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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.
Indicateur Pine Script®
par EdgeTools
8
Weekly Covered Calls Strategy with IV & Delta LogicWhat Does the Indicator Do? this is interactive you must use it with your options chain to input data based on the contract you want to trade. Visualize three strike price levels for covered calls based on: Aggressive (closest to price, riskier). Moderate (mid-range, balanced). Low Delta (farthest, safer). Incorporate Implied Volatility (IV) from the options chain to make strike predictions more realistic and aligned with market sentiment. Adjust the risk tolerance by modifying Delta inputs and IV values. Risk is defined for example .30 delta means 30% chance of your shares being assigned. If you want to generate steady income with your shares you might want to lower the risk of them being assigned to .05 or 5% etc. How to Use the Indicator with the Options Chain Start with the Options Chain: Look for the following data points from your options chain: Implied Volatility (IV Mid): Average IV for a particular strike price. Delta: ~0.30 Delta: Closest strike (Aggressive). ~0.15–0.20 Delta: Mid-range strike (Moderate). ~0.05–0.10 Delta: Far OTM, safer (Low Delta). Strike Price: Identify strike prices for the desired Deltas. Open Interest: Check liquidity; higher OI ensures tighter spreads. Input IV into the Indicator: Enter the IV Mid value (e.g., 0.70 for 70%) from the options chain into the Implied Volatility field of the indicator. Adjust Delta Inputs Based on Risk Tolerance: Aggressive Delta: Increase if you want strikes closer to the current price (riskier, higher premium). Default: 0.2 (20% chance of shares being assigned). Moderate Delta: Balanced risk/reward. Default: 0.12 (12%) Low Delta: Decrease for safer, farther OTM strikes. Default: 0.05 (5%) Visualize the Chart: Once inputs are updated: Red Line: Aggressive Strike (closest, riskiest, higher premium). Blue Line: Moderate Strike (mid-range). Green Line: Low Delta Strike (farthest, safer). Step-by-Step Workflow Example Open the options chain and note: Implied Volatility (IV Mid): Example 71.5% → input as 0.715. Delta for desired strikes: Aggressive: 0.30 Delta → Closest strike ~ $455. Moderate: 0.15 Delta → Mid-range strike ~ $470. Low Delta: 0.05 Delta → Farther strike ~ $505. Open the indicator and adjust: IV Mid: Enter 0.715. Aggressive Delta: Leave at 0.12 (or adjust to bring strikes closer). Moderate Delta: Leave at 0.18. Low Delta: Adjust to 0.25 for safer, farther strikes. View the chart: Compare the indicator's strikes (red, blue, green) with actual options chain strikes. Use the visualization to: Validate the risk/reward for each strike. Align strikes with technical trends, support/resistance. Adjusting Inputs Based on Risk Tolerance Higher Risk: Increase Aggressive Delta (e.g., 0.15) for closer strikes. Use higher IV values for volatile stocks. Moderate Risk: Use default values (0.12–0.18 Delta). Balance premiums and probability. Lower Risk: Increase Low Delta (e.g., 0.30) for farther, safer strikes. Focus on higher IV stocks with good open interest. Key Benefits Simplifies Strike Selection: Visualizes the three risk levels directly on the chart. Aligns with Market Sentiment: Incorporates IV for realistic forecasts. Customizable for Risk: Adjust inputs to match personal risk tolerance. By combining the options chain (IV, Delta, and liquidity) with the technical chart, you get a powerful, visually intuitive tool for covered call strategies.
Indicateur Pine Script®
par renderingnature1
1
Aggregated Long Short Ratio (Binance + Bybit)This indicator displays the Long/Short Ratio (LSR) from Binance and Bybit exchanges, plus an aggregated average. LSR shows the ratio between traders holding long positions vs short positions. Settings AvailableExchanges Group: ☑️Show Binance - Display Binance LSR line ☑️ Show Bybit - Display Bybit LSR line ☑️ Show Aggregated LSR - Display combined average Timeframe - Choose data timeframe (leave empty for chart timeframe) Visualization Group: 🎨 Binance Color - Default: Yellow 🎨 Bybit Color - Default: Orange 🎨 Aggregated Color - Default: White 📖 How to Read the Indicator ⚠️ CRITICAL: Always analyze LSR together with Open Interest (OI) Key Levels: LSR = 1.0 (gray dashed line) = Balance - Equal longs and shorts LSR > 1.0 = More longs than shorts (bullish sentiment) LSR < 1.0 = More shorts than longs (bearish sentiment) Extreme Zones: LSR > 1.5 (green zone) = Very bullish - Possible market top LSR < 0.5 (red zone) = Very bearish - Possible market bottom Why Open Interest Matters: LSR alone doesn't tell the full story. You MUST check Open Interest: Rising OI + High LSR (>1.5) = New longs opening → Strong momentum OR potential trap Rising OI + Low LSR (<0.5) = New shorts opening → Strong momentum OR potential trap Falling OI + Extreme LSR = Positions closing → Weak signal, avoid trading Stable OI + Extreme LSR = No new positions → Less reliable signal 💡 Trading Interpretation ⚠️ ALWAYS combine LSR with Open Interest analysis! Contrarian Strategy (High Leverage Zones): High LSR (>1.5) + Rising OI → Many new longs → Potential short squeeze OR reversal down Low LSR (<0.5) + Rising OI → Many new shorts → Potential long squeeze OR reversal up Trend Confirmation: Rising LSR + Rising price + Rising OI = Strong bullish trend with new positions Falling LSR + Falling price + Rising OI = Strong bearish trend with new positions Weak Signals (Avoid): Extreme LSR + Falling OI = Positions closing → Low conviction Extreme LSR + Stable OI = No new money → Wait for confirmation Divergences: Price higher highs but LSR falling + Rising OI = Bearish divergence (shorts accumulating) Price lower lows but LSR rising + Rising OI = Bullish divergence (longs accumulating) Best Setups: Reversal: Extreme LSR (>1.5 or <0.5) + Rising OI + Price rejection Trend: LSR trending with price + Steadily rising OI Caution: Extreme LSR + Falling OI = Ignore signal Built-in Alerts The indicator includes 4 preset alerts: LSR Crossed Above 1.0 - Market turned bullish LSR Crossed Below 1.0 - Market turned bearish LSR Very High - Above 1.5 (possible top) LSR Very Low - Below 0.5 (possible bottom) To Set Up Alerts: Click the "..." on the indicator Select "Add Alert" Choose the condition you want Configure notification method Best Practices MANDATORY: Always add Open Interest indicator to your chart alongside LSR To add OI: Click Indicators → Search "Open Interest" → Add official TradingView OI Use on perpetual futures charts (symbols ending in .P) Works best on USDT pairs (BTCUSDT, ETHUSDT, etc.) Combine LSR + OI + price action + support/resistance levels Higher timeframes (4h, 1D) give more reliable signals Don't trade LSR extremes without confirming OI direction Golden Rule: Rising OI = Strong signal | Falling OI = Weak signal ⚠️ Important Notes Indicator requires TradingView Premium or above (uses Request library) Only works on crypto perpetual futures Data availability depends on exchange API NA values mean data is not available for that exchange/symbol Never use LSR without Open Interest context
Indicateur Pine Script®
par carlosbucci
Larry Williams POIV A/D [tradeviZion]Larry Williams' POIV A/D - Release Notes v1.0 ================================================= Release Date: 01 April 2025 OVERVIEW -------- The Larry Williams POIV A/D (Price, Open Interest, Volume Accumulation/Distribution) indicator implements Williams' original formula while adding advanced divergence detection capabilities. This powerful tool combines price movement, open interest, and volume data to identify potential trend reversals and continuations. FEATURES -------- - Implements Larry Williams' original POIV A/D formula - Divergence detection system: * Regular divergences for trend reversal signals * Hidden divergences for trend continuation signals - Fast Mode option for earlier pivot detection - Customizable sensitivity for divergence filtering - Dynamic color visualization based on indicator direction - Adjustable smoothing to reduce noise - Automatic fallback to OBV when Open Interest is unavailable FORMULA ------- POIV A/D = CumulativeSum(Open Interest * (Close - Close ) / (True High - True Low)) + OBV Where: - Open Interest: Current period's open interest - Close - Close : Price change from previous period - True High - True Low: True Range - OBV: On Balance Volume DIVERGENCE TYPES --------------- 1. Regular Divergences (Reversal Signals): - Bullish: Price makes lower lows while indicator makes higher lows - Bearish: Price makes higher highs while indicator makes lower highs 2. Hidden Divergences (Continuation Signals): - Bullish: Price makes higher lows while indicator makes lower lows - Bearish: Price makes lower highs while indicator makes higher highs REQUIREMENTS ----------- - Works best with futures and other instruments that provide Open Interest data - Automatically adapts to work with any instrument by using OBV when OI is unavailable USAGE GUIDE ----------- 1. Apply the indicator to any chart 2. Configure settings: - Adjust sensitivity for divergence detection - Enable/disable Fast Mode for earlier signals - Customize visual settings as needed 3. Look for divergence signals: - Regular divergences for potential trend reversals - Hidden divergences for trend continuation opportunities 4. Use the alerts system for automated divergence detection KNOWN LIMITATIONS ---------------- - Requires Open Interest data for full functionality - Fast Mode may generate more signals but with lower reliability ACKNOWLEDGEMENTS --------------- This indicator is based on Larry Williams' work on Open Interest analysis. The implementation includes additional features for divergence detection while maintaining the integrity of the original formula.
Indicateur Pine Script®
par TradeVizion
7
Liquidity Heatmap [BigBeluga]The Liquidity Heatmap is an indicator designed to spot possible resting liquidity or potential stop loss using volume or Open interest. The Open interest is the total number of outstanding derivative contracts for an asset—such as options or futures—that have not been settled. Open interest keeps track of every open position in a particular contract rather than tracking the total volume traded. The Volume is the total quantity of shares or contracts traded for the current timeframe. 🔶 HOW IT WORKS Based on the user choice between Volume or OI, the idea is the same for both. On each candle, we add the data (volume or OI) below or above (long or short) that should be the hypothetical liquidation levels; More color of the liquidity level = more reaction when the price goes through it. Gradient color is calculated between an average of 2 points that the user can select. For example: 500, and the script will take the average of the highest data between 500 and 250 (half of the user's choice), and the gradient will be based on that. If we take volume as an example, a big volume spike will mean a lot of long or short activity in that candle. A liquidity level will be displayed below/above the set leverage (4.5 = 20x leverage as an example) so when the price revisits that zone, all the 20x leverage should be liquidated. Huge volume = a lot of activity Huge OI = a lot of positions opened More volume / OI will result in a stronger color that will generate a stronger reaction. 🔶 ROUTE Here's an example of a route for long liquidity: Enable the filter = consider only green candles. Set the leverage to 4.5 (20x). Choose Data = Volume. Process: A green candle is formed. A liquidity level is established. The level is placed below to simulate the 20x leverage. Color is applied, considering the average volume within the chosen area. Route completed. 🔶 FEATURE Possibility to change the color of both long and short liquidity Manual opacity value Manual opacity average Leverage Autopilot - set a good average automatically of the opacity value Enable both long or short liquidity visualization Filtering - grab only red/green candle of the corresponding side or grab every candle Data - nzVolume - Volume - nzOI - OI 🔶 TIPS Since the limit of the line is 500, it's best to plot 2 scripts: one with only long and another with only short. 🔶 CONCLUSION The liquidity levels are an interesting way to think about possible levels, and those are not real levels.
