Back to COT Dashboard
Market Sentiment
Sell
Based on the latest 13 weeks of non-commercial positioning data. ℹ️

PJM.AEP-DAYTON HUB_mo_off_rtp (Non-Commercial)

13-Wk Max 25,915 1,570 5,730 710 25,480
13-Wk Min 19,490 375 -662 -425 18,690
13-Wk Avg 24,633 730 360 59 23,904
Report Date Long Short Change Long Change Short Net Position Rate of Change (ROC) ℹ️ Open Int.
May 13, 2025 23,693 1,570 -290 710 22,123 -4.32% 73,195
May 6, 2025 23,983 860 -380 210 23,123 -2.49% 71,859
April 29, 2025 24,363 650 -662 -50 23,713 -2.52% 74,207
April 22, 2025 25,025 700 10 0 24,325 0.04% 75,108
April 15, 2025 25,015 700 -225 -75 24,315 -0.61% 75,714
April 8, 2025 25,240 775 -75 390 24,465 -1.87% 75,028
April 1, 2025 25,315 385 -600 -250 24,930 -1.38% 78,851
March 25, 2025 25,915 635 0 200 25,280 -0.78% 78,811
March 18, 2025 25,915 435 600 60 25,480 2.17% 77,951
March 11, 2025 25,315 375 -430 -425 24,940 -0.02% 76,711
March 4, 2025 25,745 800 525 0 24,945 2.15% 78,406
February 25, 2025 25,220 800 5,730 0 24,420 30.66% 76,226
February 18, 2025 19,490 800 480 0 18,690 2.64% 73,126

Net Position (13 Weeks) - Non-Commercial

Change in Long and Short Positions (13 Weeks) - Non-Commercial

COT Interpretation for ELECTRICITY

Comprehensive Guide to COT Reports for Commodity Natural Resources Markets

1. Introduction to COT Reports

What are COT Reports?

The Commitments of Traders (COT) reports are weekly publications released by the U.S. Commodity Futures Trading Commission (CFTC) that show the positions of different types of traders in U.S. futures markets, including natural resources commodities such as oil, natural gas, gold, silver, and agricultural products.

Historical Context

COT reports have been published since the 1920s, but the modern format began in 1962. Over the decades, the reports have evolved to provide more detailed information about market participants and their positions.

Importance for Natural Resource Investors

COT reports are particularly valuable for natural resource investors and traders because they:

  • Provide transparency into who holds positions in commodity markets
  • Help identify potential price trends based on positioning changes
  • Show how different market participants are reacting to fundamental developments
  • Serve as a sentiment indicator for commodity markets

Publication Schedule

COT reports are released every Friday at 3:30 p.m. Eastern Time, showing positions as of the preceding Tuesday. During weeks with federal holidays, the release may be delayed until Monday.

2. Understanding COT Report Structure

Types of COT Reports

The CFTC publishes several types of reports:

  1. Legacy COT Report: The original format classifying traders as Commercial, Non-Commercial, and Non-Reportable.
  2. Disaggregated COT Report: Offers more detailed breakdowns, separating commercials into producers/merchants and swap dealers, and non-commercials into managed money and other reportables.
  3. Supplemental COT Report: Focuses on 13 select agricultural commodities with additional index trader classifications.
  4. Traders in Financial Futures (TFF): Covers financial futures markets.

For natural resource investors, the Disaggregated COT Report generally provides the most useful information.

Data Elements in COT Reports

Each report contains:

  • Open Interest: Total number of outstanding contracts for each commodity
  • Long and Short Positions: Broken down by trader category
  • Spreading: Positions held by traders who are both long and short in different contract months
  • Changes: Net changes from the previous reporting period
  • Percentages: Proportion of open interest held by each trader group
  • Number of Traders: Count of traders in each category

3. Trader Classifications

Legacy Report Classifications

  1. Commercial Traders ("Hedgers"):
    • Primary business involves the physical commodity
    • Use futures to hedge price risk
    • Include producers, processors, and merchants
    • Example: Oil companies hedging future production
  2. Non-Commercial Traders ("Speculators"):
    • Do not have business interests in the physical commodity
    • Trade for investment or speculative purposes
    • Include hedge funds, CTAs, and individual traders
    • Example: Hedge funds taking positions based on oil price forecasts
  3. Non-Reportable Positions ("Small Traders"):
    • Positions too small to meet reporting thresholds
    • Typically represent retail traders and smaller entities
    • Considered "noise traders" by some analysts

