Daily Key Levels + VWAPThis indicator is daily price levels and previous day's VWAP for precision intraday trading decisions.
Educational
📊 Monitor F&M - RLYONSCRIPT OBJECTIVE
It's a confluence system that combines four key indicators to identify high-probability trading setups. It basically tells you when and where to enter the market with greater confidence.
🔧 THE 4 BASE INDICATORS
1. ADX (Average Directional Index)
What it measures: The strength of the trend (not the direction)
How to use it:
ADX ≥ 25 = STRONG trend ✅
ADX < 25 = Weak or sideways trend
What it does: Filters trades. You only look for entries when there is real strength in the market.
2. DI+ and DI- (Directional Indicators)
What it measures: The direction of the trend.
How to use it:
DI+ > DI- = Bullish trend 📈
DI- > DI+ = Bearish trend 📉
What it does: Defines whether you are looking for buys or sells.
3. TTM Squeeze (Bollinger Bands + Keltner Channels)
What it measures: Volatility compression and explosion.
States:
Squeeze ON 🔴: Volatility compressed (like a tightened spring).
Squeeze OFF 🟢: Volatility released (the spring is released = strong movement).
Transition 🔵: Changing state.
Momentum: The green/red histogram shows the direction of the movement.
Green rising = Strong bullish trend.
Red falling = Strong bearish trend.
4. RSI (Relative Strength Index)
What it measures: Whether the price is overbought or oversold.
Zones:
RSI > 70 = Overbought ⚠️ (be careful with purchases)
RSI < 30 = Oversold ✅ (bullish opportunity zone)
RSI 40-60 = Neutral zone/ideal for pullbacks
🎯 THE 2 MAIN STRATEGIES
STRATEGY 1: MOMENTUM (The strongest) 🚀
BUY setup:
✅ Squeeze released (changed from ON to OFF)
✅ Momentum green AND growing
✅ ADX ≥ 25 (strong trend)
✅ RSI not overbought (< 70)
SELL setup:
✅ Squeeze released (changed from ON to OFF)
✅ Momentum red AND Decreasing
✅ ADX ≥ 25 (strong trend)
✅ RSI not oversold (> 30)
When to trade: When you see the triangle 🚀 on the chart
STRATEGY 2: PULLBACK (Established trend) 📈📉
BUY setup:
✅ DI+ > DI- (established uptrend)
✅ ADX ≥ 25 (strong trend)
✅ RSI between 40-55 (healthy pullback)
✅ Momentum starting to turn upward
SELL setup:
✅ DI- > DI+ (established downtrend)
✅ ADX ≥ 25 (strong trend)
✅ RSI between 45-60 (healthy pullback)
✅ Momentum starting to turn downward
When to trade: When you see the "PB" circle in the graph
BATIK SMC🌀 BATIK SMC — Smart Money Concepts by YB Pips
BATIK SMC is a professional-grade Smart Money Concepts system refined under the Batik Syndicate methodology.
It combines institutional structure logic with precision-engineered visualization tools for traders who operate with discipline and intent.
🧭 Core Functions
Market Structure: automatic detection of BOS (Break of Structure) & CHoCH (Change of Character)
Order Blocks: internal & swing OB identification with real-time mitigation updates
Fair Value Gaps (FVG): dynamic detection across multiple timeframes
Equal Highs / Lows: liquidity points & sweep detection
Premium / Discount Zones: clear equilibrium mapping for high-RR setups
Smart Candle Coloring: visualize real-time trend bias directly on chart
Custom Alerts: receive instant BOS, CHoCH, OB breakout, and FVG notifications
💎 Why BATIK SMC
Developed for traders who follow structure, liquidity, and imbalance — not indicators.
It retains full Smart Money logic while carrying the signature Batik visual identity and philosophy:
“Trade where institutions position themselves — not where the crowd reacts.”
Multi-Condition Alert Builder⚡ Multi-Condition Alert Builder — Modular Alert Framework
The Multi-Condition Alert Builder is a powerful, code-free alert engine for TradingView. It allows traders to build complex multi-condition Buy/Sell alerts using simple dropdown menus — no Pine Script experience required.
Combine up to five separate conditions per side and trigger alerts based on your own custom logic.
🧠 How It Works
Each “Buy” and “Sell” side includes up to five configurable slots, where you can define:
Two data sources (indicators, price, or custom inputs)
A comparison or crossover condition
A static value (optional)
Once your slots are defined, the script combines these individual conditions according to your chosen mode:
Any – triggers when any enabled condition is true
All – same bar – triggers only when all enabled conditions occur on the same bar
All – within bars – allows conditions to complete within a user-defined lookback window
This gives traders fine-grained control to design powerful, adaptive alert logic directly in the chart — no coding required.
⚙️ Key Features
🧩 Up to 5 Buy and 5 Sell Slots – Fully customizable condition slots
🧠 Combine Logic Modes – Any / All / Within Bars flexibility
🔔 Custom Alerts – Generates separate Buy, Sell, or combined alert events
⏱️ Close-Bar Confirmation Option – Avoids premature signals on open candles
💡 Visual Signals – Plots arrows on chart for clear alert visualization
🔄 Indicator-Agnostic – Works with any sources or indicators available in your chart
🧮 Combine Logic Modes Explained
Mode Description
Any Triggers an alert if any active condition is met
All – same bar Requires all active slots to confirm on the same candle
All – within bars Conditions may complete within a set lookback window
🧭 Example Use Cases
Combine RSI, MACD, and MA crossovers for precision entries
Create alert triggers for momentum confluence setups
Build “stacked signal” logic (e.g., RSI < 30 and MACD crossover within 3 bars)
Quickly prototype and test multi-factor alert conditions
🧠 Usage Tip
Once your conditions are set, simply add TradingView alerts tied to:
“BUY↟” for long signals
“SELL↡” for short signals
“ANY ALERT” to trigger on either event
The Alert Builder becomes especially powerful when combined with your favorite custom indicators — enabling smart, automated alerts without extra coding.
⚡ In Short
Build. Combine. Alert.
The Multi-Condition Alert Builder gives you total flexibility to design complex alert logic — visually, intuitively, and efficiently — right on your chart.
Bollinger Band Width Oscillator %🧠 Bollinger Band Width Oscillator %
The Bollinger Band Width Oscillator % is a volatility-focused tool that measures the relative width of Bollinger Bands and transforms it into an oscillator format. It helps traders visualize volatility expansions and contractions directly in an indicator pane — a powerful way to anticipate breakout or consolidation phases.
🔍 How It Works
Band Width %: Calculates the percentage distance between the upper and lower Bollinger Bands relative to the basis (SMA).
Smoothed Output: The raw bandwidth is smoothed using a moving average for cleaner, more stable signals.
Dynamic Volatility Zones: The script automatically computes average, high, and low volatility thresholds — each dynamically adapting to market conditions.
User-Adjustable Multipliers: Control how sensitive your high/low zones are with the High Zone Multiplier and Low Zone Multiplier inputs.
⚙️ Key Features
📊 Oscillator Format – Easy-to-read visualization of volatility compression and expansion.
🔥 High/Low Volatility Detection – Automatic labeling and color-coded alerts for shifts in volatility.
🧩 Dynamic Thresholds – Zones adjust automatically with market activity for adaptive sensitivity.
🧠 Hysteresis Logic – Prevents rapid signal flipping, improving clarity and reliability.
🎨 Custom Visuals – Adjustable smoothing and background highlights for quick interpretation.
📈 Trading Applications
Identify Breakouts: Rising bandwidth often precedes price breakouts.
Spot Consolidations: Low bandwidth indicates tightening volatility and potential range trades.
Volatility Regime Analysis: Understand market rhythm and adapt strategies accordingly.
⚡ Inputs
Parameter Description
Band Length Period for Bollinger Band calculation
Band Multiplier Standard deviation multiplier for the bands
Source Price source (default: close)
Smoothing Period for smoothing the oscillator line
High Zone Multiplier Adjusts the high-volatility threshold
Low Zone Multiplier Adjusts the low-volatility threshold
Highlight Volatility Zones Optional background color overlay
🧊 Usage Tip
Combine this indicator with momentum tools or price action analysis to confirm trade setups. Watch for transitions from low to high volatility zones — these often signal the beginning of major market moves.
NY Midnight High/Low Arrows (Auto-Show)🇺🇸 English Explanation
This indicator automatically marks the daily high and low of the New York session.
It draws arrows (▼▲) at the highest and lowest prices after New York midnight (00:00),
and can optionally display small horizontal dotted lines at those levels.
It helps traders identify daily liquidity zones and key turning points in price action.
🇸🇦 الشرح بالعربية
هذا المؤشر يحدد القمة والقاع اليومية لجلسة نيويورك بشكل تلقائي.
يرسم أسهماً (▼▲) عند أعلى وأدنى سعر بعد منتصف الليل بتوقيت نيويورك (00:00)،
ويمكنه أيضًا عرض خطوط أفقية منقطة صغيرة عند تلك المستويات.
يساعد المتداول في معرفة مناطق السيولة اليومية ونقاط الانعكاس المهمة في حركة السعر.
Fixed Dollar Risk LinesFixed Dollar Risk Lines is a utility indicator that converts a user-defined dollar risk into price distance and plots risk lines above and below the current price for popular futures contracts. It helps you place stops or entries at a consistent dollar risk per trade, regardless of the market’s tick value or tick size.
What it does:
-You choose a dollar amount to risk (e.g., $100) and a futures contract (ES, NQ, GC, YM, RTY, PL, SI, CL, BTC).
The script automatically:
-Looks up the contract’s tick value and tick size
-Converts your dollar risk into number of ticks
-Converts ticks into price distance
Plots:
-Long Risk line below current price
-Short Risk line above current price
-Optional labels show exact price levels and an information table summarizes your settings.
Key features
-Consistent dollar risk across instruments
-Supports major futures contracts with built‑in tick values and sizes
-Toggle Long and Short risk lines independently
-Customizable line width and colors (lines and labels)
-Right‑axis price level display for quick reading
-Compact info table with contract, risk, and computed prices
Typical use
-Long setups: use the green line as a stop level below entry to match your chosen dollar risk.
-Short setups: use the red line as a stop level above entry to match your chosen dollar risk.
-Quickly compare how the same dollar risk translates to distance on different contracts.
Inputs
-Risk Amount (USD)
-Futures Contract (ES, NQ, GC, YM, RTY, PL, SI, CL, BTC)
-Show Long/Short lines (toggles)
-Line Width
-Colors for lines and labels
Notes
-Designed for futures symbols that match the listed contracts’ tick specs. If your symbol has different tick value/size than the defaults, results will differ.
-Intended for educational/informational use; not financial advice.
-This tool streamlines risk placement so you can focus on execution while keeping dollar risk consistent across markets.
Bollinger Band Spread (Dunk)Bollinger Band Width measures the distance between the upper and lower Bollinger Bands. It reflects market volatility—wider bands mean higher volatility, narrower bands mean lower volatility.
When the width contracts to low levels, it can signal price consolidation and potential breakouts. When the width expands, it indicates active markets or strong trends.
Traders use it to spot volatility squeezes, confirm breakouts, and compare relative volatility across assets or timeframes.
COT IndexTHE HIDDEN INTELLIGENCE IN FUTURES MARKETS
What if you could see what the smartest players in the futures markets are doing before the crowd catches on? While retail traders chase momentum indicators and moving averages, obsess over Japanese candlestick patterns, and debate whether the RSI should be set to fourteen or twenty-one periods, institutional players leave footprints in the sand through their mandatory reporting to the Commodity Futures Trading Commission. These footprints, published weekly in the Commitment of Traders reports, have been hiding in plain sight for decades, available to anyone with an internet connection, yet remarkably few traders understand how to interpret them correctly. The COT Index indicator transforms this raw institutional positioning data into actionable trading signals, bringing Wall Street intelligence to your trading screen without requiring expensive Bloomberg terminals or insider connections.