Indicateur Pine Script®
par BigBeluga
36
Commitment of Traders: Disaggregated Metrics█  OVERVIEW This indicator displays the Commitment of Traders (​COT) Disaggregated data for futures markets. █  CONCEPTS Commitment of Traders ​(​COT) data is tallied by the Commodity ​Futures Trading Commission (CFTC) , a US federal agency that oversees the trading of derivative markets such as futures in the US. It is weekly data that provides traders with information about open interest for an asset. The CFTC oversees derivative markets traded on different exchanges, so ​COT data is available for assets that can be traded on CBOT, ​CME, NYMEX, COMEX, and ICEUS. A detailed description of the COT report can be found on the CFTC's website . COT data is separated into three notable reports: Legacy, Disaggregated, and Financial. This indicator presents data from the Disaggregated report. The disaggregated reports are broken down by agriculture, petroleum and products, natural gas and products, electricity and metals and other physical contracts. The Disaggregated reports break down the reportable open interest positions into four classifications: Producer/Merchant/Processor/User, Swap Dealers, Managed Money, and Other Reportables. Our other COT indicators are:  • Commitment of Traders: Legacy Metrics  • Commitment of Traders: Financial Metrics  • Commitment of Traders: Total █  HOW TO USE IT Load the indicator on an active chart (see here if you don't know how). By default, the indicator uses the chart's symbol to derive the COT data it displays. You can also specify a CFTC code in the "CFTC code" field of the script's inputs to display COT data from a symbol different than the chart's. The rest of this section documents the script's input fields. Metric Each metric represents a different column of the Commitment of Traders report. Details are available in the explanatory notes on the CFTC's website . Here is a summary of the metrics:  • "Open Interest" is the total of all futures and/or option contracts entered into and not yet offset by a transaction, by delivery, by exercise, etc.   The aggregate of all long open interest is equal to the aggregate of all short open interest.  • "Traders Total" is the quantity of all unique reportable traders, regardless of the trading direction.  • "Traders Producer Merchant" is the number of traders classified as a "producer/merchant/processor/user" reported holding any position with the specified direction.   A "producer/merchant/processor/user" is an entity that predominantly engages in the production, processing, packing or handling of a physical commodity and   uses the futures markets to manage or hedge risks associated with those activities.  • "Traders Swap" is the number of traders classified as "swap dealers" reported holding any position with the specified direction.   A "swap dealer" is an entity that deals primarily in swaps for a commodity and uses the futures markets to manage or hedge the risk associated with those swaps transactions.   The swap dealer’s counterparties may be speculative traders, like hedge funds, or traditional commercial clients that are managing risk arising from their dealings in the physical commodity.  • "Traders Managed Money" is the number of traders classified as "money managers" reported holding any position with the specified direction.   A "money manager" is a registered trader that is engaged in managing and conducting organized futures trading on behalf of clients.  • "Traders Other Reportable" is the number of reportable traders that are not placed in any of the three categories specified above.  • "Traders Total Reportable" is the number of traders reported holding any position with the specified direction.   To determine the total number of reportable traders in a market, a trader is counted only once whether or not the trader appears in more than one category.   As a result, the sum of the numbers of traders in each separate category typically exceeds the total number of reportable traders.  • "Producer Merchant/Swap/Managed Money/Total Reportable/Other Reportable Positions" is all positions held by the traders of the specified category.  • "Nonreportable Positions" is the long and short open interest derived by subtracting the total long and short reportable positions from the total open interest.   Accordingly, the number of traders involved and the classification of each trader are unknown.  • "Concentration Gross/Net LE 4/8 TDR" is the percentage of open interest held by 4/8 of the largest traders (entities), by Gross/Net positions, without regard to how they are classified.   The Net position ratios are computed after offsetting each trader’s equal long and short positions.   A reportable trader with relatively large, balanced long and short positions in a single market,   therefore, may be among the four and eight largest traders in both the gross long and gross short categories,   but will probably not be included among the four and eight largest traders on a net basis. Direction Each metric is available for a particular set of directions. Valid directions for each metric are specified with its name in the "Metric" field's dropdown menu. Type Possible values are: All, Old, Other. When commodities have a well-defined marketing season or crop year (e.g. Wheat or Lean Hogs futures), this determines how the data is aggregated. Detailed explanation can be found in the "Old and Other Futures" section of the CTFC Explanatory Notes linked above. The "Major Markets for Which the COT Data Is Shown by Crop Year" table in the Explanatory Notes specifies the commodities that this distinction applies to; selecting "Old" for any of the commodities not in that list will return the same data as in "All", while selecting "Other" will return 0. Futures/Options Specifies the type of Commitment of Traders data to display: data concerning only Futures, only Options, or both. CTFC Code Instead of letting the script generate the CFTC COT code from the chart and the "COT Selection Mode" input when this field is empty, you can specify an unrelated CFTC COT code here, e.g., 001602 for wheat futures. Look first. Then leap.
Indicateur Pine Script®
par TradingView
10
Commitment of Traders: Financial Metrics█  OVERVIEW This indicator displays the Commitment of Traders (​COT) financial data for futures markets. █  CONCEPTS Commitment of Traders ​(​COT) data is tallied by the Commodity ​Futures Trading Commission (CFTC) , a US federal agency that oversees the trading of derivative markets such as futures in the US. It is weekly data that provides traders with information about open interest for an asset. The CFTC oversees derivative markets traded on different exchanges, so ​COT data is available for assets that can be traded on CBOT, ​CME, NYMEX, COMEX, and ICEUS. A detailed description of the COT report can be found on the CFTC's website . COT data is separated into three notable reports: Legacy, Disaggregated, and Financial. This indicator presents data from the COT Financial (Traders in Financial Futures) report. The Financial report includes financial contracts, such as currencies, US Treasury securities, Eurodollars, stocks, VIX and Bloomberg commodity index. As such, the TFF data is limited to financial-related tickers. The TFF report breaks down the reportable open interest positions into four classifications: Dealer/Intermediary, Asset Manager/Institutional, Leveraged Funds, and Other Reportables. Our other COT indicators are:  • Commitment of Traders: Legacy Metrics  • Commitment of Traders: Disaggregated Metrics  • Commitment of Traders: Total █  HOW TO USE IT Load the indicator on an active chart (see here if you don't know how). By default, the indicator uses the chart's symbol to derive the COT data it displays. You can also specify a CFTC code in the "CFTC code" field of the script's inputs to display COT data from a symbol different than the chart's. The rest of this section documents the script's input fields. Metric Each metric represents a different column of the Commitment of Traders report. Details are available in the explanatory notes on the CFTC's website . Here is a summary of the metrics:  • "Open Interest" is the total of all futures and/or option contracts entered into and not yet offset by a transaction, by delivery, by exercise, etc.   