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.
Analisis Fundamental
Yield Curve RegimesCurrently we are seeing equities and all other risk assets rallying to new all time high. But when will this stop?
There are multiple risks/signals i am monitoring to stay at the right side of the macro trade. Macro is everything: “When you get the Big-Picture wrong you wont live long.”
So lets go through a major risk that could be the catalyst for the next deeper correction
Capital needs to begin to move BACK across the risk curve as the yield curve steepens. We don't know if the source of the the crash will be from bear steepening or bull steepening because its unclear if long end rates blowing out will be the source of the crash.
If the Fed continues to make the policy error of being overly accommodative at this high level of nominal GDP + Inflation risk, the long end of the curve will price this.
Simple: If the Fed is to lose the long end can move up to price the inflation risk, which could ultimately pull risk assets down.
We have not seen this yet because the last inflation prints came in flat, but I expect these to come in higher over the next 6 months.
This means watching long end rates and their potential drag on equities will be critical. We are not seeing this yet as the Russell is sitting at all time highs and capital continues to move into low quality factors.
Look where the long end is moving + the attribution analysis for the move.
→ Down growth risk
→ Up Inflation risk
+ look what the 2s10s & the 10s30 are pricing and how these changes in the curve connect to the current yield curve regimes.
You can get the Trading view Skript 100% free here
FCBI Brake PressureBrake Pressure (FCBI − USIRYY)
Concept
The Brake Pressure indicator quantifies whether the bond market is braking or releasing liquidity relative to real yields (USIRYY).
It is derived from the Financial-Conditions Brake Index (FCBI) and expresses the balance between long-term yield pressure and real-rate dynamics.
Formula
Brake Pressure = FCBI − USIRYY
where FCBI = (US10Y) − (USINTR) − (CPI YoY)
Purpose
While FCBI measures the intensity of financial-condition pressure, Brake Pressure shows when that brake is being applied or released.
It captures the turning point of liquidity transmission in the financial system.
How to Read
Brake Pressure < 0 (orange) → Brake engaged → financial conditions tighter than real-rate baseline; liquidity constrained.
Brake Pressure ≈ 0 → Neutral zone → transition phase between tightening and easing.
Brake Pressure > 0 (teal) → Brake released → financial conditions looser than real-rate baseline; liquidity flows freely → late-cycle setup before recession.
Zero-Cross Logic
Cross ↑ above 0 → FCBI > USIRYY → brake released → liquidity acceleration → typically 6–18 months before recession.
Cross ↓ below 0 → FCBI < USIRYY → brake re-engaged → tightening resumes.
Historical Behavior
Each major U.S. recession (2001, 2008, 2020) was preceded by a Brake Pressure cross above zero after a negative phase, signaling that long yields had stopped resisting Fed cuts and liquidity was expanding.
Practical Use
• Identify late-cycle turning points and liquidity inflection phases.
• Combine with FCBI for a complete macro transmission picture.
• Watch for sustained positive readings as early macro-recession warnings.
Current Example (Oct 2025)
FCBI ≈ −3.1, USIRYY ≈ +3.0 → Brake Pressure ≈ −6.1 → Brake still engaged. When this crosses above 0, it signals that liquidity is free flowing and the recession countdown has begun.
Summary
FCBI shows how tight the brake is. Brake Pressure shows when the brake releases.
When Brake Pressure > 0, the system has entered the liquidity-expansion phase that historically precedes a U.S. recession.
Financial-Conditions Brake Index (FCBI) — US10Y brake on USIRYYFinancial-Conditions Brake Index (FCBI) – US10Y Brake on USIRYY
Concept
The Financial-Conditions Brake Index (FCBI) measures how U.S. long-term yields (US10Y) interact with the Federal Funds Rate (USINTR) and inflation (CPI YoY) to shape real-rate conditions (USIRYY).
It visualizes whether the bond market is tightening or loosening overall financial conditions relative to the Federal Reserve’s policy stance.
Formula
FCBI = (US10Y) − (USINTR) − (CPI YoY)
How It Works
The FCBI expresses the difference between the long-term yield curve and short-term policy rates, adjusted for inflation. It shows whether the long end of the curve is amplifying or counteracting the Fed’s stance.
FCBI > +2 → Strong brake → Long yields remain elevated despite easing → tight conditions → recession delayed.
FCBI +1 to +2 → Mild brake → Financial transmission slower; lag ≈ 12–18 months.
FCBI 0 to +1 → Neutral → Typical early post-cut environment.
FCBI < 0 → Accelerator → Long yields and inflation expectations falling → liquidity flows freely → recession often follows within 6–14 months.
How to Read the Chart
Blue line (FCBI) shows the strength of the financial brake.
Red line (USIRYY) represents the real yield baseline.
Recession shading (gray) marks NBER recessions for comparison.
FCBI < USIRYY → Brake engaged → financial conditions tighter than real-rate baseline.
FCBI > USIRYY → Brake released → long end easing faster than policy → liquidity surge → late-cycle setup.
Historically, U.S. recessions begin on average about 14 months after the first Fed rate cut, and a decline of the FCBI below zero often precedes that window.
Practical Use
Use the FCBI to identify when policy transmission is blocked (brake engaged) or flowing (brake released).
Cross-check with yield-curve inversions, Fed policy shifts, and inflation expectations to estimate macro timing windows.
Current Example (Oct 2025)
FCBI ≈ −3.1, USIRYY ≈ +3.0 → Brake still engaged.
Once FCBI rises above USIRYY and crosses positive, it signals the “brake released” phase — historically the final liquidity surge before a U.S. recession.
Summary
FCBI shows how tight the brake is.
USIRYY shows how fast the car is moving.
When FCBI rises above USIRYY, the brake is released — liquidity accelerates and the historical recession countdown begins.
