ค้นหาในสคริปต์สำหรับ "price action"
Synapse Dynamics - Market Structure📊 SYNAPSE DYNAMICS - MARKET STRUCTURE INDICATOR
An educational tool for learning and practicing Smart Money Concepts (SMC) methodology through visual representation of institutional price action patterns.
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🎯 WHAT THIS INDICATOR DISPLAYS
This indicator visualizes Smart Money Concepts patterns on your chart:
- Order Blocks (OB) - Supply and demand zones based on institutional order flow theory. The indicator identifies these areas using price action criteria including the final opposing candle before a strong directional move.
- Breaker Blocks - Failed order blocks that may act as support/resistance. These occur when an order block is invalidated but price returns to the zone, potentially reversing its role.
- Fair Value Gaps (FVG) - Three-candle imbalance patterns where price gaps create inefficiencies. The indicator marks these zones for reference in analysis.
- Market Structure - Break of Structure (BOS) and Change of Character (CHoCH) patterns based on swing high/low breaks. These help identify potential trend continuation or reversal points.
- Reference Entry Signals - The indicator calculates potential entry zones with accompanying stop loss and take profit reference levels based on order block and FVG locations. These are for educational reference only.
- Higher Timeframe Context - Optional filter that displays the higher timeframe trend direction to provide additional market context.
- Information Panel - On-screen dashboard showing active reference signals, their status, and relevant price levels.
- Swing Point Mapping - Labels recent higher highs (HH), higher lows (HL), lower highs (LH), and lower lows (LL) based on configurable swing detection parameters.
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⚙️ HOW IT WORKS
The indicator uses the following methodology:
**Order Block Detection:** Identifies the last opposing candle before a strong directional move that breaks structure. Filters blocks by size to reduce noise.
**Market Structure Analysis:** Tracks swing points and identifies when price breaks previous highs/lows to determine BOS or CHoCH patterns.
**Fair Value Gap Identification:** Detects three-candle patterns where candle 1's high/low doesn't overlap with candle 3's low/high, creating an imbalance zone.
**Reference Signal Generation:** Combines order block proximity, FVG presence, and market structure breaks to suggest potential study areas. Optional HTF trend filter can be enabled.
**Timeframe Adaptation:** Automatically adjusts swing detection sensitivity based on the chart timeframe (using multipliers for intraday vs. higher timeframes).
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📚 EDUCATIONAL PURPOSE & IMPORTANT LIMITATIONS
**This indicator is designed as an educational tool for:**
- Learning Smart Money Concepts methodology
- Practicing pattern recognition
- Understanding institutional price action theories
- Analyzing market structure visually
**Critical Understanding:**
- All signals and levels are REFERENCE POINTS for study - not trading recommendations
- The indicator displays patterns based on historical price action - it cannot predict future movements
- Smart Money Concepts is a theoretical framework - market behavior varies
- Backtested or historical results shown do not guarantee future performance
- No indicator can account for all market variables, news events, or changing conditions
**Proper Use:**
This tool is meant to assist in learning technical analysis concepts. Users must develop their own analysis skills, risk management strategies, and trading plans. The displayed patterns require interpretation within broader market context.
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⚙️ CUSTOMIZATION OPTIONS
**Adjustable Parameters:**
- Order Block: Minimum size threshold, maximum count displayed
- Fair Value Gaps: Toggle visibility, maximum count
- Market Structure: Swing detection length, BOS/CHoCH display
- Signals: Entry/SL/TP calculation method, HTF filter toggle
- Visual Settings: Colors, line styles, label sizes, panel position
**Timeframe Compatibility:**
Works on all timeframes from 1-minute to monthly charts. The swing detection automatically scales based on timeframe.
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⚠️ DISCLAIMER
This indicator is for educational and informational purposes only. It does not constitute financial advice or trading recommendations. Trading involves substantial risk of loss. Past patterns and historical analysis do not indicate future results. Users are responsible for their own trading decisions and risk management. The author assumes no liability for trading losses.
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🔧 ALERT FUNCTIONALITY
Built-in alert conditions notify you when:
- New order blocks are detected
- Market structure changes occur (BOS/CHoCH)
- Reference entry signals appear
Configure alerts through TradingView's alert system.
ICT Donchian Smart Money Structure (Expo)█ Concept Overview
The Inner Circle Trader (ICT) methodology is focused on understanding the actions and implications of the so-called "smart money" - large institutions and professional traders who often influence market movements. Key to this is the concept of market structure and how it can provide insights into potential price moves.
Over time, however, there has been a notable shift in how some traders interpret and apply this methodology. Initially, it was designed with a focus on the fractal nature of markets. Fractals are recurring patterns in price action that are self-similar across different time scales, providing a nuanced and dynamic understanding of market structure.
However, as the ICT methodology has grown in popularity, there has been a drift away from this fractal-based perspective. Instead, many traders have started to focus more on pivot points as their primary tool for understanding market structure.
Pivot points provide static levels of potential support and resistance. While they can be useful in some contexts, relying heavily on them could provide a skewed perspective of market structure. They offer a static, backward-looking view that may not accurately reflect real-time changes in market sentiment or the dynamic nature of markets.
This shift from a fractal-based perspective to a pivot point perspective has significant implications. It can lead traders to misinterpret market structure and potentially make incorrect trading decisions.
To highlight this issue, you've developed a Donchian Structure indicator that mirrors the use of pivot points. The Donchian Channels are formed by the highest high and the lowest low over a certain period, providing another representation of potential market extremes. The fact that the Donchian Structure indicator produces the same results as pivot points underscores the inherent limitations of relying too heavily on these tools.
While the Donchian Structure indicator or pivot points can be useful tools, they should not replace the original, fractal-based perspective of the ICT methodology. These tools can provide a broad overview of market structure but may not capture the intricate dynamics and real-time changes that a fractal-based approach can offer.
It's essential for traders to understand these differences and to apply these tools correctly within the broader context of the ICT methodology and the Smart Money Concept Structure. A well-rounded approach that incorporates fractals, along with other tools and forms of analysis, is likely to provide a more accurate and comprehensive understanding of market structure.
█ Smart Money Concept - Misunderstandings
The Smart Money Concept is a popular concept among traders, and it's based on the idea that the "smart money" - typically large institutional investors, market makers, and professional traders - have superior knowledge or information, and their actions can provide valuable insight for other traders.
One of the biggest misunderstandings with this concept is the belief that tracking smart money activity can guarantee profitable trading.
█ Here are a few common misconceptions:
Following Smart Money Equals Guaranteed Success: Many traders believe that if they can follow the smart money, they will be successful. However, tracking the activity of large institutional investors and other professionals isn't easy, as they use complex strategies, have access to information not available to the public, and often intentionally hide their moves to prevent others from detecting their strategies.
Instantaneous Reaction and Results: Another misconception is that market movements will reflect smart money actions immediately. However, large institutions often slowly accumulate or distribute positions over time to avoid moving the market drastically. As a result, their actions might not produce an immediate noticeable effect on the market.
Smart Money Always Wins: It's not accurate to assume that smart money always makes the right decisions. Even the most experienced institutional investors and professional traders make mistakes, misjudge market conditions, or are affected by unpredictable events.
Smart Money Activity is Transparent: Understanding what constitutes smart money activity can be quite challenging. There are many indicators and metrics that traders use to try and track smart money, such as the COT (Commitments of Traders) reports, Level II market data, block trades, etc. However, these can be difficult to interpret correctly and are often misleading.
Assuming Uniformity Among Smart Money: 'Smart Money' is not a monolithic entity. Different institutional investors and professional traders have different strategies, risk tolerances, and investment horizons. What might be a good trade for a long-term institutional investor might not be a good trade for a short-term professional trader, and vice versa.
█ Market Structure
The Smart Money Concept Structure deals with the interpretation of price action that forms the market structure, focusing on understanding key shifts or changes in the market that may indicate where 'smart money' (large institutional investors and professional traders) might be moving in the market.
█ Three common concepts in this regard are Change of Character (CHoCH), and Shift in Market Structure (SMS), Break of Structure (BMS/BoS).
Change of Character (CHoCH): This refers to a noticeable change in the behavior of price movement, which could suggest that a shift in the market might be about to occur. This might be signaled by a sudden increase in volatility, a break of a trendline, or a change in volume, among other things.
Shift in Market Structure (SMS): This is when the overall structure of the market changes, suggesting a potential new trend. It usually involves a sequence of lower highs and lower lows for a downtrend, or higher highs and higher lows for an uptrend.
Break of Structure (BMS/BoS): This is when a previously defined trend or pattern in the price structure is broken, which may suggest a trend continuation.
A key component of this approach is the use of fractals, which are repeating patterns in price action that can give insights into potential market reversals. They appear at all scales of a price chart, reflecting the self-similar nature of markets.
█ Market Structure - Misunderstandings
One of the biggest misunderstandings about the ICT approach is the over-reliance or incorrect application of pivot points. Pivot points are a popular tool among traders due to their simplicity and easy-to-understand nature. However, when it comes to the Smart Money Concept and trying to follow the steps of professional traders or large institutions, relying heavily on pivot points can create misconceptions and lead to confusion. Here's why:
Delayed and Static Information: Pivot points are inherently backward-looking because they're calculated based on the previous period's data. As such, they may not reflect real-time market dynamics or sudden changes in market sentiment. Furthermore, they present a static view of market structure, delineating pre-defined levels of support and resistance. This static nature can be misleading because markets are fundamentally dynamic and constantly changing due to countless variables.
Inadequate Representation of Market Complexity: Markets are influenced by a myriad of factors, including economic indicators, geopolitical events, institutional actions, and market sentiment, among others. Relying on pivot points alone for reading market structure oversimplifies this complexity and can lead to a myopic understanding of market dynamics.
False Signals and Misinterpretations: Pivot points can often give false signals, especially in volatile markets. Prices might react to these levels temporarily but then continue in the original direction, leading to potential misinterpretation of market structure and sentiment. Also, a trader might wrongly perceive a break of a pivot point as a significant market event, when in fact, it could be due to random price fluctuations or temporary volatility.
Over-simplification: Viewing market structure only through the lens of pivot points simplifies the market to static levels of support and resistance, which can lead to misinterpretation of market dynamics. For instance, a trader might view a break of a pivot point as a definite sign of a trend, when it could just be a temporary price spike.
Ignoring the Fractal Nature of Markets: In the context of the Smart Money Concept Structure, understanding the fractal nature of markets is crucial. Fractals are self-similar patterns that repeat at all scales and provide a more dynamic and nuanced understanding of market structure. They can help traders identify shifts in market sentiment or direction in real-time, providing more relevant and timely information compared to pivot points.
The key takeaway here is not that pivot points should be entirely avoided or that they're useless. They can provide valuable insights and serve as a useful tool in a trader's toolbox when used correctly. However, they should not be the sole or primary method for understanding the market structure, especially in the context of the Smart Money Concept Structure.
█ Fractals
Instead, traders should aim for a comprehensive understanding of markets that incorporates a range of tools and concepts, including but not limited to fractals, order flow, volume analysis, fundamental analysis, and, yes, even pivot points. Fractals offer a more dynamic and nuanced view of the market. They reflect the recursive nature of markets and can provide valuable insights into potential market reversals. Because they appear at all scales of a price chart, they can provide a more holistic and real-time understanding of market structure.
In contrast, the Smart Money Concept Structure, focusing on fractals and comprehensive market analysis, aims to capture a more holistic and real-time view of the market. Fractals, being self-similar patterns that repeat at different scales, offer a dynamic understanding of market structure. As a result, they can help to identify shifts in market sentiment or direction as they happen, providing a more detailed and timely perspective.
Furthermore, a comprehensive market analysis would consider a broader set of factors, including order flow, volume analysis, and fundamental analysis, which could provide additional insights into 'smart money' actions.
█ Donchian Structure
Donchian Channels are a type of indicator used in technical analysis to identify potential price breakouts and trends, and they may also serve as a tool for understanding market structure. The channels are formed by taking the highest high and the lowest low over a certain number of periods, creating an envelope of price action.
Donchian Channels (or pivot points) can be useful tools for providing a general view of market structure, and they may not capture the intricate dynamics associated with the Smart Money Concept Structure. A more nuanced approach, centered on real-time fractals and a comprehensive analysis of various market factors, offers a more accurate understanding of 'smart money' actions and market structure.
█ Here is why Donchian Structure may be misleading:
Lack of Nuance: Donchian Channels, like pivot points, provide a simplified view of market structure. They don't take into account the nuanced behaviors of price action or the complex dynamics between buyers and sellers that can be critical in the Smart Money Concept Structure.
Limited Insights into 'Smart Money' Actions: While Donchian Channels can highlight potential breakout points and trends, they don't necessarily provide insights into the actions of 'smart money'. These large institutional traders often use sophisticated strategies that can't be easily inferred from price action alone.
█ Indicator Overview
We have built this Donchian Structure indicator to show that it returns the same results as using pivot points. The Donchian Structure indicator can be a useful tool for market analysis. However, it should not be seen as a direct replacement or equivalent to the original Smart Money concept, nor should any indicator based on pivot points. The indicator highlights the importance of understanding what kind of trading tools we use and how they can affect our decisions.
The Donchian Structure Indicator displays CHoCH, SMS, BoS/BMS, as well as premium and discount areas. This indicator plots everything in real-time and allows for easy backtesting on any market and timeframe. A unique candle coloring has been added to make it more engaging and visually appealing when identifying new trading setups and strategies. This candle coloring is "leading," meaning it can signal a structural change before it actually happens, giving traders ample time to plan their next trade accordingly.
█ How to use
The indicator is great for traders who want to simplify their view on the market structure and easily backtest Smart Money Concept Strategies. The added candle coloring function serves as a heads-up for structure change or can be used as trend confirmation. This new candle coloring feature can generate many new Smart Money Concepts strategies.
█ Features
Market Structure
The market structure is based on the Donchian channel, to which we have added what we call 'Structure Response'. This addition makes the indicator more useful, especially in trending markets. The core concept involves traders buying at a discount and selling or shorting at a premium, depending on the order flow. Structure response enables traders to determine the order flow more clearly. Consequently, more trading opportunities will appear in trending markets.
Structure Candles
Structure Candles highlight the current order flow and are significantly more responsive to structural changes. They can provide traders with a heads-up before a break in structure occurs
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
RAVP+Fib[Trendoscope®+ChartPrime][Surge.Guru]This is a sophisticated multi-dimensional technical analysis indicator that combines two powerful analytical tools: Rolling Angled Volume Profile and Fibonacci Trend Analysis. Here's a comprehensive breakdown of its functionality:
🎯 Core Purpose
The indicator provides a comprehensive market structure analysis by combining volume-based support/resistance levels with Fibonacci-based trend and retracement levels.
📊 Component 1: Rolling Angled Volume Profile (Trendoscope®)
What It Does:
Creates a dynamic, angled volume profile that shows where trading volume has occurred over a specified period
Unlike traditional horizontal volume profiles, this one angles with price movement, making it more relevant for trending markets
Key Features:
Loopback Period (50 bars default): Determines how many bars to analyze for volume distribution
Angled Calculation: The profile lines follow the price trend angle between start and end points
Volume-Based Width: Profile lines are thicker/wider where more volume was traded
Dynamic Box: Creates a bounding box around the active volume area
Visual Output:
Dotted lines (customizable) showing volume concentration
Yellow-colored profile lines with high transparency (98% by default)
Volume hotspots appear as denser line clusters
📈 Component 2: Fibonacci Trend (ChartPrime)
What It Does:
Uses a Supertrend indicator to determine market direction
Draws Fibonacci retracement levels based on the detected trend extremes
Provides dynamic support/resistance levels that extend into the future
Key Features:
Trend Detection: Uses Supertrend with customizable factor (2.0 default)
Auto-Extending Levels: Fibonacci lines automatically extend as new highs/lows are made
Multiple Fibonacci Levels: 0.236, 0.382, 0.5, 0.618, 0.786
Visual Fill: Colored areas between key Fibonacci levels (0.618-0.786 and 0.5-0.786)
Visual Output:
Uptrend: Green coloring (#26905d)
Downtrend: Purple coloring (#74286d)
Fibonacci Lines: Horizontal levels with price labels
Extension: Projects 15 bars into the future
🔄 Synergistic Benefits
1. Multi-Timeframe Analysis
Volume Profile shows historical volume distribution
Fibonacci shows current trend structure and potential reversal zones
2. Confirmation System
When Fibonacci levels align with volume profile dense areas → stronger support/resistance
Volume validates Fibonacci levels with actual trading activity
3. Dynamic Adaptation
Both components update in real-time as market structure evolves
Volume profile "rolls" with price action
Fibonacci levels extend with trend continuation
⚙️ Key Input Parameters
Volume Profile Settings:
Loopback: Analysis period (50 bars)
Number of Profile Lines: Density of volume display (2000 lines)
Use Confirmed Bars: Wait for bar completion
Fibonacci Settings:
Trend Factor: Supertrend sensitivity (2.0)
Extend: Future projection bars (15)
Fibonacci Levels: Customizable retracement ratios
🎨 Visual Design
Subtle Volume Profile: High transparency (98%) prevents chart clutter
Clear Fibonacci Levels: Bold colors for easy trend identification
Professional Layout: Clean, organized display without overwhelming the price chart
💡 Trading Applications
For Trend Following:
Use Fibonacci levels for entry points in direction of trend
Volume profile shows optimal accumulation zones
For Reversal Trading:
Watch for price reactions at Fibonacci levels that align with volume clusters
Volume profile indicates potential reversal zones
For Risk Management:
Fibonacci levels provide natural stop-loss and take-profit targets
Volume areas show high-liquidity zones for optimal positioning
🚀 Unique Value Proposition
This indicator stands out because it:
Combines volume and price analysis in one tool
Adapts to market angles rather than using static horizontal levels
Provides forward-looking projections through Fibonacci extensions
Offers customizable visibility for each component
Maintains chart clarity despite displaying complex information
Liquidity Grab + RSI Divergence═══════════════════════════════════════════════════════════════
LIQUIDITY GRAB + RSI DIVERGENCE INDICATOR
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📌 OVERVIEW
This indicator identifies high-probability reversals by combining:
• Liquidity sweeps (stop hunts)
• RSI divergence confirmation
• Filters false breakouts automatically
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🟢 BUY SIGNAL (Green Triangle Up)
REQUIRES BOTH CONDITIONS:
1. Liquidity Grab Below Previous Low
• Price breaks BELOW recent low
• Candle CLOSES ABOVE that low
• Traps sellers who shorted the breakdown
2. Bullish RSI Divergence
• Price: Lower Low (LL)
• RSI: Higher Low (HL)
• Shows weakening downward momentum
➜ Result: Potential bullish reversal
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🔴 SELL SIGNAL (Red Triangle Down)
REQUIRES BOTH CONDITIONS:
1. Liquidity Grab Above Previous High
• Price breaks ABOVE recent high
• Candle CLOSES BELOW that high
• Traps buyers who bought the breakout
2. Bearish RSI Divergence
• Price: Higher High (HH)
• RSI: Lower High (LH)
• Shows weakening upward momentum
➜ Result: Potential bearish reversal
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📊 VISUAL INDICATORS
Main Signals:
🔺 Large Green Triangle = BUY (Liq Grab + Bullish Div)
🔻 Large Red Triangle = SELL (Liq Grab + Bearish Div)
Reference Levels:
━ Red Line = Previous High Level
━ Green Line = Previous Low Level
Additional Markers (Optional):
○ Small Green Circle = Liquidity grab low only
○ Small Red Circle = Liquidity grab high only
✕ Small Blue Cross = Bullish divergence only
✕ Small Orange Cross = Bearish divergence only
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⚙️ SETTINGS
1. Lookback Period (Default: 20)
• Range: 5-100
• Sets how far back to identify previous highs/lows
• Higher = fewer but stronger levels
• Lower = more frequent but weaker levels
2. RSI Length (Default: 14)
• Range: 5-50
• Standard RSI calculation period
• 14 is industry standard
3. RSI Divergence Lookback (Default: 5)
• Range: 3-20
• Controls pivot point sensitivity
• Higher = fewer divergence signals
• Lower = more divergence signals
4. Show Labels (Default: ON)
• Toggle BUY/SELL text labels
• Disable for cleaner chart view
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💡 HOW TO USE
Step 1: WAIT FOR CONFIRMATION
• Only trade LARGE TRIANGLE signals
• Ignore small circles/crosses alone
Step 2: CHECK TIMEFRAME
• Best on: 15min, 1H, 4H, Daily
• Avoid: 1min, 5min (too noisy)
Step 3: CONFIRM CONTEXT
• Check overall market trend
• Identify key support/resistance
• Look for confluence with price action
Step 4: ENTRY & RISK MANAGEMENT
• Enter on signal candle close or pullback
• Stop loss below/above the liquidity grab wick
• Target: Previous swing high/low or key levels
• Risk/Reward: Minimum 1:2 ratio
Step 5: SET ALERTS
• Create alert for "BUY Signal"
• Create alert for "SELL Signal"
• Never miss opportunities
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✅ BEST PRACTICES
DO:
✓ Use on multiple timeframes for confluence
✓ Combine with support/resistance zones
✓ Wait for both conditions (liq grab + divergence)
✓ Practice on demo account first
✓ Use proper position sizing
DON'T:
✗ Trade every small circle/cross
✗ Use on very low timeframes (<15min)
✗ Ignore overall market context
✗ Trade without stop loss
✗ Risk more than 1-2% per trade
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⚠️ IMPORTANT NOTES
• This is a CONFIRMATION tool, not a holy grail
• No indicator is 100% accurate
• Combine with your trading strategy
• Backtest on your preferred instruments
• Adjust parameters for your trading style
• Higher timeframes = more reliable signals
• Always use risk management
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🔔 ALERTS INCLUDED
Two alert conditions are built-in:
1. "BUY Signal" - Liquidity Grab + Bullish RSI Divergence
2. "SELL Signal" - Liquidity Grab + Bearish RSI Divergence
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📈 RECOMMENDED SETTINGS BY TIMEFRAME
5-15 Min Charts:
• Lookback: 10-15
• RSI Length: 14
• RSI Div Lookback: 3-5
1H-4H Charts:
• Lookback: 20-30
• RSI Length: 14
• RSI Div Lookback: 5-7
Daily Charts:
• Lookback: 30-50
• RSI Length: 14
• RSI Div Lookback: 7-10
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Good luck and trade safe! 🚀
Market Zone Analyzer[BullByte]Understanding the Market Zone Analyzer
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1. Purpose of the Indicator
The Market Zone Analyzer is a Pine Script™ (version 6) indicator designed to streamline market analysis on TradingView. Rather than scanning multiple separate tools, it unifies four core dimensions—trend strength, momentum, price action, and market activity—into a single, consolidated view. By doing so, it helps traders:
• Save time by avoiding manual cross-referencing of disparate signals.