The aggregate of all long open interest is equal to the aggregate of all short open interest.  • "Traders Total" is the number of all unique reportable traders, regardless of the trading direction.  • "Traders Dealer" is the number of traders classified as a "Dealer/Intermediary" reported holding any position with the specified direction.   A "producer/merchant/processor/user" is an entity typically described as the “sell side” of the market.   Though they may not predominately sell futures, they do design and sell various financial assets to clients.   They tend to have matched books or offset their risk across markets and clients.   Futures contracts are part of the pricing and balancing of risk associated with the products they sell and their activities.  • "Traders Asset Manager" is the number of traders classified as "Asset Manager/Institutional" reported holding any position with the specified direction.   These are institutional investors, including pension funds, endowments, insurance companies,   mutual funds and those portfolio/investment managers whose clients are predominantly institutional.  • "Traders Leveraged Funds" is the number of traders classified as "Leveraged Funds" reported holding any position with the specified direction.   These are typically hedge funds and various types of money managers. The traders may be engaged in managing and   conducting proprietary futures trading and trading on behalf of speculative clients.  • "Traders Other Reportable" is the number of reportable traders that are not placed in any of the three categories specified above.   The traders in this category mostly are using markets to hedge business risk, whether that risk is related to foreign exchange, equities or interest rates.   This category includes corporate treasuries, central banks, smaller banks, mortgage originators, credit unions and any other reportable traders not assigned to the other three categories.  • "Traders Total Reportable" is the number of all traders reported holding any position with the specified direction.   To determine the total number of reportable traders in a market, a trader is counted only once whether or not the trader appears in more than one category.   As a result, the sum of the numbers of traders in each separate category typically exceeds the total number of reportable traders.  • "Dealer/Asset Manager/Leveraged Funds/Total Reportable/Other Reportable Positions -- all positions held by the traders of the specified category.  • "Nonreportable Positions" is the long and short open interest derived by subtracting the total long and short reportable positions from the total open interest.   Accordingly, the number of traders involved and the commercial/non-commercial classification of each trader are unknown.  • "Concentration Gross/Net LT 4/8 TDR" is the percentage of open interest held by 4/8 of the largest traders, by gross/net positions,   without regard to whether they are classified as commercial or non-commercial. The Net position ratios are computed after offsetting each trader’s equal long and short positions.   A reportable trader with relatively large, balanced long and short positions in a single market, therefore,   may be among the four and eight largest traders in both the gross long and gross short categories, but will probably not be included among the four and eight largest traders on a net basis. Direction Each metric is available for a particular set of directions. Valid directions for each metric are specified with its name in the "Metric" field's dropdown menu. COT Selection Mode This field's value determines how the script determines which COT data to return from the chart's symbol: - "Root" uses the root of a futures symbol ("ES" for "ESH2020"). - "Base currency" uses the base currency in a forex pair ("EUR" for "EURUSD"). - "Currency" uses the quote currency, i.e., the currency the symbol is traded in ("JPY" for "TSE:9984" or "USDJPY"). - "Auto" tries all modes, in turn. If no COT data can be found, a runtime error is generated. Note that if the "CTFC Code" input field contains a code, it will override this input. Futures/Options Specifies the type of Commitment of Traders data to display: data concerning only Futures, only Options, or both. CTFC Code Instead of letting the script generate the CFTC COT code from the chart and the "COT Selection Mode" input when this field is empty, you can specify an unrelated CFTC COT code here, e.g., 001602 for wheat futures. Look first. Then leap.
Indicateur Pine Script®
par TradingView
6
LB | SB | OH | OL (Auto Futures OI)This indicator is for trading purposes, particularly in futures markets given the inclusion of open interest (OI) data. Indicator Name and Overlay: The indicator is named "LB | SB | OH | OL" and is set to overlay on the price chart (overlay=true). Override Symbol Input: Users can input a symbol to override the default symbol for analysis. Open Interest Data Retrieval: It retrieves open interest data for the specified symbol and time frame. If no data is found, it generates a runtime error. Dashboard Configuration: Users can choose to display a dashboard either at the top right, bottom right, or bottom left of the chart. Calculations: It calculates the percentage change in open interest (oi_change). It calculates the percentage change in price compared to the previous day's close (price_change). Build Up Conditions: Long Build Up: When there's a significant increase in open interest (OIChange threshold) and price rises (PriceChange threshold). Short Build Up: When there's a significant increase in open interest (OIChange threshold) and price falls (PriceChange threshold). Display Table: It creates a table on the chart showing the build-up conditions, open interest change percentage, and price change percentage. Labeling: It allows for the labeling of buy and sell conditions based on price movements. Overall, this indicator provides a visual representation of open interest and price movements, helping traders identify potential trading opportunities based on build-up conditions and price behavior. The "LB | SB | OH | OL" indicator is a tool designed to assist traders in analyzing price movements and open interest (OI) changes in FNO markets. This indicator combines various elements to provide insights into long build-up (LB), short build-up (SB), open-high (OH), and open-low (OL) scenarios. Key features of the indicator include: Override Symbol Input: Traders can override the default symbol and input their preferred symbol for analysis. Open Interest Data: The indicator retrieves open interest data for the selected symbol and time frame, facilitating analysis based on changes in open interest. Dashboard: The indicator features a customizable dashboard that displays key information such as build-up conditions, OI change, and price change. Build-Up Conditions: The indicator identifies long build-up and short build-up scenarios based on user-defined thresholds for OI change and price change percentages. Customization Options: Traders have the flexibility to customize various aspects of the indicator, including colors for long build-up, short build-up, positive OI change, negative OI change, positive price change, and negative price change. Label Plots: Buy and sell labels are plotted on the chart to highlight potential trading opportunities. Traders can customize the colors and text colors of these labels based on their preferences. Overall, the "LB | SB | OH | OL" indicator offers traders a comprehensive tool for analyzing price movements and open interest changes, helping them make informed trading decisions in the FNO markets.
