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Market Sentiment
Neutral (Overbought)
Based on the latest 13 weeks of non-commercial positioning data. ℹ️

NYISO ZONE J DA PEAK (Non-Commercial)

13-Wk Max 520 25 125 25 495
13-Wk Min 250 0 -25 0 250
13-Wk Avg 397 23 19 2 373
Report Date Long Short Change Long Change Short Net Position Rate of Change (ROC) ℹ️ Open Int.
May 13, 2025 495 25 0 0 470 0.00% 9,509
May 6, 2025 495 25 -25 0 470 -5.05% 9,509
April 29, 2025 520 25 0 0 495 0.00% 10,053
April 22, 2025 520 25 70 0 495 16.47% 9,715
April 15, 2025 450 25 125 0 425 41.67% 9,585
April 8, 2025 325 25 -25 0 300 -7.69% 9,084
April 1, 2025 350 25 0 0 325 0.00% 9,596
March 25, 2025 350 25 0 0 325 0.00% 8,803
March 18, 2025 350 25 0 0 325 0.00% 8,683
March 11, 2025 350 25 0 0 325 0.00% 8,703
March 4, 2025 350 25 0 0 325 0.00% 9,334
February 25, 2025 350 25 100 25 325 30.00% 9,234
February 18, 2025 250 0 0 0 250 0.00% 8,980

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 Neutral (Overbought)
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.

Trading Strategy Based on COT Report Analysis for NYISO Zone J DA Peak Electricity (IFED)

This strategy leverages the Commitment of Traders (COT) report to inform trading decisions for NYISO Zone J DA Peak Electricity futures (IFED) traded on the ICE Futures Energy Division. It's designed for both retail traders and market investors, adapting to different risk tolerances and time horizons.

Understanding the NYISO Zone J DA Peak Market:

  • NYISO Zone J: Refers to a specific region within the New York Independent System Operator (NYISO) electricity grid, typically covering New York City and surrounding areas.
  • DA Peak (Day-Ahead Peak): Represents the highest electricity demand period during the next day, typically during daylight hours.
  • IFED: The ICE Futures Energy Division offers futures contracts based on this specific delivery point and period, allowing participants to hedge or speculate on electricity prices.

I. COT Report Basics:

  • Source: The CFTC (Commodity Futures Trading Commission) publishes the COT report weekly, typically on Fridays, summarizing positions held by different trader categories.
  • Key Categories:
    • Commercials (Hedgers): Entities that use the futures market to hedge their exposure to electricity prices (e.g., power generators, utilities). Their primary purpose is to manage risk, not profit from speculation.
    • Non-Commercials (Speculators): Traders who use the futures market for speculative purposes, aiming to profit from price movements. This category is further broken down into managed money (e.g., hedge funds, CTAs) and other reportables.
    • Non-Reportable Positions: Small traders whose positions are below the reporting threshold. Their collective position is reported but not individually identified.
  • Data Points:
    • Long Positions: Contracts held where the trader profits from a price increase.
    • Short Positions: Contracts held where the trader profits from a price decrease.
    • Net Positions: The difference between long and short positions (Long - Short). A positive net position indicates a bullish outlook; a negative net position indicates a bearish outlook.

II. Trading Strategy Framework:

A. Data Acquisition and Preparation:

  1. COT Report Access: Download the COT report data from the CFTC website or a data provider. Look for the "Disaggregated Futures Only" report format for a detailed breakdown. Specify the CFTC market code: IFED.
  2. Historical Data: Collect historical COT report data for NYISO Zone J DA Peak electricity (IFED) for at least 2-3 years to establish a baseline and identify patterns.
  3. Price Data: Gather historical price data for the IFED futures contract from your broker or a data provider. Match the frequency of the COT report (weekly).
  4. Data Analysis Tools: Use spreadsheet software (Excel, Google Sheets) or statistical software (Python with Pandas/NumPy, R) to analyze the data.

B. COT Indicator Development:

Create the following indicators to gain insights from the COT data:

  1. Net Position Change (Commercials/Non-Commercials): Calculate the weekly change in net positions for both Commercials and Non-Commercials. This indicates the direction and intensity of their sentiment.

    • Change in Commercial Net Position = Current Week Commercial Net Position - Previous Week Commercial Net Position
    • Change in Non-Commercial Net Position = Current Week Non-Commercial Net Position - Previous Week Non-Commercial Net Position

  2. Commercial Hedgers Index (CHI): A contrarian indicator. Measures the current Commercial Net Position as a percentage of its historical range. Values approaching 100% (relatively long) suggest potential overbought conditions and a possible price reversal downwards. Values approaching 0% (relatively short) suggest potential oversold conditions and a possible price reversal upwards.

    • CHI = (Current Commercial Net Position - Historical Commercial Net Position Low) / (Historical Commercial Net Position High - Historical Commercial Net Position Low)

  3. Non-Commercial COT Index (NCI): A trend-following indicator. Measures the current Non-Commercial Net Position as a percentage of its historical range. Values approaching 100% (relatively long) suggest a strong uptrend. Values approaching 0% (relatively short) suggest a strong downtrend.

    • NCI = (Current Non-Commercial Net Position - Historical Non-Commercial Net Position Low) / (Historical Non-Commercial Net Position High - Historical Non-Commercial Net Position Low)

  4. Commercial vs. Non-Commercial Ratio (C/NC Ratio): Divide the Commercial Net Position by the Non-Commercial Net Position. A rising ratio suggests that Commercials are becoming more bullish relative to Non-Commercials (potentially bearish for the price). A falling ratio suggests that Commercials are becoming more bearish relative to Non-Commercials (potentially bullish for the price).