The uncomfortable truth is this: Most retail traders operate in a binary world. Long or short. Buy or sell. They apply technical analysis to individual positions, constrained by limited capital that forces them to concentrate risk in single directional bets. Meanwhile, institutional traders operate in an entirely different dimension. They manage portfolios dynamically weighted across multiple markets, adjusting exposure based on evolving market conditions, correlation shifts, and risk assessments that retail traders never see. A hedge fund might be simultaneously long gold, short oil, neutral on copper, and overweight agricultural commodities, with position sizes calibrated to volatility and portfolio Greeks. When they increase gold exposure from five percent to eight percent of portfolio allocation, this rebalancing decision reflects sophisticated analysis of opportunity cost, risk parity, and cross-market dynamics that no individual chart pattern can capture.
This portfolio reweighting activity, multiplied across hundreds of institutional participants, manifests in the aggregate positioning data published weekly by the CFTC. The Commitment of Traders report does not show individual trades or strategies. It shows the collective footprint of how actual commercial hedgers and large speculators have allocated their capital across different markets. When mining companies collectively increase forward gold sales to hedge thirty percent more production than last quarter, they are not reacting to a moving average crossover. They are making strategic allocation decisions based on production forecasts, cost structures, and price expectations derived from operational realities invisible to outside observers. This is portfolio management in action, revealed through positioning data rather than price charts.
If you want to understand how institutional capital actually flows, how sophisticated traders genuinely position themselves across market cycles, the COT report provides a rare window into that hidden world. But understand what you are getting into. This is not a tool for scalpers seeking confirmation of the next five-minute move. This is not an oscillator that flashes oversold at market bottoms with convenient precision. COT analysis operates on a timescale measured in weeks and months, revealing positioning shifts that precede major market turns but offer no precision timing. The data arrives three days stale, published only once per week, capturing strategic positioning rather than tactical entries.
If you need instant gratification, if you trade intraday moves, if you demand mechanical signals with ninety percent accuracy, close this document now. COT analysis rewards patience, position sizing discipline, and tolerance for being early. It punishes impatience, overleveraging, and the expectation that any single indicator can substitute for market understanding.
The premise is deceptively simple. Every Tuesday, large traders in futures markets must report their positions to the CFTC. By Friday afternoon, this data becomes public. Academic research spanning three decades has consistently shown that not all market participants are created equal. Some traders consistently profit while others consistently lose. Some anticipate major turning points while others chase trends into exhaustion. Bessembinder and Chan (1992) demonstrated in their seminal study that commercial hedgers, those with actual exposure to the underlying commodity or financial instrument, possess superior forecasting ability compared to speculators. Their research, published in the Journal of Finance, found statistically significant predictive power in commercial positioning, particularly at extreme levels. This finding challenged the efficient market hypothesis and opened the door to a new approach to market analysis based on positioning rather than price alone.
Think about what this means. Every week, the government publishes a report showing you exactly how the most informed market participants are positioned. Not their opinions. Not their predictions. Their actual money at risk. When agricultural producers collectively hold their largest short hedge in five years, they are not making idle speculation. They are locking in prices for crops they will harvest, informed by private knowledge of weather conditions, soil quality, inventory levels, and demand expectations invisible to outside observers. When energy companies aggressively hedge forward production at current prices, they reveal information about expected supply that no analyst report can capture. This is not technical analysis based on past prices. This is not fundamental analysis based on publicly available data. This is behavioral analysis based on how the smartest money is actually positioned, how institutions allocate capital across portfolios, and how those allocation decisions shift as market conditions evolve.
WHY SOME TRADERS KNOW MORE THAN OTHERS
Building on this foundation, Sanders, Boris and Manfredo (2004) conducted extensive research examining the behaviour patterns of different trader categories. Their work, which analyzed over a decade of COT data across multiple commodity markets, revealed a fascinating dynamic that challenges much of what retail traders are taught. Commercial hedgers consistently positioned themselves against market extremes, buying when speculators were most bearish and selling when speculators reached peak bullishness. The contrarian positioning of commercials was not random noise but rather reflected their superior information about supply and demand fundamentals. Meanwhile, large speculators, primarily hedge funds and commodity trading advisors, exhibited strong trend-following behaviour that often amplified market moves beyond fundamental values. Small traders, the retail participants, consistently entered positions late in trends, frequently near turning points, making them reliable contrary indicators.
Wang (2003) extended this research by demonstrating that the predictive power of commercial positioning varies significantly across different commodity sectors. His analysis of agricultural commodities showed particularly strong forecasting ability, with commercial net positions explaining up to fifteen percent of return variance in subsequent weeks. This finding suggests that the informational advantages of hedgers are most pronounced in markets where physical supply and demand fundamentals dominate, as opposed to purely financial markets where information asymmetries are smaller. When a corn farmer hedges six months of expected harvest, that decision incorporates private observations about rainfall patterns, crop health, pest pressure, and local storage capacity that no distant analyst can match. When an oil refinery hedges crude oil purchases and gasoline sales simultaneously, the spread relationships reveal expectations about refining margins that reflect operational realities invisible in public data.
The theoretical mechanism underlying these empirical patterns relates to information asymmetry and different participant motivations. Commercial hedgers engage in futures markets not for speculative profit but to manage business risks. An agricultural producer selling forward six months of expected harvest is not making a bet on price direction but rather locking in revenue to facilitate financial planning and ensure business viability. However, this hedging activity necessarily incorporates private information about expected supply, inventory levels, weather conditions, and demand trends that the hedger observes through their commercial operations (Irwin and Sanders, 2012). When aggregated across many participants, this private information manifests in collective positioning.
Consider a gold mining company deciding how much forward production to hedge. Management must estimate ore grades, recovery rates, production costs, equipment reliability, labor availability, and dozens of other operational variables that determine whether locking in prices at current levels makes business sense. If the industry collectively hedges more aggressively than usual, it suggests either exceptional production expectations or concern about sustaining current price levels or combination of both. Either way, this positioning reveals information unavailable to speculators analyzing price charts and economic data. The hedger sees the physical reality behind the financial abstraction.
Large speculators operate under entirely different incentives and constraints. Commodity Trading Advisors managing billions in assets typically employ systematic, trend-following strategies that respond to price momentum rather than fundamental supply and demand. When crude oil rallies from sixty dollars to seventy dollars per barrel, these systems generate buy signals. As the rally continues to eighty dollars, position sizes increase. The strategy works brilliantly during sustained trends but becomes a liability at reversals. By the time oil reaches ninety dollars, trend-following funds are maximally long, having accumulated positions progressively throughout the rally. At this point, they represent not smart money anticipating further gains but rather crowded money vulnerable to reversal. Sanders, Boris and Manfredo (2004) documented this pattern across multiple energy markets, showing that extreme speculator positioning typically marked late-stage trend exhaustion rather than early-stage trend development.
Small traders, the retail participants who fall below reporting thresholds, display the weakest forecasting ability. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns, meaning their aggregate positioning served as a reliable contrary indicator. The explanation combines several factors. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, entering trends after mainstream media coverage when institutional participants are preparing to exit. Perhaps most importantly, they trade with emotion, buying into euphoria and selling into panic at precisely the wrong times.
At major turning points, the three groups often position opposite each other with commercials extremely bearish, large speculators extremely bullish, and small traders piling into longs at the last moment. These high-divergence environments frequently precede increased volatility and trend reversals. The insiders with business exposure quietly exit as the momentum traders hit maximum capacity and retail enthusiasm peaks. Within weeks, the reversal begins, and positions unwind in the opposite sequence.
FROM RAW DATA TO ACTIONABLE SIGNALS
The COT Index indicator operationalizes these academic findings into a practical trading tool accessible through TradingView. At its core, the indicator normalizes net positioning data onto a zero to one hundred scale, creating what we call the COT Index. This normalization is critical because absolute position sizes vary dramatically across different futures contracts and over time. A commercial trader holding fifty thousand contracts net long in crude oil might be extremely bullish by historical standards, or it might be quite neutral depending on the context of total market size and historical ranges. Raw position numbers mean nothing without context. The COT Index solves this problem by calculating where current positioning stands relative to its range over a specified lookback period, typically two hundred fifty-two weeks or approximately five years of weekly data.
The mathematical transformation follows the methodology originally popularized by legendary trader Larry Williams, though the underlying concept appears in statistical normalization techniques across many fields. For any given trader category, we calculate the highest and lowest net position values over the lookback period, establishing the historical range for that specific market and trader group. Current positioning is then expressed as a percentage of this range, where zero represents the most bearish positioning ever seen in the lookback window and one hundred represents the most bullish extreme. A reading of fifty indicates positioning exactly in the middle of the historical range, suggesting neither extreme optimism nor pessimism relative to recent history (Williams and Noseworthy, 2009).
This index-based approach allows for meaningful comparison across different markets and time periods, overcoming the scaling problems inherent in analyzing raw position data. A commercial index reading of eighty-five in gold carries the same interpretive meaning as an eighty-five reading in wheat or crude oil, even though the absolute position sizes differ by orders of magnitude. This standardization enables systematic analysis across entire futures portfolios rather than requiring market-specific expertise for each contract.
The lookback period selection involves a fundamental tradeoff between responsiveness and stability. Shorter lookback periods, perhaps one hundred twenty-six weeks or approximately two and a half years, make the index more sensitive to recent positioning changes. However, it also increases noise and produces more false signals. Longer lookback periods, perhaps five hundred weeks or approximately ten years, create smoother readings that filter short-term noise but become slower to recognize regime changes. The indicator settings allow users to adjust this parameter based on their trading timeframe, risk tolerance, and market characteristics.
UNDERSTANDING CFTC DATA STRUCTURES
The indicator supports both Legacy and Disaggregated COT report formats, reflecting the evolution of CFTC reporting standards over decades of market development. Legacy reports categorize market participants into three broad groups: commercial traders (hedgers with underlying business exposure), non-commercial traders (large speculators seeking profit without commercial interest), and non-reportable traders (small speculators below reporting thresholds). Each category brings distinct motivations and information advantages to the market (CFTC, 2020).
The Disaggregated reports, introduced in September 2009 for physical commodity markets, provide finer granularity by splitting participants into five categories (CFTC, 2009). Producer and merchant positions capture those actually producing, processing, or merchandising the physical commodity. Swap dealers represent financial intermediaries facilitating derivative transactions for clients. Managed money includes commodity trading advisors and hedge funds executing systematic or discretionary strategies. Other reportables encompasses diverse participants not fitting the main categories. Small traders remain as the fifth group, representing retail participation.
This enhanced categorization reveals nuances invisible in Legacy reports, particularly distinguishing between different types of institutional capital and their distinct behavioural patterns. The indicator automatically detects which report type is appropriate for each futures contract and adjusts the display accordingly.
Importantly, Disaggregated reports exist only for physical commodity futures. Agricultural commodities like corn, wheat, and soybeans have Disaggregated reports because clear producer, merchant, and swap dealer categories exist. Energy commodities like crude oil and natural gas similarly have well-defined commercial hedger categories. Metals including gold, silver, and copper also receive Disaggregated treatment (CFTC, 2009). However, financial futures such as equity index futures, Treasury bond futures, and currency futures remain available only in Legacy format. The CFTC has indicated no plans to extend Disaggregated reporting to financial futures due to different market structures and participant categories in these instruments (CFTC, 2020).
THE BEHAVIORAL FOUNDATION
Understanding which trader perspective to follow requires appreciation of their distinct trading styles, success rates, and psychological profiles. Commercial hedgers exhibit anticyclical behaviour rooted in their fundamental knowledge and business imperatives. When agricultural producers hedge forward sales during harvest season, they are not speculating on price direction but rather locking in revenue for crops they will harvest. Their business requires converting volatile commodity exposure into predictable cash flows to facilitate planning and ensure survival through difficult periods. Yet their aggregate positioning reveals valuable information because these hedging decisions incorporate private information about supply conditions, inventory levels, weather observations, and demand expectations that hedgers observe through their commercial operations (Bessembinder and Chan, 1992).
