KCP Algo Test 38 [Dr.K.C.PRAKASH]KCP Algo Test 15 identifies trend shifts using two volume-weighted exponential moving averages named KCP 1 and KCP 2. A BUY signal appears when KCP 1 crosses above KCP 2 with both slopes rising, indicating strengthening market momentum. A SELL signal triggers when KCP 1 crosses below KCP 2 with both slopes declining, showing weakening momentum. The VWAP line acts as the KCP Trend Line to help visually confirm the market’s directional bias.
Educational
Nifty Intraday 9:30- 3 Min Candle By Trade Prime Algo.Nifty Intraday 9:30 – 3 Min Candle Strategy by Trade Prime Algo
This strategy is designed to help traders identify intraday long entries, stop-loss, and multi-target levels on the Nifty Spot / Nifty Futures based on the first 3-minute candle breakout after 9:30 AM.
It automates trade detection, entry marking, target plotting, and trailing stop-loss logic, allowing traders to visualize complete trade flow with clarity and precision.
The system offers:
✅ Auto identification of long entries based on candle breakout logic
✅ Configurable stop-loss, trailing SL, and four partial profit targets
✅ Dynamic plotting of entry, TSL, and targets on chart
✅ Custom alert messages for each event (Entry, TP1–TP4, SL, Close)
✅ Adjustable time session and test periods for backtesting
⚙️ How to Use
1️⃣ Set your desired start time (default: 9:15–9:30 AM).
2️⃣ Choose your stop-loss type — percentage or points.
3️⃣ Adjust target levels (TP1–TP4) and trailing SL settings as per your risk appetite.
4️⃣ Use this strategy for educational backtesting and research only — not for live trading signals.
5️⃣ The tool can be combined with price action zones or higher-timeframe analysis for best results.
⚠️ Disclaimer (SEBI & Risk Disclosure)
This strategy is developed strictly for educational and research purposes.
The creator of this script and Trade Prime Algo are not SEBI-registered advisors.
This tool does not guarantee any specific profit or performance.
Trading involves risk; users may incur partial or total capital loss.
All decisions taken using this indicator or strategy are solely at the user’s discretion and risk.
The creator assumes no liability for profit, loss, or any consequences arising from the use of this script.
Always perform your own due diligence and trade responsibly.
ETD-A BELLIndicator Overview
Early Trend Detection with Engulfing Pattern Recognition
This indicator is designed to identify early trading trend reversals by combining trend-based EMA analysis with candlestick pattern detection. It automatically plots up and down arrows on the chart to signal potential bullish or bearish shifts in market momentum.
A key feature of this tool is its Engulfing Pattern Detection, which highlights strong reversal zones confirmed by price action. When an engulfing pattern aligns with an EMA crossover or momentum change, the indicator provides an early alert helping traders anticipate potential entry or exit points before larger market moves occur.
Key Features:
Detects early bullish and bearish trend reversals.
Displays Up (▲) and Down (▼) arrows for clear visual cues.
Integrates Engulfing Candle Detection for enhanced accuracy.
Works across multiple timeframes and asset classes.
Ideal for both swing and intraday traders seeking early trend insights.
AI Scalping Signals# 🤖 AI-Powered Scalping Indicator - Ultra-Fast Trading Signals
## Overview
This advanced AI-driven **scalping indicator** is specifically engineered for high-frequency traders operating on smaller timeframes. Designed exclusively for **1-minute, 3-minute, and 5-minute charts**, this system combines multiple sophisticated technical analysis methods to identify rapid-fire, high-probability trade entries and exits. The AI algorithms analyze market momentum, micro-trend strength, and instant price dynamics in real-time, delivering lightning-fast BUY and SELL signals perfect for scalping strategies.