Indicateur Pine Script®
par vighnesh15b
Currency Futures StatisticsThe "Currency Futures Statistics" indicator provides comprehensive insights into the performance and characteristics of various currency futures. This indicator is crucial for portfolio management as it combines multiple metrics that are instrumental in evaluating currency futures' risk and return profiles. Metrics Included: Historical Volatility: Definition: Historical volatility measures the standard deviation of returns over a specified period, scaled to an annual basis. Importance: High volatility indicates greater price fluctuations, which translates to higher risk. Investors and portfolio managers use volatility to gauge the stability of a currency future and to make informed decisions about risk management and position sizing (Hull, J. C. (2017). Options, Futures, and Other Derivatives). Open Interest: Definition: Open interest represents the total number of outstanding futures contracts that are held by market participants. Importance: High open interest often signifies liquidity in the market, meaning that entering and exiting positions is less likely to impact the price significantly. It also reflects market sentiment and the degree of participation in the futures market (Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities). Year-over-Year (YoY) Performance: Definition: YoY performance calculates the percentage change in the futures contract's price compared to the same week from the previous year. Importance: This metric provides insight into the long-term trend and relative performance of a currency future. Positive YoY performance suggests strengthening trends, while negative values indicate weakening trends (Fama, E. F. (1991). Efficient Capital Markets: II). 200-Day Simple Moving Average (SMA) Position: Definition: This metric indicates whether the current price of the currency future is above or below its 200-day simple moving average. Importance: The 200-day SMA is a widely used trend indicator. If the price is above the SMA, it suggests a bullish trend, while being below indicates a bearish trend. This information is vital for trend-following strategies and can help in making buy or sell decisions (Bollinger, J. (2001). Bollinger on Bollinger Bands). Why These Metrics are Important for Portfolio Management: Risk Assessment: Historical volatility and open interest provide essential information for assessing the risk associated with currency futures. Understanding the volatility helps in estimating potential price swings, which is crucial for managing risk and setting appropriate stop-loss levels. Liquidity and Market Participation: Open interest is a critical indicator of market liquidity. Higher open interest usually means tighter bid-ask spreads and better liquidity, which facilitates smoother trading and better execution of trades. Trend Analysis: YoY performance and the SMA position help in analyzing long-term trends. This analysis is crucial for making strategic investment decisions and adjusting the portfolio based on changing market conditions. Informed Decision-Making: Combining these metrics allows for a holistic view of the currency futures market. This comprehensive view helps in making informed decisions, balancing risks and returns, and optimizing the portfolio to align with investment goals. In summary, the "Currency Futures Statistics" indicator equips investors and portfolio managers with valuable data points that are essential for effective risk management, liquidity assessment, trend analysis, and overall portfolio optimization.
Indicateur Pine Script®
par EdgeTools
Commitment of Traders: Legacy Metrics█  OVERVIEW This indicator displays the Commitment of Traders (​COT) legacy data for futures markets. █  CONCEPTS Commitment of Traders ​(​COT) data is tallied by the Commodity ​Futures Trading Commission (CFTC) , a US federal agency that oversees the trading of derivative markets such as futures in the US. It is weekly data that provides traders with information about open interest for an asset. The CFTC oversees derivative markets traded on different exchanges, so ​COT data is available for assets that can be traded on CBOT, ​CME, NYMEX, COMEX, and ICEUS. A detailed description of the COT report can be found on the CFTC's website . COT data is separated into three notable reports: Legacy, Disaggregated, and Financial. This indicator presents data from the legacy report, which is broken down by exchange. Legacy reports break down the reportable open interest positions into two classifications: non-commercial and commercial traders. Our other COT indicators are:  • Commitment of Traders: Disaggregated Metrics  • Commitment of Traders: Financial Metrics  • Commitment of Traders: Total █  HOW TO USE IT Load the indicator on an active chart (see here if you don't know how). By default, the indicator uses the chart's symbol to derive the COT data it displays. You can also specify a CFTC code in the "CFTC code" field of the script's inputs to display COT data from a symbol different than the chart's. The rest of this section documents the script's input fields. Metric Each metric represents a different column of the Commitment of Traders report. Details are available in the explanatory notes on the CFTC's website . Here is a summary of the metrics:  • "Open Interest" is the total of all futures and/or option contracts entered into and not yet offset by a transaction, by delivery, by exercise, etc.   The aggregate of all long open interest is equal to the aggregate of all short open interest.  • "Traders Total" is the number of all unique reportable traders, regardless of the trading direction.  • "Traders Total Reportable/Traders Noncommercial/Traders Commercial" are the quantities of traders reported to hold any position with the specified direction.   All of a trader's reported futures positions in a commodity are classified as commercial if the trader uses futures contracts in that particular commodity for hedging.   To determine the total number of reportable traders in a market, a trader is counted only once, whether or not the trader appears in more than one category.  • "Total Reportable/Noncommercial/Commercial Positions" are all positions held by all reportable/non-commercial/commercial traders.  • "Non-reportable Positions" is derived by subtracting total long and short "Reportable Positions" from the total open interest.   Accordingly, the number of traders involved and the commercial/non-commercial classification of each trader are unknown.  • "Concentration Gross/Net LT 4/8 TDR" is the percentage of open interest held by 4/8 of the largest traders, by gross/net positions,   without regard to whether they are classified as commercial or non-commercial. The Net position ratios are computed after offsetting each trader’s equal long and short positions.   A reportable trader with relatively large, balanced long and short positions in a single market, therefore,   may be among the four and eight largest traders in both the gross long and gross short categories, but will probably not be included among the four and eight largest traders on a net basis. Direction Each metric is available for a particular set of directions. Valid directions for each metric are specified with its name in the "Metric" field's dropdown menu. Type Possible values are: All, Old, Other. When commodities have a well-defined marketing season or crop year (e.g. Wheat or Lean Hogs futures), this determines how the data is aggregated. Detailed explanation can be found in the "Old and Other Futures" section of the CTFC Explanatory Notes linked above. The "Major Markets for Which the COT Data Is Shown by Crop Year" table in the Explanatory Notes specifies the commodities that this distinction applies to; selecting "Old" for any of the commodities not in that list will return the same data as in "All", while selecting "Other" will return 0. COT Selection Mode This field's value determines how the script determines which COT data to return from the chart's symbol: - "Root" uses the root of a futures symbol ("ES" for "ESH2020"). - "Base currency" uses the base currency in a forex pair ("EUR" for "EURUSD"). - "Currency" uses the quote currency, i.e., the currency the symbol is traded in ("JPY" for "TSE:9984" or "USDJPY"). - "Auto" tries all modes, in turn. If no COT data can be found, a runtime error is generated. Note that if the "CTFC Code" input field contains a code, it will override this input. Futures/Options Specifies the type of Commitment of Traders data to display: data concerning only Futures, only Options, or both. CTFC Code Instead of letting the script generate the CFTC COT code from the chart and the "COT Selection Mode" input when this field is empty, you can specify an unrelated CFTC COT code here, e.g., 001602 for wheat futures. Look first. Then leap.