10Y–2Y Treasury Yield Curve Spread & MES % Change📝 Description:
This indicator tracks the U.S. 10-Year minus 2-Year Treasury yield spread — a powerful macroeconomic signal often used by professional traders to gauge market sentiment and recession risk — and overlays an optional MES % change line to help intraday futures traders spot macro–price divergences in real time.
Features:
🏦 Plots the 10Y–2Y spread, with optional EMA smoothing.
📉 Highlights yield curve inversion (background turns red when spread < 0).
📊 Optional MES % change line from daily or RTH open for directional bias.
🔔 Alert conditions for:
Yield curve inversion / un-inversion.
Sudden spread spikes in basis points (customizable).
🧮 Optional correlation plot to visualize relationship strength between MES and the yield curve.
🧭 Z-score normalization allows both series to be viewed in one pane without scaling issues.
Why it matters:
A falling or inverted 2s10s spread often signals risk-off behavior and pressure on equities.
A steepening curve tends to support risk-on rallies.
Divergences between MES price action and the spread can provide early warning signals of reversals or fakeouts.
Best used with:
MES (MES1!) or MYM charts for intraday & swing bias.
Fed event days, CPI/NFP, or any macro-sensitive sessions.
VWAP or structure-based intraday trading strategies.
⚠️ Note: This indicator is for informational purposes only and does not constitute financial advice. Always combine macro context with your own trade plan and risk management.
Stock Fundamental Overlay [DarwinDarma]Stock Fundamental Overlay
Stock Fundamental Overlay is a comprehensive valuation indicator that displays multiple fundamental analysis metrics directly on your price chart.
Key Features:
• Graham Number - Benjamin Graham's intrinsic value formula
• Book Value Per Share (BVPS) - Net asset value baseline
• DCF Valuation - Discounted Cash Flow analysis (non-financial stocks)
• DDM Valuation - Dividend Discount Model (dividend-paying stocks)
• Visual Value Zones - Color-coded undervalued/overvalued regions
• Real-time Fundamental Table - Live metrics and valuations
• Price vs Graham Comparison - Quick valuation assessment
• Built-in Alerts - Notification when price crosses key levels
Valuation Models:
• Graham Number: √(22.5 × EPS × BVPS)
• DCF: Customizable discount rate, growth rate, and forecast period
• DDM: Gordon Growth Model for dividend analysis
Visual Elements:
• Plot lines for BVPS, Graham Number, and DCF values
• Shaded value zone between BVPS and Graham Number
• Background coloring: Deep value (below BVPS), Undervalued (below Graham), Overvalued (>1.5x Graham)
• Dynamic table showing all metrics with theme-aware text colors
Special Handling:
• Financial sector detection - DCF disabled for banks/financials where FCF metrics are distorted
• Automatic light/dark theme adaptation
• TTM (Trailing Twelve Months) data for current metrics
How to Use - Value Investing Approach:
1. Identifying Undervalued Stocks:
• Look for price trading BELOW the Graham Number (green zone) - potential value opportunity
• Deep value: Price below BVPS indicates trading below net asset value
• Check "Price vs Graham" % in table - negative values suggest undervaluation
• Compare multiple models: When price is below Graham, DCF, and BVPS simultaneously, stronger buy signal
2. Margin of Safety:
• Benjamin Graham recommended buying at 2/3 of intrinsic value (33% margin of safety)
• Monitor the gap between current price and valuation lines
• Larger gaps = greater margin of safety = lower downside risk
• Use the shaded "Value Zone" as your target buying range
3. Setting Alerts:
• "Price Below Graham Number" - Notifies when stock enters value territory
• "Price Below Book Value" - Extreme value alert for deep value hunters
• "Price Below DCF Value" - Cash flow-based value signal
• Set alerts on watchlist stocks to catch value opportunities
4. Customizing for Your Strategy:
• Conservative investors: Use lower growth rates (3-4%) and higher discount rates (12-15%)
• Growth-value investors: Adjust growth rate (6-8%) for quality compounders
• Dividend investors: Focus on DDM value and Div/Share metrics
• Adjust forecast years based on business predictability (stable = 10 years, cyclical = 5 years)
5. Red Flags to Avoid:
• Negative EPS or FCF (red values in table) - proceed with caution
• Financial sector stocks - Use DDM and Graham, ignore DCF
• Price far above Graham (>1.5x) with red background = overvalued territory
• No fundamental data = "N/A" in table - stock may lack reporting or be too small
• Stock persistently below BVPS for extended periods - potential value trap or business in distress
• Price significantly above ALL models (BVPS, Graham, DCF) - sentiment-driven, lacks intrinsic value foundation (fragile)
⚠️ Important Value Investing Warnings:
• Value Trap Alert: A stock staying below BVPS for months/years may signal fundamental deterioration, asset impairments, or dying industry - not just "cheap." Investigate WHY it's cheap before buying
• Sentiment Bubble Risk: When price trades far above BVPS, Graham Number, AND DCF simultaneously, the stock has no intrinsic value basis. Examples: commodity stocks during boom cycles (gold miners in gold rallies), meme stocks, hype-driven sectors. These are highly fragile and vulnerable to mean reversion
• Cyclical Trap: Commodity/cyclical stocks can appear "cheap" at peak earnings (low P/E, high FCF) but are actually expensive. Normalize earnings across the cycle before valuing
• Quality Matters: Some excellent businesses (asset-light, high ROIC) naturally trade above book value. Don't avoid quality - adjust expectations for business model
6. Monitoring Positions:
• Watch for price approaching or exceeding Graham Number - consider taking profits
• Track EPS and FCF trends quarter-to-quarter in the table
• If fundamentals deteriorate (falling BVPS, negative FCF), reassess thesis
• Use background colors for quick visual check: green = hold/buy, red = overvalued
Perfect for:
Value investors seeking multi-model fundamental analysis, long-term investors comparing intrinsic value to market price, dividend investors evaluating yield stocks, and fundamental traders looking for entry/exit signals.
