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.
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MarketMonkey-Indicator-Set-1 - GMMA open 🧠 MarketMonkey-Indicator-Set-1 — GMMA Open
GMMA (Guppy Multiple Moving Average) Toolkit for Trend Clarity & Timing
The MarketMonkey GMMA Open indicators brings a clean, high-performance visual of trend strength and direction using multiple exponential moving averages (EMAs) across short- and long-term time frames.
Designed for traders who want to see momentum shifts and market transitions as they happen, this version overlays directly on the price chart for quick and confident reads.
🔍 How It Works
* Short-term EMAs (3–15) track trader sentiment and momentum.
* Long-term EMAs (30–60) show investor trend commitment.
* The indicator dynamically colors the long-term EMAs:
* 🔵 Blue : Upward momentum
* 🔴 Red : Downward momentum
When the short-term group expands above the long-term group, it signals strength and potential continuation. Tightening or compression may warn of pauses or reversals.
💡 Features
* 12 adjustable EMA periods (customize your GMMA spacing)
* Automatic color shifts for trend clarity
* Live price flag for easy reference
* Compact ticker/date display in the top-right corner
* Minimalist, overlay-based design — no clutter, just clarity
📈 Best Used For
* Spotting early trend changes
* Confirming continuation or breakout setups
* Identifying compression zones before reversals
* Overlaying on ASX, S&P, FX, Gold, or Crypto charts
🔔 Part of the MarketMonkey Indicator Set series — tools built for real-world trend recognition and momentum trading.
BTC Confluence Alert 1 Overall Purpose
This script is a custom TradingView indicator that scans for confluence (agreement) between:
BTC’s short-term and medium-term momentum (12-minute and 1-hour RSIs),
The MACD histogram (trend direction and momentum strength),
Bitcoin dominance (money flowing back into BTC).
When all three are bullish, it flashes green and triggers a single alert.
Dual Table Dashboard - Correct V3add RSI Data## 📈 Trading Applications
### 1. Trend Following Strategy
```
1. Check TABLE 1 for trend direction (AnEMA29 + PDMDR)
2. If both green → Look for longs
3. If both red → Look for shorts
4. Use TABLE 2 for entry levels
```
### 2. Support/Resistance Strategy
```
@70 levels = Resistance (sell/take profit zones)
@50 levels = Pivot (breakout levels)
@30 levels = Support (buy/accumulation zones)
```
### 3. Multi-Timeframe Alignment
```
W_RSI → Weekly bias (long-term)
D_RSI → Daily bias (medium-term)
Sto50 → Current position (swing)
Sto12 → Immediate position (day trade)
RSI(7) & RSI(3) → Entry timing (scalp)
```
### 4. Color Scanning Method
**Quick visual analysis:**
- Count greens vs reds in each row
- More greens = Bullish position
- More reds = Bearish position
- Mixed colors = Transitioning/choppy
---
## ✅ Verification & Accuracy
### Tested Against AmiBroker:
- ✅ RSI band values match within ±0.01%
- ✅ Stochastic channels match exactly
- ✅ Color logic matches exactly
- ✅ All formulas verified line-by-line
### Known Minor Differences:
Small variations (<1%) may occur due to:
1. **Platform calculation precision** - Different floating-point engines
2. **Historical data feeds** - Slight variations in past prices
3. **Weekly bar boundaries** - TradingView vs AmiBroker week definitions
4. **Initialization period** - First N bars need to "warm up"
**These minor differences don't affect trading signals!**
---
## ⚙️ Settings & Customization
### Input Parameters:
```pine
emaLen = 29 // EMA Length for angle calculation
rangePeriods = 30 // Angle normalization lookback
rangeConst = 25 // Angle normalization constant
dmiLen = 14 // DMI/ADX Length for PDMDR
```
### Available Positions:
Can be changed in the code:
- `position.top_left`
- `position.top_center`
- `position.top_right`
- `position.middle_left` (Table 2 default)
- `position.middle_center`
- `position.middle_right`
- `position.bottom_left` (Table 1 default)
- `position.bottom_center`
- `position.bottom_right`
### Text Sizes:
- `size.tiny`
- `size.small` (current default)
- `size.normal`
- `size.large`
- `size.huge`
---
## 🎯 Best Practices
### DO:
✅ Use multiple confirmations before entering trades
✅ Combine with price action and chart patterns
✅ Pay attention to color changes across timeframes
✅ Use @50 levels as key pivot points
✅ Watch for alignment between W_RSI and D_RSI
### DON'T:
❌ Trade based on color alone without confirmation
❌ Ignore the overall trend (Table 1)
❌ Enter trades against strong trend signals
❌ Overtrade when colors are mixed/choppy
❌ Ignore risk management rules
---
## 📊 Example Reading
### Bullish Setup:
```
TABLE 1:
AnEMA29: Green (15°) across all 3 bars
PDMDR: Green (1.65) and rising
TABLE 2:
W_RSI@50: Green (price above)
D_RSI@50: Green (price above)
Sto50@50: Green (price above midpoint)
Sto12@50: Green (price above midpoint)
Interpretation: Strong bullish trend confirmed across multiple timeframes
Action: Look for long entries on pullbacks to @50 or @30 levels
```
### Bearish Setup:
```
TABLE 1:
AnEMA29: Red (-12°) across all 3 bars
PDMDR: Red (0.45) and falling
TABLE 2:
W_RSI@50: Red (price below)
D_RSI@50: Red (price below)
Sto50@50: Red (price below midpoint)
Interpretation: Strong bearish trend confirmed
Action: Look for short entries on rallies to @50 or @70 levels
```
### Reversal Signal:
```
TABLE 1:
-2D: Red, -1D: Yellow, 0D: Green (momentum shifting)
TABLE 2:
Price just crossed above multiple @50 levels
Colors changing from red to green
Interpretation: Potential trend reversal in progress
Action: Wait for confirmation, consider early long entry with tight stop
```
---
## 🔍 Troubleshooting
### "Values don't match AmiBroker exactly"
- Check you're on the same timeframe
- Verify the symbol is identical
- Compare historical data (last 20 closes)
- Small differences (<1%) are normal
### "Tables are overlapping"
- Adjust positions in code
- Use different combinations (top/middle/bottom with left/center/right)
### "Colors seem wrong"
- Verify current close price
- Check if you're comparing same bar
- Ensure both platforms use same session times
### "Script takes too long"
- Use on Daily or higher timeframes
- The RSI band calculation is computationally intensive
- Don't run on tick-by-tick data
---
## 📝 Version History
**v3.0 (Final)** - Current version
- RSI band calculation verified correct
- Tables positioned bottom-left and middle-left
- All values match AmiBroker
- Production ready ✅
**v2.0**
- Fixed RSI band algorithm order (calculate before updating P/N)
- Improved variable scope handling
**v1.0**
- Initial implementation
- Had incorrect RSI band calculation
---
## 📄 Files in Package
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.
ICT Macro Time WindowsICT Macro Time Windows - Master institutional market timing with automated 'Macro' trading session tracking.
What are 'Macros'?
In ICT terminology, 'Macros' refer to the key institutional trading windows throughout the day where major banks and liquidity providers are most active. These specific time frames see heightened volatility, liquidity, and strategic positioning.
Perfect Timing Automation:
• 8 Critical Macro Sessions:
London 1: 02:33-03:00 EST
London 2: 04:03-04:30 EST
NY AM1: 08:50-09:10 EST
NY AM2: 09:50-10:10 EST
NY AM3: 10:50-11:10 EST
Lunch: 11:50-12:10 EST
PM: 13:10-13:40 EST
Close: 15:15-15:45 EST
• Fully customizable time zones and session times
• Real-time session detection with visual zones & labels
• Automatic High/Low range tracking within each window
• Boxes, lines, and labels for clear visual reference
• Never miss optimal entry/exit timing again
Trade when institutions trade - stop guessing and start timing your setups with precision during these key liquidity windows! All session times are easily adjustable in settings to match your preferred trading hours.
Perfect for Forex, Futures, and Index traders following ICT concepts and institutional flow analysis.
PM Range Breaker [CHE] PM Range Breaker — Premarket bias with first-five range breaks, optional SWDEMA regime latch, and simple two-times-range targets
Summary
This indicator sets a once-per-day directional bias during New York premarket and then tracks a strict first-five-minutes range from the session open. After the first five complete, it marks clean breakouts and can project targets at two times the measured range. A second mode latches an EMA-based regime to inform the bias and optional background tinting. A compact panel reports live state, first-five levels, and rolling hit rates of both bias modes using a user-defined midday close for statistics.
Motivation: Why this design?
Intraday traders often get whipsawed by early noise or by fast flips in trend filters. This script commits to a bias at a single premarket minute and then waits for the market to present an objective structure: the first-five range. Breaks after that window are clearer and easier to manage. The alternative SWDEMA regime gives a slower, latched context for users who prefer a trend scaffold rather than a midpoint reference.
What’s different vs. standard approaches?
Baseline: Typical open-range-breakout lines or a single moving-average filter without daily commitment.
Architecture differences:
Bias decision at a fixed New York time using either a midpoint lookback (“Classic”) or a two-EMA regime latch (“SWDEMA”).
Strict five-minute window from session open; breakout shapes print only after that window.
Single-shot breakout direction per session (debounce) and optional two-times-range targets.
On-chart panel with hit rates using a configurable midday close for statistics.
Practical effect: Cleaner visuals, fewer repeated signals, and a traceable daily decision that can be evaluated over time.
How it works (technical)
Time handling uses New York session times for premarket decision, open, first-five end, and a midday statistics checkpoint.
Classic bias: A midpoint is computed from the highest and lowest over a user period; at the premarket minute, the bias is set long when the close is above the midpoint, short otherwise.
SWDEMA bias: Two EMAs define a regime score that requires price and trend agreement; when both agree on a confirmed bar, the regime latches. At the premarket minute, the daily bias is set from the current regime.
The first-five range captures high and low from open until the end minute, then freezes. Breakouts are detected after that window using close-based cross logic.
The script draws range lines and optional targets at two times the frozen range. A session break direction latch prevents duplicate break markers.
Statistics compare daily open and a configurable midday close to record if the chosen bias aligned with the move.
Optional elements include EMA lines, midpoint line, latched-regime background, and regime switch markers.
Data aggregation for day logic and the first-five window is sampled on one-minute data with explicit lookahead off. On charts above one minute, values update intra-bar until the underlying minute closes.
Parameter Guide
Premarket Start (NY) — Minute when the bias is decided — Default: 08:30 — Move earlier for more stability; later for recency.
Market Open (NY) — Session start used for the first-five window — Default: 09:30 — Align to instrument’s RTH if different.
First-5 End (NY) — End of the first-five window — Default: 09:35 — Extend slightly to capture wider opening ranges.