Indicateur Pine Script®
par TradingView
36
Commitments of Traders Report [Advanced]This indicator displays the Commitment of Traders (COT) report data in a clear, table format similar to an Excel spreadsheet, with additional functionalities to analyze open interest and position changes. The COT report, published weekly by the Commodity Futures Trading Commission (CFTC), provides valuable insights into market sentiment by revealing the positioning of various trader categories. Display: Release Date: When the data was released. Open Interest: Shows the total number of open contracts for the underlying instrument held by selected trader category. Net Contracts: Shows the difference between long and short positions for selected trader category. Long/Short OI: Displays the long and short positions held by selected trader category. Change in Long/Short OI: Displays the change in long and short positions since the previous reporting period. This can highlight buying or selling pressure. Long & Short Percentage: Displays the percentage of total long and short positions held by each category. Trader Categories (Configurable) Commercials: Hedgers who use futures contracts to manage risk associated with their underlying business (e.g., producers, consumers). Non-Commercials (Large Speculators): Speculative traders with large positions who aim to profit from price movements (e.g., hedge funds, investment banks). Non-Reportable (Small Speculators/Retail Traders): Smaller traders with positions below the CFTC reporting thresholds. CFTC Code: If the indicator fails to retrieve data, you can manually enter the CFTC code for the specific instrument. The code for instrument can be found on CFTC's website. Using the Indicator Effectively Market Sentiment Gauge: Analyze the positioning of each trader category to gauge overall market sentiment. High net longs by commercials might indicate a bullish outlook, while high net shorts could suggest bearish sentiment. Changes in open interest and long/short positions can provide additional insights into buying and selling pressure. Trend Confirmation: Don't rely solely on COT data for trade signals. Use it alongside price action and other technical indicators for confirmation. Identify Potential Turning Points: Extreme readings in COT data, combined with significant changes in open interest or positioning, might precede trend reversals, but exercise caution and combine with other analysis tools. Disclaimer Remember, the COT report is just one piece of the puzzle. It should not be used for making isolated trading decisions. Consider incorporating it into a comprehensive trading strategy that factors in other technical and fundamental analysis. Credit A big shoutout to Nick from Transparent FX ! His expertise and thoughtful analysis have been a major inspiration in developing this COT Report indicator. To know more about this indicator and how to use it, be sure to check out his work.
Indicateur Pine Script®
par a_guy_from_wall_street
11
Info TableOverview The Info Table V1 is a versatile TradingView indicator tailored for intraday futures traders, particularly those focusing on MESM2 (Micro E-mini S&P 500 futures) on 1-minute charts. It presents essential market insights through two customizable tables: the Main Table for predictive and macro metrics, and the New Metrics Table for momentum and volatility indicators. Designed for high-activity sessions like 9:30 AM–11:00 AM CDT, this tool helps traders assess price alignment, sentiment, and risk in real-time. Metrics update dynamically (except weekly COT data), with optional alerts for key conditions like volatility spikes or momentum shifts. This indicator builds on foundational concepts like linear regression for predictions and adapts open-source elements for enhanced functionality. Gradient code is adapted from TradingView's Color Library. QQE logic is adapted from LuxAlgo's QQE Weighted Oscillator, licensed under CC BY-NC-SA 4.0. The script is released under the Mozilla Public License 2.0. Key Features Two Customizable Tables: Positioned independently (e.g., top-right for Main, bottom-right for New Metrics) with toggle options to show/hide for a clutter-free chart. Gradient Coloring: User-defined high/low colors (default green/red) for quick visual interpretation of extremes, such as overbought/oversold or high volatility. Arrows for Directional Bias: In the New Metrics Table, up (↑) or down (↓) arrows appear in value cells based on metric thresholds (top/bottom 25% of range), indicating bullish/high or bearish/low conditions. Consensus Highlighting: The New Metrics Table's title cells ("Metric" and "Value") turn green if all arrows are ↑ (strong bullish consensus), red if all are ↓ (strong bearish consensus), or gray otherwise. Predicted Price Plot: Optional line (default blue) overlaying the ML-predicted price for visual comparison with actual price action. Alerts: Notifications for high/low Frahm Volatility (≥8 or ≤3) and QQE Bias crosses (bullish/bearish momentum shifts). Main Table Metrics This table focuses on predictive, positional, and macro insights: ML-Predicted Price: A linear regression forecast using normalized price, volume, and RSI over a customizable lookback (default 500 bars). Gradient scales from low (red) to high (green) relative to the current price ± threshold (default 100 points). Deviation %: Percentage difference between current price and predicted price. Gradient highlights extremes (±0.5% default threshold), signaling potential overextensions. VWAP Deviation %: Percentage difference from Volume Weighted Average Price (VWAP). Gradient indicates if price is above (green) or below (red) fair value (±0.5% default). FRED UNRATE % Change: Percentage change in U.S. unemployment rate (via FRED data). Cell turns red for increases (economic weakness), green for decreases (strength), gray if zero or disabled. Open Interest: Total open MESM2 futures contracts. Gradient scales from low (red) to high (green) up to a hardcoded 300,000 threshold, reflecting market participation. COT Commercial Long/Short: Weekly Commitment of Traders data for commercial positions. Long cell green if longs > shorts (bullish institutional sentiment); Short cell red if shorts > longs (bearish); gray otherwise. New Metrics Table Metrics This table emphasizes technical momentum and volatility, with arrows for quick bias assessment: QQE Bias: Smoothed RSI vs. trailing stop (default length 14, factor 4.236, smooth 5). Green for bullish (RSI > stop, ↑ arrow), red for bearish (RSI < stop, ↓ arrow), gray for neutral. RSI: Relative Strength Index (default period 14). Gradient from oversold (red, <30 + threshold offset, ↓ arrow if ≤40) to overbought (green, >70 - offset, ↑ arrow if ≥60). ATR Volatility: Score (1–20) based on Average True Range (default period 14, lookback 50). High scores (green, ↑ if ≥15) signal swings; low (red, ↓ if ≤5) indicate calm. ADX Trend: Average Directional Index (default period 14). Gradient from weak (red, ↓ if ≤0.25×25 threshold) to strong trends (green, ↑ if ≥0.75×25). Volume