Disaggregated Report Classifications

  1. Producer/Merchant/Processor/User:
    • Entities that produce, process, pack, or handle the physical commodity
    • Use futures markets primarily for hedging
    • Example: Gold miners, oil producers, refineries
  2. Swap Dealers:
    • Entities dealing primarily in swaps for commodities
    • Hedging swap exposures with futures contracts
    • Often represent positions of institutional investors
  3. Money Managers:
    • Professional traders managing client assets
    • Include CPOs, CTAs, hedge funds
    • Primarily speculative motives
    • Often trend followers or momentum traders
  4. Other Reportables:
    • Reportable traders not in above categories
    • Example: Trading companies without physical operations
  5. Non-Reportable Positions:
    • Same as in the Legacy report
    • Small positions held by retail traders

Significance of Each Classification

Understanding the motivations and behaviors of each trader category helps interpret their position changes:

  • Producers/Merchants: React to supply/demand fundamentals and often trade counter-trend
  • Swap Dealers: Often reflect institutional flows and longer-term structural positions
  • Money Managers: Tend to be trend followers and can amplify price movements
  • Non-Reportables: Sometimes used as a contrarian indicator (small traders often wrong at extremes)

4. Key Natural Resource Commodities

Energy Commodities

  1. Crude Oil (WTI and Brent)
    • Reporting codes: CL (NYMEX), CB (ICE)
    • Key considerations: Seasonal patterns, refinery demand, geopolitical factors
    • Notable COT patterns: Producer hedging often increases after price rallies
  2. Natural Gas
    • Reporting code: NG (NYMEX)
    • Key considerations: Extreme seasonality, weather sensitivity, storage reports
    • Notable COT patterns: Commercials often build hedges before winter season
  3. Heating Oil and Gasoline
    • Reporting codes: HO, RB (NYMEX)
    • Key considerations: Seasonal demand patterns, refinery throughput
    • Notable COT patterns: Refiners adjust hedge positions around maintenance periods

Precious Metals

  1. Gold
    • Reporting code: GC (COMEX)
    • Key considerations: Inflation expectations, currency movements, central bank buying
    • Notable COT patterns: Commercial shorts often peak during price rallies
  2. Silver
    • Reporting code: SI (COMEX)
    • Key considerations: Industrial vs. investment demand, gold ratio
    • Notable COT patterns: More volatile positioning than gold, managed money swings
  3. Platinum and Palladium
    • Reporting codes: PL, PA (NYMEX)
    • Key considerations: Auto catalyst demand, supply constraints
    • Notable COT patterns: Smaller markets with potentially more concentrated positions

Base Metals

  1. Copper
    • Reporting code: HG (COMEX)
    • Key considerations: Global economic growth indicator, construction demand
    • Notable COT patterns: Producer hedging often increases during supply surpluses
  2. Aluminum, Nickel, Zinc (COMEX/LME)
    • Note: CFTC reports cover U.S. exchanges only
    • Key considerations: Manufacturing demand, energy costs for production
    • Notable COT patterns: Limited compared to LME positioning data

Agricultural Resources

  1. Lumber
    • Reporting code: LB (CME)
    • Key considerations: Housing starts, construction activity
    • Notable COT patterns: Producer hedging increases during price spikes
  2. Cotton
    • Reporting code: CT (ICE)
    • Key considerations: Global textile demand, seasonal growing patterns
    • Notable COT patterns: Merchant hedging follows harvest cycles

5. Reading and Interpreting COT Data

Key Metrics to Monitor

  1. Net Positions
    • Definition: Long positions minus short positions for each trader category
    • Calculation: Net Position = Long Positions - Short Positions
    • Significance: Shows overall directional bias of each group
  2. Position Changes
    • Definition: Week-over-week changes in positions
    • Calculation: Current Net Position - Previous Net Position
    • Significance: Identifies new money flows and sentiment shifts
  3. Concentration Ratios
    • Definition: Percentage of open interest held by largest traders
    • Significance: Indicates potential market dominance or vulnerability
  4. Commercial/Non-Commercial Ratio
    • Definition: Ratio of commercial to non-commercial positions
    • Calculation: Commercial Net Position / Non-Commercial Net Position
    • Significance: Highlights potential divergence between hedgers and speculators
  5. Historical Percentiles
    • Definition: Current positions compared to historical ranges
    • Calculation: Typically 1-3 year lookback periods
    • Significance: Identifies extreme positioning relative to history