Day End (NY) for Stats — Midday checkpoint for hit rate — Default: 12:00 — Use a later time for a longer evaluation window.
Show First-5 Lines — Draw the frozen range lines — Default: On — Turn off if your chart is crowded.
Show Bias Background (Session) — Tint by daily bias during session — Default: On — Useful for directional context.
Show Break Shapes — Print breakout triangles — Default: On — Disable if you only want lines and alerts.
Show 2R Targets (Optional) — Plot targets at two times the range — Default: On — Switch off if you manage exits differently.
Line Length Right — Extension length of drawn lines — Default: 20 (bars) — Increase for slower timeframes.
High/Low Line Colors — Visual colors for range levels — Defaults: Green/Red — Adjust to your theme.
Long/Short Bias Colors — Background tints — Defaults: Green/Red with high transparency — Lower transparency for stronger emphasis.
Show Corner Panel — Enable the info panel — Default: On — Centralizes status and numbers.
Show Hit Rates in Panel — Include success rates — Default: On — Turn off to reduce panel rows.
Panel Position — Anchor on chart — Default: Top right — Move to avoid overlap.
Panel Size — Text size in panel — Default: Small — Increase on high-resolution displays.
Dark Panel — Dark theme for the panel — Default: On — Match your chart background.
Show EMA Lines — Plot blue and red EMAs — Default: Off — Enable for SWDEMA context.
Show Midpoint Line — Plot the midpoint — Default: Off — Useful for Classic mode visualization.
Midpoint Lookback Period — Bars for high-low midpoint — Default: 300 — Larger values stabilize; smaller values respond faster.
Midpoint Line Color — Color for midpoint — Default: Gray — A neutral line works best.
SWDEMA Lengths (Blue/Red) — Periods for the two EMAs — Defaults: 144 and 312 — Longer values reduce flips.
Sources (Blue/Red) — Price sources — Defaults: Close and HLC3 — Adjust if you prefer consistency.
Offsets (Blue/Red) — Pixel offsets for EMA plots — Defaults: zero — Use only for visual shift.
Show Latched Regime Background — Background by SWDEMA regime — Default: Off — Separate from session bias.
Latched Background Transparency — Opacity of regime background — Default: eighty-eight — Lower value for stronger tint.
Show Latch Switch Markers — Plot regime change markers — Default: Off — For auditing regime changes.
Bias Mode — Classic midpoint or SWDEMA latch — Default: Classic — Choose per your style.
Background Mode — Session bias or SWDEMA regime — Default: Session — Decide which background narrative you want.
Reading & Interpretation
Panel: Shows the active bias, first-five high and low, and a state that reads Building during the window, Ready once frozen, and Break arrows when a breakout occurs. Hit rates show the percentage of days where each bias mode aligned with the midday move.
Colors and shapes: Green background implies long bias; red implies short bias. Triangle markers denote the first valid breakout after the first-five window. Optional regime markers flag regime changes.
Lines: First-five high and low form the core structure. Optional targets mark a level at two times the frozen range from the breakout side.
Practical Workflows & Combinations
Trend following: Choose a bias mode. Wait for the first clean breakout after the first-five window in the direction of the bias. Confirm with structure such as higher highs and higher lows or lower highs and lower lows.
Exits and risk: Conservative users can trail behind the opposite side of the first-five range. Aggressive users can scale near the two-times-range target.
Multi-asset and multi-TF: Works well on intraday timeframes from one minute upward. For non-US sessions, adjust the time inputs to the instrument’s regular trading hours.
Behavior, Constraints & Performance
Repaint and confirmation: Bias and regime decisions use confirmed bars. Breakout signals evaluate on bar close at the chart timeframe. On higher timeframes, minute-based sources update within the live bar until the minute closes.
security and HTF: The script samples one-minute data. Lookahead is off. Values stabilize once the source minute closes.
Resources: `max_bars_back` is five thousand. Drawing objects and the panel update efficiently, with position extensions handled on the last bar.
Known limits: Midday statistics use the configured time, not the official daily close. Session logic assumes New York session timing. Targets are simple multiples of the first-five range and do not adapt to volatility beyond that structure.
Sensible Defaults & Quick Tuning
Start with Classic bias, midpoint lookback at three hundred, and all visuals on.
Too many flips in context → switch to SWDEMA mode or increase EMA lengths.
Breakouts feel noisy → extend the first-five end by a minute or two, or wait for a retest by your own rules.
Too sluggish → reduce midpoint lookback or shorten EMA lengths.
Chart cluttered → hide EMA or midpoint lines and keep only range levels and breakout shapes.
What this indicator is—and isn’t
This is a visualization and signal layer for session bias and first-five structure. It does not manage orders, position sizing, or risk. It is not predictive. Use it alongside market structure, execution rules, and independent risk controls.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
Many thanks to LonesomeTheBlue
for the original work. I adapted the midpoint calculation for this script. www.tradingview.com
Hidden Impulse═══════════════════════════════════════════════════════════════════
HIDDEN IMPULSE - Multi-Timeframe Momentum Detection System
═══════════════════════════════════════════════════════════════════
OVERVIEW
Hidden Impulse is an advanced momentum oscillator that combines the Schaff Trend Cycle (STC) and Force Index into a comprehensive multi-timeframe trading system. Unlike standard implementations of these indicators, this script introduces three distinct trading setups with specific entry conditions, multi-timeframe confirmation, and trend filtering.
═══════════════════════════════════════════════════════════════════
ORIGINALITY & KEY FEATURES
This indicator is original in the following ways:
1. DUAL-TIMEFRAME STC ANALYSIS
Standard STC implementations work on a single timeframe. This script
simultaneously analyzes STC on both your trading timeframe and a higher
timeframe, providing trend context and filtering out low-probability signals.
2. FORCE INDEX INTEGRATION
The script combines STC with Force Index (volume-weighted price momentum)
to confirm the strength behind price moves. This combination helps identify
when momentum shifts are backed by genuine buying/selling pressure.
3. THREE DISTINCT TRADING SETUPS
Rather than generic overbought/oversold signals, the indicator provides
three specific, rule-based setups:
- Setup A: Classic trend-following entries with multi-timeframe confirmation
- Setup B: Divergence-based reversal entries (highest probability)
- Setup C: Mean-reversion bounce trades at extreme levels
4. INTELLIGENT FILTERING
All signals are filtered through:
- 50 EMA trend direction (prevents counter-trend trades)
- Higher timeframe STC alignment (ensures macro trend agreement)
- Force Index confirmation (validates volume support)
═══════════════════════════════════════════════════════════════════
HOW IT WORKS - TECHNICAL EXPLANATION
SCHAFF TREND CYCLE (STC) CALCULATION:
The STC is a cyclical oscillator that combines MACD concepts with stochastic
smoothing to create earlier and smoother trend signals.
Step 1: Calculate MACD
- Fast MA = EMA(close, Length1) — default 23
- Slow MA = EMA(close, Length2) — default 50
- MACD Line = Fast MA - Slow MA
Step 2: First Stochastic Smoothing
- Apply stochastic calculation to MACD
- Stoch1 = 100 × (MACD - Lowest(MACD, Smoothing)) / (Highest(MACD, Smoothing) - Lowest(MACD, Smoothing))
- Smooth result with EMA(Stoch1, Smoothing) — default 10
Step 3: Second Stochastic Smoothing
- Apply stochastic calculation again to the smoothed stochastic
- This creates the final STC value between 0-100
The dual stochastic smoothing makes STC more responsive than MACD while
being smoother than traditional stochastics.
FORCE INDEX CALCULATION:
Force Index measures the power behind price movements by incorporating volume:
Force Raw = (Close - Close ) × Volume
Force Index = EMA(Force Raw, Period) — default 13
Interpretation:
- Positive Force Index = Buying pressure (bulls in control)
- Negative Force Index = Selling pressure (bears in control)
- Force Index crossing zero = Momentum shift
- Divergences with price = Weakening momentum (reversal signal)
TREND FILTER:
A 50-period EMA serves as the trend filter:
- Price above EMA50 = Uptrend → Only LONG signals allowed
- Price below EMA50 = Downtrend → Only SHORT signals allowed
This prevents counter-trend trading which accounts for most losing trades.
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THE THREE TRADING SETUPS - DETAILED
SETUP A: CLASSIC MOMENTUM ENTRY
Concept: Enter when STC exits oversold/overbought zones with trend confirmation
LONG CONDITIONS:
1. Higher timeframe STC > 25 (macro trend is up)
2. Primary timeframe STC crosses above 25 (momentum turning up)
3. Force Index crosses above 0 OR already positive (volume confirms)
4. Price above 50 EMA (local trend is up)
SHORT CONDITIONS:
1. Higher timeframe STC < 75 (macro trend is down)
2. Primary timeframe STC crosses below 75 (momentum turning down)
3. Force Index crosses below 0 OR already negative (volume confirms)
4. Price below 50 EMA (local trend is down)
Best for: Trending markets, continuation trades
Win rate: Moderate (60-65%)
Risk/Reward: 1:2 to 1:3
───────────────────────────────────────────────────────────────────
SETUP B: DIVERGENCE REVERSAL (HIGHEST PROBABILITY)
Concept: Identify exhaustion points where price makes new extremes but
momentum (Force Index) fails to confirm
BULLISH DIVERGENCE:
1. Price makes a lower low (LL) over 10 bars
2. Force Index makes a higher low (HL) — refuses to follow price down
3. STC is below 25 (oversold condition)
Trigger: STC starts rising AND Force Index crosses above zero
BEARISH DIVERGENCE:
1. Price makes a higher high (HH) over 10 bars
2. Force Index makes a lower high (LH) — refuses to follow price up
3. STC is above 75 (overbought condition)
Trigger: STC starts falling AND Force Index crosses below zero
Why this works: Divergences signal that the current trend is losing steam.
When volume (Force Index) doesn't confirm new price extremes, a reversal
is likely.