    • C/NC Ratio = Commercial Net Position / Non-Commercial Net Position

C. Trading Rules:

These rules combine COT analysis with price action and technical analysis. Adapt the parameters to your risk tolerance and trading style.

1. Contrarian Strategy (Following Commercials - for Longer-Term Investors):

*   **Buy Signal:**
    *   CHI is below 20% (Commercials are heavily short).
    *   Price shows signs of bullish reversal (e.g., bullish candlestick pattern, breaking above a resistance level).
    *   Confirm with other technical indicators (e.g., RSI oversold).
*   **Sell Signal:**
    *   CHI is above 80% (Commercials are heavily long).
    *   Price shows signs of bearish reversal (e.g., bearish candlestick pattern, breaking below a support level).
    *   Confirm with other technical indicators (e.g., RSI overbought).
*   **Stop Loss:** Place stop loss below a recent swing low (for long positions) or above a recent swing high (for short positions).
*   **Target:**  Consider taking profits when the CHI moves towards its median range (around 50%) or when price reaches a predefined resistance (for long positions) or support (for short positions) level.

2. Trend-Following Strategy (Following Non-Commercials - for Shorter-Term Traders):

*   **Buy Signal:**
    *   NCI is above 70% and rising (Non-Commercials are increasingly bullish).
    *   Price is in an uptrend (e.g., above a moving average, making higher highs and higher lows).
    *   Volume confirms the uptrend.
*   **Sell Signal:**
    *   NCI is below 30% and falling (Non-Commercials are increasingly bearish).
    *   Price is in a downtrend (e.g., below a moving average, making lower highs and lower lows).
    *   Volume confirms the downtrend.
*   **Stop Loss:** Place stop loss below a recent swing low (for long positions) or above a recent swing high (for short positions). Consider using a trailing stop to lock in profits as the trend progresses.
*   **Target:**  Set profit targets based on trend continuation patterns (e.g., Fibonacci extensions) or when the NCI shows signs of reversing.

3. C/NC Ratio Divergence Strategy:

*   **Buy Signal:**
    *   Price is making lower lows, but the C/NC Ratio is rising (Commercials are becoming more bullish relative to Non-Commercials despite the price decline).
    *   This suggests that the current downtrend might be losing momentum and a potential bullish reversal is imminent.
    *   Confirm with price action and other technical indicators.
*   **Sell Signal:**
    *   Price is making higher highs, but the C/NC Ratio is falling (Commercials are becoming more bearish relative to Non-Commercials despite the price increase).
    *   This suggests that the current uptrend might be losing momentum and a potential bearish reversal is imminent.
    *   Confirm with price action and other technical indicators.
*   **Stop Loss and Target:** Use similar stop loss and target techniques as described in the Contrarian and Trend-Following strategies.

D. Risk Management:

  1. Position Sizing: Risk only a small percentage of your capital on each trade (e.g., 1-2%). Adjust position size based on the volatility of the IFED contract.
  2. Stop-Loss Orders: Always use stop-loss orders to limit potential losses. Adjust stop-loss levels based on market volatility and your risk tolerance.
  3. Diversification: Don't put all your eggs in one basket. Diversify your portfolio across different asset classes and trading strategies.
  4. Monitoring and Adjustment: Continuously monitor your trades and adjust your strategy based on market conditions and your performance.

III. Considerations for Retail Traders vs. Market Investors:

  • Retail Traders: Typically have smaller capital and shorter time horizons.
    • Focus on shorter-term strategies (e.g., Trend-Following).
    • Use tighter stop-loss orders.
    • Be more active in managing positions.
  • Market Investors: May have larger capital and longer time horizons.
    • Can consider longer-term strategies (e.g., Contrarian).
    • Use wider stop-loss orders.
    • May be less active in managing positions.

IV. Additional Factors to Consider:

  • Seasonality: Electricity demand is highly seasonal. Demand typically peaks in the summer (air conditioning) and winter (heating). Consider these seasonal patterns when interpreting the COT data and making trading decisions.
  • Weather Forecasts: Weather forecasts can significantly impact electricity prices. Extreme temperatures can lead to increased demand and higher prices.
  • NYISO Market Reports: Monitor NYISO market reports for information on grid conditions, generation outages, and other factors that could affect electricity prices.
  • Regulatory Changes: Changes in regulations can impact the electricity market. Stay informed about any regulatory changes that could affect the IFED contract.
  • Economic Data: General economic data (e.g., GDP growth, industrial production) can influence electricity demand.

V. Backtesting and Optimization:

  • Backtesting: Thoroughly backtest your trading strategy using historical data to assess its performance.
  • Optimization: Optimize your strategy by adjusting parameters (e.g., COT indicator thresholds, stop-loss levels) to improve its profitability and risk-adjusted returns.
  • Paper Trading: Practice your strategy in a simulated environment before risking real capital.

VI. Disclaimer:

This trading strategy is for educational purposes only and should not be construed as financial advice. Trading futures involves significant risk of loss. You should carefully consider your investment objectives, risk tolerance, and financial situation before trading. Past performance is not indicative of future results.

By combining COT report analysis with other technical and fundamental factors, you can develop a more informed and potentially profitable trading strategy for NYISO Zone J DA Peak electricity futures. Remember to manage your risk carefully and continuously adapt your strategy to changing market conditions.