Basic Interpretation Approaches

  1. Trend Following with Managed Money
    • Premise: Follow the trend of managed money positions
    • Implementation: Go long when managed money increases net long positions
    • Rationale: Managed money often drives momentum in commodity markets
  2. Commercial Hedging Analysis
    • Premise: Commercials are "smart money" with fundamental insight
    • Implementation: Look for divergences between price and commercial positioning
    • Rationale: Commercials often take counter-trend positions at market extremes
  3. Extreme Positioning Identification
    • Premise: Extreme positions often precede market reversals
    • Implementation: Identify when any group reaches historical extremes (90th+ percentile)
    • Rationale: Crowded trades must eventually unwind
  4. Divergence Analysis
    • Premise: Divergences between trader groups signal potential turning points
    • Implementation: Watch when commercials and managed money move in opposite directions
    • Rationale: Opposing forces creating potential market friction

Visual Analysis Examples

Typical patterns to watch for:

  1. Bull Market Setup:
    • Managed money net long positions increasing
    • Commercial short positions increasing (hedging against higher prices)
    • Price making higher highs and higher lows
  2. Bear Market Setup:
    • Managed money net short positions increasing
    • Commercial long positions increasing (hedging against lower prices)
    • Price making lower highs and lower lows
  3. Potential Reversal Pattern:
    • Price making new highs/lows
    • Position extremes across multiple trader categories
    • Changes in positioning not confirming price moves (divergence)

6. Using COT Reports in Trading Strategies

Fundamental Integration Strategies

  1. Supply/Demand Confirmation
    • Approach: Use COT data to confirm fundamental analysis
    • Implementation: Check if commercials' positions align with known supply/demand changes
    • Example: Increasing commercial shorts in natural gas despite falling inventories could signal hidden supply
  2. Commercial Hedging Cycle Analysis
    • Approach: Track seasonal hedging patterns of producers
    • Implementation: Create yearly overlay charts of producer positions
    • Example: Oil producers historically increase hedging in Q2, potentially pressuring prices
  3. Index Roll Impact Assessment
    • Approach: Monitor position changes during index fund roll periods
    • Implementation: Track swap dealer positions before/after rolls
    • Example: Energy contracts often see price pressure during standard roll periods

Technical Integration Strategies

  1. COT Confirmation of Technical Patterns
    • Approach: Use COT data to validate chart patterns
    • Implementation: Confirm breakouts with appropriate positioning changes
    • Example: Gold breakout with increasing managed money longs has higher probability
  2. COT-Based Support/Resistance Levels
    • Approach: Identify price levels where significant position changes occurred
    • Implementation: Mark price points of major position accumulation
    • Example: Price levels where commercials accumulated large positions often act as support
  3. Sentiment Extremes as Contrarian Signals
    • Approach: Use extreme positioning as contrarian indicators
    • Implementation: Enter counter-trend when positions reach historical extremes (90th+ percentile)
    • Example: Enter long gold when managed money short positioning reaches 95th percentile historically

Market-Specific Strategies

  1. Energy Market Strategies
    • Crude Oil: Monitor producer hedging relative to current term structure
    • Natural Gas: Analyze commercial positioning ahead of storage injection/withdrawal seasons
    • Refined Products: Track seasonal changes in dealer/refiner positioning
  2. Precious Metals Strategies
    • Gold: Monitor swap dealer positioning as proxy for institutional sentiment
    • Silver: Watch commercial/managed money ratio for potential squeeze setups
    • PGMs: Analyze producer hedging for supply insights
  3. Base Metals Strategies
    • Copper: Track managed money positioning relative to global growth metrics
    • Aluminum/Nickel: Monitor producer hedging for production cost signals

Strategy Implementation Framework

  1. Data Collection and Processing
    • Download weekly COT data from CFTC website
    • Calculate derived metrics (net positions, changes, ratios)
    • Normalize data using Z-scores or percentile ranks
  2. Signal Generation
    • Define position thresholds for each trader category
    • Establish change-rate triggers
    • Create composite indicators combining multiple COT signals
  3. Trade Setup
    • Entry rules based on COT signals
    • Position sizing based on signal strength
    • Risk management parameters
  4. Performance Tracking
    • Track hit rate of COT-based signals
    • Monitor lead/lag relationship between positions and price
    • Regularly recalibrate thresholds based on performance