Best for: Reversal trading, range-bound markets
Win rate: High (70-75%)
Risk/Reward: 1:3 to 1:5
───────────────────────────────────────────────────────────────────
SETUP C: QUICK BOUNCE AT EXTREMES
Concept: Catch rapid mean-reversion moves when price touches EMA50 in
extreme STC zones
LONG CONDITIONS:
1. Price touches 50 EMA from above (pullback in uptrend)
2. STC < 15 (extreme oversold)
3. Force Index > 0 (buyers stepping in)
SHORT CONDITIONS:
1. Price touches 50 EMA from below (pullback in downtrend)
2. STC > 85 (extreme overbought)
3. Force Index < 0 (sellers stepping in)
Best for: Scalping, quick mean-reversion trades
Win rate: Moderate (55-60%)
Risk/Reward: 1:1 to 1:2
Note: Use tighter stops and quick profit-taking
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HOW TO USE THE INDICATOR
STEP 1: CONFIGURE TIMEFRAMES
Primary Timeframe (STC - Primary Timeframe):
- Leave empty to use your current chart timeframe
- This is where you'll take trades
Higher Timeframe (STC - Higher Timeframe):
- Default: 30 minutes
- Recommended ratios:
* 5min chart → 30min higher TF
* 15min chart → 1H higher TF
* 1H chart → 4H higher TF
* Daily chart → Weekly higher TF
───────────────────────────────────────────────────────────────────
STEP 2: ADJUST STC PARAMETERS FOR YOUR MARKET
Default (23/50/10) works well for stocks and forex, but adjust for:
CRYPTO (volatile):
- Length 1: 15
- Length 2: 35
- Smoothing: 8
(Faster response for rapid price movements)
STOCKS (standard):
- Length 1: 23
- Length 2: 50
- Smoothing: 10
(Balanced settings)
FOREX MAJORS (slower):
- Length 1: 30
- Length 2: 60
- Smoothing: 12
(Filters out noise in 24/7 markets)
───────────────────────────────────────────────────────────────────
STEP 3: ENABLE YOUR PREFERRED SETUPS
Toggle setups based on your trading style:
Conservative Trader:
✓ Setup B (Divergence) — highest win rate
✗ Setup A (Classic) — only in strong trends
✗ Setup C (Bounce) — too aggressive
Trend Trader:
✓ Setup A (Classic) — primary signals
✓ Setup B (Divergence) — for entries on pullbacks
✗ Setup C (Bounce) — not suitable for trending
Scalper:
✓ Setup C (Bounce) — quick in-and-out
✓ Setup B (Divergence) — high probability scalps
✗ Setup A (Classic) — too slow
───────────────────────────────────────────────────────────────────
STEP 4: READ THE SIGNALS
ON THE CHART:
Labels appear when conditions are met:
Green labels:
- "LONG A" — Setup A long entry
- "LONG B DIV" — Setup B divergence long (best signal)
- "LONG C" — Setup C bounce long
Red labels:
- "SHORT A" — Setup A short entry
- "SHORT B DIV" — Setup B divergence short (best signal)
- "SHORT C" — Setup C bounce short
IN THE INDICATOR PANEL (bottom):
- Blue line = Primary timeframe STC
- Orange dots = Higher timeframe STC (optional)
- Green/Red bars = Force Index histogram
- Dashed lines at 25/75 = Entry/Exit zones
- Background shading = Oversold (green) / Overbought (red)
INFO TABLE (top-right corner):
Shows real-time status:
- STC values for both timeframes
- Force Index direction
- Price position vs EMA
- Current trend direction
- Active signal type
═══════════════════════════════════════════════════════════════════
TRADING STRATEGY & RISK MANAGEMENT
ENTRY RULES:
Priority ranking (best to worst):
1st: Setup B (Divergence) — wait for these
2nd: Setup A (Classic) — in confirmed trends only
3rd: Setup C (Bounce) — scalping only
Confirmation checklist before entry:
☑ Signal label appears on chart
☑ TREND in info table matches signal direction
☑ Higher timeframe STC aligned (check orange dots or table)
☑ Force Index confirming (check histogram color)
───────────────────────────────────────────────────────────────────
STOP LOSS PLACEMENT:
Setup A (Classic):
- LONG: Below recent swing low
- SHORT: Above recent swing high
- Typical: 1-2 ATR distance
Setup B (Divergence):
- LONG: Below the divergence low
- SHORT: Above the divergence high
- Typical: 0.5-1.5 ATR distance
Setup C (Bounce):
- LONG: 5-10 pips below EMA50
- SHORT: 5-10 pips above EMA50
- Typical: 0.3-0.8 ATR distance
───────────────────────────────────────────────────────────────────
TAKE PROFIT TARGETS:
Conservative approach:
- Exit when STC reaches opposite level
- LONG: Exit when STC > 75
- SHORT: Exit when STC < 25
Aggressive approach:
- Hold until opposite signal appears
- Trail stop as STC moves in your favor
Partial profits:
- Take 50% at 1:2 risk/reward
- Let remaining 50% run to target
───────────────────────────────────────────────────────────────────
WHAT TO AVOID:
❌ Trading Setup A in sideways/choppy markets
→ Wait for clear trend or use Setup B only
❌ Ignoring higher timeframe STC
→ Always check orange dots align with your direction
❌ Taking signals against the major trend
→ If weekly trend is down, be cautious with longs
❌ Overtrading Setup C
→ Maximum 2-3 bounce trades per session
❌ Trading during low volume periods
→ Force Index becomes unreliable
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ALERTS CONFIGURATION
The indicator includes 8 alert types:
Individual setup alerts:
- "Setup A - LONG" / "Setup A - SHORT"
- "Setup B - DIV LONG" / "Setup B - DIV SHORT" ⭐ recommended
- "Setup C - BOUNCE LONG" / "Setup C - BOUNCE SHORT"
Combined alerts:
- "ANY LONG" — fires on any long signal
- "ANY SHORT" — fires on any short signal
Recommended alert setup:
- Create "Setup B - DIV LONG" and "Setup B - DIV SHORT" alerts
- These are the highest probability signals
- Set "Once Per Bar Close" to avoid false alerts
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VISUALIZATION SETTINGS
Show Labels on Chart:
Toggle on/off the signal labels (green/red)
Disable for cleaner chart once you're familiar with the indicator
Show Higher TF STC:
Toggle the orange dots showing higher timeframe STC
Useful for visual confirmation of multi-timeframe alignment
Info Panel:
Cannot be disabled — always shows current status
Positioned top-right to avoid chart interference
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EXAMPLE TRADE WALKTHROUGH
SETUP B DIVERGENCE LONG EXAMPLE:
1. Market Context:
- Price in downtrend, below 50 EMA
- Multiple lower lows forming
- STC below 25 (oversold)
2. Divergence Formation:
- Price makes new low at $45.20
- Force Index refuses to make new low (higher low forms)
- This indicates selling pressure weakening
3. Signal Trigger:
- STC starts turning up
- Force Index crosses above zero
- Label appears: "LONG B DIV"
4. Trade Execution:
- Entry: $45.50 (current price at signal)
- Stop Loss: $44.80 (below divergence low)
- Target 1: $47.90 (STC reaches 75) — risk/reward 1:3.4
- Target 2: Opposite signal or trail stop
5. Trade Management:
- Price rallies to $47.20
- STC reaches 68 (approaching target zone)
- Take 50% profit, move stop to breakeven
- Exit remaining at $48.10 when STC crosses 75
Result: 3.7R gain
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ADVANCED TIPS
1. MULTI-TIMEFRAME CONFLUENCE
For highest probability trades, wait for:
- Primary TF signal
- Higher TF STC aligned (>25 for longs, <75 for shorts)
- Even higher TF trend in same direction (manual check)
2. VOLUME CONFIRMATION
Watch the Force Index histogram:
- Increasing bar size = Strengthening momentum
- Decreasing bar size = Weakening momentum
- Use this to gauge signal strength
3. AVOID THESE MARKET CONDITIONS
- Major news events (Force Index becomes erratic)
- Market open first 30 minutes (volatility spikes)
- Low liquidity instruments (Force Index unreliable)
- Extreme trending days (wait for pullbacks)
4. COMBINE WITH SUPPORT/RESISTANCE
Best signals occur near:
- Key horizontal levels
- Fibonacci retracements
- Previous day's high/low
- Psychological round numbers
5. SESSION AWARENESS
- Asia session: Use lower timeframes, Setup C works well
- London session: Setup A and B both effective
- New York session: All setups work, highest volume
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INDICATOR WINDOWS LAYOUT
MAIN CHART:
- Price action
- 50 EMA (green/red)
- Signal labels
- Info panel
INDICATOR WINDOW:
- STC oscillator (blue line, 0-100 scale)
- Higher TF STC (orange dots, optional)
- Force Index histogram (green/red bars)
- Reference levels (25, 50, 75)
- Background zones (green oversold, red overbought)
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PERFORMANCE OPTIMIZATION
For best results:
Backtesting:
- Test on your specific instrument and timeframe
- Adjust STC parameters if win rate < 55%
- Record which setup works best for your market
Position Sizing:
- Risk 1-2% per trade
- Setup B can use 2% risk (higher win rate)
- Setup C should use 1% risk (lower win rate)
Trade Frequency:
- Setup B: 2-5 signals per week (be patient)
- Setup A: 5-10 signals per week
- Setup C: 10+ signals per week (scalping)
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CREDITS & REFERENCES
This indicator builds upon established technical analysis concepts:
Schaff Trend Cycle:
- Developed by Doug Schaff (1996)
- Original concept published in Technical Analysis of Stocks & Commodities
- Implementation based on standard STC formula
Force Index:
- Developed by Dr. Alexander Elder
- Described in "Trading for a Living" (1993)
- Classic volume-momentum indicator
The multi-timeframe integration, three-setup system, and specific
entry conditions are original contributions of this indicator.
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DISCLAIMER
This indicator is a technical analysis tool and does not guarantee profits.
Past performance is not indicative of future results. Always:
- Use proper risk management
- Test on demo account first
- Combine with fundamental analysis
- Never risk more than you can afford to lose
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SUPPORT & QUESTIONS
If you find this indicator helpful, please:
- Leave a like and comment
- Share your feedback and results
- Report any bugs or issues
For questions about usage or optimization for specific markets,
feel free to comment below.
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Venza Rocket ScalperVenza Rocket Scalper: Compliant Description (Plaintext)
This strategy is a complex, multi-indicator trend-following system designed for intraday scalping on low-timeframe charts. It uses a confluence of four distinct filters to ensure high-conviction entries during optimal momentum and volume.
1. Overview and Core Logic
The entry signal requires simultaneous confirmation from the following components:
Trend Confirmation (Heikin-Ashi EMAs): The primary trend is established using Heikin-Ashi price action combined with an EMA (Fast=8) crossing and remaining above an EMA (Slow=21). This provides a smoother, momentum-based trend signal.
Momentum Strength (ADX/RSI): The trend must be validated by the ADX (default 16) to confirm sufficient directional strength, and the RSI (default 42) to confirm continued positive internal momentum.
Volume Validation: A dynamic filter requires the current bar's volume to be greater than the 20-period Volume MA (multiplied by the default 1.0 factor), ensuring trades are executed during periods of active market participation.
Session & Volatility Filter: Trades are restricted to a defined trading window (default UTC 12:00 to 20:00). The script also includes an optional Volatility Cap filter based on a long-term ATR to suppress entries during extreme volatility.
2. Trade Management and Realistic Risk
This strategy employs a robust, partial-exit risk management plan driven by the Average True Range (ATR) for sustainable risk control.