• Reduce decision-making errors that can arise from juggling multiple indicators.
• Gain a clear, reliable read on whether the market is in a bullish, bearish, or sideways phase, so they can more confidently decide to enter, exit, or hold a position.
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2. Why a Trader Should Use It
• Unified View: Combines all essential market dimensions into one easy-to-read score and dashboard, eliminating the need to piece together signals manually.
• Adaptability: Automatically adjusts its internal weighting for trend, momentum, and price action based on current volatility. Whether markets are choppy or calm, the indicator remains relevant.
• Ease of Interpretation: Outputs a simple “BULLISH,” “BEARISH,” or “SIDEWAYS” label, supplemented by an intuitive on-chart dashboard and an oscillator plot that visually highlights market direction.
• Reliability Features: Built-in smoothing of the net score and hysteresis logic (requiring consecutive confirmations before flips) minimize false signals during noisy or range-bound phases.
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3. Why These Specific Indicators?
This script relies on a curated set of well-established technical tools, each chosen for its particular strength in measuring one of the four core dimensions:
1. Trend Strength:
• ADX/DMI (Average Directional Index / Directional Movement Index): Measures how strong a trend is, and whether the +DI line is above the –DI line (bullish) or vice versa (bearish).
• Moving Average Slope (Fast MA vs. Slow MA): Compares a shorter-period SMA to a longer-period SMA; if the fast MA sits above the slow MA, it confirms an uptrend, and vice versa for a downtrend.
• Ichimoku Cloud Differential (Senkou A vs. Senkou B): Provides a forward-looking view of trend direction; Senkou A above Senkou B signals bullishness, and the opposite signals bearishness.
2. Momentum:
• Relative Strength Index (RSI): Identifies overbought (above its dynamically calculated upper bound) or oversold (below its lower bound) conditions; changes in RSI often precede price reversals.
• Stochastic %K: Highlights shifts in short-term momentum by comparing closing price to the recent high/low range; values above its upper band signal bullish momentum, below its lower band signal bearish momentum.
• MACD Histogram: Measures the difference between the MACD line and its signal line; a positive histogram indicates upward momentum, a negative histogram indicates downward momentum.
3. Price Action:
• Highest High / Lowest Low (HH/LL) Range: Over a defined lookback period, this captures breakout or breakdown levels. A closing price near the recent highs (with a positive MA slope) yields a bullish score, and near the lows (with a negative MA slope) yields a bearish score.
• Heikin-Ashi Doji Detection: Uses Heikin-Ashi candles to identify indecision or continuation patterns. A small Heikin-Ashi body (doji) relative to recent volatility is scored as neutral; a larger body in the direction of the MA slope is scored bullish or bearish.
• Candle Range Measurement: Compares each candle’s high-low range against its own dynamic band (average range ± standard deviation). Large candles aligning with the prevailing trend score bullish or bearish accordingly; unusually small candles can indicate exhaustion or consolidation.
4. Market Activity:
• Bollinger Bands Width (BBW): Measures the distance between BB upper and lower bands; wide bands indicate high volatility, narrow bands indicate low volatility.
• Average True Range (ATR): Quantifies average price movement (volatility). A sudden spike in ATR suggests a volatile environment, while a contraction suggests calm.
• Keltner Channels Width (KCW): Similar to BBW but uses ATR around an EMA. Provides a second layer of volatility context, confirming or contrasting BBW readings.
• Volume (with Moving Average): Compares current volume to its moving average ± standard deviation. High volume validates strong moves; low volume signals potential lack of conviction.
By combining these tools, the indicator captures trend direction, momentum strength, price-action nuances, and overall market energy, yielding a more balanced and comprehensive assessment than any single tool alone.
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4. What Makes This Indicator Stand Out
• Multi-Dimensional Analysis: Rather than relying on a lone oscillator or moving average crossover, it simultaneously evaluates trend, momentum, price action, and activity.
• Dynamic Weighting: The relative importance of trend, momentum, and price action adjusts automatically based on real-time volatility (Market Activity State). For example, in highly volatile conditions, trend and momentum signals carry more weight; in calm markets, price action signals are prioritized.
• Stability Mechanisms:
• Smoothing: The net score is passed through a short moving average, filtering out noise, especially on lower timeframes.
• Hysteresis: Both Market Activity State and the final bullish/bearish/sideways zone require two consecutive confirmations before flipping, reducing whipsaw.
• Visual Interpretation: A fully customizable on-chart dashboard displays each sub-indicator’s value, regime, score, and comment, all color-coded. The oscillator plot changes color to reflect the current market zone (green for bullish, red for bearish, gray for sideways) and shows horizontal threshold lines at +2, 0, and –2.
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5. Recommended Timeframes
• Short-Term (5 min, 15 min): Day traders and scalpers can benefit from rapid signals, but should enable smoothing (and possibly disable hysteresis) to reduce false whipsaws.
• Medium-Term (1 h, 4 h): Swing traders find a balance between responsiveness and reliability. Less smoothing is required here, and the default parameters (e.g., ADX length = 14, RSI length = 14) perform well.
• Long-Term (Daily, Weekly): Position traders tracking major trends can disable smoothing for immediate raw readings, since higher-timeframe noise is minimal. Adjust lookback lengths (e.g., increase adxLength, rsiLength) if desired for slower signals.
Tip: If you keep smoothing off, stick to timeframes of 1 h or higher to avoid excessive signal “chatter.”
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6. How Scoring Works
A. Individual Indicator Scores
Each sub-indicator is assigned one of three discrete scores:
• +1 if it indicates a bullish condition (e.g., RSI above its dynamically calculated upper bound).
• 0 if it is neutral (e.g., RSI between upper and lower bounds).
• –1 if it indicates a bearish condition (e.g., RSI below its dynamically calculated lower bound).
Examples of individual score assignments:
• ADX/DMI:
• +1 if ADX ≥ adxThreshold and +DI > –DI (strong bullish trend)
• –1 if ADX ≥ adxThreshold and –DI > +DI (strong bearish trend)
• 0 if ADX < adxThreshold (trend strength below threshold)
• RSI:
• +1 if RSI > RSI_upperBound
• –1 if RSI < RSI_lowerBound
• 0 otherwise
• ATR (as part of Market Activity):
• +1 if ATR > (ATR_MA + stdev(ATR))
• –1 if ATR < (ATR_MA – stdev(ATR))
• 0 otherwise
Each of the four main categories shares this same +1/0/–1 logic across their sub-components.
B. Category Scores
Once each sub-indicator reports +1, 0, or –1, these are summed within their categories as follows:
• Trend Score = (ADX score) + (MA slope score) + (Ichimoku differential score)
• Momentum Score = (RSI score) + (Stochastic %K score) + (MACD histogram score)
• Price Action Score = (Highest-High/Lowest-Low score) + (Heikin-Ashi doji score) + (Candle range score)
• Market Activity Raw Score = (BBW score) + (ATR score) + (KC width score) + (Volume score)
Each category’s summed value can range between –3 and +3 (for Trend, Momentum, and Price Action), and between –4 and +4 for Market Activity raw.
C. Market Activity State and Dynamic Weight Adjustments
Rather than contributing directly to the netScore like the other three categories, Market Activity determines how much weight to assign to Trend, Momentum, and Price Action:
1. Compute Market Activity Raw Score by summing BBW, ATR, KCW, and Volume individual scores (each +1/0/–1).
2. Bucket into High, Medium, or Low Activity:
• High if raw Score ≥ 2 (volatile market).
• Low if raw Score ≤ –2 (calm market).
• Medium otherwise.
3. Apply Hysteresis (if enabled): The state only flips after two consecutive bars register the same high/low/medium label.
4. Set Category Weights:
• High Activity: Trend = 50 %, Momentum = 35 %, Price Action = 15 %.
• Low Activity: Trend = 25 %, Momentum = 20 %, Price Action = 55 %.
• Medium Activity: Use the trader’s base weight inputs (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 % by default).
D. Calculating the Net Score
5. Normalize Base Weights (so that the sum of Trend + Momentum + Price Action always equals 100 %).
6. Determine Current Weights based on the Market Activity State (High/Medium/Low).
7. Compute Each Category’s Contribution: Multiply (categoryScore) × (currentWeight).
8. Sum Contributions to get the raw netScore (a floating-point value that can exceed ±3 when scores are strong).
9. Smooth the netScore over two bars (if smoothing is enabled) to reduce noise.
10. Apply Hysteresis to the Final Zone:
• If the smoothed netScore ≥ +2, the bar is classified as “Bullish.”
• If the smoothed netScore ≤ –2, the bar is classified as “Bearish.”
• Otherwise, it is “Sideways.”
• To prevent rapid flips, the script requires two consecutive bars in the new zone before officially changing the displayed zone (if hysteresis is on).
E. Thresholds for Zone Classification
• BULLISH: netScore ≥ +2
• BEARISH: netScore ≤ –2
• SIDEWAYS: –2 < netScore < +2
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7. Role of Volatility (Market Activity State) in Scoring
Volatility acts as a dynamic switch that shifts which category carries the most influence:
1. High Activity (Volatile):
• Detected when at least two sub-scores out of BBW, ATR, KCW, and Volume equal +1.
• The script sets Trend weight = 50 % and Momentum weight = 35 %. Price Action weight is minimized at 15 %.
• Rationale: In volatile markets, strong trending moves and momentum surges dominate, so those signals are more reliable than nuanced candle patterns.
2. Low Activity (Calm):
• Detected when at least two sub-scores out of BBW, ATR, KCW, and Volume equal –1.
• The script sets Price Action weight = 55 %, Trend = 25 %, and Momentum = 20 %.
• Rationale: In quiet, sideways markets, subtle price-action signals (breakouts, doji patterns, small-range candles) are often the best early indicators of a new move.
3. Medium Activity (Balanced):
• Raw Score between –1 and +1 from the four volatility metrics.
• Uses whatever base weights the trader has specified (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 %).
Because volatility can fluctuate rapidly, the script employs hysteresis on Market Activity State: a new High or Low state must occur on two consecutive bars before weights actually shift. This avoids constant back-and-forth weight changes and provides more stability.
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8. Scoring Example (Hypothetical Scenario)
• Symbol: Bitcoin on a 1-hour chart.
• Market Activity: Raw volatility sub-scores show BBW (+1), ATR (+1), KCW (0), Volume (+1) → Total raw Score = +3 → High Activity.
• Weights Selected: Trend = 50 %, Momentum = 35 %, Price Action = 15 %.
• Trend Signals:
• ADX strong and +DI > –DI → +1
• Fast MA above Slow MA → +1
• Ichimoku Senkou A > Senkou B → +1
→ Trend Score = +3
• Momentum Signals:
• RSI above upper bound → +1
• MACD histogram positive → +1
• Stochastic %K within neutral zone → 0
→ Momentum Score = +2
• Price Action Signals:
• Highest High/Lowest Low check yields 0 (close not near extremes)
• Heikin-Ashi doji reading is neutral → 0
• Candle range slightly above upper bound but trend is strong, so → +1
→ Price Action Score = +1
• Compute Net Score (before smoothing):
• Trend contribution = 3 × 0.50 = 1.50
• Momentum contribution = 2 × 0.35 = 0.70
• Price Action contribution = 1 × 0.15 = 0.15
• Raw netScore = 1.50 + 0.70 + 0.15 = 2.35
• Since 2.35 ≥ +2 and hysteresis is met, the final zone is “Bullish.”
Although the netScore lands at 2.35 (Bullish), smoothing might bring it slightly below 2.00 on the first bar (e.g., 1.90), in which case the script would wait for a second consecutive reading above +2 before officially classifying the zone as Bullish (if hysteresis is enabled).
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9. Correlation Between Categories
The four categories—Trend Strength, Momentum, Price Action, and Market Activity—often reinforce or offset one another. The script takes advantage of these natural correlations:
• Bullish Alignment: If ADX is strong and pointed upward, fast MA is above slow MA, and Ichimoku is positive, that usually coincides with RSI climbing above its upper bound and the MACD histogram turning positive. In such cases, both Trend and Momentum categories generate +1 or +2. Because the Market Activity State is likely High (given the accompanying volatility), Trend and Momentum weights are at their peak, so the netScore quickly crosses into Bullish territory.
• Sideways/Consolidation: During a low-volatility, sideways phase, ADX may fall below its threshold, MAs may flatten, and RSI might hover in the neutral band. However, subtle price-action signals (like a small breakout candle or a Heikin-Ashi candle with a slight bias) can still produce a +1 in the Price Action category. If Market Activity is Low, Price Action’s weight (55 %) can carry enough influence—even if Trend and Momentum are neutral—to push the netScore out of “Sideways” into a mild bullish or bearish bias.
• Opposing Signals: When Trend is bullish but Momentum turns negative (for example, price continues up but RSI rolls over), the two scores can partially cancel. Market Activity may remain Medium, in which case the netScore lingers near zero (Sideways). The trader can then wait for either a clearer momentum shift or a fresh price-action breakout before committing.
By dynamically recognizing these correlations and adjusting weights, the indicator ensures that:
• When Trend and Momentum align (and volatility supports it), the netScore leaps strongly into Bullish or Bearish.
• When Trend is neutral but Price Action shows an early move in a low-volatility environment, Price Action’s extra weight in the Low Activity State can still produce actionable signals.
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10. Market Activity State & Its Role (Detailed)
The Market Activity State is not a direct category score—it is an overarching context setter for how heavily to trust Trend, Momentum, or Price Action. Here’s how it is derived and applied:
1. Calculate Four Volatility Sub-Scores:
• BBW: Compare the current band width to its own moving average ± standard deviation. If BBW > (BBW_MA + stdev), assign +1 (high volatility); if BBW < (BBW_MA × 0.5), assign –1 (low volatility); else 0.
• ATR: Compare ATR to its moving average ± standard deviation. A spike above the upper threshold is +1; a contraction below the lower threshold is –1; otherwise 0.
• KCW: Same logic as ATR but around the KCW mean.
• Volume: Compare current volume to its volume MA ± standard deviation. Above the upper threshold is +1; below the lower threshold is –1; else 0.
2. Sum Sub-Scores → Raw Market Activity Score: Range between –4 and +4.
3. Assign Market Activity State:
• High Activity: Raw Score ≥ +2 (at least two volatility metrics are strongly spiking).
• Low Activity: Raw Score ≤ –2 (at least two metrics signal unusually low volatility or thin volume).
• Medium Activity: Raw Score is between –1 and +1 inclusive.
4. Hysteresis for Stability:
• If hysteresis is enabled, a new state only takes hold after two consecutive bars confirm the same High, Medium, or Low label.
• This prevents the Market Activity State from bouncing around when volatility is on the fence.
5. Set Category Weights Based on Activity State:
• High Activity: Trend = 50 %, Momentum = 35 %, Price Action = 15 %.
• Low Activity: Trend = 25 %, Momentum = 20 %, Price Action = 55 %.
• Medium Activity: Use trader’s base weights (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 %).
6. Impact on netScore: Because category scores (–3 to +3) multiply by these weights, High Activity amplifies the effect of strong Trend and Momentum scores; Low Activity amplifies the effect of Price Action.
7. Market Context Tooltip: The dashboard includes a tooltip summarizing the current state—e.g., “High activity, trend and momentum prioritized,” “Low activity, price action prioritized,” or “Balanced market, all categories considered.”
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11. Category Weights: Base vs. Dynamic
Traders begin by specifying base weights for Trend Strength, Momentum, and Price Action that sum to 100 %. These apply only when volatility is in the Medium band. Once volatility shifts:
• High Volatility Overrides:
• Trend jumps from its base (e.g., 40 %) to 50 %.
• Momentum jumps from its base (e.g., 30 %) to 35 %.
• Price Action is reduced to 15 %.
Example: If base weights were Trend = 40 %, Momentum = 30 %, Price Action = 30 %, then in High Activity they become 50/35/15. A Trend score of +3 now contributes 3 × 0.50 = +1.50 to netScore; a Momentum +2 contributes 2 × 0.35 = +0.70. In total, Trend + Momentum can easily push netScore above the +2 threshold on its own.
• Low Volatility Overrides:
• Price Action leaps from its base (30 %) to 55 %.
• Trend falls to 25 %, Momentum falls to 20 %.
Why? When markets are quiet, subtle candle breakouts, doji patterns, and small-range expansions tend to foreshadow the next swing more effectively than raw trend readings. A Price Action score of +3 in this state contributes 3 × 0.55 = +1.65, which can carry the netScore toward +2—even if Trend and Momentum are neutral or only mildly positive.
Because these weight shifts happen only after two consecutive bars confirm a High or Low state (if hysteresis is on), the indicator avoids constantly flipping its emphasis during borderline volatility phases.
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12. Dominant Category Explained
Within the dashboard, a label such as “Trend Dominant,” “Momentum Dominant,” or “Price Action Dominant” appears when one category’s absolute weighted contribution to netScore is the largest. Concretely:
• Compute each category’s weighted contribution = (raw category score) × (current weight).
• Compare the absolute values of those three contributions.
• The category with the highest absolute value is flagged as Dominant for that bar.
Why It Matters:
• Momentum Dominant: Indicates that the combined force of RSI, Stochastic, and MACD (after weighting) is pushing netScore farther than either Trend or Price Action. In practice, it means that short-term sentiment and speed of change are the primary drivers right now, so traders should watch for continued momentum signals before committing to a trade.
• Trend Dominant: Means ADX, MA slope, and Ichimoku (once weighted) outweigh the other categories. This suggests a strong directional move is in place; trend-following entries or confirming pullbacks are likely to succeed.
• Price Action Dominant: Occurs when breakout/breakdown patterns, Heikin-Ashi candle readings, and range expansions (after weighting) are the most influential. This often happens in calmer markets, where subtle shifts in candle structure can foreshadow bigger moves.
By explicitly calling out which category is carrying the most weight at any moment, the dashboard gives traders immediate insight into why the netScore is tilting toward bullish, bearish, or sideways.
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13. Oscillator Plot: How to Read It
The “Net Score” oscillator sits below the dashboard and visually displays the smoothed netScore as a line graph. Key features:
1. Value Range: In normal conditions it oscillates roughly between –3 and +3, but extreme confluences can push it outside that range.
2. Horizontal Threshold Lines:
• +2 Line (Bullish threshold)
• 0 Line (Neutral midline)
• –2 Line (Bearish threshold)
3. Zone Coloring:
• Green Background (Bullish Zone): When netScore ≥ +2.
• Red Background (Bearish Zone): When netScore ≤ –2.
• Gray Background (Sideways Zone): When –2 < netScore < +2.
4. Dynamic Line Color:
• The plotted netScore line itself is colored green in a Bullish Zone, red in a Bearish Zone, or gray in a Sideways Zone, creating an immediate visual cue.
Interpretation Tips:
• Crossing Above +2: Signals a strong enough combined trend/momentum/price-action reading to classify as Bullish. Many traders wait for a clear crossing plus a confirmation candle before entering a long position.
• Crossing Below –2: Indicates a strong Bearish signal. Traders may consider short or exit strategies.
• Rising Slope, Even Below +2: If netScore climbs steadily from neutral toward +2, it demonstrates building bullish momentum.
• Divergence: If price makes a higher high but the oscillator fails to reach a new high, it can warn of weakening momentum and a potential reversal.
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14. Comments and Their Necessity
Every sub-indicator (ADX, MA slope, Ichimoku, RSI, Stochastic, MACD, HH/LL, Heikin-Ashi, Candle Range, BBW, ATR, KCW, Volume) generates a short comment that appears in the detailed dashboard. Examples:
• “Strong bullish trend” or “Strong bearish trend” for ADX/DMI
• “Fast MA above slow MA” or “Fast MA below slow MA” for MA slope
• “RSI above dynamic threshold” or “RSI below dynamic threshold” for RSI
• “MACD histogram positive” or “MACD histogram negative” for MACD Hist
• “Price near highs” or “Price near lows” for HH/LL checks
• “Bullish Heikin Ashi” or “Bearish Heikin Ashi” for HA Doji scoring
• “Large range, trend confirmed” or “Small range, trend contradicted” for Candle Range
Additionally, the top-row comment for each category is:
• Trend: “Highly Bullish,” “Highly Bearish,” or “Neutral Trend.”
• Momentum: “Strong Momentum,” “Weak Momentum,” or “Neutral Momentum.”
• Price Action: “Bullish Action,” “Bearish Action,” or “Neutral Action.”
• Market Activity: “Volatile Market,” “Calm Market,” or “Stable Market.”
Reasons for These Comments:
• Transparency: Shows exactly how each sub-indicator contributed to its category score.
• Education: Helps traders learn why a category is labeled bullish, bearish, or neutral, building intuition over time.
• Customization: If, for example, the RSI comment says “RSI neutral” despite an impending trend shift, a trader might choose to adjust RSI length or thresholds.
In the detailed dashboard, hovering over each comment cell also reveals a tooltip with additional context (e.g., “Fast MA above slow MA” or “Senkou A above Senkou B”), helping traders understand the precise rule behind that +1, 0, or –1 assignment.
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15. Real-Life Example (Consolidated)
• Instrument & Timeframe: Bitcoin (BTCUSD), 1-hour chart.
• Current Market Activity: BBW and ATR both spike (+1 each), KCW is moderately high (+1), but volume is only neutral (0) → Raw Market Activity Score = +2 → State = High Activity (after two bars, if hysteresis is on).
• Category Weights Applied: Trend = 50 %, Momentum = 35 %, Price Action = 15 %.
• Trend Sub-Scores:
1. ADX = 25 (above threshold 20) with +DI > –DI → +1.
2. Fast MA (20-period) sits above Slow MA (50-period) → +1.
3. Ichimoku: Senkou A > Senkou B → +1.
→ Trend Score = +3.
• Momentum Sub-Scores:
4. RSI = 75 (above its moving average +1 stdev) → +1.
5. MACD histogram = +0.15 → +1.
6. Stochastic %K = 50 (mid-range) → 0.
→ Momentum Score = +2.
• Price Action Sub-Scores:
7. Price is not within 1 % of the 20-period high/low and slope = positive → 0.
8. Heikin-Ashi body is slightly larger than stdev over last 5 bars with haClose > haOpen → +1.
9. Candle range is just above its dynamic upper bound but trend is already captured, so → +1.
→ Price Action Score = +2.
• Calculate netScore (before smoothing):
• Trend contribution = 3 × 0.50 = 1.50
• Momentum contribution = 2 × 0.35 = 0.70
• Price Action contribution = 2 × 0.15 = 0.30
• Raw netScore = 1.50 + 0.70 + 0.30 = 2.50 → Immediately classified as Bullish.
• Oscillator & Dashboard Output:
• The oscillator line crosses above +2 and turns green.
• Dashboard displays:
• Trend Regime “BULLISH,” Trend Score = 3, Comment = “Highly Bullish.”
