COT Index & Positions by Novatrix CapitalThis indicator visualizes the positioning of the two main groups from the CFTC COT reports: Commercials and Retail (Non-Reportables / Small Traders). Each group is displayed in two ways:
Index (0–100) – normalized net positions to identify bullish or bearish extremes (standard cycle: 26 weeks, optionally 52 weeks).
Raw Net Positions – actual long minus short positions.
Color coding on the chart:
Commercial Index: Blue
Commercial Positions: Blue
Retail Index: Red
Retail Positions: Red
Additional features:
Reference lines for neutral, overbought, and oversold levels.
Helps traders analyze market sentiment and the positioning of major participant groups.
Important notice:
Since COT data is published only once per week and the COT Index is built on cyclical multi-week analysis, the indicator is intended to be used exclusively on the weekly timeframe.
The selected cycle length (typically 26 weeks, optionally 52 weeks) determines how net positions are compared and normalized, and can influence how quickly extreme zones appear in the index lines.
Báo cáo số lượng giao dịch của các nhà đầu tư (Commitment of Traders - COT)
DXY Volatility Ranges TableThe Dollar Index (DXY) measures the US dollar's value against a basket of six major currencies, including the Euro, Japanese Yen, British Pound, Canadian Dollar, Swedish Krona, and Swiss Franc. Here are some key ranges for the DXY:
- Historical Highs and Lows:
- All-time high: 164.720 in February 1985
- All-time low: 70.698 on March 16, 2008
- Recent Trends:
- Current value: around 99.603 (as of December 5, 2025)
- 52-week high: 129.670 (November 8, 1985)
- 52-week low: 94.650 (projected target by some analysts)
- Volatility Ranges:
- Low volatility: DXY < 95
- Moderate volatility: DXY between 95-105
- High volatility: DXY > 105
- Support and Resistance Levels:
- Support: around 94.650 and 90.00
- Resistance: around 100.15/35 and 105.00
FX COT (TT314)Part of FX Dashboard, based on @lord_fed document:
www.lordfed.co.uk
CFTC Commitment of Traders - large speculators view by default.
Smart Money COTThis indicator implements the method of analysing COT data as defined by Michael Huddleston (I.E. The Inner Circle Trader). It removes all superfluous information contained in the standard COT reports and focusses only on Commercial speculators using the overall Long-Short positions.
Features
The unique feature of this indicator is its ability to look back over time and provide the following information:
Calculation of the range high and low of the specified lookback range.
Calculation of equilibrium of that range.
Automatic colour coding of net long and net short positions when the Long-Short COT calculation is above or below equilibrium of the lookback range.
Instructions
Use the Daily Timeframe only. You may get unexpected results on other timeframes.
Ensure the asset has COT data available. Script is mainly focused on commodity futures, such as ES, NQ, YM. It has not been tested against Forex.
You will need to define the "Lookback" setting in the script settings. Use the total number of trading days required for your analysis. E.g. if you want a 6 month COT analysis, use the measurement tool to count the quantity of daily candles between now and 6 months ago - use this as your Lookback setting. Adjust as needed for other lookback periods, e.g. 3 months, 12 months etc.
Other Info
The script provides the ability to customise colours in its settings.
Range High and Range Low plots can be disabled in settings.
Larry Williams COT Analysis Enhanced [tradeviZion]Larry Williams COT Analysis Enhanced - Complete Description
📖 Introduction
Welcome to the Larry Williams COT Analysis Enhanced indicator. This comprehensive description explains every setting, feature, and capability of this advanced Commitments of Traders (COT) analysis tool.
This indicator implements Larry Williams' professional COT analysis methodology with enhanced features including statistical validation, combination analysis, and adaptive signal generation.
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🎯 Quick Start
Add the indicator to your chart
The script will automatically detect your symbol's CFTC code and asset type
Review the main COT analysis table (displayed by default)
Customize settings based on your trading style
Review the Trading Edge & Signals section for signal information
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⚙️ Settings Groups Overview
The indicator is organized into 9 logical groups of settings:
1. Core COT Settings - Data source and report configuration
2. Analysis Parameters - Calculation methods and lookback periods
3. Signal Generation - Buy/sell signals and trend weighting
4. Plot Display Settings - Visual customization of chart lines
5. Smoothing Settings - Data smoothing options
6. COT Proximity Index Settings - Price-based proxy indicator configuration
7. Common Table Settings - Shared table appearance
8. Main Table Display Settings - Main analysis table customization
9. Historical Comparison Settings - Historical data table configuration
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📋 Group 1: Core COT Settings
COT Report Type
Options: Legacy | Disaggregated | Financial
What it is: Selects the type of COT report data to analyze.
Legacy - Traditional COT report format. Recommended for most users. Uses "Commercial Positions" and "Noncommercial Positions" metrics. Shows Commercial, Non-Commercial, and Small Speculator positions in the classic format.
Commercials: "Commercial Positions"
Speculators: "Noncommercial Positions"
Small Specs: "Nonreportable Positions"
Disaggregated - Separates managed money from other speculators. Uses different metrics than Legacy format.
Commercials: "Producer Merchant Positions"
Speculators: "Managed Money Positions"
Small Specs: "Nonreportable Positions"
Important: When using Disaggregated report type, the table will still show "Non-Comm" as the label, but the data displayed is actually " Managed Money Positions " (hedge funds and CTAs). The underlying data changes based on your report type selection, even though the table label remains "Non-Comm" for consistency.
Where you'll see this data:
📊 Current Positions section - The "Non-Comm" row shows Managed Money long, short, and net positions
📊 Open Interest Analysis section - "Non-Comm" net changes reflect Managed Money position changes
📈 Analysis section - "Non-Comm" percentile and LW Index values are calculated from Managed Money positions
Chart plots - The blue "Non-Commercial" line shows Managed Money net positions
Useful when you want to analyze hedge funds (Managed Money) separately from other large speculators. The "Commercial" row will show " Producer Merchant Positions " instead of general "Commercial Positions".
Financial - Designed for financial instruments (currencies, bonds, stock indices). Uses financial-specific metrics.
Commercials: "Dealer Positions"
Speculators: "Leveraged Funds Positions"
Small Specs: "Nonreportable Positions"
Important: When using Financial report type, the table will still show "Commercial" and "Non-Comm" as labels, but the data displayed is actually " Dealer Positions " (commercials) and " Leveraged Funds Positions " (speculators). The underlying data changes based on your report type selection.
Where you'll see this data:
📊 Current Positions section - "Commercial" row shows Dealer long/short/net, "Non-Comm" row shows Leveraged Funds positions
📊 Open Interest Analysis section - Net changes reflect Dealer and Leveraged Funds position changes
📈 Analysis section - Percentile and LW Index values are calculated from Dealer and Leveraged Funds positions
Chart plots - Lines show Dealer and Leveraged Funds net positions
Use this for currency futures, bond futures, and stock index futures.
Trading Use: Most traders use Legacy as it provides the most comprehensive view and works with all asset types. Switch to Disaggregated if you want to analyze managed money positions separately. Use Financial specifically for financial instruments (currencies, bonds, stock indices).
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Include Options Data
Default: Off (false)
What it is: Toggles whether to include options positions in addition to futures positions.
Trading Use: Larry Williams observed no significant difference in COT analysis when including options data. Keep this disabled unless you specifically need options data. Most traders leave it off for cleaner analysis.
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Auto-detect CFTC Code
Default: On (true)
What it is: Automatically finds the correct CFTC code for your symbol.
Trading Use: Keep this enabled unless you need a specific CFTC code. The script automatically detects codes for:
- Currency futures: CME:6E1! , CME:6B1! , CME:6J1!
- Stock index futures: CME_MINI:ES1! , CBOT_MINI:YM1! , CME_MINI:NQ1!
- Commodities: NYMEX:CL1! , COMEX:GC1! , CBOT:ZC1!
- And many more
Only disable if you're analyzing a symbol that requires a specific CFTC code not in the auto-detection database.
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Manual CFTC Code
Default: Empty
What it is: Enter a specific CFTC code manually (e.g. for E-mini S&P 500). "13874+"
Trading Use: Only used when Auto-detect CFTC Code is disabled. Most users never need this setting.
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📊 Group 2: Analysis Parameters
Display Mode
Options: COT Report | COT Index | COT Proximity Index
What it is: Controls what data is displayed on the chart and in the table.
COT Report - Shows raw position data (Long, Short, Net positions) plus analysis. Best for detailed analysis. Displays Commercial, Non-Commercial, Small Speculator, and Open Interest lines.
COT Index - Shows index values based on your selected Analysis Method (Percentile or LW Index). Best for quick sentiment analysis. Displays index lines for Commercial, Non-Commercial, Small Speculator, and Open Interest. Percentile can exceed 0-100% for extremes, LW Index stays 0-100%.
Percentile can exceed 0-100% for extremes
LW Index stays 0-100%
COT Proximity Index - Shows a price-based proxy indicator. Useful when COT data is delayed or unavailable. Calculates sentiment based on price action patterns.
Trading Use:
- Use COT Report for comprehensive analysis
- Use COT Index when you want to focus on extreme sentiment levels
- Use COT Proximity Index as a backup when COT data is delayed or unavailable.
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Analysis Method
Options: Percentile | LW Index
What it is: Selects the calculation method for position rankings.
Percentile - Professional approach. Excludes current bar from range calculation. Can show extremes (>100% or <0%) when today's value breaks historical range. More sensitive to recent extremes.
LW Index - Original Larry Williams method. Includes current bar in range, always 0-100%. Traditional approach.
Trading Use:
Percentile - Better for catching new extremes and recent market shifts
LW Index - Better for traditional Larry Williams analysis
Most traders prefer Percentile for its ability to show when positions break historical ranges.
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Lookback Mode
Options: Auto | Manual
What it is: Controls how the historical lookback period is determined.
Auto - Automatically sets lookback period based on detected asset type
Manual - Choose your own lookback period
Trading Use: Use Auto unless you have a specific reason to customize. The script automatically sets optimal periods:
Currencies: 26 weeks
Metals: 13 weeks
Grains: 26 weeks
Stocks/Indices: 13 weeks
Bonds: 52 weeks
Energies: 13 weeks
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Manual Lookback Period
Options: 1 Month | 3 Months | 6 Months | 1 Year | 3 Years | Asset-specific presets | Manual
What it is: How far back to look for historical comparison. Only used when Lookback Mode is set to Manual .
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Manual Lookback Weeks
Default: 18 weeks | Range: 1-500
What it is: Exact number of weeks to look back. Only used when Manual Lookback Period is set to Manual .
Trading Use: Set a custom period if you want precise control. 18 weeks = approximately one quarter (3 months).
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🎯 Group 3: Signal Generation
Show Signal Arrows
Default: Off (false)
What it is: Displays buy/sell arrows on the chart when extreme positions are detected.
Trading Use: Enable to get visual alerts for signals. Signals use strict multi-factor conditions requiring:
- Commercial extreme positioning
- Speculator positioning alignment
- Open Interest confirmation
- Trend consistency
- And more...
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Show Background Colors
Default: Off (false)
What it is: Colors the chart background during extreme market conditions.
Trading Use: Enable for visual market state awareness:
- Strong signals = Darker background colors
- Moderate signals = Lighter background colors
- Green background = Bullish extreme
- Red background = Bearish extreme
Useful for quick visual assessment of market conditions.
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Use Price Trend Weighting
Default: On (true)
What it is: Weights signals based on price trend alignment.
How it works:
Uptrend + Commercials long = Stronger bullish signal
Downtrend + Commercials short = Stronger bearish signal
Counter-trend signals = Harder to trigger (more conservative)
Trading Use: Keep enabled for more reliable signals. Commercials aligned with price trend are historically more accurate.
This feature makes signals easier to trigger when commercials align with the trend and harder when they're counter-trend.
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Trend MA Period
Default: 40 | Range: 1-200
What it is: Moving average period for price trend detection.
How it works:
Price above MA with the MA rising = Uptrend
Price below MA with the MA declining = Downtrend
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📈 Group 4: Plot Display Settings
Commercial Line Settings
Default Color: Red | Default Width: 2
What it is: Controls the Commercial traders net position line appearance.
Trading Use: Commercials are considered "smart money." Watch for:
Extreme long positions (high index ≥74%) = Heavy buyers = BULLISH signal
Extreme short positions (low index ≤26%) = Heavy sellers = BEARISH signal
Red is traditional for commercials. When Commercials are heavy buyers (high index), it's a bullish signal. When they're heavy sellers (low index), it's a bearish signal.
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Non-Commercial Line Settings
Default Color: Blue | Default Width: 2
What it is: Controls the Non-Commercial (Large Speculators) net position line appearance.
Trading Use: Large speculators are often trend-followers. Watch for:
Extreme long = Potential top (contrarian sell signal)
Extreme short = Potential bottom (contrarian buy signal)
They're often wrong at extremes - use as contrarian indicator.
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Small Speculator Line Settings
Default Color: Green | Default Width: 2
What it is: Controls the Small Speculators net position line appearance.
Trading Use: Small specs are typically wrong at extremes:
Extreme long = Potential top (sell signal)
Extreme short = Potential bottom (buy signal)
Exception: In Meats markets, small specs are accurate (like commercials).
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Small Speculator Multiplier
Default: 5.0x | Range: 0.1-20.0
What it is: Multiplies Small Speculator PLOTTED values for visual comparison.
Important: This only affects the visual plot line, NOT calculations or table values. Raw values used in all calculations remain unchanged.
Trading Use: Small spec positions are often much smaller than commercials. Use multiplier (default 5.0x) to scale the line for easier visual comparison.
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Open Interest Line Settings
Default Color: Black | Default Width: 1
What it is: Controls the Open Interest line appearance.
Trading Use: Open Interest shows market participation:
Rising OI = New money entering (confirms trend)
Falling OI = Money leaving (potential reversal)
Watch WHO is driving OI changes - This is critical
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Scale Open Interest
Default: On (true)
What it is: Scales Open Interest values to fit chart range.
Important: Only affects plotted lines, not table values. Scaling changes based on lookback period:
- Shorter lookback = More compressed range
- Longer lookback = Wider range
Trading Use: Keep enabled for better visual comparison. Disable if you want absolute OI values.
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Show Reference Lines
Default: Off (false)
What it is: Toggles the display of horizontal reference lines at 0%, 50%, and 100% levels on the chart.
