Net NEO Margin PositionTotal NEO Longs minus NEO Shorts in order to give you the total outstanding NEO margin debt.
ie: If 500,000 NEO has been longed, and 400,000 NEO has been shorted, then 500,000 has been bought, and 400,000 sold, leaving us with 100,000 NEO (net) remaining to be sold to give us an overall neutral margin position.
That isn't to say that the net margin position must move towards zero, but it is a sensible reference point, and historical net values may provide useful insights into the current circumstances.
(Anyone know what category this script should be in?)
在脚本中搜索"标准普尔500指数"
Everyday 0002 _ MAC 1st Trading Hour WalkoverThis is the second strategy for my Everyday project.
Like I wrote the last time - my goal is to create a new strategy everyday
for the rest of 2016 and post it here on TradingView.
I'm a complete beginner so this is my way of learning about coding strategies.
I'll give myself between 15 minutes and 2 hours to complete each creation.
This is basically a repetition of the first strategy I wrote - a Moving Average Crossover,
but I added a tiny thing.
I read that "Statistics have proven that the daily high or low is established within the first hour of trading on more than 70% of the time."
(source: )
My first Moving Average Crossover strategy, tested on VOLVB daily, got stoped out by the volatility
and because of this missed one nice bull run and a very nice bear run.
So I added this single line: if time("60", "1000-1600") regarding when to take exits:
if time("60", "1000-1600")
strategy.exit("Close Long", "Long", profit=2000, loss=500)
strategy.exit("Close Short", "Short", profit=2000, loss=500)
Sweden is UTC+2 so I guess UTC 1000 equals 12.00 in Stockholm. Not sure if this is correct, actually.
Anyway, I hope this means the strategy will only take exits based on price action which occur in the afternoon, when there is a higher probability of a lower volatility.
When I ran the new modified strategy on the same VOLVB daily it didn't get stoped out so easily.
On the other hand I'll have to test this on various stocks .
Reading and learning about how to properly test strategies is on my todo list - all tips on youtube videos or blogs
to read on this topic is very welcome!
Like I said the last time, I'm posting these strategies hoping to learn from the community - so any feedback, advice, or corrections is very much welcome and appreciated!
/pbergden
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.
Trading Lot & Margin Calculator
# 💹 Trading Lot & Margin Calculator - Professional Risk Management Tool
## 🎯 Overview
A comprehensive, all-in-one calculator dashboard that helps traders determine optimal position sizes, calculate margin requirements, and manage risk effectively across multiple asset classes. This indicator displays directly on your chart as a customizable table, providing real-time calculations based on current market prices.
## ✨ Key Features
### 📊 Three Powerful Calculation Modes:
**1. Calculate Lot Size (Risk-Based Position Sizing)**
- Input your risk percentage and stop loss in pips
- Automatically calculates the optimal lot size for your risk tolerance
- Respects margin limitations (configurable margin % cap)
- Ensures positions don't exceed minimum lot size (0.01)
- Perfect for risk management and proper position sizing
**2. Calculate Margin Cost**
- Input desired lot size
- See exactly how much margin is required
- Shows percentage of deposit used
- Displays free margin remaining
- Warns when insufficient funds
**3. Margin to Lots**
- Specify a fixed margin amount you want to use
- Calculator shows how many lots/contracts you can buy
- Ideal for traders with fixed margin budgets
## 🤖 Auto-Detection of Instruments
The calculator **automatically detects** what you're trading and adjusts calculations accordingly:
### ✅ Fully Supported:
- **💱 Forex Pairs** - All majors, minors, exotics (EURUSD, GBPJPY, etc.)
- Standard lot: 100,000 units
- JPY pairs: 0.01 pip size, others: 0.0001
- **🛢️ Commodities** - Gold, Silver, Oil
- XAUUSD (Gold): 100 oz per lot
- XAGUSD (Silver): 5,000 oz per lot
- Oil (WTI/Brent): 1,000 barrels per lot
- **📈 Indices** - US500, NAS100, US30, DAX, etc.
- Correct contract sizes per point
- **📊 Stocks** - All individual stocks
- 1 lot = 1 share
- Direct share calculations
### ⚠️ Known Limitation:
- **₿ Crypto calculations may not work properly** on all crypto pairs. Use manual contract size if needed.
## 📋 Dashboard Information Displayed:
- 🎯 Optimal/Requested Lot Size
- 💰 Margin Required
- 📊 Margin % of Deposit
- 💵 Free Margin Remaining
- 💎 Position Value
- 📈 Pip/Point Value
- ⚠️ Safety Warnings (insufficient funds, high risk, etc.)
- 🔍 Detected Instrument Type
- 📦 Contract Size
## ⚙️ Customizable Settings:
**Account Settings:**
- Account Deposit
- Leverage (1:1 to 1:1000)
- Max Margin % of Deposit (default 5% for safety)
**Risk Management:**
- Risk Percentage (for lot size calculation)
- Stop Loss in Pips
- Lot Amount (for margin cost calculation)
- Margin to Use (for margin-to-lots calculation)
**Display Options:**
- Show/Hide Dashboard
- Position: Top/Middle/Bottom, Left/Right
- Auto-detect instrument ON/OFF
- Manual contract size override
## 🎨 Professional Design
- Clean, modern table interface
- Color-coded warnings (red = danger, yellow = caution, green = safe)
- Large, readable text
- Minimal screen space usage
- Non-intrusive overlay
## 💡 Use Cases:
1. **Day Traders** - Quick position sizing based on account risk
2. **Swing Traders** - Calculate optimal positions for longer-term setups
3. **Risk Managers** - Ensure positions stay within margin limits
4. **Beginners** - Learn proper position sizing and risk management
5. **Multi-Asset Traders** - Seamlessly switch between forex, commodities, indices, and stocks
## ⚠️ Important Notes:
- ✅ Works on all timeframes
- ✅ Updates in real-time with price changes
- ✅ Minimum lot size enforced (0.01)
- ✅ Margin calculations use current chart price
- ⚠️ **Crypto calculations may be inaccurate** - verify with your broker
- 📌 Always verify calculations with your broker's specifications
- 📌 Contract sizes may vary by broker
## 🚀 How to Use:
1. Add indicator to any chart
2. Click settings ⚙️ icon
3. Enter your account details (deposit, leverage)
4. Choose calculation mode
5. Input your parameters
6. View optimal lot size and margin requirements on dashboard
## 📈 Perfect For:
- Forex traders managing multiple currency pairs
- Commodity traders (Gold, Silver, Oil)
- Index traders (S&P 500, NASDAQ, etc.)
- Stock traders
- Anyone who wants professional risk management
## 🛡️ Risk Management Features:
- Configurable margin % cap prevents over-leveraging
- Risk-based position sizing protects your account
- Warnings for high risk, insufficient funds, margin limitations
- Prevents positions below minimum lot size
---
**Trade smarter, not harder. Calculate before you trade!** 📊💪
---
## Version Notes:
- Pine Script v6
- Overlay mode for chart display
- No external dependencies
- Lightweight and fast
**Disclaimer:** This calculator is for educational and informational purposes only. Always verify calculations with your broker and trade at your own risk. Past performance does not guarantee future results.
---
EMA HI/LO Cloud Shift + Extra EMA//@version=6
indicator("EMA HI/LO Cloud Shift + Extra EMA + Shift EMA Line", overlay=true, max_lines_count=6, max_labels_count=0)
// ------------------------
// Inputs
// ------------------------
emaLength = input.int(22, "Main EMA Length", minval=1, maxval=200)
emaLineColor = input.color(color.blue, "Main EMA Lines Color")
// Main Cloud colors
cloudAboveColor = input.color(color.new(color.green, 80), "Main Cloud Color (Price Above)")
cloudBelowColor = input.color(color.new(color.red, 80), "Main Cloud Color (Price Below)")
cloudInsideColor = input.color(color.new(color.orange, 80), "Main Cloud Color (Price Inside)")
// ------------------------
// Shift EMA (new logic)
// ------------------------
showShiftEMA = input.bool(true, "Show Shift EMA Line?")
shiftEMALength = input.int(26, "Shift EMA Length", minval=1, maxval=500)
shiftEMASource = input.source(close, "Shift EMA Source") // fully customizable source
shiftEMAColor = input.color(color.purple, "Shift EMA Color")
shiftEMAWide = input.int(2, "Shift EMA Line Width", minval=1, maxval=5)
shiftEMAOffset = input.int(0, "Shift EMA Offset", minval=-100, maxval=100)
// ------------------------
// Second EMA (independent)
// ------------------------
showSecondEMA = input.bool(true, "Show Second EMA?")
secondEMALength = input.int(200, "Second EMA Length", minval=1, maxval=1000)
secondEMAColor = input.color(color.yellow, "Second EMA Color")
secondEMAWide = input.int(2, "Second EMA Line Width", minval=1, maxval=5)
// ------------------------
// Main EMA Cloud Calculations
// ------------------------
emaHigh = ta.ema(high, emaLength)
emaLow = ta.ema(low, emaLength)
// ------------------------
// Main Cloud logic
// ------------------------
priceAboveMain = close > emaHigh
priceBelowMain = close < emaLow
priceInsideMain = not priceAboveMain and not priceBelowMain
cloudColorMain = priceAboveMain ? cloudAboveColor : priceBelowMain ? cloudBelowColor : cloudInsideColor
p1_main = plot(emaHigh, title="Main EMA High", color=emaLineColor, linewidth=2)
p2_main = plot(emaLow, title="Main EMA Low", color=emaLineColor, linewidth=2)
fill(p1_main, p2_main, color=cloudColorMain, title="Main EMA Cloud")
// ------------------------
// Shift EMA Line (replaces cloud offset)
// ------------------------
shiftEMA = ta.ema(shiftEMASource, shiftEMALength)
plot(showShiftEMA ? shiftEMA : na, title="Shift EMA Line", color=shiftEMAColor, linewidth=shiftEMAWide, offset=shiftEMAOffset)
// ------------------------
// Second EMA Plot (Independent)
// ------------------------
secondEMA = ta.ema(close, secondEMALength)
plot(showSecondEMA ? secondEMA : na, title="Second EMA", color=secondEMAColor, linewidth=secondEMAWide)
LANZ Origins🔷 LANZ Origins – Multi-Framework Liquidity, Structure & Risk Management Overlay
LANZ Origins is an advanced multi-framework visualization toolkit that unifies key institutional concepts into one efficient interface. Designed for professional traders, it merges session mapping, liquidity analysis, imbalance detection, multi-account risk control, and higher-timeframe candle tracing — all in a single overlay.