7. Advanced COT Analysis Techniques

Statistical Analysis Methods

  1. Z-Score Analysis
    • Definition: Standardized measure of position extremes
    • Calculation: Z-score = (Current Net Position - Average Net Position) / Standard Deviation
    • Application: Identify positions that are statistically extreme
    • Example: Gold commercials with Z-score below -2.0 often mark potential bottoms
  2. Percentile Ranking
    • Definition: Position ranking relative to historical range
    • Calculation: Current position's percentile within 1-3 year history
    • Application: More robust than Z-scores for non-normal distributions
    • Example: Natural gas managed money in 90th+ percentile often precedes price reversals
  3. Rate-of-Change Analysis
    • Definition: Speed of position changes rather than absolute levels
    • Calculation: Weekly RoC = (Current Position - Previous Position) / Previous Position
    • Application: Identify unusual accumulation or liquidation
    • Example: Crude oil swap dealers increasing positions by >10% in a week often signals institutional flows

Multi-Market Analysis

  1. Intermarket COT Correlations
    • Approach: Analyze relationships between related commodity positions
    • Implementation: Create correlation matrices of trader positions across markets
    • Example: Gold/silver commercial positioning correlation breakdown can signal sector rotation
  2. Currency Impact Assessment
    • Approach: Analyze COT data in currency futures alongside commodities
    • Implementation: Track correlations between USD positioning and commodity positioning
    • Example: Extreme USD short positioning often coincides with commodity long positioning
  3. Cross-Asset Confirmation
    • Approach: Verify commodity COT signals with related equity or bond positioning
    • Implementation: Compare energy COT data with energy equity positioning
    • Example: Divergence between oil futures positioning and energy equity positioning can signal sector disconnects

Machine Learning Applications

  1. Pattern Recognition Models
    • Approach: Train models to identify historical COT patterns preceding price moves
    • Implementation: Use classification algorithms to categorize current positioning
    • Example: Random forest models predicting 4-week price direction based on COT features
  2. Clustering Analysis
    • Approach: Group historical COT data to identify common positioning regimes
    • Implementation: K-means clustering of multi-dimensional COT data
    • Example: Identifying whether current gold positioning resembles bull or bear market regimes
  3. Predictive Modeling
    • Approach: Create forecasting models for future price movements
    • Implementation: Regression models using COT variables as features
    • Example: LSTM networks predicting natural gas price volatility from COT positioning trends

Advanced Visualization Techniques

  1. COT Heat Maps
    • Description: Color-coded visualization of position extremes across markets
    • Application: Quickly identify markets with extreme positioning
    • Example: Heat map showing all commodity markets with positioning in 90th+ percentile
  2. Positioning Clock
    • Description: Circular visualization showing position cycle status
    • Application: Track position cycles within commodities
    • Example: Natural gas positioning clock showing seasonal accumulation patterns
  3. 3D Surface Charts
    • Description: Three-dimensional view of positions, price, and time
    • Application: Identify complex patterns not visible in 2D
    • Example: Surface chart showing commercial crude oil hedger response to price changes over time

8. Limitations and Considerations

Reporting Limitations

  1. Timing Delays
    • Issue: Data reflects positions as of Tuesday, released Friday
    • Impact: Significant market moves can occur between reporting and release
    • Mitigation: Combine with real-time market indicators
  2. Classification Ambiguities
    • Issue: Some traders could fit in multiple categories
    • Impact: Classification may not perfectly reflect true market structure
    • Mitigation: Focus on trends rather than absolute values
  3. Threshold Limitations
    • Issue: Only positions above reporting thresholds are included
    • Impact: Incomplete picture of market, especially for smaller commodities
    • Mitigation: Consider non-reportable positions as context

Interpretational Challenges

  1. Correlation vs. Causation
    • Issue: Position changes may reflect rather than cause price moves
    • Impact: Following positioning blindly can lead to false signals
    • Mitigation: Use COT as confirmation rather than primary signal
  2. Structural Market Changes
    • Issue: Market participant behavior evolves over time
    • Impact: Historical relationships may break down
    • Mitigation: Use adaptive lookback periods and recalibrate regularly
  3. Options Positions Not Included
    • Issue: Standard COT reports exclude options positions
    • Impact: Incomplete view of market exposure, especially for hedgers
    • Mitigation: Consider using COT-CIT Supplemental reports for context
  4. Exchange-Specific Coverage
    • Issue: Reports cover only U.S. exchanges
    • Impact: Incomplete picture for globally traded commodities
    • Mitigation: Consider parallel data from other exchanges where available