## Key Features
### ✨ AI-Enhanced Scalping Signal Generation
- **Machine Learning Integration**: Proprietary AI algorithms process multiple technical indicators simultaneously with millisecond precision to catch quick market moves
- **Smart Cross-Validation**: The AI system validates signals across multiple micro-conditions before generating alerts, perfect for fast-paced scalping
- **Adaptive Micro-Trend Analysis**: Intelligent momentum and trend detection optimized specifically for 1M, 3M, and 5M timeframes
- **Low-Latency Processing**: Designed for speed—signals generate instantly when conditions align for rapid trade execution
### 📊 Clean Visual Interface for Fast Trading
- **Crystal Clear Signals**: Easy-to-read BUY (green) and SELL (red) labels appear directly on your chart—no delay, no confusion
- **Background Confirmation**: Subtle background highlighting provides additional visual confirmation of scalping signals
- **No Chart Clutter**: The indicator focuses on signals only—no unnecessary lines or plots to distract from rapid price action and quick decision-making
- **Optimized for Speed**: Minimalist design allows you to spot and execute trades in seconds
### 🔔 Comprehensive Alert System for Scalpers
- **Real-Time Notifications**: Get instantly notified when AI-confirmed BUY or SELL signals are generated—critical for scalping success
- **Multi-Alert Options**: Separate alerts for buy signals, sell signals, or combined alerts for any scalping opportunity
- **Never Miss a Quick Move**: Set up alerts and let the AI monitor rapid market movements 24/7
- **Mobile-Friendly**: Receive alerts on your phone for on-the-go scalping
## How It Works
The indicator employs a sophisticated multi-layer analysis system optimized for scalping:
1. **Micro-Trend Analysis Layer**: AI algorithms analyze rapid trend shifts using advanced moving average techniques calibrated for small timeframes
2. **Momentum Spike Detection**: Smart momentum oscillators identify instant overbought and oversold conditions with scalping-level precision
3. **Price Action Validation**: Proprietary price cross-detection ensures signals align with actual market microstructure movements
4. **AI Flash Confirmation**: All conditions are processed through ultra-fast AI validation logic for immediate signal generation
### Signal Conditions
**🟢 BUY Signal (Long Scalp Entry)**
Generated when the AI system confirms:
- Bullish micro-trend alignment detected
- Price momentum shows instant strength above key thresholds
- AI-validated upward price breakout occurs on small timeframe
- Multiple technical confirmations align simultaneously for quick profit potential
**🔴 SELL Signal (Short Scalp Entry)**
Generated when the AI system confirms:
- Bearish micro-trend alignment detected
- Price momentum shows instant weakness below key thresholds
- AI-validated downward price breakdown occurs on small timeframe
- Multiple technical confirmations align simultaneously for quick profit potential
## Best Practices for Scalping
### Recommended Usage
- **⚡ Optimal Timeframes**: Specifically calibrated for **1-minute, 3-minute, and 5-minute charts** for maximum scalping performance
- **Markets**: Highly effective on forex pairs (especially majors), crypto (BTC, ETH), and high-liquidity stocks and indices
- **Session Focus**: Best results during high-volume trading sessions (London/NY overlap for forex, market open for stocks)
- **Quick Execution**: This is a scalping tool—execute trades immediately when signals appear
- **Risk Management**: Use tight stop-losses (5-15 pips for forex) and quick take-profits; scalping requires strict risk control
### Scalping Strategy Tips
- Execute trades instantly—scalping requires fast action within seconds of signal generation
- Use 1:1 or 1:2 risk-reward ratios for consistent scalping profits
- Monitor spreads and commissions—they matter significantly for scalpers
- Trade during high liquidity hours to ensure tight spreads and quick fills
- Consider trading multiple signals per session for accumulated gains
- Set mobile alerts to catch quick opportunities throughout the day
- Close positions quickly—don't let scalps turn into swing trades
- The background color change provides a split-second early warning system
## What Makes This Scalping Indicator Different?
Unlike traditional indicators designed for longer timeframes, this AI-powered scalping tool:
- ✅ **Built Exclusively for Scalping**: Optimized specifically for 1M, 3M, and 5M timeframes—not a generic indicator
- ✅ Combines multiple technical analysis methods with millisecond-precision AI processing
- ✅ Uses artificial intelligence to filter noise and validate only the fastest, cleanest scalping signals
- ✅ Eliminates the need to manually analyze multiple indicators during rapid market moves
- ✅ Provides clear, actionable signals with no interpretation required—critical for scalping speed
- ✅ Reduces false signals through multi-condition validation tuned for small timeframes
- ✅ Adapts to rapid volatility changes and micro-trend shifts in real-time
- ✅ Zero lag—signals appear instantly when conditions align for immediate execution
## Important Disclaimers
⚠️ **Scalping Risk Warning**: Scalping involves extremely high frequency trading with substantial risk of loss. This indicator is a tool to assist with fast-paced analysis and should not be the sole basis for trading decisions. Scalping requires experience, discipline, and proper risk management.
⚠️ **No Guarantee**: Past performance and backtested results do not guarantee future performance. No indicator is 100% accurate, especially in volatile scalping conditions.
⚠️ **Due Diligence**: Always conduct your own research and analysis. Use proper risk management with every single trade. Never risk more than 1-2% of your account per scalp trade.
⚠️ **Transaction Costs**: Be aware that scalping involves frequent trading, which means higher commission and spread costs. Ensure your broker offers competitive pricing for high-frequency trading.
⚠️ **Educational Tool**: This indicator is designed as an educational and analytical tool for experienced traders. Users are solely responsible for their own trading decisions.
## Settings & Customization
This is a **protected scalping indicator** with optimized parameters locked specifically for 1-minute, 3-minute, and 5-minute chart performance. The AI algorithms have been fine-tuned through extensive backtesting and live scalping optimization. No manual adjustments are needed—simply add to your small timeframe chart and start receiving rapid-fire signals.
## Support & Updates
This indicator receives regular updates to enhance AI algorithms and improve signal accuracy. For questions or support, please contact the publisher.
---
**Ready to dominate the scalping game with AI-powered lightning-fast signals?** Add this indicator to your 1M, 3M, or 5M chart and experience the difference of intelligent, validated scalping signals designed for rapid-fire trading.