What it shows:
Zero Line (0%) - Dotted gray line at 0% level
Midline (50%) - Solid gray line at 50% level
100 Line (100%) - Dotted gray line at 100% level
Trading Use: Enable when you want visual reference points for:
0% = Extreme bearish positioning
50% = Neutral/middle range
100% = Extreme bullish positioning
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🔄 Group 5: Smoothing Settings
Smoothing Method
Options: None | SMA | EMA | WMA | RMA
What it is: Selects the moving average type for smoothing data.
None - Use raw data (no smoothing)
SMA - Simple Moving Average (equal weight to all periods)
EMA - Exponential Moving Average (more weight to recent data)
WMA - Weighted Moving Average (linear weighting)
RMA - Relative Moving Average (Wilder's smoothing)
Trading Use:
None - Best for catching extremes quickly
SMA - Most common, balanced smoothing
EMA - More responsive to recent changes
WMA/RMA - Advanced smoothing methods
Smoothing reduces noise but may delay signal detection. Use None for most responsive signals.
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Smoothing Period
Default: 4 | Range: 2-20
What it is: Number of periods for the moving average smoothing.
Trading Use:
Shorter periods (2-5) = Less smoothing, more responsive
Longer periods (10-20) = More smoothing, less noise
Default 4 = Good balance
Only used when Smoothing Method is not None.
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Smooth COT Report Plots
Default: Off (false)
What it is: Applies smoothing to COT Report plotted lines (Commercial, Non-Commercial, Small Speculators, Open Interest).
Trading Use: Enable if you want smoother chart lines. Note: Smoothing affects visual display but calculations use raw data unless Smooth COT Index Plots is also enabled.
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Smooth COT Index Plots
Default: Off (false)
What it is: Applies smoothing to COT Index plotted lines.
Trading Use: Enable if you want smoother index lines. Important : When enabled, smoothed values are used in table displays and signal calculations. This affects the "user-facing" index values shown in the table and used for signals.
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📊 Group 6: COT Proximity Index Settings
Proximity Length Mode
Options: Auto | Manual
What it is: Controls how the proximity index calculation period is determined.
Auto - Calculates length based on ZigZag patterns (dynamic)
Manual - Uses fixed length setting
Trading Use: Use Auto for adaptive calculation. Use Manual if you want consistent period regardless of market conditions.
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Manual Proximity Length
Default: 8 bars | Range: 1+
What it is: Fixed number of bars for COT Proximity Index calculation. Only used when Proximity Length Mode is Manual .
Trading Use: Set based on your timeframe. 8 bars works well for weekly chart.
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Heavy Buyers Level
Default: 74% | Range: 50-100
What it is: COT Index level above which commercials are considered heavy buyers (extreme long positioning).
Trading Use: This threshold is used for:
- Signal generation
- Market state calculation
- Entry level recommendations
Default 74% means commercials are "heavy buyers" when LW Index ≥ 74%.
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Heavy Sellers Level
Default: 26% | Range: 0-50
What it is: COT Index level below which commercials are considered heavy sellers (extreme short positioning).
Trading Use: This threshold is used for:
- Signal generation
- Market state calculation
- Entry level recommendations
Default 26% means commercials are "heavy sellers" when LW Index ≤ 26%.
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ZigZag Deviation
Default: 1.0% | Range: 1-100.0
What it is: Minimum price change (%) required to create a new ZigZag pivot point.
Trading Use:
Smaller values = More sensitive, more pivots
Larger values = Less sensitive, fewer pivots
Used for Auto proximity length calculation.
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ZigZag Depth
Default: 1 | Range: 1+
What it is: Minimum number of bars between pivot points.
Trading Use: Higher values filter out minor pivots. Default 1 captures all significant pivots.
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Extend ZigZag to Last Bar
Default: Off (false)
What it is: Draws ZigZag lines to the current bar (may show incomplete patterns).
Trading Use: Enable to see current ZigZag pattern, but be aware it may change as new bars form.
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Show ZigZag Lines
Default: Off (false)
What it is: Displays ZigZag pivot lines on the chart for visual reference.
Trading Use: Enable to see the ZigZag pattern used for proximity index calculation. Useful for understanding how Auto mode works.
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🎨 Group 7: Common Table Settings
Color Theme
Options: Dark | Light | Midnight Blue | Ocean Blue | Forest Green | Amber Gold | Slate Gray
What it is: Color scheme for both main and historical comparison tables.
Trading Use: Choose based on your preference:
Dark/Light - Classic themes
Midnight Blue - Professional dark theme
Ocean Blue - Calming blue tones
Forest Green - Natural green theme
Amber Gold - Warm gold tones
Slate Gray - Modern gray theme
Theme applies to both tables simultaneously for consistency.
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📋 Group 8: Main Table Display Settings
Show COT Table
Default: On (true)
What it is: Toggles the main COT analysis table display.
Trading Use: Disable only if you want to use chart plots only. Most traders keep this enabled for comprehensive analysis.
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Table Mode
Options: Full | Compact
What it is: Controls the detail level of the main table.
Full - Complete analysis table with all sections
Compact - Essential info only (mobile-friendly)
Trading Use:
Full - Desktop trading, comprehensive analysis
Compact - Mobile trading, quick reference
See "Table Modes Explained" section below for details.
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Table Position
Options: Top Right | Top Left | Bottom Right | Bottom Left | Middle Right | Middle Left
What it is: Position of the main COT analysis table on the chart.
Trading Use: Choose based on your chart layout and preference. Top Right is default and works well for most traders.
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Table Text Size
Options: Tiny | Small | Normal | Large
What it is: Size of text in the COT analysis table.
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Section Visibility Controls
All default: On (true)
What it is: Individual toggles to show/hide specific table sections.
⚙️ Settings - Report Type, CFTC Code, Options setting
📊 Current Positions - Long, Short, Net positions for each group
📈 Analysis - LW Index, Percentile, Market State
🎯 Trading Edge & Signals - Current Signal, Entry Level, Best Setup
💡 Trading Tips - Context-aware trading insights
📈 Trend Analysis - Trend Direction, Strength, Cum Change, ROC, vs MA
🔄 Market Maker Activity - Spreading, Activity Level, Trading Edge
Trading Use: Customize your table to show only what you need:
Quick traders - Show only Trading Edge & Signals
Detailed analysis - Show all sections
Mobile users - Hide less critical sections
Each section can be toggled independently for maximum customization.
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📊 Group 9: Historical Comparison Settings
Show Historical Comparisons
Default: On (true)
What it is: Toggles the historical comparison table display.
Trading Use: This table shows how current positions rank over different time periods (1M, 3M, 6M, 1Y, 3Y, All Time). Very useful for context.
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Historical Table Mode
Options: Full | Compact
What it is: Controls the detail level of the historical comparison table.
Full - Complete historical comparison with all time periods (1M, 3M, 6M, 1Y, 3Y, All Time) and all COT groups
Compact - Essential periods only (1M, 3M, 6M, 1Y, All Time) showing Commercial % only
Trading Use:
- Full - Comprehensive historical analysis
- Compact - Quick reference, mobile-friendly
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Table Position (Historical)
Options: Top Right | Top Left | Bottom Right | Bottom Left
What it is: Position of the historical comparison table on the chart.
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Table Text Size (Historical)
Options: Tiny | Small | Normal | Large
What it is: Size of text in the historical comparison table.
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Trading Days
Options: Weekdays | 24/7
What it is: How to calculate time periods for historical comparisons.
Weekdays - Calculate based on trading days only (5 days/week)
24/7 - Include all calendar days (7 days/week), Use for 24/7 markets like cryptocurrencies
Used for both main COT data and COT Proximity Index historical comparisons.
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📊 Table Modes Explained
Full Mode - Main Table
The Full mode displays all available sections:
⚙️ Settings - Report type, CFTC code, options setting
📊 Current Positions - Long, Short, Net for Commercial, Non-Commercial, Small Speculators
📊 Open Interest Analysis - OI value, change, who's driving changes, concentration
📈 Analysis - Percentile ranks, LW Index values, Market State
🎯 Trading Edge & Signals - Current Signal, Entry Level, What to Watch, Best Setup
💡 Trading Tips - Context-aware insights
📈 Trend Analysis - Trend Direction, Strength, Consistency, Cumulative Change, ROC %, vs MA
🔄 Market Maker Activity - Spreading %, Activity Level, Interpretation, Trading Edge
Best for: Desktop trading, comprehensive analysis, detailed market assessment
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📋 Understanding Each Table Section
This section explains what each part of the main table means and how to use it for trading decisions.
⚙️ Settings Section
Report Type - Shows which COT report format you're using (Legacy, Disaggregated, or Financial). Verify this matches your asset type.
Options - Indicates if options data is included ("Included") or excluded ("Excluded"). Most traders exclude options for cleaner analysis.
CFTC Code - Unique identifier for your futures contract. Shows "Auto" when automatically detected, or displays the manual code if set.
Trading Use: Always verify your CFTC code is correct. Wrong code = wrong data = wrong signals.
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📊 Current Positions Section
Shows the actual position sizes for each trader group.
What Each Column Means:
Long - Total long contracts held by this group
Short - Total short contracts held by this group
Net - Net position (Long - Short). This is the key number.
How to Interpret:
Commercial Net Position:
- Negative (Net Short) = Commercials expect prices to fall
- Positive (Net Long) = Commercials expect prices to rise
- Commercials are "smart money" - their positioning often precedes major moves
Non-Commercial Net Position:
- Positive (Net Long) = Large speculators bullish
- Negative (Net Short) = Large speculators bearish
- Often trend-followers, can be caught at extremes
Small Spec Net Position:
- Positive (Net Long) = Small traders bullish
- Negative (Net Short) = Small traders bearish
- Often contrarian indicator - wrong at extremes
Trading Edge: Watch for extremes in Commercial net positions. When Commercials are heavy buyers (high index ≥74%), it's a bullish signal. When they're heavy sellers (low index ≤26%), it's a bearish signal.
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📊 Open Interest Analysis Section
Open Interest - Total number of outstanding contracts. Shows market participation level.
Change - Week-over-week change in Open Interest. Rising OI = new money entering, Falling OI = money leaving.
Net Changes - Shows which group is driving Open Interest changes. This is Larry Williams' most important insight.
🎯 Critical Question: Who is Driving OI Changes?
EXTREMELY BULLISH SIGNAL (Very Rare - Pay Close Attention):
- Commercials driving OI increase + Commercials raising positions + Uptrend market
- Meaning: Smart money (commercials) accumulating long positions while market is rising
- Action: Extremely bullish - very rare setup, pay close attention to this signal
- This is the strongest bullish signal possible
BULLISH SIGNAL (Strong Buy):
- Commercials driving OI increase + Commercials net long
- Meaning: Smart money accumulating long positions
- Action: Strong bullish setup
BEARISH SIGNAL (Strong Sell - Market Topping):
- Commercials exiting + OI increasing due to Small Specs + Non-Commercials
- Meaning: Smart money leaving while speculative money entering
- Action: Market top forming - most likely scenario for bearish reversal
- This indicates speculative excess and potential market top
BEARISH SIGNAL (Speculative Excess):
- Small Specs + Non-Commercials driving OI increase + They are net long
- Meaning: Speculative excess, "dumb money" driving market
- Action: Bearish reversal likely
Trading Use:
- Rising OI = New money entering (confirms trend)
- Falling OI = Money leaving (potential reversal)
- Watch WHO is driving OI changes - This is critical
- When Commercials drive OI increases while raising positions in an uptrend = Extremely bullish and very rare - pay attention
- When Commercials exit while OI increases due to Small Specs and Non-Commercials = Market topping signal
Concentration - Shows how much of the market is controlled by the largest traders:
- Top 4 - Four largest traders' share of total OI
- Top 8 - Eight largest traders' share of total OI
Trading Use: High concentration (>30%) means fewer dominant players, potential for volatility. Low concentration means more distributed positions, healthier market.
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📈 Analysis Section
Proximity Index (when in COT Proximity Index mode):
- Value: Current proximity index reading (0-100%)
- Length: Number of bars used in calculation
- Status: Heavy Buyers, Heavy Sellers, or Neutral
Analysis Method - Shows whether you're using Percentile or LW Index calculation.
Small Spec Mode - Shows how Small Speculators are interpreted:
- Contrarian (Traditional) - Small specs are wrong at extremes (default)
- Accurate (Meats) - Small specs are accurate like commercials (for Meats markets)
Market State - Overall market sentiment assessment:
- STRONG BULLISH - Multiple factors aligned bullish, strong buy signal
- MODERATE BULLISH - Several bullish factors, moderate buy signal
- LEANING BULLISH - Slight bullish bias, watch for confirmation
- NEUTRAL - Mixed signals, trade with existing trend
- LEANING BEARISH - Slight bearish bias, watch for confirmation
- MODERATE BEARISH - Several bearish factors, moderate sell signal
- STRONG BEARISH - Multiple factors aligned bearish, strong sell signal
Trading Use: Start your analysis here. Market State gives you the overall picture before diving into details.
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🎯 Trading Edge & Signals Section
Current Signal - Shows which combination is active based on current positioning extremes and its expected accuracy percentage:
- Comm+Spec+OI - All three groups at extremes (highest accuracy)
- Comm+Spec - Commercials and specs at extremes (opposite extremes - Larry Williams' favorite)
- Comm+OI - Commercials and Open Interest at extremes (smart money + participation)
- Commercials - Only Commercials at extreme (smart money indicator)
- Wait - No extremes detected, wait for setup
Entry - Trading signal based on Commercial positioning:
- LONG - Commercials are heavy buyers (≥Heavy Buyers Level), bullish signal
- SHORT - Commercials are heavy sellers (≤Heavy Sellers Level), bearish signal
- Wait - Commercials neutral, no clear signal
Best Setup - Shows the historically highest accuracy combination found in the data:
- Comm+Spec+SmallSpec+OI - All four groups aligned (strongest signal)
- Comm+Spec+OI (All) - Commercials + Speculators + Open Interest aligned
- Comm+Spec+SmallSpec - Commercials + Speculators + Small Specs aligned
- Comm+Spec (Both) - Commercials + Speculators (opposite extremes - Larry Williams' favorite)
- Comm+OI (Both) - Commercials + Open Interest (participation confirms smart money)
- Comm+SmallSpec - Commercials + Small Specs (especially strong in Meats markets)
- Commercials Alone - Commercial positioning only (baseline - smart money indicator)
Trading Use: This is your action center . Focus on Entry signals when Market State confirms. Higher accuracy setups (shown in Best Setup) are more reliable.
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💡 Trading Tips Section
Context-aware insights based on current market conditions.