Common Misinterpretations

  1. Assuming Commercials Are Always Right
    • Misconception: Commercial positions always lead price
    • Reality: Commercials can be wrong on timing and magnitude
    • Better approach: Look for confirmation across multiple signals
  2. Ignoring Position Size Context
    • Misconception: Absolute position changes are what matter
    • Reality: Position changes relative to open interest provide better context
    • Better approach: Normalize position changes by total open interest
  3. Over-Relying on Historical Patterns
    • Misconception: Historical extremes will always work the same way
    • Reality: Market regimes change, affecting positioning impact
    • Better approach: Adjust expectations based on current volatility regime
  4. Neglecting Fundamental Context
    • Misconception: COT data is sufficient standalone
    • Reality: Positioning often responds to fundamental catalysts
    • Better approach: Integrate COT analysis with supply/demand factors

Integration into Trading Workflow

  1. Weekly Analysis Routine
    • Friday: Review new COT data upon release
    • Weekend: Comprehensive analysis and strategy adjustments
    • Monday: Implement new positions based on findings
  2. Framework for Position Decisions
    • Primary signal: Identify extremes in relevant trader categories
    • Confirmation: Check for divergences with price action
    • Context: Consider fundamental backdrop
    • Execution: Define entry, target, and stop parameters
  3. Documentation Process
    • Track all COT-based signals in trading journal
    • Record hit/miss rate and profitability
    • Note market conditions where signals work best/worst
  4. Continuous Improvement
    • Regular backtest of signal performance
    • Adjustment of thresholds based on market conditions
    • Integration of new data sources as available

Case Studies: Practical Applications

  1. Natural Gas Winter Strategy
    • Setup: Monitor commercial positioning ahead of withdrawal season
    • Signal: Commercial net long position > 70th percentile
    • Implementation: Long exposure with technical price confirmation
    • Historical performance: Positive expectancy during 2015-2023 period
  2. Gold Price Reversal Strategy
    • Setup: Watch for extreme managed money positioning
    • Signal: Managed money net short position > 85th percentile historically
    • Implementation: Contrarian long position with tiered entry
    • Risk management: Stop loss at recent swing point
  3. Crude Oil Price Collapse Warning System
    • Setup: Monitor producer hedging acceleration
    • Signal: Producer short positions increasing by >10% over 4 weeks
    • Implementation: Reduce long exposure or implement hedging strategies
    • Application: Successfully flagged risk periods in 2014, 2018, and 2022

By utilizing these resources and implementing the strategies outlined in this guide, natural resource investors and traders can gain valuable insights from COT data to enhance their market analysis and decision-making processes.

Market Sell
Based on the latest 13 weeks of non-commercial positioning data.
📊 COT Sentiment Analysis Guide

This guide helps traders understand how to interpret Commitments of Traders (COT) reports to generate potential Buy, Sell, or Neutral signals using market positioning data.

🧠 How It Works
  • Recent Trend Detection: Tracks net position and rate of change (ROC) over the last 13 weeks.
  • Overbought/Oversold Check: Compares current net positions to a 1-year range using percentiles.
  • Strength Confirmation: Validates if long or short positions are dominant enough for a signal.
✅ Signal Criteria
Condition Signal
Net ↑ for 13+ weeks AND ROC ↑ for 13+ weeks AND strong long dominance Buy
Net ↓ for 13+ weeks AND ROC ↓ for 13+ weeks AND strong short dominance Sell
Net in top 20% of 1-year range AND net uptrend ≥ 3 Neutral (Overbought)
Net in bottom 20% of 1-year range AND net downtrend ≥ 3 Neutral (Oversold)
None of the above conditions met Neutral
🧭 Trader Tips
  • Trend traders: Follow Buy/Sell signals when all trend and strength conditions align.
  • Contrarian traders: Use Neutral (Overbought/Oversold) flags to anticipate reversals.
  • Swing traders: Use sentiment as a filter to increase trade confidence.
Example:
Net positions rising, strong long dominance, in top 20% of historical range.
Result: Neutral (Overbought) — uptrend may be too crowded.
  • COT data is delayed (released on Friday, based on Tuesday's positions) - it's not real-time.
  • Combine with price action, FVG, liquidity, or technical indicators for best results.
  • Use percentile filters to avoid buying at extreme highs or selling at extreme lows.