*Remember: Scalping success requires lightning-fast execution, strict discipline, proper risk management, and continuous practice. Use this tool as part of a comprehensive scalping strategy with tight stop-losses and realistic profit targets.*
369 Swing Points369 Swing Points - Digital Root Time Analysis
This indicator combines swing point detection with digital root numerology applied to intraday timestamps, filtering for times that reduce to 3, 6, or 9.
Methodology:
The script uses pivot point detection to identify swing highs and lows, then calculates the digital root of the bar's timestamp. Digital root is calculated by recursively summing the digits of a number until a single digit remains (e.g., 13:45 = 1345 → 1+3+4+5 = 13 → 1+3 = 4). Only swing points occurring at times with digital roots of 3, 6, or 9 are displayed.
What Makes This Unique:
Unlike standard swing point indicators, this filters results based on time-based numerology. The multiple calculation modes allow testing different hypotheses: whether the full timestamp (HHMM), just the minutes (MM), or either produces significant patterns. This is particularly useful for traders exploring intraday cyclical patterns or time-based market theories, especially popular in swing trading communities that follow specific time cycles.
How It Works:
Detects swing highs/lows using configurable lookback periods
Extracts the timestamp from each swing point bar
Calculates digital root using selected time mode (Full Time, Minutes Only, or Both)
Displays only swings with DR of 3, 6, or 9
Includes timezone adjustment to match your local time
Optional real-time plotting to show potential swings before confirmation
Configuration:
Swing Length: Sensitivity of pivot detection (default: 2)
Digital Root Mode: Full Time (HHMM), Minutes Only (MM), or Both
Timezone Offset: Aligns displayed times with your chart's timezone
Label customization: Text size, color, spacing options
Real-time Plotting: Shows unconfirmed swings as they develop (with transparency)
Debug mode: View all swings with their digital roots for analysis
Usage:
Works on all intraday timeframes (1min to 4H). Adjust timezone offset to ensure accurate time display. Use debug mode to verify swing detection and see digital root calculations for all pivots. Enable "Highlight 369 Digital Root Bars" to see when current bar time has a 3/6/9 digital root.
Renko Emulator Strategy # 🚀 Renko Emulator Strategy for Normal Candlestick Charts
Transform your trading with this advanced Renko-based strategy that works seamlessly on normal candlestick charts!
## ✨ What Makes This Special?
### 🎯 Smart Signal System
- **One Signal at a Time**: No confusing duplicate signals
- **Position State Tracking**: Always know your current position
- **Automatic Target Detection**: T1, T2, T3 calculated automatically
- **10 Comprehensive Alerts**: Never miss an opportunity
### 🔧 Technical Excellence
- **Renko Logic**: Filters market noise using brick formations
- **ATR-Based Sizing**: Adapts to market volatility
- **Multi-Indicator Confirmation**: EMA, RSI, MACD, Supertrend
- **Volume Validation**: Only high-probability setups
## 📊 How It Works
### Entry Signals
🟢 **LONG (BUY)**
- Reversal: Red bricks → First green brick
- Trend: 3+ consecutive green bricks
- With full technical confirmation
🔴 **SHORT (SELL)**
- Reversal: Green bricks → First red brick
- Trend: 3+ consecutive red bricks
- With full technical confirmation
### Position Management
📍 **Stop Loss**: Last opposite brick ± buffer
🎯 **Target 1**: 2× Brick size → Book 50%
🎯 **Target 2**: 3× Brick size → Book 30%
🎯 **Target 3**: 4× Brick size → Book 20%
### Exit Rules
⚠️ Opposite brick formation
⚠️ RSI extremes (>80 or <20)
⚠️ Manual exit as needed
## 🎨 Visual Features
### On Your Chart
- 📊 Renko brick overlays
- 🟢 Green triangles = BUY signals
- 🔴 Red triangles = SELL signals
- ⚪ Target hit markers (T1, T2, T3)
- 📈 Trend indicators overlay
- 🎨 Position background color
### Info Panel
Real-time dashboard showing:
- Current brick size & color
- Position status (LONG/SHORT/NONE)
- Consecutive brick count
- RSI level
- Trend direction
- Market conditions
## 🔔 Complete Alert System
**10 Alerts Available:**
✅ Long & Short Entry
✅ All 6 Target Hits (T1, T2, T3 each)
✅ Long & Short Exit
**Alert Messages Include:**
- Entry price & direction
- Profit booking instructions
- Risk management tips
- Next action guidance
## 💰 Best Instruments
### Highly Effective On:
- **Indian Markets**: Nifty 50, Bank Nifty
- **Stocks**: HDFC, Reliance, TCS, Infosys
- **Forex**: EUR/USD, GBP/USD, USD/JPY
- **Crypto**: BTC, ETH, major altcoins
- **Commodities**: Gold, Silver, Crude Oil
### Recommended Timeframes:
- **Day Trading**: 5-min, 15-min
- **Swing Trading**: 1-hour, 4-hour
- **Position Trading**: Daily
## ⚙️ Customizable Settings
### Brick Configuration
- ATR-based (automatic) or Fixed points
- Adjustable ATR period & multiplier
- Visual brick display on/off
### Indicator Parameters
- EMA length (default: 20)
- RSI period (default: 14)
- MACD settings (12, 26, 9)
- Supertrend (10, 3)