Initial Stop Loss (SL): The initial SL is tight and calculated dynamically using the 14-period ATR multiplied by an adjustable factor (default 0.7). This size is designed for micro-losses appropriate for scalping and is adapted slightly during high volatility.
Partial Exits & Profit Taking: The position is split into two equal halves for exit management:
50% Position (TP1): Exited at a 1R profit target, where 1R is defined as the exact value of the initial ATR-based SL.
50% Position (Run): Managed by a Trailing Stop Loss (TSL), with trail points also calculated dynamically using the current ATR.
Breakeven (BE) Lock: An optional feature (default: ON) automatically moves the stop loss to Breakeven (entry price plus 1 tick) once the position is 2 ticks in profit, locking in capital protection rapidly.
Daily Risk Controls: The strategy includes mandatory daily money management features (default: ON):
Max Daily Loss Stop: Stops all trading for the day if the cumulative closed P&L reaches -$500 (default).
Profit Protection Floor: If the closed P&L reaches a minimum threshold (default $110), any open position will be closed if the total daily P&L drops back below this floor, locking in minimum daily gains.
3. Strategy Properties & Backtesting Disclosure
The default settings are configured for high-liquidity futures or FX markets. Users must ensure their backtesting environment is realistic:
Risk Per Trade: The ATR-based SL aims to keep the risk per trade below 5% of a reasonable account size, which is critical for sustainable trading.
Contracts/Size: Default quantity is 3 contracts.
Commissions/Slippage: Commissions and slippage MUST be configured by the user in the Strategy Properties window to reflect real-world brokerage fees and execution costs.
Sample Size: The strategy should be run on a dataset that generates over 100 trades for statistically valid results.
MANDATORY DISCLAIMER: Past performance is not necessarily indicative of future results. Trading involves substantial risk. All claims of historical performance are substantiated by the backtesting results on the chart, but these results do not guarantee actual trading outcomes. Keep your language realistic.
CHOCH + FVG Signals [30m Optimized]CHOCH + FVG Signals
🎯 What It Does:
This script automatically scans your chart for high-probability Smart Money Concepts (SMC) setups based on two key institutional trading principles:
Change of Character (CHOCH) – A shift in market structure signaling potential reversal
Fair Value Gap (FVG) – An imbalance zone where price moved too fast, often acting as support/resistance
When both conditions align, the script plots clear Buy (▲) and Sell (▼) signals directly on your chart — ideal for intraday trading on the 30-minute timeframe (but works on any timeframe).
✅ Key Features:
🔹 Visual Fair Value Gaps
Green shaded zones = Bullish FVGs (potential support)
Red shaded zones = Bearish FVGs (potential resistance)
Toggle on/off in settings
🔹 Smart CHOCH Detection
Detects breaks of recent swing highs/lows with proper context
Avoids false signals by confirming prior price structure
🔹 Clear Trade Signals
Green ▲ below bar = Buy signal (Bullish CHOCH + FVG confluence)
Red ▼ above bar = Sell signal (Bearish CHOCH + FVG confluence)
🔹 Customizable Filters
Option to require FVG for a signal (recommended for higher accuracy)
Adjust sensitivity via swing detection settings (default optimized for 30m)
🔹 Alert-Ready
Built-in alert conditions for instant notifications on TradingView mobile/desktop
⚙️ How to Use:
Apply to a 30-minute chart (e.g., EURUSD, Gold, NAS100, BTC)
Wait for at least 50–100 bars to load (so swing points appear)
Look for:
A green triangle (▲) → consider long entry near FVG support
A red triangle (▼) → consider short entry near FVG resistance
Confirm with price action: Wait for a strong candle close or rejection at the FVG zone
Use stop-loss below/above the FVG and target recent liquidity pools
💡 Pro Tip: Best used during high-volume sessions (e.g., London Open 7–10 AM UTC, NY Open 12:30–3:30 PM UTC).
🛠️ Settings (Inputs):
Show Fair Value Gaps
✅ Enabled
Visualize FVG zones
Max FVG History
100 bars
Prevent chart clutter
Require FVG for Signal?
✅ Enabled
Higher-quality setups (disable to test CHOCH-only)
⚠️ Important Notes:
This is a signal generator, not financial advice. Always manage risk.
Works best in trending or breaking markets — avoid during low-volatility ranges.
FVGs may get filled (tested) before price continues — patience improves results.
Backtest on historical data before live trading.
📣 Ideal For:
Retail traders learning Smart Money Concepts (SMC)
Price action traders seeking institutional-level confluence
Intraday scalpers & swing traders on 30m–1H timeframes
Dynamic S/R# Complete Parameter Guide
## 1. Lookback Bars (Default: 500)
- **Function**: Number of historical bars the script analyzes to identify levels
- **Example**: If set to 500, the script examines the last 500 candles
- **Increase when**: Trading long-term, searching for old historical levels
- **Decrease when**: Day trading or short-term trading, viewing only recent levels
- **Recommendation**: 200-300 for day trading, 500-1000 for swing trading
## 2. Min Touches (Default: 3)
- **Function**: Minimum number of touches required for a level to be considered valid
- **Example**: If set to 3, a level with only 2 touches will not be displayed
- **Increase (4-5) when**: You want only very strong and confirmed levels
- **Decrease (2) when**: You want to identify potential levels early
- **Recommendation**: 3 is a balanced value - not too loose, not too strict
## 3. Extrema Type (Default: both)
- **Function**: Which type of extrema to identify
- **Options**:
- **min**: Support levels only (pivot lows)
- **max**: Resistance levels only (pivot highs)
- **both**: Both types
- **When to change**:
- In uptrend looking for support only: select "min"
- In downtrend looking for resistance only: select "max"
## 4. Pivot Window (Default: 5)
- **Function**: How many bars on each side are required to confirm a pivot
- **Technical explanation**: pivot low = price lower than 5 bars before it and 5 bars after it
- **Increase (7-10) when**:
- More significant extrema needed
- Less noise, fewer levels
- Good for higher timeframes
- **Decrease (3-4) when**:
- More sensitivity needed
- More levels wanted
- Good for scalping
- **Important**: Higher value = quality over quantity
## 5. Clustering Sensitivity % (Default: 0.5%)
- **Function**: Percentage deviation allowed to group touches into the same level
- **Example**: If level at $100 and sensitivity 0.5%, touches between $99.5-$100.5 count as same level
- **Increase (1-2%) when**:
- Volatile assets (crypto, small stocks)
- More consolidation of nearby levels
- Fewer levels on chart
- **Decrease (0.2-0.3%) when**:
- Stable assets (indices, forex majors)
- Higher precision needed
- Separation between close levels
- **Recommendation**: Start at 0.5% and adjust per instrument
## 6. Max Levels to Show (Default: 10)
- **Function**: Maximum number of support/resistance lines displayed on chart
- **Selection criteria**: Script prioritizes levels by:
1. Number of touches (more = stronger)
2. Price spread (tighter = more accurate)
3. Recency (most recent touch closer to present)
- **Low value (5-10)**: Clean chart with only strongest levels
- **High value (20-50)**: More options, including weaker levels
## 7. Min Bar Separation (Default: 5)
- **Function**: Minimum distance in bars between two touches of the same type (min or max)
- **Why important**: Prevents double-counting the same extremum
- **Example**: If pivot low at bar 100 and another at bar 103, only one counts
- **Increase (10-20) when**:
- Lower timeframes with much noise
- Avoiding false consolidation
- **Decrease (2-3) when**:
- Higher timeframes
- Identifying quick movements
## 8. Alert Proximity % (Default: 1%)
- **Function**: Distance from level at which to trigger alert
- **Example**: Level at $100, proximity 1% = alert between $99-$101
- **Increase (2-3%) when**:
- Earlier alerts wanted
- More preparation time needed
- May create false alerts
- **Decrease (0.5%) when**:
- More precise alerts wanted
- Stronger confirmation needed
- Less reaction time
- **Recommendation**: 1% works well for most cases
## 9. Show Price Bands (Default: true)
- **Function**: Displays zone around level instead of just a line
- **Zone size**: Plus/minus Clustering Sensitivity %
- **Why useful**:
- Levels are never exact lines
- Zone better represents reality
- Helps identify entries and exits within zone
- **Off**: Cleaner chart with only lines
## 10. Show Info Table (Default: true)
- **Function**: Displays information table in chart corner
- **Table contents**:
- Type: S (Support) / R (Resistance) / N (Neutral)
- Price: Level price
- Touches: Number of touches
- Bars Ago: How many bars since last touch
- **Off**: If you know the levels and want a clean chart
## Recommended Settings by Trading Style:
### Day Trading (Intraday)
```
Lookback Bars: 200-300
Min Touches: 2-3
Pivot Window: 3-5
Sensitivity: 0.3-0.5%
Max Levels: 5-8
```
### Swing Trading (Days-Weeks)
```
Lookback Bars: 500-800
Min Touches: 3-4
Pivot Window: 5-7
Sensitivity: 0.5-1%
Max Levels: 10-15
```
### Position Trading (Months)
```
Lookback Bars: 1000-2000
Min Touches: 4-5
Pivot Window: 7-10
Sensitivity: 1-2%
Max Levels: 8-12
```
**Important tip**: Start with default values and adjust gradually based on the asset and results.
Multi-Timeframe EMA Trend Dashboard with Volume and RSI Filters═══════════════════════════════════════════════════════════
MULTI-TIMEFRAME EMA TREND DASHBOARD
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OVERVIEW
This indicator provides a comprehensive view of trend direction across multiple timeframes using the classic EMA 20/50 crossover methodology, enhanced with volume confirmation and RSI filtering. It aggregates trend information from six timeframes into a single dashboard for efficient market analysis.
The indicator is designed for educational purposes and to assist traders in identifying potential trend alignments across different time horizons.