What You'll See:
Commercial positioning assessment (extreme long/short, favorable/unfavorable)
Speculator positioning (contrarian support or warning)
Open Interest guidance (who's driving changes)
Trend assessment (aligning or conflicting)
Information about entry timing, position sizing, and confirmation needs
Trading Use: Review these tips when analyzing. They provide context-specific information tailored to current conditions.
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📈 Trend Analysis Section
Trend Direction - Overall price trend:
- Bullish - Price trending up
- Bearish - Price trending down
- Mixed - No clear direction
Consistency - How stable the trend is:
- Consistent - Trend is stable and maintaining direction
- Mixed - Trend is unstable, direction changing
- Accelerating - Trend is gaining momentum
Strength - Trend intensity:
- Strong - Powerful trend
- Steady - Moderate trend
- Weak - Weak trend
This Week - Net position change this week (percentage).
Cumulative Change - Total net position change over different periods:
- 4W - 4-week cumulative change
- 13W - 13-week cumulative change (one quarter)
- 26W - 26-week cumulative change (half year)
ROC % - Rate of Change percentage over different periods. Shows momentum.
vs MA - Current net position compared to moving average:
- Positive = Above average (strong positioning)
- Negative = Below average (weak positioning)
Trading Use: Align COT signals with trend direction for higher accuracy. When COT signals align with price trend, signals are more reliable. Counter-trend signals require more confirmation.
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🔄 Market Maker Activity Section
Total Spreading - Percentage of open interest in spread positions (simultaneous long and short in different months).
Percentile - Where current spreading level ranks historically. High percentile = unusual spreading activity.
13W Trend - 13-week trend in spreading activity (+ = increasing, - = decreasing).
Activity Level - Market maker activity intensity:
- High - Very active, expect volatility
- Moderate - Normal activity
- Low - Quiet, less volatility expected
vs 13W Avg - Current activity compared to 13-week average.
Trading Edge - Interpretation of market maker activity:
- High & Rising - Expect volatility, market makers hedging risk
- High & Stable - Active hedging, monitor for changes
- Low & Falling - Reduced activity, potential for directional moves
Trading Use: High market maker activity often precedes volatility. Use this to adjust position sizing and risk management. When spreading is high and rising, expect choppy conditions.
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📋 Understanding Compact Mode Fields
The Compact mode provides essential information for quick trading decisions. Here's what each field means:
State
Shows the overall market sentiment based on combined COT analysis.
Possible Values:
- STRONG BULLISH - Multiple factors aligned bullish, strong buy signal
- MODERATE BULLISH - Several bullish factors, moderate buy signal
- LEANING BULLISH - Slight bullish bias, watch for confirmation
- NEUTRAL - Mixed signals, trade with existing trend
- LEANING BEARISH - Slight bearish bias, watch for confirmation
- MODERATE BEARISH - Several bearish factors, moderate sell signal
- STRONG BEARISH - Multiple factors aligned bearish, strong sell signal
Trading Use: Start your analysis here. Strong signals (STRONG BULLISH/BEARISH) indicate higher confidence setups. Neutral means trade with price trend.
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Entry
Your actionable trading signal based on Commercial positioning.
Possible Values:
- LONG - Commercials are heavy buyers (≥Heavy Buyers Level), bullish signal
- SHORT - Commercials are heavy sellers (≤Heavy Sellers Level), bearish signal
- Wait - Commercials neutral, no clear signal
Trading Use: This is your go/no-go decision point. Only take trades when Entry shows LONG or SHORT. When Entry = Wait, stay on sidelines until clearer signal develops.
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Comm Index
Commercial LW Index percentage showing where Commercial net position ranks historically.
Range: 0% to 100%
- 0-26% = Commercials heavy sellers (bearish positioning)
- 27-73% = Commercials neutral (no extreme)
- 74-100% = Commercials heavy buyers (bullish positioning)
Trading Use: Commercial extremes are most reliable. Values ≥74% (heavy buyers/extreme long) = BULLISH signal. Values ≤26% (heavy sellers/extreme short) = BEARISH signal. When Commercials are heavy buyers, it indicates bullish sentiment. When they're heavy sellers, it indicates bearish sentiment.
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OI Status
Open Interest condition showing market participation level and trend.
Format: Status (Percentile %)
Examples:
- High (100.0%) - OI at extreme high, strong participation
- Moderate (50.0%) - OI at average level
- Low (10.0%) - OI at extreme low, weak participation
Trend Indicators:
- Rising - OI increasing (new money entering)
- Falling - OI decreasing (money leaving)
- Stable - OI unchanged
Trading Use: High OI with rising trend = strong market participation, confirms directional moves. Falling OI = watch for potential reversals. Low OI = reduced participation, potential for volatility.
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Best Setup
Shows which combination of factors has the highest historical accuracy.
Format: Combination Name (Accuracy %)
Examples:
- Commercials Alone (75.3%) - Commercial positioning only
- Commercials + Speculators (68.2%) - Commercials and specs aligned
- Commercials + Open Interest (72.1%) - Commercials with OI confirmation
- Commercials + Speculators + OI (82.1%) - All factors aligned (strongest)
Trading Use: Higher accuracy values indicate signals with higher historical accuracy. When Best Setup shows "Commercials + Speculators + OI" with high accuracy, it indicates a combination with strong historical performance.
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Trend
13-week cumulative trend direction based on net position changes.
Possible Values:
- Bullish - Net positions trending bullish over 13 weeks
- Bearish - Net positions trending bearish over 13 weeks
- Mixed - No clear directional trend
Trading Use: Align Entry signals with Trend for higher accuracy. When Entry = LONG and Trend = Bullish, signal is stronger. When Entry = LONG but Trend = Bearish, wait for price confirmation before entering. Counter-trend signals require more confirmation.
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Full Mode - Historical Table
The Full historical mode shows:
All time periods: 1 Month, 3 Months, 6 Months, 1 Year, 3 Years, All Time
All COT groups: Commercial, Non-Commercial, Small Speculators, Open Interest
Complete header with asset type and lookback information
Best for: Comprehensive historical analysis, understanding long-term positioning
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Compact Mode - Historical Table
The Compact historical mode shows:
Essential periods only: 1M, 3M, 6M, 1Y, All Time
Commercial % only (most important indicator)
Simplified header
Best for: Quick reference, mobile-friendly, focused analysis
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🎯 How to Use Each Feature for Trading
Using Display Modes
COT Report Mode - Use for:
Understanding raw position sizes
Analyzing net position changes
Comparing absolute positions across groups
Detailed market structure analysis
COT Index Mode - Use for:
Quick sentiment assessment
Identifying extremes (Percentile can show >100% or <0%, LW Index shows 0-100%)
Comparing relative positioning
Signal generation
COT Proximity Index Mode - Use for:
When COT data is delayed
Real-time sentiment estimation
Price-action based analysis
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Using Analysis Methods
Percentile Method - Use when:
You want to catch new extremes (>100% or <0%)
You need responsive signals
You're analyzing recent market regime changes
You want to use the professional approach (excludes current bar from range)
LW Index Method - Use when:
You want traditional Larry Williams analysis
You prefer stable, conservative signals
You're doing long-term analysis
You want always 0-100% range
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Using Signal Generation
Enable Signal Arrows when:
You want visual alerts for high-quality setups
You're scanning multiple charts
You want to catch extreme positioning
Enable Background Colors when:
You want quick visual market state assessment
You're monitoring multiple timeframes
You want to see market conditions at a glance
Use Price Trend Weighting to:
Increase signal reliability
Align COT signals with price action
Filter counter-trend signals
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Using Smoothing
No Smoothing - Best for:
Catching extremes quickly
Responsive signal generation
Active trading
With Smoothing - Best for:
Reducing noise
Trend identification
Swing trading
Remember: Smoothing affects visual display. Enable "Smooth COT Index Plots" if you want smoothed values in calculations.
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Using Heavy Buyers/Sellers Levels
Default 74%/26% - Good starting point
Tighter levels (80%/20%) - More conservative, fewer signals
Wider levels (70%/30%) - More signals, less extreme
Trading Use: Adjust based on your risk tolerance and signal frequency preference.
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Using Table Sections
Settings - Verify your configuration
Current Positions - Understand current market structure
Analysis - Identify extremes and market state
Trading Edge & Signals - Most important - Entry signals based on Commercial positioning
Trading Tips - Context-aware insights
Trend Analysis - Understand momentum and direction
Market Maker Activity - Assess market maker positioning
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💡 Key Trading Concepts
Market State Interpretation
STRONG BULLISH - Multiple factors aligned bullish. Strong buy signal.
MODERATE BULLISH - Several bullish factors. Moderate buy signal.
LEANING BULLISH - Slight bullish bias. Watch for confirmation.
NEUTRAL - Mixed signals. Trade with existing trend.
LEANING BEARISH - Slight bearish bias. Watch for confirmation.
MODERATE BEARISH - Several bearish factors. Moderate sell signal.
STRONG BEARISH - Multiple factors aligned bearish. Strong sell signal.
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Entry Level Signals
LONG - Commercials are heavy buyers (≥Heavy Buyers Level). Bullish signal.
SHORT - Commercials are heavy sellers (≤Heavy Sellers Level). Bearish signal.
Wait - Commercials neutral. No clear signal.
When Commercials are heavy buyers (high index), it indicates bullish sentiment. When they're heavy sellers (low index), it indicates bearish sentiment.
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Best Setup Interpretation
The Best Setup shows the historically highest accuracy combination:
Commercials Alone - Commercial positioning is most reliable
Commercials + Speculators - Both groups aligned
Commercials + Open Interest - Commercials + OI confirmation
Commercials + Speculators + OI - All factors aligned (strongest)
Higher accuracy = More reliable signal. Use this to prioritize which signals to follow.
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Open Interest Analysis
Critical Question: Who is driving Open Interest changes?
EXTREMELY BULLISH (Very Rare):
Commercials driving OI increase + Commercials raising positions + Uptrend = EXTREMELY BULLISH
This is very rare - pay close attention when this occurs
STRONG BULLISH:
Commercials driving OI increase + Commercials long = STRONG BULLISH
BEARISH (Market Topping):
Commercials exiting + OI increasing due to Small Specs + Non-Commercials = BEARISH (market topping)
Most likely scenario for bearish reversal - speculative excess
BEARISH (Speculative Excess):
Speculators driving OI increase + Speculators long = BEARISH (speculative excess)
TREND CONFIRMATION:
Rising OI = Confirms trend (new money entering)
Falling OI = Potential reversal (money leaving)
This is one of Larry Williams' most important insights. When Commercials drive OI increases while raising positions in an uptrend, it's extremely bullish and very rare - pay attention. When Commercials exit while Small Specs and Non-Commercials drive OI increases, the market is likely topping.
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🚀 Practical Trading Workflow
Daily Analysis Routine
Check Market State - Overall assessment
Review Entry Level - Actionable signal
Check Best Setup - Signal reliability
Review Trading Tips - Context-aware insights
Analyze Trend Analysis - Momentum confirmation
Check Historical Comparison - Context over time
Verify Open Interest - Who's driving changes
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Signal Confirmation Checklist
Before taking a trade based on COT signals:
✓ Market State shows clear bias (not Neutral)
✓ Entry Level matches Market State
✓ Best Setup shows high accuracy (>60%)
✓ Price trend aligns with signal (if using trend weighting)
✓ Open Interest confirms (rising for trend continuation, falling for reversal)
✓ Historical comparison shows extreme positioning
✓ Price action confirms (wait for price confirmation)
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⚠️ Important Notes
COT data is weekly - Updates every Friday afternoon
Extremes can persist - Don't expect immediate reversals
Combine with price action - COT is one tool among many
Historical context matters - Consider market conditions
Meats markets are special - Small specs are accurate (like commercials)
Signals are rare - High-quality signals don't appear every week
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This description covers all settings and features of the Larry Williams COT Analysis Enhanced indicator. Larry Williams recommends combining COT analysis with other indicators for setup signals: Williams Sentiment Index, Williams Valuation Index, Williams True Seasonal, Pinch and Paunch Signal, along with price action, technical analysis, and fundamental factors.
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📖 Conclusion
The Larry Williams COT Analysis Enhanced indicator provides a sophisticated framework for understanding market sentiment through the lens of different participant groups. By combining mathematical analysis with behavioral insights, it displays COT positioning data, calculates index values, and generates signals based on extreme positioning.
Remember: This is a tool for analysis, not a crystal ball. Consider combining COT analysis with other Larry Williams indicators, price action, technical analysis, and fundamental factors.
Practice with the indicator, study historical signals, and develop your understanding of how different market participants behave. Signals with multiple factors aligned - Commercials at extremes, Open Interest changes driven by the right groups, and price action confirming the COT signals - have shown higher historical accuracy.
This description provides comprehensive documentation for the Larry Williams COT Analysis Enhanced indicator. For the most current data and analysis, always refer to the latest COT reports and market conditions.
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Acknowledgment
This tool builds upon the foundational work of Larry Williams, who developed the Commitments of Traders (COT) analysis methodology and the principles for interpreting COT data. It also incorporates enhancements including statistical validation, combination analysis, adaptive signal generation, and comprehensive historical comparison features.
Note: Always practice proper risk management and thoroughly test the indicator to ensure it aligns with your trading strategy. Past performance is not indicative of future results.
Cjack COT IndexHere's the updated description with the formula and additional context:
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**Cjack COT Index - Commitment of Traders Positioning Indicator**
This indicator transforms raw Commitment of Traders (COT) data into normalized 0-100 index values, making it easy to identify extreme positioning across different trader categories.
**How It Works:**
The indicator calculates a min-max normalized index for three trader groups over your chosen lookback period (default 26 weeks):
- **Large Speculators** (Non-commercial positions) - typically trend followers
- **Small Speculators** (Non-reportable positions) - retail traders
- **Commercial Hedgers** - producers and consumers hedging business risk
The normalization formula is: **Index = (Current Position - Minimum Position) / (Maximum Position - Minimum Position) × 100**
This calculation shows where current net positioning sits between the minimum and maximum levels observed in the lookback window. A reading of 100 means current positioning equals the maximum net long over that period, 0 equals the minimum (most net short), and 50 is the midpoint of the range.
**Important:** The lookback period critically affects index readings - shorter lookbacks (13-26 weeks) make the index more sensitive to recent extremes, while longer lookbacks (52-78 weeks) provide broader historical context and identify truly exceptional positioning. Min-max normalization is essential because it makes positioning comparable across different contracts and time periods, regardless of the absolute size of positions.