🧩 Core Components
🈵 Asian Range Liquidity
Automatically detects and projects the Asian session range (19:00–02:00 NY) with an optional mid-price line (50 %). This provides visual context for intraday liquidity and manipulation zones commonly referenced in ICT-style analysis.
📊 Imbalance Detector
Highlights Fair Value Gaps (FVG), Opening Gaps (OG), and Volume Imbalances (VI) directly on-chart, using separate color schemes for bullish and bearish inefficiencies. Each element can be customized by width, ATR filter, and extension length.
🕯️ Higher-Timeframe Candles (ICT Style)
Displays multi-timeframe candles (HTF1–HTF6) simultaneously — e.g., 5 m, 30 m, 1 h, 4 h, 1 D, 1 W — each rendered with independent wick, border, and fill settings. Includes remaining-time counters, timeframe labels, and optional imbalance shading between bodies.
📈 Market Structure (ZigZag 30 m)
Replicates 30-minute swing structure to all active timeframes, producing dynamic pivots with live extension. Ideal for contextualizing BOS/CHoCH events across multiple scales.
💸 Multi-Account Lot Size Panel
Calculates position size for up to five accounts simultaneously, using your defined capital, risk %, and fixed SL distance (in pips). Results appear in a clean table at the bottom-right corner of the chart.
🎨 Session Visualization
Colored backgrounds mark key trading phases:
🟢 Day division
🔴 No-action zone
🔵 Kill-zone
🟡 Hold session
⚙️ Customization & Performance
Every module can be toggled individually, with full color, opacity, and style control. The script is optimized for overlay use and supports up to 500 boxes, lines, and labels with efficient resource handling.
🧠 Best Use Case
LANZ Origins is ideal for traders who follow:
Smart Money Concepts / ICT methodology
Liquidity & Imbalance-based trading
Multi-timeframe confluence setups
Risk-based position sizing workflows
Use it to observe how price interacts with liquidity pools, higher-timeframe candles, and imbalances within key sessions — while monitoring lot size risk in real time.
📌 Recommended Setup
Timeframes: 30m - 5m – 3m
Pairs: FX
Session Timezone: New York (EST/EDT)
Combine with: LANZ Strategy series for execution and journaling
💬 Note
This indicator does not generate buy/sell signals. It’s a visual and analytical tool built to support your own decision-making process.
Economic Cycle Signal (USA)📊 Economic Cycle Signal (USA)
This indicator overlays both the U.S. Federal Reserve Funds Rate (Fed Funds) and the U.S. Inflation Rate YoY directly onto your stock market chart (e.g., S&P 500). It visually connects monetary policy and inflation dynamics with equity market performance, helping traders and analysts understand how macroeconomic shifts impact risk assets.
🔹 Key Features
• Plots the monthly U.S. Fed Funds Rate alongside your chart.
• Overlays the U.S. Inflation Rate YoY, offering a direct and realistic view of inflation pressure instead of CPI.
• Shades the background to reflect different economic cycle phases (recovery, recession, expansion, late cycle).
• Highlights how the stock market reacts during shifting monetary and inflationary conditions.
• Provides a clear traffic-light style signal for quick macro interpretation.
• Now includes dynamic inflation color logic based on the Fed’s 2% target and 5% threshold (explained below).
🔹 Inflation Line Color Logic (New)
The inflation line now changes color dynamically to show whether inflation is within or outside the Federal Reserve’s comfort zone, and whether it’s rising or falling:
Inflation Condition Interpretation Line Color
Inflation > 5% and Rising Inflation overheating (well above target) 🔴 Red
Inflation > 5% and Falling Cooling off from high levels 💚 Lime
Inflation < 5% and Falling Disinflation / stable price environment 🟢 Green
Inflation < 5% and Rising Early inflation rebound 🟡 Yellow
This color-coded logic mirrors the interest rate phase colors, giving traders an instant visual cue about inflationary pressure and possible policy turning points.
🔹 How Traders & Analysts Can Use It
• Visualize the interaction between U.S. monetary policy and inflation cycles in real time.
• Identify historically supportive phases when low or easing rates follow moderate inflation.
• Detect tightening cycles when inflation spikes first and the Fed reacts, signaling potential equity headwinds.
• Use as a macro compass to anticipate inflation pressure, policy changes, and market regime shifts.
• Combine with technical analysis, fundamentals, or leading indicators for deeper macro insights.
🔹 Color Legend (Economic Phases)
🟩 Light Green → Recovery (Early Cycle)
• Rates: low or falling
• Inflation: low/stable
🟩 Green → Recession (Down Cycle)
• Rates: cut aggressively
• Inflation: falling
🟨 Yellow → Expansion (Mid Cycle)
• Rates: rising gradually
• Inflation: moderate
🟥 Red → Overheating (Late Cycle)
• Rates: high / rising fast
• Inflation: high
🔹 Inflation Context
• Inflation typically leads the policy rate cycle, offering early insight into future Fed actions.
• The U.S. Inflation Rate YoY provides a direct measure of consumer price changes compared to the same month last year — a clearer gauge of inflation pressure than CPI.
• The new color logic helps visualize whether inflation is accelerating or cooling, relative to the Fed’s 2% target and 5% upper threshold.
• This dual-overlay makes it easy to interpret the cause (inflation) and effect (interest rate policy) in one synchronized chart.
⚠️ Disclaimer
This script is for educational and informational purposes only. It does not provide financial advice or trading signals. Always combine it with your own research, proper risk management, and professional judgment.
Pivot Regime Anchored VWAP [CHE] Pivot Regime Anchored VWAP — Detects body-based pivot regimes to classify swing highs and lows, anchoring volume-weighted average price lines directly at higher highs and lower lows for adaptive reference levels.
Summary
This indicator identifies shifts between top and bottom regimes through breakouts in candle body highs and lows, labeling swing points as higher highs, lower highs, lower lows, or higher lows. It then draws anchored volume-weighted average price lines starting from the most recent higher high and lower low, providing dynamic support and resistance that evolve with volume flow. These anchored lines differ from standard volume-weighted averages by resetting only at confirmed swing extremes, reducing noise in ranging markets while highlighting momentum shifts in trends.
Motivation: Why this design?
Traders often struggle with static reference lines that fail to adapt to changing market structures, leading to false breaks in volatile conditions or missed continuations in trends. By anchoring volume-weighted average price calculations to body pivot regimes—specifically at higher highs for resistance and lower lows for support—this design creates reference levels tied directly to price structure extremes. This approach addresses the problem of generic moving averages lagging behind swing confirmations, offering a more context-aware tool for intraday or swing trading.
What’s different vs. standard approaches?
- Baseline reference: Traditional volume-weighted average price indicators compute a running total from session start or fixed periods, often ignoring price structure.
- Architecture differences:
- Regime detection via body breakout logic switches between high and low focus dynamically.
- Anchoring limited to confirmed higher highs and lower lows, with historical recalculation for accurate line drawing.
- Polyline rendering rebuilds only on the last bar to manage performance.
- Practical effect: Charts show fewer, more meaningful lines that start at swing points, making it easier to spot confluences with structure breaks rather than cluttered overlays from continuous calculations.
How it works (technical)
The indicator first calculates the maximum and minimum of each candle's open and close to define body highs and lows. It then scans a lookback window for the highest body high and lowest body low. A top regime triggers when the body high from the lookback period exceeds the window's highest, and a bottom regime when the body low falls below the window's lowest. These regime shifts confirm pivots only when crossing from one state to the other.
For top pivots, it compares the new body high against the previous swing high: if greater, it marks a higher high and anchors a new line; otherwise, a lower high. The same logic applies inversely for bottom pivots. Anchored lines use cumulative price-volume products and volumes from the anchor bar onward, subtracting prior cumulatives to isolate the segment. On pivot confirmation, it loops backward from the current bar to the anchor, computing and storing points for the line. New points append as bars advance, ensuring the line reflects ongoing volume weighting.
Initialization uses persistent variables to track the last swing values and anchor bars, starting with neutral states. Data flows from regime detection to pivot classification, then to anchoring and point accumulation, with lines rendered globally on the final bar.
Parameter Guide
Pivot Length — Controls the lookback window for detecting body breakouts, influencing pivot frequency and sensitivity to recent action. Shorter values catch more pivots in choppy conditions; longer smooths for major swings. Default: 30 (bars). Trade-offs/Tips: Min 1; for intraday, try 10–20 to reduce lag but watch for noise; on daily, 50+ for stability.
Show Pivot Labels — Toggles display of text markers at swing points, aiding quick identification of higher highs, lower highs, lower lows, or higher lows. Default: true. Trade-offs/Tips: Disable in multi-indicator setups to declutter; useful for backtesting structure.
HH Color — Sets the line and label color for higher high anchored lines, distinguishing resistance levels. Default: Red (solid). Trade-offs/Tips: Choose contrasting hues for dark/light themes; pair with opacity for fills if added later.
LL Color — Sets the line and label color for lower low anchored lines, distinguishing support levels. Default: Lime (solid). Trade-offs/Tips: As above; green shades work well for bullish contexts without overpowering candles.
Reading & Interpretation
Higher high labels and red lines indicate potential resistance zones where volume weighting begins at a new swing top, suggesting sellers may defend prior highs. Lower low labels and lime lines mark support from a fresh swing bottom, with the line's slope reflecting buyer commitment via volume. Lower highs or higher lows appear as labels without new anchors, signaling possible range-bound action. Line proximity to price shows overextension; crosses may hint at regime shifts, but confirm with volume spikes.