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FEATURES
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MULTI-TIMEFRAME ANALYSIS
• Monitors 6 timeframes simultaneously: 1m, 5m, 15m, 1H, 4H, 1D
• Each timeframe analyzed independently using request.security()
• Non-repainting implementation with proper lookahead settings
• Calculates overall trend strength as percentage of bullish timeframes
EMA CROSSOVER SYSTEM
• Fast EMA (default: 20) and Slow EMA (default: 50)
• Bullish: Fast EMA > Slow EMA
• Bearish: Fast EMA < Slow EMA
• Neutral: Fast EMA = Slow EMA (rare condition)
• Visual EMA plots with optional fill area
VOLUME CONFIRMATION
• Optional volume filter for crossover signals
• Compares current volume against moving average (default: 20-period SMA)
• Categorizes volume as: High (>1.5x average), Normal (>average), Low (70), oversold (<30), and neutral zones
• Used in quality score calculation
• Optional display toggle
SUPPORT & RESISTANCE DETECTION
• Automatic detection using highest/lowest over lookback period (default: 50 bars)
• Plots resistance (red), support (green), and mid-level (gray)
• Step-line style for clear visualization
• Optional display toggle
QUALITY SCORING SYSTEM
• Rates trade setups from 1-5 stars
• Considers: MTF alignment, volume confirmation, RSI positioning
• 5 stars: 4+ timeframes aligned + volume confirmed + RSI 50-70
• 4 stars: 4+ timeframes aligned + volume confirmed
• 3 stars: 3+ timeframes aligned
• 2 stars: Exactly 3 timeframes aligned
• 1 star: Other conditions
VISUAL DASHBOARD
• Clean table display (position customizable)
• Color-coded trend indicators (green/red/yellow)
• Extended statistics panel (toggleable)
• Shows: Trends, Strength, Quality, RSI, Volume, Price Distance
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TECHNICAL SPECIFICATIONS
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CALCULATIONS
Trend Determination per Timeframe:
• request.security() fetches EMA values with gaps=off, lookahead=off
• Compares Fast EMA vs Slow EMA
• Returns: 1 (bullish), -1 (bearish), 0 (neutral)
Trend Strength:
• Counts number of bullish timeframes
• Formula: (bullish_count / 6) × 100
• Range: 0% (all bearish) to 100% (all bullish)
Price Distance from EMA:
• Formula: ((close - EMA) / EMA) × 100
• Positive: Price above EMA
• Negative: Price below EMA
• Warning when absolute distance > 5%
ANTI-REPAINTING MEASURES
• All request.security() calls use lookahead=barmerge.lookahead_off
• Dashboard updates only on barstate.islast
• Historical bars remain unchanged
• Crossover signals finalize on bar close
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USAGE GUIDE
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INTERPRETING THE DASHBOARD
Timeframe Rows:
• Each row shows individual timeframe trend status
• Look for alignment (multiple timeframes same direction)
• Higher timeframes generally more significant
Strength Indicator:
• >66.67%: Strong bullish (4+ timeframes bullish)
• 33.33-66.67%: Mixed/choppy conditions
• <33.33%: Strong bearish (4+ timeframes bearish)
Quality Score:
• Higher stars = better confluence of factors
• 5-star setups have strongest multi-factor confirmation
• Lower scores may indicate weaker or conflicting signals
SUGGESTED APPLICATIONS
Trend Confirmation:
• Check if multiple timeframes confirm current chart trend
• Higher agreement = stronger trend confidence
• Use for position sizing decisions
Entry Timing:
• Wait for EMA crossover on chart timeframe
• Confirm with higher timeframe alignment
• Volume above average preferred
• RSI not in extreme zones
Divergence Detection:
• When lower timeframes diverge from higher
• May indicate trend exhaustion or reversal
• Requires additional confirmation
CUSTOMIZATION
EMA Settings:
• Adjust Fast/Slow lengths for different sensitivities
• Shorter periods = more responsive, more signals
• Longer periods = smoother, fewer signals
• Common alternatives: 10/30, 12/26, 50/200
Volume Filter:
• Enable for higher-quality signals (fewer false positives)
• Disable in always-liquid markets or for more signals
• Adjust MA length based on typical volume patterns
Display Options:
• Toggle EMAs, S/R levels, extended stats as needed
• Choose dashboard position to avoid chart overlap
• Adjust colors for visibility preferences
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ALERTS
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AVAILABLE ALERT CONDITIONS
1. Bullish EMA Cross (Volume Confirmed)
2. Bearish EMA Cross (Volume Confirmed)
3. Strong Bullish Alignment (4+ timeframes)
4. Strong Bearish Alignment (4+ timeframes)
5. Trend Strength Increasing (>16.67% jump)
6. Trend Strength Decreasing (>16.67% drop)
7. Excellent Trade Setup (5-star rating)
Alert messages use standard placeholders:
• {{ticker}} - Symbol name
• {{close}} - Current close price
• {{time}} - Bar timestamp
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LIMITATIONS & CONSIDERATIONS
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KNOWN LIMITATIONS
• Lower timeframe data may not be available on all symbols
• 1-minute data typically limited to recent history
• request.security() subject to TradingView data limits
• Dashboard requires screen space (may overlap on small screens)
• More complex calculations may affect load time on slower devices
NOT SUITABLE FOR
• Highly volatile/illiquid instruments (many false signals)
• News-driven markets during announcements
• Automated trading without additional filters
• Markets where EMA strategies don't perform well
DOES NOT PROVIDE
• Exact entry/exit prices
• Stop-loss or take-profit levels
• Position sizing recommendations
• Guaranteed profit signals
• Market predictions
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BEST PRACTICES
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RECOMMENDED USAGE
✓ Combine with price action analysis
✓ Use appropriate risk management
✓ Backtest on historical data before live use
✓ Adjust settings for specific market characteristics
✓ Wait for higher-quality setups in important trades
✓ Consider overall market context and fundamentals
NOT RECOMMENDED
✗ Using as standalone trading system without confirmation
✗ Trading every signal without discretion
✗ Ignoring risk management principles
✗ Trading without understanding the methodology
✗ Applying to unsuitable markets/timeframes
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EDUCATIONAL BACKGROUND
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EMA CROSSOVER STRATEGY
The Exponential Moving Average crossover is a classical trend-following technique:
• Golden Cross: Fast EMA crosses above Slow EMA (bullish signal)
• Death Cross: Fast EMA crosses below Slow EMA (bearish signal)
• Widely used since the 1970s in various markets
• More responsive than SMA due to exponential weighting
MULTI-TIMEFRAME ANALYSIS
Analyzing multiple timeframes helps traders:
• Identify alignment between short and long-term trends
• Reduce false signals from single-timeframe noise
• Understand market context across different horizons
• Make informed decisions about trade duration
VOLUME ANALYSIS
Volume confirmation adds reliability:
• High volume suggests institutional participation
• Low volume signals may indicate false breakouts
• Volume precedes price in many market theories
• Helps distinguish genuine moves from noise
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TECHNICAL IMPLEMENTATION
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CODE STRUCTURE
• Organized in clear sections with proper commenting
• Uses explicit type declarations (int, float, bool, color, string)
• Constants defined at top (BULLISH=1, BEARISH=-1, etc.)
• Functions documented with @function, @param, @returns
• Follows PineCoders naming conventions (camelCase variables)
PERFORMANCE OPTIMIZATION
• var keyword for table (created once, not every bar)
• Calculations cached where possible
• Dashboard updates only on last bar
• Minimal redundant security() calls
SECURITY IMPLEMENTATION
• Proper gaps and lookahead parameters
• No future data leakage
• Signals finalize on bar close
• Historical bars remain static
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VERSION INFORMATION
═══════════════════════════════════════════════════════════
Current Version: 2.0
Pine Script Version: 5
Last Updated: 2024
Developed by: Zakaria Safri
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SETTINGS REFERENCE
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EMA SETTINGS
• Fast EMA Length: 1-500 (default: 20)
• Slow EMA Length: 1-500 (default: 50)
VOLUME & MOMENTUM
• Use Volume Confirmation: true/false (default: true)
• Volume MA Length: 1-500 (default: 20)
• Show RSI Levels: true/false (default: true)
• RSI Length: 1-500 (default: 14)
PRICE ACTION FEATURES
• Show Price Distance: true/false (default: true)
• Show Key Levels: true/false (default: true)
• S/R Lookback Period: 10-500 (default: 50)
DISPLAY SETTINGS
• Show EMAs on Chart: true/false (default: true)
• Fast EMA Color: customizable (default: cyan)
• Slow EMA Color: customizable (default: orange)
• EMA Line Width: 1-5 (default: 2)
• Show Fill Between EMAs: true/false (default: true)
• Show Crossover Signals: true/false (default: true)
DASHBOARD SETTINGS
• Position: Top Left/Right, Bottom Left/Right
• Show Extended Statistics: true/false (default: true)
ALERT SETTINGS
• Alert on Multi-TF Alignment: true/false (default: true)
• Alert on Trend Strength Change: true/false (default: true)
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RISK DISCLAIMER
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This indicator is provided for educational and informational purposes only. It should not be considered financial advice or a recommendation to buy or sell any security.
IMPORTANT NOTICES:
• Past performance does not indicate future results
• All trading involves risk of capital loss
• No indicator guarantees profitable trades
• Always conduct independent research and analysis
• Use proper risk management and position sizing
• Consult a qualified financial advisor before trading
• The developer assumes no liability for trading losses
By using this indicator, you acknowledge that you understand these risks and accept full responsibility for your trading decisions.
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SUPPORT & CONTRIBUTIONS
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FEEDBACK WELCOME
• Constructive comments appreciated
• Bug reports help improve the indicator
• Feature suggestions considered for future versions
• Share your experience to help other users
OPEN SOURCE
This code is published as open source for the TradingView community to:
• Learn from the implementation
• Modify for personal use
• Understand multi-timeframe analysis techniques
If you find this indicator useful, please consider:
• Leaving a thoughtful review
• Sharing with other traders who might benefit
• Following for future updates and releases
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ADDITIONAL RESOURCES
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RECOMMENDED READING
• TradingView Pine Script documentation
• PineCoders community resources
• Technical analysis textbooks on moving averages
• Multi-timeframe trading strategy guides
• Risk management principles
RELATED CONCEPTS
• Trend following strategies
• Moving average convergence/divergence
• Multiple timeframe analysis
• Volume-price relationships
• Momentum indicators
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Thank you for using this indicator. Trade responsibly and continue learning!
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Adaptive Trend 1m ### Overview
The "Adaptive Trend Impulse Parallel SL/TP 1m Realistic" strategy is a sophisticated trading system designed specifically for high-volatility markets like cryptocurrencies on 1-minute timeframes. It combines trend-following with momentum filters and adaptive parameters to dynamically adjust to market conditions, ensuring more reliable entries and risk management. This strategy uses SuperTrend for primary trend detection, enhanced by MACD, RSI, Bollinger Bands, and optional volume spikes. It incorporates parallel stop-loss (SL) and multiple take-profit (TP) levels based on ATR, with options for breakeven and trailing stops after the first TP. Optimized for realistic backtesting on short timeframes, it avoids over-optimization by adapting indicators to market speed and efficiency.
### Principles of Operation
The strategy operates on the principle of adaptive impulse trading, where entry signals are generated only when multiple conditions align to confirm a strong trend reversal or continuation:
1. **Trend Detection (SuperTrend)**: The core signal comes from an adaptive SuperTrend indicator. It calculates upper and lower bands using ATR (Average True Range) with dynamic periods and multipliers. A buy signal occurs when the price crosses above the lower band (from a downtrend), and a sell signal when it crosses below the upper band (from an uptrend). Adaptation is based on Rate of Change (ROC) to measure market speed, shortening periods in fast markets for quicker responses.
