COT IndexTHE HIDDEN INTELLIGENCE IN FUTURES MARKETS
What if you could see what the smartest players in the futures markets are doing before the crowd catches on? While retail traders chase momentum indicators and moving averages, obsess over Japanese candlestick patterns, and debate whether the RSI should be set to fourteen or twenty-one periods, institutional players leave footprints in the sand through their mandatory reporting to the Commodity Futures Trading Commission. These footprints, published weekly in the Commitment of Traders reports, have been hiding in plain sight for decades, available to anyone with an internet connection, yet remarkably few traders understand how to interpret them correctly. The COT Index indicator transforms this raw institutional positioning data into actionable trading signals, bringing Wall Street intelligence to your trading screen without requiring expensive Bloomberg terminals or insider connections.
The uncomfortable truth is this: Most retail traders operate in a binary world. Long or short. Buy or sell. They apply technical analysis to individual positions, constrained by limited capital that forces them to concentrate risk in single directional bets. Meanwhile, institutional traders operate in an entirely different dimension. They manage portfolios dynamically weighted across multiple markets, adjusting exposure based on evolving market conditions, correlation shifts, and risk assessments that retail traders never see. A hedge fund might be simultaneously long gold, short oil, neutral on copper, and overweight agricultural commodities, with position sizes calibrated to volatility and portfolio Greeks. When they increase gold exposure from five percent to eight percent of portfolio allocation, this rebalancing decision reflects sophisticated analysis of opportunity cost, risk parity, and cross-market dynamics that no individual chart pattern can capture.
This portfolio reweighting activity, multiplied across hundreds of institutional participants, manifests in the aggregate positioning data published weekly by the CFTC. The Commitment of Traders report does not show individual trades or strategies. It shows the collective footprint of how actual commercial hedgers and large speculators have allocated their capital across different markets. When mining companies collectively increase forward gold sales to hedge thirty percent more production than last quarter, they are not reacting to a moving average crossover. They are making strategic allocation decisions based on production forecasts, cost structures, and price expectations derived from operational realities invisible to outside observers. This is portfolio management in action, revealed through positioning data rather than price charts.
If you want to understand how institutional capital actually flows, how sophisticated traders genuinely position themselves across market cycles, the COT report provides a rare window into that hidden world. But understand what you are getting into. This is not a tool for scalpers seeking confirmation of the next five-minute move. This is not an oscillator that flashes oversold at market bottoms with convenient precision. COT analysis operates on a timescale measured in weeks and months, revealing positioning shifts that precede major market turns but offer no precision timing. The data arrives three days stale, published only once per week, capturing strategic positioning rather than tactical entries.
If you need instant gratification, if you trade intraday moves, if you demand mechanical signals with ninety percent accuracy, close this document now. COT analysis rewards patience, position sizing discipline, and tolerance for being early. It punishes impatience, overleveraging, and the expectation that any single indicator can substitute for market understanding.
The premise is deceptively simple. Every Tuesday, large traders in futures markets must report their positions to the CFTC. By Friday afternoon, this data becomes public. Academic research spanning three decades has consistently shown that not all market participants are created equal. Some traders consistently profit while others consistently lose. Some anticipate major turning points while others chase trends into exhaustion. Bessembinder and Chan (1992) demonstrated in their seminal study that commercial hedgers, those with actual exposure to the underlying commodity or financial instrument, possess superior forecasting ability compared to speculators. Their research, published in the Journal of Finance, found statistically significant predictive power in commercial positioning, particularly at extreme levels. This finding challenged the efficient market hypothesis and opened the door to a new approach to market analysis based on positioning rather than price alone.
Think about what this means. Every week, the government publishes a report showing you exactly how the most informed market participants are positioned. Not their opinions. Not their predictions. Their actual money at risk. When agricultural producers collectively hold their largest short hedge in five years, they are not making idle speculation. They are locking in prices for crops they will harvest, informed by private knowledge of weather conditions, soil quality, inventory levels, and demand expectations invisible to outside observers. When energy companies aggressively hedge forward production at current prices, they reveal information about expected supply that no analyst report can capture. This is not technical analysis based on past prices. This is not fundamental analysis based on publicly available data. This is behavioral analysis based on how the smartest money is actually positioned, how institutions allocate capital across portfolios, and how those allocation decisions shift as market conditions evolve.
WHY SOME TRADERS KNOW MORE THAN OTHERS
Building on this foundation, Sanders, Boris and Manfredo (2004) conducted extensive research examining the behaviour patterns of different trader categories. Their work, which analyzed over a decade of COT data across multiple commodity markets, revealed a fascinating dynamic that challenges much of what retail traders are taught. Commercial hedgers consistently positioned themselves against market extremes, buying when speculators were most bearish and selling when speculators reached peak bullishness. The contrarian positioning of commercials was not random noise but rather reflected their superior information about supply and demand fundamentals. Meanwhile, large speculators, primarily hedge funds and commodity trading advisors, exhibited strong trend-following behaviour that often amplified market moves beyond fundamental values. Small traders, the retail participants, consistently entered positions late in trends, frequently near turning points, making them reliable contrary indicators.
Wang (2003) extended this research by demonstrating that the predictive power of commercial positioning varies significantly across different commodity sectors. His analysis of agricultural commodities showed particularly strong forecasting ability, with commercial net positions explaining up to fifteen percent of return variance in subsequent weeks. This finding suggests that the informational advantages of hedgers are most pronounced in markets where physical supply and demand fundamentals dominate, as opposed to purely financial markets where information asymmetries are smaller. When a corn farmer hedges six months of expected harvest, that decision incorporates private observations about rainfall patterns, crop health, pest pressure, and local storage capacity that no distant analyst can match. When an oil refinery hedges crude oil purchases and gasoline sales simultaneously, the spread relationships reveal expectations about refining margins that reflect operational realities invisible in public data.
