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

ARGUS PROPANE SAUDI ARAMCO (Non-Commercial)

13-Wk Max 377 341 149 9 274
13-Wk Min 28 82 -57 -70 -303
13-Wk Avg 161 146 31 -18 15
Report Date Long Short Change Long Change Short Net Position Rate of Change (ROC) ℹ️ Open Int.
April 30, 2024 28 193 0 0 -165 -223.53% 1,962
February 27, 2024 43 94 0 0 -51 -124.06% 1,928
October 31, 2023 320 108 -57 5 212 -22.63% 2,601
October 24, 2023 377 103 149 5 274 110.77% 2,572
October 17, 2023 228 98 22 7 130 13.04% 2,237
October 10, 2023 206 91 6 9 115 -2.54% 2,120
October 3, 2023 200 82 30 -56 118 268.75% 1,783
September 26, 2023 170 138 0 0 32 45.45% 2,146
August 29, 2023 129 107 16 -13 22 414.29% 2,039
August 22, 2023 113 120 -4 -28 -7 77.42% 1,887
August 15, 2023 117 148 0 0 -31 79.47% 1,736
July 25, 2023 120 271 82 -70 -151 50.17% 2,031
July 18, 2023 38 341 0 0 -303 -358.97% 1,841

Net Position (13 Weeks) - Non-Commercial

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

COT Interpretation for NATURAL GAS LIQUIDS

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
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 craft a comprehensive trading strategy using COT (Commitment of Traders) data for Argus Propane Saudi Aramco, tailored for retail traders and market investors. Keep in mind that COT reports are just one piece of the puzzle and should be combined with other forms of analysis (technical, fundamental). Also, trading commodities is inherently risky.

I. Understanding the Basics

  • What are Natural Gas Liquids (NGLs)? NGLs are a group of hydrocarbons that are separated from natural gas during processing. Propane is one of the most commonly used NGLs and is derived from natural gas.
  • Argus Propane Saudi Aramco: This price assessment by Argus Media reflects the price of propane sourced from Saudi Aramco and delivered to the New York Mercantile Exchange (NYMEX). It's a key benchmark for propane pricing in the US market.
  • NYMEX (New York Mercantile Exchange): This is where the contract based on the Argus Propane Saudi Aramco assessment is traded.
  • Contract Size: One contract represents 1,000 metric tons of propane. Retail traders will likely be trading smaller contracts or options, so understanding the underlying unit is important.
  • CFTC (Commodity Futures Trading Commission): The CFTC releases the Commitment of Traders (COT) report weekly. This report details the positions held by different categories of traders in the futures market.
  • COT Report Categories (Simplified for Retail Use):
    • Commercials (Hedgers): These are companies that use propane (or produce/store it) in their actual business operations. They use futures to hedge against price fluctuations. They are considered to be the 'smart money'.
    • Non-Commercials (Large Speculators): These are large entities like hedge funds and money managers who trade futures for profit.
    • Retail Traders (Small Speculators): While not explicitly broken out, we can infer their positions by subtracting Commercials and Non-Commercials from the total open interest.

II. Data Sources and Access

  • CFTC Website: The official source for the COT report. You can find the data in both short and long formats. Look for the report specific to NYMEX and NGLs/Propane.
  • Financial News Websites: Many sites like TradingView, Bloomberg, and Reuters provide COT data in charts and tables.
  • Data Providers: Bloomberg, Refinitiv, and other financial data providers offer more in-depth COT data analysis tools, but these typically come with a subscription fee.

III. Core Trading Strategy Based on COT Data

The foundation of this strategy relies on following the "smart money" – the Commercials (Hedgers). Their actions are driven by their actual business needs, giving them a potential edge.

1. Identify Extreme Net Positions:

  • Commercials Net Short (Bearish Signal): When Commercials are heavily net short (i.e., they are selling futures contracts more than buying), it suggests they expect prices to decline. This often happens when propane supplies are high and/or demand is expected to decrease.
  • Commercials Net Long (Bullish Signal): When Commercials are heavily net long (i.e., they are buying futures contracts more than selling), it suggests they expect prices to rise. This often happens when propane supplies are tight and/or demand is expected to increase.
  • "Extreme" is Relative: You need to analyze the historical COT data (at least 1-3 years) to determine what constitutes an "extreme" position for the Commercials. Look for situations where their net position is at or near its highest or lowest level in the historical range. Consider using percentiles to quantify this (e.g., a net position in the top 10% or bottom 10% of its historical range).

2. Confirmation with Price Action (Crucial!):

  • Don't blindly follow the COT report! Use price action to confirm the signals.
  • Bearish Scenario (Commercials Net Short):
    • Look for bearish price patterns (e.g., downtrends, head and shoulders, double tops) on the Argus Propane Saudi Aramco or related Propane futures chart.
    • Confirmation: Price breaking below key support levels.
    • Potential Trade: Consider shorting (selling) propane futures/options or related ETFs/stocks.
  • Bullish Scenario (Commercials Net Long):
    • Look for bullish price patterns (e.g., uptrends, inverse head and shoulders, double bottoms) on the Argus Propane Saudi Aramco or related Propane futures chart.
    • Confirmation: Price breaking above key resistance levels.
    • Potential Trade: Consider going long (buying) propane futures/options or related ETFs/stocks.