2. **Momentum and Trend Filters**:
- **MACD**: Uses adaptive fast and slow lengths. In "Trend Filter" mode (default when "Use MACD Cross" is false), it checks if the MACD line is above/below the signal for long/short. In cross mode, it requires a crossover/crossunder.
- **RSI**: Adaptive period RSI must be above 50 for longs and below 50 for shorts, confirming overbought/oversold conditions dynamically.
- **Bollinger Bands (BB)**: Depending on the mode ("Midline" by default), it requires the price to be above/below the BB midline for longs/shorts, or a breakout in "Breakout" mode. Deviation adapts to market efficiency.
- **Volume Spike Filter** (optional): Entries require volume to exceed an adaptive multiple of its SMA, signaling strong impulse.
3. **Volatility Filter**: Entries are only allowed if current ATR percentage exceeds a historical minimum (adaptive), preventing trades in low-volatility ranges.
4. **Risk Management (Parallel SL/TP)**:
- **Stop-Loss**: Set at an adaptive ATR multiple below/above entry for long/short.
- **Take-Profits**: Three levels at adaptive ATR multiples, with partial position closures (e.g., 51% at TP1, 25% at TP2, remainder at TP3).
- **Post-TP1 Features**: Optional breakeven moves SL to entry after TP1. Trailing SL uses BB midline as a dynamic trail.
- All levels are calculated per trade using the ATR at entry, making them "realistic" for 1m charts by widening SL and tightening initial TPs.
The strategy enters long on buy signals with all filters met, and short on sell signals. It uses pyramid margin (100% long/short) for full position sizing.
Adaptation is driven by:
- **Market Speed (normSpeed)**: Based on ROC, tightens multipliers in volatile periods.
- **Efficiency Ratio (ER)**: Measures trend strength, adjusting periods for trending vs. ranging markets.
This ensures the strategy "adapts" without manual tweaks, reducing false signals in varying conditions.
### Main Advantages
- **Adaptability**: Unlike static strategies, parameters dynamically adjust to market volatility and trend strength, improving performance across ranging and trending phases without over-optimization.
- **Realistic Risk Management for 1m**: Wider SL and tiered TPs prevent premature stops in noisy short-term charts, while partial profits lock in gains early. Breakeven/trailing options protect profits in extended moves.
- **Multi-Filter Confirmation**: Combines trend, momentum, and volume for high-probability entries, reducing whipsaws. The volatility filter avoids flat markets.
- **Debug Visualization**: Built-in plots for signals, levels, and component checks (when "Show Debug" is enabled) help users verify logic on charts.
- **Efficiency**: Low computational load, suitable for real-time trading on TradingView with alerts.
Backtesting shows robust results on volatile assets, with a focus on sustainable risk (e.g., SL at 3x ATR avoids excessive drawdowns).
### Uniqueness
What sets this strategy apart is its **fully adaptive framework** integrating multiple indicators with real-time market metrics (ROC for speed, ER for efficiency). Most trend strategies use fixed parameters, leading to poor adaptation; here, every key input (periods, multipliers, deviations) scales dynamically within bounds, creating a "self-tuning" system. The "parallel SL/TP with 1m realism" adds custom handling for micro-timeframes: tightened initial TPs for quick wins and adaptive min-ATR filter to skip low-vol bars. Unlike generic mashups, it justifies the combination—SuperTrend for trend, MACD/RSI/BB for impulse confirmation, volume for conviction—working synergistically to capture "trend impulses" while filtering noise. The post-TP1 breakeven/trailing tied to BB adds a unique profit-locking mechanism not common in open-source scripts.
### Recommended Settings
These settings are optimized and recommended for trading ASTER/USDT on Bybit, with 1-minute chart, x10 leverage, and cross margin mode. They provide a balanced risk-reward for this volatile pair:
- **Base Inputs**:
- Base ATR Period: 10
- Base SuperTrend ATR Multiplier: 2.5
- Base MACD Fast: 8
- Base MACD Slow: 17
- Base MACD Signal: 6
- Base RSI Period: 9
- Base Bollinger Period: 12
- Bollinger Deviation: 1.8
- Base Volume SMA Period: 19
- Base Volume Spike Multiplier: 1.8
- Adaptation Window: 54
- ROC Length: 10
- **TP/SL Settings**:
- Use Stop Loss: True
- Base SL Multiplier (ATR): 3
- Use Take Profits: True
- Base TP1 Multiplier (ATR): 5.5
- Base TP2 Multiplier (ATR): 10.5
- Base TP3 Multiplier (ATR): 19
- TP1 % Position: 51
- TP2 % Position: 25
- Breakeven after TP1: False
- Trailing SL after TP1: False
- Base Min ATR Filter: 0.001
- Use Volume Spike Filter: True
- BB Condition: Midline
- Use MACD Cross (false=Trend Filter): True
- Show Debug: True
For backtesting, use initial capital of 30 USD, base currency USDT, order size 100 USDT, pyramiding 1, commission 0.1%, slippage 0 ticks, long/short margin 0%.
Always backtest on your platform and use risk management—risk no more than 1-2% per trade. This is not financial advice; trade at your own risk.
VWAP Retest + EMA9 Cross + Candle Pattern V2📈 VWAP Retest + EMA9 Cross + Candle Pattern Strategy_V2
Setup: This intraday momentum strategy combines 3 core elements:
• VWAP Retest: Price retests VWAP within a small buffer zone
• EMA9 Crossover: EMA9 crosses above VWAP within the last 3 bars
• Bullish Candle Pattern: At least one bullish signal — Hammer, Engulfing, or Momentum candle
A trade is triggered only during the US morning session (9:30–12:30 EST) and only if price is above yesterday’s high, suggesting strong momentum.
⚙️ Strategy Settings
• Initial Capital: $100,000
• Position Sizing: 10% of equity per trade
• Commission: 0.03% per trade
• Slippage: 1 tick
• Take Profit: +3% from entry
• Stop Loss: 0.5% below VWAP at entry
• Forced Exit: 1:00 PM EST
📊 Strategy Logic
• VWAP Retest Filter ensures entry is near a value zone.
• EMA9 Cross Confirmation aligns short-term momentum with volume-weighted price.
• Bullish Candle Patterns provide price action confirmation:
○ ✅ Hammer
○ ✅ Bullish Engulfing
○ ✅ Large momentum body
• Above Yesterday’s High (YH) acts as a bullish bias filter.
🧪 Backtest Results (Jan 2023 – Oct 2025)
• Total Trades: 120
• Win Rate: 52.5%
• Profit Factor: 1.18
• Max Drawdown: 1.22%
• Net P&L: +$1,064 (+1.06%)
Due to chart data limits, only part of the period may be visible on publication charts.
🔍 Chart Visuals
This strategy plots:
• VWAP (white) and EMA9 (orange)
• Candle pattern markers:
○ “H” = Hammer
○ “BE” = Bullish Engulfing
○ “M” = Momentum Candle
• “SETUP” label when all conditions are met
• YH/YL labels for context — previous day’s high/low
💡 Use Case
This setup is designed for intraday momentum scalping, ideal for traders who:
• Trade morning breakouts
• Use VWAP as a dynamic support/resistance
• Want clear, rule-based entries based on both trend and price action
Educational and research use - not financial advice.
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.
RSI Divergence Strategy v6 What this does
Detects regular and hidden divergences between price and RSI using confirmed RSI pivots. Adds RSI@pivot entry gates, a normalized strength + volume filter, optional volume gate, delayed entries, and transparent risk management with rigid SL and activatable trailing. Visuals are throttled for clarity and include a gap-free horizontal RSI gradient.
How it works (simple)
🧮 RSI is calculated on your selected source/period.
📌 RSI pivots are confirmed with left/right lookbacks (lbL/lbR). A pivot becomes final only after lbR bars; before that, it can move (expected).
🔎 The latest confirmed pivot is compared against the previous confirmed pivot within your bar window:
• Regular Bullish = price lower low + RSI higher low
• Hidden Bullish = price higher low + RSI lower low
• Regular Bearish = price higher high + RSI lower high
• Hidden Bearish = price lower high + RSI higher high
💪 Each divergence gets a strength score that multiplies price % change, RSI change, and a volume ratio (Volume SMA / Baseline Volume SMA).
• Set Min divergence strength to filter tiny/noisy signals.
• Turn on the volume gate to require volume ratio ≥ your threshold (e.g., 1.0).
🎯 RSI@pivot gating:
• Longs only if RSI at the bullish pivot ≤ 30 (default).
• Shorts only if RSI at the bearish pivot ≥ 70 (default).
⏱ Entry timing:
• Immediate: on divergence confirm (delay = 0).
• Delayed: after N bars if RSI is still valid.
• RSI-only mode: ignore divergences; use RSI thresholds only.
🛡 Risk:
• Rigid SL is placed from average entry.
• Trailing activates only after unrealized gain ≥ threshold; it re-anchors on new highs (long) or new lows (short).
What’s NEW here (vs. the reference) — and why you may care
• Improved pivots + bar window → fewer early/misaligned signals; cleaner drawings.
• RSI@pivot gates → entries aligned with true oversold/overbought at the exact decision bar.
• Normalized strength + volume gate → ignore weak or low-volume divergences.
• Delayed entries → require the signal to persist N bars if you want more confirmation.
• Rigid SL + activatable trailing → trailing engages only after a cushion, so it’s less noisy.
• Clutter control + gradient → readable chart with a smooth RSI band look.
Suggested starting values (clear ranges)
• RSI@pivot thresholds: LONG ≤ 30 (oversold), SHORT ≥ 70 (overbought).
• Min divergence strength:
0.0 = off
3–6 = moderate filter
7–12 = strict filter for noisy LTFs
• Volume gate (ratio):
1.0 = at least baseline volume
1.2–1.5 = strong-volume only (fewer but cleaner signals)
• Pivot lookbacks:
lbL 1–2, lbR 3–4 (raise lbR to confirm later and reduce noise)
• Bar window (between pivots):
Min 5–10, Max 30–60 (increase Min if you see micro-pivots; increase Max for wider structures)
• Risk:
Rigid SL 2–5% on liquid majors; 5–10% on higher-volatility symbols
Trailing activation 1–3%, trailing 0.5–1.5% are common intraday starts
Plain-text examples
• BTCUSDT 1h → RSI 9, lbL 1, lbR 3, Min strength 5.0, Volume gate 1.0, SL 4.5%, Trail on 2.0%, Trail 1.0%.
• SPY 15m → RSI 8, lbL 1, lbR 3, Min strength 7.0, Volume gate 1.2, SL 3.0%, Trail on 1.5%, Trail 0.8%.
• EURUSD 4h → RSI 14, lbL 2, lbR 4, Min strength 4.0, Volume gate 1.0, SL 2.5%, Trail on 1.0%, Trail 0.5%.
Notes & limitations
• Pivot confirmation means the newest candidate pivot can move until lbR confirms it (expected).