The theoretical mechanism underlying these empirical patterns relates to information asymmetry and different participant motivations. Commercial hedgers engage in futures markets not for speculative profit but to manage business risks. An agricultural producer selling forward six months of expected harvest is not making a bet on price direction but rather locking in revenue to facilitate financial planning and ensure business viability. However, this hedging activity necessarily incorporates private information about expected supply, inventory levels, weather conditions, and demand trends that the hedger observes through their commercial operations (Irwin and Sanders, 2012). When aggregated across many participants, this private information manifests in collective positioning.
Consider a gold mining company deciding how much forward production to hedge. Management must estimate ore grades, recovery rates, production costs, equipment reliability, labor availability, and dozens of other operational variables that determine whether locking in prices at current levels makes business sense. If the industry collectively hedges more aggressively than usual, it suggests either exceptional production expectations or concern about sustaining current price levels or combination of both. Either way, this positioning reveals information unavailable to speculators analyzing price charts and economic data. The hedger sees the physical reality behind the financial abstraction.
Large speculators operate under entirely different incentives and constraints. Commodity Trading Advisors managing billions in assets typically employ systematic, trend-following strategies that respond to price momentum rather than fundamental supply and demand. When crude oil rallies from sixty dollars to seventy dollars per barrel, these systems generate buy signals. As the rally continues to eighty dollars, position sizes increase. The strategy works brilliantly during sustained trends but becomes a liability at reversals. By the time oil reaches ninety dollars, trend-following funds are maximally long, having accumulated positions progressively throughout the rally. At this point, they represent not smart money anticipating further gains but rather crowded money vulnerable to reversal. Sanders, Boris and Manfredo (2004) documented this pattern across multiple energy markets, showing that extreme speculator positioning typically marked late-stage trend exhaustion rather than early-stage trend development.
Small traders, the retail participants who fall below reporting thresholds, display the weakest forecasting ability. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns, meaning their aggregate positioning served as a reliable contrary indicator. The explanation combines several factors. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, entering trends after mainstream media coverage when institutional participants are preparing to exit. Perhaps most importantly, they trade with emotion, buying into euphoria and selling into panic at precisely the wrong times.
At major turning points, the three groups often position opposite each other with commercials extremely bearish, large speculators extremely bullish, and small traders piling into longs at the last moment. These high-divergence environments frequently precede increased volatility and trend reversals. The insiders with business exposure quietly exit as the momentum traders hit maximum capacity and retail enthusiasm peaks. Within weeks, the reversal begins, and positions unwind in the opposite sequence.
FROM RAW DATA TO ACTIONABLE SIGNALS
The COT Index indicator operationalizes these academic findings into a practical trading tool accessible through TradingView. At its core, the indicator normalizes net positioning data onto a zero to one hundred scale, creating what we call the COT Index. This normalization is critical because absolute position sizes vary dramatically across different futures contracts and over time. A commercial trader holding fifty thousand contracts net long in crude oil might be extremely bullish by historical standards, or it might be quite neutral depending on the context of total market size and historical ranges. Raw position numbers mean nothing without context. The COT Index solves this problem by calculating where current positioning stands relative to its range over a specified lookback period, typically two hundred fifty-two weeks or approximately five years of weekly data.
The mathematical transformation follows the methodology originally popularized by legendary trader Larry Williams, though the underlying concept appears in statistical normalization techniques across many fields. For any given trader category, we calculate the highest and lowest net position values over the lookback period, establishing the historical range for that specific market and trader group. Current positioning is then expressed as a percentage of this range, where zero represents the most bearish positioning ever seen in the lookback window and one hundred represents the most bullish extreme. A reading of fifty indicates positioning exactly in the middle of the historical range, suggesting neither extreme optimism nor pessimism relative to recent history (Williams and Noseworthy, 2009).
This index-based approach allows for meaningful comparison across different markets and time periods, overcoming the scaling problems inherent in analyzing raw position data. A commercial index reading of eighty-five in gold carries the same interpretive meaning as an eighty-five reading in wheat or crude oil, even though the absolute position sizes differ by orders of magnitude. This standardization enables systematic analysis across entire futures portfolios rather than requiring market-specific expertise for each contract.
The lookback period selection involves a fundamental tradeoff between responsiveness and stability. Shorter lookback periods, perhaps one hundred twenty-six weeks or approximately two and a half years, make the index more sensitive to recent positioning changes. However, it also increases noise and produces more false signals. Longer lookback periods, perhaps five hundred weeks or approximately ten years, create smoother readings that filter short-term noise but become slower to recognize regime changes. The indicator settings allow users to adjust this parameter based on their trading timeframe, risk tolerance, and market characteristics.
UNDERSTANDING CFTC DATA STRUCTURES
The indicator supports both Legacy and Disaggregated COT report formats, reflecting the evolution of CFTC reporting standards over decades of market development. Legacy reports categorize market participants into three broad groups: commercial traders (hedgers with underlying business exposure), non-commercial traders (large speculators seeking profit without commercial interest), and non-reportable traders (small speculators below reporting thresholds). Each category brings distinct motivations and information advantages to the market (CFTC, 2020).
The Disaggregated reports, introduced in September 2009 for physical commodity markets, provide finer granularity by splitting participants into five categories (CFTC, 2009). Producer and merchant positions capture those actually producing, processing, or merchandising the physical commodity. Swap dealers represent financial intermediaries facilitating derivative transactions for clients. Managed money includes commodity trading advisors and hedge funds executing systematic or discretionary strategies. Other reportables encompasses diverse participants not fitting the main categories. Small traders remain as the fifth group, representing retail participation.