3. Non-Commercials (Large Speculators) - Secondary Indicator:

  • Corroboration or Divergence: Analyze the positions of the Non-Commercials to see if they are aligned with or diverging from the Commercials.
    • Confirmation: If both Commercials and Non-Commercials are moving in the same direction (e.g., both net short), it strengthens the signal.
    • Divergence: If the Non-Commercials are moving in the opposite direction of the Commercials, it can be a warning sign. The Commercials' positions are generally more reliable, but divergence warrants caution and further analysis. This can indicate a potential short-term counter-trend move before the longer-term trend (indicated by the commercials) resumes.

4. Open Interest:

  • Rising Open Interest: Generally, rising open interest confirms the trend. It suggests that new money is entering the market in the direction of the price movement.
  • Falling Open Interest: Falling open interest can weaken the signal. It suggests that the trend may be losing momentum.

IV. Risk Management

  • Stop-Loss Orders: Absolutely essential! Place stop-loss orders to limit your potential losses. Base your stop-loss placement on technical levels (e.g., recent swing highs/lows, support/resistance). A good starting point is typically 1-2% of your trading capital per trade.
  • Position Sizing: Don't risk too much on any single trade. A common guideline is to risk no more than 1-2% of your trading capital per trade.
  • Leverage: Be extremely cautious with leverage. Propane and NGL futures can be volatile. High leverage can magnify both your profits and your losses. For retail traders, using little to no leverage is often the best approach. If you are using CFDs, be aware of the leverage and the high risks involved.
  • Market Volatility: Understand the volatility of propane markets. Factors like weather, geopolitical events, and production disruptions can cause significant price swings.
  • Paper Trading: Practice your strategy on a demo account before risking real money.

V. Fundamental Analysis Considerations

The COT report helps gauge sentiment, but understanding the underlying fundamentals is crucial for long-term success. Consider these factors:

  • Weather: Propane is heavily used for heating, so weather forecasts (especially for cold snaps in winter) significantly impact demand.
  • Inventory Levels: Track propane inventory levels at major storage hubs (e.g., Conway, Kansas; Mont Belvieu, Texas). High inventories can put downward pressure on prices, while low inventories can support prices. The EIA (Energy Information Administration) publishes weekly inventory reports.
  • Production: Monitor propane production levels, particularly in the US, which is a major producer.
  • Exports: Propane exports from the US have become a significant factor. Increased exports can reduce domestic supply and support prices.
  • Crack Spread: The propane crack spread is the difference between the price of crude oil and the price of propane. It reflects the profitability of refining propane from crude oil. A widening crack spread can incentivize production and potentially lead to lower propane prices.
  • Geopolitical Events: Major political events in propane-producing regions (e.g., Middle East) can disrupt supply and impact prices.
  • Seasonality: Propane demand is seasonal, with higher demand in the winter months for heating.

VI. Example Trade Scenario (Illustrative)

  1. COT Signal: The weekly COT report shows that Commercials have reached an extreme net short position in Argus Propane Saudi Aramco futures, the lowest in the past 3 years.
  2. Price Action: The propane futures price is consolidating near a key support level.
  3. Confirmation: The price breaks below the support level, confirming the bearish signal.
  4. Trade: Enter a short position (selling) in propane futures contracts.
  5. Stop-Loss: Place a stop-loss order slightly above the recent swing high or the broken support level (now resistance).
  6. Target: Set a profit target based on a previous support level or a reasonable risk/reward ratio (e.g., 2:1). Monitor the fundamentals and COT data to adjust your target as needed.

VII. Important Considerations for Retail Traders

  • Accessibility: Trading Argus Propane Saudi Aramco directly may not be accessible to all retail traders. You might need to use:
    • Propane Futures (NYMEX): If you can access these, be aware of the large contract size.
    • Options on Propane Futures: A more capital-efficient way to trade.
    • Related ETFs: Some ETFs track energy or natural gas prices and may be correlated to propane. However, the correlation might not be perfect. Research any ETF carefully.
    • CFDs (Contracts for Difference): Be very careful with CFDs due to their high leverage and potential for rapid losses.
  • Data Costs: Accessing reliable COT data and real-time price information may require a subscription fee.
  • Time Commitment: Analyzing COT data and monitoring the propane market requires time and effort.
  • Emotional Discipline: Stick to your trading plan and avoid making impulsive decisions based on fear or greed.

VIII. Disclaimer

This is for informational and educational purposes only and should not be construed as investment advice. Trading commodities involves significant risk of loss. You should carefully consider your investment objectives, risk tolerance, and experience level before trading. Consult with a qualified financial advisor before making any investment decisions. Past performance is not indicative of future results.

In Summary

This COT-based trading strategy for Argus Propane Saudi Aramco is designed to help retail traders and market investors understand and potentially profit from market sentiment. Remember to use the COT report in conjunction with price action analysis, fundamental analysis, and robust risk management techniques. Good luck, and trade responsibly!