- Volume filter toggle
### Display Options
- Show/hide entry signals
- Show/hide target levels
- Show/hide info table
- Brick overlay transparency
## 📈 Usage Strategy
### For Beginners:
1. Add to chart with default settings
2. Wait for clear BUY/SELL arrows
3. Follow position management rules
4. Use recommended stop losses
5. Book profits at targets
### For Advanced Traders:
1. Optimize brick size per instrument
2. Fine-tune indicator parameters
3. Combine with your strategy
4. Backtest thoroughly
5. Scale position sizes
## ⚠️ Risk Management
### Built-in Protection:
- Maximum 2% risk per trade
- Clear stop loss levels
- Defined profit targets
- Position size calculator
- Daily loss limits
### Best Practices:
✅ Test on demo first
✅ Use proper position sizing
✅ Follow stop losses strictly
✅ Don't over-trade
✅ Maintain trading journal
## 🎓 What You Get
### Immediate Benefits:
- Clear entry/exit signals
- No analysis paralysis
- Reduced emotional trading
- Systematic approach
- Professional risk management
### Learning Opportunities:
- Understand Renko concepts
- Master position management
- Learn risk control
- Develop discipline
- Build consistent strategy
## 🐛 Troubleshooting
### No Signals?
- Check indicator settings
- Verify brick size not too large
- Ensure volume filter appropriate
- Try different timeframe
### Too Many Signals?
- Increase brick size
- Use higher timeframe
- Enable stricter filters
- Check signal filtering active
## 📊 Performance Notes
### Works Best In:
✅ Trending markets
✅ Clear directional moves
✅ Good liquidity
✅ Normal volatility
### Avoid Trading:
❌ Major news events
❌ Low volume periods
❌ Extreme volatility
❌ Choppy/sideways markets
## 🔄 Updates & Support
**Current Version**: 2.0
**Recent Updates:**
- ✅ Fixed duplicate signals
- ✅ Added position tracking
- ✅ Enhanced alert system
- ✅ Improved visual feedback
- ✅ Better target detection
**Future Plans:**
- Additional customization
- More alert options
- Advanced features
- Performance improvements
## 📜 Important Disclaimer
⚠️ **Please Read Carefully:**
This indicator is for **educational purposes only**. Trading involves substantial risk of loss. Past performance does not guarantee future results.
**You Must:**
- Use proper risk management
- Test strategies before live trading
- Never risk more than you can afford to lose
- Consult financial advisor if needed
- Understand your trading instrument
**The creator assumes no responsibility for trading losses incurred using this indicator.**
## 🙏 Credits
- Renko Concept: Traditional Japanese charting
- ATR Calculation: J. Welles Wilder
- Community Feedback: Beta testers & users
---
## 💬 Feedback Welcome!
If you find this helpful:
- ⭐ Like the indicator
- 💬 Share your feedback
- 🐛 Report bugs
- 💡 Suggest improvements
- 🔄 Share with traders
## 📞 Getting Started
1. **Add to Chart**: Click "Add to Chart"
2. **Configure Settings**: Adjust as needed
3. **Set Alerts**: Enable notifications
4. **Test First**: Use demo account
5. **Go Live**: Start small, scale up
---
**Happy Trading! 📈🚀**
**Trade Smart. Trade Safe. Trade Profitable.**
---
*Remember: Discipline + Risk Management + Good Strategy = Success*
*No indicator is perfect. Use as part of complete trading plan.*
QTS - MK V1 (Working)***DO NOT START USING. NEEDS UPDATE****
Highlights entry models as per SSMT between 2 consecutive quarters
PCP Arbitrage Monitor (Math by Thomas)Live monitor for Put–Call Parity (C + PV(K) = P + S) showing drift, arbitrage direction, and opportunity strength.
The PCP Arbitrage Monitor helps traders visualize and quantify deviations from the Put–Call Parity (PCP) relationship:
𝐶+𝐾𝑒−𝑟𝑇 = 𝑃+𝑆
When this equation drifts, it indicates a potential arbitrage opportunity between call, put, and underlying (spot or future).
This indicator plots the left-hand side (LHS) and right-hand side (RHS) of the PCP equation on your chart, computes the drift, and automatically highlights and displays actionable trade combinations when the deviation exceeds a set threshold.
⚙️ How It Works
Inputs
Call & Put Symbols – Select matching call and put options (same strike & expiry).
Strike (K) – The strike price for those options.
Expiry (UTC) – Option expiry date/time (used to calculate 𝑇 and PV(K)).
Risk-free Rate (r) – Annualized rate used for discounting the strike.
Lot Size / Tick Value – Used to calculate profit in INR.
Arbitrage Threshold – Minimum drift (in points) to trigger signals (default 200).