This enhanced categorization reveals nuances invisible in Legacy reports, particularly distinguishing between different types of institutional capital and their distinct behavioural patterns. The indicator automatically detects which report type is appropriate for each futures contract and adjusts the display accordingly.
Importantly, Disaggregated reports exist only for physical commodity futures. Agricultural commodities like corn, wheat, and soybeans have Disaggregated reports because clear producer, merchant, and swap dealer categories exist. Energy commodities like crude oil and natural gas similarly have well-defined commercial hedger categories. Metals including gold, silver, and copper also receive Disaggregated treatment (CFTC, 2009). However, financial futures such as equity index futures, Treasury bond futures, and currency futures remain available only in Legacy format. The CFTC has indicated no plans to extend Disaggregated reporting to financial futures due to different market structures and participant categories in these instruments (CFTC, 2020).
THE BEHAVIORAL FOUNDATION
Understanding which trader perspective to follow requires appreciation of their distinct trading styles, success rates, and psychological profiles. Commercial hedgers exhibit anticyclical behaviour rooted in their fundamental knowledge and business imperatives. When agricultural producers hedge forward sales during harvest season, they are not speculating on price direction but rather locking in revenue for crops they will harvest. Their business requires converting volatile commodity exposure into predictable cash flows to facilitate planning and ensure survival through difficult periods. Yet their aggregate positioning reveals valuable information because these hedging decisions incorporate private information about supply conditions, inventory levels, weather observations, and demand expectations that hedgers observe through their commercial operations (Bessembinder and Chan, 1992).
Consider a practical example from energy markets. Major oil companies continuously hedge portions of forward production based on price levels, operational costs, and financial planning needs. When crude oil trades at ninety dollars per barrel, they might aggressively hedge the next twelve months of production, locking in prices that provide comfortable profit margins above their extraction costs. This hedging appears as short positioning in COT reports. If oil rallies further to one hundred dollars, they hedge even more aggressively, viewing these prices as exceptional opportunities to secure revenue. Their short positioning grows increasingly extreme. To an outside observer watching only price charts, the rally suggests bullishness. But the commercial positioning reveals that the actual producers of oil find these prices attractive enough to lock in years of sales, suggesting skepticism about sustaining even higher levels. When the eventual reversal occurs and oil declines back to eighty dollars, the commercials who hedged at ninety and one hundred dollars profit while speculators who chased the rally suffer losses.
Large speculators or managed money traders operate under entirely different incentives and constraints. Their systematic, momentum-driven strategies mean they amplify existing trends rather than anticipate reversals. Trend-following systems, the most common approach among large speculators, by definition require confirmation of trend through price momentum before entering positions (Sanders, Boris and Manfredo, 2004). When crude oil rallies from sixty dollars to eighty dollars per barrel over several months, trend-following algorithms generate buy signals based on moving average crossovers, breakouts, and other momentum indicators. As the rally continues, position sizes increase according to the systematic rules.
However, this approach becomes a liability at turning points. By the time oil reaches ninety dollars after a sustained rally, trend-following funds are maximally long, having accumulated positions progressively throughout the move. At this point, their positioning does not predict continued strength. Rather, it often marks late-stage trend exhaustion. The psychological and mechanical explanation is straightforward. Trend followers by definition chase price momentum, entering positions after trends establish rather than anticipating them. Eventually, they become fully invested just as the trend nears completion, leaving no incremental buying power to sustain the rally. When the first signs of reversal appear, systematic stops trigger, creating a cascade of selling that accelerates the downturn.
Small traders consistently display the weakest track record across academic studies. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns in his analysis across multiple commodity markets. This result means that whatever small traders collectively do, the opposite typically proves profitable. The explanation for small trader underperformance combines several factors documented in behavioral finance literature. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, learning about commodity trends through mainstream media coverage that arrives after institutional participants have already positioned. Perhaps most importantly, retail traders are more susceptible to emotional decision-making, buying into euphoria and selling into panic at precisely the wrong times (Tharp, 2008).
SETTINGS, THRESHOLDS, AND SIGNAL GENERATION
The practical implementation of the COT Index requires understanding several key features and settings that users can adjust to match their trading style, timeframe, and risk tolerance. The lookback period determines the time window for calculating historical ranges. The default setting of two hundred fifty-two bars represents approximately one year on daily charts or five years on weekly charts, balancing responsiveness with stability. Conservative traders seeking only the most extreme, highest-probability signals might extend the lookback to five hundred bars or more. Aggressive traders seeking earlier entry and willing to accept more false positives might reduce it to one hundred twenty-six bars or even less for shorter-term applications.
The bullish and bearish thresholds define signal generation levels. Default settings of eighty and twenty respectively reflect academic research suggesting meaningful information content at these extremes. Readings above eighty indicate positioning in the top quintile of the historical range, representing genuine extremes rather than temporary fluctuations. Conversely, readings below twenty occupy the bottom quintile, indicating unusually bearish positioning (Briese, 2008).
However, traders must recognize that appropriate thresholds vary by market, trader category, and personal risk tolerance. Some futures markets exhibit wider positioning swings than others due to seasonal patterns, volatility characteristics, or participant behavior. Conservative traders seeking high-probability setups with fewer signals might raise thresholds to eighty-five and fifteen. Aggressive traders willing to accept more false positives for earlier entry could lower them to seventy-five and twenty-five.
The key is maintaining meaningful differentiation between bullish, neutral, and bearish zones. The default settings of eighty and twenty create a clear three-zone structure. Readings from zero to twenty represent bearish territory where the selected trader group holds unusually bearish positions. Readings from twenty to eighty represent neutral territory where positioning falls within normal historical ranges. Readings from eighty to one hundred represent bullish territory where the selected trader group holds unusually bullish positions.