Consider a practical example from energy markets. Major oil companies continuously hedge portions of forward production based on price levels, operational costs, and financial planning needs. When crude oil trades at ninety dollars per barrel, they might aggressively hedge the next twelve months of production, locking in prices that provide comfortable profit margins above their extraction costs. This hedging appears as short positioning in COT reports. If oil rallies further to one hundred dollars, they hedge even more aggressively, viewing these prices as exceptional opportunities to secure revenue. Their short positioning grows increasingly extreme. To an outside observer watching only price charts, the rally suggests bullishness. But the commercial positioning reveals that the actual producers of oil find these prices attractive enough to lock in years of sales, suggesting skepticism about sustaining even higher levels. When the eventual reversal occurs and oil declines back to eighty dollars, the commercials who hedged at ninety and one hundred dollars profit while speculators who chased the rally suffer losses.
Large speculators or managed money traders operate under entirely different incentives and constraints. Their systematic, momentum-driven strategies mean they amplify existing trends rather than anticipate reversals. Trend-following systems, the most common approach among large speculators, by definition require confirmation of trend through price momentum before entering positions (Sanders, Boris and Manfredo, 2004). When crude oil rallies from sixty dollars to eighty dollars per barrel over several months, trend-following algorithms generate buy signals based on moving average crossovers, breakouts, and other momentum indicators. As the rally continues, position sizes increase according to the systematic rules.
However, this approach becomes a liability at turning points. By the time oil reaches ninety dollars after a sustained rally, trend-following funds are maximally long, having accumulated positions progressively throughout the move. At this point, their positioning does not predict continued strength. Rather, it often marks late-stage trend exhaustion. The psychological and mechanical explanation is straightforward. Trend followers by definition chase price momentum, entering positions after trends establish rather than anticipating them. Eventually, they become fully invested just as the trend nears completion, leaving no incremental buying power to sustain the rally. When the first signs of reversal appear, systematic stops trigger, creating a cascade of selling that accelerates the downturn.
Small traders consistently display the weakest track record across academic studies. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns in his analysis across multiple commodity markets. This result means that whatever small traders collectively do, the opposite typically proves profitable. The explanation for small trader underperformance combines several factors documented in behavioral finance literature. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, learning about commodity trends through mainstream media coverage that arrives after institutional participants have already positioned. Perhaps most importantly, retail traders are more susceptible to emotional decision-making, buying into euphoria and selling into panic at precisely the wrong times (Tharp, 2008).
SETTINGS, THRESHOLDS, AND SIGNAL GENERATION
The practical implementation of the COT Index requires understanding several key features and settings that users can adjust to match their trading style, timeframe, and risk tolerance. The lookback period determines the time window for calculating historical ranges. The default setting of two hundred fifty-two bars represents approximately one year on daily charts or five years on weekly charts, balancing responsiveness with stability. Conservative traders seeking only the most extreme, highest-probability signals might extend the lookback to five hundred bars or more. Aggressive traders seeking earlier entry and willing to accept more false positives might reduce it to one hundred twenty-six bars or even less for shorter-term applications.
The bullish and bearish thresholds define signal generation levels. Default settings of eighty and twenty respectively reflect academic research suggesting meaningful information content at these extremes. Readings above eighty indicate positioning in the top quintile of the historical range, representing genuine extremes rather than temporary fluctuations. Conversely, readings below twenty occupy the bottom quintile, indicating unusually bearish positioning (Briese, 2008).
However, traders must recognize that appropriate thresholds vary by market, trader category, and personal risk tolerance. Some futures markets exhibit wider positioning swings than others due to seasonal patterns, volatility characteristics, or participant behavior. Conservative traders seeking high-probability setups with fewer signals might raise thresholds to eighty-five and fifteen. Aggressive traders willing to accept more false positives for earlier entry could lower them to seventy-five and twenty-five.
The key is maintaining meaningful differentiation between bullish, neutral, and bearish zones. The default settings of eighty and twenty create a clear three-zone structure. Readings from zero to twenty represent bearish territory where the selected trader group holds unusually bearish positions. Readings from twenty to eighty represent neutral territory where positioning falls within normal historical ranges. Readings from eighty to one hundred represent bullish territory where the selected trader group holds unusually bullish positions.
The trading perspective selection determines which participant group the indicator follows, fundamentally shaping interpretation and signal meaning. For counter-trend traders seeking reversal opportunities, monitoring commercial positioning makes intuitive sense based on the academic research discussed earlier. When commercials reach extreme bearish readings below twenty, indicating unprecedented short positioning relative to recent history, they are effectively betting against the crowd. Given their informational advantages demonstrated by Bessembinder and Chan (1992), this contrarian stance often precedes major bottoms.
Trend followers might instead monitor large speculator positioning, but with inverted logic compared to commercials. When managed money reaches extreme bullish readings above eighty, the trend may be exhausting rather than accelerating. This seeming paradox reflects their late-cycle participation documented by Sanders, Boris and Manfredo (2004). Sophisticated traders thus use speculator extremes as fade signals, entering positions opposite to speculator consensus.
Small trader monitoring serves primarily as a contrary indicator for all trading styles. Extreme small trader bullishness above seventy-five or eighty typically warns of retail FOMO at market tops. Extreme small trader bearishness below twenty or twenty-five often marks capitulation bottoms where the last weak hands have sold.
VISUALIZATION AND USER INTERFACE
The visual design incorporates multiple elements working together to facilitate decision-making and maintain situational awareness during active trading. The primary COT Index line plots in bold with adjustable line width, defaulting to two pixels for clear visibility against busy price charts. An optional glow effect, controlled by a simple toggle, adds additional visual prominence through multiple plot layers with progressively increasing transparency and width.
A twenty-one period exponential moving average overlays the index line, providing trend context for positioning changes. When the index crosses above its moving average, it signals accelerating bullish sentiment among the selected trader group regardless of whether absolute positioning is extreme. Conversely, when the index crosses below its moving average, it signals deteriorating sentiment and potentially the beginning of a reversal in positioning trends.
The EMA provides a dynamic reference line for assessing positioning momentum. When the index trades far above its EMA, positioning is not only extreme in absolute terms but also building with momentum. When the index trades far below its EMA, positioning is contracting or reversing, which may indicate weakening conviction even if absolute levels remain elevated.
The data table positioned at the top right of the chart displays eleven metrics for each trader category, transforming the indicator from a simple index calculation into an analytical dashboard providing multidimensional market intelligence. Beyond the COT Index itself, users can monitor positioning extremity, which measures how unusual current levels are compared to historical norms using statistical techniques. The extremity metric clarifies whether a reading represents the ninety-fifth or ninety-ninth percentile, with values above two standard deviations indicating genuinely exceptional positioning.
Market power quantifies each group's influence on total open interest. This metric expresses each trader category's net position as a percentage of total market open interest. A commercial entity holding forty percent of total open interest commands significantly more influence than one holding five percent, making their positioning signals more meaningful.
Momentum and rate of change metrics reveal whether positions are building or contracting, providing early warning of potential regime shifts. Position velocity measures the rate of change in positioning changes, effectively a second derivative providing even earlier insight into inflection points.
Sentiment divergence highlights disagreements between commercial and speculative positioning. This metric calculates the absolute difference between normalized commercial and large speculator index values. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals.
The table also displays concentration metrics when available, showing how positioning is distributed among the largest handful of traders in each category. High concentration indicates a few dominant players controlling most of the positioning, while low concentration suggests broad-based participation across many traders.
THE ALERT SYSTEM AND MONITORING
The alert system, comprising five distinct alert conditions, enables systematic monitoring of dozens of futures markets without constant screen watching. The bullish and bearish COT signal alerts trigger when the index crosses user-defined thresholds, indicating the selected trader group has reached extreme positioning worthy of attention. These alerts fire in real-time as new weekly COT data publishes, typically Friday afternoon following the Tuesday measurement date.
Extreme positioning alerts fire at ninety and ten index levels, representing the top and bottom ten percent of the historical range, warning of particularly stretched readings that historically precede reversals with high probability. When commercials reach a COT Index reading below ten, they are expressing their most bearish stance in the entire lookback period.
The data staleness alert notifies users when COT reports have not updated for more than ten days, preventing reliance on outdated information for trading decisions. Government shutdowns or federal holidays can interrupt the normal Friday publication schedule. Using stale signals while believing them current creates dangerous false confidence.
The indicator's watermark information display positioned in the bottom right corner provides essential context at a glance. This persistent display shows the symbol and timeframe, the COT report date timestamp, days since last update, and the current signal state. A trader analyzing a potential short entry in crude oil can glance at the watermark to instantly confirm positioning context without interrupting analysis flow.
LIMITATIONS AND REALISTIC EXPECTATIONS
Practical application requires understanding both the indicator's considerable strengths and inherent limitations. COT data inherently lags price action by three days, as Tuesday positions are not published until Friday afternoon. This delay means the indicator cannot catch rapid intraday reversals or respond to surprise news events. Traders using the COT Index for timing entries must accept this latency and focus on swing trading and position trading timeframes where three-day lags matter less than in day trading or scalping.
The weekly publication schedule similarly makes the indicator unsuitable for short-term trading strategies requiring immediate feedback. The COT Index works best for traders operating on weekly or longer timeframes, where positioning shifts measured in weeks and months align with trading horizon.
Extreme COT readings can persist far longer than typical technical indicators suggest, testing the patience and capital reserves of traders attempting to fade them. When crude oil enters a sustained bull market driven by genuine supply disruptions, commercial hedgers may maintain bearish positioning for many months as prices grind higher. A commercial COT Index reading of fifteen indicating extreme bearishness might persist for three months while prices continue rallying before finally reversing. Traders without sufficient capital and risk tolerance to weather such drawdowns will exit prematurely, precisely when the signal is about to work (Irwin and Sanders, 2012).
Position sizing discipline becomes paramount when implementing COT-based strategies. Rather than risking large percentages of capital on individual signals, successful COT traders typically allocate modest position sizes across multiple signals, allowing some to take time to mature while others work more quickly.
The indicator also cannot overcome fundamental regime changes that alter the structural drivers of markets. If gold enters a true secular bull market driven by monetary debasement, commercial hedgers may remain persistently bearish as mining companies sell forward years of production at what they perceive as favorable prices. Their positioning indicates valuation concerns from a production cost perspective, but cannot stop prices from rising if investment demand overwhelms physical supply-demand balance.
Similarly, structural changes in market participation can alter the meaning of positioning extremes. The growth of commodity index investing in the two thousands brought massive passive long-only capital into futures markets, fundamentally changing typical positioning ranges. Traders relying on COT signals without recognizing this regime change would have generated numerous false bearish signals during the commodity supercycle from 2003 to 2008.
The research foundation supporting COT analysis derives primarily from commodity markets where the commercial hedger information advantage is most pronounced. Studies specifically examining financial futures like equity indices and bonds show weaker but still present effects. Traders should calibrate expectations accordingly, recognizing that COT analysis likely works better for crude oil, natural gas, corn, and wheat than for the S&P 500, Treasury bonds, or currency futures.
Another important limitation involves the reporting threshold structure. Not all market participants appear in COT data, only those holding positions above specified minimums. In markets dominated by a few large players, concentration metrics become critical for proper interpretation. A single large trader accounting for thirty percent of commercial positioning might skew the entire category if their individual circumstances are idiosyncratic rather than representative.
GOLD FUTURES DURING A HYPOTHETICAL MARKET CYCLE
Consider a practical example using gold futures during a hypothetical but realistic market scenario that illustrates how the COT Index indicator guides trading decisions through a complete market cycle. Suppose gold has rallied from fifteen hundred to nineteen hundred dollars per ounce over six months, driven by inflation concerns following aggressive monetary expansion, geopolitical uncertainty, and sustained buying by Asian central banks for reserve diversification.