Momentum: Score (1–20) comparing current to historical volume (lookback 50). High (green, ↑ if ≥15) suggests pressure; low (red, ↓ if ≤5) implies weakness. Frahm Volatility: Score (1–20) from true range over a window (default 24 hours, multiplier 9). Dynamic gradient (green/red/yellow); ↑ if ≥7.5, ↓ if ≤2.5. Frahm Avg Candle (Ticks): Average candle size in ticks over the window. Blue gradient (or dynamic green/red/yellow); ↑ if ≥0.75 percentile, ↓ if ≤0.25. Arrows trigger on metric-specific logic (e.g., RSI ≥60 for ↑), providing directional cues without strict color ties. Customization Options Adapt the indicator to your strategy: ML Inputs: Lookback (10–5000 bars) and RSI period (2+) for prediction sensitivity—shorter for volatility, longer for trends. Timeframes: Individual per metric (e.g., 1H for QQE Bias to match higher frames; blank for chart timeframe). Thresholds: Adjust gradients and arrows (e.g., Deviation 0.1–5%, ADX 0–100, RSI overbought/oversold). QQE Settings: Length, factor, and smooth for fine-tuned momentum. Data Toggles: Enable/disable FRED, Open Interest, COT for focus (e.g., disable macro for pure intraday). Frahm Options: Window hours (1+), scale multiplier (1–10), dynamic colors for avg candle. Plot/Table: Line color, positions, gradients, and visibility. Ideal Use Case Perfect for MESM2 scalpers and trend traders. Use the Main Table for entry confirmation via predicted deviations and institutional positioning. Leverage the New Metrics Table arrows for short-term signals—enter bullish on green consensus (all ↑), avoid chop on low volatility. Set alerts to catch shifts without constant monitoring. Why It's Valuable Info Table V1 consolidates diverse metrics into actionable visuals, answering critical questions: Is price mispriced? Is momentum aligning? Is volatility manageable? With real-time updates, consensus highlights, and extensive customization, it enhances precision in fast markets, reducing guesswork for confident trades. Note: Optimized for futures; some metrics (OI, COT) unavailable on non-futures symbols. Test on demo accounts. No financial advice—use at your own risk. The provided script reuses open-source elements from TradingView's Color Library and LuxAlgo's QQE Weighted Oscillator, as noted in the script comments and description. Credits are appropriately given in both the description and code comments, satisfying the requirement for attribution. Regarding significant improvements and proportion: The QQE logic comprises approximately 15 lines of code in a script exceeding 400 lines, representing a small proportion (<5%). Adaptations include integration with multi-timeframe support via request.security, user-customizable inputs for length, factor, and smooth, and application within a broader table-based indicator for momentum bias display (with color gradients, arrows, and alerts). This extends the original QQE beyond standalone oscillator use, incorporating it as one of seven metrics in the New Metrics Table for confluence analysis (e.g., consensus highlighting when all metrics align). These are functional enhancements, not mere stylistic or variable changes. The Color Library usage is via official import (import TradingView/Color/1 as Color), leveraging built-in gradient functions without copying code, and applied to enhance visual interpretation across multiple metrics. The script complies with the rules: reused code is minimal, significantly improved through integration and expansion, and properly credited. It qualifies for open-source publication under the Mozilla Public License 2.0, as stated.
Indicateur Pine Script®
par charleyRT
TASC 2023.10 COT Commercials Indicator█ OVERVIEW This script implements the COT Commercials Indicator introduced by Alfred François Tagher in an article featured in TASC's October 2023 edition of Traders' Tips . The indicator is designed for use in futures markets and represents a fast stochastic (%K) calculated based on the commercial open interest values of an asset derived from the weekly Commitments Of Traders (COT) report . █ CONCEPTS The COT report, issued by the Commodity ​Futures Trading Commission (CFTC) , presents a breakdown of reportable open interest positions held by various trader groups—commercial, noncommercial, and nonreportable (small traders). Open interest reflects the total number of derivative contracts entered by market participants but not yet settled. Consequently, it can serve as a measure of market activity and liquidity. The indicator showcased here aims to analyze changes in the reported net values of open interest for commercial traders/hedgers (often referred to as 'smart money', as they deal directly in underlying commodities). The net values are positive when the commercial traders have more long positions than short ones and negative when they hold more short positions than long ones. Positive net values indicate that commercial traders hold more long positions than short ones, while negative values indicate the opposite. Thus, overbought and oversold conditions of the COT Commercials Indicator potentially suggest collective bullish and bearish sentiments, respectively. █ CALCULATIONS The calculations involve these steps: 1. Net open interest values are extracted from COT data using the LibraryCOT library provided by TradingView. 2. A fast stochastic indicator (%K) is then applied to normalize these net values. The script also provides an option of calculating and plotting the indicator curve for noncommercial (speculators) open interest.
Indicateur Pine Script®
par PineCodersTASC
4
Herrick Payoff Index @shrilssThis indicator combines elements of price action, volume, and open interest to provide insights into market strength and potential trend reversals. This script calculates the Herrick Payoff Index (HPI) based on a modified formula that incorporates volume and open interest adjustments. The HPI is derived from comparing the current day's mean price to the previous day's mean price, factoring in volume and open interest changes. By analyzing these factors, the indicator aims to gauge the effectiveness of market participants' positions. Key Features: - HPI Calculation: The HPI value is calculated using the formula: ((M - My) * C * V) * (1 + |OI - OI | / min(OI, OI )), where M represents the mean price for the current day, My represents the mean price for the previous day, C is a constant (set to 1), V is the volume, and OI is the open interest. This adjusted calculation accounts for changes in volume and open interest, providing a more nuanced view of market dynamics. - Moving Averages: The script also includes two Exponential Moving Averages (EMAs) of the HPI values, allowing traders to identify trends and potential reversal points. Users can customize the length of these moving averages to suit their trading strategies. - Visual Signals: The indicator visually represents the HPI values and their relationship to the moving averages. When the HPI value is above the shorter-term EMA, it suggests bullish momentum, while values below indicate bearish sentiment.