• Results vary by timeframe/symbol/settings; always forward-test.
• Educational tool — no performance or profit claims.
Credits
• RSI by J. Welles Wilder Jr. (1978).
• Reference divergence script by eemani123:
• This version by tagstrading 2025 adds: improved pivot engine, RSI@pivot gating, normalized strength + optional volume gate, delayed entries, rigid SL and activatable trailing, and a gap-free RSI gradient.
Luxy Adaptive MA Cloud - Trend Strength & Signal Tracker V2Luxy Adaptive MA Cloud - Professional Trend Strength & Signal Tracker
Next-generation moving average cloud indicator combining ultra-smooth gradient visualization with intelligent momentum detection. Built for traders who demand clarity, precision, and actionable insights.
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WHAT MAKES THIS INDICATOR SPECIAL?
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Unlike traditional MA indicators that show static lines, Luxy Adaptive MA Cloud creates a living, breathing visualization of market momentum. Here's what sets it apart:
Exponential Gradient Technology
This isn't just a simple fill between two lines. It's a professionally engineered gradient system with 26 precision layers using exponential density distribution. The result? An organic, cloud-like appearance where the center is dramatically darker (15% transparency - where crossovers and price action occur), while edges fade gracefully (75% transparency). Think of it as a visual "heat map" of trend strength.
Dynamic Momentum Intelligence
Most MA clouds only show structure (which MA is on top). This indicator shows momentum strength in real-time through four intelligent states:
- 🟢 Bright Green = Explosive bullish momentum (both MAs rising strongly)
- 🔵 Blue = Weakening bullish (structure intact, but momentum fading)
- 🟠 Orange = Caution zone (bearish structure forming, weak momentum)
- 🔴 Deep Red = Strong bearish momentum (both MAs falling)
The cloud literally tells you when trends are accelerating or losing steam.
Conditional Performance Architecture
Every calculation is optimized for speed. Disable a feature? It stops calculating entirely—not just hidden, but not computed . The 26-layer gradient only renders when enabled. Toggle signals off? Those crossover checks don't run. This makes it one of the most efficient cloud indicators available, even with its advanced visual system.
Zero Repaint Guarantee
All signals and momentum states are based on confirmed bar data only . What you see in historical data is exactly what you would have seen trading live. No lookahead bias. No repainting tricks. No signals that "magically" appear perfect in hindsight. If a signal shows in history, it would have triggered in real-time at that exact moment.
Educational by Design
Every single input includes comprehensive tooltips with:
- Clear explanations of what each parameter does
- Practical examples of when to use different settings
- Recommended configurations for scalping, day trading, and swing trading
- Real-world trading impact ("This affects entry timing" vs "This is visual only")
You're not just getting an indicator—you're learning how to use it effectively .
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THE GRADIENT CLOUD - TECHNICAL DETAILS
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Architecture:
26 precision layers for silk-smooth transitions
Exponential density curve - layers packed tightly near center (where crossovers happen), spread wider at edges
75%-15% transparency range - center is highly opaque (15%), edges fade gracefully (75%)
V-Gradient design - emphasizes the action zone between Fast and Medium MAs
The Four Momentum States:
🟢 GREEN - Strong Bullish
Fast MA above Medium MA
Both MAs rising with momentum > 0.02%
Action: Enter/hold LONG positions, strong uptrend confirmed
🔵 BLUE - Weak Bullish
Fast MA above Medium MA
Weak or flat momentum
Action: Caution - bullish structure but losing strength, consider trailing stops
🟠 ORANGE - Weak Bearish
Medium MA above Fast MA
Weak or flat momentum
Action: Warning - bearish structure developing, consider exits
🔴 RED - Strong Bearish
Medium MA above Fast MA
Both MAs falling with momentum < -0.02%
Action: Enter/hold SHORT positions, strong downtrend confirmed
Smooth Transitions: The momentum score is smoothed using an 8-bar EMA to eliminate noise and prevent whipsaws. You see the true trend , not every minor fluctuation.
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FLEXIBLE MOVING AVERAGE SYSTEM
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Three Customizable MAs:
Fast MA (default: EMA 10) - Reacts quickly to price changes, defines short-term momentum
Medium MA (default: EMA 20) - Balances responsiveness with stability, core trend reference
Slow MA (default: SMA 200, optional) - Long-term trend filter, major support/resistance
Six MA Types Available:
EMA - Exponential; faster response, ideal for momentum and day trading
SMA - Simple; smooth and stable, best for swing trading and trend following
WMA - Weighted; middle ground between EMA and SMA
VWMA - Volume-weighted; reflects market participation, useful for liquid markets
RMA - Wilder's smoothing; used in RSI/ADX, excellent for trend filters
HMA - Hull; extremely responsive with minimal lag, aggressive option
Recommended Settings by Trading Style:
Scalping (1m-5m):
Fast: EMA(5-8)
Medium: EMA(10-15)
Slow: Not needed or EMA(50)
Day Trading (5m-1h):
Fast: EMA(10-12)
Medium: EMA(20-21)
Slow: SMA(200) for bias
Swing Trading (4h-1D):
Fast: EMA(10-20)
Medium: EMA(34-50)
Slow: SMA(200)
Pro Tip: Start with Fast < Medium < Slow lengths. The gradient works best when there's clear separation between Fast and Medium MAs.
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CROSSOVER SIGNALS - CLEAN & RELIABLE
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Golden Cross ⬆ LONG Signal
Fast MA crosses above Medium MA
Classic bullish reversal or trend continuation signal
Most reliable when accompanied by GREEN cloud (strong momentum)
Death Cross ⬇ SHORT Signal
Fast MA crosses below Medium MA
Classic bearish reversal or trend continuation signal
Most reliable when accompanied by RED cloud (strong momentum)
Signal Intelligence:
Anti-spam filter - Minimum 5 bars between signals prevents noise
Clean labels - Placed precisely at crossover points
Alert-ready - Built-in ALERTS for automated trading systems
No repainting - Signals based on confirmed bars only
Signal Quality Assessment:
High-Quality Entry:
Golden Cross + GREEN cloud + Price above both MAs
= Strong bullish setup ✓
Low-Quality Entry (skip or wait):
Golden Cross + ORANGE cloud + Choppy price action
= Weak bullish setup, likely whipsaw ✗
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REAL-TIME INFO PANEL
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An at-a-glance dashboard showing:
Trend Strength Indicator:
Visual display of current momentum state
Color-coded header matching cloud color
Instant recognition of market bias
MA Distance Table:
Shows percentage distance of price from each enabled MA:
Green rows : Price ABOVE MA (bullish)
Red rows : Price BELOW MA (bearish)
Gray rows : Price AT MA (rare, decision point)
Distance Interpretation:
+2% to +5%: Healthy uptrend
+5% to +10%: Getting extended, caution
+10%+: Overextended, expect pullback
-2% to -5%: Testing support
-5% to -10%: Oversold zone
-10%+: Deep correction or downtrend
Customization:
4 corner positions
5 font sizes (Tiny to Huge)
Toggle visibility on/off
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HOW TO USE - PRACTICAL TRADING GUIDE
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STRATEGY 1: Trend Following
Identify trend : Wait for GREEN (bullish) or RED (bearish) cloud
Enter on signal : Golden Cross in GREEN cloud = LONG, Death Cross in RED cloud = SHORT
Hold position : While cloud maintains color
Exit signals :
• Cloud turns ORANGE/BLUE = momentum weakening, tighten stops
• Opposite crossover = close position
• Cloud turns opposite color = full reversal
STRATEGY 2: Pullback Entries
Confirm trend : GREEN cloud established (bullish bias)
Wait for pullback : Price touches or crosses below Fast MA
Enter when : Price rebounds back above Fast MA with cloud still GREEN
Stop loss : Below Medium MA or recent swing low
Target : Previous high or when cloud weakens
STRATEGY 3: Momentum Confirmation
Your setup triggers : (e.g., chart pattern, support/resistance)
Check cloud color :
• GREEN = proceed with LONG
• RED = proceed with SHORT
• BLUE/ORANGE = skip or reduce size
Use gradient as confluence : Not as primary signal, but as momentum filter
Risk Management Tips:
Never enter against the cloud color (don't LONG in RED cloud)
Reduce position size during BLUE/ORANGE (transition periods)
Place stops beyond Medium MA for swing trades
Use Slow MA (200) as final trend filter - don't SHORT above it in uptrends
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PERFORMANCE & OPTIMIZATION
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Tested On:
Crypto: BTC, ETH, major altcoins
Stocks: SPY, AAPL, TSLA, QQQ
Forex: EUR/USD, GBP/USD, USD/JPY
Indices: S&P 500, NASDAQ, DJI
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TRANSPARENCY & RELIABILITY
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Educational Focus:
Detailed tooltips on every input
Clear documentation of methodology
Practical examples in descriptions
Teaches you why , not just what
Open Logic:
Momentum calculation: (Fast slope + Medium slope) / 2
Smoothing: 8-bar EMA to reduce noise
Thresholds: ±0.02% for strong momentum classification
Everything is transparent and explainable
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COMPLETE FEATURE LIST
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Visual Components:
26-layer exponential gradient cloud
3 customizable moving average lines
Golden Cross / Death Cross labels
Real-time info panel with trend strength
MA distance table
Calculation Features:
6 MA types (EMA, SMA, WMA, VWMA, RMA, HMA)
Momentum-based cloud coloring
Smoothed trend strength scoring
Conditional performance optimization
Customization Options:
All MA lengths adjustable
All colors customizable (when gradient disabled)
Panel position (4 corners)
Font sizes (5 options)
Toggle any feature on/off
Signal Features:
Anti-spam filter (configurable gap)
Clean, non-overlapping labels
Built-in alert conditions
No repainting guarantee
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IMPORTANT DISCLAIMERS
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This indicator is for educational and informational purposes only
Not financial advice - always do your own research
Past performance does not guarantee future results
Use proper risk management - never risk more than you can afford to lose
Test on paper/demo accounts before using with real money
Combine with other analysis methods - no single indicator is perfect
Works best in trending markets; less effective in choppy/sideways conditions
Signals may perform differently in different timeframes and market conditions
The indicator uses historical data for MA calculations - allow sufficient lookback period
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CREDITS & TECHNICAL INFO
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Version: 2.0
Release: October 2025
Special Thanks:
TradingView community for feedback and testing
Pine Script documentation for technical reference
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SUPPORT & UPDATES
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Found a bug? Comment below with:
Ticker symbol
Timeframe
Screenshot if possible
Steps to reproduce
Feature requests? I'm always looking to improve! Share your ideas in the comments.
Questions? Check the tooltips first (hover over any input) - most answers are there. If still stuck, ask in comments.
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Happy Trading!
Remember: The best indicator is the one you understand and use consistently. Take time to learn how the cloud behaves in different market conditions. Practice on paper before going live. Trade smart, manage risk, and may the trends be with you! 🚀
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.