Practical Workflows & Combinations
- Trend following: Enter longs above a rising lower low anchored line after higher low confirmation; filter with rising higher highs for uptrends. Use line breaks as trailing stops.
- Exits/Stops: In downtrends, exit shorts below a higher high line; set aggressive stops above it for scalps, conservative below for swings. Pair with momentum oscillators for divergence.
- Multi-asset/Multi-TF: Defaults suit forex/stocks on 1H–4H; on crypto 15M, shorten length to 15. Scale colors for dark themes; combine with higher timeframe anchors for confluence.
Behavior, Constraints & Performance
Closed-bar logic ensures pivots confirm after the lookback period, with no repainting on historical bars—live bars may adjust until regime shift. No higher timeframe calls, so minimal repaint risk beyond standard delays. Resources include a 2000-bar history limit, label/polyline caps at 200/50, and loops for historical point filling (up to current bar count from anchor, typically under 500 iterations). Known limits: In extreme gaps or low-volume periods, anchors may skew; lines absent until first pivots.
Sensible Defaults & Quick Tuning
Start with the 30-bar length for balanced pivot detection across most assets. For too-frequent pivots in ranges, increase to 50 for fewer signals. If lines lag in trends, reduce to 20 and enable labels for visual cues. In low-volatility assets, widen color contrasts; test on 100-bar history to verify stability.
What this indicator is—and isn’t
This is a structure-aware visualization layer for anchoring volume-weighted references at swing extremes, enhancing manual analysis of regimes and levels. It is not a standalone signal generator or predictive model—always integrate with broader context like order flow or news. Use alongside risk management and position sizing, not as isolated buy/sell triggers.
Many thanks to LuxAlgo for the original script "McDonald's Pattern ". The implementation for body pivots instead of wicks uses a = max(open, close), b = min(open, close) and then highest(a, length) / lowest(b, length). This filters noise from the wicks and detects breakouts over/under bodies. Unusual and targeted, super innovative.
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
HermesHERMES STRATEGY - TRADINGVIEW DESCRIPTION
OVERVIEW
Hermes is an adaptive trend-following strategy that uses dual ALMA (Arnaud Legoux Moving Average) filters to identify high-quality entry and exit points. It's designed for swing and position traders who want smooth, low-lag signals with minimal whipsaws.
Unlike traditional moving averages that operate on price, Hermes analyzes price returns (percentage changes) to create signals that work consistently across any asset class and price range.
HOW IT WORKS
DUAL ALMA SYSTEM
The strategy uses two ALMA lines applied to price returns:
• Fast ALMA (Blue Line): Short-term trend signal (default: 80 periods)
• Slow ALMA (Black Line): Long-term baseline trend (default: 250 periods)
ALMA is superior to simple or exponential moving averages because it provides:
• Smoother curves with less noise
• Significantly reduced lag
• Natural resistance to outliers and flash crashes
TRADING LOGIC
BUY SIGNAL:
• Fast ALMA crosses above Slow ALMA (bullish regime)
• Price makes new N-bar high (momentum confirmation)
• Optional: Price above 200 EMA (macro trend filter)
• Optional: ALMA lines sufficiently separated (strength filter)
SELL SIGNAL:
• Fast ALMA crosses below Slow ALMA (bearish regime)
• Optional: Price makes new N-bar low (momentum confirmation)
The strategy stays in position during the entire bullish regime, allowing you to ride trends for weeks or months.
VISUAL INDICATORS
LINES:
• Blue Line: Fast ALMA (short-term signal)
• Black Line: Slow ALMA (long-term baseline)
TRADE MARKERS:
• Green Triangle Up: Buy executed
• Red Triangle Down: Sell executed
• Orange "M": Buy blocked by momentum filter
• Purple "W": Buy blocked by weak crossover strength
KEY PARAMETERS
ALMA SETTINGS:
• Short Period (default: 30) - Fast signal responsiveness
• Long Period (default: 250) - Baseline stability
• ALMA Offset (default: 0.90) - Balance between lag and smoothness
• ALMA Sigma (default: 7.5) - Gaussian curve width
ENTRY/EXIT FILTERS:
• Buy Lookback (default: 7) - Bars for momentum confirmation (required)
• Sell Lookback (default: 0) - Exit momentum bars (0 = disabled for faster exits)
• Min Crossover Strength (default: 0.0) - Required ALMA separation (0 = disabled)
• Use Macro Filter (default: true) - Only enter above 200 EMA
BEST PRACTICES
RECOMMENDED ASSETS - Works well on:
• Cryptocurrencies (Bitcoin, Ethereum, etc.)
• Major indices (S&P 500, Nasdaq)
• Large-cap stocks
• Commodities (Gold, Oil)
RECOMMENDED TIMEFRAMES:
• Daily: Primary timeframe for swing trading
• 4-Hour: More active trading (increase trade frequency)
• Weekly: Long-term position trading
PARAMETER TUNING:
• More trades: Lower Short Period (60-80)
• Fewer trades: Raise Short Period (100-120)
• Faster exits: Set Sell Lookback = 0
• Safer entries: Enable Macro Filter (Use Macro Filter = true)
STRATEGY ADVANTAGES
1. Low Lag - ALMA provides faster signals than traditional moving averages
2. Smooth Signals - Minimal whipsaws compared to crossover strategies
3. Asset Agnostic - Same parameters work across different markets
4. Trend Capture - Stays positioned during entire bullish regimes
5. Risk Management - Multiple filters prevent poor entries
6. Visual Clarity - Easy to interpret regime and filter states
WHEN TO USE HERMES
BEST FOR:
• Trending markets (crypto bull runs, equity uptrends)
• Swing trading (hold days to weeks)
• Position trading (hold weeks to months)
• Clear trend identification
• Risk-managed exposure
NOT SUITABLE FOR:
• Ranging/sideways markets
• Scalping or day trading
• High-frequency trading
• Mean reversion strategies
RISK DISCLAIMER
This indicator is for educational purposes only. Past performance does not guarantee future results. Always use proper position sizing and risk management. Test thoroughly on historical data before live trading.
CREDITS
Inspired by Giovanni Santostasi's Power Law Volatility Indicator, generalized for universal application across all assets using adaptive ALMA filtering.
Strategy by Hermes Trading Systems
QUICK START
1. Add indicator to chart
2. Use on daily timeframe for best results
3. Look for green buy signals when blue line crosses above black line
4. Exit on red sell signals when blue line crosses below black line
5. Adjust parameters based on your trading style:
• Conservative: Enable Macro Filter, increase Buy Lookback to 10
• Aggressive: Disable Macro Filter, lower Short Period to 60
• Default settings work well for most assets
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.
═══════════════════════════════════════════════
WHAT MAKES THIS INDICATOR SPECIAL?
═══════════════════════════════════════════════
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 .
═══════════════════════════════════════════════
THE GRADIENT CLOUD - TECHNICAL DETAILS
═══════════════════════════════════════════════
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.
═══════════════════════════════════════════════
FLEXIBLE MOVING AVERAGE SYSTEM
═══════════════════════════════════════════════
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.
═══════════════════════════════════════════════
CROSSOVER SIGNALS - CLEAN & RELIABLE
═══════════════════════════════════════════════
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 ✗
═══════════════════════════════════════════════
REAL-TIME INFO PANEL
═══════════════════════════════════════════════
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
═══════════════════════════════════════════════
HOW TO USE - PRACTICAL TRADING GUIDE
═══════════════════════════════════════════════
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
═══════════════════════════════════════════════
PERFORMANCE & OPTIMIZATION
═══════════════════════════════════════════════
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
═══════════════════════════════════════════════
TRANSPARENCY & RELIABILITY
═══════════════════════════════════════════════
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
═══════════════════════════════════════════════
COMPLETE FEATURE LIST
═══════════════════════════════════════════════
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
═══════════════════════════════════════════════
IMPORTANT DISCLAIMERS
═══════════════════════════════════════════════
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
═══════════════════════════════════════════════
CREDITS & TECHNICAL INFO
═══════════════════════════════════════════════
Version: 2.0
Release: October 2025
Special Thanks:
TradingView community for feedback and testing
Pine Script documentation for technical reference
═══════════════════════════════════════════════
SUPPORT & UPDATES
═══════════════════════════════════════════════
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.