Large speculators, operating primarily trend-following strategies, have accumulated increasingly bullish positions throughout this rally. Their COT Index has climbed progressively from forty-five to eighty-five. The table display shows that large speculators now hold net long positions representing thirty-two percent of total open interest, their highest in four years. Momentum indicators show positive readings, indicating positions are still building though at a decelerating rate. Position velocity has turned negative, suggesting the pace of position building is slowing.
Meanwhile, commercial hedgers have responded to the rally by aggressively selling forward production and inventory. Their COT Index has moved inversely to price, declining from fifty-five to twenty. This bearish commercial positioning represents mining companies locking in forward sales at prices they view as attractive relative to production costs. The table shows commercials now hold net short positions representing twenty-nine percent of total open interest, their most bearish stance in five years. Concentration metrics indicate this positioning is broadly distributed across many commercial entities, suggesting the bearish stance reflects collective industry view rather than idiosyncratic positioning by a single firm.
Small traders, attracted by mainstream financial media coverage of gold's impressive rally, have recently piled into long positions. Their COT Index has jumped from forty-five to seventy-eight as retail investors chase the trend. Television financial networks feature frequent segments on gold with bullish guests. Internet forums and social media show surging retail interest. This retail enthusiasm historically marks late-stage trend development rather than early opportunity.
The COT Index indicator, configured to monitor commercial positioning from a contrarian perspective, displays a clear bearish signal given the extreme commercial short positioning. The table displays multiple confirming metrics: positioning extremity shows commercials at the ninety-sixth percentile of bearishness, market power indicates they control twenty-nine percent of open interest, and sentiment divergence registers sixty-five, indicating massive disagreement between commercial hedgers and large speculators. This divergence, the highest in three years, places the market in the historically high-risk category for reversals.
The interpretation requires nuance and consideration of context beyond just COT data. Commercials are not necessarily predicting an imminent crash. Rather, they are hedging business operations at what they collectively view as favorable price levels. However, the data reveals they have sold unusually large quantities of forward production, suggesting either exceptional production expectations for the year ahead or concern about sustaining current price levels or combination of both. Combined with extreme speculator positioning indicating a crowded long trade, and small trader enthusiasm confirming retail FOMO, the confluence suggests elevated reversal risk even if the precise timing remains uncertain.
A prudent trader analyzing this situation might take several actions based on COT Index signals. Existing long positions could be tightened with closer stop losses. Profit-taking on a portion of long exposure could lock in gains while maintaining some participation. Some traders might initiate modest short positions as portfolio hedges, sizing them appropriately for the inherent uncertainty in timing reversals. Others might simply move to the sidelines, avoiding new long entries until positioning normalizes.
The key lesson from case study analysis is that COT signals provide probabilistic edges rather than deterministic predictions. They work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five percent win rate with proper risk management produces substantial profits over time, yet still means forty-five percent of signals will be premature or wrong. Traders must embrace this probabilistic reality rather than seeking the impossible goal of perfect accuracy.
INTEGRATION WITH TRADING SYSTEMS
Integration with existing trading systems represents a natural and powerful use case for COT analysis, adding a positioning dimension to price-based technical approaches or fundamental analytical frameworks. Few traders rely exclusively on a single indicator or methodology. Rather, they build systems that synthesize multiple information sources, with each component addressing different aspects of market behavior.
Trend followers might use COT extremes as regime filters, modifying position sizing or avoiding new trend entries when positioning reaches levels historically associated with reversals. Consider a classic trend-following system based on moving average crossovers and momentum breakouts. Integration of COT analysis adds nuance. When large speculator positioning exceeds ninety or commercial positioning falls below ten, the regime filter recognizes elevated reversal risk. The system might reduce position sizing by fifty percent for new signals during these high-risk periods (Kaufman, 2013).
Mean reversion traders might require COT signal confluence before fading extended moves. When crude oil becomes technically overbought and large speculators show extreme long positioning above eighty-five, both signals confirm. If only technical indicators show extremes while positioning remains neutral, the potential short signal is rejected, avoiding fades of trends with underlying institutional support (Kaufman, 2013).
Discretionary traders can monitor the indicator as a continuous awareness tool, informing bias and position sizing without dictating mechanical entries and exits. A discretionary trader might notice commercial positioning shifting from neutral to progressively more bullish over several months. This trend informs growing positive bias even without triggering mechanical signals.
Multi-timeframe analysis represents another powerful integration approach. A trader might use daily charts for trade execution and timing while monitoring weekly COT positioning for strategic context. When both timeframes align, highest-probability opportunities emerge.
Portfolio construction for futures traders can incorporate COT signals as an additional selection criterion. Markets showing strong technical setups AND favorable COT positioning receive highest allocations. Markets with strong technicals but neutral or unfavorable positioning receive reduced allocations.
ADVANCED METRICS AND INTERPRETATION
The metrics table transforms simple positioning data into multidimensional market intelligence. Position extremity, calculated as the absolute deviation from the historical mean normalized by standard deviation, helps identify truly unusual readings versus routine fluctuations. A reading above two standard deviations indicates ninety-fifth percentile or higher extremity. Above three standard deviations indicates ninety-ninth percentile or higher, genuinely rare positioning that historically precedes major events with high probability.
Market power, expressed as a percentage of total open interest, reveals whose positioning matters most from a mechanical market impact perspective. Consider two scenarios in gold futures. In scenario one, commercials show a COT Index reading of fifteen while their market power metric shows they hold net shorts representing thirty-five percent of open interest. This is a high-confidence bearish signal. In scenario two, commercials also show a reading of fifteen, but market power shows only eight percent. While positioning is extreme relative to this category's normal range, their limited market share means less mechanical influence on price.
The rate of change and momentum metrics highlight whether positions are accelerating or decelerating, often providing earlier warnings than absolute levels alone. A COT Index reading of seventy-five with rapidly building momentum suggests continued movement toward extremes. Conversely, a reading of eighty-five with decelerating or negative momentum indicates the positioning trend is exhausting.
Position velocity measures the rate of change in positioning changes, effectively a second derivative. When velocity shifts from positive to negative, it indicates that while positioning may still be growing, the pace of growth is slowing. This deceleration often precedes actual reversal in positioning direction by several weeks.
Sentiment divergence calculates the absolute difference between normalized commercial and large speculator index values. When commercials show extreme bearish positioning at twenty while large speculators show extreme bullish positioning at eighty, the divergence reaches sixty, representing near-maximum disagreement. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals. The mechanism is intuitive. Extreme divergence indicates the informed hedgers and momentum-following speculators have positioned opposite each other with conviction. One group will prove correct and profit while the other proves incorrect and suffers losses. The resolution of this disagreement through price movement often involves volatility.
The table also displays concentration metrics when available. High concentration indicates a few dominant players controlling most of the positioning within a category, while low concentration suggests broad-based participation. Broad-based positioning more reliably reflects collective market intelligence and industry consensus. If mining companies globally all independently decide to hedge aggressively at similar price levels, it suggests genuine industry-wide view about price valuations rather than circumstances specific to one firm.
DATA QUALITY AND RELIABILITY
The CFTC has maintained COT reporting in various forms since the nineteen twenties, providing nearly a century of positioning data across multiple market cycles. However, data quality and reporting standards have evolved substantially over this long period. Modern electronic reporting implemented in the late nineteen nineties and early two thousands significantly improved accuracy and timeliness compared to earlier paper-based systems.
Traders should understand that COT reports capture positions as of Tuesday's close each week. Markets remain open three additional days before publication on Friday afternoon, meaning the reported data is three days stale when received. During periods of rapid market movement or major news events, this lag can be significant. The indicator addresses this limitation by including timestamp information and staleness warnings.
The three-day lag creates particular challenges during extreme volatility episodes. Flash crashes, surprise central bank interventions, geopolitical shocks, and other high-impact events can completely transform market positioning within hours. Traders must exercise judgment about whether reported positioning remains relevant given intervening events.
Reporting thresholds also mean that not all market participants appear in disaggregated COT data. Traders holding positions below specified minimums aggregate into the non-reportable or small trader category. This aggregation affects different markets differently. In highly liquid contracts like crude oil with thousands of participants, reportable traders might represent seventy to eighty percent of open interest. In thinly traded contracts with only dozens of active participants, a few large reportable positions might represent ninety-five percent of open interest.
Another data quality consideration involves trader classification into categories. The CFTC assigns traders to commercial or non-commercial categories based on reported business purpose and activities. However, this process is not perfect. Some entities engage in both commercial and speculative activities, creating ambiguity about proper classification. The transition to Disaggregated reports attempted to address some of these ambiguities by creating more granular categories.
COMPARISON WITH ALTERNATIVE APPROACHES
Several alternative approaches to COT analysis exist in the trading community beyond the normalization methodology employed by this indicator. Some analysts focus on absolute position changes week-over-week rather than index-based normalization. This approach calculates the change in net positioning from one week to the next. The emphasis falls on momentum in positioning changes rather than absolute levels relative to history. This method potentially identifies regime shifts earlier but sacrifices cross-market comparability (Briese, 2008).
Other practitioners employ more complex statistical transformations including percentile rankings, z-score standardization, and machine learning classification algorithms. Ruan and Zhang (2018) demonstrated that machine learning models applied to COT data could achieve modest improvements in forecasting accuracy compared to simple threshold-based approaches. However, these gains came at the cost of interpretability and implementation complexity.
The COT Index indicator intentionally employs a relatively straightforward normalization methodology for several important reasons. First, transparency enhances user understanding and trust. Traders can verify calculations manually and develop intuitive feel for what different readings mean. Second, academic research suggests that most of the predictive power in COT data comes from extreme positioning levels rather than subtle patterns requiring complex statistical methods to detect. Third, robust methods that work consistently across many markets and time periods tend to be simpler rather than more complex, reducing the risk of overfitting to historical data. Fourth, the complexity costs of implementation matter for retail traders without programming teams or computational infrastructure.
PSYCHOLOGICAL ASPECTS OF COT TRADING
Trading based on COT data requires psychological fortitude that differs from momentum-based approaches. Contrarian positioning signals inherently mean betting against prevailing market sentiment and recent price action. When commercials reach extreme bearish positioning, prices have typically been rising, sometimes for extended periods. The price chart looks bullish, momentum indicators confirm strength, moving averages align positively. The COT signal says bet against all of this. This psychological difficulty explains why COT analysis remains underutilized relative to trend-following methods.
Human psychology strongly predisposes us toward extrapolation and recency bias. When prices rally for months, our pattern-matching brains naturally expect continued rally. The recent price action dominates our perception, overwhelming rational analysis about positioning extremes and historical probabilities. The COT signal asking us to sell requires overriding these powerful psychological impulses.
The indicator design attempts to support the required psychological discipline through several features. Clear threshold markers and signal states reduce ambiguity about when signals trigger. When the commercial index crosses below twenty, the signal is explicit and unambiguous. The background shifts to red, the signal label displays bearish, and alerts fire. This explicitness helps traders act on signals rather than waiting for additional confirmation that may never arrive.
The metrics table provides analytical justification for contrarian positions, helping traders maintain conviction during inevitable periods of adverse price movement. When a trader enters short positions based on extreme commercial bearish positioning but prices continue rallying for several weeks, doubt naturally emerges. The table display provides reassurance. Commercial positioning remains extremely bearish. Divergence remains high. The positioning thesis remains intact even though price action has not yet confirmed.
Alert functionality ensures traders do not miss signals due to inattention while also not requiring constant monitoring that can lead to emotional decision-making. Setting alerts for COT extremes enables a healthier relationship with markets. When meaningful signals occur, alerts notify them. They can then calmly assess the situation and execute planned responses.
However, no indicator design can completely overcome the psychological difficulty of contrarian trading. Some traders simply cannot maintain short positions while prices rally. For these traders, COT analysis might be better employed as an exit signal for long positions rather than an entry signal for shorts.