Displayed Data
LHS = C + PV(K) (Call + discounted Strike)
RHS = P + S (Put + Spot/Future)
Drift = LHS – RHS
Bookable Profit (INR)
Action Suggestion (only when |drift| ≥ threshold)
Background Highlight
🟩 Green – Call side expensive → Sell Call + Buy Put + Buy Fut
🟥 Red – Call side cheap → Buy Call + Sell Put + Sell Fut
Table
Displays all key values live in the top-right corner:
Option prices
LHS, RHS
Drift (points)
Time to expiry
Lot size
Bookable profit (INR)
Trade action (only if |drift| ≥ threshold)
📈 How to Use
Open a NIFTY Spot or Futures chart (works on both).
Enter the exact option symbols (e.g., NSE:NIFTY24DEC21900CE and NSE:NIFTY24DEC21900PE).
Adjust Strike (K) and Expiry to match those options.
Observe:
The green/red background highlights large deviations (≥ threshold).
The Action cell displays the arbitrage combination and expected profit.
A drift beyond the threshold suggests a potential risk-free arbitrage if executed simultaneously across all legs.
Hold positions till expiry if margin allows; the profit is theoretically locked in.
💡 Tips
Works on both Spot and Futures charts — the script auto-uses the chart’s close as 𝑆
Set smoothing to 0 to see raw parity values.
Adjust threshold based on costs and margin — e.g., 150–200 points for NIFTY is practical.
Only valid when options are European (no early exercise risk).
Ensure both option symbols are liquid and from the same expiry.
⚠️ Disclaimer
This tool is for educational and analytical purposes.
Real arbitrage execution depends on liquidity, bid-ask spread, slippage, and margin requirements.
Always validate prices with your broker before trading.
MACD ZoneThe MACD Zone Indicator is a custom technical analysis tool built to visualize market momentum by combining the MACD (Moving Average Convergence Divergence) with dynamic zone levels for enhanced trend interpretation.
🔧 Key Features
1. MACD Histogram & Signal Lines:
Displays a color-coded MACD histogram that highlights bullish and bearish momentum shifts for quick visual analysis.
2. Neutral Zone Logic:
For Nifty and its option charts, the default neutral zone threshold (h2) is set to 5, which can be customized by the user through settings.
For other symbols, h2 is dynamically calculated as price / 2000, adapting automatically to varying price levels.
3. Zone Visualization:
Four horizontal levels (h1, h2, h3, h4) define the bullish, neutral, and bearish zones, helping traders quickly gauge trend strength, potential reversals, and momentum transitions.
Daily Key Levels + VWAPThis indicator is daily price levels and previous day's VWAP for precision intraday trading decisions.
📊 Monitor F&M - RLYONSCRIPT OBJECTIVE
It's a confluence system that combines four key indicators to identify high-probability trading setups. It basically tells you when and where to enter the market with greater confidence.
🔧 THE 4 BASE INDICATORS
1. ADX (Average Directional Index)
What it measures: The strength of the trend (not the direction)
How to use it:
ADX ≥ 25 = STRONG trend ✅
ADX < 25 = Weak or sideways trend
What it does: Filters trades. You only look for entries when there is real strength in the market.
2. DI+ and DI- (Directional Indicators)
What it measures: The direction of the trend.
How to use it:
DI+ > DI- = Bullish trend 📈
DI- > DI+ = Bearish trend 📉
What it does: Defines whether you are looking for buys or sells.
3. TTM Squeeze (Bollinger Bands + Keltner Channels)
What it measures: Volatility compression and explosion.
States:
Squeeze ON 🔴: Volatility compressed (like a tightened spring).
Squeeze OFF 🟢: Volatility released (the spring is released = strong movement).
Transition 🔵: Changing state.
Momentum: The green/red histogram shows the direction of the movement.
Green rising = Strong bullish trend.
Red falling = Strong bearish trend.
4. RSI (Relative Strength Index)
What it measures: Whether the price is overbought or oversold.
Zones:
RSI > 70 = Overbought ⚠️ (be careful with purchases)
RSI < 30 = Oversold ✅ (bullish opportunity zone)
RSI 40-60 = Neutral zone/ideal for pullbacks
🎯 THE 2 MAIN STRATEGIES
STRATEGY 1: MOMENTUM (The strongest) 🚀
BUY setup:
✅ Squeeze released (changed from ON to OFF)
✅ Momentum green AND growing
✅ ADX ≥ 25 (strong trend)
✅ RSI not overbought (< 70)
SELL setup:
✅ Squeeze released (changed from ON to OFF)
✅ Momentum red AND Decreasing
✅ ADX ≥ 25 (strong trend)
✅ RSI not oversold (> 30)
When to trade: When you see the triangle 🚀 on the chart
STRATEGY 2: PULLBACK (Established trend) 📈📉
BUY setup:
✅ DI+ > DI- (established uptrend)
✅ ADX ≥ 25 (strong trend)
✅ RSI between 40-55 (healthy pullback)
✅ Momentum starting to turn upward
SELL setup:
✅ DI- > DI+ (established downtrend)
✅ ADX ≥ 25 (strong trend)
✅ RSI between 45-60 (healthy pullback)
✅ Momentum starting to turn downward
When to trade: When you see the "PB" circle in the graph
BATIK SMC🌀 BATIK SMC — Smart Money Concepts by YB Pips
BATIK SMC is a professional-grade Smart Money Concepts system refined under the Batik Syndicate methodology.