The trading perspective selection determines which participant group the indicator follows, fundamentally shaping interpretation and signal meaning. For counter-trend traders seeking reversal opportunities, monitoring commercial positioning makes intuitive sense based on the academic research discussed earlier. When commercials reach extreme bearish readings below twenty, indicating unprecedented short positioning relative to recent history, they are effectively betting against the crowd. Given their informational advantages demonstrated by Bessembinder and Chan (1992), this contrarian stance often precedes major bottoms.
Trend followers might instead monitor large speculator positioning, but with inverted logic compared to commercials. When managed money reaches extreme bullish readings above eighty, the trend may be exhausting rather than accelerating. This seeming paradox reflects their late-cycle participation documented by Sanders, Boris and Manfredo (2004). Sophisticated traders thus use speculator extremes as fade signals, entering positions opposite to speculator consensus.
Small trader monitoring serves primarily as a contrary indicator for all trading styles. Extreme small trader bullishness above seventy-five or eighty typically warns of retail FOMO at market tops. Extreme small trader bearishness below twenty or twenty-five often marks capitulation bottoms where the last weak hands have sold.
VISUALIZATION AND USER INTERFACE
The visual design incorporates multiple elements working together to facilitate decision-making and maintain situational awareness during active trading. The primary COT Index line plots in bold with adjustable line width, defaulting to two pixels for clear visibility against busy price charts. An optional glow effect, controlled by a simple toggle, adds additional visual prominence through multiple plot layers with progressively increasing transparency and width.
A twenty-one period exponential moving average overlays the index line, providing trend context for positioning changes. When the index crosses above its moving average, it signals accelerating bullish sentiment among the selected trader group regardless of whether absolute positioning is extreme. Conversely, when the index crosses below its moving average, it signals deteriorating sentiment and potentially the beginning of a reversal in positioning trends.
The EMA provides a dynamic reference line for assessing positioning momentum. When the index trades far above its EMA, positioning is not only extreme in absolute terms but also building with momentum. When the index trades far below its EMA, positioning is contracting or reversing, which may indicate weakening conviction even if absolute levels remain elevated.
The data table positioned at the top right of the chart displays eleven metrics for each trader category, transforming the indicator from a simple index calculation into an analytical dashboard providing multidimensional market intelligence. Beyond the COT Index itself, users can monitor positioning extremity, which measures how unusual current levels are compared to historical norms using statistical techniques. The extremity metric clarifies whether a reading represents the ninety-fifth or ninety-ninth percentile, with values above two standard deviations indicating genuinely exceptional positioning.
Market power quantifies each group's influence on total open interest. This metric expresses each trader category's net position as a percentage of total market open interest. A commercial entity holding forty percent of total open interest commands significantly more influence than one holding five percent, making their positioning signals more meaningful.
Momentum and rate of change metrics reveal whether positions are building or contracting, providing early warning of potential regime shifts. Position velocity measures the rate of change in positioning changes, effectively a second derivative providing even earlier insight into inflection points.
Sentiment divergence highlights disagreements between commercial and speculative positioning. This metric calculates the absolute difference between normalized commercial and large speculator index values. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals.
The table also displays concentration metrics when available, showing how positioning is distributed among the largest handful of traders in each category. High concentration indicates a few dominant players controlling most of the positioning, while low concentration suggests broad-based participation across many traders.
THE ALERT SYSTEM AND MONITORING
The alert system, comprising five distinct alert conditions, enables systematic monitoring of dozens of futures markets without constant screen watching. The bullish and bearish COT signal alerts trigger when the index crosses user-defined thresholds, indicating the selected trader group has reached extreme positioning worthy of attention. These alerts fire in real-time as new weekly COT data publishes, typically Friday afternoon following the Tuesday measurement date.
Extreme positioning alerts fire at ninety and ten index levels, representing the top and bottom ten percent of the historical range, warning of particularly stretched readings that historically precede reversals with high probability. When commercials reach a COT Index reading below ten, they are expressing their most bearish stance in the entire lookback period.
The data staleness alert notifies users when COT reports have not updated for more than ten days, preventing reliance on outdated information for trading decisions. Government shutdowns or federal holidays can interrupt the normal Friday publication schedule. Using stale signals while believing them current creates dangerous false confidence.
The indicator's watermark information display positioned in the bottom right corner provides essential context at a glance. This persistent display shows the symbol and timeframe, the COT report date timestamp, days since last update, and the current signal state. A trader analyzing a potential short entry in crude oil can glance at the watermark to instantly confirm positioning context without interrupting analysis flow.
LIMITATIONS AND REALISTIC EXPECTATIONS
Practical application requires understanding both the indicator's considerable strengths and inherent limitations. COT data inherently lags price action by three days, as Tuesday positions are not published until Friday afternoon. This delay means the indicator cannot catch rapid intraday reversals or respond to surprise news events. Traders using the COT Index for timing entries must accept this latency and focus on swing trading and position trading timeframes where three-day lags matter less than in day trading or scalping.
The weekly publication schedule similarly makes the indicator unsuitable for short-term trading strategies requiring immediate feedback. The COT Index works best for traders operating on weekly or longer timeframes, where positioning shifts measured in weeks and months align with trading horizon.
Extreme COT readings can persist far longer than typical technical indicators suggest, testing the patience and capital reserves of traders attempting to fade them. When crude oil enters a sustained bull market driven by genuine supply disruptions, commercial hedgers may maintain bearish positioning for many months as prices grind higher. A commercial COT Index reading of fifteen indicating extreme bearishness might persist for three months while prices continue rallying before finally reversing. Traders without sufficient capital and risk tolerance to weather such drawdowns will exit prematurely, precisely when the signal is about to work (Irwin and Sanders, 2012).
Position sizing discipline becomes paramount when implementing COT-based strategies. Rather than risking large percentages of capital on individual signals, successful COT traders typically allocate modest position sizes across multiple signals, allowing some to take time to mature while others work more quickly.