═══════════════════════════════════════════════
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! 🚀
4h 相对超跌筛选器 · Webhook v2.0## 指标用途
用于你的「框架第2步」：在**美股 RTH**里，按**4h 收盘**（06:30–10:30 PT 为首根）筛出相对大盘/行业**显著超跌**且结构健康的候选标的，并可**通过 Webhook 自动推送**`symbol + ts`给下游 AI 执行新闻甄别（第3步）与进出场评估（第4步）。
## 工作原理（核心逻辑）
* **结构健康**：最近 80 根 4h 中，收盘 > 4h_SMA50 的占比 ≥ 阈值（默认 55%）。
* **跌深条件**：4h 跌幅 ≤ −4%，且近两根累计（≈8h）≤ −6%。
* **相对劣化**：相对大盘（SPY/QQQ）与相对行业（XLK/XLF/… 或 KWEB/CQQQ）各 ≤ −3%。
* **流动性与价格**：ADV20_USD ≥ 2000 万；价格 ≥ 3 美元。
* **只在 4h 收盘刻评估与触发**，历史点位全部保留，便于回放核验。
* **冷却**：同一标的信号间隔 ≥ N 天（默认 10）。
## 主要输入参数
* **bench / sector**：大盘与行业基准（例：SPY/QQQ，XLK/XLF/XLY；中概用 KWEB/CQQQ）。
* **advMinUSD / priceMin**：20 日美元成交额下限、最小价格。
* **pctAboveTh**：结构健康阈值（%）。
* **drop4hTh / drop8hTh**：4h/8h 跌幅阈值（%）。
* **relMktTh / relSecTh**：相对大盘/行业阈值（%）。
* **coolDays**：冷却天数。
* **fromDate**：仅显示此日期后的历史信号（图表拥挤时可用）。
* **showTable / tableRows**：是否显示右上角“最近信号表”及行数。
## 图表信号
* **S2 绿点**：当根 4h 收盘满足全部筛选条件。
* **右上角表格**：滚动列出最近 N 条命中（`SYMBOL @ yyyy-MM-dd HH:mm`，按图表本地时区）。
## Webhook 联动（生产用）
1. 添加指标 → 🔔 新建警报（Alert）：
* **Condition**：`Any alert() function call`
* **Options**：`Once per bar close`
* **Webhook URL**：填你的接收地址（可带 `?token=...`）
* **Message**：留空（脚本内部 `alert(payload)` 会发送 JSON）。
2. 典型 JSON 载荷（举例）：
```json
{
"event": "step2_signal",
"symbol": "LULU",
"symbol_id": "NASDAQ:LULU",
"venue": "NASDAQ",
"bench": "SPY",
"sector": "XLY",
"ts_bar_close_ms": 1754524200000,
"ts_bar_close_local": "2025-06-06 10:30",
"price_close": 318.42,
"ret_4h_pct": -5.30,
"ret_8h_pct": -7.45,
"rel_mkt_pct": -4.90,
"rel_sec_pct": -3.80
}
```
> 建议以 `symbol + ts_bar_close_ms` 做去重键；接收端先快速 `200 OK`，后续异步处理并交给第3步 AI。
## 使用建议
* **时间框架**：任意周期可用，指标内部统一拉取 240 分钟数据并仅在 4h 收盘刻触发。
* **行业映射**：尽量选与个股业务最贴近的 ETF；中国 ADR 可用 `PGJ/KWEB/CQQQ` 叠加细分行业对照。
* **回放验证**：Bar Replay **不发送真实 Webhook**；仅用于查看历史命中与表格。测试接收端请用 Alert 面板的 **Test**。
## 适配说明
* Pine Script **v5**。
* 不含成分筛查逻辑（请在你的 500–600 只候选池内使用）。
* 数字常量不使用下划线分隔；如需大数可用 `20000000` 或 `2e7`。
## 常见问题
* ⛔️ 报错 `tostring(...)`：Pine 无时间格式化重载，脚本已内置 `timeToStr()`。
* ⛔️ `syminfo.exchange` 不存在：已改用 `syminfo.prefix`（交易所前缀）。
* ⛔️ 多行字符串拼接报 `line continuation`：本脚本已用括号包裹或 `str.format` 规避。
## 免责声明
该指标仅供筛选与研究使用，不构成投资建议。请结合你的第3步新闻/基本面甄别与第4步执行规则共同决策。
ICT First Presented FVG with Volume Imbalance [1st P. FVG + VI]The indicator identifies and highlights the first presented Fair Value Gap (FVG) occurringthe morning (09:30–10:00) and afternoon (13:30–14:00) session's first 30 minutes. It includes an optional feature to extend FVG zones when a volume imbalance (V.I.) is detected, providing additional context for areas of potential price inefficiency. This powerful combination helps traders identify significant market structure gaps that often act as support/resistance zones and potential price targets.
What is an FVG?
A Fair Value Gap, often abbreviated as FVG, is a price range on a chart where there is an inefficiency or imbalance in trading. This typically happens when price moves rapidly in one direction, leaving a gap between the wicks or bodies of three consecutive candles. For example, in a bullish move, if the low of the third candle is higher than the high of the first candle, the space between them is the FVG.
What is a Volume Imbalance?
A volume imbalance is a smaller, more precise inefficiency within price action, often visible as a "crack" or thin area in the price delivery. It represents a spot where the volume traded was not balanced between buyers and sellers, often seen as a thin wick or a gap between candle bodies.
FVG + Volume Imbalance:
When you have a fair value gap that contains a volume imbalance, it becomes a more significant area of interest. ICT teaches that you should not ignore a volume imbalance if it’s part of an FVG. In fact, you should use the volume imbalance in conjunction with the FVG to define your trading range more accurately
📊 Volume Imbalance Integration
Toggle Option: Enable/disable volume imbalance detection based on preference
Extended Boundaries: When enabled, FVG boundaries expand to include volume imbalance zones
Accurate Gap Sizing: Total gap calculation includes volume imbalance extensions
Multi-Scenario Support: Handles volume imbalances at start, end, or both sides of FVG formations
📈 Multiple Display Modes
Current Day: Shows only today's FVGs for clean chart analysis
Current Week: Displays all weekly FVGs for broader context
Forward Extension: Extends FVG boxes and CE, Upper/Lower Quadrant lines into the future
📊 Visualization
Bullish FVGs appear in semi-transparent blue or purple zones (depending on session).
Bearish FVGs appear in red or orange zones.
Optional dotted lines mark the CE (midpoint) of each FVG for additional reference.
Quadrant Division: Additional 25%/75% lines for large FVGs (configurable minimum gap size)
🎯 Smart Filtering
First Presentation Only: Only displays the initial FVG in each session, avoiding clutter
Minimum Gap Size: Configurable tick-based thresholds for AM and PM sessions
Core FVG Validation: Ensures only valid Fair Value Gaps are displayed
⚙️ Configuration Options
Display Settings
Show Mode: Current Day or Current Week view
Forward Extension: 1-500 bars projection
Day Labels: Toggle weekday labels in weekly mode
Text Color: Customizable label colors
Volume Imbalance Settings
Include Volume Imbalance: Master toggle for enhanced boundary calculation
Automatic Detection: Identifies imbalance scenarios without additional input
Session-Specific Settings
AM Session (09:30-10:00):
Enable/disable AM FVG detection
Customizable bullish/bearish colors
CE line visibility and coloring
Minimum gap size in ticks
PM Session (13:30-14:00):
Enable/disable PM FVG detection
Customizable bullish/bearish colors
CE line visibility and coloring
Minimum gap size in ticks
Quadrant Settings
Enable/Disable: Toggle quadrant line display
Minimum Gap: Tick threshold for quadrant activation
Line Style: Dotted, dashed, or solid
Color: Customizable quadrant line color
How It Works
FVG Boundary Calculation
Traditional FVG: High to Low (bullish) or Low to High (bearish)
Enhanced FVG: Extended boundaries to include volume imbalance zones when enabled
Total Gap Size: Calculated including any volume imbalance extensions
Volume Imbalance Detection
The indicator identifies volume imbalances by detecting bars where:
Bullish Imbalance: Current bar's body is completely above previous bar's body
Bearish Imbalance: Current bar's body is completely below previous bar's body
⚠️ Disclaimer
This script is a technical visualization tool only.
It does not provide financial advice, signals, or predictions. Always perform independent analysis and manage risk appropriately before making trading decisions.
Swing High/Low ExtensionsSwing High/Low — Extensions (2 Plots + Drawings + Touch Signal)
What it does
This indicator finds Swing Highs/Lows using a symmetric length (same bars left & right), then creates horizontal extension levels that run to the right and stop at the first price touch (“extend until future intersection”).
It outputs:
Two plots showing the most recent active High/Low extension (great for alerts & strategy logic).
Line drawings for every detected swing (historical levels stay on the chart and end at the touch bar).
A hidden TouchSignal used to color bars and trigger alerts without distorting the price scale.
The design mirrors Sierra Chart’s “Swing High and Low” with “Extend Swings Until Future Intersection”, but implemented natively in Pine.
How it determines swings
Uses ta.pivothigh() / ta.pivotlow() with length bars left and right.
A swing is confirmed only after there are length bars to the right of the center.
How extensions/lines end
High extensions end when High ≥ level.
Low extensions end when Low ≤ level.
The corresponding line drawing is frozen on the touch bar; the most recent active line continues to extend each new bar.
Inputs
Swing Strength (Bars Left = Right) – symmetric pivot length.
Offset as Percentage – 1 = +1%; positive values push levels outward (High up / Low down), negative pull them inward.
Draw “Extend…Until Future Intersection” Lines – toggle line drawings on/off.
Line Width (Plots + Drawings) – thickness for plots and drawings.
HighExt Color / LowExt Color – colors for the two plots and drawings.
Touch Color – color to paint bars on the touch bar (doesn’t affect scale).
HighExt/LowExt Line Style – choose line style (Solid/Dashed/Dotted) for drawings.
Color Bars on Touch? – enable/disable bar coloring.
Bar Color on High Touch / Low Touch – separate bar colors for each side.
Bar Color Transparency (0..100) – opacity for the bar painting.
Plots
HighExt – latest active high extension only.
LowExt – latest active low extension only.
(Internally there is also a hidden “TouchSignal” plot used for bar coloring & alerts; it’s not displayed to keep the chart scale clean.)
Alerts
Three built-in alertconditions:
Any Extension Touched — triggers when either side is hit.
High Extension Touched — only high level touch.
Low Extension Touched — only low level touch.
Create alerts from the indicator’s “More” (⋯) menu → Add alert → choose one of the conditions.
Styling
Drawings use your selected style (Solid/Dashed/Dotted), color, and width.
Existing historical lines adopt new styles when the script recalculates.
Bar coloring highlights the exact touch candle; disable it if you prefer clean candles.
Notes & tips
Scale-safe: the TouchSignal is hidden (display=none), so it won’t distort the Y-axis.
Performance: TradingView limits scripts to ~500 line objects; this script uses max_lines_count=500. If you hit the cap on long histories, either increase timeframe or disable drawings and rely on the two plots + alerts.
Works on any symbol/timeframe; levels are rounded to the instrument’s minimum tick.
Intended use
For discretionary levels, alerting, and rule-based entries that react to first touch of recent swing extensions. Not financial advice—use at your own risk.
Nq/ES daily CME risk intervalReverse engineering the risk interval for CME (Chicago Mercantile Exchange) products based on margin requirements involves understanding the relationship between margin requirements, volatility, and the risk interval (price movement assumed for margin calculation)
The CME uses a methodology called SPAN (Standard Portfolio Analysis of Risk) to calculate margins. At a high level, the initial margin is derived from:
Initial Margin = Risk Interval × Contract Size × Volatility Adjustment Factor
Where:
Risk Interval: The price movement range used in the margin calculation.