Ultimately, successful COT trading requires developing comfort with probabilistic thinking rather than certainty-seeking. The signals work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five or sixty percent win rate with proper risk management produces substantial profits over years, yet still means forty to forty-five percent of signals will be premature or wrong. COT analysis provides genuine edge, but edge means probability advantage, not elimination of losing trades.
EDUCATIONAL RESOURCES AND CONTINUOUS LEARNING
The indicator provides extensive built-in educational resources through its documentation, detailed tooltips, and transparent calculations. However, mastering COT analysis requires study beyond any single tool or resource. Several excellent resources provide valuable extensions of the concepts covered in this guide.
Books and practitioner-focused monographs offer accessible entry points. Stephen Briese published The Commitments of Traders Bible in two thousand eight, offering detailed breakdowns of how different markets and trader categories behave (Briese, 2008). Briese's work stands out for its empirical focus and market-specific insights. Jack Schwager includes discussion of COT analysis within the broader context of market behavior in his book Market Sense and Nonsense (Schwager, 2012). Perry Kaufman's Trading Systems and Methods represents perhaps the most rigorous practitioner-focused text on systematic trading approaches including COT analysis (Kaufman, 2013).
Academic journal articles provide the rigorous statistical foundation underlying COT analysis. The Journal of Futures Markets regularly publishes research on positioning data and its predictive properties. Bessembinder and Chan's earlier work on systematic risk, hedging pressure, and risk premiums in futures markets provides theoretical foundation (Bessembinder, 1992). Chang's examination of speculator returns provides historical context (Chang, 1985). Irwin and Sanders provide essential skeptical perspective in their two thousand twelve article (Irwin and Sanders, 2012). Wang's two thousand three article provides one of the most empirical analyses of COT data across multiple commodity markets (Wang, 2003).
Online resources extend beyond academic and book-length treatments. The CFTC website provides free access to current and historical COT reports in multiple formats. The explanatory materials section offers detailed documentation of report construction, category definitions, and historical methodology changes. Traders serious about COT analysis should read these official CFTC documents to understand exactly what they are analyzing.
Commercial COT data services such as Barchart provide enhanced visualization and analysis tools beyond raw CFTC data. TradingView's educational materials, published scripts library, and user community provide additional resources for exploring different approaches to COT analysis.
The key to mastering COT analysis lies not in finding a single definitive source but rather in building understanding through multiple perspectives and information sources. Academic research provides rigorous empirical foundation. Practitioner-focused books offer practical implementation insights. Direct engagement with data through systematic backtesting develops intuition about how positioning dynamics manifest across different market conditions.
SYNTHESIZING KNOWLEDGE INTO PRACTICE
The COT Index indicator represents the synthesis of academic research, trading experience, and software engineering into a practical tool accessible to retail traders equipped with nothing more than a TradingView account and willingness to learn. What once required expensive data subscriptions, custom programming capabilities, statistical software, and institutional resources now appears as a straightforward indicator requiring only basic parameter selection and modest study to understand. This democratization of institutional-grade analysis tools represents a broader trend in financial markets over recent decades.
Yet technology and data access alone provide no edge without understanding and discipline. Markets remain relentlessly efficient at eliminating edges that become too widely known and mechanically exploited. The COT Index indicator succeeds only when users invest time learning the underlying concepts, understand the limitations and probability distributions involved, and integrate signals thoughtfully into trading plans rather than applying them mechanically.
The academic research demonstrates conclusively that institutional positioning contains genuine information about future price movements, particularly at extremes where commercial hedgers are maximally bearish or bullish relative to historical norms. This informational content is neither perfect nor deterministic but rather probabilistic, providing edge over many observations through identification of higher-probability configurations. Bessembinder and Chan's finding that commercial positioning explained modest but significant variance in future returns illustrates this probabilistic nature perfectly (Bessembinder and Chan, 1992). The effect is real and statistically significant, yet it explains perhaps ten to fifteen percent of return variance rather than most variance. Much of price movement remains unpredictable even with positioning intelligence.
The practical implication is that COT analysis works best as one component of a trading system rather than a standalone oracle. It provides the positioning dimension, revealing where the smart money has positioned and where the crowd has followed, but price action analysis provides the timing dimension. Fundamental analysis provides the catalyst dimension. Risk management provides the survival dimension. These components work together synergistically.
The indicator's design philosophy prioritizes transparency and education over black-box complexity, empowering traders to understand exactly what they are analyzing and why. Every calculation is documented and user-adjustable. The threshold markers, background coloring, tables, and clear signal states provide multiple reinforcing channels for conveying the same information.
This educational approach reflects a conviction that sustainable trading success comes from genuine understanding rather than mechanical system-following. Traders who understand why commercial positioning matters, how different trader categories behave, what positioning extremes signify, and where signals fit within probability distributions can adapt when market conditions change. Traders mechanically following black-box signals without comprehension abandon systems after normal losing streaks.
The research foundation supporting COT analysis comes primarily from commodity markets where commercial hedger informational advantages are most pronounced. Agricultural producers hedging crops know more about supply conditions than distant speculators. Energy companies hedging production know more about operating costs than financial traders. Metals miners hedging output know more about ore grades than index funds. Financial futures markets show weaker but still present effects.
The journey from reading this documentation to profitable trading based on COT analysis involves several stages that cannot be rushed. Initial reading and basic understanding represents the first stage. Historical study represents the second stage, reviewing past market cycles to observe how positioning extremes preceded major turning points. Paper trading or small-size real trading represents the third stage to experience the psychological challenges. Refinement based on results and personal psychology represents the fourth stage.
Markets will continue evolving. New participant categories will emerge. Regulatory structures will change. Technology will advance. Yet the fundamental dynamics driving COT analysis, that different market participants have different information, different motivations, and different forecasting abilities that manifest in their positioning, will persist as long as futures markets exist. While specific thresholds or optimal parameters may shift over time, the core logic remains sound and adaptable.
The trader equipped with this indicator, understanding of the theory and evidence behind COT analysis, realistic expectations about probability rather than certainty, discipline to maintain positions through adverse volatility, and patience to allow signals time to develop possesses genuine edge in markets. The edge is not enormous, markets cannot allow large persistent inefficiencies without arbitraging them away, but it is real, measurable, and exploitable by those willing to invest in learning and disciplined application.
REFERENCES
Bessembinder, H. (1992) Systematic risk, hedging pressure, and risk premiums in futures markets, Review of Financial Studies, 5(4), pp. 637-667.
Bessembinder, H. and Chan, K. (1992) The profitability of technical trading rules in the Asian stock markets, Pacific-Basin Finance Journal, 3(2-3), pp. 257-284.
Briese, S. (2008) The Commitments of Traders Bible: How to Profit from Insider Market Intelligence. Hoboken: John Wiley & Sons.
Chang, E.C. (1985) Returns to speculators and the theory of normal backwardation, Journal of Finance, 40(1), pp. 193-208.
Commodity Futures Trading Commission (CFTC) (2009) Explanatory Notes: Disaggregated Commitments of Traders Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Commodity Futures Trading Commission (CFTC) (2020) Commitments of Traders: About the Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Irwin, S.H. and Sanders, D.R. (2012) Testing the Masters Hypothesis in commodity futures markets, Energy Economics, 34(1), pp. 256-269.
Kaufman, P.J. (2013) Trading Systems and Methods. 5th edn. Hoboken: John Wiley & Sons.
Ruan, Y. and Zhang, Y. (2018) Forecasting commodity futures prices using machine learning: Evidence from the Chinese commodity futures market, Applied Economics Letters, 25(12), pp. 845-849.
Sanders, D.R., Boris, K. and Manfredo, M. (2004) Hedgers, funds, and small speculators in the energy futures markets: an analysis of the CFTC's Commitments of Traders reports, Energy Economics, 26(3), pp. 425-445.
Schwager, J.D. (2012) Market Sense and Nonsense: How the Markets Really Work and How They Don't. Hoboken: John Wiley & Sons.
Tharp, V.K. (2008) Super Trader: Make Consistent Profits in Good and Bad Markets. New York: McGraw-Hill.
Wang, C. (2003) The behavior and performance of major types of futures traders, Journal of Futures Markets, 23(1), pp. 1-31.
Williams, L.R. and Noseworthy, M. (2009) The Right Stock at the Right Time: Prospering in the Coming Good Years. Hoboken: John Wiley & Sons.
FURTHER READING
For traders seeking to deepen their understanding of COT analysis and futures market positioning beyond this documentation, the following resources provide valuable extensions:
Academic Journal Articles:
Fishe, R.P.H. and Smith, A. (2012) Do speculators drive commodity prices away from supply and demand fundamentals?, Journal of Commodity Markets, 1(1), pp. 1-16.
Haigh, M.S., Hranaiova, J. and Overdahl, J.A. (2007) Hedge funds, volatility, and liquidity provision in energy futures markets, Journal of Alternative Investments, 9(4), pp. 10-38.
Kocagil, A.E. (1997) Does futures speculation stabilize spot prices? Evidence from metals markets, Applied Financial Economics, 7(1), pp. 115-125.
Sanders, D.R. and Irwin, S.H. (2011) The impact of index funds in commodity futures markets: A systems approach, Journal of Alternative Investments, 14(1), pp. 40-49.
Books and Practitioner Resources:
Murphy, J.J. (1999) Technical Analysis of the Financial Markets: A Guide to Trading Methods and Applications. New York: New York Institute of Finance.
Pring, M.J. (2002) Technical Analysis Explained: The Investor's Guide to Spotting Investment Trends and Turning Points. 4th edn. New York: McGraw-Hill.
Federal Reserve and Research Institution Publications:
Federal Reserve Banks regularly publish working papers examining commodity markets, futures positioning, and price discovery mechanisms. The Federal Reserve Bank of San Francisco and Federal Reserve Bank of Kansas City maintain active research programs in this area.
Online Resources:
The CFTC website provides free access to current and historical COT reports, explanatory materials, and regulatory documentation.
Barchart offers enhanced COT data visualization and screening tools.
TradingView's community library contains numerous published scripts and educational materials exploring different approaches to positioning analysis.
Strat 1-2 Break AlertsThe Strat 1-2 Break Alerts
by Yolanda Marie Dixon
This indicator automatically identifies Inside Bars (1) and alerts when price breaks out into a 2-1-2 Bullish or 2-1-2 Bearish setup — two of the most actionable patterns in The Strat methodology created by Rob Smith.
📊 What It Does:
Marks Inside Bars with a yellow triangle below the candle.
Plots a green “2-1-2↑” triangle when a bullish breakout occurs.
Plots a red “2-1-2↓” triangle when a bearish breakdown occurs.
Provides built-in alerts so traders never miss a 2-1-2 setup.
💡 How to Use It:
Add the indicator to your chart, then go to Alerts → Create Alert → Condition: Strat 1-2 Break Alerts, and choose either 2-1-2 Up or 2-1-2 Down.
Perfect for traders who follow The Strat and want simple, reliable visual and alert-based signals for 1-2 setups.
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🔔 Stay ready, stay Stratified.
Master The Strat with instant alerts for every 2-1-2 breakout.
HTF Supply & Demand Zones 📊 Overview
Advanced supply and demand zone indicator that automatically detects institutional-level price zones on higher timeframes and dynamically adapts zone colors based on price position. Zones below price act as demand (support) and zones above price act as supply (resistance).