Indicateur Pine Script®
par shrilss
2
Biddles OIWAP-Price SpreadThis indicator is the companion to my OIWAP (Open Interested-Weighted Average price) open source indicator. In observing the OIWAP, what seemed most interesting was the distance between price and OIWAP. This indicator plots that spread in a histogram. It seems when price is too high above all OIWAPs, it's locally overbought (sentiment is overly bullish), and vice versa when it's too far below all OIWAPs (sentiment is overly bearish). But I think there are more unique observations to be made beyond that - I am still in discovery phase myself. For example: Looking at the SPX while using the ticker override to display BINANCE:BTCUSDT.P OI-Price spread data. It works on any asset that Tradingview has OI data for. But it's also interesting to view correlated assets by using ticker override in the indicator settings (open the correlated asset w/o OI data in your chart, then set ticker override to a symbol with OI data, like the SPX example above). >> If you find any interesting observations using it, have suggestions for improving the script, etc., hit me up on Twitter! >>> @thalamu_
Indicateur Pine Script®
par b_biddles
Biddles OI Weighted Average PriceAhoy! This script calculates Open Interested Weighted Average Price for the following lookback periods: - 7, 30, 60 e.g. On the 1D chart, you will see OIWAP for the past 7, 30, and 60 days. It works on any timeframe though. It works with any ticker that TV's OI indicator supports, and has ticker override if you are looking at an exchange that's unsupported, but for an asset that is. e.g. If you're looking at Bybit's BTCUSDT.P which is unsupported- you can override to get OI data from Binance's BTCUSDT.P which is supported. ===== Open-Sourced + Crowd Sourcing Goals ===== I am open sourcing this in hopes we can work together to find interesting signal/observation, and make the script better. The only way I could think of to calculate the OIWAP for the lookback periods was to manually factor in each period in the formula. e.g. For the 60-period lookback, it's manually taking price and OI for each individual period. I am also hoping other folks will make interesting observations. With the few hours I've spent thus far, they seem to operate much like MA bands, with crossovers having similar implications. But I feel like there are many other observations left unnoticed! If you find any, hmu on twitter: @thalamu_ ===== Interesting Calculations in the Script, but not Plotted on the Chart ===== There are calculations for up to 60 days of OIWAP taking change in OI rather than just OI. There's one set for absolute value of change in OI, and one set for raw change in OI. I didn't notice anything spectacularly interesting - but perhaps you will if you tinker with it! ===== Find something cool? Have an improvement? ===== Hmu on twitter: @thalamu_
Indicateur Pine Script®
par b_biddles
5
MS POIVThis indicator was introduced by Larry Williams in 2007 and is very similar to the well known OBV indicator. As such, it should be examined for convergence and divergence with the price trend. The interpretation can be done using the Wyckoff principles. * Price rises, POIV stays behind => no subsequent demand * Price meets resistance, POIV reaches new highs => supply (distribution) in the background * Price and POIV rise synchronously => price trend is intact These statements can of course be applied correspondingly to falling prices. Larry Williams wrote for explanation: Despite the problem, volume indictors have proven their worth, but while it is a good idea to watch the cumulative flow of buying and selling pressure, you should not assign all of this buying and selling to bulls and bears. Combined with other concepts, such as keying off the open, we can focus on something more germane to trading based just on volume, or what some might consider related volatility indicators, such as daily ranges. Futures traders can consider at least one solution to this problem: open interest. Open interest is the number of outstanding contracts in a particular market. (...)) The formula is calculating the cumulative sum of open interest times the net change in price, divided by the true range. We then add the OBV value to this cumulative sum. So we first take the net change in price (today’s close minus yesterday’s close) to get a percentage of where within the range the close was. Not all of the activity will be buying or selling; the market “tells” us what percentage of open interest goes to the buy or sell side. Not only that, it also means we are incorporating price and trend change into the formula. (...) One note of warning is necessary. The Williams POIV AD is a specific formula that compensates for the close within the range relationship, as well telling us how much OI to use, but it is an indicator, not a trading system. In practice, it is useful to confirm a trade or to focus attention on a potential trade. It is not intended to stand as the sole reason to initiate a position in the market.
Indicateur Pine Script®
par waynem59
14
Interest Rate and GDP Dashboard by toodegreesDescription: The Interest Rate and GDP Dashboard is a powerful tool designed to provide traders with valuable insights into Interest Rate and Gross Domestic Product (GDP) of the largest Central Banks. Interest Rates are closely monitored from all around the world, and play a massive role in Interbank Institutional Trading. Although mainly used by Forex traders, it's important for all types of analysts to understand risk-on and risk-off environments in respective currencies, or other asset classes, based on a global financial landscape. Forex Pair Dashboard ( FOREXCOM:EURUSD ): Non-Forex Pair Dashboard ( CME_MINI:ES1! ): This tool displays the Live Interest Rates (as well as latest Interest Rate Change) and GDP, of the following countries/regions: Australia Canada Europe Japan New Zealand Switzerland United Kingdom United States Further, analysts will be able to see Interest Rate Change labels directly on chart, to monitor Time and price relationship following rate hikes or rate cuts. The labels will display according to the impact of the Interest Rate Change on the current asset on chart, and their tooltips will display the %Change: Analysts can also choose to mark Interest Rate Changes with vertical lines, to aid in marking changes in sentiment or global financial environment: The real power and value provided by this tool is its tailored Interest Rate (and GDP) Differential feature for Forex markets, based on the Interest Rate Differential concept as taught by the Inner Circle Trader (ICT). Using Interest Rate Differentials as a further Long Term Bias factor was introduced by ICT in conjunction with other higher Timeframe principles like Seasonal Tendency, Commitment of Traders, and Open Interest. This fusion ensures a holistic approach to dissecting specific Forex pairs, and the involvement of Institutional traders. Key Features: Dynamically calculates and organizes the dashboard to display the interest rate differential of the chart's forex pair, or displays all if outside of forex markets. Pinpoint historical interest rate changes with precision using vertical lines and/or dynamic labels with tooltips. Other Features: Toggle Options: Customize your viewing experience by toggling the display of previous rate changes, enabling or disabling GDP visibility, and tailoring the size and location of the dashboard. Fine-tune Visuals: Adjust the size and style of the previous interest rate labels and lines to suit your preferences, offering a personalized touch to your analytical workspace. Usage Guidance: Add the Interest Rate and GDP Dashboard to your Tradingview chart. Tailor your experience by customizing the table and style to be in line with your analytical preferences, ensuring a visually engaging and personalized chart. Observe where and when key Interest Rate decisions impact the macro trend or market environment. Leverage this invaluable information to shape your Higher Timeframe narrative in confluence with other tools.
Indicateur Pine Script®
par toodegrees
8