**What It's Good For:**
The indicator excels at identifying **crowded trades** and potential reversals by tracking contrarian setups where commercials (smart money) position opposite to speculators. Background highlighting automatically flags:
- **Long setups** (green): Commercials heavily long while speculators are heavily short
- **Short setups** (red): Commercials heavily short while speculators are heavily long
The "Shift Index" option (enabled by default) displays last week's tradeable COT data aligned with current price action, ensuring you're working with actionable information since COT reports publish with a delay.
Works on weekly timeframes and below for commodities and futures with available COT data.
COT IndexTHE HIDDEN INTELLIGENCE IN FUTURES MARKETS
What if you could see what the smartest players in the futures markets are doing before the crowd catches on? While retail traders chase momentum indicators and moving averages, obsess over Japanese candlestick patterns, and debate whether the RSI should be set to fourteen or twenty-one periods, institutional players leave footprints in the sand through their mandatory reporting to the Commodity Futures Trading Commission. These footprints, published weekly in the Commitment of Traders reports, have been hiding in plain sight for decades, available to anyone with an internet connection, yet remarkably few traders understand how to interpret them correctly. The COT Index indicator transforms this raw institutional positioning data into actionable trading signals, bringing Wall Street intelligence to your trading screen without requiring expensive Bloomberg terminals or insider connections.
The uncomfortable truth is this: Most retail traders operate in a binary world. Long or short. Buy or sell. They apply technical analysis to individual positions, constrained by limited capital that forces them to concentrate risk in single directional bets. Meanwhile, institutional traders operate in an entirely different dimension. They manage portfolios dynamically weighted across multiple markets, adjusting exposure based on evolving market conditions, correlation shifts, and risk assessments that retail traders never see. A hedge fund might be simultaneously long gold, short oil, neutral on copper, and overweight agricultural commodities, with position sizes calibrated to volatility and portfolio Greeks. When they increase gold exposure from five percent to eight percent of portfolio allocation, this rebalancing decision reflects sophisticated analysis of opportunity cost, risk parity, and cross-market dynamics that no individual chart pattern can capture.
This portfolio reweighting activity, multiplied across hundreds of institutional participants, manifests in the aggregate positioning data published weekly by the CFTC. The Commitment of Traders report does not show individual trades or strategies. It shows the collective footprint of how actual commercial hedgers and large speculators have allocated their capital across different markets. When mining companies collectively increase forward gold sales to hedge thirty percent more production than last quarter, they are not reacting to a moving average crossover. They are making strategic allocation decisions based on production forecasts, cost structures, and price expectations derived from operational realities invisible to outside observers. This is portfolio management in action, revealed through positioning data rather than price charts.
If you want to understand how institutional capital actually flows, how sophisticated traders genuinely position themselves across market cycles, the COT report provides a rare window into that hidden world. But understand what you are getting into. This is not a tool for scalpers seeking confirmation of the next five-minute move. This is not an oscillator that flashes oversold at market bottoms with convenient precision. COT analysis operates on a timescale measured in weeks and months, revealing positioning shifts that precede major market turns but offer no precision timing. The data arrives three days stale, published only once per week, capturing strategic positioning rather than tactical entries.
If you need instant gratification, if you trade intraday moves, if you demand mechanical signals with ninety percent accuracy, close this document now. COT analysis rewards patience, position sizing discipline, and tolerance for being early. It punishes impatience, overleveraging, and the expectation that any single indicator can substitute for market understanding.
The premise is deceptively simple. Every Tuesday, large traders in futures markets must report their positions to the CFTC. By Friday afternoon, this data becomes public. Academic research spanning three decades has consistently shown that not all market participants are created equal. Some traders consistently profit while others consistently lose. Some anticipate major turning points while others chase trends into exhaustion. Bessembinder and Chan (1992) demonstrated in their seminal study that commercial hedgers, those with actual exposure to the underlying commodity or financial instrument, possess superior forecasting ability compared to speculators. Their research, published in the Journal of Finance, found statistically significant predictive power in commercial positioning, particularly at extreme levels. This finding challenged the efficient market hypothesis and opened the door to a new approach to market analysis based on positioning rather than price alone.
Think about what this means. Every week, the government publishes a report showing you exactly how the most informed market participants are positioned. Not their opinions. Not their predictions. Their actual money at risk. When agricultural producers collectively hold their largest short hedge in five years, they are not making idle speculation. They are locking in prices for crops they will harvest, informed by private knowledge of weather conditions, soil quality, inventory levels, and demand expectations invisible to outside observers. When energy companies aggressively hedge forward production at current prices, they reveal information about expected supply that no analyst report can capture. This is not technical analysis based on past prices. This is not fundamental analysis based on publicly available data. This is behavioral analysis based on how the smartest money is actually positioned, how institutions allocate capital across portfolios, and how those allocation decisions shift as market conditions evolve.
WHY SOME TRADERS KNOW MORE THAN OTHERS
Building on this foundation, Sanders, Boris and Manfredo (2004) conducted extensive research examining the behaviour patterns of different trader categories. Their work, which analyzed over a decade of COT data across multiple commodity markets, revealed a fascinating dynamic that challenges much of what retail traders are taught. Commercial hedgers consistently positioned themselves against market extremes, buying when speculators were most bearish and selling when speculators reached peak bullishness. The contrarian positioning of commercials was not random noise but rather reflected their superior information about supply and demand fundamentals. Meanwhile, large speculators, primarily hedge funds and commodity trading advisors, exhibited strong trend-following behaviour that often amplified market moves beyond fundamental values. Small traders, the retail participants, consistently entered positions late in trends, frequently near turning points, making them reliable contrary indicators.
Wang (2003) extended this research by demonstrating that the predictive power of commercial positioning varies significantly across different commodity sectors. His analysis of agricultural commodities showed particularly strong forecasting ability, with commercial net positions explaining up to fifteen percent of return variance in subsequent weeks. This finding suggests that the informational advantages of hedgers are most pronounced in markets where physical supply and demand fundamentals dominate, as opposed to purely financial markets where information asymmetries are smaller. When a corn farmer hedges six months of expected harvest, that decision incorporates private observations about rainfall patterns, crop health, pest pressure, and local storage capacity that no distant analyst can match. When an oil refinery hedges crude oil purchases and gasoline sales simultaneously, the spread relationships reveal expectations about refining margins that reflect operational realities invisible in public data.
The theoretical mechanism underlying these empirical patterns relates to information asymmetry and different participant motivations. Commercial hedgers engage in futures markets not for speculative profit but to manage business risks. An agricultural producer selling forward six months of expected harvest is not making a bet on price direction but rather locking in revenue to facilitate financial planning and ensure business viability. However, this hedging activity necessarily incorporates private information about expected supply, inventory levels, weather conditions, and demand trends that the hedger observes through their commercial operations (Irwin and Sanders, 2012). When aggregated across many participants, this private information manifests in collective positioning.
Consider a gold mining company deciding how much forward production to hedge. Management must estimate ore grades, recovery rates, production costs, equipment reliability, labor availability, and dozens of other operational variables that determine whether locking in prices at current levels makes business sense. If the industry collectively hedges more aggressively than usual, it suggests either exceptional production expectations or concern about sustaining current price levels or combination of both. Either way, this positioning reveals information unavailable to speculators analyzing price charts and economic data. The hedger sees the physical reality behind the financial abstraction.
Large speculators operate under entirely different incentives and constraints. Commodity Trading Advisors managing billions in assets typically employ systematic, trend-following strategies that respond to price momentum rather than fundamental supply and demand. When crude oil rallies from sixty dollars to seventy dollars per barrel, these systems generate buy signals. As the rally continues to eighty dollars, position sizes increase. The strategy works brilliantly during sustained trends but becomes a liability at reversals. By the time oil reaches ninety dollars, trend-following funds are maximally long, having accumulated positions progressively throughout the rally. At this point, they represent not smart money anticipating further gains but rather crowded money vulnerable to reversal. Sanders, Boris and Manfredo (2004) documented this pattern across multiple energy markets, showing that extreme speculator positioning typically marked late-stage trend exhaustion rather than early-stage trend development.
Small traders, the retail participants who fall below reporting thresholds, display the weakest forecasting ability. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns, meaning their aggregate positioning served as a reliable contrary indicator. The explanation combines several factors. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, entering trends after mainstream media coverage when institutional participants are preparing to exit. Perhaps most importantly, they trade with emotion, buying into euphoria and selling into panic at precisely the wrong times.
At major turning points, the three groups often position opposite each other with commercials extremely bearish, large speculators extremely bullish, and small traders piling into longs at the last moment. These high-divergence environments frequently precede increased volatility and trend reversals. The insiders with business exposure quietly exit as the momentum traders hit maximum capacity and retail enthusiasm peaks. Within weeks, the reversal begins, and positions unwind in the opposite sequence.
FROM RAW DATA TO ACTIONABLE SIGNALS
The COT Index indicator operationalizes these academic findings into a practical trading tool accessible through TradingView. At its core, the indicator normalizes net positioning data onto a zero to one hundred scale, creating what we call the COT Index. This normalization is critical because absolute position sizes vary dramatically across different futures contracts and over time. A commercial trader holding fifty thousand contracts net long in crude oil might be extremely bullish by historical standards, or it might be quite neutral depending on the context of total market size and historical ranges. Raw position numbers mean nothing without context. The COT Index solves this problem by calculating where current positioning stands relative to its range over a specified lookback period, typically two hundred fifty-two weeks or approximately five years of weekly data.
The mathematical transformation follows the methodology originally popularized by legendary trader Larry Williams, though the underlying concept appears in statistical normalization techniques across many fields. For any given trader category, we calculate the highest and lowest net position values over the lookback period, establishing the historical range for that specific market and trader group. Current positioning is then expressed as a percentage of this range, where zero represents the most bearish positioning ever seen in the lookback window and one hundred represents the most bullish extreme. A reading of fifty indicates positioning exactly in the middle of the historical range, suggesting neither extreme optimism nor pessimism relative to recent history (Williams and Noseworthy, 2009).
This index-based approach allows for meaningful comparison across different markets and time periods, overcoming the scaling problems inherent in analyzing raw position data. A commercial index reading of eighty-five in gold carries the same interpretive meaning as an eighty-five reading in wheat or crude oil, even though the absolute position sizes differ by orders of magnitude. This standardization enables systematic analysis across entire futures portfolios rather than requiring market-specific expertise for each contract.
The lookback period selection involves a fundamental tradeoff between responsiveness and stability. Shorter lookback periods, perhaps one hundred twenty-six weeks or approximately two and a half years, make the index more sensitive to recent positioning changes. However, it also increases noise and produces more false signals. Longer lookback periods, perhaps five hundred weeks or approximately ten years, create smoother readings that filter short-term noise but become slower to recognize regime changes. The indicator settings allow users to adjust this parameter based on their trading timeframe, risk tolerance, and market characteristics.
UNDERSTANDING CFTC DATA STRUCTURES
The indicator supports both Legacy and Disaggregated COT report formats, reflecting the evolution of CFTC reporting standards over decades of market development. Legacy reports categorize market participants into three broad groups: commercial traders (hedgers with underlying business exposure), non-commercial traders (large speculators seeking profit without commercial interest), and non-reportable traders (small speculators below reporting thresholds). Each category brings distinct motivations and information advantages to the market (CFTC, 2020).
The Disaggregated reports, introduced in September 2009 for physical commodity markets, provide finer granularity by splitting participants into five categories (CFTC, 2009). Producer and merchant positions capture those actually producing, processing, or merchandising the physical commodity. Swap dealers represent financial intermediaries facilitating derivative transactions for clients. Managed money includes commodity trading advisors and hedge funds executing systematic or discretionary strategies. Other reportables encompasses diverse participants not fitting the main categories. Small traders remain as the fifth group, representing retail participation.
This enhanced categorization reveals nuances invisible in Legacy reports, particularly distinguishing between different types of institutional capital and their distinct behavioural patterns. The indicator automatically detects which report type is appropriate for each futures contract and adjusts the display accordingly.
Importantly, Disaggregated reports exist only for physical commodity futures. Agricultural commodities like corn, wheat, and soybeans have Disaggregated reports because clear producer, merchant, and swap dealer categories exist. Energy commodities like crude oil and natural gas similarly have well-defined commercial hedger categories. Metals including gold, silver, and copper also receive Disaggregated treatment (CFTC, 2009). However, financial futures such as equity index futures, Treasury bond futures, and currency futures remain available only in Legacy format. The CFTC has indicated no plans to extend Disaggregated reporting to financial futures due to different market structures and participant categories in these instruments (CFTC, 2020).
THE BEHAVIORAL FOUNDATION
Understanding which trader perspective to follow requires appreciation of their distinct trading styles, success rates, and psychological profiles. Commercial hedgers exhibit anticyclical behaviour rooted in their fundamental knowledge and business imperatives. When agricultural producers hedge forward sales during harvest season, they are not speculating on price direction but rather locking in revenue for crops they will harvest. Their business requires converting volatile commodity exposure into predictable cash flows to facilitate planning and ensure survival through difficult periods. Yet their aggregate positioning reveals valuable information because these hedging decisions incorporate private information about supply conditions, inventory levels, weather observations, and demand expectations that hedgers observe through their commercial operations (Bessembinder and Chan, 1992).
Consider a practical example from energy markets. Major oil companies continuously hedge portions of forward production based on price levels, operational costs, and financial planning needs. When crude oil trades at ninety dollars per barrel, they might aggressively hedge the next twelve months of production, locking in prices that provide comfortable profit margins above their extraction costs. This hedging appears as short positioning in COT reports. If oil rallies further to one hundred dollars, they hedge even more aggressively, viewing these prices as exceptional opportunities to secure revenue. Their short positioning grows increasingly extreme. To an outside observer watching only price charts, the rally suggests bullishness. But the commercial positioning reveals that the actual producers of oil find these prices attractive enough to lock in years of sales, suggesting skepticism about sustaining even higher levels. When the eventual reversal occurs and oil declines back to eighty dollars, the commercials who hedged at ninety and one hundred dollars profit while speculators who chased the rally suffer losses.
Large speculators or managed money traders operate under entirely different incentives and constraints. Their systematic, momentum-driven strategies mean they amplify existing trends rather than anticipate reversals. Trend-following systems, the most common approach among large speculators, by definition require confirmation of trend through price momentum before entering positions (Sanders, Boris and Manfredo, 2004). When crude oil rallies from sixty dollars to eighty dollars per barrel over several months, trend-following algorithms generate buy signals based on moving average crossovers, breakouts, and other momentum indicators. As the rally continues, position sizes increase according to the systematic rules.
