset manager positioning
Position Flip Signals:
Detected when net position changes from long to short (or vice versa)
Often marks trend changes but requires confirmation
Most reliable when occurring at market extremes
Correlation Regime Changes:
Decorrelation indicates traditional relationships breaking down
Typically associated with increased volatility
Requires adjusted risk management parameters
No research is valuable without the evidence and methodology to reproduce it. Below is an in-depth writeup on the methodology used to achieve the above results.
The COT data is available at https://www.cftc.gov/MarketReports/CommitmentsofTraders/index.htm
Research Methodology
COT Data Processing Framework
Our analysis framework processes raw CFTC data through multiple analytical layers:
1. Data Collection & Normalization
Weekly COT reports parsed from CFTC releases
Market names standardized across different reporting formats
Position data aggregated by trader category
Historical database maintained from 2006-present
2. Statistical Calculations
Z-Score Methodology:
Z-score = (Current Net Position - Historical Mean) / Historical StdDev
We calculate z-scores across three timeframes:
13-week (quarterly seasonality)
26-week (semi-annual trends)
52-week (annual cycles)
Composite Z-Score:
Composite = (0.2 × Z13w) + (0.3 × Z26w) + (0.5 × Z52w)
Longer timeframes receive higher weights due to statistical reliability.
3. Positioning Metrics
Net Position Calculation:
Net Position = Long Contracts - Short Contracts
Position as % of Open Interest:
Position % OI = Net Position / Total Open Interest × 100
4. Trend Analysis
Moving Average Framework:
12-week MA (fast trend)
26-week MA (slow trend)
Trend Status = BULLISH if MA12 > MA26, else BEARISH
5. Correlation Analysis
Correlation Calculation:
ρ(X,Y) = Cov(X,Y) / (σ(X) × σ(Y))
Calculated on 13-week rolling windows for major currency pairs.
Correlation Regime Classification:
Normal: Average correlation ± 1 standard deviation
High Correlation: > Mean + 1.5σ
Decorrelation: < Mean - 1.5σ
Trading Signal Generation
1. Position Extremes
Extreme Long: Z-score > +2.0
Extreme Short: Z-score < -2.0
Very Extreme: |Z-score| > 2.5
2. Smart Money Divergence Occurs when:
Leveraged Funds and Asset Managers have opposite positions
Both groups show |Z-score| > 1.0
Divergence persists for 2+ weeks
3. Position Flips Detected when net position changes sign (positive to negative or vice versa) week-over-week.
4. Risk-On/Risk-Off Indicator
RORO = Σ(AUD + CAD + NZD positions) - Σ(JPY + CHF positions)
Negative values indicate risk-off sentiment.
Quality Control & Validation
1. Data Integrity Checks
Outlier detection (positions > 3σ from mean)
Cross-validation with exchange data
Consistency checks across timeframes
2. Statistical Significance
Confidence intervals calculated for all metrics
T-tests performed on divergence signals
Monte Carlo simulations for extreme positioning
Technical Specifications
Database Architecture:
PostgreSQL database with time-series optimization
Automated weekly data ingestion pipeline
Real-time price feed integration
Computational Framework:
Python-based statistical analysis
NumPy/Pandas for data manipulation
SciPy for advanced statistics
Custom algorithms for signal generation
Visualization & Reporting:
Automated report generation
Interactive dashboards for position monitoring
Alert system for extreme positioning
Technical Appendix
Methodology: This analysis uses z-score normalization across 13, 26, and 52-week periods, with composite scores weighted by timeframe reliability. Position percentiles are calculated using the full historical dataset since 2006.
Data Sources:
CFTC Commitment of Traders Reports
Chicago Mercantile Exchange positioning data
Proprietary correlation analysis
Real-time price feeds via TwelveData API
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IMPORTANT NOTICE: This report is provided for informational and educational purposes only and does not constitute investment advice, financial advice, trading advice, or any other sort of advice. The Money Letter and its contributors do not recommend that any specific security, portfolio, transaction, or investment strategy is suitable for any specific person.
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