• Momentum Regime “BULLISH,” Momentum Score = 2, Comment = “Strong Momentum.”
• Price Action Regime “BULLISH,” Price Action Score = 2, Comment = “Bullish Action.”
• Market Activity State “High,” Comment = “Volatile Market.”
• Weights: Trend 50 %, Momentum 35 %, Price Action 15 %.
• Dominant Category: Trend (because 1.50 > 0.70 > 0.30).
• Overall Score: 2.50, posCount = (three +1s in Trend) + (two +1s in Momentum) + (two +1s in Price Action) = 7 bullish signals, negCount = 0.
• Final Zone = “BULLISH.”
• The trader sees that both Trend and Momentum are reinforcing each other under high volatility. They might wait one more candle for confirmation but already have strong evidence to consider a long.
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• .
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Disclaimer
This indicator is strictly a technical analysis tool and does not constitute financial advice. All trading involves risk, including potential loss of capital. Past performance is not indicative of future results. Traders should:
• Always backtest the “Market Zone Analyzer ” on their chosen symbols and timeframes before committing real capital.
• Combine this tool with sound risk management, position sizing, and, if possible, fundamental analysis.
• Understand that no indicator is foolproof; always be prepared for unexpected market moves.
Goodluck
-BullByte!
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Advanced ICT Theory - A-ICT📊 Advanced ICT Theory (A-ICT): The Institutional Manipulation Detector
Are you tired of being the liquidity? Stop chasing shadows and start tracking the architects of price movement.
This is not another lagging indicator. This is a complete framework for viewing the market through the lens of institutional traders. Advanced ICT Theory (A-ICT) is an all-in-one, military-grade analysis engine designed to decode the complex language of "Smart Money." It automates the core tenets of Inner Circle Trader (ICT) methodology, moving beyond simple patterns to build a dynamic, real-time narrative of market manipulation, liquidity engineering, and institutional order flow.
AIT provides a living blueprint of the market, identifying high-probability zones, tracking structural shifts, and scoring the quality of setups with a sophisticated, multi-factor algorithm. This is your X-ray into the market's true intentions.
🔬 THE CORE ENGINE: DECODING THE THEORY & FORMULAS
A-ICT is built upon a sophisticated, multi-layered logic system that interprets price action as a story of cause and effect. It does not guess; it confirms. Here is the foundational theory that drives the engine:
1. Market Structure: The Blueprint of Trend
The script first establishes a deep understanding of the market's skeleton through multi-level pivot analysis. It uses ta.pivothigh and ta.pivotlow to identify significant swing points.
Internal Structure (iBOS): Minor swings that show the short-term order flow. A break of internal structure is the first whisper of a potential shift.
External Structure (eBOS): Major swing points that define the primary trend. A confirmed break of external structure is a powerful statement of trend continuation. AIT validates this with optional Volume Confirmation (volume > volumeSMA * 1.2) and Candle Confirmation to ensure the break is driven by institutional force, not just a random spike.
Change of Character (CHoCH): This is the earthquake. A CHoCH occurs when a confirmed eBOS happens against the prevailing trend (e.g., a bearish eBOS in a clear uptrend). A-ICT flags this immediately, as it is the strongest signal that the primary trend is under threat of reversal.
2. Liquidity Engineering: The Fuel of the Market
Institutions don't buy into strength; they buy into weakness. They need liquidity. A-ICT maps these liquidity pools with forensic precision:
Buyside & Sellside Liquidity (BSL/SSL): Using ta.highest and ta.lowest, AIT identifies recent highs and lows where clusters of stop-loss orders (liquidity) are resting. These are institutional targets.
Liquidity Sweeps: This is the "manipulation" part of the detector. AIT has a specific formula to detect a sweep: high > bsl and close < bsl . This signifies that institutions pushed price just high enough to trigger buy-stops before aggressively selling—a classic "stop hunt." This event dramatically increases the quality score of subsequent patterns.
3. The Element Lifecycle: From Potential to Power
This is the revolutionary heart of A-ICT. Zones are not static; they have a lifecycle. AIT tracks this with its dynamic classification engine.
Phase 1: PENDING (Yellow): The script identifies a potential zone of interest based on a specific candle formation (a "displacement"). It is marked as "Pending" because its true nature is unknown. It is a question.
Phase 2: CLASSIFICATION: After the zone is created, AIT watches what happens next. The zone's identity is defined by its actions:
ORDER BLOCK (Blue): The highest-grade element. A zone is classified as an Order Block if it directly causes a Break of Structure (BOS) . This is the footprint of institutions entering the market with enough force to validate the new trend direction.
TRAP ZONE (Orange): A zone is classified as a Trap Zone if it is directly involved in a Liquidity Sweep . This indicates the zone was used to engineer liquidity, setting a "trap" for retail traders before a reversal.
REVERSAL / S&R ZONE (Green): If a zone is not powerful enough to cause a BOS or a major sweep, but still serves as a pivot point, it's classified as a general support/resistance or reversal zone.
4. Market Inefficiencies: Gaps in the Matrix
Fair Value Gaps (FVG): AIT detects FVGs—a 3-bar pattern indicating an imbalance—with a strict formula: low > high (for a bullish FVG) and gapSize > atr14 * 0.5. This ensures only significant, volatile gaps are shown. An FVG co-located with an Order Block is a high-confluence setup.
5. Premium & Discount: The Law of Value
Institutions buy at wholesale (Discount) and sell at retail (Premium). AIT uses a pdLookback to define the current dealing range and divides it into three zones: Premium (sell zone), Discount (buy zone), and Equilibrium. An element's quality score is massively boosted if it aligns with this principle (e.g., a bullish Order Block in a Discount zone).
⚙️ THE CONTROL PANEL: A COMPLETE GUIDE TO THE INPUTS MENU
Every setting is a lever, allowing you to tune the AIT engine to your exact specifications. Master these to unlock the script's full potential.
🎯 A-ICT Detection Engine
Min Displacement Candles: Controls the sensitivity of element detection. How it works: It defines the number of subsequent candles that must be "inside" a large parent candle. Best practice: Use 2-3 for a balanced view on most timeframes. A higher number (4-5) will find only major, more significant zones, ideal for swing trading. A lower number (1) is highly sensitive, suitable for scalping.
Mitigation Method: Defines when a zone is considered "used up" or mitigated. How it works: Cross triggers as soon as price touches the zone's boundary. Close requires a candle to fully close beyond it. Best practice: Cross is more responsive for fast-moving markets. Close is more conservative and helps filter out fake-outs caused by wicks, making it safer for confirmations.
Min Element Size (ATR): A crucial noise filter. How it works: It requires a detected zone to be at least this multiple of the Average True Range (ATR). Best practice: Keep this around 0.5. If you see too many tiny, irrelevant zones, increase this value to 0.8 or 1.0. If you feel the script is missing smaller but valid zones, decrease it to 0.3.
Age Threshold & Pending Timeout: These manage visual clutter. How they work: Age Threshold removes old, mitigated elements after a set number of bars. Pending Timeout removes a "Pending" element if it isn't classified within a certain window. Best practice: The default settings are optimized. If your chart feels cluttered, reduce the Age Threshold. If pending zones disappear too quickly, increase the Pending Timeout.
Min Quality Threshold: Your primary visual filter. How it works: It hides all elements (boxes, lines, labels) that do not meet this minimum quality score (0-100). Best practice: Start with the default 30. To see only A- or B-grade setups, increase this to 60 or 70 for an exceptionally clean, high-probability view.
🏗️ Market Structure
Lookbacks (Internal, External, Major): These define the sensitivity of the trend analysis. How they work: They set the number of bars to the left and right for pivot detection. Best practice: Use smaller values for Internal (e.g., 3) to see minor structure and larger values for External (e.g., 10-15) to map the main trend. For a macro, long-term view, increase the Major Swing Lookback.
Require Volume/Candle Confirmation: Toggles for quality control on BOS/CHoCH signals. Best practice: It is highly recommended to keep these enabled. Disabling them will result in more structure signals, but many will be false alarms. They are your filter against market noise.
... (Continue this detailed breakdown for every single input group: Display Configuration, Zones Style, Levels Appearance, Colors, Dashboards, MTF, Liquidity, Premium/Discount, Sessions, and IPDA).
📊 THE INTELLIGENCE DASHBOARDS: YOUR COMMAND CENTER
The dashboards synthesize all the complex analysis into a simple, actionable intelligence briefing.
Main Dashboard (Bottom Right)
ICT Metrics & Breakdown: This is your statistical overview. Total Elements shows how much structure the script is tracking. High Quality instantly tells you if there are any A/B grade setups nearby. Unmitigated vs. Mitigated shows the balance of fresh opportunities versus resolved price action. The breakdown by Order Blocks, Trap Zones, etc., gives you a quick read on the market's recent character.
Structure & Market Context: This is your core bias. Order Flow tells you the current script-determined trend. Last BOS shows you the most recent structural event. CHoCH Active is a critical warning. HTF Bias shows if you are aligned with the higher timeframe—the checkmark (✓) for alignment is one of the most important confluence factors.
Smart Money Flow: A volume-based sentiment gauge. Net Flow shows the raw buying vs. selling pressure, while the Bias provides an interpretation (e.g., "STRONG BULLISH FLOW").
Key Guide (Large Dashboard only): A built-in legend so you never have to guess. It defines every pattern, structure type, and special level visually.
📖 Narrative Dashboard (Bottom Left)
This is the "story" of the market, updated in real-time. It's designed to build your trading thesis.
Recent Elements Table: A live list of the most recent, high-quality setups. It displays the Type , its Narrative Role (e.g., "Bullish OB caused BOS"), its raw Quality percentage, and its final Trade Score grade. This is your at-a-glance opportunity scanner.
Market Narrative Section: This is the soul of A-ICT. It combines all data points into a human-readable story:
📍 Current Phase: Tells you if you are in a high-volatility Killzone or a consolidation phase like the Asian Range.
🎯 Bias & Alignment: Your primary direction, with a clear indicator of HTF alignment or conflict.
🔗 Events: A causal sequence of recent events, like "💧 Sell-side liquidity swept →
📊 Bullish BOS → 🎯 Active Order Block".
🎯 Next Expectation: The script's logical conclusion. It provides a specific, forward-looking hypothesis, such as "📉 Pullback expected to bullish OB at 1.2345 before continuation up."
🎨 READING THE BATTLEFIELD: A VISUAL INTERPRETATION GUIDE
Every color and line is a piece of information. Learn to read them together to see the full picture.
The Core Zones (Boxes):
Blue Box (Order Block): Highest probability zone for trend continuation. Look for entries here.
Orange Box (Trap Zone): A manipulation footprint. Expect a potential reversal after price interacts with this zone.
Green Box (Reversal/S&R): A standard pivot area. A good reference point but requires more confluence.
Purple Box (FVG): A market imbalance. Acts as a magnet for price. An FVG inside an Order Block is an A+ confluence.
The Structural Lines:
Green/Red Line (eBOS): Confirms the trend direction. A break above the green line is bullish; a break below the red line is bearish.
Thick Orange Line (CHoCH): WARNING. The previous trend is now in question. The market character has changed.
Blue/Red Lines (BSL/SSL): Liquidity targets. Expect price to gravitate towards these lines. A dotted line with a checkmark (✓) means the liquidity has been "swept" or "purged."
How to Synthesize: The magic is in the confluence. A perfect setup might look like this: Price sweeps below a red SSL line , enters a green Discount Zone during the NY Killzone , and forms a blue Order Block which then causes a green eBOS . This sequence, visible at a glance, is the story of a high-probability long setup.
🔧 THE ARCHITECT'S VISION: THE DEVELOPMENT JOURNEY
A-ICT was forged from the frustration of using lagging indicators in a market that is forward-looking. Traditional tools are reactive; they tell you what happened. The vision for A-ICT was to create a proactive engine that could anticipate institutional behavior by understanding their objectives: liquidity and efficiency. The development process was centered on creating a "lifecycle" for price patterns—the idea that a zone's true meaning is only revealed by its consequence. This led to the post-breakout classification system and the narrative-building engine. It's designed not just to show you patterns, but to tell you their story.
⚠️ RISK DISCLAIMER & BEST PRACTICES
Advanced ICT Theory (A-ICT) is a professional-grade analytical tool and does not provide financial advice or direct buy/sell signals. Its analysis is based on historical price action and probabilities. All forms of trading involve substantial risk. Past performance is not indicative of future results. Always use this tool as part of a comprehensive trading plan that includes your own analysis and a robust risk management strategy. Do not trade based on this indicator alone.
観の目つよく、見の目よわく
"Kan no me tsuyoku, ken no me yowaku"
— Miyamoto Musashi, The Book of Five Rings
English: "Perceive that which cannot be seen with the eye."
— Dskyz, Trade with insight. Trade with anticipation.
Uptrick: Dual Moving Average Volume Oscillator
Title: Uptrick: Dual Moving Average Volume Oscillator (DPVO)
### Overview
The "Uptrick: Dual Moving Average Volume Oscillator" (DPVO) is an advanced trading tool designed to enhance market analysis by integrating volume data with price action. This indicator is specially developed to provide traders with deeper insights into market dynamics, making it easier to spot potential entry and exit points based on volume and price interactions. The DPVO stands out by offering a sophisticated approach to traditional volume analysis, setting it apart from typical volume indicators available on the TradingView platform.
### Unique Features
Unlike traditional indicators that analyze volume and price movements separately, the DPVO combines these two critical elements to offer a comprehensive view of market behavior. By calculating the Volume Impact, which involves the product of the exponential moving averages (EMAs) of volume and the price range (close - open), this indicator highlights significant trading activities that could indicate strong buying or selling pressure. This method allows traders to see not just the volume spikes, but how those spikes relate to price movements, providing a clearer picture of market sentiment.
### Customization and Inputs
The DPVO is highly customizable, catering to various trading styles and strategies:
- **Oscillator Length (`oscLength`)**: Adjusts the period over which the volume and price difference is analyzed, allowing traders to set it according to their trading timeframe.
- **Fast and Slow Moving Averages (`fastMA` and `slowMA`)**: These parameters control the responsiveness of the DPVO. A shorter `fastMA` coupled with a longer `slowMA` can help in identifying trends quicker or smoothing out market noise for more conservative approaches.
- **Signal Smoothing (`signalSmooth`)**: This input helps in reducing signal noise, making the crossover and crossunder points between the DVO and its smoothed signal line clearer and easier to interpret.
### Functionality Details
The DPVO operates through a sequence of calculated steps that integrate volume data with price movement:
1. **Volume Impact Calculation**: This is the foundational step where the product of the EMA of volume and the EMA of price range (close - open) is calculated. This metric highlights trading sessions where significant volume accompanies substantial price movements, suggesting a strong market response.
2. **Dynamic Volume Oscillator (DVO)**: The heart of the indicator, the DVO, is derived by calculating the difference between the fast EMA and the slow EMA of the Volume Impact. This result is then normalized by dividing by the EMA of the volume over the same period to scale the output, making it consistent across various trading environments.
3. **Signal Generation**: The final output is smoothed using a simple moving average of the DVO to filter out market noise. Buy and sell signals are generated based on the crossover and crossunder of the DVO with its smoothed version, providing clear cues for market entry or exit.
### Originality
The DPVO's originality lies in its innovative integration of volume and price movement, a novel approach not typically observed in other volume indicators. By analyzing the product of volume and price change EMAs, the DPVO captures the essence of market dynamics more holistically than traditional tools, which often only reflect volume levels without contextualizing them with price actions. This dual analysis provides traders with a deeper understanding of market forces, enabling them to make more informed decisions based on a combination of volume surges and significant price movements. The DPVO also introduces a unique normalization and smoothing technique that refines the oscillator's output, offering cleaner and more reliable signals that are adaptable to various market conditions and trading styles.
### Practical Application
The DPVO excels in environments where volume plays a crucial role in validating price movements. Traders can utilize the buy and sell signals generated by the DPVO to enhance their decision-making process. The signals are plotted directly on the trading chart, with buy signals appearing below the price bars and sell signals above, ensuring they are prominent and actionable. This setup is particularly useful for day traders and swing traders who rely on timely and accurate signals to maximize their trading opportunities.
### Best Practices
To maximize the effectiveness of the DPVO, traders should consider the following best practices:
- **Market Selection**: Use the DPVO in markets known for strong volume-price correlation such as major forex pairs, popular stocks, and cryptocurrencies.
- **Signal Confirmation**: While the DPVO provides powerful signals, confirming these signals with additional indicators such as RSI or MACD can increase trade reliability.
- **Risk Management**: Always use stop-loss orders to manage risks associated with trading signals. Adjust the position size based on the volatility of the asset to avoid significant losses.
### Practical Example + How to use it
Practical Example1: Day Trading Cryptocurrencies
For a day trader focusing on the highly volatile cryptocurrency market, the DPVO can be an effective tool on a 15-minute chart. Suppose a trader is monitoring Bitcoin (BTC) during a period of high market activity. The DPVO might show an upward crossover of the DVO above its smoothed signal line while also indicating a significant increase in volume. This could signal that strong buying pressure is entering the market, suggesting a potential short-term rally. The trader could enter a long position based on this signal, setting a stop-loss just below the recent support level to manage risk. If the DPVO later shows a crossover in the opposite direction with decreasing volume, it might signal a good exit point, allowing the trader to lock in profits before a potential pullback.
- **Swing Trading Stocks**: For a swing trader looking at stocks, the DPVO could be applied on a daily chart. If the oscillator shows a consistent downward trend along with increasing volume, this could suggest a potential sell-off, providing a sell signal before a significant downturn.
You can look for:
--> Increase in volume - You can use indicators like 24-hour-Volume to have a better visualization
--> Uptrend/Downtrend in the indicator (HH, HL, LL, LH)
--> Confirmation (Buy signal/Sell signal)
--> Correct Price action (Not too steep moves up or down. Stable moves.) (Optional)
--> Confirmation with other indicators (Optional)
Quick image showing you an example of a buy signal on SOLANA:
### Technical Notes
- **Calculation Efficiency**: The DPVO utilizes exponential moving averages (EMAs) in its calculations, which provides a balance between responsiveness and smoothing. EMAs are favored over simple moving averages in this context because they give more weight to recent data, making the indicator more sensitive to recent market changes.
- **Normalization**: The normalization of the DVO by the EMA of the volume ensures that the oscillator remains consistent across different assets and timeframes. This means the indicator can be used on a wide variety of markets without needing significant adjustments, making it a versatile tool for traders.
- **Signal Line Smoothing**: The final signal line is smoothed using a simple moving average (SMA) to reduce noise. The choice of SMA for smoothing, as opposed to EMA, is intentional to provide a more stable signal that is less prone to frequent whipsaws, which can occur in highly volatile markets.
- **Lag and Sensitivity**: Like all moving average-based indicators, the DPVO may introduce a slight lag in signal generation. However, this is offset by the indicator’s ability to filter out market noise, making it a reliable tool for identifying genuine trends and reversals. Adjusting the `fastMA`, `slowMA`, and `signalSmooth` inputs allows traders to fine-tune the sensitivity of the DPVO to match their specific trading strategy and market conditions.
- **Platform Compatibility**: The DPVO is written in Pine Script™ v5, ensuring compatibility with the latest features and functionalities offered by TradingView. This version takes advantage of optimized functions for performance and accuracy in calculations, making it well-suited for real-time analysis.
Conclusion
The "Uptrick: Dual Moving Average Volume Oscillator" is a revolutionary tool that merges volume analysis with price movement to offer traders a more nuanced understanding of market trends and reversals. Its ability to provide clear, actionable signals based on a unique combination of volume and price changes makes it an invaluable addition to any trader's toolkit. Whether you are managing long-term positions or looking for quick trades, the DPVO provides insights that can help refine any trading strategy, making it a standout choice in the crowded field of technical indicators.
Nothing from this indicator or any other Uptrick Indicators is financial advice. Only you are ultimately responsible for your choices.
Composite Indicator (Donchian + OBV)Composite Indicator (Donchian + OBV)
The Composite Indicator (Donchian + OBV) is a powerful tool designed to evaluate the strength of market breakouts and momentum trends , offering traders a comprehensive perspective on price action. This indicator combines the Donchian Channel with On-Balance Volume (OBV) to create a dynamic and easy-to-interpret metric scaled between -1 and 1 .
Key Features
Breakout Strength Analysis:
- The indicator assesses the strength of price breakouts relative to the upper and lower bounds of the Donchian Channel.
- Positive values close to 1 indicate a strong bullish breakout.
- Negative values close to -1 indicate a strong bearish breakout.
Momentum Detection with OBV:
- On-Balance Volume (OBV) tracks the cumulative buying and selling volume to gauge market momentum.
- The smoothed OBV trend ensures the momentum component aligns with price action, reducing noise.
Integrated Composite Value:
- Combines breakout strength and OBV momentum into a single metric for enhanced clarity.
- The final composite value highlights whether the market is bullish, bearish, or neutral.
Divergence Detection:
- Spot bullish divergences when the indicator rises while price falls, suggesting a potential upward reversal.
- Identify bearish divergences when the indicator falls while price rises, hinting at a potential downward reversal.
How It Works
Donchian Channel Analysis:
- Calculates the highest high and lowest low over a user-defined period to establish the upper and lower channels .
- Breakouts beyond these channels contribute to the breakout strength component.
OBV Momentum:
- Measures cumulative volume trends to validate price movements.
- Momentum is derived from the rate of change in smoothed OBV values.
Composite Calculation:
- Combines breakout strength and OBV momentum, normalized and scaled to -1 to 1 for clarity.
How to Use
Bullish Breakout:
- When the indicator value approaches 1 , it signals a strong upward breakout supported by positive OBV momentum.
- Example Action: Consider a Buy if price breaks the upper Donchian Channel with increasing OBV.
Bearish Breakout:
- When the indicator value approaches -1 , it indicates a strong downward breakout supported by negative OBV momentum.
- Example Action: Consider a Sell if price breaks the lower Donchian Channel with decreasing OBV.
Neutral Market:
- When the value is near 0 , the market is likely balanced with no significant breakout or momentum detected.
Divergence Opportunities:
- Bullish Divergence: Price makes lower lows, but the indicator trends upward → Potential upward reversal.
- Bearish Divergence: Price makes higher highs, but the indicator trends downward → Potential downward reversal.
Customization Options
Donchian Channel Length: Adjust the period for the upper and lower bounds.
OBV Smoothing Length: Modify the smoothing period for OBV to fine-tune momentum detection.
Scaling Adjustments: The composite value is automatically normalized for consistency across timeframes.
Ideal Use Cases
Breakout Trading: Identify and confirm strong breakouts in volatile markets.
Momentum Confirmation: Validate price movements with volume-based momentum.
Reversal Detection: Leverage divergences to spot potential market reversals.
Example Applications
Strong Bullish Signal:
- Price breaks the upper channel , and OBV shows increasing volume → Composite value near 1 .