It combines institutional structure logic with precision-engineered visualization tools for traders who operate with discipline and intent.
🧭 Core Functions
Market Structure: automatic detection of BOS (Break of Structure) & CHoCH (Change of Character)
Order Blocks: internal & swing OB identification with real-time mitigation updates
Fair Value Gaps (FVG): dynamic detection across multiple timeframes
Equal Highs / Lows: liquidity points & sweep detection
Premium / Discount Zones: clear equilibrium mapping for high-RR setups
Smart Candle Coloring: visualize real-time trend bias directly on chart
Custom Alerts: receive instant BOS, CHoCH, OB breakout, and FVG notifications
💎 Why BATIK SMC
Developed for traders who follow structure, liquidity, and imbalance — not indicators.
It retains full Smart Money logic while carrying the signature Batik visual identity and philosophy:
“Trade where institutions position themselves — not where the crowd reacts.”
NY Midnight High/Low Arrows (Auto-Show)🇺🇸 English Explanation
This indicator automatically marks the daily high and low of the New York session.
It draws arrows (▼▲) at the highest and lowest prices after New York midnight (00:00),
and can optionally display small horizontal dotted lines at those levels.
It helps traders identify daily liquidity zones and key turning points in price action.
🇸🇦 الشرح بالعربية
هذا المؤشر يحدد القمة والقاع اليومية لجلسة نيويورك بشكل تلقائي.
يرسم أسهماً (▼▲) عند أعلى وأدنى سعر بعد منتصف الليل بتوقيت نيويورك (00:00)،
ويمكنه أيضًا عرض خطوط أفقية منقطة صغيرة عند تلك المستويات.
يساعد المتداول في معرفة مناطق السيولة اليومية ونقاط الانعكاس المهمة في حركة السعر.
Fixed Dollar Risk LinesFixed Dollar Risk Lines is a utility indicator that converts a user-defined dollar risk into price distance and plots risk lines above and below the current price for popular futures contracts. It helps you place stops or entries at a consistent dollar risk per trade, regardless of the market’s tick value or tick size.
What it does:
-You choose a dollar amount to risk (e.g., $100) and a futures contract (ES, NQ, GC, YM, RTY, PL, SI, CL, BTC).
The script automatically:
-Looks up the contract’s tick value and tick size
-Converts your dollar risk into number of ticks
-Converts ticks into price distance
Plots:
-Long Risk line below current price
-Short Risk line above current price
-Optional labels show exact price levels and an information table summarizes your settings.
Key features
-Consistent dollar risk across instruments
-Supports major futures contracts with built‑in tick values and sizes
-Toggle Long and Short risk lines independently
-Customizable line width and colors (lines and labels)
-Right‑axis price level display for quick reading
-Compact info table with contract, risk, and computed prices
Typical use
-Long setups: use the green line as a stop level below entry to match your chosen dollar risk.
-Short setups: use the red line as a stop level above entry to match your chosen dollar risk.
-Quickly compare how the same dollar risk translates to distance on different contracts.
Inputs
-Risk Amount (USD)
-Futures Contract (ES, NQ, GC, YM, RTY, PL, SI, CL, BTC)
-Show Long/Short lines (toggles)
-Line Width
-Colors for lines and labels
Notes
-Designed for futures symbols that match the listed contracts’ tick specs. If your symbol has different tick value/size than the defaults, results will differ.
-Intended for educational/informational use; not financial advice.
-This tool streamlines risk placement so you can focus on execution while keeping dollar risk consistent across markets.
Bollinger Band Spread (Dunk)Bollinger Band Width measures the distance between the upper and lower Bollinger Bands. It reflects market volatility—wider bands mean higher volatility, narrower bands mean lower volatility.
When the width contracts to low levels, it can signal price consolidation and potential breakouts. When the width expands, it indicates active markets or strong trends.
Traders use it to spot volatility squeezes, confirm breakouts, and compare relative volatility across assets or timeframes.
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.
Trading Toolkit - Comprehensive AnalysisTrading Toolkit – Comprehensive Analysis
A unified trading analysis toolkit with four sections:
📊 Company Info
Fundamentals, market cap, sector, and earnings countdown.
📅 Performance
Date‑range analysis with key metrics.
🎯 Market Sentiment
CNN‑style Fear & Greed Index (7 components) + 150‑SMA positioning.
🛡️ Risk Levels
ATR/MAD‑based stop‑loss and take‑profit calculations.
Key Features
CNN‑style Fear & Greed approximation using:
Momentum: S&P 500 vs 125‑DMA
Price Strength: NYSE 52‑week highs vs lows
Market Breadth: McClellan Volume Summation (Up/Down volume)
Put/Call Ratio: 5‑day average (inverted)
Volatility: VIX vs 50‑DMA (inverted)
Safe‑Haven Demand: 20‑day SPY–IEF return spread
Junk‑Bond Demand: HY vs IG credit spread (inverted)
Normalization: z‑score → percentile (0–100) with ±3 clipping.