✨ Key Features
🎯 Dynamic Zone Recognition
- Smart Color Adaptation: Zones automatically change from demand (green) to supply (red) when price crosses them
- Higher Timeframe Analysis: Detect zones from any timeframe while trading on lower timeframes
- Base/Blast Pattern Detection**: Identifies strong institutional zones using base-blast candle methodology
- Automatic Zone Flipping: Broken demand zones become supply and vice versa
📈 Zone Detection Method
Uses the proven Base & Blast candle pattern:
- Base Candle: Small consolidation candle with minimal wick
- Blast Candle: Strong momentum candle breaking from the base
- Customizable Ratio: Adjust base/blast body size ratio (default 8:1)
- Wick Filter: Ensures clean base candles for higher probability zones
🎨 Visual Features
- Clean Zone Boxes: Extended zones with customizable colors and transparency
- Smart Labels: Display zone type and touch count
- Touch Counter: Track how many times price has tested each zone
- Info Dashboard: Real-time statistics in top-right corner
⚙️ Zone Management
- Auto-Delete After X Touches**: Remove zones after specified number of tests (default: 5)
- Optional Break Deletion**: Choose whether to delete zones when price breaks through
- Maximum Zone Limit**: Control chart cleanliness by limiting displayed zones
- Extended Zones**: All zones extend to the right for forward visibility
🔧 Settings
Detection Parameters
- Higher Timeframe: Select any timeframe for zone detection (empty = current timeframe)
- Base/Blast Ratio: 4.0 to 30.0 (default: 8.0) - Higher = stronger zones, fewer signals
- Wick Threshold: 0.1 to 0.5 (default: 0.3) - Maximum base candle wick size
Display Options
- Toggle demand/supply zones independently
- Maximum zones to display (1-50)
- Show/hide zone labels
- Customizable colors for demand and supply zones
- Adjustable border width
Zone Management
- Delete after X touches (1-30 touches)
- Delete on break option
- Touch counter displays current/max touches
💡 How to Use
For Swing Trading
1. Set timeframe to Daily or Weekly
2. Use 8:1 ratio for high-quality zones
3. Enable auto-delete after 3-5 touches
4. Trade pullbacks to green zones (demand) for longs
5. Trade rallies to red zones (supply) for shorts
For Day Trading
1. Set timeframe to 1H or 4H
2. Use 6:1 ratio for more zones
3. Watch for zone color changes as confirmation
4. Enter when price retests zones in the direction of the higher timeframe trend
For Scalping
1. Set timeframe to 15m or 1H
2. Use 5:1 ratio for frequent signals
3. Focus on first touch of fresh zones
4. Use lower timeframes for precise entries
📋 Best Practices
✅ DO:
- Use zones from higher timeframes for better reliability
- Wait for zone color change as confirmation of flip
- Focus on first 2-3 touches of a zone
- Combine with trend analysis
- Use zones as targets and entry levels
❌ DON'T:
- Trade every zone - quality over quantity
- Ignore the touch counter
- Use on very low timeframes without HTF context
- Trade zones that have been tested many times
🎓 Understanding Dynamic Colors
Green Zones (Demand) = Below current price = Support = Look for bounces
Red Zones (Supply) = Above current price = Resistance = Look for rejections
When price breaks a green zone downward, it flips to red (former support becomes resistance)
When price breaks a red zone upward, it flips to green (former resistance becomes support)
📊 Info Dashboard
The top-right table displays:
- Active timeframe
- Current demand zones count (below price)
- Current supply zones count (above price)
- Active base/blast ratio
- Maximum touches setting
🔔 Trading Signals
High Probability Setups:
- Fresh zones (0-1 touches) on higher timeframes
- Zones that align with major support/resistance
- First test after a zone color flip
- Multiple timeframe confluence
Avoid:
- Zones with 4+ touches
- Zones in choppy/ranging markets
- Counter-trend zones during strong momentum
⚡ Performance Notes
- Maximum 500 boxes and lines supported
- Optimized for real-time scanning
- Minimal resource usage
- No repainting - all zones are confirmed
🎯 Recommended Settings by Trading Style
Conservative (Higher Quality)
- Ratio: 10:1
- Wick Threshold: 0.2
- Delete After: 3 touches
Balanced (Default)
- Ratio: 8:1
- Wick Threshold: 0.3
- Delete After: 5 touches
Aggressive (More Signals)
- Ratio: 6:1
- Wick Threshold: 0.4
- Delete After: 7 touches
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📖 Additional Resources
For more information on supply and demand trading:
- Study institutional order flow
- Learn base and blast candle patterns
- Understand market structure and liquidity zones
- Practice on demo before live trading
Risk Warning: This indicator is a tool for technical analysis. Always use proper risk management and combine with your trading strategy. Past performance does not guarantee future results.
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Compatible with all markets: Forex, Stocks, Crypto, Futures, and Indices
Version: 1.0 | Language: Pine Script v5
Support & Resistance Zones + FVG**Overview:**
This tool automatically identifies **key support and resistance levels** and highlights **Fair Value Gaps (FVGs)** on the chart. It helps traders of all levels **visualize important price areas**, spot potential market reactions, and make better-informed trading decisions.
Support and resistance zones are areas where price tends to **reverse, stall, or accelerate**, making them essential for entries, exits, and stop-loss placement. Fair Value Gaps represent rapid price movements that leave temporary imbalances, which often act as **future targets or reversal points**. Together, these features provide a **comprehensive view of market structure**.
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## Features:
1. **Automatic Support & Resistance Detection:**
* Detects zones based on recent price action patterns:
* **Bull-to-Bear transitions** → resistance zones
* **Bear-to-Bull transitions** → support zones
* Dynamically calculates **zone heights** based on recent candle ranges, adapting to market volatility.
2. **Broken Zones & Proximity Alerts:**
* Highlights zones that have been broken, helping traders **focus on relevant levels**.
* Optional proximity alerts indicate broken zones that are **near the current price**, showing potential retests.
3. **Fair Value Gaps (FVGs):**
* Detects bullish and bearish gaps automatically.
* Options to **ignore narrow gaps** and **remove fully crossed FVGs**.
* Acts as a guide for potential **price targets or reversal areas**.
4. **Clean Chart & Customization:**
* Hides overlapping or invalid zones to reduce clutter.
* Fully adjustable inputs, including:
* Zone length
* Lookback range
* Zone height multiplier
* FVG extension
* Display and opacity settings
5. **Timeframe-Independent:**
* Works on **any chart interval**, from scalping to long-term swing charts.
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## How It Helps Traders:
* **Identify Key Levels Automatically:**
Visualizes areas where the price has historically reacted. These zones act as **natural barriers** guiding entries and exits.
* **Spot Broken Zones:**
Broken zones may lose significance but could act as **future retest points**, helping assess trend continuation or reversal.
* **Visualize Price Gaps (FVGs):**
Gaps left by rapid price movement often act as **price magnets**, providing potential targets or reversal points.
* **Reduce Noise:**
Automatically hides overlapping or invalid zones for a **cleaner, easier-to-read chart**, highlighting only the most significant levels.
* **Adaptable to Any Trading Style:**
Useful for **swing trading, intraday trading, or scalping**, showing where buyers and sellers are most active.
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## Practical Usage Guide:
1. **Enable Support & Resistance Zones:**
* Visualize critical price levels.
* Adjust **zone length, lookback range, and height multiplier** to fit your trading style and volatility.
2. **Enable FVGs:**
* Highlights gaps created by rapid price movements.
* Customize **minimum gap size, extension, and filtering options** to reduce chart noise.
3. **Observe Price Reactions:**
* **Bounce at support:** Potential buy opportunity.
* **Reversal at resistance:** Potential sell/short opportunity.
* **Breakout:** Watch for price breaking a zone for trend continuation trades.
4. **Risk Management:**
* Place stop-loss orders just outside zones to protect trades.
* Use broken zones as **profit targets** or areas to tighten stops.
5. **Trend Analysis:**
* Understand where buyers and sellers are concentrated.
* Identify strong trends by observing multiple zones being respected or broken.
6. **Multi-Timeframe Application:**
* Apply on different timeframes to **align short-term entries with longer-term structure**, improving trade probability.
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## Key Inputs & Customizations:
* **Support & Resistance Zones:**
* Show/Hide Zones
* Zone Length Extend (bars)
* Loopback Range (bars)
* Zone Height Multiplier
* Hide Overlapped Zones
* Hide Broken Zones
* Ignore Last N Candles for Break Check
* Show Proximity Broken Zones
* Proximity Range for Broken Zones
* **Fair Value Gaps (FVGs):**
* Show/Hide FVGs
* Extend FVGs (bars)
* Minimum FVG Size
* Ignore Narrow FVGs
* Ignore Fully Crossed FVGs
* Up and Down Colors with Opacity
---
**Summary:**
This indicator saves **time, improves chart clarity, and highlights key market levels**. It is suitable for beginners who want to **understand market structure visually**, as well as experienced traders seeking **precise entry, exit, and target zones**. By combining support/resistance detection with Fair Value Gaps, it provides a **complete visual guide to price behavior**, helping traders make **more confident and informed decisions**.
KD-10-20 Cross No Chop ChopThis strategy avoids 80% of the choppy trades. Take entry when it gives a buy signal and put TP and SL as per your convenience. Happy Trading!
Liquidity Sniper V3 (ANTI-FAKEOUT)An advanced institutional trading indicator combining liquidity pool targeting, smart money concepts, and momentum-based entries with comprehensive risk management.
🎯 CORE FEATURES:
- Liquidity Sniper Module: Identifies and targets major liquidity pools (PDH/PDL, PWH/PWL, Equal Highs/Lows, HVN/LVN edges)
- Anti-Fakeout Stack: 10-layer confirmation system including VWAP reclaim, micro BOS, displacement, relative volume, and mitigation entries
- Momentum Engulf Add-On: Catches high-velocity impulsive moves with engulfing candles, volume spikes, and volatility breakouts
- GARCH Volatility Filter: Dynamic volatility analysis to avoid choppy conditions
- Multi-Timeframe Confirmation: Ensures alignment across timeframes before entries
📊 SIGNAL CLASSIFICATION:
- BEST (Green): Highest probability setups with all confirmations aligned - 6.0+ score
- BETTER (Medium Green): Strong setups with most confirmations - 4.5-6.0 score
- GOOD (Light Green): Valid setups with basic confirmations - 3.0-4.5 score
🔍 TRADE SCENARIOS:
S1: Liquidity Reversal - Sweeps + reversals at key levels with displacement
S2: Continuation - Trend following with VWAP mean reversion
S3: Mean Reversion - Extreme deviations (2σ+) with Fibonacci exhaustion
S4: Deep Sweep - 3σ sweeps at major liquidity with high confluence
⚡ MOMENTUM TRIGGERS:
- MET (Momentum Engulf): Bullish/bearish engulfing with 1.5x+ volume spike and ATR impulse
- VBT (Volatility Breakout): Range breakouts with sigma bursts and participation
🛡️ RISK MANAGEMENT:
- Dynamic TP/SL based on ATR, VWAP bands, and liquidity pools
- 3-tier targets (T1: VWAP, T2: Nearest pool, T3: 5R extension)
- Early invalidation tracking (0.5R movement monitoring)
- Minimum 2:1 RR requirement with cooldown periods
- RTH session filters and anti-spam protection
📈 TECHNICAL EDGE:
- SMT Divergence detection vs ES correlation
- CVD (Cumulative Volume Delta) divergence confirmation
- FVG (Fair Value Gap) and Order Block mitigation entries
- Equal highs/lows clustering analysis
- Volume profile HVN/LVN identification
⚙️ FULLY CUSTOMIZABLE:
All parameters adjustable including cooldowns, proximity thresholds, ATR multipliers, RR floors, and scenario weights.
Perfect for: ES/NQ futures, forex majors, and liquid stocks. Works on 1-15 min timeframes. Best results during NY session (9:35-11:00 AM & 1:30-3:30 PM ET).
Created for serious traders seeking institutional-grade edge with quantifiable risk/reward and high-probability setups
Trappin Previous Timeframe LevelsTrappin Previous Timeframe Levels (Trappin PTL)
Overview
Trappin PTL is a comprehensive multi-timeframe support and resistance indicator that displays key price levels from multiple timeframes on a single chart. This indicator helps traders identify critical price zones where reversals or breakouts are likely to occur, making it ideal for both intraday and swing trading strategies.