- Action: Enter a Buy position and set a Stop Loss below the upper channel.
Strong Bearish Signal:
- Price breaks the lower channel , and OBV shows decreasing volume → Composite value near -1 .
- Action: Enter a Sell position and set a Stop Loss above the lower channel.
Neutral Market:
- Composite value near 0 suggests indecision or consolidation. Wait for a breakout.
Limitations
Best used alongside additional tools like RSI or MACD for filtering noise and improving decision-making.
Requires careful parameter tuning based on the asset and timeframe.
Final Thoughts
The Composite Indicator (Donchian + OBV) offers traders a versatile tool to navigate complex markets. By blending breakout analysis with volume-based momentum, this indicator provides an actionable edge for identifying high-probability opportunities and potential reversals.
Multipower Entry SecretMultipower Entry Secret indicator is designed to be the ultimate trading companion for traders of all skill levels—especially those who struggle with decision-making due to unclear or overwhelming signals. Unlike conventional trading systems cluttered with too many lines and confusing alerts, this indicator provides a clear, adaptive, and actionable guide for market entries and exits.
Key Points:
Clear Buy/Sell/Wait Signals:
The script dynamically analyzes price action, candle patterns, volume, trend strength, and higher time frame context. This means it gives you “Buy,” “Sell,” or “Wait” signals based on real, meaningful market information—filtering out the noise and weak trades.
Multi-Timeframe Adaptive Analysis:
It synchronizes signals between higher and current timeframes, ensuring you get the most reliable direction—reducing the risk of getting caught in fake moves or sudden reversals.
Automatic Support, Resistance & Liquidity Zones:
Key levels like support, resistance, and liquidity zones are auto-detected and displayed directly on the chart, helping you make precise decisions without manual drawing.
Real-Time Dashboard:
All relevant information, such as trend strength, market intent, volume sentiment, and the reason behind each signal, is neatly summarized in a dashboard—making monitoring effortless and intuitive.
Customizable & Beginner-Friendly:
Whether you’re a newcomer wanting straightforward guidance or a professional needing advanced customization, the indicator offers flexible options to adjust analysis depth, timeframes, sensitivity, and more.
Visual & Clutter-Free:
The design ensures that your chart remains clean and readable, showing only the most important information. This minimizes mental overload and allows for instant decision-making.
Who Will Benefit?
Beginners who want to learn trading logic, avoid common traps, and see the exact reason behind every signal.
Advanced traders who require adaptive multi-timeframe analytics, fast execution, and stress-free monitoring.
Anyone who wants to save screen time, reduce analysis paralysis, and have more confidence in every trade they take.
1. No Indicator Clutter
Intent:
Many traders get confused by charts filled with too many indicators and signals. This often leads to hesitation, missed trades, or taking random, risky trades.
In this Indicator:
You get a clean and clutter-free chart. Only the most important buy/sell/wait signals and relevant support/resistance/liquidity levels are shown. These update automatically, removing the “overload” and keeping your focus sharp, so your decision-making is faster and stress-free.
2. Exact Entry Guide
Intent:
Traders often struggle with entry timing, leading to FOMO (fear of missing out) or getting trapped in sudden market reversals.
In this Indicator:
The system uses powerful adaptive logic to filter out weak signals and only highlight the strongest market moves. This not only prevents you from entering late or on noise, but also helps avoid losses from false breakouts or whipsaws. You get actionable suggestions—when to enter, when to hold back—so your entries are high-conviction and disciplined.
3. HTF+LTF Logic: Multitimeframe Sync Analysis
Intent:
Most losing trades happen when you act only on the short-term chart, ignoring the bigger market trend.
In this Indicator:
Signals are based on both the current chart timeframe (LTF) and a higher (HTF, like hourly/daily) timeframe. The indicator synchronizes trend direction, momentum, and structure across both levels, quickly adapting to show you when both are aligned. This filtering results in “only trade with the bigger trend”—dramatically increasing your win rate and market confidence.
4. Auto Support/Resistance & Liquidity Zones
Intent:
Drawing support/resistance and liquidity zones manually is time-consuming and error-prone, especially for beginners.
In this Indicator:
The system automatically identifies and plots the most crucial support/resistance levels and liquidity zones on your chart. This is based on adaptive, real-time price and volume analysis. These zones highlight where major institutional activity, trap setups, or real breakouts/reversals are most likely, removing guesswork and giving you a clear reference for entries, exits, and stop placements.
5. Clear Action/Direction
Intent:
Traders need certainty—what does the market want right now? Most indicators are vague.
In this Indicator:
Your dashboard always displays in plain words (like “BUY”, “SELL”, or “WAIT”) what action makes sense in the current market phase. Whether it’s a bull trap, volume spike, wick reversal, or exhaustion—it’s interpreted and explained clearly. No more confusion—just direct, real-time advice.
6. For Everyone (Beginner to Pro)
Intent:
Most advanced indicators are overwhelming for new traders; simple ones lack depth for professionals.
In this Indicator:
It is simple enough for a beginner—just add it to the chart and instantly see what action to consider. At the same time, it includes advanced adaptive analysis, multi-timeframe logic, and customizable settings so professional traders can fine-tune it for their strategies.
7. Ideal Usage and User Benefits
Instant Decision Support:
Whenever you’re unsure about a trade, just look at the indicator’s suggestion for clarity.
Entry Learning:
Beginners get real-time “practice” by not only seeing signals, but also the reason behind them—improving your chart reading and market understanding.
Screen Time & Stress Reduction:
Clear, relevant information only; no noise, less fatigue, faster decisions.
Makes Trading Confident & Simple:
The smart dashboard splits actionable levels (HTF, LTF, action) so you never miss a move, avoid traps, and stay aligned with high-probability trades.
8. Advanced Input Settings (Smart Customization)
Explained with Examples:
Enable Wick Analysis:
Finds candles with strong upper/lower wicks (signs of rejection/buying/selling force), alerting you to hidden reversals and protecting from FOMO entries.
Enable Absorption:
Detects when heavy order flow from one side is “absorbed” by the other (shows where institutional buyers/sellers are likely active, helps spot fake breakouts).
Enable Unusual Breakout:
Highlights real breakouts—large volatility plus high volume—so you catch genuine moves and avoid random spikes.
Enable Range/Expansion:
Smartly flags sudden range expansions—when the market goes from quiet to volatile—so you can act at the start of real trends.
Trend Bar Lookback:
Adjusts how many bars/candles are used in trend calculations. Short (fast trades, more signals), long (more reliability, fewer whipsaws).
Bull/Bear Bars for Strong Trend Min:
Sets how many candles in a row must support a trend before calling it “strong”—prevents flipping signals, keeps you disciplined.
Volume MA Length:
Lets you adjust how many bars back volume is averaged—fine-tune for your asset and trading style for best volume signals.
Swing Lookback Bars:
Set how many bars to use for swing high/low detection—short (quick swing levels), long (stronger support/resistance).
HTF (Bias Window):
Decide which higher timeframe the indicator should use for big-picture market mood. Adjustable for any style (scalp, swing, position).
Adaptive Lookback (HTF):
Choose how much HTF history is used for detecting major extremes/zones. Quick adjust for more/less sensitivity.
Show Support/Resistance, Liquidity Zones, Trendlines:
Toggle them on/off instantly per your needs—keeps your chart relevant and tailored.
9. Live Dashboard Sections Explained
Intent HTF:
Shows if the bigger timeframe currently has a Bullish, Bearish, or Neutral (“Chop”) intent, based on strict volume/price body calculations. Instant clarity—no more guessing on trend bias.
HTF Bias:
Clear message about which side (buy/sell/sideways) controls the market on the higher timeframe, so you always trade with the “big money.”
Chart Action:
The central action for the current bar—Whether to Buy, Sell, or Wait—calculated from all indicator logic, not just one rule.
TrendScore Long/Short:
See how many candles in your chosen window were bullish or bearish, at a glance. Instantly gauge market momentum.
Reason (WHY):
Every time a signal appears, the “reason” cell tells you the primary logic (breakout, wick, strong trend, etc.) behind it. Full transparency and learning—never trade blindly.
Strong Trend:
Shows if the market is currently in a powerful trend or not—helping you avoid choppy, risky entries.
HTF Vol/Body:
Displays current higher timeframe volume and candle body %—helping spot when big players are active for higher probability trades.
Volume Sentiment:
A real-time analysis of market psychology (strong bullish/bearish, neutral)—making your decision-making much more confident.
10. Smart and User-Friendly Design
Multi-timeframe Adaptive:
All calculations can now be drawn from your choice of higher or current timeframe, ensuring signals are filtered by larger market context.
Flexible Table Position:
You can set the live dashboard/summary anywhere on the chart for best visibility.
Refined Zone Visualization:
Liquidity and order blocks are visually highlighted, auto-tuning for your settings and always cleaning up to stay clutter-free.
Multi-Lingual & Beginner Accessible:
With Hindi and simple English support, descriptions and settings are accessible for a wide audience—anyone can start using powerful trading logic with zero language barrier.
Efficient Labels & Clear Reasoning:
Signal labels and reasons are shown/removed dynamically so your chart stays informative, not messy.
Every detail of this indicator is designed to make trading both simpler and smarter—helping you avoid the common pitfalls, learn real price action, stay in sync with the market’s true mood, and act with discipline for higher consistency and confidence.
This indicator makes professional-grade market analysis accessible to everyone. It’s your trusted assistant for making smarter, faster, and more profitable trading decisions—providing not just signals, but also the “why” behind every action. With auto-adaptive logic, clear visuals, and strong focus on real trading needs, it lets you focus on capturing the moves that matter—every single time.
Smart MTF S/R Levels[BullByte]
Smart MTF S/R Levels
Introduction & Motivation
Support and Resistance (S/R) levels are the backbone of technical analysis. However, most traders face two major challenges:
Manual S/R Marking: Drawing S/R levels by hand is time-consuming, subjective, and often inconsistent.
Multi-Timeframe Blind Spots: Key S/R levels from higher or lower timeframes are often missed, leading to surprise reversals or missed opportunities.
Smart MTF S/R Levels was created to solve these problems. It is a fully automated, multi-timeframe, multi-method S/R detection and visualization tool, designed to give traders a complete, objective, and actionable view of the market’s most important price zones.
What Makes This Indicator Unique?
Multi-Timeframe Analysis: Simultaneously analyzes up to three user-selected timeframes, ensuring you never miss a critical S/R level from any timeframe.
Multi-Method Confluence: Integrates several respected S/R detection methods—Swings, Pivots, Fibonacci, Order Blocks, and Volume Profile—into a single, unified system.
Zone Clustering: Automatically merges nearby levels into “zones” to reduce clutter and highlight areas of true market consensus.
Confluence Scoring: Each zone is scored by the number of methods and timeframes in agreement, helping you instantly spot the most significant S/R areas.
Reaction Counting: Tracks how many times price has recently interacted with each zone, providing a real-world measure of its importance.
Customizable Dashboard: A real-time, on-chart table summarizes all key S/R zones, their origins, confluence, and proximity to price.
Smart Alerts: Get notified when price approaches high-confluence zones, so you never miss a critical trading opportunity.
Why Should a Trader Use This?
Objectivity: Removes subjectivity from S/R analysis by using algorithmic detection and clustering.
Efficiency: Saves hours of manual charting and reduces analysis fatigue.
Comprehensiveness: Ensures you are always aware of the most relevant S/R zones, regardless of your trading timeframe.
Actionability: The dashboard and alerts make it easy to act on the most important levels, improving trade timing and risk management.
Adaptability: Works for all asset classes (stocks, forex, crypto, futures) and all trading styles (scalping, swing, position).
The Gap This Indicator Fills
Most S/R indicators focus on a single method or timeframe, leading to incomplete analysis. Manual S/R marking is error-prone and inconsistent. This indicator fills the gap by:
Automating S/R detection across multiple timeframes and methods
Objectively scoring and ranking zones by confluence and reaction
Presenting all this information in a clear, actionable dashboard
How Does It Work? (Technical Logic)
1. Level Detection
For each selected timeframe, the script detects S/R levels using:
SW (Swing High/Low): Recent price pivots where reversals occurred.
Pivot: Classic floor trader pivots (P, S1, R1).
Fib (Fibonacci): Key retracement levels (0.236, 0.382, 0.5, 0.618, 0.786) over the last 50 bars.
Bull OB / Bear OB: Institutional price zones based on bullish/bearish engulfing patterns.
VWAP / POC: Volume Weighted Average Price and Point of Control over the last 50 bars.
2. Level Clustering
Levels within a user-defined % distance are merged into a single “zone.”
Each zone records which methods and timeframes contributed to it.
3. Confluence & Reaction Scoring
Confluence: The number of unique methods/timeframes in agreement for a zone.
Reactions: The number of times price has touched or reversed at the zone in the recent past (user-defined lookback).
4. Filtering & Sorting
Only zones within a user-defined % of the current price are shown (to focus on actionable areas).
Zones can be sorted by confluence, reaction count, or proximity to price.
5. Visualization
Zones: Shaded boxes on the chart (green for support, red for resistance, blue for mixed).
Lines: Mark the exact level of each zone.
Labels: Show level, methods by timeframe (e.g., 15m (3 SW), 30m (1 VWAP)), and (if applicable) Fibonacci ratios.
Dashboard Table: Lists all nearby zones with full details.
6. Alerts
Optional alerts trigger when price approaches a zone with confluence above a user-set threshold.
Inputs & Customization (Explained for All Users)
Show Timeframe 1/2/3: Enable/disable analysis for each timeframe (e.g., 15m, 30m, 1h).
Show Swings/Pivots/Fibonacci/Order Blocks/Volume Profile: Select which S/R methods to include.
Show levels within X% of price: Only display zones near the current price (default: 3%).
How many swing highs/lows to show: Number of recent swings to include (default: 3).
Cluster levels within X%: Merge levels close together into a single zone (default: 0.25%).
Show Top N Zones: Limit the number of zones displayed (default: 8).
Bars to check for reactions: How far back to count price reactions (default: 100).
Sort Zones By: Choose how to rank zones in the dashboard (Confluence, Reactions, Distance).
Alert if Confluence >=: Set the minimum confluence score for alerts (default: 3).
Zone Box Width/Line Length/Label Offset: Control the appearance of zones and labels.
Dashboard Size/Location: Customize the dashboard table.
How to Read the Output
Shaded Boxes: Represent S/R zones. The color indicates type (green = support, red = resistance, blue = mixed).
Lines: Mark the precise level of each zone.
Labels: Show the level, methods by timeframe (e.g., 15m (3 SW), 30m (1 VWAP)), and (if applicable) Fibonacci ratios.
Dashboard Table: Columns include:
Level: Price of the zone
Methods (by TF): Which S/R methods and how many, per timeframe (see abbreviation key below)
Type: Support, Resistance, or Mixed
Confl.: Confluence score (higher = more significant)
React.: Number of recent price reactions
Dist %: Distance from current price (in %)
Abbreviations Used
SW = Swing High/Low (recent price pivots where reversals occurred)
Fib = Fibonacci Level (key retracement levels such as 0.236, 0.382, 0.5, 0.618, 0.786)
VWAP = Volume Weighted Average Price (price level weighted by volume)
POC = Point of Control (price level with the highest traded volume)
Bull OB = Bullish Order Block (institutional support zone from bullish price action)
Bear OB = Bearish Order Block (institutional resistance zone from bearish price action)
Pivot = Pivot Point (classic floor trader pivots: P, S1, R1)
These abbreviations appear in the dashboard and chart labels for clarity.
Example: How to Read the Dashboard and Labels (from the chart above)
Suppose you are trading BTCUSDT on a 15-minute chart. The dashboard at the top right shows several S/R zones, each with a breakdown of which timeframes and methods contributed to their detection:
Resistance zone at 119257.11:
The dashboard shows:
5m (1 SW), 15m (2 SW), 1h (3 SW)
This means the level 119257.11 was identified as a resistance zone by one swing high (SW) on the 5-minute timeframe, two swing highs on the 15-minute timeframe, and three swing highs on the 1-hour timeframe. The confluence score is 6 (total number of method/timeframe hits), and there has been 1 recent price reaction at this level. This suggests 119257.11 is a strong resistance zone, confirmed by multiple swing highs across all selected timeframes.
Mixed zone at 118767.97:
The dashboard shows:
5m (2 SW), 15m (2 SW)
This means the level 118767.97 was identified by two swing points on both the 5-minute and 15-minute timeframes. The confluence score is 4, and there have been 19 recent price reactions at this level, indicating it is a highly reactive zone.
Support zone at 117411.35:
The dashboard shows:
5m (2 SW), 1h (2 SW)
This means the level 117411.35 was identified as a support zone by two swing lows on the 5-minute timeframe and two swing lows on the 1-hour timeframe. The confluence score is 4, and there have been 2 recent price reactions at this level.
Mixed zone at 118291.45:
The dashboard shows:
15m (1 SW, 1 VWAP), 5m (1 VWAP), 1h (1 VWAP)
This means the level 118291.45 was identified by a swing and VWAP on the 15-minute timeframe, and by VWAP on both the 5-minute and 1-hour timeframes. The confluence score is 4, and there have been 12 recent price reactions at this level.
Support zone at 117103.10:
The dashboard shows:
15m (1 SW), 1h (1 SW)
This means the level 117103.10 was identified by a single swing low on both the 15-minute and 1-hour timeframes. The confluence score is 2, and there have been no recent price reactions at this level.
Resistance zone at 117899.33:
The dashboard shows:
5m (1 SW)
This means the level 117899.33 was identified by a single swing high on the 5-minute timeframe. The confluence score is 1, and there have been no recent price reactions at this level.
How to use this:
Zones with higher confluence (more methods and timeframes in agreement) and more recent reactions are generally more significant. For example, the resistance at 119257.11 is much stronger than the resistance at 117899.33, and the mixed zone at 118767.97 has shown the most recent price reactions, making it a key area to watch for potential reversals or breakouts.
Tip:
“SW” stands for Swing High/Low, and “VWAP” stands for Volume Weighted Average Price.
The format 15m (2 SW) means two swing points were detected on the 15-minute timeframe.
Best Practices & Recommendations
Use with Other Tools: This indicator is most powerful when combined with your own price action analysis and risk management.
Adjust Settings: Experiment with timeframes, clustering, and methods to suit your trading style and the asset’s volatility.
Watch for High Confluence: Zones with higher confluence and more reactions are generally more significant.
Limitations
No Future Prediction: The indicator does not predict future price movement; it highlights areas where price is statistically more likely to react.
Not a Standalone System: Should be used as part of a broader trading plan.
Historical Data: Reaction counts are based on historical price action and may not always repeat.
Disclaimer
This indicator is a technical analysis tool and does not constitute financial advice or a recommendation to buy or sell any asset. Trading involves risk, and past performance is not indicative of future results. Always use proper risk management and consult a financial advisor if needed.
Neon Juliet - PreviewThere is no TLDR, but there is a summary at the end. I strongly encourage to read full description before trying it out. Enjoy!
Background
=========
Having successful and adamant trading systems typically consists of two (oversimplified) elements: signals and risk management system. In most zero-sum games, such as trading, signals must offer an advantage against the market, and risk management system provides a safety mechanism to allow the system to exist in the future. Let me explain.
Say, I have a solid risk management system: it is diversified, with take profit and stop loss thresholds set for low risk, on average I trade less than 3% of my assets, and there’s a loss recovery mechanism, etc. Hypothetically, it’s pristine. Now, let’s trade this portfolio against a flip of a coin, essentially a signal that provides 50% probability of things turning out in my favour. How profitable is such system? My answer: it isn’t. I might be able to sustain this system for some time, but eventually this system is going to have to loosen risk restrictions to stay ahead of the commissions and borrowing costs, resulting in overtime detrimental trend.
Conversely, if the signals provide greater than 50% confidence of things turning out in my favour, but risk management is poor, I’d expect such system to end up in a disaster soon, perhaps after a few euphoric gains. (I’d isolate a top-notch signals, say >90% confidence, in another bucket, but this idealistic system is non-achievable in my practice, so I’ll leave it be)
Neon Juliet was developed to offer an advantage against given markets. Probabilities generated by this model are statistical historical outcomes. This model developed using only price action and is unable to consume any other data or price data across instruments. In other words, it doesn’t know anything you don’t see already on a chart.
Neon J performs best on complex instruments where there’s great diversity of actors and considerable daily volume .
Methodology
==========
In principle, Neon J is based on Bayes’ Theorem. Simply put, prior knowledge of price action ( aka patterns) provides basis for probability of future price action development (ex. long or short trend).
The training process is implemented outside of this script mainly due to Pine Script limitations. This script, however, contains inference portion of the model.
As input for training, daily candle data is used. From this data, feature engineering step of the training develops features, like price average divergence/convergence (think MACD ), price strength (think RSI , ADX ); multiple periods used to diversify long and short patterns. This is done to develop a “state” that is reflective of recent price development. Ex. what we’d call a trend is just a strong and consistent upward price action, but we’d need to look at most recent N candles and their pattern to know that.
Once features are developed, I train a model using Reinforcement Learning technique. Simply put, this technique allows an agent to interact with a trading simulator and take actions (ex. go long, go short, etc.). After many iterations, the agent learns conditions (patterns) that lead to positive outcomes and those that lead to negative outcomes. This learning is quantitative, which means there’s a way to tell which probabilities are strong and which are weak. These probabilities are indicated by this script.
Trained Neon J models are instruments-specific. Meaning, that model for DJI is not compatible with SP500 or any other instrument. Experimentally, I proved that such approach over-performs generalizable models (those that are trained on data from multiple instruments)
Neon J currently only support daily time frame. The limitation is purely practical to reduce the development load and model size.
Results
======
Tests show 60%-70% success rate (on average, some instruments are worse than that, some better) of individual signal when threshold is set to 0.3 (roughly equivalent to 65% probability). This is calculated with Pine Script Strategy with the following entry/exit rules:
Entry when individual signal (a dot) is above 0.3 (long) or below -0.3 (short)
Exit when 14-period smooth signal (a column) is above 0.0 (short exit) or below 0.0 (long exit)
No stop loss or take profit levels.
Pyramiding is set to 100 (to allow unrestricted action of all signals)
All trades are closed on last tested bar (to conclude all signals in-flight)
Percent Profitable is what we take as success rate in the context of this assessment. This number represents how many signals were profitable vs all signals actioned.
It is also worth noting that this assessment was performed on a time period previously unseen by the model. Simply put, we only train a model with data up until date X, then we test starting from date X onward. This ensures that the assessment is unbiased by the model already “knowing” the future. In practice, this gives confidence that future (unknown) market dynamics is going to be representative of our test results.
Be aware, the above “strategy” is not my recommended usage of this signal, it is simply an assessment technique that is meant to be as simple and unconstrained as possible.