The indicator also cannot overcome fundamental regime changes that alter the structural drivers of markets. If gold enters a true secular bull market driven by monetary debasement, commercial hedgers may remain persistently bearish as mining companies sell forward years of production at what they perceive as favorable prices. Their positioning indicates valuation concerns from a production cost perspective, but cannot stop prices from rising if investment demand overwhelms physical supply-demand balance.
Similarly, structural changes in market participation can alter the meaning of positioning extremes. The growth of commodity index investing in the two thousands brought massive passive long-only capital into futures markets, fundamentally changing typical positioning ranges. Traders relying on COT signals without recognizing this regime change would have generated numerous false bearish signals during the commodity supercycle from 2003 to 2008.
The research foundation supporting COT analysis derives primarily from commodity markets where the commercial hedger information advantage is most pronounced. Studies specifically examining financial futures like equity indices and bonds show weaker but still present effects. Traders should calibrate expectations accordingly, recognizing that COT analysis likely works better for crude oil, natural gas, corn, and wheat than for the S&P 500, Treasury bonds, or currency futures.
Another important limitation involves the reporting threshold structure. Not all market participants appear in COT data, only those holding positions above specified minimums. In markets dominated by a few large players, concentration metrics become critical for proper interpretation. A single large trader accounting for thirty percent of commercial positioning might skew the entire category if their individual circumstances are idiosyncratic rather than representative.
GOLD FUTURES DURING A HYPOTHETICAL MARKET CYCLE
Consider a practical example using gold futures during a hypothetical but realistic market scenario that illustrates how the COT Index indicator guides trading decisions through a complete market cycle. Suppose gold has rallied from fifteen hundred to nineteen hundred dollars per ounce over six months, driven by inflation concerns following aggressive monetary expansion, geopolitical uncertainty, and sustained buying by Asian central banks for reserve diversification.
Large speculators, operating primarily trend-following strategies, have accumulated increasingly bullish positions throughout this rally. Their COT Index has climbed progressively from forty-five to eighty-five. The table display shows that large speculators now hold net long positions representing thirty-two percent of total open interest, their highest in four years. Momentum indicators show positive readings, indicating positions are still building though at a decelerating rate. Position velocity has turned negative, suggesting the pace of position building is slowing.
Meanwhile, commercial hedgers have responded to the rally by aggressively selling forward production and inventory. Their COT Index has moved inversely to price, declining from fifty-five to twenty. This bearish commercial positioning represents mining companies locking in forward sales at prices they view as attractive relative to production costs. The table shows commercials now hold net short positions representing twenty-nine percent of total open interest, their most bearish stance in five years. Concentration metrics indicate this positioning is broadly distributed across many commercial entities, suggesting the bearish stance reflects collective industry view rather than idiosyncratic positioning by a single firm.
Small traders, attracted by mainstream financial media coverage of gold's impressive rally, have recently piled into long positions. Their COT Index has jumped from forty-five to seventy-eight as retail investors chase the trend. Television financial networks feature frequent segments on gold with bullish guests. Internet forums and social media show surging retail interest. This retail enthusiasm historically marks late-stage trend development rather than early opportunity.
The COT Index indicator, configured to monitor commercial positioning from a contrarian perspective, displays a clear bearish signal given the extreme commercial short positioning. The table displays multiple confirming metrics: positioning extremity shows commercials at the ninety-sixth percentile of bearishness, market power indicates they control twenty-nine percent of open interest, and sentiment divergence registers sixty-five, indicating massive disagreement between commercial hedgers and large speculators. This divergence, the highest in three years, places the market in the historically high-risk category for reversals.
The interpretation requires nuance and consideration of context beyond just COT data. Commercials are not necessarily predicting an imminent crash. Rather, they are hedging business operations at what they collectively view as favorable price levels. However, the data reveals they have sold unusually large quantities of forward production, suggesting either exceptional production expectations for the year ahead or concern about sustaining current price levels or combination of both. Combined with extreme speculator positioning indicating a crowded long trade, and small trader enthusiasm confirming retail FOMO, the confluence suggests elevated reversal risk even if the precise timing remains uncertain.
A prudent trader analyzing this situation might take several actions based on COT Index signals. Existing long positions could be tightened with closer stop losses. Profit-taking on a portion of long exposure could lock in gains while maintaining some participation. Some traders might initiate modest short positions as portfolio hedges, sizing them appropriately for the inherent uncertainty in timing reversals. Others might simply move to the sidelines, avoiding new long entries until positioning normalizes.
The key lesson from case study analysis is that COT signals provide probabilistic edges rather than deterministic predictions. They work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five percent win rate with proper risk management produces substantial profits over time, yet still means forty-five percent of signals will be premature or wrong. Traders must embrace this probabilistic reality rather than seeking the impossible goal of perfect accuracy.
INTEGRATION WITH TRADING SYSTEMS
Integration with existing trading systems represents a natural and powerful use case for COT analysis, adding a positioning dimension to price-based technical approaches or fundamental analytical frameworks. Few traders rely exclusively on a single indicator or methodology. Rather, they build systems that synthesize multiple information sources, with each component addressing different aspects of market behavior.
Trend followers might use COT extremes as regime filters, modifying position sizing or avoiding new trend entries when positioning reaches levels historically associated with reversals. Consider a classic trend-following system based on moving average crossovers and momentum breakouts. Integration of COT analysis adds nuance. When large speculator positioning exceeds ninety or commercial positioning falls below ten, the regime filter recognizes elevated reversal risk. The system might reduce position sizing by fifty percent for new signals during these high-risk periods (Kaufman, 2013).