ATR-Normalized MACD w/ Visual BackgroundChatGPT said:
Absolutely! Let’s break down the YON MACD indicator in detail so you understand what it does, how it works, and how to use it.
1. Purpose
The YON MACD is a volatility-adjusted version of the classic MACD. Instead of just using EMA differences, it normalizes the MACD by the Average True Range (ATR), which means:
High-volatility markets → the MACD signal is scaled down.
Low-volatility markets → the MACD signal is scaled up.
This gives a more consistent momentum signal across different market conditions, avoiding false spikes during high volatility.
2. Components
a. Fast and Slow EMAs
fastEMA → Typically 12-period EMA of price.
slowEMA → Typically 26-period EMA of price.
The difference between them measures short-term momentum.
b. ATR Normalization
atr → Average True Range over a specified period (default 26).
Formula:
YON MACD=fastEMA - slowEMAATR×100
YON MACD=
ATR
fastEMA - slowEMA
×100
This adjusts the MACD for market volatility.
c. Signal Line
EMA of the YON MACD (default 9 periods).
Acts like a trigger line for crossovers.
d. Histogram
hist = YON MACD - Signal Line
Visualizes divergence: how far the MACD is from the signal line.
Positive histogram → bullish momentum, negative → bearish momentum.
3. Visual Features
Plot Lines
YON MACD → colored green (rising), red (falling), gray (unchanged).
Signal line → always blue.
Histogram → columns: green (positive), red (negative).
Background Coloring
Green → MACD rising + histogram positive (bullish momentum).
Red → MACD falling + histogram negative (bearish momentum).
Yellow/Orange → histogram flips (early momentum change).
This makes trend and momentum immediately visible without having to study the panel in detail.
4. Alerts
MACD Cross Alerts
YON MACD crosses above the signal → potential buy.
YON MACD crosses below the signal → potential sell.
Histogram Flip Alerts
Histogram flips from negative → positive → early bullish signal.
Histogram flips from positive → negative → early bearish signal.
This allows automation or notifications for momentum changes.
5. How to Use
Trend Confirmation
Green background + MACD above signal → trend is bullish.
Red background + MACD below signal → trend is bearish.
Entry/Exit Signals
Buy: MACD crosses above signal or histogram flips positive.
Sell: MACD crosses below signal or histogram flips negative.
Volatility Adjustment
Since the MACD is ATR-normalized, it avoids overreacting in volatile conditions and highlights true momentum shifts.
Summary
The YON MACD is a trend-following and momentum indicator with:
Volatility normalization (ATR)
MACD cross signals
Histogram divergence visualization
Background colors for instant momentum reading
Alerts for crossovers and early momentum flips
It’s a powerful all-in-one momentum tool that can work for day trading, swing trading, or even longer-term analysis.
DAMMU SWING TRADING PROScalping and swing trading tool for 15-min and 1-min charts.
Designed for trend, pullback, and reversal analysis.
Works optionally with Heikin Ashi candles.
Indicators Used
EMAs:
EMA89/EMA75 (green)
EMA200/EMA180 (blue)
EMA633/EMA540 (black)
EMA5-12 channel & EMA12-36 ribbon for short-term trends
Price Action Channel (PAC) – EMA high/low/close, length adjustable
Fractals & Pristine Fractals (BW filter)
Higher High (HH), Lower High (LH), Higher Low (HL), Lower Low (LL) detection
Pivot Points – optional, disables fractals automatically
Bar color coding based on PAC:
Blue → Close above PAC
Red → Close below PAC
Gray → Close inside PAC
Trading Signals
PAC swing alerts: arrows or shapes when price exits PAC with optional 200 EMA filter.
RSI 14 signals (if added):
≥50 → BUY
<50 → SELL
Chart Setup
Two panes: 15-min (trend anchor) + 1-min (entry)
Optional Heikin Ashi candles
Use Sweetspot Gold2 for support/resistance “00” and “0” lines
Trendlines can be drawn using HH/LL or Pivot points
Usage Notes
Trade long only if price above EMA200; short only if below EMA200
Pullback into EMA channels/ribbons signals potential continuation
Fractals or pivot points help define trend reversals
PAC + EMA36 used for strong momentum confirmation
Alerts
Up/Down PAC exit alerts configurable with big arrows or labels
RSI labels show buy/sell zones (optional)
Works on both 15-min and 1-min timeframes
If you want, I can make an even shorter “super cheat-sheet” version for 1-page quick reference for trading. It will list only inputs, signals, and colors.
EU & US Open Signals (Triple Confirmation)🚀 EU & US Open Signals: Triple Confirmation Breakouts
💡 What This Indicator Does
This indicator plots the opening prices of the European (EU) and American (US) trading sessions as key daily support/resistance levels. Its main function is to generate three distinct levels of signals based on the breakout of these levels, strictly filtered by candlestick type.
The goal is to easily spot market momentum following the opening of the major trading sessions.
🔑 Key Features
1. Daily Session Levels
The indicator plots two crucial horizontal lines that reset daily:
EU Open (Black Line): The European session opening price (Default: 06:00 UTC).
US Open (Dark Blue Line): The American session opening price (Default: 12:00 UTC).
2. Triple Signaling Logic
All signals are only generated if the breakout candle is bullish (for Buy) or bearish (for Sell), and only within your set trading hours.
Logic I. EU Single Break
Condition: Close breaks above/below the EU Open Level + Bullish/Bearish candle.
Visual Plot: Small Cross (Green for Buy / Red for Sell).
Meaning: Early signal focused on EU session momentum.
Logic II. US Single Break
Condition: Close breaks above/below the US Open Level + Bullish/Bearish candle.
Visual Plot: Small Cross (Green for Buy / Red for Sell).
Meaning: Signal focused on US session momentum.
Logic III. Double Break (HIGH CONFIRMATION)
Condition: Close breaks above Both Levels (EU and US) for Buy, or below Both Levels for Sell + Bullish/Bearish candle.
Visual Plot: Large Triangle (Dark Green/Red).
⚠️ Crucial: This is the strongest signal, indicating strong momentum as both key daily levels have been broken.
⚙️ Simple Settings
Easily customize the indicator to fit your strategy:
EU/US Session Open Hour (UTC): Set the exact session open times.
Time Zone: Select your time zone for accurate level calculation.
Start/End Trading Hour: Define the daily window for when signals will be active.
🎯 Recommended Use
High Confidence: Focus on the Double Break (Logic III) - Large Triangle for your most confirmed entries.
Context: Use the Single Break signals (I and II) to confirm early market bias.
Risk: The Open Levels can serve as natural points for setting Stop-Loss orders.
Disclaimer: Trading carries risk. This tool is for analysis purposes only. Use it at your own discretion.
DAMMU Swing Trading PRODammu Scalping Pro – Short Notes
1️⃣ Purpose:
Scalping and swing trading tool for 15-min and 1-min charts.
Designed for trend continuation, pullbacks, and reversals.
Works well with Heikin Ashi candles (optional).
2️⃣ Core Components:
EMAs:
Fast: EMA5-12
Medium: EMA12-36 Ribbon
Long: EMA75/89 (1-min), EMA180/200 (15-min), EMA540/633
Price Action Channel (PAC): EMA-based High, Low, Close channel.
Fractals: Regular & filtered (BW) fractals for swing recognition.
Higher Highs / Lower Highs / Higher Lows / Lower Lows (HH, LH, HL, LL).
Pivot Points: Optional display with labels.
3️⃣ Bar Coloring:
Blue: Close above PAC
Red: Close below PAC
Gray: Close inside PAC
4️⃣ Alerts:
Swing Buy/Sell arrows based on PAC breakout and EMA200 filter.
Optional “Big Arrows” mode for visibility.
Alert messages: "SWING_UP" and "SWING_DN"
5️⃣ Workflow / Usage Tips:
Set chart to 15-min (for trend) + 1-min (for entry).
Optionally enable Heikin Ashi candles.
Trade long only above EMA200, short only below EMA200.
Watch for pullbacks into EMA channels or ribbons.
Confirm trend resumption via PAC breakout & bar color change.
Use fractals and pivot points to draw trendlines and locate support/resistance.
6️⃣ Optional Filters:
Filter PAC signals with 200 EMA.
Filter fractals for “Pristine/Ideal” patterns (BW filter).
7️⃣ Visuals:
EMA ribbons, PAC fill, HH/LL squares, fractal triangles.
Pivot labels & candle numbering for patterns.
8️⃣ Notes:
No extra indicators needed except optionally SweetSpot Gold2 for major S/R levels.
Suitable for scalping pullbacks with trend confirmation.
If you want, I can make an even shorter “one-screen cheat sheet” with colors, alerts, and EMAs, perfect for real-time chart reference.
Do you want me to do that?
DAMMU Swing Trading PRODammu Scalping Pro – Short Notes
1️⃣ Purpose:
Scalping and swing trading tool for 15-min and 1-min charts.
Designed for trend continuation, pullbacks, and reversals.
Works well with Heikin Ashi candles (optional).
2️⃣ Core Components:
EMAs:
Fast: EMA5-12
Medium: EMA12-36 Ribbon
Long: EMA75/89 (1-min), EMA180/200 (15-min), EMA540/633
Price Action Channel (PAC): EMA-based High, Low, Close channel.
Fractals: Regular & filtered (BW) fractals for swing recognition.
Higher Highs / Lower Highs / Higher Lows / Lower Lows (HH, LH, HL, LL).
Pivot Points: Optional display with labels.
3️⃣ Bar Coloring:
Blue: Close above PAC
Red: Close below PAC
Gray: Close inside PAC
4️⃣ Alerts:
Swing Buy/Sell arrows based on PAC breakout and EMA200 filter.
Optional “Big Arrows” mode for visibility.
Alert messages: "SWING_UP" and "SWING_DN"
5️⃣ Workflow / Usage Tips:
Set chart to 15-min (for trend) + 1-min (for entry).
Optionally enable Heikin Ashi candles.
Trade long only above EMA200, short only below EMA200.
Watch for pullbacks into EMA channels or ribbons.
Confirm trend resumption via PAC breakout & bar color change.
Use fractals and pivot points to draw trendlines and locate support/resistance.
6️⃣ Optional Filters:
Filter PAC signals with 200 EMA.
Filter fractals for “Pristine/Ideal” patterns (BW filter).
7️⃣ Visuals:
EMA ribbons, PAC fill, HH/LL squares, fractal triangles.
Pivot labels & candle numbering for patterns.
8️⃣ Notes:
No extra indicators needed except optionally SweetSpot Gold2 for major S/R levels.
Suitable for scalping pullbacks with trend confirmation.
If you want, I can make an even shorter “one-screen cheat sheet” with colors, alerts, and EMAs, perfect for real-time charT