How to use this script
================
The script calculates a probability. A term probability here is used in a loose form and means “a numeric value in roughly -1 to 1 space that represents the likelyhood of bullish or bearish price action”. Keep in mind that probability values can go over 1.0 or below -1.0. This is due to the fact that these value are normalized to -1/1 space using 95-percentile (this detail is largely unimportant for usability’s sake).
Indications
--------------
Dots (circles) indicate individual probability value on any given bar. Indicated value on a given bar indicates the probability of future price action. High (positive) values indicate high probability of long action in the future. Low (negative) values indicate high probability of short action in the future. You should interpret future as a gradient (a trend developing slowly over time) instead of being isolated to what’s immediately follows (ex. next bar)
Columns (histogram) provided as convenient view of smoothed probabilities of last N bars. This is controlled by the Smoothing parameter and defaults to 14.
Parameters
---------------
Model parameter is the backbone of this script. It is a required parameter and it is unique for each instrument. Example models provided at the end (see below). This parameter is a long 10000+ character representation of a model.
The script has two additional parameters for configuring interpretation: Threshold and Smoothing.
Threshold controls the level at which values change color (ex. above 0.3, turn neon blue, and below -0.3 turn neon purple).
Smoothing parameter provides a way to smooth out individual probabilities into a exponential moving average with the periods provided. This average is indicated using columns on the indicator.
Model expiration
----------------------
Models are valid for 1 month after training. This is done by design to prevent model deterioration. A month is proven to be a maximum period of time to hold model performance steady. After that, deterioration is likely to occur. Optimal time for model lifetime is 10 days (this is what I use for live trading), and of course most optimal (but unpractical for now) is to re-train daily.
Validity indicated with blue-tinted indicator background, while red-tinted background indicates expired period.
Preview
======
This script is released as a public script for anyone to try. My motives for this release are two-fold:
To subject the model to a variety of conditions, including traders with different experiences trading different instruments (subject to specific models offered of course). Essentially, my own testing is not enough to grasp a full breadths of scenarios. I’d like to harden it and understand where it is strong and where it might fall short (pun intended).
Get an idea on how Neon J might be useful when making trading decision. I tried to make the representation of the signals unconstrained and unopinionated, so there’s room to explore and experiment. I found that Neon J can be packaged in a number of different ways.
At this moment the script is closed-source. I might consider open-sourcing this script in future depending on how much feedback I get from this submission and whether it’d be deemed useful to others.
Summary
=======
Neon J is a set of probabilistic models for predicting future price action with ~65% accuracy. It indicates individual signals (circles) for probability of price action in a foreseeable future, while smoothed signals (columns) are provided for a more dynamic view of probable price action. Blue circle - strong long probability; Purple circle - strong short probability. Blue column - strong long trend ahead or in-progress; Purple column - strong short trend ahead or in-progress.
To use it, copy models below and provide them an input to “model” parameter when applying to a chart. Models are instrument-specific. Only daily (D) charts should be used.
The script is provided for evaluation purposes.
Models!
======
At last, here are the models (a piece of text you need to input in script parameters for each instrument)
TVC:DJI :
DJI|20121220|20221220|0.597,-0.032,0.0,-0.121,0.0,0.866,-0.046,0.0,-0.091,0.0|1.492,0.1,0.0,-0.162,0.0,-0.669,-0.037,0.0,-0.042,0.0|0.07,0.374,0.0,0.305,0.0,0.085,0.488,0.0,0.26,0.0|0.249,-0.257,0.0,0.529,0.0,-0.018,-0.233,0.0,0.502,0.0|0,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,30,10,10,10,10,10,10,10,30,10,10,10,10,10,10,30,30,10,10,10,10,60,10,10,10,20,10,40,10,10,10,80,10,10,60,10,10,10,10,10,10,20,10,10,10,10,10,10,10,10,20,20,10,10,10,10,10,10,20,10,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,20,10,10,10,20,10,10,10,20,10,10,10,10,20,10,20,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,30,10,10,10,10,20,50,10,10,10,10,10,10,30,10,10,10,10,10,10,10,30,10,10,10,10,10,10,10,10,10,10,10,10,10,10,30,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,10,10,20,30,10,10,10,10,10,50,10,10,10,10,60,10,10,10,10,10,40,10,10,10,10,10,20,30,10,10,10,10,60,10,10,10,10,10,20,10,10,10,10,10,10,10,40,10,10,10,10,10,10,10,30,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,10,10,30,10,10,10,10,10,10,10,40,10,10,10,10,10,10,40,10,10,10,10,10,50,10,10,10,10,10,50,10,10,10,10,50,10,10,10,10,10,40,10,10,10,10,10,10,40,10,10,10,10,10,10,40,10,10,10,10,10,10,10,30,10,10,10,10,10,10,10,20,20,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,40,10,10,10,10,10,10,40,10,10,10,10,10,10,30,10,10,10,10,10,10,40,10,10,10,10,10,40,10,10,10,10,10,10,10,30,10,10,10,10,10,10,20,20,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,30,10,10,10,10,10,10,10,20,10,10,10,10,10,10,20,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,10,20,20,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,30,10,10,10,10,10,10,40,10,10,10,10,10,20,20,10,10,10,10,10,10,40,10,10,10,10,10,10,10,10,30,10,10,10,10,10,30,20,10,10,10,10,10,20,20,10,10,10,10,10,10,10,10,30,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,30,10,10,10,10,10,10,40,10,10,10,10,10,10,40,10,10,10,10,10,50,10,10,10,10,20,40,10,10,10,70,10,10,10,10,60,10,10,10,10,10,10,40,10,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,30,10,10,10,10,10,10,40,10,10,10,10,10,10,40,10,10,10,10,10,50,10,10,10,10,70,10,10,10|-645,-188,-7,-97,-4,29,-18,90,60,-7,-30,117,-226,-82,-49,77,-245,53,78,221,-72,280,245,400,683,268,-74,-15,-106,-102,-3,251,302,536,47,3,-6,-179,-56,101,-62,172,176,98,-15,-71,-18,200,61,-249,-30,-38,1,94,-2,-9,47,79,-35,-15,34,-30,76,120,39,96,-47,-11,-61,-21,124,-704,0,-248,112,-193,143,-27,-14,133,170,-20,-17,-2,-120,61,-98,-32,-2,79,-2,109,-35,-16,132,-44,-63,-168,205,-28,919,235,-34,-53,-23,-243,-68,-26,-35,-54,60,-37,28,-91,-3,-21,-47,79,-127,229,61,59,-49,-139,-63,-43,91,201,-19,-80,-27,120,-122,-141,-100,-32,-25,-98,-27,50,-2,-65,-138,-7,-36,-9,53,-36,-36,-64,-11,216,-5,-664,-19,74,82,-83,-3,-66,21,386,-454,-1002,-282,-7,-52,-30,-9,-16,-148,-131,112,-484,-96,97,93,-13,-162,-49,38,31,-5,-199,-22,205,153,-29,14,-41,-222,-225,-145,107,70,-3,-8,-7,-20,-247,37,96,268,362,-95,706,-69,60,70,120,-34,-65,-152,-69,-7,69,-76,71,-5,384,109,-102,-484,-3,34,60,-20,380,244,678,292,-48,-2,-154,-17,-62,105,486,597,212,-26,-21,-310,-29,-22,-90,285,-204,-92,-290,-6,-516,-42,-16,127,-47,-7,-72,-247,76,-47,-13,43,-26,43,89,-38,30,-21,-106,-78,113,-19,-13,-8,-12,-12,362,247,-4,50,76,64,-14,-52,-16,-93,-172,53,-1,32,99,22,-75,-4,-9,31,70,116,-54,-61,-3,-55,-19,-15,176,143,-11,134,144,-11,-28,-47,-29,-136,-75,99,64,-9,-2,-24,-43,30,-161,-179,82,175,129,115,-71,-396,-202,101,-9,139,-6,-31,-312,-111,2,0,-234,-21,-52,-31,-12,-26,-37,-144,-23,68,23,-16,149,60,-64,10,-7,-8,46,210,393,-5,96,-56,89,48,475,176,20,-10,-31,-29,34,76,41,178,38,-32,-94,-33,76,-5,91,-15,123,72,-46,-13,-11,0,-37,-244,-161,155,-8,-3,165,23,77,16,-117,35,-74,-5,-107,-286,-24,-263,-14,-37,-5,-196,-290,-576,-188,41,-20,-98,-34,-45,-45,-242,40,60,-7,-10,-17,-43,73,48,-25,-8,-40,-27,-2,-5,42,73,-6,-23,8,-16,63,167,21,-99,-47,-119,-36,-59,192,158,115,123,54,-28,-1,-90,-169,-71,-72,114,156,-141,155,64,42,-88,69,-75,76,94,-4,65,102,152,-9,10,-17,-192,67,-10,-343,-90,-43,-106,12,-9,-79,-10,-73,-461,-509,-75,99,-57,0,-27,80,-156,-198,-642,-363,33,47,-28,-40,-43,-8,9,-27,-67,41,26,0,6,-49,-29,-60,32,70,34,-2,-9,-40,-240,-152,21,189,49,67,12,-12,-2,16,31,200,193,211,-150,-84,-45,58,75,44,260,128,105,-9,-11,-1,82,-94,184,-53,266,326,-55,-209,-9,54,85,308,-14,60,420,160,-39,-81,-17,-10,77,108,-28,257,-104,-53,-59,-128,-5,-13,8,119,-20,-130,-49,-9,-3,-23,-46,150,194,263,-214,-12,72,-6,-22,25,-10,290,-41,-21,-18,-1,-17,-42,-14,-21,0,-4,-23,-1,-1,-13,172,-9,224,86,-9,-2,-22,176,-6,33,186,-61,-187,-46,-33,94,172,0,16,-12,-37,59,103,118,194,1000,44,4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VANTAGE:SP500 :
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BINANCE:BTCUSD
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For more models, see a link on bio (description length limitation in this description restricts me to publish more).
Unimportant details
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“Neon” is the project code name, “J” is the iteration (versions “A” to “I” all led to a solid “J”)
Formatting options here make formatting very difficult, so forgive me poor readability.
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.
ADM Indicator [CHE] Comprehensive Description of the Three Market Phases for TradingView
Introduction
Financial markets often exhibit patterns that reflect the collective behavior of participants. Recognizing these patterns can provide traders with valuable insights into potential future price movements. The ADM Indicator is designed to help traders identify and capitalize on these patterns by detecting three primary market phases:
1. Accumulation Phase
2. Manipulation Phase
3. Distribution Phase
This indicator places labels on the chart to signify these phases, aiding traders in making informed decisions. Below is an in-depth explanation of each phase, including how the ADM Indicator detects them.
1. Accumulation Phase
Definition
The Accumulation Phase is a period where informed investors or institutions discreetly purchase assets before a potential price increase. During this phase, the price typically moves within a confined range between established highs and lows.
Characteristics
- Price Range Bound: The asset's price stays within the previous high and low after a timeframe change.
- Low Volatility: Minimal price movement indicates a balance between buyers and sellers.
- Steady Volume: Trading volume may remain relatively constant or show slight increases.
- Market Sentiment: General market interest is low, as the accumulation is not yet apparent to the broader market.
Detection with ADM Indicator
- Criteria: An accumulation is detected when the price remains within the previous high and low after a timeframe change.
- Indicator Action: At the end of the period, if accumulation has occurred, the indicator places a label "Accumulation" on the chart.
- Visual Cues: A yellow semi-transparent background highlights the accumulation phase, enhancing visual recognition.
Implications for Traders
- Entry Opportunity: Consider preparing for potential long positions before a possible upward move.
- Risk Management: Use tight stop-loss orders below the support level due to the defined trading range.
2. Manipulation Phase
Definition
The Manipulation Phase, also known as the Shakeout Phase, occurs when dominant market players intentionally move the price to trigger stop-loss orders and create panic among less-informed traders. This action generates liquidity and better entry prices for large positions.
Characteristics
- False Breakouts: The price moves above the previous high or below the previous low but quickly reverses.
- Increased Volatility: Sharp price movements occur without fundamental reasons.
- Stop-Loss Hunting: The price targets common stop-loss areas, triggering them before reversing.
- Emotional Trading: Retail traders may react impulsively, leading to poor trading decisions.
Detection with ADM Indicator
- Manipulation Up:
- Criteria: Detected when the price rises above the previous high and then falls back below it.
- Indicator Action: Places a label "Manipulation Up" on the chart at the point of detection.
- Manipulation Down:
- Criteria: Detected when the price falls below the previous low and then rises back above it.
- Indicator Action: Places a label "Manipulation Down" on the chart at the point of detection.
- Visual Cues:
- Manipulation Up: Blue background highlights the phase.
- Manipulation Down: Orange background highlights the phase.
Implications for Traders
- Caution Advised: Be wary of false signals and avoid overreacting to sudden price changes.
- Preparation for Next Phase: Use this phase to anticipate potential distribution and adjust strategies accordingly.
3. Distribution Phase
Definition
The Distribution Phase occurs when the institutions or informed investors who accumulated positions start selling to the general market at higher prices. This phase often follows a Manipulation Phase and may signal an impending trend reversal.
Characteristics
- Price Reversal: The price moves in the opposite direction of the prior manipulation.
- High Trading Volume: Increased selling activity as large players offload positions.
- Trend Weakening: The previous trend loses momentum, indicating a potential shift.
- Market Sentiment Shift: Optimism fades, and uncertainty or pessimism may emerge.
Detection with ADM Indicator
- Distribution Up:
- Criteria: Detected after a verified Manipulation Up when the price subsequently falls below the previous low.
- Indicator Action: Places a label "Distribution Up" on the chart.
- Distribution Down:
- Criteria: Detected after a verified Manipulation Down when the price subsequently rises above the previous high.
- Indicator Action: Places a label "Distribution Down" on the chart.
- Visual Cues:
- Distribution Up: Purple background highlights the phase.
- Distribution Down: Maroon background highlights the phase.
Implications for Traders
- Exit Signals: Consider closing long positions if in a Distribution Up phase.
- Short Selling Opportunities: Potential to enter short positions anticipating a downtrend.
Using the ADM Indicator on TradingView
Indicator Overview
The ADM Indicator automates the detection of Accumulation, Manipulation, and Distribution phases by analyzing price movements relative to previous highs and lows on a selected timeframe. It provides visual cues and labels on the chart, helping traders quickly identify the current market phase.
Features
- Multi-Timeframe Analysis: Choose from auto, multiplier, or manual timeframe settings.
- Visual Labels: Clear labeling of market phases directly on the chart.
- Background Highlighting: Distinct background colors for each phase.
- Customizable Settings: Adjust colors, styles, and display options.
- Period Separators: Optional separators delineate different timeframes.
Interpreting the Indicator
1. Accumulation Phase
- Detection: Price stays within the previous high and low after a timeframe change.
- Label: "Accumulation" placed at the period's end if detected.
- Background: Yellow semi-transparent color.
- Action: Prepare for potential long positions.
2. Manipulation Phase
- Detection:
- Manipulation Up: Price rises above previous high and then falls back below.
- Manipulation Down: Price falls below previous low and then rises back above.
- Labels: "Manipulation Up" or "Manipulation Down" placed at detection.
- Background:
- Manipulation Up: Blue color.
- Manipulation Down: Orange color.
- Action: Exercise caution; avoid impulsive trades.
3. Distribution Phase
- Detection:
- Distribution Up: After a Manipulation Up, price falls below previous low.
- Distribution Down: After a Manipulation Down, price rises above previous high.
- Labels: "Distribution Up" or "Distribution Down" placed at detection.
- Background:
- Distribution Up: Purple color.
- Distribution Down: Maroon color.
- Action: Consider exiting positions or entering counter-trend trades.
Configuring the Indicator
- Timeframe Type: Select Auto, Multiplier, or Manual for analysis timeframe.
- Multiplier: Set a custom multiplier when using "Multiplier" type.
- Manual Resolution: Define a specific timeframe with "Manual" option.
- Separator Settings: Customize period separators for visual clarity.
- Label Display Options: Choose to display all labels or only the most recent.
- Visualization Settings: Adjust colors and styles for personal preference.
Practical Tips
- Combine with Other Analysis Tools: Use alongside volume indicators, trend lines, or other technical tools.
- Backtesting: Review historical data to understand how the indicator signals would have impacted past trades.
- Stay Informed: Keep abreast of market news that might affect price movements beyond technical analysis.
- Risk Management: Always employ stop-loss orders and position sizing strategies.
Conclusion
The ADM Indicator is a valuable tool for traders seeking to understand and leverage market phases. By detecting Accumulation, Manipulation, and Distribution phases through specific price action criteria, it provides actionable insights into market dynamics.
Understanding the precise conditions under which each phase is detected empowers traders to make more informed decisions. Whether preparing for potential breakouts during accumulation, exercising caution during manipulation, or adjusting positions during distribution, the ADM Indicator aids in navigating the complexities of the financial markets.
Disclaimer:
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
This indicator is inspired by the Super 6x Indicators: RSI, MACD, Stochastic, Loxxer, CCI, and Velocity . A special thanks to Loxx for their relentless effort, creativity, and contributions to the TradingView community, which served as a foundation for this work.
Best regards Chervolino
Overview of the Timeframe Levels in the `autotimeframe()` Function
The `autotimeframe()` function automatically adjusts the higher timeframe based on the current chart timeframe. Here are the specific timeframe levels used in the function:
- Current Timeframe ≤ 1 Minute
→ Higher Timeframe: 240 Minutes (4 Hours)
- Current Timeframe ≤ 5 Minutes
→ Higher Timeframe: 1 Day
- Current Timeframe ≤ 1 Hour
→ Higher Timeframe: 3 Days
- Current Timeframe ≤ 4 Hours
→ Higher Timeframe: 7 Days
- Current Timeframe ≤ 12 Hours
→ Higher Timeframe: 1 Month
- Current Timeframe ≤ 1 Day
→ Higher Timeframe: 3 Months
- Current Timeframe ≤ 7 Days
→ Higher Timeframe: 6 Months
- For All Higher Timeframes (over 7 Days)
→ Higher Timeframe: 12 Months
Summary:
The function assigns a corresponding higher timeframe based on the current timeframe to optimize the analysis:
- 1 Minute or Less → 4 Hours
- Up to 5 Minutes → 1 Day
- Up to 1 Hour → 3 Days
- Up to 4 Hours → 7 Days
- Up to 12 Hours → 1 Month
- Up to 1 Day → 3 Months
- Up to 7 Days → 6 Months
- Over 7 Days → 12 Months
This automated adjustment ensures that the indicator works effectively across different chart timeframes without requiring manual changes.
Adaptive McGinley Cloud V1 Trend & Trade SignalsAdaptive McGinley Cloud V1 Trend & Trade Signals is a dynamic trend-following indicator designed to help traders identify market trends, trade opportunities, and manage risk. The script is built on the McGinley Dynamic, which adjusts the moving average based on the price and its volatility, providing a smoother and more adaptive trend-following tool. Here's a breakdown of its key features:
McGinley Dynamic: The core of the indicator, the McGinley Dynamic, is calculated to track price movements more closely than traditional moving averages. It reacts more quickly to price changes in volatile markets, making it more adaptive.
Upper and Lower Bands: The indicator uses standard deviation to calculate upper and lower bands around the McGinley Dynamic, which represent potential levels of market volatility and trend strength. These bands help define whether the price is trending strongly or consolidating.
Cloud Visualization: A cloud fills the area between the upper and lower bands. The cloud's color changes based on the strength of the trend, with the opacity reflecting how far the price is from the McGinley Dynamic. When the trend is bullish, the cloud color shifts to purple, and when the trend is bearish, the cloud becomes more transparent.
Trend Indicators: The script detects trend changes by comparing the price with the McGinley Dynamic and the bands. A bullish trend is signaled when the price is above the McGinley Dynamic and upper band, while a bearish trend is signaled when the price is below the lower band.
Buy and Sell Signals: The indicator generates buy (long) and sell (short) signals when the trend crosses from bearish to bullish (or vice versa). These signals are marked with upward and downward arrows on the chart.
Take Profit (TP) and Stop Loss (SL) Levels: The script calculates potential take profit and stop loss levels based on the distance between the price and the upper and lower bands. These levels adjust dynamically as the price moves, helping traders manage risk.
Alerts: Alerts are built into the script for key events such as trend changes, take profit conditions, and stop loss conditions. Traders can set alerts to be notified when these events occur.
This indicator is designed to provide traders with a comprehensive tool for identifying trends, spotting potential entry and exit points, and managing trades effectively. By using adaptive calculations and providing visual cues such as the cloud and arrows, it offers an intuitive way to follow market movements and make informed decisions.
1drv.ms
Dynamic Cloud and Trend Identification
Original McGinley Dynamic Indicator: The original McGinley Dynamic (MD) was essentially a smoothed moving average designed to adapt to changing market speeds. It is typically used to indicate the direction of the trend based on its relationship with the price.
Modified Indicator: The modified version of the McGinley Dynamic is enhanced by the addition of a cloud that visually represents the price volatility using standard deviation bands around the MD line. The cloud is filled based on the gradient, with colors indicating the trend’s strength and direction. The script adds:
Upper and Lower Bands: These are plotted based on the standard deviation, which dynamically adjusts with market volatility. These bands help to assess the range within which the price should move.
Cloud Fill: The area between the upper and lower bands is filled with color, making it easier to visually identify periods of strong trends and consolidations. The cloud color changes based on the gradient, indicating whether the market is moving in a bullish or bearish direction.
Why This Helps Traders:
The cloud visually highlights the strength of the trend, making it easier for traders to identify trend reversals and potential breakout points.
The gradient fill within the cloud allows traders to spot trends before they become obvious from just the price action, giving them an early warning of trend strength.
Trend Reversal and Signal Indicators
Original McGinley Dynamic Indicator: The original MD only provided the smoothed average to indicate the trend direction. It didn't offer specific buy/sell signals or a way to easily spot trend changes.
Modified Indicator: The modified version introduces trend reversal signals:
Up Arrows (▲): These are plotted when the price crosses above the McGinley Dynamic, signaling a potential bullish trend.
Down Arrows (▼): These are plotted when the price crosses below the McGinley Dynamic, signaling a potential bearish trend.
Why This Helps Traders:
The inclusion of clear trend reversal signals gives traders a visual cue for when a potential trend change is occurring, which they can use to time their entries.
The arrows provide an additional layer of confirmation when combined with the cloud, giving traders more confidence in making trading decisions.
Dynamic Take Profit (TP) and Stop Loss (SL) Levels
Original McGinley Dynamic Indicator: The original MD indicator does not include any functionality for setting or calculating take profit or stop loss levels. It’s primarily used to identify the trend.
Modified Indicator: The modified version uses the upper and lower bands to define levels for take profit (TP) and stop loss (SL). These levels are dynamically calculated based on the McGinley Dynamic and its surrounding bands.