Mean reversion traders might require COT signal confluence before fading extended moves. When crude oil becomes technically overbought and large speculators show extreme long positioning above eighty-five, both signals confirm. If only technical indicators show extremes while positioning remains neutral, the potential short signal is rejected, avoiding fades of trends with underlying institutional support (Kaufman, 2013).
Discretionary traders can monitor the indicator as a continuous awareness tool, informing bias and position sizing without dictating mechanical entries and exits. A discretionary trader might notice commercial positioning shifting from neutral to progressively more bullish over several months. This trend informs growing positive bias even without triggering mechanical signals.
Multi-timeframe analysis represents another powerful integration approach. A trader might use daily charts for trade execution and timing while monitoring weekly COT positioning for strategic context. When both timeframes align, highest-probability opportunities emerge.
Portfolio construction for futures traders can incorporate COT signals as an additional selection criterion. Markets showing strong technical setups AND favorable COT positioning receive highest allocations. Markets with strong technicals but neutral or unfavorable positioning receive reduced allocations.
ADVANCED METRICS AND INTERPRETATION
The metrics table transforms simple positioning data into multidimensional market intelligence. Position extremity, calculated as the absolute deviation from the historical mean normalized by standard deviation, helps identify truly unusual readings versus routine fluctuations. A reading above two standard deviations indicates ninety-fifth percentile or higher extremity. Above three standard deviations indicates ninety-ninth percentile or higher, genuinely rare positioning that historically precedes major events with high probability.
Market power, expressed as a percentage of total open interest, reveals whose positioning matters most from a mechanical market impact perspective. Consider two scenarios in gold futures. In scenario one, commercials show a COT Index reading of fifteen while their market power metric shows they hold net shorts representing thirty-five percent of open interest. This is a high-confidence bearish signal. In scenario two, commercials also show a reading of fifteen, but market power shows only eight percent. While positioning is extreme relative to this category's normal range, their limited market share means less mechanical influence on price.
The rate of change and momentum metrics highlight whether positions are accelerating or decelerating, often providing earlier warnings than absolute levels alone. A COT Index reading of seventy-five with rapidly building momentum suggests continued movement toward extremes. Conversely, a reading of eighty-five with decelerating or negative momentum indicates the positioning trend is exhausting.
Position velocity measures the rate of change in positioning changes, effectively a second derivative. When velocity shifts from positive to negative, it indicates that while positioning may still be growing, the pace of growth is slowing. This deceleration often precedes actual reversal in positioning direction by several weeks.
Sentiment divergence calculates the absolute difference between normalized commercial and large speculator index values. When commercials show extreme bearish positioning at twenty while large speculators show extreme bullish positioning at eighty, the divergence reaches sixty, representing near-maximum disagreement. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals. The mechanism is intuitive. Extreme divergence indicates the informed hedgers and momentum-following speculators have positioned opposite each other with conviction. One group will prove correct and profit while the other proves incorrect and suffers losses. The resolution of this disagreement through price movement often involves volatility.
The table also displays concentration metrics when available. High concentration indicates a few dominant players controlling most of the positioning within a category, while low concentration suggests broad-based participation. Broad-based positioning more reliably reflects collective market intelligence and industry consensus. If mining companies globally all independently decide to hedge aggressively at similar price levels, it suggests genuine industry-wide view about price valuations rather than circumstances specific to one firm.
DATA QUALITY AND RELIABILITY
The CFTC has maintained COT reporting in various forms since the nineteen twenties, providing nearly a century of positioning data across multiple market cycles. However, data quality and reporting standards have evolved substantially over this long period. Modern electronic reporting implemented in the late nineteen nineties and early two thousands significantly improved accuracy and timeliness compared to earlier paper-based systems.
Traders should understand that COT reports capture positions as of Tuesday's close each week. Markets remain open three additional days before publication on Friday afternoon, meaning the reported data is three days stale when received. During periods of rapid market movement or major news events, this lag can be significant. The indicator addresses this limitation by including timestamp information and staleness warnings.
The three-day lag creates particular challenges during extreme volatility episodes. Flash crashes, surprise central bank interventions, geopolitical shocks, and other high-impact events can completely transform market positioning within hours. Traders must exercise judgment about whether reported positioning remains relevant given intervening events.
Reporting thresholds also mean that not all market participants appear in disaggregated COT data. Traders holding positions below specified minimums aggregate into the non-reportable or small trader category. This aggregation affects different markets differently. In highly liquid contracts like crude oil with thousands of participants, reportable traders might represent seventy to eighty percent of open interest. In thinly traded contracts with only dozens of active participants, a few large reportable positions might represent ninety-five percent of open interest.
Another data quality consideration involves trader classification into categories. The CFTC assigns traders to commercial or non-commercial categories based on reported business purpose and activities. However, this process is not perfect. Some entities engage in both commercial and speculative activities, creating ambiguity about proper classification. The transition to Disaggregated reports attempted to address some of these ambiguities by creating more granular categories.
COMPARISON WITH ALTERNATIVE APPROACHES
Several alternative approaches to COT analysis exist in the trading community beyond the normalization methodology employed by this indicator. Some analysts focus on absolute position changes week-over-week rather than index-based normalization. This approach calculates the change in net positioning from one week to the next. The emphasis falls on momentum in positioning changes rather than absolute levels relative to history. This method potentially identifies regime shifts earlier but sacrifices cross-market comparability (Briese, 2008).
Other practitioners employ more complex statistical transformations including percentile rankings, z-score standardization, and machine learning classification algorithms. Ruan and Zhang (2018) demonstrated that machine learning models applied to COT data could achieve modest improvements in forecasting accuracy compared to simple threshold-based approaches. However, these gains came at the cost of interpretability and implementation complexity.