Note: Only works with stocks that have financial data available. Not applicable to crypto, forex, or futures. This indicator provides analysis tools; always conduct thorough research and due diligence before investing.
Alerts v6The strategy includes:
✅ EMA-based trend direction (fast vs slow)
✅ RSI filtering for overbought/oversold control
✅ ADX confirmation for strong trend validation
✅ Pullback & BOS detection for precision entries
✅ Per-bar change logic for adaptive entry timing
✅ Session/day gating to control trading hours
✅ JSON alert integration for AI trading bots or webhooks
This script is Pine Script v6 compatible and optimized for automated alert-based trading setups such as AI trading bots, webhook systems, and VPS-linked executions.
Recommended Timeframes: 5m, 15m, 30m
Markets: XAUUSD, FX pairs, indices, and metals
Asia & London Session High/Low – EOD Segments (v4.5)What it does
Plots the Asia and London session high & low each day.
When a session ends, its high/low are locked (non-repainting) and drawn as horizontal segments that auto-extend to the end of that same day (no infinite rays).
Optional labels show the exact level at session close.
Toggle whether to keep prior days on the chart or auto-clear them on the first bar of a new day.
Why traders use it
Quickly see overnight liquidity levels that often act as magnets or barriers during the U.S. session.
Map session range extremes for breakout/reversal planning, partials, and invalidation.
Works great alongside VWAP, 8/20/200 MAs, or your NY session tools to build confluence.
How it works
You define the session windows (defaults: Asia 00:00–06:00, London 07:00–11:00).
While a session is active, the script tracks running high/low.
On the bar after the session ends, the level is finalized and drawn; the segment’s right edge updates each bar until EOD, then stops automatically.
Inputs
Session Timezone: “Exchange”, UTC, or a specific region (set this to match your venue).
Asia / London Session: editable HHMM-HHMM windows.
Show Asia / Show London: enable either/both sessions.
Keep history: keep or auto-delete previous days.
Show labels: price labels at session close.
Colors & width: customize high/low colors and line width.
Best practices
Use on intraday timeframes (1–60m).
For equities/futures, set timezone to your exchange (e.g., America/New_York). For FX/crypto, pick what matches your workflow.
Common tweak: London 08:00–12:00 local; Asia 00:00–05:00 or your broker’s definition.
Notes
Non-repainting: levels only print once the session is complete.
Designed to be light and reliable—no boxes, just clean lines and labels.
If you want NY session levels, midlines (50%), anchored stop-time, or alerts on touches, this script can be extended.
For educational use only. Not financial advice.
Economic Cycle Signal (USA)📊 Economic Cycle Signal (USA)
This indicator overlays both the U.S. Federal Reserve Funds Rate (Fed Funds) and the U.S. Inflation Rate YoY directly onto your stock market chart (e.g., S&P 500). It visually connects monetary policy and inflation dynamics with equity market performance, helping traders and analysts understand how macroeconomic shifts impact risk assets.
🔹 Key Features
• Plots the monthly U.S. Fed Funds Rate alongside your chart.
• Overlays the U.S. Inflation Rate YoY, offering a direct and realistic view of inflation pressure instead of CPI.
• Shades the background to reflect different economic cycle phases (recovery, recession, expansion, late cycle).
• Highlights how the stock market reacts during shifting monetary and inflationary conditions.
• Provides a clear traffic-light style signal for quick macro interpretation.
• Now includes dynamic inflation color logic based on the Fed’s 2% target and 5% threshold (explained below).
🔹 Inflation Line Color Logic (New)
The inflation line now changes color dynamically to show whether inflation is within or outside the Federal Reserve’s comfort zone, and whether it’s rising or falling:
Inflation Condition Interpretation Line Color
Inflation > 5% and Rising Inflation overheating (well above target) 🔴 Red
Inflation > 5% and Falling Cooling off from high levels 💚 Lime
Inflation < 5% and Falling Disinflation / stable price environment 🟢 Green
Inflation < 5% and Rising Early inflation rebound 🟡 Yellow
This color-coded logic mirrors the interest rate phase colors, giving traders an instant visual cue about inflationary pressure and possible policy turning points.
🔹 How Traders & Analysts Can Use It
• Visualize the interaction between U.S. monetary policy and inflation cycles in real time.
• Identify historically supportive phases when low or easing rates follow moderate inflation.
• Detect tightening cycles when inflation spikes first and the Fed reacts, signaling potential equity headwinds.
• Use as a macro compass to anticipate inflation pressure, policy changes, and market regime shifts.
• Combine with technical analysis, fundamentals, or leading indicators for deeper macro insights.
🔹 Color Legend (Economic Phases)
🟩 Light Green → Recovery (Early Cycle)
• Rates: low or falling
• Inflation: low/stable
🟩 Green → Recession (Down Cycle)
• Rates: cut aggressively
• Inflation: falling
🟨 Yellow → Expansion (Mid Cycle)
• Rates: rising gradually
• Inflation: moderate
🟥 Red → Overheating (Late Cycle)
• Rates: high / rising fast
• Inflation: high
🔹 Inflation Context
• Inflation typically leads the policy rate cycle, offering early insight into future Fed actions.
• The U.S. Inflation Rate YoY provides a direct measure of consumer price changes compared to the same month last year — a clearer gauge of inflation pressure than CPI.
• The new color logic helps visualize whether inflation is accelerating or cooling, relative to the Fed’s 2% target and 5% upper threshold.
• This dual-overlay makes it easy to interpret the cause (inflation) and effect (interest rate policy) in one synchronized chart.
⚠️ Disclaimer
This script is for educational and informational purposes only. It does not provide financial advice or trading signals. Always combine it with your own research, proper risk management, and professional judgment.