However, this approach becomes a liability at turning points. By the time oil reaches ninety dollars after a sustained rally, trend-following funds are maximally long, having accumulated positions progressively throughout the move. At this point, their positioning does not predict continued strength. Rather, it often marks late-stage trend exhaustion. The psychological and mechanical explanation is straightforward. Trend followers by definition chase price momentum, entering positions after trends establish rather than anticipating them. Eventually, they become fully invested just as the trend nears completion, leaving no incremental buying power to sustain the rally. When the first signs of reversal appear, systematic stops trigger, creating a cascade of selling that accelerates the downturn.
Small traders consistently display the weakest track record across academic studies. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns in his analysis across multiple commodity markets. This result means that whatever small traders collectively do, the opposite typically proves profitable. The explanation for small trader underperformance combines several factors documented in behavioral finance literature. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, learning about commodity trends through mainstream media coverage that arrives after institutional participants have already positioned. Perhaps most importantly, retail traders are more susceptible to emotional decision-making, buying into euphoria and selling into panic at precisely the wrong times (Tharp, 2008).
SETTINGS, THRESHOLDS, AND SIGNAL GENERATION
The practical implementation of the COT Index requires understanding several key features and settings that users can adjust to match their trading style, timeframe, and risk tolerance. The lookback period determines the time window for calculating historical ranges. The default setting of two hundred fifty-two bars represents approximately one year on daily charts or five years on weekly charts, balancing responsiveness with stability. Conservative traders seeking only the most extreme, highest-probability signals might extend the lookback to five hundred bars or more. Aggressive traders seeking earlier entry and willing to accept more false positives might reduce it to one hundred twenty-six bars or even less for shorter-term applications.
The bullish and bearish thresholds define signal generation levels. Default settings of eighty and twenty respectively reflect academic research suggesting meaningful information content at these extremes. Readings above eighty indicate positioning in the top quintile of the historical range, representing genuine extremes rather than temporary fluctuations. Conversely, readings below twenty occupy the bottom quintile, indicating unusually bearish positioning (Briese, 2008).
However, traders must recognize that appropriate thresholds vary by market, trader category, and personal risk tolerance. Some futures markets exhibit wider positioning swings than others due to seasonal patterns, volatility characteristics, or participant behavior. Conservative traders seeking high-probability setups with fewer signals might raise thresholds to eighty-five and fifteen. Aggressive traders willing to accept more false positives for earlier entry could lower them to seventy-five and twenty-five.
The key is maintaining meaningful differentiation between bullish, neutral, and bearish zones. The default settings of eighty and twenty create a clear three-zone structure. Readings from zero to twenty represent bearish territory where the selected trader group holds unusually bearish positions. Readings from twenty to eighty represent neutral territory where positioning falls within normal historical ranges. Readings from eighty to one hundred represent bullish territory where the selected trader group holds unusually bullish positions.
The trading perspective selection determines which participant group the indicator follows, fundamentally shaping interpretation and signal meaning. For counter-trend traders seeking reversal opportunities, monitoring commercial positioning makes intuitive sense based on the academic research discussed earlier. When commercials reach extreme bearish readings below twenty, indicating unprecedented short positioning relative to recent history, they are effectively betting against the crowd. Given their informational advantages demonstrated by Bessembinder and Chan (1992), this contrarian stance often precedes major bottoms.
Trend followers might instead monitor large speculator positioning, but with inverted logic compared to commercials. When managed money reaches extreme bullish readings above eighty, the trend may be exhausting rather than accelerating. This seeming paradox reflects their late-cycle participation documented by Sanders, Boris and Manfredo (2004). Sophisticated traders thus use speculator extremes as fade signals, entering positions opposite to speculator consensus.
Small trader monitoring serves primarily as a contrary indicator for all trading styles. Extreme small trader bullishness above seventy-five or eighty typically warns of retail FOMO at market tops. Extreme small trader bearishness below twenty or twenty-five often marks capitulation bottoms where the last weak hands have sold.
VISUALIZATION AND USER INTERFACE
The visual design incorporates multiple elements working together to facilitate decision-making and maintain situational awareness during active trading. The primary COT Index line plots in bold with adjustable line width, defaulting to two pixels for clear visibility against busy price charts. An optional glow effect, controlled by a simple toggle, adds additional visual prominence through multiple plot layers with progressively increasing transparency and width.
A twenty-one period exponential moving average overlays the index line, providing trend context for positioning changes. When the index crosses above its moving average, it signals accelerating bullish sentiment among the selected trader group regardless of whether absolute positioning is extreme. Conversely, when the index crosses below its moving average, it signals deteriorating sentiment and potentially the beginning of a reversal in positioning trends.
The EMA provides a dynamic reference line for assessing positioning momentum. When the index trades far above its EMA, positioning is not only extreme in absolute terms but also building with momentum. When the index trades far below its EMA, positioning is contracting or reversing, which may indicate weakening conviction even if absolute levels remain elevated.
The data table positioned at the top right of the chart displays eleven metrics for each trader category, transforming the indicator from a simple index calculation into an analytical dashboard providing multidimensional market intelligence. Beyond the COT Index itself, users can monitor positioning extremity, which measures how unusual current levels are compared to historical norms using statistical techniques. The extremity metric clarifies whether a reading represents the ninety-fifth or ninety-ninth percentile, with values above two standard deviations indicating genuinely exceptional positioning.
Market power quantifies each group's influence on total open interest. This metric expresses each trader category's net position as a percentage of total market open interest. A commercial entity holding forty percent of total open interest commands significantly more influence than one holding five percent, making their positioning signals more meaningful.
Momentum and rate of change metrics reveal whether positions are building or contracting, providing early warning of potential regime shifts. Position velocity measures the rate of change in positioning changes, effectively a second derivative providing even earlier insight into inflection points.
Sentiment divergence highlights disagreements between commercial and speculative positioning. This metric calculates the absolute difference between normalized commercial and large speculator index values. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals.
The table also displays concentration metrics when available, showing how positioning is distributed among the largest handful of traders in each category. High concentration indicates a few dominant players controlling most of the positioning, while low concentration suggests broad-based participation across many traders.
THE ALERT SYSTEM AND MONITORING
The alert system, comprising five distinct alert conditions, enables systematic monitoring of dozens of futures markets without constant screen watching. The bullish and bearish COT signal alerts trigger when the index crosses user-defined thresholds, indicating the selected trader group has reached extreme positioning worthy of attention. These alerts fire in real-time as new weekly COT data publishes, typically Friday afternoon following the Tuesday measurement date.
Extreme positioning alerts fire at ninety and ten index levels, representing the top and bottom ten percent of the historical range, warning of particularly stretched readings that historically precede reversals with high probability. When commercials reach a COT Index reading below ten, they are expressing their most bearish stance in the entire lookback period.
The data staleness alert notifies users when COT reports have not updated for more than ten days, preventing reliance on outdated information for trading decisions. Government shutdowns or federal holidays can interrupt the normal Friday publication schedule. Using stale signals while believing them current creates dangerous false confidence.
The indicator's watermark information display positioned in the bottom right corner provides essential context at a glance. This persistent display shows the symbol and timeframe, the COT report date timestamp, days since last update, and the current signal state. A trader analyzing a potential short entry in crude oil can glance at the watermark to instantly confirm positioning context without interrupting analysis flow.
LIMITATIONS AND REALISTIC EXPECTATIONS
Practical application requires understanding both the indicator's considerable strengths and inherent limitations. COT data inherently lags price action by three days, as Tuesday positions are not published until Friday afternoon. This delay means the indicator cannot catch rapid intraday reversals or respond to surprise news events. Traders using the COT Index for timing entries must accept this latency and focus on swing trading and position trading timeframes where three-day lags matter less than in day trading or scalping.
The weekly publication schedule similarly makes the indicator unsuitable for short-term trading strategies requiring immediate feedback. The COT Index works best for traders operating on weekly or longer timeframes, where positioning shifts measured in weeks and months align with trading horizon.
Extreme COT readings can persist far longer than typical technical indicators suggest, testing the patience and capital reserves of traders attempting to fade them. When crude oil enters a sustained bull market driven by genuine supply disruptions, commercial hedgers may maintain bearish positioning for many months as prices grind higher. A commercial COT Index reading of fifteen indicating extreme bearishness might persist for three months while prices continue rallying before finally reversing. Traders without sufficient capital and risk tolerance to weather such drawdowns will exit prematurely, precisely when the signal is about to work (Irwin and Sanders, 2012).
Position sizing discipline becomes paramount when implementing COT-based strategies. Rather than risking large percentages of capital on individual signals, successful COT traders typically allocate modest position sizes across multiple signals, allowing some to take time to mature while others work more quickly.
The indicator also cannot overcome fundamental regime changes that alter the structural drivers of markets. If gold enters a true secular bull market driven by monetary debasement, commercial hedgers may remain persistently bearish as mining companies sell forward years of production at what they perceive as favorable prices. Their positioning indicates valuation concerns from a production cost perspective, but cannot stop prices from rising if investment demand overwhelms physical supply-demand balance.
Similarly, structural changes in market participation can alter the meaning of positioning extremes. The growth of commodity index investing in the two thousands brought massive passive long-only capital into futures markets, fundamentally changing typical positioning ranges. Traders relying on COT signals without recognizing this regime change would have generated numerous false bearish signals during the commodity supercycle from 2003 to 2008.
The research foundation supporting COT analysis derives primarily from commodity markets where the commercial hedger information advantage is most pronounced. Studies specifically examining financial futures like equity indices and bonds show weaker but still present effects. Traders should calibrate expectations accordingly, recognizing that COT analysis likely works better for crude oil, natural gas, corn, and wheat than for the S&P 500, Treasury bonds, or currency futures.
Another important limitation involves the reporting threshold structure. Not all market participants appear in COT data, only those holding positions above specified minimums. In markets dominated by a few large players, concentration metrics become critical for proper interpretation. A single large trader accounting for thirty percent of commercial positioning might skew the entire category if their individual circumstances are idiosyncratic rather than representative.
GOLD FUTURES DURING A HYPOTHETICAL MARKET CYCLE
Consider a practical example using gold futures during a hypothetical but realistic market scenario that illustrates how the COT Index indicator guides trading decisions through a complete market cycle. Suppose gold has rallied from fifteen hundred to nineteen hundred dollars per ounce over six months, driven by inflation concerns following aggressive monetary expansion, geopolitical uncertainty, and sustained buying by Asian central banks for reserve diversification.
Large speculators, operating primarily trend-following strategies, have accumulated increasingly bullish positions throughout this rally. Their COT Index has climbed progressively from forty-five to eighty-five. The table display shows that large speculators now hold net long positions representing thirty-two percent of total open interest, their highest in four years. Momentum indicators show positive readings, indicating positions are still building though at a decelerating rate. Position velocity has turned negative, suggesting the pace of position building is slowing.
Meanwhile, commercial hedgers have responded to the rally by aggressively selling forward production and inventory. Their COT Index has moved inversely to price, declining from fifty-five to twenty. This bearish commercial positioning represents mining companies locking in forward sales at prices they view as attractive relative to production costs. The table shows commercials now hold net short positions representing twenty-nine percent of total open interest, their most bearish stance in five years. Concentration metrics indicate this positioning is broadly distributed across many commercial entities, suggesting the bearish stance reflects collective industry view rather than idiosyncratic positioning by a single firm.
Small traders, attracted by mainstream financial media coverage of gold's impressive rally, have recently piled into long positions. Their COT Index has jumped from forty-five to seventy-eight as retail investors chase the trend. Television financial networks feature frequent segments on gold with bullish guests. Internet forums and social media show surging retail interest. This retail enthusiasm historically marks late-stage trend development rather than early opportunity.
The COT Index indicator, configured to monitor commercial positioning from a contrarian perspective, displays a clear bearish signal given the extreme commercial short positioning. The table displays multiple confirming metrics: positioning extremity shows commercials at the ninety-sixth percentile of bearishness, market power indicates they control twenty-nine percent of open interest, and sentiment divergence registers sixty-five, indicating massive disagreement between commercial hedgers and large speculators. This divergence, the highest in three years, places the market in the historically high-risk category for reversals.
The interpretation requires nuance and consideration of context beyond just COT data. Commercials are not necessarily predicting an imminent crash. Rather, they are hedging business operations at what they collectively view as favorable price levels. However, the data reveals they have sold unusually large quantities of forward production, suggesting either exceptional production expectations for the year ahead or concern about sustaining current price levels or combination of both. Combined with extreme speculator positioning indicating a crowded long trade, and small trader enthusiasm confirming retail FOMO, the confluence suggests elevated reversal risk even if the precise timing remains uncertain.
A prudent trader analyzing this situation might take several actions based on COT Index signals. Existing long positions could be tightened with closer stop losses. Profit-taking on a portion of long exposure could lock in gains while maintaining some participation. Some traders might initiate modest short positions as portfolio hedges, sizing them appropriately for the inherent uncertainty in timing reversals. Others might simply move to the sidelines, avoiding new long entries until positioning normalizes.
The key lesson from case study analysis is that COT signals provide probabilistic edges rather than deterministic predictions. They work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five percent win rate with proper risk management produces substantial profits over time, yet still means forty-five percent of signals will be premature or wrong. Traders must embrace this probabilistic reality rather than seeking the impossible goal of perfect accuracy.
INTEGRATION WITH TRADING SYSTEMS
Integration with existing trading systems represents a natural and powerful use case for COT analysis, adding a positioning dimension to price-based technical approaches or fundamental analytical frameworks. Few traders rely exclusively on a single indicator or methodology. Rather, they build systems that synthesize multiple information sources, with each component addressing different aspects of market behavior.
Trend followers might use COT extremes as regime filters, modifying position sizing or avoiding new trend entries when positioning reaches levels historically associated with reversals. Consider a classic trend-following system based on moving average crossovers and momentum breakouts. Integration of COT analysis adds nuance. When large speculator positioning exceeds ninety or commercial positioning falls below ten, the regime filter recognizes elevated reversal risk. The system might reduce position sizing by fifty percent for new signals during these high-risk periods (Kaufman, 2013).
Mean reversion traders might require COT signal confluence before fading extended moves. When crude oil becomes technically overbought and large speculators show extreme long positioning above eighty-five, both signals confirm. If only technical indicators show extremes while positioning remains neutral, the potential short signal is rejected, avoiding fades of trends with underlying institutional support (Kaufman, 2013).