Contract Size: The unit size of the futures contract.
Volatility Adjustment Factor: A measure of how much price fluctuation is expected, often tied to historical volatility.
To calculate an approximate of the daily CME risk interval, we need:
Initial Margin Requirement: Available on the CME Group website or broker platforms.
Contract Size: The size of one futures contract (e.g., for the S&P 500 E-mini, it is $50 × index points).
Volatility Adjustment Factor: This is derived from historical volatility or CME's implied volatility estimates.
As we do not have access to CME calculations , the volatility adjustment factor can be estimated using historical volatility: We calculate the standard deviation of daily returns over a specific period (e.g., 20 or 30 or 60 days).
Key Considerations
The exact formulas and parameters used by CME for CME's implied volatility estimates are proprietary, so this calculation based on standard deviation of daily returns is an approximation.
How to use:
Input the maintenance margin obtained from the CME website.
Adjust volatility period calculation.
The indicator displays the range high and low for the trading day.
1.Lines can be used as targets intraday
2.Market tends to snap back in between the lines and close the day in the range
Dollar Volume Ownership Gauge Dollar Volume Ownership Gauge (DVOG)
By: Mando4_27
Version: 1.0 — Pine Script® v6
Overview
The Dollar Volume Ownership Gauge (DVOG) is designed to measure the intensity of real money participation behind each price bar.
Instead of tracking raw share volume, this tool converts every bar’s trading activity into dollar volume (price × volume) and highlights the transition points where institutional capital begins to take control of a move.
DVOG’s mission is simple:
Show when the crowd is trading vs. when the institutions are buying control.
Core Concept
Most retail traders focus on share count (volume) — but institutions think in dollar exposure.
A small-cap printing a 1-million-share candle at $1 is very different from a 1-million-share candle at $10.
DVOG normalizes this by displaying total traded dollar value per bar, then color-codes and alerts when the volume of money crosses key thresholds.
This exposes the exact moments when ownership is shifting — often before major breakouts, reclaims, or exhaustion reversals.
How It Works
Dollar Volume Calculation
Each candle’s dollar volume is computed as close × volume.
Data is aggregated from the 5-minute timeframe regardless of your current chart, allowing consistent institutional-flow detection on any resolution.
Threshold Logic
Two customizable levels define interest zones:
$500K Threshold → Early or moderate institutional attention.
$1M Threshold → High-conviction or aggressive accumulation.
Both levels can be edited to fit different market caps or trading styles.
Bar Coloring Scheme
Red = Dollar Volume ≥ $1,000,000 → Significant institutional activity / control bar.
Green = Dollar Volume ≥ $500,000 and < $1,000,000 → Emerging accumulation / transition bar.
Black = Below $500,000 → Retail or low-interest zone.
(Colors are intentionally inverted from standard expectation: when volume intensity spikes, the bar turns hotter in tone.)
Plot Display
Histogram style plot displays 5-minute aggregated dollar volume per bar.
Dotted reference lines mark $500K and $1M levels, with live right-hand labels for quick reading.
Optional debug label shows current bar’s dollar value, closing price, and raw volume for transparency.
Alerts & Conditions
DVOG includes three alert triggers for hands-off monitoring:
Alert Name Trigger Message Purpose
Green Bar Alert – Dollar Volume ≥ $500K When dollar volume first crosses $500K “Institutional interest starting on ” Signals early money entering.
Dollar Volume ≥ $500K Same as above, configurable “Early institutional interest detected…” Broad alert option.
Dollar Volume ≥ $1M When dollar volume first crosses $1M “Significant money flow detected…” Indicates heavy institutional presence or ignition bar.
You can enable or disable alerts via checkbox inputs, allowing you to monitor just the levels that fit your style.
Interpretation & Use Cases
Identify Institutional “Ignition” Points:
Watch for sudden green or red DVOG bars after long low-volume consolidation — these often precede explosive continuation moves.
Confirm Breakouts & Reclaims:
If price reclaims a key level (HOD, neckline, or coil top) and DVOG flashes green/red, odds strongly favor follow-through.
Spot Trap Exhaustion:
After a flush or low-volume fade, the first strong green/red DVOG bar can mark the institutional reclaim — the moment retail control ends.
Filter Noise:
Ignore standard volume spikes. DVOG only reacts when dollar ownership materially changes hands, not when small traders churn shares.
Customization
Setting Default Description
$500K Threshold 500,000 Lower limit for “Green” institutional attention.
$1M Threshold 1,000,000 Upper limit for “Red” heavy institutional control.
Show Alerts ✅ Enable or disable global alerts.
Alert on Green Bars ✅ Toggle only the $500K crossover alerts.
Adjust thresholds to match the liquidity of your preferred tickers — for example, micro-caps may use $100K/$300K, while large-caps might use $5M/$20M.
Reading the Output
Black baseline = Noise / retail chop.
First Green bar = Smart money starts building position.
Red bar(s) = Ownership shift confirmed — institutions active.
Flat-to-rising pattern in DVOG = Sustained accumulation; often aligns with strong trend continuation.
Summary
DVOG transforms raw volume into actionable context — showing you when capital, not hype, is moving.
It’s particularly effective for:
Momentum and breakout traders
Liquidity trap reclaims (Kuiper-style setups)
Identifying early ignition bars before halts
Confirming frontside strength in micro-caps
Use DVOG as your ownership radar — the visual cue for when the market stops being retail and starts being real.
Multi-Timeframe Trend Indicator with Signals═══════════════════════════════════════════════════════════════
Multi-Timeframe Trend Indicator with Signals
by Zakaria Safri
═══════════════════════════════════════════════════════════════
⚠️ IMPORTANT DISCLAIMERS:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
• This indicator may REPAINT on unconfirmed bars
• Signals appear in real-time but may change or disappear
• FOR EDUCATIONAL PURPOSES ONLY - NOT FINANCIAL ADVICE
• Past performance does not guarantee future results
• Always do your own research and use proper risk management
• The Risk Management feature is VISUAL ONLY - does not execute trades
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📊 OVERVIEW:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
This indicator combines multiple technical analysis tools to help identify
potential trend directions and entry/exit points across different timeframes.
It uses SuperTrend, EMAs, ADX, RSI, and Keltner Channels to generate signals.
🎯 KEY FEATURES:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📍 SIGNAL TYPES:
• All Signals: Shows all SuperTrend crossovers
• Filtered Signals: Additional EMA filter for potentially higher quality signals
• Signals use barstate.isconfirmed to reduce (but not eliminate) repainting
📈 TREND ANALYSIS:
• Trend Ribbon: 8 EMAs creating a visual trend direction indicator
• Trend Cloud: EMA 150/250 cloud for long-term trend context
• Chaos Trend Line: Dynamic support/resistance trend line
• Multi-timeframe dashboard showing trend across 8 timeframes (3m to Daily)
📊 TECHNICAL INDICATORS:
• Keltner Channels: Dynamic price channels
• RSI Background: Visual overbought/oversold zones
• Candlestick Coloring: Three modes (CleanScalper/Trend Ribbon/Moving Average)
• ADX-based trend strength analysis for MTF dashboard
🎯 VISUAL TOOLS:
• Order Blocks: Supply/demand zones (optional)
• Channel Breakouts: Pivot-based support/resistance levels
• Reversal Signals: RSI-based potential reversal indicators
• Visual TP/SL Lines: For reference only - does NOT execute trades
📊 DASHBOARD:
• Real-time multi-timeframe trend analysis
• Volatility indicator (Very Low to Very High)
• Current RSI value with color coding
• Customizable position and size
⚙️ SETTINGS:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
MAIN SETTINGS:
• Sensitivity: Controls signal frequency (lower = more signals)
• Signal Type: Choose between All Signals or Filtered Signals
• Factor: ATR multiplier for SuperTrend calculation
TREND SETTINGS:
• Toggle Trend Ribbon, Trend Cloud, Chaos Trend, Order Blocks
• Moving Average: Customizable EMA (default 200)
ADVANCED SETTINGS:
• Candlestick coloring with 3 different modes
• Overbought/Oversold background coloring
• Channel breakout levels
• Show/hide signals
RISK MANAGEMENT (VISUAL ONLY):
• ⚠️ Does NOT execute trades automatically
• Shows potential Take Profit levels (TP1, TP2, TP3)
• Shows potential Stop Loss level
• Adjustable TP strength multiplier
• For educational reference only
📖 HOW TO USE:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
1. SIGNAL INTERPRETATION:
• "Buy" signals appear below candles when conditions are met
• "Sell" signals appear above candles when conditions are met
• Wait for bar close confirmation to avoid repainting
• Use multiple timeframes for confluence
2. TREND CONFIRMATION:
• Check the multi-timeframe dashboard for trend alignment
• Use Trend Ribbon for visual trend direction
• Trend Cloud shows longer-term market bias
• Green candles = potential uptrend, Red = potential downtrend
3. ENTRY/EXIT STRATEGY:
• Combine signals with other analysis tools
• Check volatility status before entering trades
• Use support/resistance levels for confirmation
• The visual TP/SL lines are for planning only
4. RISK MANAGEMENT:
• Always use stop losses (indicator shows suggested levels only)
• Position size according to your risk tolerance
• Never risk more than you can afford to lose
• The indicator does NOT manage trades automatically
⚠️ LIMITATIONS & RISKS:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
REPAINTING:
• Signals may appear and disappear on unconfirmed bars
• Always wait for bar close before taking action
• Historical performance may look better than real-time results
FALSE SIGNALS:
• No indicator is 100% accurate
• Signals can fail in ranging/choppy markets
• Use additional confirmation methods
• Consider market context and fundamentals
VISUAL TP/SL:
• Lines are for reference/planning only
• Does NOT place or manage actual trades
• You must manually set your own stop losses
• TP levels are calculated estimates, not guarantees
🔧 TECHNICAL DETAILS:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
• Version: Pine Script v5
• Overlay: Yes (displays on main chart)
• Anti-repaint measures: Uses barstate.isconfirmed on signals
• Security function: Uses lookahead protection for higher timeframes
• Dynamic requests: Enabled for MTF analysis
• Max labels: 500
📚 COMPONENTS EXPLAINED:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
SUPERTREND:
• Core signal generator using ATR-based bands
• Crossovers indicate potential trend changes
• Adjustable via Sensitivity and Factor inputs
EMA FILTER:
• Uses 200 EMA as trend filter (customizable)
• Filtered signals require price above/below EMA
• Helps reduce false signals in ranging markets
ADX TREND QUALITY:
• Measures trend strength across timeframes
• Used in multi-timeframe dashboard
• Shows Bullish/Bearish/Neutral states
KELTNER CHANNELS:
• Multiple bands showing volatility zones
• Color-coded based on RSI levels
• Helps identify overbought/oversold conditions
ORDER BLOCKS:
• Identifies supply/demand zones
• Based on price structure and pivots
• Can extend to the right for projection
💡 BEST PRACTICES:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
✓ Use multiple timeframe confirmation
✓ Wait for bar close before acting on signals
✓ Combine with support/resistance analysis
✓ Check overall market conditions
✓ Use proper risk management (1-2% per trade)
✓ Backtest on your specific market/timeframe
✓ Paper trade before using real money
✓ Keep a trading journal
✓ Adjust settings to your trading style
✗ Don't rely solely on this indicator
✗ Don't ignore risk management
✗ Don't trade on unconfirmed signals
✗ Don't overtrade every signal
✗ Don't use without understanding how it works
✗ Don't expect the TP/SL feature to trade for you
📞 SUPPORT & UPDATES:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Creator: Zakaria Safri
Version: 4.3 (Compliance Update)
For questions or feedback, please use TradingView's comment section.