CNN‑aligned thresholds:
Extreme Fear: 0–24 | Fear: 25–44 | Neutral: 45–54 | Greed: 55–74 | Extreme Greed: 75+.
Risk tools: ATR & MAD volatility measures with configurable multipliers.
Flexible layout: vertical or side‑by‑side columns.
Data Sources
S&P 500: CBOE:SPX or AMEX:SPY
NYSE: INDEX:HIGN, INDEX:LOWN, USI:UVOL, USI:DVOL
Options: USI:PCC (Total PCR), fallback INDEX:CPCS (Equity PCR)
Volatility: CBOE:VIX
Treasuries: NASDAQ:IEF
Credit Spreads: FRED:BAMLH0A0HYM2, FRED:BAMLC0A0CM
Risk Management
ATR risk bands: 🟢 ≤3%, 🟡 3–6%, ⚪ 6–10%, 🟠 10–15%, 🔴 >15%
MAD‑based stop‑loss and take‑profit calculations.
Author: Daniel Dahan
(AI Generated, Merged & enhanced version with CNN‑style Fear & Greed)
Strat 1-2 Break AlertsThe Strat 1-2 Break Alerts
by Yolanda Marie Dixon
This indicator automatically identifies Inside Bars (1) and alerts when price breaks out into a 2-1-2 Bullish or 2-1-2 Bearish setup — two of the most actionable patterns in The Strat methodology created by Rob Smith.
📊 What It Does:
Marks Inside Bars with a yellow triangle below the candle.
Plots a green “2-1-2↑” triangle when a bullish breakout occurs.
Plots a red “2-1-2↓” triangle when a bearish breakdown occurs.
Provides built-in alerts so traders never miss a 2-1-2 setup.
💡 How to Use It:
Add the indicator to your chart, then go to Alerts → Create Alert → Condition: Strat 1-2 Break Alerts, and choose either 2-1-2 Up or 2-1-2 Down.
Perfect for traders who follow The Strat and want simple, reliable visual and alert-based signals for 1-2 setups.
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🔔 Stay ready, stay Stratified.
Master The Strat with instant alerts for every 2-1-2 breakout.
Market Opens + Killzones — New York, Tokyo & London (SMC/ICT)Market Opens + Killzones — New York, London & Tokyo (SMC/ICT) — TradingATH
Precision. Timing. Liquidity.
This refined overlay defines the world’s three dominant trading sessions — New York , London & Tokyo — plus their critical overlap. Each Opening and Killzone is plotted with full-height visual blocks and precise time anchoring, giving you an immediate understanding of when and where true price delivery begins.
Designed for ICT and SMC Traders , it provides a disciplined structure to navigate intraday volatility — aligning executions with the moments institutional liquidity enters the market.
What You’ll See
New York Killzone (08:30 – 10:30 NY) → Gray full-height Block
London Killzone (07:00 – 10:00 London) → Dark-gray Block
Tokyo Killzone (09:00 – 11:00 Tokyo) → Black Block
London–New York Overlap (13:30 – 16:00 London) → Blue Block
Session Opening Lines : Precise vertical markers with optional labels and customizable color, style, and width.
Every Block extends from chart top to bottom — forming crystal-clear time partitions that highlight where volatility and liquidity converge.
Features
True global time synchronization — automatic daylight-saving adjustment; no manual offset needed.
Full-height killzones — visually structured blocks that scale seamlessly across any timeframe.
Configurable session openings — control color, line width, label visibility, and transparency.
Daily auto-reset — clean, non-repainting visuals with no overlap or drift.
Lightweight performance — optimized rendering with zero lag, even on lower timeframes.
Perfect For
Intraday and Scalping Traders timing executions around session volatility.
ICT / Smart Money Concepts practitioners focusing on liquidity windows.
Traders seeking precise, time-based market context for entries and exits.
Recommended Settings
Line Width: 3–4 px for optimal visibility.
Block Transparency: 60 – 75 % for clean chart integration.
Focus: London + New York sessions for highest liquidity.
In Short
Simple. Accurate. Powerful.
Market Opens + Killzones — New York, London & Tokyo (SMC/ICT) delivers a clean, professional mapping of institutional trading hours — allowing you to trade exactly when the market moves with purpose.
Created by: TradingATH
HTF Supply & Demand Zones 📊 Overview
Advanced supply and demand zone indicator that automatically detects institutional-level price zones on higher timeframes and dynamically adapts zone colors based on price position. Zones below price act as demand (support) and zones above price act as supply (resistance).