Economic Cycle Signal (Pakistan)📊 Economic Cycle Signal (Pakistan)
This indicator overlays both the Pakistan Policy Rate (PKINTR) and the Pakistan Inflation Rate YoY (PKIRYY) directly onto your KSE or Pakistan market chart. It visually connects monetary policy and inflation dynamics with market performance, helping traders and analysts understand how shifts in economic conditions impact risk assets in Pakistan.
🔹 Key Features
• Plots the monthly Pakistan Policy Rate alongside your chart.
• Overlays the Pakistan Inflation Rate YoY to track how price pressures evolve before policy rate adjustments.
• Shades the background to reflect different economic cycle phases (recovery, recession, expansion, late cycle).
• Highlights how equities and other risk assets react during shifting monetary and inflationary conditions.
• Provides a clear traffic-light style signal for quick macro interpretation.
• Now includes dynamic inflation color logic based on the State Bank of Pakistan’s (SBP) 5–7% target range and thresholds for overheating or cooling inflation.
🔹 Inflation Line Color Logic (New)
The inflation line color dynamically reflects whether inflation is within or outside SBP’s target range, and whether it’s rising or falling:
Inflation Condition Interpretation Line Color
Inflation > 7% and Rising Inflation overheating (well above SBP target) 🔴 Red
Inflation > 7% and Falling Cooling off from high levels 💚 Lime
Inflation < 5% and Falling Disinflation / stable price environment 🟢 Green
Inflation < 5% and Rising Early inflation rebound 🟡 Yellow
This adaptive color logic mirrors the interest rate cycle signals, helping traders instantly interpret Pakistan’s inflation trajectory and anticipate potential monetary policy turning points.
🔹 How Traders & Analysts Can Use It
• Visualize Pakistan’s monetary policy cycles and inflation trends in real time.
• Identify supportive phases when rate cuts or low policy rates follow controlled inflation.
• Detect tightening cycles when inflation spikes and the SBP reacts with rate hikes, often creating headwinds for equities.
• Use as a macro compass to anticipate inflation pressure, potential policy actions, and shifts in market risk appetite.
• Combine with technical analysis, fundamentals, or macro indicators for deeper insights into Pakistan’s economic conditions.
🔹 Color Legend (Economic Phases)
🟩 Light Green → Recovery (Early Cycle)
• Rates: low or falling
• Inflation: low/stable
🟩 Green → Recession (Down Cycle)
• Rates: cut aggressively
• Inflation: falling
🟨 Yellow → Expansion (Mid Cycle)
• Rates: rising gradually
• Inflation: moderate
🟥 Red → Overheating (Late Cycle)
• Rates: high / rising fast
• Inflation: high
🔹 Inflation Context
• SBP’s medium-term inflation target range is 5–7%, aimed at balancing growth and price stability.
• The script applies the same visual logic used in the U.S. version, now calibrated to Pakistan’s macro environment.
• The Pakistan Inflation Rate YoY (PKIRYY) line color shifts dynamically — clearly showing when inflation is rising above target, cooling, or stabilizing.
• This dual-overlay helps interpret both the cause (inflation) and effect (policy response) within Pakistan’s economic cycle, giving investors a clear macro perspective.
⚠️ Disclaimer
This script is for educational and informational purposes only. It does not provide financial advice or trading signals. Always combine it with your own research, proper risk management, and professional judgment.
Gold–Bitcoin Correlation (Offset Model) by KManus88This indicator analyzes the correlation between Gold (XAU/USD) and Bitcoin (BTC/USD) using a time-offset model adjustable by the user.
The goal is to detect cyclical leads or lags between both assets, highlighting how capital flows into Gold may precede or follow movements in the crypto market.
Key Features:
Dynamic correlation calculation between Gold and Bitcoin.
Adjustable offset in days (default: 107) to fine-tune the temporal shift.
Automatic labels and on-chart visualization.
Compatible with multiple timeframes and logarithmic scales.
Interpretation:
Positive correlation suggests synchronized trends between both assets.
Negative correlation signals divergence or rotation of liquidity.
The time-offset parameter helps estimate when a shift in Gold could later reflect in Bitcoin.
Recommended use:
For macro-financial and global liquidity cycle analysis.
As a complementary tool in cross-asset momentum strategies.
© 2025 – Developed by KManus88 | Inspired by monetary correlation studies and global liquidity cycles.
This script is for educational purposes only and does not constitute financial advice.
Index of Civilization DevelopmentIndex of Civilization Development Indicator
This Pine Script (version 6) creates a custom technical indicator for TradingView, titled Index of Civilization Development. It generates a composite index by averaging normalized stock market performances from a selection of global country indices. The normalization is relative to each index's 100-period simple moving average (SMA), scaled to a percentage (100% baseline). This allows for a comparable "development" or performance metric across diverse markets, potentially highlighting trends in global economic or "civilizational" progress based on equity markets.The indicator plots as a single line in a separate pane (non-overlay) and is designed to handle up to 40 symbols to respect TradingView's request.security() call limits.Key FeaturesComposite Index Calculation: Fetches the previous bar's close (close ) and its 100-period SMA for each selected symbol.
Normalizes each: (close / SMA(100)) * 100.
Averages the valid normalizations (ignores invalid/NA data) to produce a single "Index (%)" value.
Symbol Selection Modes:Top N Countries: Selects from a predefined list of the top 50 global stock indices (by market cap/importance, e.g., SPX for USA, SHCOMP for China). Options: Top 5, 15, 25, or 50.
Democratic Countries: ~38 symbols from democracies (e.g., SPX, NI225, NIFTY; based on democracy indices ≥6/10, including flawed/parliamentary systems).