⚖️ FINAL DISCLAIMER:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
This indicator is provided for EDUCATIONAL and INFORMATIONAL purposes only.
It is NOT financial advice, investment advice, or a recommendation to buy/sell.
Trading involves substantial risk of loss. Past performance, whether actual or
indicated by historical tests of strategies, is not indicative of future results.
The creator assumes NO responsibility for your trading results. You are solely
responsible for your own investment decisions and due diligence.
Always consult with a qualified financial advisor before making investment decisions.
By using this indicator, you acknowledge and accept these risks and limitations.
TASC 2025.11 The Points and Line Chart█ OVERVIEW
This script implements the Points and Line Chart described by Mohamed Ashraf Mahfouz and Mohamed Meregy in the November 2025 edition of the TASC Traders' Tips , "Efficient Display of Irregular Time Series”. This novel chart type interprets regular time series chart data to create an irregular time series chart.
█ CONCEPTS
When formatting data for display on a price chart, there are two main categorizations of chart types: regular time series (RTS) and irregular time series (ITS).
RTS charts, such as a typical candlestick chart, collect data over a specified amount of time and display it at one point. A one-minute candle, for example, represents the entirety of price movements within the minute that it represents.
ITS charts display data only after certain conditions are met. Since they do not plot at a consistent time period, they are called “irregular”.
Typically, ITS charts, such as Point and Figure (P&F) and Renko charts, focus on price change, plotting only when a certain threshold of change occurs.
The Points and Line (P&L) chart operates similarly to a P&F chart, using price change to determine when to plot points. However, instead of plotting the price in points, the P&L chart (by default) plots the closing price from RTS data. In other words, the P&L chart plots its points at the actual RTS close, as opposed to (price) intervals based on point size. This approach creates an ITS while still maintaining a reference to the RTS data, allowing us to gain a better understanding of time while consolidating the chart into an ITS format.
█ USAGE
Because the P&L chart forms bars based on price action instead of time, it displays displays significantly more history than a typical RTS chart. With this view, we are able to more easily spot support and resistance levels, which we could use when looking to place trades.
In the chart below, we can see over 13 years of data consolidated into one single view.
To view specific chart details, hover over each point of the chart to see a list of information.
In addition to providing a compact view of price movement over larger periods, this new chart type helps make classic chart patterns easier to interpret. When considering breakouts, the closing price provides a clearer representation of the actual breakout, as opposed to point size plots which are limited.
Because P&L is a new charting type, this script still requires a standard RTS chart for proper calculations. However, the main price chart is not intended for interpretation alongside the P&L chart; users can hide the main price series to keep the chart clean.
█ DISPLAYS
This indicator creates two displays: the "Price Display" and the "Data Display".
With the "Price display" setting, users can choose between showing a line or OHLC candles for the P&L drawing. The line display shows the close price of the P&L chart. In the candle display, the close price remains the same, while the open, high, and low values depend on the price action between points.
With the "Data display" setting, users can enable the display of a histogram that shows either the total volume or days/bars between the points in the P&L chart. For example, a reading of 12 days would indicate that the time since the last point was 12 days.
Note: The "Days" setting actually shows the number of chart bars elapsed between P&L points. The displayed value represents days only if the chart uses the "1D" timeframe.
The "Overlay P&L on chart" input controls whether the P&L line or candles appear on the main chart pane or in a separate pane.
Users can deactivate either display by selecting "None" from the corresponding input.
Technical Note: Due to drawing limitations, this indicator has the following display limits:
The line display can show data to 10,000 P&L points.
The candle display and tooltips show data for up to 500 points.
The histograms show data for up to 3,333 points.
█ INPUTS
Reversal Amount: The number of points/steps required to determine a reversal.
Scale size Method: The method used to filter price movements. By default, the P&L chart uses the same scaling method as the P&F chart. Optionally, this scaling method can be changed to use ATR or Percent.
P&L Method: The prices to plot and use for filtering:
“Close” plots the closing price and uses it to determine movements.
“High/Low” uses the high price on upside moves and low price on downside moves.
"Point Size" uses the closing price for filtration, but locks the price to plot at point size intervals.
US30 Quarter Levels (125-point grid) by FxMogul🟦 US30 Quarter Levels — Trade the Index Like the Banks
Discover the Dow’s hidden rhythm.
This indicator reveals the institutional quarter levels that govern US30 — spaced every 125 points, e.g. 45125, 45250, 45375, 45500, 45625, 45750, 45875, 46000, and so on.
These are the liquidity magnets and reaction zones where smart money executes — now visualized directly on your chart.
💼 Why You Need It
See institutional precision: The Dow respects 125-point cycles — this tool exposes them.
Catch reversals before retail sees them: Every impulse and retracement begins at one of these zones.
Build confluence instantly: Perfectly aligns with your FVGs, OBs, and session highs/lows.
Trade like a professional: Turn chaos into structure, and randomness into rhythm.
⚙️ Key Features
Automatically plots US30 quarter levels (…125 / …250 / …375 / …500 / …625 / …750 / …875 / …000).
Color-coded hierarchy:
🟨 xx000 / xx500 → major institutional levels
⚪ xx250 / xx750 → medium-impact levels
⚫ xx125 / xx375 / xx625 / xx875 → intraday liquidity pockets
Customizable window size, label spacing, and line extensions.
Works across all timeframes — from 1-minute scalps to 4-hour macro swings.
Optimized for clean visualization with no clutter.
🎯 How to Use It
Identify liquidity sweeps: Smart money hunts stops at these quarter zones.
Align structure: Combine with session opens, order blocks, or FVGs.
Set precision entries & exits: Trade reaction-to-reaction with tight risk.
Plan daily bias: Watch how New York respects these 125-point increments.
🧭 Designed For
Scalpers, day traders, and swing traders who understand that US30 doesn’t move randomly — it moves rhythmically.
Perfect for traders using ICT, SMC, or liquidity-based frameworks.
⚡ Creator’s Note
“Every 125 points, the Dow breathes. Every 1000, it shifts direction.
Once you see the rhythm, you’ll never unsee it.”
— FxMogul
Time Line Indicator - by LMTime Line Indicator – by LM
Description:
The Time Line Indicator is a simple, clean, and customizable tool designed to visualize specific time periods within each hour directly in a dedicated indicator pane. It allows traders to mark important intraday minute ranges across multiple past hours, providing a clear visual reference for time-based analysis. This indicator is perfect for identifying recurring hourly windows, session patterns, or custom time-based events in your charts.
Unlike traditional overlays, this indicator does not interfere with price candles and draws its lines in a separate pane at the bottom of your chart for clarity.
Key Features:
Custom Hourly Lines:
Draw horizontal lines for a specific minute range within each hour, e.g., from the 45th minute to the 15th minute of the next hour.
Multi-Hour Support:
Choose how many past hours to display. The indicator will replicate the line for each selected hourly period, following the same minute logic.
Automatic Start/End Logic:
If your chosen start minute is in the previous hour, the line correctly begins at that time.
The end minute can cross into the next hour when applicable.
If the selected end minute does not yet exist in the current chart data, the line will extend to the latest available bar.
Dedicated Indicator Pane:
Lines appear in a fixed, non-intrusive y-axis within the indicator pane (overlay=false), keeping your price chart clean.
Customizable Appearance:
Line Color: Choose any color to match your chart theme.
Line Thickness: Adjust the width of the lines for better visibility.
Inputs:
Input Name Type Default Description
Line Color Color Orange The color of the horizontal lines.
Line Thickness Integer 2 The thickness of each line (1–5).
Start Minute Integer 5 The minute within the hour where the line begins (0–59).
End Minute Integer 25 The minute within the hour where the line ends (0–59).
Hours Back Integer 3 Number of past hours to display lines for.
Use Cases:
Intraday Analysis: Quickly visualize recurring minute ranges across multiple hours.
Session Tracking: Mark critical time windows for trading sessions or market events.
Pattern Recognition: Easily identify time-based patterns or setups without cluttering the price chart.
How It Works:
The indicator calculates the nearest bars corresponding to your start and end minutes.