✨ Key Features
🎯 Dynamic Zone Recognition
- Smart Color Adaptation: Zones automatically change from demand (green) to supply (red) when price crosses them
- Higher Timeframe Analysis: Detect zones from any timeframe while trading on lower timeframes
- Base/Blast Pattern Detection**: Identifies strong institutional zones using base-blast candle methodology
- Automatic Zone Flipping: Broken demand zones become supply and vice versa
📈 Zone Detection Method
Uses the proven Base & Blast candle pattern:
- Base Candle: Small consolidation candle with minimal wick
- Blast Candle: Strong momentum candle breaking from the base
- Customizable Ratio: Adjust base/blast body size ratio (default 8:1)
- Wick Filter: Ensures clean base candles for higher probability zones
🎨 Visual Features
- Clean Zone Boxes: Extended zones with customizable colors and transparency
- Smart Labels: Display zone type and touch count
- Touch Counter: Track how many times price has tested each zone
- Info Dashboard: Real-time statistics in top-right corner
⚙️ Zone Management
- Auto-Delete After X Touches**: Remove zones after specified number of tests (default: 5)
- Optional Break Deletion**: Choose whether to delete zones when price breaks through
- Maximum Zone Limit**: Control chart cleanliness by limiting displayed zones
- Extended Zones**: All zones extend to the right for forward visibility
🔧 Settings
Detection Parameters
- Higher Timeframe: Select any timeframe for zone detection (empty = current timeframe)
- Base/Blast Ratio: 4.0 to 30.0 (default: 8.0) - Higher = stronger zones, fewer signals
- Wick Threshold: 0.1 to 0.5 (default: 0.3) - Maximum base candle wick size
Display Options
- Toggle demand/supply zones independently
- Maximum zones to display (1-50)
- Show/hide zone labels
- Customizable colors for demand and supply zones
- Adjustable border width
Zone Management
- Delete after X touches (1-30 touches)
- Delete on break option
- Touch counter displays current/max touches
💡 How to Use
For Swing Trading
1. Set timeframe to Daily or Weekly
2. Use 8:1 ratio for high-quality zones
3. Enable auto-delete after 3-5 touches
4. Trade pullbacks to green zones (demand) for longs
5. Trade rallies to red zones (supply) for shorts
For Day Trading
1. Set timeframe to 1H or 4H
2. Use 6:1 ratio for more zones
3. Watch for zone color changes as confirmation
4. Enter when price retests zones in the direction of the higher timeframe trend
For Scalping
1. Set timeframe to 15m or 1H
2. Use 5:1 ratio for frequent signals
3. Focus on first touch of fresh zones
4. Use lower timeframes for precise entries
📋 Best Practices
✅ DO:
- Use zones from higher timeframes for better reliability
- Wait for zone color change as confirmation of flip
- Focus on first 2-3 touches of a zone
- Combine with trend analysis
- Use zones as targets and entry levels
❌ DON'T:
- Trade every zone - quality over quantity
- Ignore the touch counter
- Use on very low timeframes without HTF context
- Trade zones that have been tested many times
🎓 Understanding Dynamic Colors
Green Zones (Demand) = Below current price = Support = Look for bounces
Red Zones (Supply) = Above current price = Resistance = Look for rejections
When price breaks a green zone downward, it flips to red (former support becomes resistance)
When price breaks a red zone upward, it flips to green (former resistance becomes support)
📊 Info Dashboard
The top-right table displays:
- Active timeframe
- Current demand zones count (below price)
- Current supply zones count (above price)
- Active base/blast ratio
- Maximum touches setting
🔔 Trading Signals
High Probability Setups:
- Fresh zones (0-1 touches) on higher timeframes
- Zones that align with major support/resistance
- First test after a zone color flip
- Multiple timeframe confluence
Avoid:
- Zones with 4+ touches
- Zones in choppy/ranging markets
- Counter-trend zones during strong momentum
⚡ Performance Notes
- Maximum 500 boxes and lines supported
- Optimized for real-time scanning
- Minimal resource usage
- No repainting - all zones are confirmed
🎯 Recommended Settings by Trading Style
Conservative (Higher Quality)
- Ratio: 10:1
- Wick Threshold: 0.2
- Delete After: 3 touches
Balanced (Default)
- Ratio: 8:1
- Wick Threshold: 0.3
- Delete After: 5 touches
Aggressive (More Signals)
- Ratio: 6:1
- Wick Threshold: 0.4
- Delete After: 7 touches
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📖 Additional Resources
For more information on supply and demand trading:
- Study institutional order flow
- Learn base and blast candle patterns
- Understand market structure and liquidity zones
- Practice on demo before live trading
Risk Warning: This indicator is a tool for technical analysis. Always use proper risk management and combine with your trading strategy. Past performance does not guarantee future results.
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Compatible with all markets: Forex, Stocks, Crypto, Futures, and Indices
Version: 1.0 | Language: Pine Script v5
SUPER BUY SELL(INDICATOR)Hello Trader Welcome Back To My SUPER BUY SELL(INDICATOR).
This Indicator Made By Pivot Point Demand Supply Zone And Super trend Indicator Formula.
Uses Of Instruction :-
When Market Up To The Super Trend Indicator's Then We Should To Take BUY Entry . Don't Take SELL Entry .
When Market Down To The Super Trend Indicator's Then We Should To Take SELL Entry . Don't Take BUY Entry .
This Indicator Educational Purpose Only Not For Sale.