Dictatorships: ~12 symbols from authoritarian/hybrid regimes (e.g., SHCOMP, TASI, IMOEX; scores <6/10).
Customization:Line color (default: blue).
Line width (1-5, default: 2).
Line style: Solid line (default), Stepline, or Circles.
Data Handling:Uses request.security() with lookahead enabled for real-time accuracy, gaps off, and invalid symbol ignoring.
Runs calculations on every bar, with max_bars_back=2000 for historical depth.
Arrays are populated only on the first bar (barstate.isfirst) for efficiency.
Predefined Symbol Lists (Examples)Top 50: SPX (USA), SHCOMP (China), NI225 (Japan), ..., BAX (Bahrain).
Democratic: Focuses on free-market democracies like USA, Japan, UK, Canada, EU nations, Australia, etc.
Dictatorships: Authoritarian markets like China, Saudi Arabia, Russia, Turkey, etc.
Usage TipsAdd to any chart (e.g., daily/weekly timeframe) to view the composite line.
Ideal for macro analysis: Compare democratic vs. authoritarian performance, or track "top world" equity health.
Potential Limitations: Relies on TradingView's symbol availability; some exotic indices (e.g., KWSEIDX) may fail if not supported. The 40-symbol cap prevents errors.
Interpretation: Values >100 indicate above-trend performance; <100 suggest underperformance relative to recent averages.
This script blends financial data with geopolitical categorization for a unique "civilization index" perspective on global markets. For modifications, ensure symbol tickers match TradingView's format.
global credit spread with global yield curveglobal credit spread with global yield curve designed to give short term and longer term asset price reversal
Global Risk-On / Risk-Off: Global 2s10s + Credit SpreadGlobal Risk-On / Risk-Off: Global 2s10s + Credit Spread
Moving Average Trend Strategy V2.1 — With Stop Loss and Add Posi**Strategy Feature Description:**
---
### **Entry Logic:**
* When **MA7** crosses **MA15**, and the distance between **MA15** and **MA99** is less than **0.5%**
* When **MA15** crosses **MA99**, and the distance between **MA7** and **MA15** is less than **0.5%**
* When the distance among all three MAs (**MA7**, **MA15**, **MA99**) is less than **0.5%** (adjustable via parameters)
---
### **Capital Management:**
* Initial capital: **$100**
* Each position uses **15%** of total capital
* Opens **both long and short positions simultaneously** (dual-direction mode)
---
### **Risk Control:**
* **Long position stop-loss:** Entry price − 2%
* **Short position stop-loss:** Entry price + 2%
* Uses a **five-level take-profit grid**:
* Every 5% profit → close 20% of position
* Any pending take-profit orders are automatically canceled when stop-loss triggers
---
### **Visualization Features:**
* Real-time display of the three moving averages
* Chart annotations for entry signal points
* All trade signals and performance can be viewed through **TradingView backtest reports**
---
### **Notes:**
* Parameters can be adjusted based on the volatility of the instrument (historical backtesting is recommended first)
* Dual-direction positions may generate **hedging costs** — recommended for low-fee markets
* Real trading must consider **exchange minimum order size limits**
* Suggest enabling a **volume filter mechanism** (extension interface already reserved)
* Always perform **historical backtesting and parameter optimization** in TradingView before connecting to live trading systems
Liquidity Index with Advanced Statistical NormalizationLiquidity Index with Advanced Statistical Normalization
An open-source TradingView indicator for analyzing global liquidity cycles using robust statistical methods
Overview
This Pine Script indicator combines multiple macroeconomic data sources to construct a composite liquidity index that tracks global financial conditions. It employs advanced statistical techniques typically found in quantitative finance research, adapted for real-time charting.
Key Features
📊 Multi-Source Data Integration
- Federal Reserve Components: Fed Funds Rate, Reverse Repo (RRP), Treasury General Account (TGA)
- PBOC Components: China M2 Money Stock adjusted by CNY/USD exchange rate
- Volatility Index: MOVE Index (bond market volatility)
🔬 Advanced Statistical Methods
1. Theil-Sen Estimator: Robust trend detection resistant to outliers
2. Triple Normalization:
- Z-score normalization
- MAD (Median Absolute Deviation) normalization
- Quantile normalization via inverse normal CDF
3. Multi-Timeframe Analysis: Short (8-bar) and long (34-bar) windows with blended composite
📈 Signal Processing
- Log-transformation for non-linear relationships
- Smoothing via customizable SMA
- Composite signal averaging across normalization methods
Why This Approach?
Traditional liquidity indicators often suffer from:
- Sensitivity to outliers in economic data
- Assumption of normal distributions
- Single-timeframe bias
This script addresses these issues by:
- Using median-based robust statistics (Theil-Sen, MAD)
- Applying multiple normalization techniques
- Blending short and long-term perspectives
Customization Options
short_length // Short window (default: 8)
long_length // Long window (default: 34)
show_short // Display short composite
show_long // Display long composite
show_blended // Display blended signal
smoothing_length // SMA smoothing period (default: 10)
How to Use
1. Liquidity Expansion (positive values): Risk-on environment, favorable for asset prices
2. Liquidity Contraction (negative values): Risk-off environment, potential market stress
3. Divergences: Compare indicator direction vs. price action for early warnings
Potential Improvements
Community members are encouraged to enhance:
- Additional data sources (ECB balance sheet, BOJ operations, etc.)
- Alternative normalization methods (robust scaling, rank transformation)
- Machine learning integration (LSTM forecasting, regime detection)
- Alert conditions for liquidity inflection points
- Volatility-adjusted weighting schemes
Technical Notes
- Uses request.security() for multi-symbol data fetching
- All calculations handle missing data via nz() functions
- Median-based statistics computed via array operations
- Custom inverse CDF approximation (no external libraries required)
Contributing
This is a foundation for liquidity analysis. Potential extensions:
- LLM Integration: Use language models to parse Fed/PBOC meeting minutes and adjust weights dynamically
- Sentiment Layer: Incorporate crypto funding rates or options skew
- Adaptive Parameters: Auto-tune window lengths based on market regime
- Cross-Asset Validation: Backtest signals against BTC, equities, bonds
---
License: Open source - modify and redistribute freelyDisclaimer: For educational purposes only. Not financial advice.