Discretionary traders can monitor the indicator as a continuous awareness tool, informing bias and position sizing without dictating mechanical entries and exits. A discretionary trader might notice commercial positioning shifting from neutral to progressively more bullish over several months. This trend informs growing positive bias even without triggering mechanical signals.
Multi-timeframe analysis represents another powerful integration approach. A trader might use daily charts for trade execution and timing while monitoring weekly COT positioning for strategic context. When both timeframes align, highest-probability opportunities emerge.
Portfolio construction for futures traders can incorporate COT signals as an additional selection criterion. Markets showing strong technical setups AND favorable COT positioning receive highest allocations. Markets with strong technicals but neutral or unfavorable positioning receive reduced allocations.
ADVANCED METRICS AND INTERPRETATION
The metrics table transforms simple positioning data into multidimensional market intelligence. Position extremity, calculated as the absolute deviation from the historical mean normalized by standard deviation, helps identify truly unusual readings versus routine fluctuations. A reading above two standard deviations indicates ninety-fifth percentile or higher extremity. Above three standard deviations indicates ninety-ninth percentile or higher, genuinely rare positioning that historically precedes major events with high probability.
Market power, expressed as a percentage of total open interest, reveals whose positioning matters most from a mechanical market impact perspective. Consider two scenarios in gold futures. In scenario one, commercials show a COT Index reading of fifteen while their market power metric shows they hold net shorts representing thirty-five percent of open interest. This is a high-confidence bearish signal. In scenario two, commercials also show a reading of fifteen, but market power shows only eight percent. While positioning is extreme relative to this category's normal range, their limited market share means less mechanical influence on price.
The rate of change and momentum metrics highlight whether positions are accelerating or decelerating, often providing earlier warnings than absolute levels alone. A COT Index reading of seventy-five with rapidly building momentum suggests continued movement toward extremes. Conversely, a reading of eighty-five with decelerating or negative momentum indicates the positioning trend is exhausting.
Position velocity measures the rate of change in positioning changes, effectively a second derivative. When velocity shifts from positive to negative, it indicates that while positioning may still be growing, the pace of growth is slowing. This deceleration often precedes actual reversal in positioning direction by several weeks.
Sentiment divergence calculates the absolute difference between normalized commercial and large speculator index values. When commercials show extreme bearish positioning at twenty while large speculators show extreme bullish positioning at eighty, the divergence reaches sixty, representing near-maximum disagreement. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals. The mechanism is intuitive. Extreme divergence indicates the informed hedgers and momentum-following speculators have positioned opposite each other with conviction. One group will prove correct and profit while the other proves incorrect and suffers losses. The resolution of this disagreement through price movement often involves volatility.
The table also displays concentration metrics when available. High concentration indicates a few dominant players controlling most of the positioning within a category, while low concentration suggests broad-based participation. Broad-based positioning more reliably reflects collective market intelligence and industry consensus. If mining companies globally all independently decide to hedge aggressively at similar price levels, it suggests genuine industry-wide view about price valuations rather than circumstances specific to one firm.
DATA QUALITY AND RELIABILITY
The CFTC has maintained COT reporting in various forms since the nineteen twenties, providing nearly a century of positioning data across multiple market cycles. However, data quality and reporting standards have evolved substantially over this long period. Modern electronic reporting implemented in the late nineteen nineties and early two thousands significantly improved accuracy and timeliness compared to earlier paper-based systems.
Traders should understand that COT reports capture positions as of Tuesday's close each week. Markets remain open three additional days before publication on Friday afternoon, meaning the reported data is three days stale when received. During periods of rapid market movement or major news events, this lag can be significant. The indicator addresses this limitation by including timestamp information and staleness warnings.
The three-day lag creates particular challenges during extreme volatility episodes. Flash crashes, surprise central bank interventions, geopolitical shocks, and other high-impact events can completely transform market positioning within hours. Traders must exercise judgment about whether reported positioning remains relevant given intervening events.
Reporting thresholds also mean that not all market participants appear in disaggregated COT data. Traders holding positions below specified minimums aggregate into the non-reportable or small trader category. This aggregation affects different markets differently. In highly liquid contracts like crude oil with thousands of participants, reportable traders might represent seventy to eighty percent of open interest. In thinly traded contracts with only dozens of active participants, a few large reportable positions might represent ninety-five percent of open interest.
Another data quality consideration involves trader classification into categories. The CFTC assigns traders to commercial or non-commercial categories based on reported business purpose and activities. However, this process is not perfect. Some entities engage in both commercial and speculative activities, creating ambiguity about proper classification. The transition to Disaggregated reports attempted to address some of these ambiguities by creating more granular categories.
COMPARISON WITH ALTERNATIVE APPROACHES
Several alternative approaches to COT analysis exist in the trading community beyond the normalization methodology employed by this indicator. Some analysts focus on absolute position changes week-over-week rather than index-based normalization. This approach calculates the change in net positioning from one week to the next. The emphasis falls on momentum in positioning changes rather than absolute levels relative to history. This method potentially identifies regime shifts earlier but sacrifices cross-market comparability (Briese, 2008).
Other practitioners employ more complex statistical transformations including percentile rankings, z-score standardization, and machine learning classification algorithms. Ruan and Zhang (2018) demonstrated that machine learning models applied to COT data could achieve modest improvements in forecasting accuracy compared to simple threshold-based approaches. However, these gains came at the cost of interpretability and implementation complexity.
The COT Index indicator intentionally employs a relatively straightforward normalization methodology for several important reasons. First, transparency enhances user understanding and trust. Traders can verify calculations manually and develop intuitive feel for what different readings mean. Second, academic research suggests that most of the predictive power in COT data comes from extreme positioning levels rather than subtle patterns requiring complex statistical methods to detect. Third, robust methods that work consistently across many markets and time periods tend to be simpler rather than more complex, reducing the risk of overfitting to historical data. Fourth, the complexity costs of implementation matter for retail traders without programming teams or computational infrastructure.
PSYCHOLOGICAL ASPECTS OF COT TRADING
Trading based on COT data requires psychological fortitude that differs from momentum-based approaches. Contrarian positioning signals inherently mean betting against prevailing market sentiment and recent price action. When commercials reach extreme bearish positioning, prices have typically been rising, sometimes for extended periods. The price chart looks bullish, momentum indicators confirm strength, moving averages align positively. The COT signal says bet against all of this. This psychological difficulty explains why COT analysis remains underutilized relative to trend-following methods.
Human psychology strongly predisposes us toward extrapolation and recency bias. When prices rally for months, our pattern-matching brains naturally expect continued rally. The recent price action dominates our perception, overwhelming rational analysis about positioning extremes and historical probabilities. The COT signal asking us to sell requires overriding these powerful psychological impulses.
The indicator design attempts to support the required psychological discipline through several features. Clear threshold markers and signal states reduce ambiguity about when signals trigger. When the commercial index crosses below twenty, the signal is explicit and unambiguous. The background shifts to red, the signal label displays bearish, and alerts fire. This explicitness helps traders act on signals rather than waiting for additional confirmation that may never arrive.
The metrics table provides analytical justification for contrarian positions, helping traders maintain conviction during inevitable periods of adverse price movement. When a trader enters short positions based on extreme commercial bearish positioning but prices continue rallying for several weeks, doubt naturally emerges. The table display provides reassurance. Commercial positioning remains extremely bearish. Divergence remains high. The positioning thesis remains intact even though price action has not yet confirmed.
Alert functionality ensures traders do not miss signals due to inattention while also not requiring constant monitoring that can lead to emotional decision-making. Setting alerts for COT extremes enables a healthier relationship with markets. When meaningful signals occur, alerts notify them. They can then calmly assess the situation and execute planned responses.
However, no indicator design can completely overcome the psychological difficulty of contrarian trading. Some traders simply cannot maintain short positions while prices rally. For these traders, COT analysis might be better employed as an exit signal for long positions rather than an entry signal for shorts.
Ultimately, successful COT trading requires developing comfort with probabilistic thinking rather than certainty-seeking. The signals work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five or sixty percent win rate with proper risk management produces substantial profits over years, yet still means forty to forty-five percent of signals will be premature or wrong. COT analysis provides genuine edge, but edge means probability advantage, not elimination of losing trades.
EDUCATIONAL RESOURCES AND CONTINUOUS LEARNING
The indicator provides extensive built-in educational resources through its documentation, detailed tooltips, and transparent calculations. However, mastering COT analysis requires study beyond any single tool or resource. Several excellent resources provide valuable extensions of the concepts covered in this guide.
Books and practitioner-focused monographs offer accessible entry points. Stephen Briese published The Commitments of Traders Bible in two thousand eight, offering detailed breakdowns of how different markets and trader categories behave (Briese, 2008). Briese's work stands out for its empirical focus and market-specific insights. Jack Schwager includes discussion of COT analysis within the broader context of market behavior in his book Market Sense and Nonsense (Schwager, 2012). Perry Kaufman's Trading Systems and Methods represents perhaps the most rigorous practitioner-focused text on systematic trading approaches including COT analysis (Kaufman, 2013).
Academic journal articles provide the rigorous statistical foundation underlying COT analysis. The Journal of Futures Markets regularly publishes research on positioning data and its predictive properties. Bessembinder and Chan's earlier work on systematic risk, hedging pressure, and risk premiums in futures markets provides theoretical foundation (Bessembinder, 1992). Chang's examination of speculator returns provides historical context (Chang, 1985). Irwin and Sanders provide essential skeptical perspective in their two thousand twelve article (Irwin and Sanders, 2012). Wang's two thousand three article provides one of the most empirical analyses of COT data across multiple commodity markets (Wang, 2003).
Online resources extend beyond academic and book-length treatments. The CFTC website provides free access to current and historical COT reports in multiple formats. The explanatory materials section offers detailed documentation of report construction, category definitions, and historical methodology changes. Traders serious about COT analysis should read these official CFTC documents to understand exactly what they are analyzing.
Commercial COT data services such as Barchart provide enhanced visualization and analysis tools beyond raw CFTC data. TradingView's educational materials, published scripts library, and user community provide additional resources for exploring different approaches to COT analysis.
The key to mastering COT analysis lies not in finding a single definitive source but rather in building understanding through multiple perspectives and information sources. Academic research provides rigorous empirical foundation. Practitioner-focused books offer practical implementation insights. Direct engagement with data through systematic backtesting develops intuition about how positioning dynamics manifest across different market conditions.
SYNTHESIZING KNOWLEDGE INTO PRACTICE
The COT Index indicator represents the synthesis of academic research, trading experience, and software engineering into a practical tool accessible to retail traders equipped with nothing more than a TradingView account and willingness to learn. What once required expensive data subscriptions, custom programming capabilities, statistical software, and institutional resources now appears as a straightforward indicator requiring only basic parameter selection and modest study to understand. This democratization of institutional-grade analysis tools represents a broader trend in financial markets over recent decades.
Yet technology and data access alone provide no edge without understanding and discipline. Markets remain relentlessly efficient at eliminating edges that become too widely known and mechanically exploited. The COT Index indicator succeeds only when users invest time learning the underlying concepts, understand the limitations and probability distributions involved, and integrate signals thoughtfully into trading plans rather than applying them mechanically.
The academic research demonstrates conclusively that institutional positioning contains genuine information about future price movements, particularly at extremes where commercial hedgers are maximally bearish or bullish relative to historical norms. This informational content is neither perfect nor deterministic but rather probabilistic, providing edge over many observations through identification of higher-probability configurations. Bessembinder and Chan's finding that commercial positioning explained modest but significant variance in future returns illustrates this probabilistic nature perfectly (Bessembinder and Chan, 1992). The effect is real and statistically significant, yet it explains perhaps ten to fifteen percent of return variance rather than most variance. Much of price movement remains unpredictable even with positioning intelligence.
The practical implication is that COT analysis works best as one component of a trading system rather than a standalone oracle. It provides the positioning dimension, revealing where the smart money has positioned and where the crowd has followed, but price action analysis provides the timing dimension. Fundamental analysis provides the catalyst dimension. Risk management provides the survival dimension. These components work together synergistically.
The indicator's design philosophy prioritizes transparency and education over black-box complexity, empowering traders to understand exactly what they are analyzing and why. Every calculation is documented and user-adjustable. The threshold markers, background coloring, tables, and clear signal states provide multiple reinforcing channels for conveying the same information.
This educational approach reflects a conviction that sustainable trading success comes from genuine understanding rather than mechanical system-following. Traders who understand why commercial positioning matters, how different trader categories behave, what positioning extremes signify, and where signals fit within probability distributions can adapt when market conditions change. Traders mechanically following black-box signals without comprehension abandon systems after normal losing streaks.
The research foundation supporting COT analysis comes primarily from commodity markets where commercial hedger informational advantages are most pronounced. Agricultural producers hedging crops know more about supply conditions than distant speculators. Energy companies hedging production know more about operating costs than financial traders. Metals miners hedging output know more about ore grades than index funds. Financial futures markets show weaker but still present effects.
The journey from reading this documentation to profitable trading based on COT analysis involves several stages that cannot be rushed. Initial reading and basic understanding represents the first stage. Historical study represents the second stage, reviewing past market cycles to observe how positioning extremes preceded major turning points. Paper trading or small-size real trading represents the third stage to experience the psychological challenges. Refinement based on results and personal psychology represents the fourth stage.
Markets will continue evolving. New participant categories will emerge. Regulatory structures will change. Technology will advance. Yet the fundamental dynamics driving COT analysis, that different market participants have different information, different motivations, and different forecasting abilities that manifest in their positioning, will persist as long as futures markets exist. While specific thresholds or optimal parameters may shift over time, the core logic remains sound and adaptable.
The trader equipped with this indicator, understanding of the theory and evidence behind COT analysis, realistic expectations about probability rather than certainty, discipline to maintain positions through adverse volatility, and patience to allow signals time to develop possesses genuine edge in markets. The edge is not enormous, markets cannot allow large persistent inefficiencies without arbitraging them away, but it is real, measurable, and exploitable by those willing to invest in learning and disciplined application.
REFERENCES
Bessembinder, H. (1992) Systematic risk, hedging pressure, and risk premiums in futures markets, Review of Financial Studies, 5(4), pp. 637-667.
Bessembinder, H. and Chan, K. (1992) The profitability of technical trading rules in the Asian stock markets, Pacific-Basin Finance Journal, 3(2-3), pp. 257-284.
Briese, S. (2008) The Commitments of Traders Bible: How to Profit from Insider Market Intelligence. Hoboken: John Wiley & Sons.