Take Profit (TP): If the price moves beyond the upper band, it might indicate an overbought condition or a trend continuation point where a trader could take profits.
Stop Loss (SL): If the price moves below the lower band, it could indicate an oversold condition, and traders may consider stopping out of a position.
Why This Helps Traders:
Dynamic TP and SL levels based on market volatility (standard deviation) help to manage risk better than static levels, adjusting to market conditions as they change.
Traders can use these levels to protect profits and minimize losses automatically, making the indicator more useful for those who require risk management tools alongside trend identification.
Trend Strength and Gradient Visualization
Original McGinley Dynamic Indicator: The original MD smooths the price data and shows the direction of the trend but does not offer a visual measure of the strength of that trend.
Modified Indicator: The modified version calculates the gradient of the McGinley Dynamic relative to the price. This gradient value is used to assess the strength of the trend, which is then visualized in the form of a color gradient for the cloud.
The gradient is measured by calculating the difference between the McGinley Dynamic and the price, and then normalizing it to a 0-100 scale. This gives traders a clearer view of how strong the current trend is and whether it's likely to continue or reverse.
The cloud color also dynamically changes based on this gradient, so traders can visually gauge trend strength.
Why This Helps Traders:
By visualizing trend strength, the trader gets a better sense of whether the market is in a strong, sustained move or just a weak pullback. This helps them avoid false signals and stick to the more powerful trends.
The gradient-based cloud provides a more intuitive view of market conditions than just the price line alone.
Visual Trade and Trend Indicators
Original McGinley Dynamic Indicator: It would simply show the MD line without providing visual indicators of trends, reversals, or other key levels.
Modified Indicator: In addition to the cloud, arrows, and gradient, the modified version adds visual signals for potential trade actions:
Trend Change Indicators: The indicator plots up and down arrows to indicate trend changes, making it easier for traders to know when to enter or exit trades based on the McGinley Dynamic.
Bullish and Bearish Signals: Visual cues like arrows and shapes give more context and actionable data for trading decisions.
Why This Helps Traders:
The arrows and other visual signals allow traders to quickly recognize trend changes without needing to interpret complex data, making it easier to trade actively.
The clarity in trend shifts ensures that traders can time their entries and exits more effectively.
Summary: How the Modified Indicator Helps Traders
Enhanced Trend Detection: The modified indicator provides a clear visual representation of trends through the cloud and McGinley Dynamic, helping traders identify trends and reversals more easily.
Dynamic Risk Management: The automatic calculation of TP and SL levels based on market volatility and the McGinley Dynamic helps traders manage risk more effectively.
Visual Confirmation: With trend reversal arrows and a gradient-based cloud, traders get multiple confirmations for when to enter or exit trades, improving the accuracy of their decisions.
Trend Strength: The indicator not only shows the direction of the trend but also its strength, allowing traders to assess the sustainability of the current market conditions.
In conclusion, the modified McGinley Dynamic indicator is a powerful tool that not only tracks trends but also provides dynamic risk management, trend strength visualization, and clear entry/exit signals. It improves on the original by adding these features, making it a more comprehensive tool for traders looking for an automated way to assess market conditions and manage trades.
BeeQuant - Hive Bars🔶 OVERVIEW
The "Hive Bars" indicator is a truly revolutionary analytical instrument, meticulously engineered to transcend the limitations of conventional price charting and unveil the profound, underlying essence of market dynamics. Imagine possessing a sophisticated visual engine that intelligently reconstructs raw price data into unique, dynamically consolidated "Hive Bars." These specialized constructs intuitively reveal the dominant market momentum and highlight high-conviction signals often obscured by the ubiquitous noise of traditional candlesticks. This indicator acts as a precision filter, illuminating exactly when pivotal shifts are occurring by coloring these reconstructed units with an adaptive, unparalleled accuracy. It is expertly crafted for the discerning trader seeking an undeniable analytical advantage, offering a fresh, meticulously refined perspective that enables the discernment of concealed patterns, fostering more decisive and confident trading actions. Crucially, "Hive Bars" now feature proactive, real-time alert capabilities, ensuring no critical market inflection point ever goes unnoticed.
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🧠 CONCEPTS
At its intellectual core, the "Hive Bars" indicator operates upon an advanced, proprietary framework that fundamentally reinterprets market data. It presents this refined information through its unique "Hive Bars"—specialized visual constructs that dynamically encapsulate the consolidated spirit and true directional bias of price action, delivering unparalleled clarity.
⬜ Smart Bar Reconstruction: Hive Bars don’t follow time, they follow the market. They are derived through a sophisticated, multi-faceted internal process that precisely captures the dominant price influence and momentum over variable periods. This structure adapts dynamically to changing conditions, letting you see the real pressure behind price moves with consistency that time-based candles can’t match. This proprietary reconstruction creates a new, inherently consistent, and highly focused visual narrative of underlying market flow, effectively stripping away extraneous "noise" and revealing the market's authentic directional intent.
⬜ Multi-Layered Internal Analysis: A dynamic and live, adaptive line powers the core of Hive Bars. It recalibrates constantly, tracking market structure in real time. Every bar is formed in relation to this internal baseline, giving immediate context to price behavior. You choose the data that drives this line—open, close, high, low, or custom blends—to match your style.
⬜ Intelligent Bar Formation Sequences: Bars are created when the market speaks, not when the clock ticks. A built-in pattern engine reads the flow and waits for real structure to form. This allows the indicator to autonomously consolidate price action, presenting a cleaner, more coherent visualization of trend development as it truly unfolds, rather than fragmented snapshots based on time.
⬜ Visual Signal Precision: "Hive Bars" spring to life with an intuitively powerful coloring system. While primary colors (Green for upward bias, Red for downward bias) denote the prevailing market direction, the "Hive Bars" indicator introduces distinctively colored "Signal Hive Bars". These specialized bars emerge when the market price exhibits a particularly robust, high-conviction interaction with the adaptive internal baseline, standing out instantly and often mark key turning points or breakouts you want to act on.
⬜ Daily Reset Option: For intraday traders, there’s a reset feature that clears the internal build-up at the start of each new trading day. This ensures fresh, unbiased perspectives that are meticulously tailored to the distinct market dynamics and cyclic rhythms of the current trading day.
⬜ Adjustable Sensitivity: With Hive Smoothing, you’re in full control. This setting lets you fine-tune how sensitive the bars are to price movement. Want tighter, faster signals? Dial it down. Prefer broader, more filtered setups? Turn it up. You decide when a new Hive Bar forms—and when a Signal Bar confirms. It’s all based on how you trade and how your asset moves. No guesswork, no one-size-fits-all defaults. Hive Bars adapts to your strategy and trading style, not the other way around.
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✨ FEATURES
The "Hive Bars" indicator is equipped with a comprehensive suite of cutting-edge features, designed for unparalleled clarity, adaptive responsiveness, augmented analytical depth, seamless interoperability with your broader analytical toolkit, and proactive real-time notifications:
🔹Proprietary Hive Bar Reconstruction
Experience a uniquely advanced visual representation of price action that dynamically consolidates market data, leading to enhanced trend and momentum clarity that goes beyond standard charting and candlestick data.
🔹Customizable Internal Analysis Line
Gain precise control over the underlying adaptive baseline's calculation by selecting various internal price source options, ensuring its alignment with your specific analytical focus.
🔹 Smart Alerts for Key Events 🔔
Get notified in real time when:
◦ A new Hive Bar completes – signaling a fresh structural range reset
◦ A new Signal Hive Bar closes – identifying a potential overbought or oversold condition
Built-in alert conditions make it easy to stay ahead of shifts without watching every candle manually.
🔹Intelligent Bar Formation Sequencing
Diamond-shaped markers clearly indicate the start of the indicator's internal combination logic for enhanced visual understanding.
🔹High-Conviction "Signal Hive Bars" (Distinct Colors)
Receive specialized, uniquely colored visual alerts when Hive Bars exhibit strong, decisive movements relative to the adaptive baseline, indicating moments of heightened market conviction and potential opportunity.
🔹Session-Based Reconstruction
Opt for the "Daily New Start" to intelligently reset the indicator's perspective with each new trading day, providing fresh, session-aligned insights tailored for intraday precision.
🔹Unrivaled External Indicator Collaboration
A truly unique and powerful advantage of "Hive Bars" is its capability to seamlessly integrate and profoundly enhance the performance of other external indicators. By outputting clean, smoothed price data, it lets you feed a higher-quality source into tools like RSI, MACD, moving averages etc. Use close for indicators like RSI, and close for moving averages. The result is better clarity, fewer false signals, and a stronger edge across your setup. Hive Bars isn’t just an indicator, it’s an upgrade for everything you use.
🔹Non-Repainting Historical Integrity
Hive Bars never repaints. Each bar is locked in only after all internal conditions are fully met. This means you can trust every historical signal—it won’t shift or vanish after the fact. What you see in hindsight is exactly what was shown in real time.
🔹Universal Timeframe Compatibility
Whether you're scalping on the 1-minute chart or analyzing multi-month trends, Hive Bars delivers consistent, clean insights. Its architecture adapts to any timeframe without losing fidelity, making it a reliable tool for any strategy or style.
🔹Cross-Market Versatility
Hive Bars is engineered to perform with precision across all major markets—whether you're trading forex, commodities, stocks, or indices. Its adaptive logic automatically aligns with the unique volatility and structure of each asset class, delivering consistently reliable insights no matter where you trade.
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⚙️ USAGE
Integrating the "Hive Bars" indicator into your daily analytical regimen is an intuitive process that will profoundly enhance your ability to discern crucial market dynamics and spot high-conviction opportunities with unprecedented clarity:
💁 Effortless Application
Simply add the "Hive Bars" indicator to any chart within your TradingView platform. Note that it plots on a separate panel below your main price chart to provide its unique visual output without obscuring the primary price action.
📊 Strategic Calibration
Access the indicator's comprehensive settings panel to meticulously calibrate its powerful engines and unlock its full potential:
⚙ "Internal EMA Config"
Configure the internal adaptive baseline by choosing its source (e.g., CLOSE, HL/2) and its specific EMA length. This shapes the core reference point for the dynamic formation of the "Hive Bars."
🤖 "CONFIG Group"
Here, you decide if you want "Daily New Start" for session-based analytical resets (particularly beneficial for intraday strategies). The "Hive Smoothing" input allows you to control a further layer of consolidation for the "Hive Bars."
🟩🟥 "Color": Customize the appearance of both standard "Hive Bars" and "Signal Hive Bars" to suit your visual preferences, enhancing their immediate interpretability.
🧭 Empirical Exploration
Experimentation with these parameters is paramount. Dedicate time to exploring different combinations across various assets and timeframes to discover the optimal configuration that resonates with your unique trading methodology and the inherent volatility of the market being analyzed.
👀 Interpreting the Unveiled Market Reality: Once calibrated, the "Hive Bars" will present a strikingly clear and actionable picture of market dynamics:
+ Green/Red Hive Bars: These visually denote the consolidated directional bias of the market over the reconstructed period. A sustained sequence of Green "Hive Bars" suggests pervasive bullish pressure and an upward path of least resistance, while a series of Red "Hive Bars" indicates dominant bearish control and a clear downward momentum.
+ "Signal Hive Bars" (Distinct Colors): Pay close attention to these specially colored "Hive Bars." They signify critical moments where the reconstructed price action exhibits a particularly strong, high-conviction interaction with its adaptive internal baseline. These often precede or confirm significant market movements and serve as your clearest, most reliable visual triggers for potential shifts in market control.
⛓️ Intermittent Appearance: Observe that "Hive Bars" do not necessarily appear for every single native time unit of your chart. They are intelligently reconstructed and consolidated representations of price action, appearing only when specific internal conditions are met to present a coherent, high-impact view of distinct market phases.
🔗 Harnessing Advanced External Synergy: To unlock a new dimension of analytical power, profoundly enhance your existing indicator suite by integrating the output of "Hive Bars" as the data source for other external indicators. When adding or configuring indicators such as RSI, Stochastic Oscillators, various Moving Averages (EMA, SMA), or any other indicator that prompts for a 'source' input, you can now select the purified output of the "Hive Bars" as your desired data stream.
For oscillators (e.g., RSI, MACD), select the close or a similar relevant output from "Hive Bars" as your source. This allows the oscillator to react to the purified, consolidated momentum of the "Hive Bars" rather than the potentially noisy raw price data, leading to smoother and more meaningful oscillator signals.
For moving averages (e.g., EMA, SMA), utilize the close or other pertinent "Hive Bar" output as your source. This provides an exceptionally smooth, highly responsive, and less choppy average that precisely tracks the true underlying trend as identified by "Hive Bars." This unique capability allows for the construction of powerfully layered and synergistic trading strategies.
📢 Setting Up Proactive Alerts for Critical Events: Leverage the newly incorporated alert capabilities to maintain real-time awareness of pivotal market developments, even when not actively monitoring your charts.
You can now choose to be alerted specifically when a "New Hive Bar Closed" (signifying the definitive completion of a major market phase as identified by the indicator) or when a "New Signal Hive Bar Closed" (highlighting a high-conviction market event that warrants immediate attention due to its pronounced significance).
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⚠️ LIMITATIONS
While the "Hive Bars" indicator is an incredibly powerful and advanced tool for dissecting market dynamics, it is vital to understand its inherent design parameters and the prevailing platform-specific constraints for optimal and informed utilization:
👉 Visual Gaps in Plotting: Due to current platform limitations pertaining to custom candle plotting functionality, you may occasionally observe visual gaps or intermittent non-contiguous plotting between "Hive Bars" on the chart. They’re not missing data, but a result of strict plotting rules. A bar is only drawn when all internal conditions are met. This ensures accuracy, even if the chart shows some spacing.
👉 Complementary Tool: This indicator excels at providing high-conviction directional insights and identifying significant market phases. However, it is fundamentally designed as a sophisticated complementary tool to a broader trading strategy, not as a standalone, all-encompassing system. Its true power is unlocked when integrated with other analytical methods.
👉 Input Calibration Essential: The efficacy and depth of insights derived from the "Hive Bars" are highly dependent on the careful and thoughtful calibration of its input parameters, including the "Internal EMA Config," "Hive Smoothing" setting. Optimal results necessitate empirical user experimentation and fine-tuning to discover the configurations best suited for specific assets, analytical objectives, and market conditions.
👉 Exclusion of Auxiliary Data: The "Hive Bars" indicator's primary focus is exclusively on transforming and presenting price data. It does not natively incorporate other vital market information such as fundamental economic data, or news events. Integrating these additional analytical layers remains an essential aspect of constructing a truly comprehensive and robust trading strategy.
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🎯 CONCLUSION
The "Hive Bars" indicator offers an unparalleled, intuitively accessible, and highly adaptable framework for instantly grasping true price momentum and direction through its intelligent, non-repainting reconstruction of market data. By transforming chaotic raw data into strikingly clear, high-conviction "Hive Bars" and dynamic signals, and now with proactive alerts to highlight critical moments, it empowers you to cut through distractions and identify market currents with unprecedented ease. Think of it as a custom lens for the market. It filters out the clutter and shows you the real structure—bars formed not by time, but by intent. It's about seeing the unseen, with enhanced clarity and a deeper understanding of market forces, now with the power to supercharge all your other tools and keep you informed. No fluff. No hype. Just an edge you can actually see—and use.
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🚨 RISK DISCLAIMER
Engagement in financial market speculation inherently carries a substantial degree of inherent risk, and the potential for capital diminution, potentially exceeding initial deposits, is a pervasive and non-trivial consideration. All content, algorithmic tools, scripts, articles, and educational materials disseminated by "Hive Bars" are exclusively purposed for informational and pedagogical objectives, strictly for reference. Historical performance data, whether explicitly demonstrated or implicitly suggested, offers no infallible assurance or guarantee of future outcomes. Users bear sole and ultimate accountability for their individual trading decisions and are emphatically urged to meticulously assess their financial disposition, risk tolerance parameters, and conduct independent due diligence prior to engaging in any speculative activity.
Fusion MFI RSIHello fellas,
This superb indicator summons two monsters called Relative Strength Index (RSI) and Money Flow Index (MFI) and plays the Yu-Gi-Oh! card "Polymerization" to combine them.
Overview
The Fusion MFI RSI Indicator is an advanced analytical tool designed to provide a nuanced understanding of market dynamics by combining the Relative Strength Index (RSI) and the Money Flow Index (MFI). Enhanced with sophisticated smoothing techniques and the Inverse Fisher Transform (IFT), this indicator excels in identifying key market conditions such as overbought and oversold states, trends, and potential reversal points.
Key Features (Brief Overview)
Fusion of RSI and MFI: Integrates momentum and volume for a comprehensive market analysis.
Advanced Smoothing Techniques: Employs Hann Window, Jurik Moving Average (JMA), T3 Smoothing, and Super Smoother to refine signals.
Inverse Fisher Transform (IFT) Enhances the clarity and distinctiveness of indicator outputs.
Detailed Feature Analysis
Fusion of RSI and MFI
RSI (Relative Strength Index): Developed by J. Welles Wilder Jr., the RSI measures the speed and magnitude of directional price movements. Wilder recommended using a 14-day period and identified overbought conditions above 70 and oversold conditions below 30.
MFI (Money Flow Index): Created by Gene Quong and Avrum Soudack, the MFI combines price and volume to measure trading pressure. It is typically calculated using a 14-day period, with over 80 considered overbought and under 20 as oversold.
Application in Fusion: By combining RSI and MFI, the indicator leverages RSI's sensitivity to price changes with MFI's volume-weighted confirmation, providing a robust analysis tool. This combination is particularly effective in confirming the strength behind price movements, making the signals more reliable.
Advanced Smoothing Techniques
Hann Window: Traditionally used to reduce the abrupt data discontinuities at the edges of a sample, it is applied here to smooth the price data.
Jurik Moving Average (JMA): Known for preserving the timing and smoothness of the data, JMA reduces market noise effectively without significant lag.
T3 Smoothing: Developed to respond quickly to market changes, T3 provides a smoother response to price fluctuations.
Super Smoother: Filters out high-frequency noise while retaining important trends.
Application in Fusion: These techniques are chosen to refine the output of the combined RSI and MFI values, ensuring the indicator remains responsive yet stable, providing clearer and more actionable signals.
Inverse Fisher Transform (IFT):
Developed by John Ehlers, the IFT transforms oscillator outputs to enhance the clarity of extreme values. This is particularly useful in this fusion indicator to make critical turning points more distinct and actionable.
Mathematical Calculations for the Fusion MFI RSI Indicator
RSI (Relative Strength Index)
The RSI is calculated using the following steps:
Average Gain and Average Loss: First, determine the average gain and average loss over the specified period (typically 14 days). This is done by summing all the gains and losses over the period and then dividing each by the period.
Average Gain = (Sum of Gains over the past 14 periods) / 14
Average Loss = (Sum of Losses over the past 14 periods) / 14
Relative Strength (RS): This is the ratio of average gain to average loss.
RS = Average Gain / Average Loss
RSI: Finally, the RSI is calculated using the RS value:
RSI = 100 - (100 / (1 + RS))
MFI (Money Flow Index)
The MFI is calculated using several steps that incorporate both price and volume:
Typical Price: Calculate the typical price for each period.
Typical Price = (High + Low + Close) / 3
Raw Money Flow: Multiply the typical price by the volume for the period.
Raw Money Flow = Typical Price * Volume
Positive and Negative Money Flow: Compare the typical price of the current period to the previous period to determine if the money flow is positive or negative.
If today's Typical Price > Yesterday's Typical Price, then Positive Money Flow = Raw Money Flow; Negative Money Flow = 0
If today's Typical Price < Yesterday's Typical Price, then Negative Money Flow = Raw Money Flow; Positive Money Flow = 0
Money Flow Ratio: Calculate the ratio of the sum of Positive Money Flows to the sum of Negative Money Flows over the past 14 periods.
Money Flow Ratio = (Sum of Positive Money Flows over 14 periods) / (Sum of Negative Money Flows over 14 periods)
MFI: Finally, calculate the MFI using the Money Flow Ratio.
MFI = 100 - (100 / (1 + Money Flow Ratio))
Fusion of RSI and MFI
The final Fusion MFI RSI value could be calculated by averaging the IFT-transformed values of RSI and MFI, providing a single oscillator value that reflects both momentum and volume-weighted price action:
Fusion MFI RSI = (MFI weight * MFI) + (RSI weight * RSI)
Suggested Settings and Trading Rules
Original Usage
RSI: Wilder suggested buying when the RSI moves above 30 from below (enter long) and selling when the RSI moves below 70 from above (enter short). He recommended exiting long positions when the RSI reaches 70 or higher and exiting short positions when the RSI falls below 30.
MFI: Quong and Soudack recommended buying when the MFI is below 20 and starts rising (enter long), and selling when it is above 80 and starts declining (enter short). They suggested exiting long positions when the MFI reaches 80 or higher and exiting short positions when the MFI falls below 20.
Fusion Application
Settings: Use a 14-day period for this indicator's calculations to maintain consistency with the original settings suggested by the inventors.
Trading Rules:
Enter Long Signal: Consider entering a long position when both RSI and MFI are below their respective oversold levels and begin to rise. This indicates strong buying pressure supported by both price momentum and volume.
Exit Long Signal: Exit the long position when either RSI or MFI reaches its respective overbought threshold, suggesting a potential reversal or decrease in buying pressure.
Enter Short Signal: Consider entering a short position when both indicators are above their respective overbought levels and begin to decline, suggesting that selling pressure is mounting.
Exit Short Signal: Exit the short position when either RSI or MFI falls below its respective oversold threshold, indicating diminishing selling pressure and a potential upward reversal.
How to Use the Indicator
Select Source and Timeframe: Choose the data source and the timeframe for analysis.
Configure Fusion Settings: Adjust the weights for RSI and MFI.
Choose Smoothing Technique: Select and configure the desired smoothing method to suit the market conditions and personal preference.
Enable Fisherization: Optionally apply the Inverse Fisher Transform to enhance signal clarity.
Customize Visualization: Set up gradient coloring, background plots, and bands according to your preferences.
Interpret the Indicator: Use the Fusion value and visual cues to identify market conditions and potential trading opportunities.
Conclusion
The Fusion MFI RSI Indicator integrates classical and modern technical analysis concepts to provide a comprehensive tool for market analysis. By combining RSI and MFI with advanced smoothing techniques and the Inverse Fisher Transform, this indicator offers enhanced insights, aiding traders in making more informed and timely trading decisions. Customize the settings to align with your trading strategy and leverage this powerful tool to navigate financial markets effectively.
Best regards,
simwai
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Credits to:
@loxx – T3
@everget – JMA
@cheatcountry – Hann Window
Luxy Adaptive MA Cloud - Trend Strength & Signal Tracker V2Luxy Adaptive MA Cloud - Professional Trend Strength & Signal Tracker
Next-generation moving average cloud indicator combining ultra-smooth gradient visualization with intelligent momentum detection. Built for traders who demand clarity, precision, and actionable insights.