💡 Origin Story
I got tired of manually drawing these lines that I learned from watching Wallstreet Trapper on Trappin Tuesdays YouTube live streams. After repeatedly marking the same previous timeframe levels on every chart, I decided to automate the process. Hope it helps you as much as it helps me!
Key Features
📊 Multiple Timeframe Levels
The indicator tracks and displays high/low levels from:
Previous Hour (PHH/PHL) - Purple lines
Previous Day (PDH/PDL) - Green lines
Previous Week (PWH/PWL) - Yellow lines
Previous Month (PMH/PML) - Blue lines
All-Time High (ATH) - Red line
52-Week High - Orange line
🎨 Fully Customizable
Colors - Change the color of each timeframe independently
Line Styles - Choose between Solid, Dashed, or Dotted lines
Line Widths - Adjust thickness from 1-4 pixels
All settings organized in intuitive groups for easy access
📍 Smart Line Extension
Lines extend back to show when the level was established
Lines project forward to show current relevance
Historical context helps identify key support/resistance zones
🏷️ Clear Price Labels
Each level displays its exact price value (no currency symbols)
Labels positioned horizontally to avoid overlap
Adaptive text color for visibility on any chart theme (dark or light mode)
Why "Trappin"?
The name is a tribute to Wallstreet Trapper and his Trappin Tuesdays YouTube live streams, where I learned the importance of marking previous timeframe levels. The name also reflects the indicator's purpose: identifying price levels where traders often get "trapped" - whether it's bulls getting trapped below resistance or bears getting trapped above support. These levels represent zones where significant order flow and liquidity exist, making them prime areas for reversals or breakouts.
Credits
Created by resoh
Inspired by Wallstreet Trapper and Trappin Tuesdays YouTube live streams
This indicator is provided for educational and informational purposes. Always practice proper risk management and conduct your own analysis before making trading decisions.
Version History
v1.0 - Initial Release
Multi-timeframe high/low levels
All-time high tracking
52-week high tracking
Fully customizable colors, styles, and widths
Adaptive labels with price display
Smart line extension showing historical context
[PS] Planetary Movements & Nakshatras - Adv Astrological Trading🌟 Planetary Movements & Nakshatras - Advanced Astrological Trading Indicator
📊 Overview
Planetary Movements & Nakshatras is a comprehensive Pine Script indicator that bridges ancient Vedic astrology with modern technical analysis. This powerful tool overlays planetary positions, transitions, alignments, and nakshatras (lunar mansions) directly on your price charts, providing unique insights into potential market movements based on celestial patterns.
🎯 Key Features
1. Real-Time Planetary Tracking
Displays current positions of 7 major celestial bodies: Sun ☉, Moon ☽, Mercury ☿, Venus ♀, Mars ♂, Jupiter ♃, and Saturn ♄
Shows each planet's current zodiac sign and nakshatra
Optional degree display for precise astronomical positioning
Color-coded labels for easy identification
2. Industry-Specific Intelligence
Choose from 15 industry classifications with customized planetary and nakshatra associations:
Technology - Mercury, Rahu, Uranus (Innovation & Communication)
Finance/Banking - Jupiter, Mercury, Venus (Wealth & Trade)
Healthcare/Pharma - Sun, Moon, Jupiter (Vitality & Healing)
Energy/Oil - Sun, Mars (Power & Energy)
Agriculture - Moon, Venus, Jupiter (Growth & Fertility)
Real Estate - Saturn, Mars, Venus (Property & Construction)
Media/Entertainment - Venus, Mercury, Moon (Arts & Creativity)
Transportation - Mars, Mercury, Moon (Movement & Travel)
Metals/Mining - Saturn, Mars, Sun (Minerals & Iron)
FMCG/Retail - Venus, Mercury, Moon (Commerce & Consumer Goods)
Telecom - Mercury, Rahu (Communication & Networks)
Automobile - Mars, Saturn, Mercury (Machinery & Engineering)
Defense - Mars, Sun, Saturn (War & Discipline)
Education - Jupiter, Mercury, Moon (Knowledge & Learning)
General - All planets (Universal application)
Primary planets for each industry are marked with ★ and highlighted with vibrant colors, while secondary planets appear muted.
3. 27 Nakshatras (Lunar Mansions)
Complete coverage of all 27 Vedic nakshatras from Ashwini to Revati:
Each nakshatra spans 13.33° of the zodiac
Industry-specific favorable nakshatras marked with ✓
Visual nakshatra boundaries with dotted lines
Configurable display: Lines, Labels, Both, or None
Enhanced visualization for auspicious nakshatras
4. Planetary Transitions & Sign Changes
Track when planets change zodiac signs (every 30°):
Triangle markers indicate sign transitions
Historical price impact displayed with each transition
Shows average upward ↑% and downward ↓% swing following the event
Significant transitions highlighted at chart bottom
Regular transitions appear at chart top
5. Planetary Alignments & Aspects
Detects major astronomical events:
Conjunctions - Planets in the same position (customizable orb: 1-15°)
Oppositions - Planets 180° apart (customizable orb: 1-15°)
Sun-Moon Conjunctions (New Moon) - Powerful market turning points
Sun-Moon Oppositions (Full Moon) - High volatility periods
Jupiter-Saturn Conjunctions - Major cycle indicators (every 20 years)
Background highlighting for major alignments
6. Advanced Pattern Detection System
Machine learning-inspired historical analysis:
Automatic Pattern Recognition - Identifies recurring planetary configurations
Swing Analysis - Calculates price movements following each event
Configurable Parameters:
Minimum Swing Threshold (0.5% - 50%)
Lookforward Period (5-180 days)
Minimum Occurrences (1-10 instances)
Statistical Tracking:
Count of pattern occurrences
Average upward swing percentage
Average downward swing percentage
Maximum upward swing
Maximum downward swing
Industry Relevance Filtering - Focus only on patterns relevant to your sector
7. Three Interactive Information Tables
📋 Industry Planet Guide Table (Configurable Position)
Shows primary planets to watch for your selected industry
Lists favorable nakshatras for optimal timing
Legend explaining symbols (★ = Primary, ✓ = Favorable)
Compact format with color-coded information
📊 Pattern Statistics Table (Configurable Position)
Historical performance data for all detected patterns
Sortable by significance
Columns: Pattern Name, Count, Avg↑%, Avg↓%, Max↑%, Max↓%, Relevance
Color-coded thresholds (green for bullish, red for bearish)
Industry relevance marked with ★
Shows up to 15 most significant patterns
🔮 Future Events Table (Configurable Position)
Projects planetary events up to 365 days into the future
Lists upcoming transitions, conjunctions, and oppositions
Shows historical average price impacts for each future event
Date, Event type, Sign/Nakshatra, Expected swing percentages
Significant events marked with ★
Displays up to 20 upcoming events
Table Positioning: Each table can be placed in any of 9 positions:
Top: Left, Center, Right
Middle: Left, Center, Right
Bottom: Left, Center, Right
8. Visual Enhancements
Nakshatra Boundary Lines - Dotted vertical lines every 27 bars
Color-Coded Events - Orange (Sun), Silver (Moon), Yellow (Mercury), Green (Venus), Red (Mars), Purple (Jupiter), Blue (Saturn)
Significance Highlighting - Bright colors for high-impact events, muted for regular events
Background Shading - Subtle yellow for Sun-Moon conjunctions, purple for Jupiter-Saturn conjunctions
Responsive Labels - Adjustable size (tiny, small, normal, large)
9. Astronomical Calculations
Julian Day Number conversion for precise date handling
Keplerian Orbital Elements for planetary position calculation
J2000 Epoch (January 1, 2000) as reference point
Accurate for historical, current, and future dates
Accounts for mean longitude and orbital mechanics
🎛️ Comprehensive Settings
Industry Settings
15 industry types with pre-configured planetary associations
Planets Group
Toggle planetary positions display
Toggle transition markers
Toggle alignment indicators
Planet Selection
Individual on/off switches for all 7 planets
Mix and match based on your trading strategy
Pattern Detection
Enable/disable pattern recognition
Minimum swing threshold (%)
Days to measure swing impact
Minimum pattern occurrences for validity
Highlight significant events
Filter by industry-relevant planets
Alignments
Conjunction orb (1-15°)
Opposition orb (1-15°)
Customizable sensitivity
Display Options
Label size selection
Show/hide degree measurements
Toggle all three information tables
Nakshatra display modes
Table Settings
Show/hide Future Events Table
Show/hide Pattern Statistics Table
Show/hide Industry Guide Table
Configure position for each table (9 positions)
Adjust future projection days (30-365)
Nakshatras
Display modes: Lines, Labels, Both, or None
Automatic favorable nakshatra highlighting
💡 Use Cases
Timing Market Entries & Exits
Identify high-probability periods using planetary alignments
Watch for favorable nakshatra transits in your industry
Track historical success rates of specific planetary configurations
Risk Management
Be aware of volatile periods (Full Moons, major transitions)
Reduce position sizes during unfavorable planetary periods
Increase exposure during auspicious nakshatra alignments
Industry-Specific Analysis
Technology stocks may respond to Mercury movements
Banking stocks may correlate with Jupiter-Venus alignments
Energy stocks may track Sun-Mars aspects
Long-Term Cycle Analysis
Jupiter-Saturn conjunctions mark major market cycles (20-year cycles)
Saturn transitions indicate sector rotation (2.5-year cycles)
Jupiter transitions show expansion/contraction phases (1-year cycles)
Intraday & Swing Trading
Moon transitions every 2.5 days for short-term timing
Mercury retrogrades for communication/tech sector volatility
Venus transitions for consumer goods and luxury items
Pattern Backtesting
Quantify historical price impacts of specific events
Build confidence in planetary timing strategies
Compare multiple patterns for optimal selection
📈 Performance & Optimization
Efficient Calculations - Optimized algorithms for minimal lag
Smart Pattern Storage - Tracks only significant patterns
Configurable Display Limits - Control label and line counts
Future Projection - Pre-calculates events without real-time overhead
Industry Filtering - Reduces noise by focusing on relevant patterns
🔧 Technical Specifications
Pine Script Version: 6
Chart Type: Overlay (true)
Max Labels: 500
Max Lines: 500
Max Boxes: 500
Calculation Method: Simplified Keplerian orbital mechanics
Date Range: Works for past, present, and future dates
Zodiac System: Tropical (Western) zodiac with Vedic nakshatras
🌙 Nakshatra Reference
All 27 nakshatras are supported with industry-specific favorable classifications:
Ashwini - Swift action, healing, pioneering (Tech, Auto, Transport)
Bharani - Transformation, restraint (Defense, Entertainment)
Krittika - Purification, cutting through (Energy, Real Estate, Metals)
Rohini - Growth, beauty, fertility (Finance, Agriculture, FMCG)
Mrigashira - Seeking, curiosity (Agriculture, Auto)
Ardra - Storm, transformation, breakthroughs (Tech, Telecom)
Punarvasu - Renewal, expansion (Agriculture, Transport, Telecom, Education)
Pushya - Nourishment, prosperity (Finance, Healthcare, Agriculture, Education)
Ashlesha - Control, mysticism (Healthcare)
Magha - Power, authority, leadership (Energy, Metals, Defense)
... and 17 more nakshatras with specific industry associations
🎨 Color Scheme
Sun ☉ - Orange (vitality, authority)
Moon ☽ - Silver (emotions, public)
Mercury ☿ - Yellow (communication, intellect)
Venus ♀ - Green (beauty, wealth, harmony)
Mars ♂ - Red (action, energy, conflict)
Jupiter ♃ - Purple (expansion, wisdom, fortune)
Saturn ♄ - Blue (restriction, discipline, structure)
📚 Trading Strategy Ideas
The Industry-Specific Strategy
Select your stock's industry classification
Focus only on primary planet transitions (marked with ★)
Wait for favorable nakshatra alignments (marked with ✓)
Check Pattern Statistics Table for historical success rate
Enter on confluence of favorable conditions
The Alignment Trading Strategy
Monitor Sun-Moon conjunctions (New Moons) for trend reversals
Track Sun-Moon oppositions (Full Moons) for volatility spikes
Use conjunction orb settings to fine-tune sensitivity
Compare with technical support/resistance levels
The Pattern Recognition Strategy
Enable Pattern Detection with your preferred parameters
Set minimum swing threshold based on your risk tolerance
Focus on patterns with high occurrence counts (5+)
Use Future Events Table to plan entries in advance
Backtest patterns in Pattern Statistics Table
The Nakshatra Timing Strategy
Identify favorable nakshatras for your industry
Wait for Moon to transit through favorable nakshatras
Combine with planetary transitions for stronger signals
Use nakshatra boundary lines for visual confirmation
⚠️ Disclaimer
This indicator is for educational and research purposes only. Planetary positions and astrological calculations should not be the sole basis for trading decisions. Always combine with fundamental analysis, technical analysis, and proper risk management. Past performance of planetary patterns does not guarantee future results. Trading involves substantial risk of loss.