Live Volume TickerGives current real-time volume of tick movements denoted in the timeframe of the current candle.
PPI Inflation Monitor (Change YoY & MoM)📊 PPI Inflation Monitor - Leading Inflation Indicator
The Producer Price Index (PPI) measures wholesale/producer-level prices and serves as a critical leading indicator for consumer inflation trends. This tool helps you anticipate CPI movements and identify corporate margin pressures before they show up in earnings.
🎯 KEY FEATURES:
- Dual Perspective Analysis:
- Year-over-Year (YoY): Histogram bars showing annual producer price inflation
- Month-over-Month (MoM): Line overlay showing monthly wholesale price changes
- Visual Reference System:
- Dashed line at 2% (typical target for producer price inflation)
- Dotted line at 0.17% (equivalent monthly target)
- Color-coded bars: Red above target, Green below target
- Real-Time Data Table:
- Current PPI Index value
- YoY inflation rate with color coding
- MoM inflation rate with color coding
- Deviation from target level
- Automated Alerts:
- YoY crosses above/below target
- MoM crosses above/below target
- Early warning system for inflation trends
📈 WHY PPI IS YOUR EARLY WARNING SYSTEM:
PPI typically leads CPI by 1-3 months because:
- Producers face cost increases first
- These costs are eventually passed to consumers
- Shows whether companies can maintain pricing power
Rising PPI with stable CPI = Margin compression → Bearish for stocks
Rising PPI followed by rising CPI = Broad inflation → Fed hawkishness incoming
Falling PPI = Disinflationary trend starting → Positive for risk assets
🔍 TRADING APPLICATIONS:
1. Lead Time Advantage: Position before CPI confirms PPI trends
2. Sector Rotation: High PPI = favor companies with pricing power
3. Margin Analysis: PPI-CPI divergence = margin pressure/expansion signals
4. Fed Anticipation: PPI acceleration = Fed likely to turn hawkish soon
💡 STRATEGIC USE CASES:
- Value vs. Growth: Rising PPI favors value stocks with pricing power
- Commodities: PPI often correlates with commodity price trends
- Small Caps: More vulnerable to input cost increases (high PPI = cautious)
- Corporate Earnings: Anticipate margin pressure before quarterly reports
🔄 COMBINE WITH:
- CPI: Confirm if producer costs reach consumers
- PCE: Validate Fed's preferred inflation metric response
- Fed Funds Rate: Assess if Fed is behind/ahead of curve
📊 DATA SOURCE:
Official PPI data from FRED (Federal Reserve Economic Data), updated monthly when new data releases occur.
🎨 CUSTOMIZATION:
Fully customizable:
- Toggle YoY/MoM displays
- Adjust reference target levels
- Customize colors
- Show/hide absolute PPI values
Perfect for: Macro traders, fundamental analysts, earnings traders, and investors seeking early inflation signals before they appear in consumer prices.
⚡ Remember: PPI leads CPI. Use this advantage to position ahead of the crowd.
PCE Inflation Monitor (Change YoY & MoM)📊 PCE Inflation Monitor - The Fed's Most Important Metric
Personal Consumption Expenditures (PCE) is the Federal Reserve's preferred inflation measure and THE metric they target for their 2% inflation goal. If you want to predict Fed policy, you need to watch PCE.
🎯 KEY FEATURES:
- Dual Perspective Analysis:
- Year-over-Year (YoY): Histogram bars showing annual PCE inflation
- Month-over-Month (MoM): Line overlay showing monthly consumption price changes
- Visual Reference System:
- Dashed line at 2% (Fed's official PCE inflation target)
- Dotted line at 0.17% (equivalent monthly target)
- Color-coded bars: Red above Fed target, Green below target
- Real-Time Data Table:
- Current PCE Index value
- YoY inflation rate vs. Fed's 2% target
- MoM inflation rate with color coding
- Exact deviation from Fed target (critical for policy predictions)
- Automated Alerts:
- PCE crosses Fed's 2% target (major policy signal!)
- MoM crosses monthly target
- Stay informed of Fed-relevant inflation changes
📈 WHY PCE IS DIFFERENT (AND MORE IMPORTANT):
PCE vs. CPI differences:
- Flexible basket: PCE adjusts for substitution (beef → chicken if prices rise)
- Broader coverage: Includes healthcare paid by insurance/government
- Lower readings: Typically 0.2-0.4% below CPI
- Fed's choice: Explicitly stated as their target metric
Most importantly: When Powell speaks about "our 2% target," he means PCE, not CPI!
🔍 TRADING IMPLICATIONS:
PCE Above 2% (Red Zone):
→ Fed under pressure to maintain/raise rates
→ Hawkish policy stance likely
→ Negative for growth stocks, crypto
→ Positive for USD, bearish for gold
PCE Below 2% (Green Zone):
→ Fed has flexibility to cut rates
→ Dovish policy stance possible
→ Positive for risk assets, growth stocks
→ Negative for USD, bullish for commodities
PCE Approaching 2% from Above:
→ Fed "mission accomplished" narrative
→ Rate cut cycle becomes possible
→ Major bullish signal for equities/crypto
💡 ADVANCED STRATEGIES:
1. Fed Meeting Preparation: Check PCE before FOMC meetings for policy clues
2. Dot Plot Predictions: PCE trend determines Fed's rate forecast updates
3. Pivot Timing: When PCE MoM turns negative, Fed pivot becomes realistic
4. Press Conference Analysis: Compare Powell's comments to PCE deviation
🎯 KEY LEVELS TO WATCH:
- 2.0% YoY: Fed's official target - crossing this level is major news
- 2.5% YoY: "Uncomfortably high" - Fed forced to stay restrictive
- 3.0% YoY: "Crisis mode" - Fed turns very hawkish
- 1.5% YoY: "Below target" - Rate cuts become likely
🔄 COMBINE WITH:
- CPI: Public perception vs. Fed's metric (often diverge)
- Core PCE: Even more important (excludes food/energy volatility)
- Fed Funds Rate: Is Fed responding appropriately to PCE?