It draws horizontal lines at a fixed y-axis value within the indicator pane.
Lines are drawn for each selected past hour, replicating the chosen minute span.
All logic respects the actual chart data; lines never extend into the future beyond the most recent bar.
Notes:
Overlay is set to false, so lines appear in a dedicated pane below the price chart.
The indicator is fully compatible with any timeframe. Lines adjust automatically to match the chart’s bar spacing.
You can change the number of hours displayed at any time without affecting existing lines.
If you want, I can also draft a shorter “TradingView Store / Public Library description” version under 500 characters for the “Short Description” field — concise and punchy for users scrolling through indicators.
YM & NQ Directional Strength PanelA real-time momentum visualization tool for tracking directional strength across three major U.S. equity index futures (YM, NQ, ES). The indicator displays RSI-based momentum readings for each contract using a color-coded histogram that transitions from bright green (bullish, above 50) to bright red (bearish, below 50).
Live momentum tracking for Dow (YM), Nasdaq (NQ), and S&P 500 (ES) micro contracts
Customizable moving average types (ALMA, EMA, SuperSmoother) with adjustable parameters
Visual confirmation of multi-index alignment - quickly spot when all three indices agree on direction
Dynamic color gradient showing overbought (top) and oversold (bottom) zones
Ideal for scalpers and day traders who need quick confirmation of market directional bias across multiple indices without cluttering their charts.
Forecast PriceTime Oracle [CHE] Forecast PriceTime Oracle — Prioritizes quality over quantity by using Power Pivots via RSI %B metric to forecast future pivot highs/lows in price and time
Summary
This indicator identifies potential pivot highs and lows based on out-of-bounds conditions in a modified RSI %B metric, then projects future occurrences by estimating time intervals and price changes from historical medians. It provides visual forecasts via diagonal and horizontal lines, tracks achievement with color changes and symbols, and displays a dashboard for statistical overview including hit rates. Signals are robust due to median-based aggregation, which reduces outlier influence, and optional tolerance settings for near-misses, making it suitable for anticipating reversals in ranging or trending markets.
Motivation: Why this design?
Standard pivot detection often lags or generates false signals in volatile conditions, missing the timing of true extrema. This design leverages out-of-bounds excursions in RSI %B to capture "Power Pivots" early—focusing on quality over quantity by prioritizing significant extrema rather than every minor swing—then uses historical deltas in time and price to forecast the next ones, addressing the need for proactive rather than reactive analysis. It assumes that pivot spacing follows statistical patterns, allowing users to prepare entries or exits ahead of confirmation.
What’s different vs. standard approaches?
- Reference baseline: Diverges from traditional ta.pivothigh/low, which require fixed left/right lengths and confirm only after bars close, often too late for dynamic markets.
- Architecture differences:
- Detects extrema during OOB runs rather than post-bar symmetry.
- Aggregates deltas via medians (or alternatives) over a user-defined history, capping arrays to manage resources.
- Applies tolerance thresholds for hit detection, with options for percentage, absolute, or volatility-adjusted (ATR) flexibility.
- Freezes achieved forecasts with visual states to avoid clutter.
- Practical effect: Charts show proactive dashed projections instead of retrospective dots; the dashboard reveals evolving hit rates, helping users gauge reliability over time without manual calculation.
How it works (technical)
The indicator first computes a smoothed RSI over a specified length, then applies Bollinger Bands to derive %B, flagging out-of-bounds below zero or above one hundred as potential run starts. During these runs, it tracks the extreme high or low price and bar index. Upon exit from the OOB state, it confirms the Power Pivot at that extreme and records the time delta (bars since prior) and price change percentage to rolling arrays.
For forecasts, it calculates the median (or selected statistic) of recent deltas, subtracts the confirmation delay (bars from apex to exit), and projects ahead by that adjusted amount. Price targets use the median change applied to the origin pivot value. Lines are drawn from the apex to the target bar and price, with a short horizontal at the endpoint. Arrays store up to five active forecasts, pruning oldest on overflow.
Tolerance adjusts hit checks: for highs, if the high reaches or exceeds the target (adjusted by tolerance); for lows, if the low drops to or below. Once hit, the forecast freezes, changing colors and symbols, and extends the horizontal to the hit bar. Persistent variables maintain last pivot states across bars; arrays initialize empty and grow until capped at history length.
Parameter Guide
Source: Specifies the data input for the RSI computation, influencing how price action is captured. Default is close. For conservative signals in noisy environments, switch to high; using low boosts responsiveness but may increase false positives.
RSI Length: Sets the smoothing period for the RSI calculation, with longer values helping to filter out whipsaws. Default is 32. Opt for shorter lengths like 14 to 21 on faster timeframes for quicker reactions, or extend to 50 or more in strong trends to enhance stability at the cost of some lag.
BB Length: Defines the period for the Bollinger Bands applied to %B, directly affecting how often out-of-bounds conditions are triggered. Default is 20. Align it with the RSI length: shorter periods detect more potential runs but risk added noise, while longer ones provide better filtering yet might overlook emerging extrema.
BB StdDev: Controls the multiplier for the standard deviation in the bands, where wider settings reduce false out-of-bounds alerts. Default is 2.0. Narrow it to 1.5 for highly volatile assets to catch more signals, or broaden to 2.5 or higher to emphasize only major movements.
Show Price Forecast: Enables or disables the display of diagonal and target lines along with their updates. Default is true. Turn it off for simpler chart views, or keep it on to aid in trade planning.
History Length: Determines the number of recent pivot samples used for median-based statistics, where more history leads to smoother but potentially less current estimates. Default is 50. Start with a minimum of 5 to build data; limit to 100 to 200 to prevent outdated regimes from skewing results.
Max Lookahead: Limits the number of bars projected forward to avoid overly extended lines. Default is 500. Reduce to 100 to 200 for intraday focus, or increase for longer swing horizons.
Stat Method: Selects the aggregation technique for time and price deltas: Median for robustness against outliers, Trimmed Mean (20%) for a balanced trim of extremes, or 75th Percentile for a conservative upward tilt. Default is Median. Use Median for even distributions; switch to Percentile when emphasizing potential upside in trending conditions.
Tolerance Type: Chooses the approach for flexible hit detection: None for exact matches, Percentage for relative adjustments, Absolute for fixed point offsets, or ATR for scaling with volatility. Default is None. Begin with Percentage at 0.5 percent for currency pairs, or ATR for adapting to cryptocurrency swings.
Tolerance %: Provides the relative buffer when using Percentage mode, forgiving small deviations. Default is 0.5. Set between 0.2 and 1.0 percent; higher values accommodate gaps but can overstate hit counts.
Tolerance Points: Establishes a fixed offset in price units for Absolute mode. Default is 0.0010. Tailor to the asset, such as 0.0001 for forex pairs, and validate against past wick behavior.
ATR Length: Specifies the period for the Average True Range in dynamic tolerance calculations. Default is 14. This is the standard setting; shorten to 10 to reflect more recent volatility.
ATR Multiplier: Adjusts the ATR scale for tolerance width in ATR mode. Default is 0.5. Range from 0.3 for tighter precision to 0.8 for greater leniency.
Dashboard Location: Positions the summary table on the chart. Default is Bottom Right. Consider Top Left for better visibility on mobile devices.
Dashboard Size: Controls the text scaling for dashboard readability. Default is Normal. Choose Tiny for dense overlays or Large for detailed review sessions.
Text/Frame Color: Sets the color scheme for dashboard text and borders. Default is gray. Align with your chart theme, opting for lighter shades on dark backgrounds.
Reading & Interpretation
Forecast lines appear as dashed diagonals from confirmed pivots to projected targets, with solid horizontals at endpoints marking price levels. Open targets show a target symbol (🎯); achieved ones switch to a trophy symbol (🏆) in gray, with lines fading to gray. The dashboard summarizes median time/price deltas, sample counts, and hit rates—rising rates indicate improving forecast alignment. Colors differentiate highs (red) from lows (lime); frozen states signal validated projections.
Practical Workflows & Combinations
- Trend following: Enter long on low forecast hits during uptrends (higher highs/lower lows structure); filter with EMA crossovers to ignore counter-trend signals.
- Reversal setups: Short above high projections in overextended rallies; use volume spikes as confirmation to reduce false breaks.
- Exits/Stops: Trail stops to prior pivot lows; conservative on low hit rates (below 50%), aggressive above 70% with tight tolerance.
- Multi-TF: Apply on 1H for entries, 4H for time projections; combine with Ichimoku clouds for confluence on targets.
- Risk management: Position size inversely to delta uncertainty (wider history = smaller bets); avoid low-liquidity sessions.
Behavior, Constraints & Performance
Confirmation occurs on OOB exit, so live-bar pivots may adjust until close, but projections update only on events to minimize repaint. No security or HTF calls, so no external lookahead issues. Arrays cap at history length with shifts; forecasts limited to five active, pruning FIFO. Loops iterate over small fixed sizes (e.g., up to 50 for stats), efficient on most hardware. Max lines/labels at 500 prevent overflow.
Known limits: Sensitive to OOB parameter tuning—too tight misses runs; assumes stationary pivot stats, which may shift in regime changes like low vol. Gaps or holidays distort time deltas.
Sensible Defaults & Quick Tuning
Defaults suit forex/crypto on 1H–4H: RSI 32/BB 20 for balanced detection, Median stats over 50 samples, None tolerance for exactness.
- Too many false runs: Increase BB StdDev to 2.5 or RSI Length to 50 for filtering.
- Lagging forecasts: Shorten History Length to 20; switch to 75th Percentile for forward bias.
- Missed near-hits: Enable Percentage tolerance at 0.3% to capture wicks without overcounting.
- Cluttered charts: Reduce Max Lookahead to 200; disable dashboard on lower TFs.
What this indicator is—and isn’t
This is a forecasting visualization layer for pivot-based analysis, highlighting statistical projections from historical patterns. It is not a standalone system—pair with price action, volume, and risk rules. Not predictive of all turns; focuses on OOB-derived extrema, ignoring volume or news impacts.