🔄 Updates & Support
This indicator combines ancient wisdom with modern data analysis. While planetary positions are calculated using established astronomical formulas, the correlation between celestial events and market movements is a subject of ongoing research and debate. Use this tool as one component of a comprehensive trading strategy.
Aude - Minimal Session IndicatorMinimal Session Indicator
- The indicator allows users to highlight specific sessions (time range) on the chart.
- There are options to change the visual settings of the session box (BG color, Border color, Border style).
- Max 500 sessions drawn
VIX Overnight Unch or Up AlertThis indicator alerts when VIX opens the day unchanged or higher on the day. If in fact VIX opens up unchanged or higher, it will display near the first bar of the day, previous day's close time and level and the opening time and level. The close time is typically 16:15 New York Time and the opening time is 09:30 or the first print a few minutes later. I use TVC:VIX instead of CBOT because TVC for me is real time. I also use the 1 minute chart and the script is coded as 1 minute.
Market Profile based Support/ResistanceBrought to you by Stock Kaka - Your trading sidekick 🦜📈 - pay your visit at stockkaka.my.canva.site or find us on X #StockKaka
📊 What This Indicator Does
Ever wish the market would just tell you where the important levels are? Well, buckle up, because this indicator is like having a market whisperer on your chart!
Based on cutting-edge hierarchical market structure analysis (fancy words for "smart support and resistance"), this bad boy uses ATR-based Directional Change to identify turning points that actually matter. No more guessing where price might bounce or break—let the algorithm do the heavy lifting while you sip your coffee ☕
🎯 The Five Levels Explained (From Noisy to Mighty)
Think of these levels like a pyramid of importance. Level 0 is your chatty friend who notices everything, while Level 4 is the wise oracle who only speaks when it really matters.
Level 0: The Hyperactive Scout 🐿️
What it does: Catches every little zigzag in price using ATR confirmation
Significance: Very short-term, intraday noise
Best for: Scalpers who love action every few minutes
Trader Type: "I refresh my chart 100 times an hour"
Reliability: ⭐⭐ (It's enthusiastic but easily excitable)
Level 1: The Day Trader's Buddy 🎯
What it does: Filters Level 0 to show minor swing highs/lows
Significance: Intraday support/resistance, hourly structure
Best for: Day traders, scalpers looking for better entries
Trader Type: "I close all positions before dinner"
Reliability: ⭐⭐⭐ (Solid for quick moves)
Level 2: The Swing Trader's Sweet Spot 🎪
What it does: Identifies multi-day to weekly structure points
Significance: Intermediate support/resistance where battles happen
Best for: Swing traders, position traders
Trader Type: "I hold for days, not minutes"
Reliability: ⭐⭐⭐⭐ (Now we're talking real structure!)
Level 3: The Big Money Magnet 💰
What it does: Shows major market structure—where the whales play
Significance: Weekly to monthly levels, institutional zones
Best for: Position traders, trend followers
Trader Type: "I think in weeks and months, not hours"
Reliability: ⭐⭐⭐⭐⭐ (These levels have gravitational pull!)
Level 4: The Market Prophet 🔮
What it does: Reveals ultra-major turning points (think: quarterly/yearly pivots)
Significance: Long-term macro structure, investment-grade levels
Best for: Investors, long-term position traders
Trader Type: "Warren Buffett is my spirit animal"
Reliability: ⭐⭐⭐⭐⭐⭐ (When these break, market's rewrite the story)
⚙️ Parameter Setup Guide (The Secret Sauce)
The magic ingredient is the ATR Lookback Period—think of it as teaching the indicator your timeframe's "dialect." Here's your cheat sheet:
2-Minute Chart ⚡
ATR Lookback: 720 (24 hours of 2-min bars)
Who uses this: Crypto degens, futures scalpers, adrenaline junkies
Show Levels: L0, L1, L2 (L3+ won't budge much)
Pro Tip: Enable only L1 and L2 or your chart will look like spaghetti
5-Minute Chart 🏃
ATR Lookback: 288 (24 hours of 5-min bars)
Who uses this: Active day traders, news traders
Show Levels: L1, L2, L3
Pro Tip: L2 is your best friend here—perfect for intraday swings
15-Minute Chart 📈
ATR Lookback: 96 (24 hours of 15-min bars)
Who uses this: Swing traders, patient day traders
Show Levels: L1, L2, L3
Pro Tip: This is the "Goldilocks zone"—not too fast, not too slow
1-Hour Chart ⏰
ATR Lookback: 168 (1 week of hourly bars)
Who uses this: Swing traders, position traders
Show Levels: L2, L3, L4
Pro Tip: L3 levels here are like magnets for price action
Daily Chart 📅
ATR Lookback: 30 to 50 (1-2 months)
Who uses this: Investors, long-term traders, people with patience
Show Levels: L2, L3, L4
Pro Tip: L4 on dailies = "Don't fight this level, respect it"
🎨 How to Use This Thing
Add to Chart - Duh! 😄
Set Your ATR Lookback - Use the guide above (don't wing it!)
Enable Relevant Levels - Less is more! Turn off levels that just clutter
Watch the Magic - See horizontal lines appear at key S/R zones
Check the Table - Top-right corner shows current levels (fancy!)
Set Alerts - Get notified when price approaches or breaks levels
Trading Strategies 🎲
The Bounce Play:
Price approaches Level 2 or 3 support → Look for bullish reversal signals
Take profit at the next level resistance
Stop loss just below the support level
The Breakout Play:
Price breaks through Level 2/3 resistance with volume → Go long
Next level becomes your target
Failed breakout? Level becomes resistance again (classic fake-out)
The Confluence Play:
When Level 3 aligns with your favorite indicator (RSI oversold, moving average, Fibonacci) → Chef's kiss! 👨🍳💋
These multi-confirmation setups are where the money lives
🚨 Important Notes (Read This or Blame Yourself Later)
⚠️ This indicator REPAINTS on the current bar until an extreme is confirmed. That's not a bug, it's how directional change works. The past levels are solid as a rock, but the pending one is still... pending.
⚠️ More levels ≠ Better results. Showing all 5 levels is like having 5 GPS apps shouting directions at once. Pick 2-3 levels max.
⚠️ ATR Lookback matters! Wrong setting = garbage results. Use the guide above or experiment carefully.
⚠️ Volatile markets (crypto, meme stocks) work GREAT with this. Choppy, range-bound markets? Meh.
⚠️ Combine with other tools! This shows you WHERE, not WHEN. Use momentum indicators, volume, or your favorite chicken entrails for timing 🐔
🦜 Final Word from Stock Kaka
Remember: Indicators don't make money, traders do. This tool shows you where the market has historically respected structure. What you do with that info? That's on you, champ!
Use proper risk management, don't YOLO your rent money, and may your stops never get hunted 🎯
Trade smart, trade safe, and let Stock Kaka be your guide!
📝 Credits
Algorithm: neurotrader888 (Python implementation)
Pine Script Conversion: Your friendly neighborhood Stock Kaka team!!
Inspiration: Ginger chai, market inefficiencies, and a dash of chaos
📌 Tags
support-and-resistance market-structure atr directional-change multi-timeframe swing-trading day-trading levels hierarchical-analysis algo-trading
Price–Volume Anomaly DetectorDescription
This indicator identifies unusual relationships between price strength and trading volume. By analyzing expected intraday volume behavior and comparing it with current activity, it highlights potential exhaustion, absorption, or expansion events that may signal changing market dynamics.
How It Works
The script profiles average volume by time of day and compares current volume against this adaptive baseline. Combined with normalized price movement (ATR-based), it detects conditions where price and volume diverge:
Exhaustion: Strong price move on low volume (potential fade)
Absorption: Weak price move on high volume (potential reversal)
Expansion: Strong price move on high volume (momentum continuation)
Key Features
Adaptive time-based volume normalization
Configurable sensitivity thresholds
Optional visibility for each anomaly type
Adjustable label transparency and offset
Light Mode support: label text automatically adjusts for dark or light chart backgrounds
Lightweight overlay design
Inputs Overview
Volume Profile Resolution: Defines time bucket size for expected volume
[* ]Lookback Days: Controls how quickly the profile adapts
Price / Volume Thresholds: Tune anomaly sensitivity
Show Expansion / Exhaustion / Absorption: Toggle specific labels
Label Transparency & Offset: Adjust chart visibility
How to Use:
Apply the indicator to any chart or timeframe.
Observe where labels appear:
🔴 Exhaustion: strong price, weak volume
🔵 Absorption: weak price, strong volume
🟢 Expansion: strong price, strong volume
Use these as context clues, not trade signals — combine with broader volume or trend analysis.
How It Helps
Reveals hidden price–volume imbalances
Highlights areas where momentum may be fading or strengthening
Enhances understanding of market behavior beyond raw price action
⚠️Disclaimer:
This script is provided for educational and informational purposes only. It is not financial advice and should not be considered a recommendation to buy, sell, or hold any financial instrument. Trading involves significant risk of loss and is not suitable for every investor. Users should perform their own due diligence and consult with a licensed financial advisor before making any trading decisions. The author does not guarantee any profits or results from using this script, and assumes no liability for any losses incurred. Use this script at your own risk.
Opening Range + Daily LevelsA comprehensive multi-timeframe indicator designed for intraday traders who need critical support/resistance levels and EMAs all in one clean display.
Features:
📊 EMAs
9 EMA (default: white)
21 EMA (default: orange)
📅 Previous Day Levels
Yesterday's High, Low, Open, and Close
Lines extend progressively through the current session
Clean visual separation between trading days
📈 Previous Week Levels
Last Week's High, Low, Open, and Close
Dotted lines that extend through the current week
Perfect for identifying major support/resistance zones
🌙 Pre-Market Session (12:00 AM - 7:30 AM)
Pre-Market High and Low
Tracks overnight price action
Extends through the trading day
⏰ 15-Minute Opening Range (7:30 AM - 7:45 AM)
Opening Range High and Low with shaded box
Fibonacci retracement levels (0.382, 0.5, 0.618)
Golden ratio levels (0.382 & 0.618) in gold, midpoint (0.5) in dotted gray
Customization:
Adjustable timezone settings
Fully customizable colors for all levels
Adjustable line widths
Toggle Fibonacci levels on/off
Perfect For:
Day traders who need key levels at a glance
Price action traders using previous session data
Opening range breakout strategies
Multi-timeframe analysis
All levels update automatically and extend progressively as the day progresses, with labels staying visible at the current bar edge.
FX Sessions by m_cptForex Intraday Sessions Indicator, config time in UTC-4. Support 4 main sessions, smooth end-to-start candles mode, without gaps if your sessions has config like:
1) 19:00 - 03:00
2) 02:00 - 03:00
3) 03:00 -11:00
No excluded last candles issue on all TFs.
Working on LTF up to 1h TF since its intraday sessions indicator.