📊 DATA SOURCE:
Official PCE data from FRED (Federal Reserve Economic Data), updated monthly typically in the last week of each month (after CPI/PPI releases).
🎨 CUSTOMIZATION:
Fully customizable:
- Toggle YoY/MoM displays
- Adjust Fed target if needed
- Customize colors
- Show/hide absolute PCE values
Perfect for: Fed watchers, macro traders, policy analysts, and serious investors who want to predict monetary policy changes before they happen.
⚠️ CRITICAL INSIGHT: While media focuses on CPI, the Fed focuses on PCE. Trade what the Fed trades, not what the headlines say.
🎓 Pro Tip: Fed members often mention "Core PCE" (excluding food/energy). Consider adding that indicator alongside this one for complete Fed policy analysis.
CPI Inflation Monitor (Change YoY & MoM)📊 CPI Inflation Monitor - Complete Macro Analysis Tool
This indicator provides a comprehensive view of Consumer Price Index (CPI) inflation trends, essential for understanding monetary policy, market conditions, and making informed trading decisions.
🎯 KEY FEATURES:
- Dual Perspective Analysis:
- Year-over-Year (YoY): Histogram bars showing annual inflation rate
- Month-over-Month (MoM): Line overlay showing monthly price changes
- Visual Reference System:
- Dashed line at 2% (Fed's official inflation target for YoY)
- Dotted line at 0.17% (equivalent monthly target for MoM)
- Color-coded bars: Red above target, Green below target
- Real-Time Data Table:
- Current CPI Index value
- YoY inflation rate with color coding
- MoM inflation rate with color coding
- Deviation from Fed target
- Automated Alerts:
- YoY crosses above/below 2% target
- MoM crosses above/below 0.17% target
- Perfect for staying informed without constant monitoring
📈 WHY THIS MATTERS FOR TRADERS:
CPI is the most widely reported inflation metric and directly influences:
- Federal Reserve interest rate decisions
- Bond yields and currency valuations
- Stock market sentiment (especially growth vs. value rotation)
- Cryptocurrency and risk asset performance
Rising inflation (red bars) typically leads to:
→ Higher interest rates → Negative for growth stocks, crypto
→ Stronger USD → Pressure on commodities
Falling inflation (green bars) typically leads to:
→ Rate cut expectations → Positive for growth stocks, crypto
→ Weaker USD → Support for commodities
🔍 HOW TO USE:
1. Strategic Positioning: Use YoY trend (thick bars) for long-term asset allocation
2. Tactical Timing: Use MoM trend (thin line) to identify turning points early
3. Divergence Trading: When MoM falls but YoY remains high, anticipate trend reversal
4. Fed Policy Prediction: Distance from 2% target indicates Fed's likely hawkishness
💡 PRO TIPS:
- Multiple months of MoM above 0.3% = Accelerating inflation → Fed turns hawkish
- MoM turning negative while YoY still elevated = Peak inflation → Position for pivot
- Compare with PPI and PCE indicators for complete inflation picture
- Use alerts to catch important threshold crossings automatically
📊 DATA SOURCE:
Official CPI data from FRED (Federal Reserve Economic Data), updated monthly mid-month when new data releases occur.
🎨 CUSTOMIZATION:
Fully customizable through settings:
- Toggle YoY/MoM displays
- Adjust target levels
- Customize colors for visual preference
- Show/hide absolute CPI values
Perfect for: Macro traders, swing traders, long-term investors, and anyone wanting to understand the inflation environment affecting their portfolio.
Note: This indicator works on any chart timeframe as it loads external monthly economic data.
CMF, RSI, CCI, MACD, OBV, Fisher, Stoch RSI, ADX (+DI/-DI)Eight normalized indicators are used in conjunction with the CMF, CCI, MACD, and Stoch RSI indicators. You can track buy and sell decisions by tracking swings. The zero line is for reversal tracking at -20, +20, +50, and +80. You can use any of the nine indicators individually or in combination.
Intrinsic Value AnalyzerThe Intrinsic Value Analyzer is an all-in-one valuation tool that automatically calculates the fair value of a stock using industry-standard valuation techniques. It estimates intrinsic value through Discounted Cash Flow (DCF), Enterprise Value to Revenue (EV/REV), Enterprise Value to EBITDA (EV/EBITDA), and Price to Earnings (P/EPS). The model features adjustable parameters and a built-in alert system that notifies investors in real time when valuation multiples reach predefined thresholds. It also includes a comprehensive, color-coded table that compares the company’s historical average growth rates, valuation multiples, and financial ratios with the most recent values, helping investors quickly assess how current values align with historical averages.
The model calculates the historical Compounded Annual Growth Rates (CAGR) and average valuation multiples over the selected Lookback Period. It then projects Revenue, Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), Earnings per Share (EPS), and Free Cash Flow (FCF) for the selected Forecast Period and discounts their future values back to the present using the Weighted Average Cost of Capital (WACC) or the Cost of Equity. By default, the model automatically applies the historical averages displayed in the table as the growth forecasts and target multiples. These assumptions can be modified in the menu by entering custom REV-G, EBITDA-G, EPS-G, and FCF-G growth forecasts, as well as EV/REV, EV/EBITDA, and P/EPS target multiples. When new input values are entered, the model recalculates the fair value in real time, allowing users to see how changes in these assumptions affect the company’s fair value.