The COT Index indicator intentionally employs a relatively straightforward normalization methodology for several important reasons. First, transparency enhances user understanding and trust. Traders can verify calculations manually and develop intuitive feel for what different readings mean. Second, academic research suggests that most of the predictive power in COT data comes from extreme positioning levels rather than subtle patterns requiring complex statistical methods to detect. Third, robust methods that work consistently across many markets and time periods tend to be simpler rather than more complex, reducing the risk of overfitting to historical data. Fourth, the complexity costs of implementation matter for retail traders without programming teams or computational infrastructure.
PSYCHOLOGICAL ASPECTS OF COT TRADING
Trading based on COT data requires psychological fortitude that differs from momentum-based approaches. Contrarian positioning signals inherently mean betting against prevailing market sentiment and recent price action. When commercials reach extreme bearish positioning, prices have typically been rising, sometimes for extended periods. The price chart looks bullish, momentum indicators confirm strength, moving averages align positively. The COT signal says bet against all of this. This psychological difficulty explains why COT analysis remains underutilized relative to trend-following methods.
Human psychology strongly predisposes us toward extrapolation and recency bias. When prices rally for months, our pattern-matching brains naturally expect continued rally. The recent price action dominates our perception, overwhelming rational analysis about positioning extremes and historical probabilities. The COT signal asking us to sell requires overriding these powerful psychological impulses.
The indicator design attempts to support the required psychological discipline through several features. Clear threshold markers and signal states reduce ambiguity about when signals trigger. When the commercial index crosses below twenty, the signal is explicit and unambiguous. The background shifts to red, the signal label displays bearish, and alerts fire. This explicitness helps traders act on signals rather than waiting for additional confirmation that may never arrive.
The metrics table provides analytical justification for contrarian positions, helping traders maintain conviction during inevitable periods of adverse price movement. When a trader enters short positions based on extreme commercial bearish positioning but prices continue rallying for several weeks, doubt naturally emerges. The table display provides reassurance. Commercial positioning remains extremely bearish. Divergence remains high. The positioning thesis remains intact even though price action has not yet confirmed.
Alert functionality ensures traders do not miss signals due to inattention while also not requiring constant monitoring that can lead to emotional decision-making. Setting alerts for COT extremes enables a healthier relationship with markets. When meaningful signals occur, alerts notify them. They can then calmly assess the situation and execute planned responses.
However, no indicator design can completely overcome the psychological difficulty of contrarian trading. Some traders simply cannot maintain short positions while prices rally. For these traders, COT analysis might be better employed as an exit signal for long positions rather than an entry signal for shorts.
Ultimately, successful COT trading requires developing comfort with probabilistic thinking rather than certainty-seeking. The signals work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five or sixty percent win rate with proper risk management produces substantial profits over years, yet still means forty to forty-five percent of signals will be premature or wrong. COT analysis provides genuine edge, but edge means probability advantage, not elimination of losing trades.
EDUCATIONAL RESOURCES AND CONTINUOUS LEARNING
The indicator provides extensive built-in educational resources through its documentation, detailed tooltips, and transparent calculations. However, mastering COT analysis requires study beyond any single tool or resource. Several excellent resources provide valuable extensions of the concepts covered in this guide.
Books and practitioner-focused monographs offer accessible entry points. Stephen Briese published The Commitments of Traders Bible in two thousand eight, offering detailed breakdowns of how different markets and trader categories behave (Briese, 2008). Briese's work stands out for its empirical focus and market-specific insights. Jack Schwager includes discussion of COT analysis within the broader context of market behavior in his book Market Sense and Nonsense (Schwager, 2012). Perry Kaufman's Trading Systems and Methods represents perhaps the most rigorous practitioner-focused text on systematic trading approaches including COT analysis (Kaufman, 2013).
Academic journal articles provide the rigorous statistical foundation underlying COT analysis. The Journal of Futures Markets regularly publishes research on positioning data and its predictive properties. Bessembinder and Chan's earlier work on systematic risk, hedging pressure, and risk premiums in futures markets provides theoretical foundation (Bessembinder, 1992). Chang's examination of speculator returns provides historical context (Chang, 1985). Irwin and Sanders provide essential skeptical perspective in their two thousand twelve article (Irwin and Sanders, 2012). Wang's two thousand three article provides one of the most empirical analyses of COT data across multiple commodity markets (Wang, 2003).
Online resources extend beyond academic and book-length treatments. The CFTC website provides free access to current and historical COT reports in multiple formats. The explanatory materials section offers detailed documentation of report construction, category definitions, and historical methodology changes. Traders serious about COT analysis should read these official CFTC documents to understand exactly what they are analyzing.
Commercial COT data services such as Barchart provide enhanced visualization and analysis tools beyond raw CFTC data. TradingView's educational materials, published scripts library, and user community provide additional resources for exploring different approaches to COT analysis.
The key to mastering COT analysis lies not in finding a single definitive source but rather in building understanding through multiple perspectives and information sources. Academic research provides rigorous empirical foundation. Practitioner-focused books offer practical implementation insights. Direct engagement with data through systematic backtesting develops intuition about how positioning dynamics manifest across different market conditions.
SYNTHESIZING KNOWLEDGE INTO PRACTICE
The COT Index indicator represents the synthesis of academic research, trading experience, and software engineering into a practical tool accessible to retail traders equipped with nothing more than a TradingView account and willingness to learn. What once required expensive data subscriptions, custom programming capabilities, statistical software, and institutional resources now appears as a straightforward indicator requiring only basic parameter selection and modest study to understand. This democratization of institutional-grade analysis tools represents a broader trend in financial markets over recent decades.
Yet technology and data access alone provide no edge without understanding and discipline. Markets remain relentlessly efficient at eliminating edges that become too widely known and mechanically exploited. The COT Index indicator succeeds only when users invest time learning the underlying concepts, understand the limitations and probability distributions involved, and integrate signals thoughtfully into trading plans rather than applying them mechanically.