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
Breakdown or Buyable Dip? Pullback Depth Can HelpAs a common adage says, “the market doesn’t move in a straight line.” But when prices have fallen, it’s not always clear whether buying makes sense. That’s where today’s script may help.
Most traditional indicators judge movement based on price. That’s obviously important, but time can also be helpful. After all, there’s a big difference between probing a low from 2-3 weeks ago versus a low from months or even years in the past.
Pullback Depth clearly illustrates this by answering the question: “Today’s low is the lowest in how many bars?”
The resulting integer is plotted in a simple histogram. Values are always negative because bars with higher absolute values (meaning more negative, or further below zero) are potentially more bearish.
The study also has a maximum lookback period to avoid overwhelming the study with too many bars. Its default setting of 125 bars includes enough history to illustrate the trend.
The stock market’s recent run has seen only shallow pullbacks. Most dips have probed 1-2 weeks in the past, while Friday’s selloff only turned back the clock a month.
Consider two other previous moments.
First, the great bull run of 1995 saw only shallow pullbacks. (None exceeded 50 days.):
In contrast, early 2022 saw the S&P 500 test levels more than 100 candles into the past. It soon fell into an official “bear market:”
TradeStation has, for decades, advanced the trading industry, providing access to stocks, options and futures. If you're born to trade, we could be for you. See our Overview for more.
Past performance, whether actual or indicated by historical tests of strategies, is no guarantee of future performance or success. There is a possibility that you may sustain a loss equal to or greater than your entire investment regardless of which asset class you trade (equities, options or futures); therefore, you should not invest or risk money that you cannot afford to lose. Online trading is not suitable for all investors. View the document titled Characteristics and Risks of Standardized Options at www.TradeStation.com . Before trading any asset class, customers must read the relevant risk disclosure statements on www.TradeStation.com . System access and trade placement and execution may be delayed or fail due to market volatility and volume, quote delays, system and software errors, Internet traffic, outages and other factors.
Securities and futures trading is offered to self-directed customers by TradeStation Securities, Inc., a broker-dealer registered with the Securities and Exchange Commission and a futures commission merchant licensed with the Commodity Futures Trading Commission). TradeStation Securities is a member of the Financial Industry Regulatory Authority, the National Futures Association, and a number of exchanges.
TradeStation Securities, Inc. and TradeStation Technologies, Inc. are each wholly owned subsidiaries of TradeStation Group, Inc., both operating, and providing products and services, under the TradeStation brand and trademark. When applying for, or purchasing, accounts, subscriptions, products and services, it is important that you know which company you will be dealing with. Visit www.TradeStation.com for further important information explaining what this means.
Tweezer & Kangaroo Zones [WavesUnchained]Tweezer & Kangaroo Zones
Pattern Recognition with Supply/Demand Zones
Indicator that detects tweezer and kangaroo tail (pin bar) reversal patterns and creates supply and demand zones. Includes volume validation, trend context, and confluence scoring.
What You See on Your Chart
Pattern Labels:
"T" (Red) - Tweezer Top detected above price → Bearish reversal signal
"T" (Green) - Tweezer Bottom detected below price → Bullish reversal signal
"K" (Red) - Kangaroo Bear (Pin Bar rejection from top) → Bearish signal
"K" (Green) - Kangaroo Bull (Pin Bar rejection from bottom) → Bullish signal
Label Colors Indicate Pattern Strength:
Dark Green/Red - Strong pattern (score ≥8.0)
Medium Green/Red - Good pattern (score ≥6.0)
Light Green/Red - Valid pattern (score <6.0)
Zone Boxes:
Red Boxes - Supply Zones (resistance, potential short areas)
Green Boxes - Demand Zones (support, potential long areas)
White Border - Active zone (fresh, not tested yet)
Gray Border - Inactive zone (expired or invalidated)
Pattern Detection
Tweezer Patterns (Classic Double-Top/Bottom):
Flexible Lookback - Detects patterns up to 3 bars apart (not just consecutive)
Precision Matching - 0.2% level tolerance for high-quality signals
Wick Similarity Check - Both candles must show similar rejection wicks
Volume Validation - Second candle requires elevated volume (0.8x average)
Pattern Strength Score - 0-1 quality rating based on level match + wick similarity
Optional Trend Context - Can require trend alignment (default: OFF for more signals)
Kangaroo Tail / Pin Bar Patterns:
No Pivot Delay - Instant detection without waiting for pivot confirmation
Body Position Check - Body must be at candle extremes (30% tolerance)
Volume Spike - Rejection must occur with volume (0.9x average)
Rejection Strength - Scores based on wick length (0.5-0.9 of range)
Optional Trend Context - Bearish in uptrends, Bullish in downtrends (default: OFF)
Zone Management
Auto-Created Zones - Every valid pattern creates a supply/demand zone
Overlap Prevention - Zones too close together (50% overlap) are not duplicated
Lifetime Control - Zones expire after 400 bars (configurable)
Smart Invalidation - Zones invalidate when price closes through them
Styling Options - Choose between Solid, Dashed, or Dotted borders
Border Width - 2px width for better visibility
Confluence Scoring System
Multi-factor confluence scoring (0-10 scale) with configurable weights:
Regime (EMA+HTF) - Trend alignment across timeframes (Weight: 2.0)
HTF Stack - Multi-timeframe trend confluence (Weight: 3.0)
Structure - Higher lows / Lower highs confirmation (Weight: 1.0)
Relative Volume - Volume surge validation (Weight: 1.0)
Chop Advantage - Favorable market conditions (Weight: 1.0)
Zone Thinness - Tight zones = better R/R (Weight: 1.0)
Supertrend - Trend indicator alignment (Weight: 1.0)
MOST - Moving Stop alignment (Weight: 1.0)
Pattern Strength - Quality of detected pattern (Weight: 1.5)
Zone Retest Signals
Signals generated when zones are retested:
BUY Signal - Price retests demand zone from above (score ≥4.5)
SELL Signal - Price retests supply zone from below (score ≥5.5)
Normalized Score - Displayed as 0-10 for easy interpretation
Optional Trend Gate - Require trend alignment for signals (default: OFF)
Alert Ready - Built-in alertconditions for automation
Additional Features
Auto-Threshold Tuning - Adapts to ATR and Choppiness automatically
Session Profiles - Different settings for RTH vs ETH sessions
Organized Settings - 15+ input groups for easy configuration
Optional Panels - HTF Stack overview and performance metrics (default: OFF)
Data Exports - Hidden plots for strategy/library integration
RTA Health Monitoring - Built-in performance tracking
Setup & Configuration
Quick Start:
1. Apply indicator to any timeframe
2. Patterns and zones appear automatically
3. Adjust pattern detection sensitivity if needed
4. Configure zone styling (Solid/Dashed/Dotted)
5. Set up alerts for zone retests
Key Settings to Adjust:
Pattern Detection:
• Min RelVolume: Lower = more signals (0.8 Tweezer, 0.9 Kangaroo)
• Require trend context: Enable for stricter, higher-quality patterns
• Check wick similarity: Ensures proper rejection structure
Zone Management:
• Zone lifetime: How long zones remain active (default: 400 bars)
• Invalidate on close-through: Remove zones when price breaks through
• Max overlap: Prevent duplicate zones (default: 50%)
Scoring:
• Min Score BUY/SELL: Higher = fewer but better signals (default: 4.5/5.5)
• Component weights: Customize what factors matter most
• Signals require trend gate: OFF = more signals, ON = higher quality
Visual Customization
Zone Colors - Light red/green with 85% transparency (non-intrusive)
Border Styles - Solid, Dashed, or Dotted
Label Intensity - Darker greens for better readability
Clean Charts - All panels OFF by default
Understanding the Zones
Supply Zones (Red):
Created from bearish patterns (Tweezer Tops, Kangaroo Bears). Price made a high attempt to push higher, but was rejected. These become resistance areas where sellers may step in again.
Demand Zones (Green):
Created from bullish patterns (Tweezer Bottoms, Kangaroo Bulls). Price made a low with strong rejection. These become support areas where buyers may step in again.
Zone Quality Indicators:
• White border = Fresh zone, not tested yet
• Gray border = Zone expired or invalidated
• Thin zones (tight range) = Better risk/reward ratio
• Thick zones = Less precise, wider stop required
Trading Applications
Reversal Trading - Enter at pattern detection with tight stops
Zone Retest Trading - Wait for retests of established zones
Trend Confluence - Trade only when patterns align with trend
Risk Management - Use zone boundaries for stop placement
Target Setting - Opposite zones become profit targets
Pro Tips
Best signals occur when pattern + zone retest + trend all align
Lower timeframes = more signals but more noise
Higher timeframes = fewer but more reliable signals
Start with default settings, adjust based on your market
Combine with other analysis (structure, key levels, etc.)
Use alerts to avoid staring at charts all day
Important Notes
Not all patterns will lead to successful trades
Use proper risk management and position sizing
Patterns work best in trending or range-bound markets
Very choppy conditions may produce lower-quality signals
Always confirm with your own analysis before trading
Technical Specifications
• Pine Script v6
• RTA-Core integration
• RTA Core Library integration
• Maximum 200 boxes, 500 labels
• Auto-tuning based on ATR and Choppiness
• Session-aware threshold adjustments
• Memory-optimized zone management
What's Included
Tweezer Top/Bottom detection
Kangaroo Tail / Pin Bar detection
Automatic supply/demand zone creation
Volume validation system
Pattern strength scoring
Zone retest signals
Multi-factor confluence scoring
Optional HTF Stack panel
Optional performance metrics
Session profile support
Auto-threshold tuning
Alert conditions
Data exports for strategies
Author Waves Unchained
Version 1.0
Status Public Indicator
Summary
Reversal pattern detection with zone management, volume validation, and confluence scoring for tweezer and kangaroo tail patterns.
---
Disclaimer: This indicator is for educational and informational purposes only. Trading involves risk. Past performance does not guarantee future results. Always practice proper risk management.