DCF = (Sum of (FCF × (1 + FCF-G) ^ t ÷ (1 + WACC) ^ t) for each year t until Forecast Period + ((FCF × (1 + FCF-G) ^ Forecast Period × (1 + LT Growth)) ÷ ((WACC - LT Growth) × (1 + WACC) ^ Forecast Period)) + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding
EV/REV = ((Revenue × (1 + REV-G) ^ Forecast Period × EV/REV Target) ÷ (1 + WACC) ^ Forecast Period + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding
EV/EBITDA = ((EBITDA × (1 + EBITDA-G) ^ Forecast Period × EV/EBITDA Target) ÷ (1 + WACC) ^ Forecast Period + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding
P/EPS = (EPS × (1 + EPS-G) ^ Forecast Period × P/EPS Target) ÷ (1 + Cost of Equity) ^ Forecast Period
The discounted one-year average analyst price target (1Y PT) is also displayed alongside the valuation labels to provide an overview of consensus estimates. For the DCF model, the terminal long-term FCF growth rate (LT Growth) is based on the selected country to reflect expected long-term nominal GDP growth and can be modified in the menu. For metrics involving FCF, users can choose between reported FCF, calculated as Cash From Operations (CFO) - Capital Expenditures (CAPEX), or standardized FCF, calculated as Earnings Before Interest and Taxes (EBIT) × (1 - Average Tax Rate) + Depreciation and Amortization - Change in Net Working Capital - CAPEX. Historical average values displayed in the left column of the table are based on Fiscal Year (FY) data, while the latest values in the right column use the most recent Trailing Twelve Month (TTM) or Fiscal Quarter (FQ) data. The indicator displays color-coded price labels for each fair value estimate, showing the percentage upside or downside from the current price. Green indicates undervaluation, while red indicates overvaluation. The table follows a separate color logic:
REV-G, EBITDA-G, EPS-G, FCF-G = Green indicates positive annual growth when the CAGR is positive. Red indicates negative annual growth when the CAGR is negative.
EV/REV = Green indicates undervaluation when EV/REV ÷ REV-G is below 1. Red indicates overvaluation when EV/REV ÷ REV-G is above 2. Gray indicates fair value.
EV/EBITDA = Green indicates undervaluation when EV/EBITDA ÷ EBITDA-G is below 1. Red indicates overvaluation when EV/EBITDA ÷ EBITDA-G is above 2. Gray indicates fair value.
P/EPS = Green indicates undervaluation when P/EPS ÷ EPS-G is below 1. Red indicates overvaluation when P/EPS ÷ EPS-G is above 2. Gray indicates fair value.
EBITDA% = Green indicates profitable operations when the EBITDA margin is positive. Red indicates unprofitable operations when the EBITDA margin is negative.
FCF% = Green indicates strong cash conversion when FCF/EBITDA > 50%. Red indicates unsustainable FCF when FCF/EBITDA is negative. Gray indicates normal cash conversion.
ROIC = Green indicates value creation when ROIC > WACC. Red indicates value destruction when ROIC is negative. Gray indicates positive but insufficient returns.
ND/EBITDA = Green indicates low leverage when ND/EBITDA is below 1. Red indicates high leverage when ND/EBITDA is above 3. Gray indicates moderate leverage.
YIELD = Green indicates positive shareholder return when Shareholder Yield > 1%. Red indicates negative shareholder return when Shareholder Yield < -1%.
The Return on Invested Capital (ROIC) is calculated as EBIT × (1 - Average Tax Rate) ÷ (Average Debt + Average Equity - Average Cash). Shareholder Yield (YIELD) is calculated as the CAGR of Dividend Yield - Change in Shares Outstanding. The Weighted Average Cost of Capital (WACC) is displayed at the top left of the table and is derived from the current Market Cap (MC), Debt, Cost of Equity, and Cost of Debt. The Cost of Equity is calculated using the Equity Beta, Index Return, and Risk-Free Rate, which are based on the selected country. The Equity Beta (β) is calculated as the 5-year Blume-adjusted beta between the weekly logarithmic returns of the underlying stock and the selected country’s stock market index. For accurate calculations, it is recommended to use the stock ticker listed on the primary exchange corresponding to the company’s main index.
Cost of Debt = (Interest Expense on Debt ÷ Average Debt) × (1 - Average Tax Rate)
Cost of Equity = Risk-Free Rate + Equity Beta (β) × (Index Return - Risk-Free Rate)
WACC = (MC ÷ (MC + Debt)) × Cost of Equity + (Debt ÷ (MC + Debt)) × Cost of Debt
This indicator works best for operationally stable and profitable companies that are primarily valued based on fundamentals rather than speculative growth, such as those in the industrial, consumer, technology, and healthcare sectors. It is less suitable for early-stage, unprofitable, or highly cyclical companies, including energy, real estate, and financial institutions, as these often have irregular cash flows or distorted balance sheets. It is also worth noting that TradingView’s financial data provider, FactSet, standardizes financial data from official company filings to align with a consistent accounting framework. While this improves comparability across companies, industries, and countries, it may also result in differences from officially reported figures.
In summary, the Intrinsic Value Analyzer is a comprehensive valuation tool designed to help long-term investors estimate a company’s fair value while comparing historical averages with the latest values. Fair value estimates are driven by growth forecasts, target multiples, and discount rates, and should always be interpreted within the context of the underlying assumptions. By default, the model applies historical averages and current discount rates, which may not accurately reflect future conditions. Investors are therefore encouraged to adjust inputs in the menu to better understand how changes in these key assumptions influence the company’s fair value.