Okay, let's break down a potential trading strategy for electricity (specifically PJM AEP-DAYTON HUB) based on Commitment of Traders (COT) report analysis, tailored for retail traders and market investors. Keep in mind that this is a simplified approach and actual electricity trading can be incredibly complex. It involves understanding grid operations, weather patterns, demand forecasting, and regulatory factors. The COT report is only one piece of the puzzle.

I. Understanding the Basics

  • Commodity: Electricity (PJM AEP-DAYTON HUB_mo_off_rtp - NODAL EXCHANGE)
  • Contract Unit: Megawatt Hours (MWh) - This is the standard unit of electricity delivery.
  • CFTC Market Code: NODX - This is the unique identifier used by the Commodity Futures Trading Commission (CFTC) to track this specific contract.
  • Market Exchange: PJM.AEP-DAYTON HUB_mo_off_rtp - NODAL EXCHANGE - This indicates the specific delivery point (AEP-DAYTON HUB within the PJM Interconnection) and the type of product (monthly, off-peak, real-time pricing - RTP).
  • COT Report: The Commitments of Traders (COT) report is a weekly publication by the CFTC that breaks down the open interest (number of outstanding contracts) in futures markets by different types of traders. It helps identify the positioning of commercial and non-commercial traders.

II. Key COT Report Categories & Interpretation for Electricity (PJM AEP-DAYTON HUB)

The COT report categorizes traders into:

  • Commercials (Hedgers): These are entities that use the futures market to hedge their exposure to the underlying commodity. In the case of electricity, these would be power generators, utilities, large industrial consumers, and energy marketers. They are producers and consumers of electricity. Their primary goal is not speculation but to manage price risk.
  • Non-Commercials (Speculators): These are traders who use the futures market to profit from price movements. They include hedge funds, managed money, and other speculative entities. They don't typically have any physical involvement in electricity. Their primary goal is to profit from price changes.
  • Non-Reportable Positions: Small traders whose positions are below the reporting threshold. Typically, their collective impact is minor, but it's good to be aware they exist.

Interpreting the Data (General Principles):

  • Commercial Net Position: Typically, commercials are net short (selling) because they are hedging their production or consumption. A significant shift towards a less short or net long position by commercials might suggest they anticipate higher prices. Conversely, a more short position suggests they might anticipate lower prices or are hedging against potential price declines.
  • Non-Commercial Net Position: Typically, non-commercials follow trends. They tend to be net long (buying) in uptrends and net short (selling) in downtrends. Large and rapidly increasing net long positions by non-commercials can suggest an overbought market. Large and rapidly increasing net short positions can suggest an oversold market.
  • Divergence: Pay attention to divergences between commercial and non-commercial positions. For example, if prices are rising, but commercials are increasing their short positions while non-commercials are increasing their long positions, this could signal a potential trend reversal. The commercials are "fading" the rally, which can be a sign of smart money betting against the trend.
  • Open Interest: Changes in open interest can also provide clues. Increasing open interest in a rising market can confirm the uptrend. Decreasing open interest in a rising market might suggest the rally is losing steam.

III. A Retail Trader/Market Investor Strategy Based on the COT Report (PJM AEP-DAYTON HUB)

Disclaimer: This is a simplified strategy for educational purposes. Electricity trading is very risky and requires a deep understanding of the market. Never risk more than you can afford to lose. Past performance is not indicative of future results.

Assumptions:

  • You have access to the weekly COT report data (available on the CFTC website).
  • You can access price charts for the PJM AEP-DAYTON HUB electricity contract (through a brokerage or data provider that offers electricity futures or forwards).
  • You understand basic technical analysis (support/resistance, trendlines, moving averages).

Steps:

  1. Data Collection: Download the COT report data for the NODX (PJM AEP-DAYTON HUB) contract weekly. Create a spreadsheet to track the net positions of commercials and non-commercials over time.

  2. Identify Trends: Analyze the historical COT data to identify trends in commercial and non-commercial positioning. Are commercials typically net short? Are non-commercials trend followers?