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WHAT MAKES THIS INDICATOR SPECIAL?
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Unlike traditional MA indicators that show static lines, Luxy Adaptive MA Cloud creates a living, breathing visualization of market momentum. Here's what sets it apart:
Exponential Gradient Technology
This isn't just a simple fill between two lines. It's a professionally engineered gradient system with 26 precision layers using exponential density distribution. The result? An organic, cloud-like appearance where the center is dramatically darker (15% transparency - where crossovers and price action occur), while edges fade gracefully (75% transparency). Think of it as a visual "heat map" of trend strength.
Dynamic Momentum Intelligence
Most MA clouds only show structure (which MA is on top). This indicator shows momentum strength in real-time through four intelligent states:
- 🟢 Bright Green = Explosive bullish momentum (both MAs rising strongly)
- 🔵 Blue = Weakening bullish (structure intact, but momentum fading)
- 🟠 Orange = Caution zone (bearish structure forming, weak momentum)
- 🔴 Deep Red = Strong bearish momentum (both MAs falling)
The cloud literally tells you when trends are accelerating or losing steam.
Conditional Performance Architecture
Every calculation is optimized for speed. Disable a feature? It stops calculating entirely—not just hidden, but not computed . The 26-layer gradient only renders when enabled. Toggle signals off? Those crossover checks don't run. This makes it one of the most efficient cloud indicators available, even with its advanced visual system.
Zero Repaint Guarantee
All signals and momentum states are based on confirmed bar data only . What you see in historical data is exactly what you would have seen trading live. No lookahead bias. No repainting tricks. No signals that "magically" appear perfect in hindsight. If a signal shows in history, it would have triggered in real-time at that exact moment.
Educational by Design
Every single input includes comprehensive tooltips with:
- Clear explanations of what each parameter does
- Practical examples of when to use different settings
- Recommended configurations for scalping, day trading, and swing trading
- Real-world trading impact ("This affects entry timing" vs "This is visual only")
You're not just getting an indicator—you're learning how to use it effectively .
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THE GRADIENT CLOUD - TECHNICAL DETAILS
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Architecture:
26 precision layers for silk-smooth transitions
Exponential density curve - layers packed tightly near center (where crossovers happen), spread wider at edges
75%-15% transparency range - center is highly opaque (15%), edges fade gracefully (75%)
V-Gradient design - emphasizes the action zone between Fast and Medium MAs
The Four Momentum States:
🟢 GREEN - Strong Bullish
Fast MA above Medium MA
Both MAs rising with momentum > 0.02%
Action: Enter/hold LONG positions, strong uptrend confirmed
🔵 BLUE - Weak Bullish
Fast MA above Medium MA
Weak or flat momentum
Action: Caution - bullish structure but losing strength, consider trailing stops
🟠 ORANGE - Weak Bearish
Medium MA above Fast MA
Weak or flat momentum
Action: Warning - bearish structure developing, consider exits
🔴 RED - Strong Bearish
Medium MA above Fast MA
Both MAs falling with momentum < -0.02%
Action: Enter/hold SHORT positions, strong downtrend confirmed
Smooth Transitions: The momentum score is smoothed using an 8-bar EMA to eliminate noise and prevent whipsaws. You see the true trend , not every minor fluctuation.
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FLEXIBLE MOVING AVERAGE SYSTEM
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Three Customizable MAs:
Fast MA (default: EMA 10) - Reacts quickly to price changes, defines short-term momentum
Medium MA (default: EMA 20) - Balances responsiveness with stability, core trend reference
Slow MA (default: SMA 200, optional) - Long-term trend filter, major support/resistance
Six MA Types Available:
EMA - Exponential; faster response, ideal for momentum and day trading
SMA - Simple; smooth and stable, best for swing trading and trend following
WMA - Weighted; middle ground between EMA and SMA
VWMA - Volume-weighted; reflects market participation, useful for liquid markets
RMA - Wilder's smoothing; used in RSI/ADX, excellent for trend filters
HMA - Hull; extremely responsive with minimal lag, aggressive option
Recommended Settings by Trading Style:
Scalping (1m-5m):
Fast: EMA(5-8)
Medium: EMA(10-15)
Slow: Not needed or EMA(50)
Day Trading (5m-1h):
Fast: EMA(10-12)
Medium: EMA(20-21)
Slow: SMA(200) for bias
Swing Trading (4h-1D):
Fast: EMA(10-20)
Medium: EMA(34-50)
Slow: SMA(200)
Pro Tip: Start with Fast < Medium < Slow lengths. The gradient works best when there's clear separation between Fast and Medium MAs.
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CROSSOVER SIGNALS - CLEAN & RELIABLE
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Golden Cross ⬆ LONG Signal
Fast MA crosses above Medium MA
Classic bullish reversal or trend continuation signal
Most reliable when accompanied by GREEN cloud (strong momentum)
Death Cross ⬇ SHORT Signal
Fast MA crosses below Medium MA
Classic bearish reversal or trend continuation signal
Most reliable when accompanied by RED cloud (strong momentum)
Signal Intelligence:
Anti-spam filter - Minimum 5 bars between signals prevents noise
Clean labels - Placed precisely at crossover points
Alert-ready - Built-in ALERTS for automated trading systems
No repainting - Signals based on confirmed bars only
Signal Quality Assessment:
High-Quality Entry:
Golden Cross + GREEN cloud + Price above both MAs
= Strong bullish setup ✓
Low-Quality Entry (skip or wait):
Golden Cross + ORANGE cloud + Choppy price action
= Weak bullish setup, likely whipsaw ✗
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REAL-TIME INFO PANEL
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An at-a-glance dashboard showing:
Trend Strength Indicator:
Visual display of current momentum state
Color-coded header matching cloud color
Instant recognition of market bias
MA Distance Table:
Shows percentage distance of price from each enabled MA:
Green rows : Price ABOVE MA (bullish)
Red rows : Price BELOW MA (bearish)
Gray rows : Price AT MA (rare, decision point)
Distance Interpretation:
+2% to +5%: Healthy uptrend
+5% to +10%: Getting extended, caution
+10%+: Overextended, expect pullback
-2% to -5%: Testing support
-5% to -10%: Oversold zone
-10%+: Deep correction or downtrend
Customization:
4 corner positions
5 font sizes (Tiny to Huge)
Toggle visibility on/off
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HOW TO USE - PRACTICAL TRADING GUIDE
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STRATEGY 1: Trend Following
Identify trend : Wait for GREEN (bullish) or RED (bearish) cloud
Enter on signal : Golden Cross in GREEN cloud = LONG, Death Cross in RED cloud = SHORT
Hold position : While cloud maintains color
Exit signals :
• Cloud turns ORANGE/BLUE = momentum weakening, tighten stops
• Opposite crossover = close position
• Cloud turns opposite color = full reversal
STRATEGY 2: Pullback Entries
Confirm trend : GREEN cloud established (bullish bias)
Wait for pullback : Price touches or crosses below Fast MA
Enter when : Price rebounds back above Fast MA with cloud still GREEN
Stop loss : Below Medium MA or recent swing low
Target : Previous high or when cloud weakens
STRATEGY 3: Momentum Confirmation
Your setup triggers : (e.g., chart pattern, support/resistance)
Check cloud color :
• GREEN = proceed with LONG
• RED = proceed with SHORT
• BLUE/ORANGE = skip or reduce size
Use gradient as confluence : Not as primary signal, but as momentum filter
Risk Management Tips:
Never enter against the cloud color (don't LONG in RED cloud)
Reduce position size during BLUE/ORANGE (transition periods)
Place stops beyond Medium MA for swing trades
Use Slow MA (200) as final trend filter - don't SHORT above it in uptrends
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PERFORMANCE & OPTIMIZATION
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Tested On:
Crypto: BTC, ETH, major altcoins
Stocks: SPY, AAPL, TSLA, QQQ
Forex: EUR/USD, GBP/USD, USD/JPY
Indices: S&P 500, NASDAQ, DJI
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TRANSPARENCY & RELIABILITY
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Educational Focus:
Detailed tooltips on every input
Clear documentation of methodology
Practical examples in descriptions
Teaches you why , not just what
Open Logic:
Momentum calculation: (Fast slope + Medium slope) / 2
Smoothing: 8-bar EMA to reduce noise
Thresholds: ±0.02% for strong momentum classification
Everything is transparent and explainable
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COMPLETE FEATURE LIST
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Visual Components:
26-layer exponential gradient cloud
3 customizable moving average lines
Golden Cross / Death Cross labels
Real-time info panel with trend strength
MA distance table
Calculation Features:
6 MA types (EMA, SMA, WMA, VWMA, RMA, HMA)
Momentum-based cloud coloring
Smoothed trend strength scoring
Conditional performance optimization
Customization Options:
All MA lengths adjustable
All colors customizable (when gradient disabled)
Panel position (4 corners)
Font sizes (5 options)
Toggle any feature on/off
Signal Features:
Anti-spam filter (configurable gap)
Clean, non-overlapping labels
Built-in alert conditions
No repainting guarantee
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IMPORTANT DISCLAIMERS
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This indicator is for educational and informational purposes only
Not financial advice - always do your own research
Past performance does not guarantee future results
Use proper risk management - never risk more than you can afford to lose
Test on paper/demo accounts before using with real money
Combine with other analysis methods - no single indicator is perfect
Works best in trending markets; less effective in choppy/sideways conditions
Signals may perform differently in different timeframes and market conditions
The indicator uses historical data for MA calculations - allow sufficient lookback period
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CREDITS & TECHNICAL INFO
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Version: 2.0
Release: October 2025
Special Thanks:
TradingView community for feedback and testing
Pine Script documentation for technical reference
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SUPPORT & UPDATES
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Found a bug? Comment below with:
Ticker symbol
Timeframe
Screenshot if possible
Steps to reproduce
Feature requests? I'm always looking to improve! Share your ideas in the comments.
Questions? Check the tooltips first (hover over any input) - most answers are there. If still stuck, ask in comments.
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Happy Trading!
Remember: The best indicator is the one you understand and use consistently. Take time to learn how the cloud behaves in different market conditions. Practice on paper before going live. Trade smart, manage risk, and may the trends be with you! 🚀
Syndicate Bias Universal (Auto)Syndicate Bias Universal (Auto): A Masterclass in Time-Based Trading
Chapter 1: The Modern Trader's Dilemma—A New Framework for a Noisy Market
In today's hyper-connected financial markets, the modern trader is faced with a profound paradox: we have access to more information than ever before, yet achieving consistent clarity has never been more challenging. We are inundated with a relentless stream of price data, countless indicators, breaking news, and expert opinions. This information overload often leads not to better decision-making, but to analysis paralysis, emotional trading, and a chronic sense of being one step behind the market's true intentions.
The fundamental problem that Syndicate Bias Universal (Auto) addresses is this struggle for clarity amidst the noise. It challenges the conventional approach of relying solely on price- and volume-based indicators, which are inherently lagging and often produce conflicting signals. Instead, it introduces a crucial, and often overlooked, third dimension to technical analysis: time.
This indicator is not merely another tool to be added to a cluttered chart; it is a comprehensive, systematic framework designed to reinterpret market dynamics through the structured lens of trading sessions. Its core function is to deconstruct any trading period—from an entire week down to the smallest intraday segments—into a clear, four-part narrative structure, which we call "Quarters."
Many traders can correctly identify a market's general direction but consistently struggle with the critical question of when to act. This timing issue leads to the most common trading errors: entering positions too early only to be stopped out by volatility, entering too late and catching the tail-end of a move, or being whipsawed by directionless chop. This script provides a logical, rules-based solution by identifying a specific, high-probability time window within each session where reversal setups are most likely to occur. It is built for the discerning trader who is ready to evolve—to move beyond reactive, emotionally-driven decisions and adopt a structured, patient, and objective methodology for market engagement. It is, in essence, an operating system for disciplined trading.
Chapter 2: The Core Philosophy—Viewing the Market as a Four-Quarter Game
At its heart, this indicator operates on a powerful principle: market sessions, regardless of their duration, exhibit a discernible rhythm and structure, much like a four-quarter game of football, a four-act theatrical play, or the four seasons of a year. Price action is not a chaotic, random walk. It is a story unfolding, driven by the collective psychology of millions of participants. This story often follows a recurring pattern of opening, exploration, climax, and resolution.
By dividing trading sessions into four distinct quarters, we can better contextualize this narrative. This temporal structure acts as a powerful filter, cutting through the incessant noise of minor price fluctuations and focusing the trader's attention on the moments that truly matter.
Quarter 1 (The Opening Act): This is the period of price discovery. The market is absorbing overnight news, and early participants are establishing their initial positions. The character of this quarter—whether it is quiet and rotational or strong and directional—provides crucial clues about the session's potential.
Quarter 2 (The Exploration): Following the initial open, the market begins to test the levels established in Q1. This is often a period of consolidation or early trend development, where weaker hands are shaken out.
Quarter 3 (The Climax): Often, this is where the session's primary, decisive move occurs. It can be a powerful trend continuation or, critically, a major reversal point where the initial momentum shows signs of exhaustion.
Quarter 4 (The Resolution): This is the closing period, characterized by profit-taking, late-day position adjustments, and a general decrease in volume as the session winds down.
This is not a "black box" system promising guaranteed results. It is a transparent methodology built on a clear, logical foundation of session analysis. Its purpose is to empower you with a deeper understanding of market behavior, transforming you from a mere participant, tossed about by the market's waves, into a patient observer who waits for specific, high-probability conditions to align before acting. Embracing this philosophy is the first and most crucial step to unlocking the tool's full potential.
Chapter 3: The Engine—Key Features & In-Depth Principles
This section dissects the sophisticated mechanics that power the indicator. Each feature is designed to work in concert, creating a robust and adaptive analytical engine.
Feature 1: Universal Market Adaptability—A Global, Intelligent Tool
A significant weakness of many trading tools is their inherent rigidity. An indicator fine-tuned for the unique volatility profile and session times of the New York open will invariably underperform or provide false signals when applied to the different rhythms of the Indian or Asian markets. Syndicate Bias Universal eradicates this problem with a sophisticated, dual-mode adaptability engine.
Intelligent Auto-Detection: This is the default and recommended setting for most traders. When the "Market Type" input is set to "Auto," the script becomes a dynamic, context-aware tool. It intelligently queries the exchange information (syminfo.prefix) of the instrument you are currently viewing. It automatically recognizes major Indian exchanges (NSE, BSE, MCX) and all other global exchanges. Based on this identification, it seamlessly applies the correct session timing logic—using "Asia/Kolkata" for Indian instruments and "America/New_York" for global instruments (Forex, Commodities, US Equities, etc.).
This allows traders with a diverse watchlist to move effortlessly from analyzing the NIFTY 50 to EUR/USD to Crude Oil, confident that the underlying temporal analysis remains precise, relevant, and correctly calibrated to the dominant trading hours of each asset. There is no need for manual adjustment or multiple chart templates; the indicator handles the complex work of timezone alignment for you.
Focused Manual Override: For the advanced trader, the manual override provides an indispensable layer of analytical control. There are specific scenarios where locking the indicator to a particular time zone, regardless of the asset being viewed, is crucial.
Cross-Market Influence Analysis: A European trader analyzing the DAX index might want to lock the indicator to "Global" (New York) time during the afternoon to see how the US open influences the German market's behavior in its final hours.
Commodity and Forex Trading: A trader in Asia specializing in WTI Crude Oil or Gold knows that these markets are heavily dominated by the New York session. By locking the indicator to "Global," they can apply the correct temporal structure to their analysis, even if their local time is different.
Consistent Strategy Application: A trader who has developed a strategy based purely on the London/New York session overlap can lock the indicator to "Global" and apply this single, consistent framework across any and all instruments they trade.
This dual-mode system ensures that the indicator is both effortlessly simple for those who need it to be and powerfully flexible for those who require granular control.
Feature 2: Fractal Quarter-Based Analysis—Structure at Every Scale
The term "fractal" in market analysis refers to the principle that the same patterns of collective human behavior—driven by greed, fear, hope, and indecision—manifest repeatedly across all timeframes. A pattern that takes months to unfold on a weekly chart can play out in a matter of minutes on a one-minute chart. The Syndicate Bias Universal indicator is built on this very principle, applying its Four-Quarter structure consistently from the highest macro view down to the lowest micro view.
This provides a unified, coherent framework for analysis, regardless of your trading style.
The Weekly Quarter (The Position Trader's View): At this macro level, the trading week is divided into four primary segments (e.g., Monday, Tuesday, Wednesday, Thursday). This perspective is invaluable for position traders and long-term investors. It helps answer critical strategic questions: Is the week's opening action on Monday establishing a trend that will likely hold, or is it creating the conditions for a mid-week reversal? The weekly quarters help contextualize the larger battle between long-term buyers and sellers.
The Daily Quarter (The Swing Trader's View): Here, the full 24-hour global trading day is partitioned into four 6-hour quarters. This is the ideal lens for swing traders and day traders who aim to capture the dominant move of the day or a multi-day swing. It helps them avoid the morning "chop" by understanding the initial price discovery phase and position themselves for the more decisive moves that often occur in the later quarters of the global session.
Intraday Quarters: 90min, Micro, and Nano (The Day Trader's & Scalper's View): For traders operating on the front lines of intraday price action, the script drills down with surgical precision. It breaks down shorter sessions into their own complete four-quarter cycles. This granular view is essential for timing precise entries, managing trades with tight stop-losses, and understanding the micro-rhythms of order flow. It helps scalpers identify high-probability windows to trade, while allowing them to step back and avoid periods of low liquidity or erratic price action.
To keep you anchored, the script automatically selects and displays the relevant analysis timeframe ("Auto TF") in a non-intrusive display on your chart. This seemingly simple feature is a crucial navigational tool, constantly reminding you of the specific temporal context the engine is currently analyzing, ensuring your decisions are always aligned with the appropriate structural scale.
Feature 3: The "S-Quarter" Timing Window—The Art of Strategic Patience
This is the intellectual core of the indicator and its most powerful feature. It is the mechanism that transforms trading from a constant, stressful hunt for opportunities into a calm, disciplined, and strategic wait. The S-Quarter (Search Quarter) engine enforces patience by activating its search for trade setups only within a specific, algorithmically determined time window.
The Q1 Volatility Profile Analysis: The process begins at the start of a new session. The indicator's logic performs a sophisticated analysis of the price action within the first quarter (Q1). It looks beyond simple direction and evaluates its character. This involves assessing the nature of the opening period's volatility. Is the range expanding or contracting? Is the price action rotational and indecisive, or is it directional and backed by momentum? A quiet, low-volatility Q1 suggests a different market psychology and implies a very different probabilistic path for the rest of the session compared to a strong, high-volume, trend-setting Q1.
Dynamic and Adaptive Window Selection: Based on this nuanced Q1 profile, the script makes a critical, forward-looking determination: which of the subsequent quarters (Q2, Q3, or Q4) is most likely to host a significant market turning point, a liquidity grab, or an exhaustion event. This designated period is the "S-Quarter." The selection is dynamic and adaptive:
If Q1 was a powerful, trending move, the engine might identify Q3 as the S-Quarter, anticipating that the initial momentum will wane, drawing in late trend-followers just in time for a sharp reversal.
If Q1 was a tight, rotational range, the engine might identify Q2 as the S-Quarter, anticipating that the first breakout attempt from this range will likely be a "head fake" designed to trap traders before the real move begins in the opposite direction.
This intelligent selection is what sets the tool apart. It doesn't use a fixed, one-size-fits-all timing window. It adapts its search to the unique, unfolding conditions of each individual trading session. The S-Quarter is the only time the script will actively look for and display trade setups. This powerful filter is the key to mastering trading psychology. It prevents impulsive entries, eliminates the fear of missing out (FOMO), dramatically reduces exposure to choppy and unpredictable market periods, and aligns your actions with the moments of highest probabilistic edge.
Feature 4: Contrarian Reversal Setups—Identifying Market Exhaustion
The setups generated by this indicator are contrarian by design. They are not trend-following signals. They are based on the principle of identifying moments where a prevailing short-term move is reaching a point of exhaustion, often culminating in a "liquidity grab."
The Mechanics of a Liquidity Grab: Within the pre-defined S-Quarter, the script vigilantly monitors short-term market structure, specifically the pivot highs and pivot lows. A break of a recent, significant pivot is a critical event. The script's logic posits that during the S-Quarter, these breakouts are often not the beginning of a sustained new trend. Instead, they are frequently a calculated move by institutional players to "run the stops"—a stop hunt designed to trigger the stop-loss orders of retail traders who are positioned on the wrong side of the market. This action injects a surge of liquidity into the market, which is precisely what larger players need to fill their large orders in the opposite direction.
Bullish Reversal Setup (Fading the Low): This setup is triggered by a break below a recent structural low during the S-Quarter. This event signals that the sellers who pushed the price to a new low may have exhausted their power in the process of running the stops. The trap has been set, and this alert serves as a potential turning point where buyers are likely to step in with force.
Bearish Reversal Setup (Fading the High): This setup is triggered by a break above a recent structural high during the S-Quarter. This suggests that the final, euphoric wave of buying pressure may be culminating in a liquidity grab. The last of the breakout buyers have been drawn in at the worst possible price, presenting an opportunity for informed sellers to take control and initiate a move downwards.
It is absolutely essential to understand that these are high-probability setups, not automated entry signals. They are sophisticated alerts that tell you, "The conditions are now ripe for a potential reversal within our strategic time window." The final decision to execute a trade, and the management of that trade, always rests with you, the trader.
Chapter 4: The Workflow—A Step-by-Step Guide to Practical Application
This section provides a clear, actionable workflow for integrating the Syndicate Bias Universal indicator into your daily trading routine.
Step 1: Initial Configuration (The Pre-Flight Check). Begin by setting the "Market Type." For maximum efficiency across a varied watchlist, leave it on "Auto." If you are a specialist who focuses on one specific market session, manually select "Global" or "Indian" to lock in your preferred analytical framework. Ensure other visual settings, like "Show Active Quarter Boxes," are enabled.
Step 2: Contextualize the Session (Reading the Field). At the start of your trading day, observe the quarter boxes as they begin to form. Pay attention to the story they tell. Is the Q1 box narrow and tight, suggesting indecision? Is it wide and directional, suggesting a strong opening sentiment? This visual context helps you build an intuitive feel for the session's rhythm long before any signal appears.
Step 3: Exercise Strategic Patience (The Professional's Edge). This is the most critical and often the most difficult step. The script will automatically perform its Q1 analysis and silently determine the S-Quarter. Your job is to wait. Resist the urge to trade during the other quarters. This disciplined inaction is not passive; it is an active strategy. It conserves your mental and financial capital for the moments that count the most.