Chang, E.C. (1985) Returns to speculators and the theory of normal backwardation, Journal of Finance, 40(1), pp. 193-208.
Commodity Futures Trading Commission (CFTC) (2009) Explanatory Notes: Disaggregated Commitments of Traders Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Commodity Futures Trading Commission (CFTC) (2020) Commitments of Traders: About the Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Irwin, S.H. and Sanders, D.R. (2012) Testing the Masters Hypothesis in commodity futures markets, Energy Economics, 34(1), pp. 256-269.
Kaufman, P.J. (2013) Trading Systems and Methods. 5th edn. Hoboken: John Wiley & Sons.
Ruan, Y. and Zhang, Y. (2018) Forecasting commodity futures prices using machine learning: Evidence from the Chinese commodity futures market, Applied Economics Letters, 25(12), pp. 845-849.
Sanders, D.R., Boris, K. and Manfredo, M. (2004) Hedgers, funds, and small speculators in the energy futures markets: an analysis of the CFTC's Commitments of Traders reports, Energy Economics, 26(3), pp. 425-445.
Schwager, J.D. (2012) Market Sense and Nonsense: How the Markets Really Work and How They Don't. Hoboken: John Wiley & Sons.
Tharp, V.K. (2008) Super Trader: Make Consistent Profits in Good and Bad Markets. New York: McGraw-Hill.
Wang, C. (2003) The behavior and performance of major types of futures traders, Journal of Futures Markets, 23(1), pp. 1-31.
Williams, L.R. and Noseworthy, M. (2009) The Right Stock at the Right Time: Prospering in the Coming Good Years. Hoboken: John Wiley & Sons.
FURTHER READING
For traders seeking to deepen their understanding of COT analysis and futures market positioning beyond this documentation, the following resources provide valuable extensions:
Academic Journal Articles:
Fishe, R.P.H. and Smith, A. (2012) Do speculators drive commodity prices away from supply and demand fundamentals?, Journal of Commodity Markets, 1(1), pp. 1-16.
Haigh, M.S., Hranaiova, J. and Overdahl, J.A. (2007) Hedge funds, volatility, and liquidity provision in energy futures markets, Journal of Alternative Investments, 9(4), pp. 10-38.
Kocagil, A.E. (1997) Does futures speculation stabilize spot prices? Evidence from metals markets, Applied Financial Economics, 7(1), pp. 115-125.
Sanders, D.R. and Irwin, S.H. (2011) The impact of index funds in commodity futures markets: A systems approach, Journal of Alternative Investments, 14(1), pp. 40-49.
Books and Practitioner Resources:
Murphy, J.J. (1999) Technical Analysis of the Financial Markets: A Guide to Trading Methods and Applications. New York: New York Institute of Finance.
Pring, M.J. (2002) Technical Analysis Explained: The Investor's Guide to Spotting Investment Trends and Turning Points. 4th edn. New York: McGraw-Hill.
Federal Reserve and Research Institution Publications:
Federal Reserve Banks regularly publish working papers examining commodity markets, futures positioning, and price discovery mechanisms. The Federal Reserve Bank of San Francisco and Federal Reserve Bank of Kansas City maintain active research programs in this area.
Online Resources:
The CFTC website provides free access to current and historical COT reports, explanatory materials, and regulatory documentation.
Barchart offers enhanced COT data visualization and screening tools.
TradingView's community library contains numerous published scripts and educational materials exploring different approaches to positioning analysis.
Objective COTAutomated COT-based forex sentiment tool using CFTC data to highlight buy/sell zones via commercial hedgers' net positions. Spots extremes in pairs like EURUSD.
Features:
- Auto base/quote code detection.
- Custom thresholds (e.g., BUY: Base ≥55%, Quote ≤45%).
- 5-week % change filter for Commercials/Small Traders.
- Separate long/short colors for base/quote.
- Weekly confirmation, debug table, alerts.
- Futures/options selection.
Perfect for sentiment trading on daily/weekly charts. Backtest; not advice. Free!
COT Non-Commercial Net PositionsThis indicator displays the net position of Non-Commercial traders (speculators) in futures markets by subtracting short positions from long positions, based on CFTC COT data. It fetches the relevant COT long and short values weekly (or as per the user-selected timeframe) and plots the net positions relative to zero.
Position Sizing Risk TablePosition Sizing Risk Table - swing trading. Allowing for a 0,25; 0,5 and 1% risk based on NAV
Fundamental Analysis & Economic-Based Stock ValuationFundamental Analysis & Economic-Based Stock Valuation
The Fundamental Analysis & Economic-Based Stock Valuation is a powerful tool designed to give traders and investors a quick, comprehensive overview of a company’s financial health. This horizontal, color-coded table includes live financial data, progress indicators, and smart health insights for informed decision-making. Below are the key financial metrics included in the table:
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1. Market Capitalization (Market Cap)
Definition: Market Cap is calculated as the total number of outstanding shares multiplied by the current stock price.
Importance: This gives investors an idea of the company’s size and valuation.
How to Use:
• Large-cap stocks (> $10B) are typically stable, established companies.
• Small- or mid-cap stocks may offer higher growth but come with more volatility.
aiTrendview Feature: Progress bars visually represent the company's size. This helps users quickly gauge whether the stock is a micro-cap, mid-cap, or large-cap investment opportunity.
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2. Earnings Yield (%)
Definition: Earnings Yield = (EPS / Price) × 100. It shows how much a company earns relative to its stock price.
Importance: It’s the inverse of the P/E ratio and is used to compare returns from equity with bond yields.
How to Use:
• A yield > 10% may indicate undervaluation.
• Lower yield (< 3%) may indicate an overpriced stock.
aiTrendview Feature: Health indicators like “STRONG”, “FAIR”, or “POOR” and a progress bar help investors assess return potential relative to risk.
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3. Price-to-Book Ratio (P/B Ratio)
Definition: P/B Ratio = Market Price / Book Value per Share.
Importance: Measures market valuation relative to the company's net assets.
How to Use:
• A ratio < 1 can mean the stock is undervalued.
• 3 might indicate overvaluation unless justified by high ROE.
aiTrendview Feature: Color-coded health markers show if the company is UNDERVALUED, FAIR, or OVERVALUED, making valuation analysis visual.
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4. Price-to-Earnings Ratio (P/E Ratio)
Definition: P/E = Price / Earnings per Share. It tells you how much investors are paying for each unit of earnings.
Importance: One of the most commonly used valuation metrics.
How to Use:
• A low P/E (< 15) might indicate undervaluation.
• High P/E (> 30) could mean overvaluation or growth expectations.
aiTrendview Feature: The health indicator ("CHEAP", "FAIR", "HIGH", "EXPENSIVE") with a visual bar helps judge sentiment and valuation instantly.
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5. Price-to-Sales Ratio (P/S Ratio)
Definition: Market Cap / Revenue. Indicates how much investors pay per dollar of sales.
Importance: Useful for valuing companies with low or negative earnings.
How to Use:
• < 2 is attractive in most industries.
• Higher ratios need to be justified by strong growth.
aiTrendview Feature: P/S-based health tags and progress bars help traders decide whether the stock is reasonably priced on revenue.
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6. EBITDA (Earnings Before Interest, Taxes, Depreciation & Amortization)
Definition: A measure of a company's core operational profitability.
Importance: Strips out non-operational costs and is used for comparative analysis.
How to Use:
• Positive EBITDA suggests financial strength.
• Compare year-over-year for growth consistency.
aiTrendview Feature: Visual score and health indicator classify profitability status as “PROFIT” or “LOSS”.
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7. Total Revenue
Definition: The total income from sales before expenses.
Importance: Indicates the scale of business operations.
How to Use:
• Rising revenue over quarters = growth.
• Compare with competitors for market share insight.
aiTrendview Feature: Categorizes revenue scale as “MICRO”, “SMALL”, “MEDIUM”, or “LARGE” – useful for gauging company tier.
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8. Net Income
Definition: Profit after all expenses, taxes, and interest.
Importance: Shows the company’s actual profitability.
How to Use:
• Positive Net Income = healthy bottom line.
• Use for EPS and ROE calculations.
aiTrendview Feature: Margin percentage + status label (“PROFIT” or “LOSS”) instantly convey financial strength.
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9. Book Value Per Share (BVPS)
Definition: Total equity divided by the number of outstanding shares.
Importance: Indicates the liquidation value per share.
How to Use:
• Compare with current market price.
• Price < BVPS can mean undervaluation.
aiTrendview Feature: Shows whether the stock is trading at “DISCOUNT” or “PREMIUM” to its actual value.
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10. Earnings Per Share (EPS)
Definition: Net income divided by outstanding shares.
Importance: Measures profitability on a per-share basis.
How to Use:
• Key input for valuation and dividend decisions.
• Positive EPS is essential for investment appeal.
aiTrendview Feature: Labeled “PROFIT” or “LOSS” and enhanced with visual status for clarity.
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11. Symbol & Exchange Info
Definition: Displays the trading symbol and exchange (e.g., NSE, NYSE).
Importance: Ensures clarity when analyzing or sharing screenshots.
How to Use:
• Useful for verifying ticker and confirming data source.
aiTrendview Feature: Clearly displayed with "LIVE" tag for credibility.
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12. Fundamental Health Score
Definition: aiTrendview computes a composite score (0–100) based on 5 core metrics: Net Income, EPS, P/E, P/B, and EBITDA.
Importance: Provides a single summary score to assess the company's overall financial strength.
How to Use:
• Use this as a filter to shortlist strong candidates.
• Score > 80 = “EXCELLENT”; 60–80 = “GOOD”; < 40 = “POOR”.
aiTrendview Feature: A professional horizontal progress bar with color-coded grade makes it visually intuitive.
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⚠️ Disclaimer from aiTrendview
The information provided in this Fundamental Analysis dashboard is for educational and informational purposes only. While the data is sourced live and computed dynamically, it should not be interpreted as investment advice. Traders and investors must do their own due diligence and consider risk appetite, macroeconomic factors, and other indicators before making any financial decisions. aiTrendview.com or its affiliates shall not be held liable for any loss arising from the use of this tool. Markets are risky — trade wisely and responsibly.
Info TableOverview
The Info Table V1 is a versatile TradingView indicator tailored for intraday futures traders, particularly those focusing on MESM2 (Micro E-mini S&P 500 futures) on 1-minute charts. It presents essential market insights through two customizable tables: the Main Table for predictive and macro metrics, and the New Metrics Table for momentum and volatility indicators. Designed for high-activity sessions like 9:30 AM–11:00 AM CDT, this tool helps traders assess price alignment, sentiment, and risk in real-time. Metrics update dynamically (except weekly COT data), with optional alerts for key conditions like volatility spikes or momentum shifts.
This indicator builds on foundational concepts like linear regression for predictions and adapts open-source elements for enhanced functionality. Gradient code is adapted from TradingView's Color Library. QQE logic is adapted from LuxAlgo's QQE Weighted Oscillator, licensed under CC BY-NC-SA 4.0. The script is released under the Mozilla Public License 2.0.
Key Features
Two Customizable Tables: Positioned independently (e.g., top-right for Main, bottom-right for New Metrics) with toggle options to show/hide for a clutter-free chart.
Gradient Coloring: User-defined high/low colors (default green/red) for quick visual interpretation of extremes, such as overbought/oversold or high volatility.
Arrows for Directional Bias: In the New Metrics Table, up (↑) or down (↓) arrows appear in value cells based on metric thresholds (top/bottom 25% of range), indicating bullish/high or bearish/low conditions.
Consensus Highlighting: The New Metrics Table's title cells ("Metric" and "Value") turn green if all arrows are ↑ (strong bullish consensus), red if all are ↓ (strong bearish consensus), or gray otherwise.
Predicted Price Plot: Optional line (default blue) overlaying the ML-predicted price for visual comparison with actual price action.
Alerts: Notifications for high/low Frahm Volatility (≥8 or ≤3) and QQE Bias crosses (bullish/bearish momentum shifts).
Main Table Metrics
This table focuses on predictive, positional, and macro insights:
ML-Predicted Price: A linear regression forecast using normalized price, volume, and RSI over a customizable lookback (default 500 bars). Gradient scales from low (red) to high (green) relative to the current price ± threshold (default 100 points).
Deviation %: Percentage difference between current price and predicted price. Gradient highlights extremes (±0.5% default threshold), signaling potential overextensions.
VWAP Deviation %: Percentage difference from Volume Weighted Average Price (VWAP). Gradient indicates if price is above (green) or below (red) fair value (±0.5% default).
FRED UNRATE % Change: Percentage change in U.S. unemployment rate (via FRED data). Cell turns red for increases (economic weakness), green for decreases (strength), gray if zero or disabled.
Open Interest: Total open MESM2 futures contracts. Gradient scales from low (red) to high (green) up to a hardcoded 300,000 threshold, reflecting market participation.
COT Commercial Long/Short: Weekly Commitment of Traders data for commercial positions. Long cell green if longs > shorts (bullish institutional sentiment); Short cell red if shorts > longs (bearish); gray otherwise.
New Metrics Table Metrics
This table emphasizes technical momentum and volatility, with arrows for quick bias assessment:
QQE Bias: Smoothed RSI vs. trailing stop (default length 14, factor 4.236, smooth 5). Green for bullish (RSI > stop, ↑ arrow), red for bearish (RSI < stop, ↓ arrow), gray for neutral.
RSI: Relative Strength Index (default period 14). Gradient from oversold (red, <30 + threshold offset, ↓ arrow if ≤40) to overbought (green, >70 - offset, ↑ arrow if ≥60).
ATR Volatility: Score (1–20) based on Average True Range (default period 14, lookback 50). High scores (green, ↑ if ≥15) signal swings; low (red, ↓ if ≤5) indicate calm.
ADX Trend: Average Directional Index (default period 14). Gradient from weak (red, ↓ if ≤0.25×25 threshold) to strong trends (green, ↑ if ≥0.75×25).
Volume Momentum: Score (1–20) comparing current to historical volume (lookback 50). High (green, ↑ if ≥15) suggests pressure; low (red, ↓ if ≤5) implies weakness.
Frahm Volatility: Score (1–20) from true range over a window (default 24 hours, multiplier 9). Dynamic gradient (green/red/yellow); ↑ if ≥7.5, ↓ if ≤2.5.
Frahm Avg Candle (Ticks): Average candle size in ticks over the window. Blue gradient (or dynamic green/red/yellow); ↑ if ≥0.75 percentile, ↓ if ≤0.25.
Arrows trigger on metric-specific logic (e.g., RSI ≥60 for ↑), providing directional cues without strict color ties.