  3. Look for Extremes and Divergences:

    • Extreme Commercial Positioning: Look for periods where commercials have reached unusually large net short or net long positions relative to their historical average. A substantial shift from their normal positioning can be a strong signal.
    • Extreme Non-Commercial Positioning: Look for periods where non-commercials have built up very large net long or net short positions. This can indicate overbought or oversold conditions.
    • Divergence Between Commercials and Non-Commercials: This is often the most valuable signal. For example:
      • Price is Rising, Commercials are Increasing Shorts, Non-Commercials are Increasing Longs: Potentially bearish signal. Commercials may believe the price is overvalued and are hedging against a future decline.
      • Price is Falling, Commercials are Increasing Longs, Non-Commercials are Increasing Shorts: Potentially bullish signal. Commercials may believe the price is undervalued and are buying in anticipation of a rebound.

  4. Combine COT Data with Price Action and Technical Analysis: This is crucial. The COT report is not a crystal ball. You must confirm any COT signals with price action on the chart.

    • Support and Resistance: Look for COT signals near key support and resistance levels. For example, if commercials are increasing their long positions near a strong support level, it strengthens the bullish case.
    • Trendlines: See if COT signals align with trendlines. A divergence at a trendline break can be a powerful signal.
    • Moving Averages: Use moving averages to identify the overall trend. If the trend is up and the COT report suggests a potential pullback (e.g., commercials increasing shorts), you might look for a buying opportunity after a retracement to a moving average.
    • Candlestick Patterns: Use candlestick patterns to confirm potential entries and exits.

  5. Risk Management:

    • Stop-Loss Orders: Always use stop-loss orders to limit your potential losses. Place your stop-loss order based on technical analysis (e.g., below a support level).
    • Position Sizing: Do not risk more than a small percentage of your capital on any single trade (e.g., 1-2%).
    • Understand Leverage: Be very cautious with leverage. Electricity markets can be volatile.
    • Consider Time Decay: Electricity contracts are usually shorter-dated (monthly). Be aware of time decay as the contract approaches expiration.

Example Trade Scenario:

  1. Observation: The price of the PJM AEP-DAYTON HUB monthly off-peak contract has been in an uptrend for several weeks. Non-commercials have built up a large net long position.
  2. COT Signal: The latest COT report shows that commercials have started to increase their net short positions, while non-commercials are still adding to their long positions. This is a bearish divergence.
  3. Technical Confirmation: The price is approaching a resistance level, and a bearish candlestick pattern (e.g., a shooting star) has formed.
  4. Trade: Enter a short position near the resistance level. Place a stop-loss order just above the resistance level.
  5. Target: Set a target price near a previous support level or a moving average.

Important Considerations Specific to Electricity Trading:

  • Weather: Electricity demand is highly dependent on weather conditions (temperature, humidity). Extreme weather events can cause significant price spikes.
  • Grid Operations: Understand the basics of how the PJM Interconnection grid operates. Factors like transmission constraints, generator outages, and renewable energy output can impact prices.
  • Demand Forecasting: Learn how to interpret demand forecasts. High demand can lead to higher prices.
  • Fuel Prices: The cost of fuels used to generate electricity (natural gas, coal, nuclear) can influence electricity prices.
  • Regulatory Environment: Electricity markets are heavily regulated. Changes in regulations can affect prices.
  • Off-Peak vs. On-Peak: PJM AEP-DAYTON HUB_mo_off_rtp focuses on off-peak hours. Understand the differences between on-peak and off-peak pricing.

IV. Limitations of COT Report Analysis for Electricity

  • Lagging Indicator: The COT report is released on Friday, reflecting data from the previous Tuesday. Market conditions can change significantly in the intervening days.
  • Aggregation: The COT report aggregates all commercial traders into one category. This can mask important differences between the positions of different types of commercial traders (e.g., generators vs. utilities).
  • Complexity of Electricity Markets: The COT report is just one piece of the puzzle. Electricity markets are influenced by a wide range of factors, many of which are not reflected in the COT report.
  • Speculative Bubbles/Crashes: COT data alone cannot prevent losses from broader market phenomena unrelated to its specific insights.

V. Conclusion

The COT report can be a useful tool for electricity traders, but it should not be used in isolation. Combine COT data with price action, technical analysis, and a thorough understanding of the factors that influence electricity prices. Electricity trading is complex and risky, so start with small positions and always use risk management techniques. Before trading, engage in significant education and ideally, obtain guidance from experienced mentors. Remember, this information is for educational purposes only and not financial advice. Always do your own research before making any trading decisions.