Step 4: The Alert (The Call to Action). When a label—"Look for Bullish/Bearish reversal"—appears on your chart, it is your cue to shift from a passive, observational state to an active, analytical one. This is the moment you have been waiting for. Do not instantly click "buy" or "sell." The alert is a call to focus your attention, not a command to act blindly.
Step 5: The Confirmation Process (Your Personal Edge). The setup is the start, not the end, of your trade analysis. This is where you apply your own skills to confirm the validity of the setup. For example, upon seeing a Bullish Reversal Setup:
Candlestick Analysis: Look for confirmation candles like a powerful bullish engulfing bar, a hammer, or a dragonfly doji forming right after the new low was made.
Volume Analysis: Check if the move to the new low was on high, climactic volume that suddenly dried up, followed by an increase in volume as the price starts to reverse.
Indicator Confluence: Look for bullish divergence on an oscillator like the RSI or MACD, where price makes a new low but the indicator makes a higher low.
This confirmation process is what integrates the indicator into your unique trading style, making it exponentially more powerful.
Step 6: Execute and Manage Risk (The Business of Trading). Once you have your confirmation, execute your trade according to your plan. Risk management is paramount. A logical stop-loss for a Bullish Reversal Setup would typically be placed just below the low of the liquidity grab candle. Your take-profit targets should be based on your analysis of key resistance levels. Always ensure the potential reward of the trade justifies the initial risk. A setup is a probabilistic edge, not a certainty.
Chapter 5: The Trader's Mind—Mastering the Psychology of Time
Integrating this tool effectively is as much about mastering psychology as it is about technical analysis. Its very design encourages the development of a professional trading mindset.
From Impulsive to Patient: The S-Quarter forces you to wait for the market to come to you, curing the impulsive need to be "in a trade" at all times.
From Reactive to Proactive: You are no longer reacting to every price tick. You have a proactive plan: you know which time window you are interested in and what condition you are waiting for. This puts you in a position of mental control.
Building Unshakeable Discipline: By consistently following the framework, you are building the muscle of discipline. You learn that often the most profitable action is no action at all.
Conquering FOMO (Fear Of Missing Out): FOMO is driven by unstructured, random trading. When you know you are only interested in a specific type of setup within a specific time window, the moves that happen outside of that framework become irrelevant noise. You cannot miss a move you were never supposed to take.
Gaining Confidence Through Structure: The clarity and structure provided by the Four-Quarter framework build immense confidence. You are not guessing; you are executing a well-defined plan based on a logical, repeatable methodology.
Chapter 6: Frequently Asked Questions & Scenarios
Q: What happens if no setup appears during the S-Quarter?
A: This is one of the most valuable outcomes the indicator can provide. It means that during the high-probability window, the market did not produce a clear exhaustion or liquidity grab event. The script has effectively told you that the conditions were not optimal for a high-probability reversal, and the correct decision was to preserve your capital. A null signal is a powerful signal in itself.
Q: Can I use this indicator with my existing trend-following strategy?
A: Absolutely. In fact, it's a perfect combination. You can use your macro trend-following tools to establish the dominant weekly or daily direction. Then, you can use the Syndicate Bias Universal indicator on a lower timeframe to look for contrarian setups that signal the end of a pullback, allowing you to enter the trade in the direction of the larger trend at a much better price.
Q: Which analysis timeframe ("Auto TF") is the 'best' one to use?
A: There is no "best" timeframe; there is only the timeframe that is right for your trading style. This is precisely why the fractal design is so powerful. A long-term swing trader might focus primarily on the signals generated by the Daily quarters, while a high-frequency scalper will live within the Micro and Nano quarters. The indicator adapts to you, not the other way around. Experiment and find the resolution that best suits your personality and trading goals.
Paul_BDT Osc. MACD, ADX, CHOP, RSI & CVD🔧 Overview
Modular multi-oscillator engine designed for actionable and filtered trading signals. It combines the power of MACD, ADX, CHOP, RSI, and CVD, integrates advanced divergence detection, a multi-timeframe dashboard, and a built-in risk management system.
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🚨 Alert System
Alerts are organized by signal type, oscillator used, and timeframe block, with precision controls for filtering and sensitivity.
1. Oscillator Alerts (Osc.)
Triggers ▲ / ▼ triangle markers based on trend momentum shifts detected on the selected oscillator:
• MACD: triggers when histogram crosses 0 with bullish or bearish slope
• ADX: triggers on directional breakout with increasing trend strength
• CHOP: signals trend resumption after choppy market phase
• RSI: breakout from dynamic support/resistance using pivot detection
• CVD: shift in buy/sell pressure based on aggregated volume delta
✅ All signals optionally trigger on bar close only (if enabled)
2. Divergence Alerts (Div.)
Automatic detection of:
• 🔼 Regular Divergences
• Bullish: Lower lows in price, higher lows in oscillator
• Bearish: Higher highs in price, lower highs in oscillator
• 🔁 Hidden Divergences
• Hidden Bullish: Higher lows in price, lower lows in oscillator
• Hidden Bearish: Lower highs in price, higher highs in oscillator
Alert trigger logic:
• Divergences only trigger if confirmed by price action:
→ breakout from wick or close beyond BB/RSI dynamic bands
• Alerts are non-repeating (fires only on signal change)
🔔 divergeUP and divergeDN are fired when divergence AND price condition are met.
3. Reversal Alerts (Rev.)
Strict combo alert:
• reverseUP = divergeUP AND bullish wick breakout
• reverseDN = divergeDN AND bearish wick breakout
🧠 These are high-conviction signals, ideal for swing entries or reversion trades.
📊 Multi-Timeframe Support (4 Blocks)
4 independent blocks:
• Scalp, Intra, Swing, Custom
• Each block accepts 3 sorted timeframes
• You can individually enable:
• Oscillator alerts
• Divergences
• Reversals
Example:
• Scalp: RSI only, no divergence
• Intra: CVD + reversal only
• Swing: MACD + divergence + reversal
Each timeframe is dynamically sorted and shown in a structured dashboard grid (TF01 to TF12), making the multi-timeframe readout seamless.
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⚙️ Additional Features
• Full visual panel with color-coded trend indicators
• Take Profit/Exit Alerts available on a custom timeframe
• Built-in Money Management:
• % or USD risk
• Configurable R/R ratio
• Minimum PnL threshold (filter out low-return setups)
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✅ Best Use Cases
• High-frequency scalping (1s–1min) with real-time oscillator breakouts
• Structured intraday/swing planning using divergence + reversal logic
• Manual backtesting and alert-based discretionary entries
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🧠 Fonctionnalités
• Oscillateurs personnalisables : activez un indicateur à la fois (MACD, ADX, CHOP, RSI, ou CVD) pour une analyse ciblée et lisible.
• Détection des divergences :
• Divergences classiques (bullish/bearish),
• Divergences cachées (hidden bullish/bearish),
• Filtres avancés pour ne détecter que les signaux pertinents (crossover/crossunder + break de mèche).
• Multi-timeframes :
• Jusqu’à 4 blocs configurables (scalp, intra, swing, custom),
• Tri automatique des UT,
• Alertes différenciées par bloc et par type de signal.
• Visualisation modulaire :
• Tableau de synthèse personnalisable, affichant l’état de chaque indicateur par UT,
• Affichage hors graphique ou directement sur le chart,
• Couleurs dynamiques pour les signaux haussiers, baissiers ou neutres.
• Gestion du risque intégrée :
• Paramétrez le risque en % du capital ou en valeur absolue (USD),
• Ratio risk/reward configurable pour filtrer les signaux,
• Seuil de profit minimum (PnL) configurable pour filtrer les signaux.
• Support de volumes agrégés multi-exchange pour CVD : compatible avec les plateformes crypto (BITGET, BINANCE, etc).
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⚙️ Personnalisation
• Choix du type de moyenne mobile (EMA, RMA, VWAP, etc.).
• Activation sélective des signaux (Oscillateur, Divergence, Renversement) pour chaque bloc de timeframes.
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📈 Alertes intégrées
• Compatibles avec les alertes automatiques de TradingView,
• Détection de signaux d’entrée (achat/vente), divergences, renversements,
• Configuration des alertes par type de signal et par timeframe (scalp/intra/swing/custom).
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🔍 Utilisations recommandées
• Scalping haute fréquence (1s à 1min),
• Intraday en multi-UT (5 à 30min),
• Swing trading (1H à 1D),
• Analyse technique avancée sur crypto, indices, forex ou actions.
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📌 Conclusion
Ce script combine précision algorithmique et flexibilité de personnalisation.
RVL Unreal Edge (concept build)Designed with a purpose, this script was intended for use by bots automating trading of XLM using a 6hr timeframe.
It's now being shaped into fantastic indicator on its own with very actionable signals and essentially zero lag. Much of the power behind it is derived from standard deviation/mean reversion strategies, and John Ehlers' incredible CG oscillator.
John Ehlers was an electrical engineer and Raytheon employee who began trading in the 1970's. He is best known for his work creating super-smoothing algorithms and methods of analysing cycle length and behaviour, and his work in the field of zero-lag indicators - indicators that don't follow the price action but are in fact capable of leading it actionably and responding with essentially zero lag.
By approaching the price action as a sine wave with a demonstrably fractal nature and thus subject to the phenomena of spectral dilation, Ehler's makes a number of important advancements. His CG indicator is derived from calculations typically used to derive the centre of gravity in a physical object. It effectively works as a band-pass filter, and is possibly one of the very best leading indicators available.
This script catches breakouts, tops and bottoms, leads reversals and the start/end of cycles. It functions as an excellent way to secure entries/exits around support and resistance. There are some methods of charting support and resistance built into the script currently, and lots more to add. One of the next major adjustments will be to hide or reduce the strength of buy/sell signals when price might be overextended (seen by the larger triangles, and + x symbols - these signal that a reversion back to the mean may be imminent).
The early version of this script had a 65% winrate and fantastic profit factor.
Stay tuned!
Support/Resistance:
The Ichimoku cloud, in this case has been custom tuned to the XLM 6 hour chart.
The 42 period EMA is a moving average that gets notable reactions from the price.
The 200 period EMA is the same.
The automatic Pitchfork almost always provides relevant Fibonacci based levels, but can sometimes require manually flicking through a few different presets to find a combination that fits the current price action. This will be automated in future.
CandelaCharts - ICT Weekly Profiles📝 Overview
The indicator provides a pattern-based approach to the ICT Weekly Profiles, emphasizing a line that marks the Open, High, Low, and Close of the week. This line allows you to instantly visualize and identify the Weekly Profile.
The profile detection relies on the week’s high and low, delivering a clear and concise representation of the weekly profile.
ICT Weekly Profiles are structured conceptual frameworks designed to outline typical patterns of price behavior over the course of a trading week. These profiles serve as analytical tools, offering traders insights into recurring market tendencies and helping them identify potential opportunities and risks.
The ICT Weekly Profiles indicator offers two distinct types of profiles to provide a clearer understanding of weekly price action:
ICT Weekly Profiles
ICT Missing Weekly Profiles
The toolkit automatically detects and marks these ICT Weekly Profiles and ICT Missing Weekly Profiles on the chart, enabling traders to quickly pinpoint critical zones for analysis and decision-making.
📦 Features
The ICT Weekly Profiles toolkit offers a comprehensive set of features designed to enhance trading precision and decision-making. Key features include:
Weekly Profiles
Missing Weekly Profiles
Advanced Styling
Scanner
The indicator supports the following profiles:
ICT Weekly Profiles
Classic Tuesday Low Of The Week Bullish
Classic Tuesday High Of The Week Bearish
Wednesday Low Of The Week Bullish
Wednesday High Of The Week Bearish
Consolidation Thursday Reversal Bullish
Consolidation Thursday Reversal Bearish
Consolidation Midweek Rally Bullish
Consolidation Midweek Rally Bearish
Wednesday Weekly Reversal Bullish
Wednesday Weekly Reversal Bearish
Seek And Destroy Bullish Friday
Seek And Destroy Bearish Friday
ICT Missing Weekly Profiles
Monday Low Tuesday High Bullish
Monday High Tuesday Low Bearish
Monday Low Wednesday High Bullish
Monday High Wednesday Low Bearish
Monday Low Thursday High Bullish
Monday High Thursday Low Bearish
Tuesday Low Wednesday High Bullish
Tuesday High Wednesday Low Bearish
Tuesday Low Friday High Bullish
Tuesday High Friday Low Bearish
Wednesday Low Thursday High Bullish
Wednesday High Thursday Low Bearish
Monday Low Friday High Bullish
Monday Friday Bearish Rally
Monday High/Low Range
Tuesday High/Low Range
Wednesday High/Low Range
Thursday High/Low Range
Friday High/Low Range
⚙️ Settings
History: Controls how many profiles are displayed on the chart.
Timeframe Limit: Sets the timeframe up to which profiles will be drawn.
Show OHLC Lines: Display the lines for OHLC.
Show Profile Line: Display the Weekly Profile line.
Use NY Midnight Open: Controls from where a profile will start detection.
Open: Style for Open line.
High: Style for High line.
Low: Style for Low line.
Midline: Style for Profile Midline.
Label: Controls the position of the Weekly Profile name.
Scanner: Display the Scanner
⚡️ Showcase
ICT (Inner Circle Trader) weekly profile templates are analytical frameworks that categorize and describe typical patterns of price action observed during a trading week.
ICT Weekly Profiles
ICT Missing Weekly Profiles
Scanner
📒 Usage
The primary objective of the ICT Weekly Profiles indicator is to provide traders with a comprehensive and actionable overview of the Weekly Previous, Current, and Future Profile. This allows traders to interpret market structure, anticipate price behavior, and align their trading decisions with higher time-frame trends.
Load the indicator on the chart
Enable Scanner
See the Predicted Profiles list
Predicted Profiles represent all potential scenarios for the current week, generated by a profile detection algorithm.
By visualizing potential outcomes through Predicted Profiles, the ICT Weekly Profiles indicator provides traders with a strategic edge, allowing them to remain flexible, prepared, and aligned with the most probable market movements.
🚨 Alerts
The indicator does not provide any alerts!
🔹 Notes
ICT Weekly Profiles
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ICT Missing Weekly Profiles
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⚠️ Disclaimer
These tools are exclusively available on the TradingView platform.
Our charting tools are intended solely for informational and educational purposes and should not be regarded as financial, investment, or trading advice. They are not designed to predict market movements or offer specific recommendations. Users should be aware that past performance is not indicative of future results and should not rely on these tools for financial decisions. By using these charting tools, the purchaser agrees that the seller and creator hold no responsibility for any decisions made based on information provided by the tools. The purchaser assumes full responsibility and liability for any actions taken and their consequences, including potential financial losses or investment outcomes that may result from the use of these products.
By purchasing, the customer acknowledges and accepts that neither the seller nor the creator is liable for any undesired outcomes stemming from the development, sale, or use of these products. Additionally, the purchaser agrees to indemnify the seller from any liability. If invited through the Friends and Family Program, the purchaser understands that any provided discount code applies only to the initial purchase of Candela's subscription. The purchaser is responsible for canceling or requesting cancellation of their subscription if they choose not to continue at the full retail price. In the event the purchaser no longer wishes to use the products, they must unsubscribe from the membership service, if applicable.
We do not offer reimbursements, refunds, or chargebacks. Once these Terms are accepted at the time of purchase, no reimbursements, refunds, or chargebacks will be issued under any circumstances.
By continuing to use these charting tools, the user confirms their understanding and acceptance of these Terms as outlined in this disclaimer.
TrendScope:TrendScope Indicator Description with First-Time User Tutorial
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Overview:
The TrendScope indicator is designed to give traders a comprehensive view of the market by combining multiple filter sets that analyze different aspects of price action. The filter sets allow you to switch between different views effortlessly and avoid indicator clutter. Whether you're scalping, swing trading, or identifying breakout opportunities, TrendScope helps you make informed decisions by assessing momentum, volatility, trade timing, and trend direction. It also includes a scalp setup you can use to execute trades and manage risk.
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TrendScope Filter Sets with First-Time User Setup & Tutorial
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Filter Set A: Short-Term Momentum
Goal:
This filter focuses on the immediate market sentiment without any additional indicators. It reveals where retail traders might enter the market, potentially highlighting areas where they could be stopped out. The goal is to identify these weak spots and anticipate likely price movements that could follow.
No Additional Indicators Required:
This filter set uses moving averages (SMA 20, SMA 50, SMA 100) to determine the short-term trend.
Tutorial:
- To Confirm an Uptrend: Ensure all moving averages are aligned in sequence: SMA 20 above SMA 50, and SMA 50 above SMA 100, all trending upwards.
Action: Consider going long using the scalper in Filter Set D.
- To Confirm a Downtrend: Ensure all moving averages are aligned in sequence: SMA 20 below SMA 50, and SMA 50 below SMA 100, all trending downwards.
Action: Consider going short using the scalper in Filter Set D.
- To Confirm Consolidation: If the moving averages are not aligned or are intertwined, the market is either about to or already trending sideways. The market is in a consolidation phase.
Action: Switch to Filter Set C for further analysis.
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Filter Set B: Long-Term Momentum
Goal:
Similar to the short-term filter, but with a broader perspective. It helps in understanding the bigger picture, providing insights into longer-term trends and potential reversals for swing trade entries.
No Additional Indicators Required:
This filter set uses moving averages (SMA 20, SMA 100, SMA 200) to determine the long-term trend.
Tutorial:
- To Confirm an Uptrend: Ensure all moving averages are aligned in sequence: SMA 20 above SMA 100, and SMA 100 above SMA 200, all trending upwards.
Action: Consider going long using the scalper in Filter Set D.
- To Confirm a Downtrend: Ensure all moving averages are aligned in sequence: SMA 20 below SMA 100, and SMA 100 below SMA 200, all trending downwards.
Action: Consider going short using the scalper in Filter Set D.
- To Confirm Consolidation: If the moving averages are not aligned or are intertwined, the market is either about to or already trending sideways. The market is in a consolidation phase.
Action: Switch to Filter Set C for further analysis.
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Filter Set C: Trading Range
This filter uses Bollinger Bands, Volume, and Volume-Weighted Relative Volume Profile (VRVP) to identify trading ranges and predict breakouts and trade timing. In short, when Bollinger Bands contract and volume is below average, the VRVP highlights low-volume areas that can serve as breakout targets, offering a timing edge.
Goal:
Anticipate breakouts in a sideways market.
Additional Indicators Required:
- VRVP: For visualizing volume at specific price levels.
- Volume Indicator: With a 100-period moving average for anticipating low market participation.
Tutorial:
1. Setup Screen: Zoom out to see the entire consolidation phase.
2. Identify Support & Resistance:
- Use VRVP to determine VAH (upper range) and VAL (lower range) support or resistance levels.
- Identify the POC (Point of Control) as the area with the highest support or resistance.
3. Wait for Setup:
- Wait for Bollinger Bands to contract and volume to dip below the average.
- Go short if the price is at VAH, go long if the price is at VAL.
4. Action: Switch to Filter Set D for precise entry, target, and risk management.
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Filter Set D: Scalper
After determining the market condition using the previous filter sets, you can use this filter set to hunt for trades. Designed for use with Heikin Ashi candles, this filter allows you to enter when there’s high momentum and provides a trailing stop along the way.
Goal:
Execute trades in harmony with the established trend.
Setup Rules:
1. Condition 1: You know the current trend direction as per filter set guidance (A, B, & C), and the trend is up, and you are going long.
2. Condition 2: Wait for the price to close 3 consecutive flat-bottom Heikin Ashi candles above the 7 MA. Then Enter on the open of the fourth Candle.
3. Condition 3: The 3x candles have to be above the 7 MA (red line), and the 7 MA has to be above the 50 EMA (yellow line).
Trade Management:
Use the 50 EMA (Yellow Line) as a trailing stop and hold the position until a candle opens and closes below the 7 SMA (Red Line).
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Additional Filter Sets
These filter sets are designed to accommodate various trading strategies, allowing for flexibility depending on the trader's approach.
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Filter Set E: VWAP
When using the VWAP filter, load the On-Balance Volume (OBV) indicator to complement your analysis. This combination can help confirm volume trends and potential price movements.
Tips:
Look for instances where the VWAP aligns with OBV divergences to confirm or negate potential trade setups.
Tutorial:
- Complement with OBV: Look for volume confirmations.
- Usage: Switch the candles to a line chart. Wait for both the line to close above the VWAP and OBV above the Smoothing Line. Then, switch to Filter Set D and hunt for a long entry as per the strategy. Do the opposite for hunting short entries.
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Filter Set F: Super Trend
This filter is most effective when paired with the Ichimoku Cloud (using custom settings) along with the MACD and ADX indicators.
Goal:
Gauge trend strength, momentum, and support and resistance levels.
Tutorial:
- Load Ichimoku, MACD, and ADX: To gauge trend strength and momentum.
- Usage Tips:
I use the cloud to look for long periods where the clouds print horizontal levels and use them for support and resistance levels. Alternatively, use the ADX. When the price breaks up through the super trend downtrend line and retraces back to the top of the Ichimoku cloud, switch to Filter Set D and hunt for a long scalp entry. For a short entry, wait for the price to break through the Up Trend Line and retrace back up to the cloud. Then, switch to Filter Set D and use the setup to hunt for a short.
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Filter Set G: Keltner Channels
Combine this filter with Donchian Channels and the Average True Range (ATR) for enhanced volatility analysis. This filter set works similarly to Filter Set C.
Goal:
Measure volatility and predict breakouts.
Tutorial:
- Load Donchian Channels or ATR: To measure volatility and breakouts.
- Usage Tips:
Look for the price to fall through the Keltner lower line and the ATR making a higher low. Then, use the scalper for entries, with Donchian boundaries as take-profit estimates.
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Filter Set H: Pivot Points
This filter works with the RSI to spot divergences that could signal a trend change or reversal.
Goal:
Identify divergences and trend reversals.
Tutorial:
- Load RSI: For identifying divergences.
- Usage Tips:
Use RSI in conjunction with pivot points to identify divergences. Then, switch to Filter Set D and use the scalper to hunt for swing entries in the divergence direction.
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Filter Set I: Opening Range Breakout
This filter uses the Seasonality indicator to gauge investor sentiment and prediction sentiment.
Goal:
Assess market sentiment and predict breakout directions.
Tutorial:
- Load Seasonality Indicator: To assess market sentiment.
- Usage Tips:
Use seasonal trends to gauge potential breakout directions. Use on the daily timeframe only. Risk on investment zones are when the price is close to the ORB low level. Realize investment profit when the price is nearing the ORB high level, considering that there has to be divergence as determined using Filter Set H.
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By following this structured approach, traders can learn to navigate different market conditions, using TrendScope to make informed decisions based on a comprehensive analysis of momentum, trend, and volatility. The goal is to go through all the filter sets and combine them with the scalp setup in Filter Set D, using the additional filters to adapt to various strategies and market conditions.