Customization Options
Adapt the indicator to your strategy:
ML Inputs: Lookback (10–5000 bars) and RSI period (2+) for prediction sensitivity—shorter for volatility, longer for trends.
Timeframes: Individual per metric (e.g., 1H for QQE Bias to match higher frames; blank for chart timeframe).
Thresholds: Adjust gradients and arrows (e.g., Deviation 0.1–5%, ADX 0–100, RSI overbought/oversold).
QQE Settings: Length, factor, and smooth for fine-tuned momentum.
Data Toggles: Enable/disable FRED, Open Interest, COT for focus (e.g., disable macro for pure intraday).
Frahm Options: Window hours (1+), scale multiplier (1–10), dynamic colors for avg candle.
Plot/Table: Line color, positions, gradients, and visibility.
Ideal Use Case
Perfect for MESM2 scalpers and trend traders. Use the Main Table for entry confirmation via predicted deviations and institutional positioning. Leverage the New Metrics Table arrows for short-term signals—enter bullish on green consensus (all ↑), avoid chop on low volatility. Set alerts to catch shifts without constant monitoring.
Why It's Valuable
Info Table V1 consolidates diverse metrics into actionable visuals, answering critical questions: Is price mispriced? Is momentum aligning? Is volatility manageable? With real-time updates, consensus highlights, and extensive customization, it enhances precision in fast markets, reducing guesswork for confident trades.
Note: Optimized for futures; some metrics (OI, COT) unavailable on non-futures symbols. Test on demo accounts. No financial advice—use at your own risk.
The provided script reuses open-source elements from TradingView's Color Library and LuxAlgo's QQE Weighted Oscillator, as noted in the script comments and description. Credits are appropriately given in both the description and code comments, satisfying the requirement for attribution.
Regarding significant improvements and proportion:
The QQE logic comprises approximately 15 lines of code in a script exceeding 400 lines, representing a small proportion (<5%).
Adaptations include integration with multi-timeframe support via request.security, user-customizable inputs for length, factor, and smooth, and application within a broader table-based indicator for momentum bias display (with color gradients, arrows, and alerts). This extends the original QQE beyond standalone oscillator use, incorporating it as one of seven metrics in the New Metrics Table for confluence analysis (e.g., consensus highlighting when all metrics align). These are functional enhancements, not mere stylistic or variable changes.
The Color Library usage is via official import (import TradingView/Color/1 as Color), leveraging built-in gradient functions without copying code, and applied to enhance visual interpretation across multiple metrics.
The script complies with the rules: reused code is minimal, significantly improved through integration and expansion, and properly credited. It qualifies for open-source publication under the Mozilla Public License 2.0, as stated.
Briese CoT Movement IndexThis Briese CoT (Commitments of Traders) Movement Index histogram indicator was built based on the formula by Stephen Briese in his book "The Commitments of Traders Bible":
"...difference between the COT Index and its reading of one or several weeks prior. I use six." —Chapter 7, page 75.
The code is a bit of a remix of the "ICT Commitment of Traders°" indicator by toodegrees and is meant for use in a new pane below a Weekly Chart .
The upper and lower thresholds are +40/-40. Some context: "A ± 40 point surge in the COT Index within a six-week period frequently marks the end of a counter-trend price reaction"
40 Point CoT Surge Rules (Commercials) from page 76
"During a correction from a prevailing uptrend, a +40 point movement in the CoT Index within a six-week period often marks the end of a corrective pullback, and the resumption of the major uptrend."
"During a reaction in a prevailing downtrend, a -40 point movement in the CoT Index within a six-week period frequently marks the end of a price reaction, and the resumption of the established downtrend."
"The failure of a ± point CoT Movement Index signal to restart the prevailing trend is a tip-off to a major trend change"
I'd recommend reading Briese's book for examples on how to properly interpret this indictor.
This indicator can be used in conjunction with another one I've published called the "Williams x Briese Hybrid CoT Index" which can be found on my scripts page.
CoT MK DashboardThis indicator provides a compact, visual table overview of the most important Commitments of Traders (CoT) metrics:
• Commercials (Long, Short, Net)
• Speculators (Long, Short, Net)
• Open Interest
• Commercials Short/OI %
• WillCo Index
Each metric is shown with its current value and a simple sentiment signal (Long, Neutral, Short) based on dynamic quantile levels.
Quantile thresholds are calculated over a customizable lookback period (in weeks), so you can adapt the sensitivity of the signals to your own needs.
Purpose:
Quickly assess the overall positioning and sentiment of different market participants at a glance, without needing to analyze each data series individually.
Recommended Workflow:
Use this dashboard as a first step to identify potential market extremes or notable positioning.
If you spot interesting signals (e.g., multiple metrics showing “Long”), you can then take a deeper look using the specialized indicators from the CoT MK suite, such as:
• CoT MK Commercials
• CoT MK_Speculators Percentile
• CoT MK OI-Short Percentile Oscillator
• CoT_MK_WillCo_Index
Who is it for?
Active traders, position traders, and anyone who wants to quickly monitor institutional and speculative activity in futures markets.
CoT MK OI-Short Percentile OscillatorCoT MK OI-Short Percentile Oscillator is a weekly indicator that tracks overall market participation and commercial hedger pessimism by plotting total Open Interest and the ratio of Commercials’ short positions to Open Interest. It fetches both data series on a 1-week resolution, then calculates the user-defined upper and lower percentiles (default 80%/20%) over a configurable lookback period (default 208 weeks) entirely within the weekly timeframe. The main plots show rounded Open Interest in blue and Commercials Short/OI% in red, while the red upper bands flag overbought or over-hedged extremes and the green lower bands highlight underbought or under-hedged conditions. Traders use these percentile bands to identify when crowd participation or hedger pessimism reaches extremes that often presage market turns.
CoT MK CommercialsCoT MK Commercials is a weekly tool that visualizes how Commercial hedgers are positioned in the futures market by plotting their Long, Short (inverted if desired), and Net exposures alongside upper and lower percentile bands. It fetches Commercial Long and Short data from the CFTC Legacy CoT report, computes the chosen upper and lower percentiles (default 75 % and 25 %) of each series over a user-defined lookback period (default 208 weeks), and overlays these bands to highlight extreme sentiment. Green bands mark bullish extremes (e.g. many longs or few shorts), while red bands mark bearish extremes (e.g. many shorts or few longs). You can toggle Long, Short, and Net series on or off, choose to display shorts as negative values for symmetry, and adjust the lookback and percentile levels to suit your analysis. Traders use CoT MK Commercials to track smart-money positioning and to identify potential turning points when Commercials reach unusually high or low exposure.
CoT MK_Speculators PercentileCoT MK Speculators Percentile
This indicator visualizes the weekly positioning of Non-Commercial traders (Speculators) from the CFTC’s Legacy CoT report, plotting their Long, Short, and Net positions alongside user-defined percentile bands.
• Data Source: Weekly Non-Commercial Long and Short positions via the TradingView CoT Library.
• Percentile Bands: Calculates the chosen upper and lower percentiles (default 80% and 20%) of each series over a configurable lookback period (default 208 weeks).
• Negative Shorts: Optionally inverts Short values so that larger Short exposure appears deeper below zero, improving symmetry with Long data.
• Usage:
• Speculators Long/Short/Net: Show raw or inverted position curves.
• Upper Percentile (red): Marks extreme Speculator exposure (bearish contrarian zone).
• Lower Percentile (green): Identifies low Speculator engagement (bullish contrarian zone).
Traders use these percentile bands as contrarian signals: extreme Speculator positioning often precedes market reversals.
CoT_MK_WillCo_IndexThe WillCo MK Index (Commercials, volume-adjusted) is a weekly oscillator that measures how strongly Commercial traders are positioned relative to total market size (Open Interest). It calculates the net Commercial position (Long minus Short), divides it by Open Interest to normalize for market volume, then scales that ratio to a 0–100 range over a user-defined lookback period (default 26 weeks). Readings near 100 indicate exceptionally strong Commercial net-long exposure (bullish extreme), while readings near 0 reflect heavy Commercial shorting or lack of longs (bearish extreme). Traders use WillCo MK to spot potential turning points by following smart-money extremes that often anticipate price reversals.
CoT-Mike-Long&Short Quantile CoT-Mike-Long&Short visualizes the weekly net positions of commercial traders (Commitments of Traders, COT) for both long and short sides. The indicator calculates the highest and lowest values of commercial long and short positions over a user-defined lookback period (default: 208 weeks) and plots customizable quantile lines (default: 75% and 25%) for both sides.
By default, only the short side is displayed, but you can enable the long side in the settings.
The 25% and 75% quantile lines are used to identify extreme positioning of commercials. When commercial positions reach these extreme levels, it often signals potential market turning points, as commercials are considered the "smart money" and tend to act against the prevailing market trend. These quantile levels help traders spot overbought or oversold conditions and filter out less significant signals, supporting a more disciplined and contrarian trading approach.
Features:
• Weekly-based calculation, independent of the chart timeframe
• Customizable lookback period (in weeks)
• Adjustable quantile levels (e.g. 75%/25%, 80%/20%, etc.)
• Option to display only shorts, only longs, or both
• Visualizes key levels for both long and short positions
• Useful for identifying extreme positioning and potential market turning points
How to use:
Add the indicator to your chart
Adjust the lookback period and quantile levels if needed
Enable or disable the display of long/short positions as desired
Use the quantile lines to spot overbought/oversold conditions in commercial positioning
Note: The data is always calculated on a weekly basis, regardless of your current chart timeframe.
Planting & Harvesting SeasonsHello all,
as a commodity trader, I use a lot of seasonal patterns in my analysis. Some time ago, I came up with the idea to develop a simple script that visually overlays the typical planting and harvesting periods for key agricultural futures directly on the chart.
This script automatically detects the underlying commodity based on the symbol (e.g. ZC, ZW, ZS, CT) and displays color-coded zones for each seasonal window. These zones are based on historical crop calendars and help identify when planting or harvesting typically takes place. The goal is to better align technical setups with fundamental seasonal factors.
This is a basic version and meant as a visual aid — not a trading signal in itself.
Hope you enjoy it and any feedback is highly appreciated!
High-Impact News Events with ALERTHigh-Impact News Events with ALERT
This indicator is builds upon the original by adding alert capabilities, allowing traders to receive notifications before and after economic events to manage risk effectively.
This indicator is updated version of the Live Economic Calendar by @toodegrees ( ) which allows user to set alert for the news events.
Key Features
Customizable Alert Selection: Users can choose which impact levels to restrict (High, Medium, Low).
User-Defined Restriction Timing: Set alerts to X minutes before or after the event.
Real-Time Economic Event Detection: Fetches live news data from Forex Factory.
Multi-Event Support: Detects and processes multiple news events dynamically.
Automatic Trading Restriction: user can use this script to stop trades in news events.
Visual Markers:
Vertical dashed lines indicate the start and end of restriction periods.
Background color changes during restricted trading times.
Alerts notify traders during the news events.
How It Works
The user selects which news impact levels should restrict trading.
The script retrieves real-time economic event data from Forex Factory.
Trading can be restricted for X minutes before and after each event.
The script highlights restricted periods with a background color.
Alerts notify traders all time during the news events is active as per the defined time to prevent unexpected volatility exposure.
Customization Options
Choose which news impact levels (High, Medium, Low) should trigger trading restrictions.
Define time limits before and after each news event for restriction.
Enable or disable alerts for restricted trading periods.
How to Use
Apply the indicator to any TradingView chart.
Configure the news event impact levels you want to restrict.
Set the pre- and post-event restriction durations as needed.
The indicator will automatically apply restrictions, plot visual markers, and trigger alerts accordingly.
Limitations
This script relies on Forex Factory data and may have occasional update delays.
TradingView does not support external API connections, so data is updated through internal methods.
The indicator does not execute trades automatically; it only provides visual alerts and restriction signals.
Reference & Credit
This script is based on the Live Economic Calendar by @toodegrees ( ), adding enhanced pre- and post-event alerting capabilities to help traders prepare for market-moving news.
Disclaimer
This script is for informational purposes only and does not constitute financial advice. Users should verify economic data independently and exercise caution when trading around news events. Past performance is not indicative of future results.
The Commitment of Traders (COT) IndexThe COT Index indicator is used to measure the positioning of different market participants (Large Traders, Small Traders, and Commercial Hedgers) relative to their historical positioning over a specified lookback period. It helps traders identify extreme positioning, which can signal potential reversals or trend continuations.
Key Features of the Indicator:
COT Data Retrieval
The script pulls COT report data from the TradingView COT Library TradingView/LibraryCOT/3).
It retrieves long and short positions for three key groups:
Large Traders (Non-commercial positions) – Speculators such as hedge funds.
Small Traders (Non-reportable positions) – Small retail traders.
Commercial Hedgers (Commercial positions) – Institutions that hedge real-world positions.
Threshold Zones for Extreme Positioning:
Upper Zone Threshold (Default: 90%)
Signals potential overbought conditions (excessive buying).
Lower Zone Threshold (Default: 10%)
Signals potential oversold conditions (excessive selling).
The indicator plots these zones using horizontal lines.
The COT Index should be used in conjunction with technical analysis (support/resistance, trends, etc.). A high COT Index does not mean the market will reverse immediately—it’s an indication of extreme sentiment.
Note:
If the script does not recognize or can't find the ticker currently viewed in the COT report, the COT indicator will default to U.S. Dollar.
COT Index and OI IndexCOT Index and OI Index
It calculates COT Index for Commercials and Open Interest Index. Both for a custom period depending of the asset nature. These periods can be adjusted. You can also choose to have signals drawn on the chart when the following conditions are met
If "Extreme Positions" option is chosen:
* COT Index for Commercials > Extreme Long (adjustable parameter in %) and COT Index for Large Speculators and Small Speculators < Extreme Short (adjustable parameter in %). This will show a green triangle up in the last weekly bar/candle
* COT Index for Commercials < Extreme Short (adjustable parameter in %) and COT Index for Large Speculators and Small Speculators > Extreme Long (adjustable parameter in %). This will show a red triangle down in the last weekly bar/candle
If "Extreme Commercials and OI Index" option is chosen:
* COT Index for Commercials > Extreme Long (adjustable parameter in %) and OI Index < Extreme Short (adjustable parameter in %). This will show a green triangle up in the last weekly bar/candle
* COT Index for Commercials < Extreme Short (adjustable parameter in %) and OI Index > Extreme Long (adjustable parameter in %). This will show a red triangle down in the last weekly bar/candle