The academic research demonstrates conclusively that institutional positioning contains genuine information about future price movements, particularly at extremes where commercial hedgers are maximally bearish or bullish relative to historical norms. This informational content is neither perfect nor deterministic but rather probabilistic, providing edge over many observations through identification of higher-probability configurations. Bessembinder and Chan's finding that commercial positioning explained modest but significant variance in future returns illustrates this probabilistic nature perfectly (Bessembinder and Chan, 1992). The effect is real and statistically significant, yet it explains perhaps ten to fifteen percent of return variance rather than most variance. Much of price movement remains unpredictable even with positioning intelligence.
The practical implication is that COT analysis works best as one component of a trading system rather than a standalone oracle. It provides the positioning dimension, revealing where the smart money has positioned and where the crowd has followed, but price action analysis provides the timing dimension. Fundamental analysis provides the catalyst dimension. Risk management provides the survival dimension. These components work together synergistically.
The indicator's design philosophy prioritizes transparency and education over black-box complexity, empowering traders to understand exactly what they are analyzing and why. Every calculation is documented and user-adjustable. The threshold markers, background coloring, tables, and clear signal states provide multiple reinforcing channels for conveying the same information.
This educational approach reflects a conviction that sustainable trading success comes from genuine understanding rather than mechanical system-following. Traders who understand why commercial positioning matters, how different trader categories behave, what positioning extremes signify, and where signals fit within probability distributions can adapt when market conditions change. Traders mechanically following black-box signals without comprehension abandon systems after normal losing streaks.
The research foundation supporting COT analysis comes primarily from commodity markets where commercial hedger informational advantages are most pronounced. Agricultural producers hedging crops know more about supply conditions than distant speculators. Energy companies hedging production know more about operating costs than financial traders. Metals miners hedging output know more about ore grades than index funds. Financial futures markets show weaker but still present effects.
The journey from reading this documentation to profitable trading based on COT analysis involves several stages that cannot be rushed. Initial reading and basic understanding represents the first stage. Historical study represents the second stage, reviewing past market cycles to observe how positioning extremes preceded major turning points. Paper trading or small-size real trading represents the third stage to experience the psychological challenges. Refinement based on results and personal psychology represents the fourth stage.
Markets will continue evolving. New participant categories will emerge. Regulatory structures will change. Technology will advance. Yet the fundamental dynamics driving COT analysis, that different market participants have different information, different motivations, and different forecasting abilities that manifest in their positioning, will persist as long as futures markets exist. While specific thresholds or optimal parameters may shift over time, the core logic remains sound and adaptable.
The trader equipped with this indicator, understanding of the theory and evidence behind COT analysis, realistic expectations about probability rather than certainty, discipline to maintain positions through adverse volatility, and patience to allow signals time to develop possesses genuine edge in markets. The edge is not enormous, markets cannot allow large persistent inefficiencies without arbitraging them away, but it is real, measurable, and exploitable by those willing to invest in learning and disciplined application.
REFERENCES
Bessembinder, H. (1992) Systematic risk, hedging pressure, and risk premiums in futures markets, Review of Financial Studies, 5(4), pp. 637-667.
Bessembinder, H. and Chan, K. (1992) The profitability of technical trading rules in the Asian stock markets, Pacific-Basin Finance Journal, 3(2-3), pp. 257-284.
Briese, S. (2008) The Commitments of Traders Bible: How to Profit from Insider Market Intelligence. Hoboken: John Wiley & Sons.
Chang, E.C. (1985) Returns to speculators and the theory of normal backwardation, Journal of Finance, 40(1), pp. 193-208.
Commodity Futures Trading Commission (CFTC) (2009) Explanatory Notes: Disaggregated Commitments of Traders Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Commodity Futures Trading Commission (CFTC) (2020) Commitments of Traders: About the Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Irwin, S.H. and Sanders, D.R. (2012) Testing the Masters Hypothesis in commodity futures markets, Energy Economics, 34(1), pp. 256-269.
Kaufman, P.J. (2013) Trading Systems and Methods. 5th edn. Hoboken: John Wiley & Sons.
Ruan, Y. and Zhang, Y. (2018) Forecasting commodity futures prices using machine learning: Evidence from the Chinese commodity futures market, Applied Economics Letters, 25(12), pp. 845-849.
Sanders, D.R., Boris, K. and Manfredo, M. (2004) Hedgers, funds, and small speculators in the energy futures markets: an analysis of the CFTC's Commitments of Traders reports, Energy Economics, 26(3), pp. 425-445.
Schwager, J.D. (2012) Market Sense and Nonsense: How the Markets Really Work and How They Don't. Hoboken: John Wiley & Sons.
Tharp, V.K. (2008) Super Trader: Make Consistent Profits in Good and Bad Markets. New York: McGraw-Hill.
Wang, C. (2003) The behavior and performance of major types of futures traders, Journal of Futures Markets, 23(1), pp. 1-31.
Williams, L.R. and Noseworthy, M. (2009) The Right Stock at the Right Time: Prospering in the Coming Good Years. Hoboken: John Wiley & Sons.
FURTHER READING
For traders seeking to deepen their understanding of COT analysis and futures market positioning beyond this documentation, the following resources provide valuable extensions:
Academic Journal Articles:
Fishe, R.P.H. and Smith, A. (2012) Do speculators drive commodity prices away from supply and demand fundamentals?, Journal of Commodity Markets, 1(1), pp. 1-16.
Haigh, M.S., Hranaiova, J. and Overdahl, J.A. (2007) Hedge funds, volatility, and liquidity provision in energy futures markets, Journal of Alternative Investments, 9(4), pp. 10-38.
Kocagil, A.E. (1997) Does futures speculation stabilize spot prices? Evidence from metals markets, Applied Financial Economics, 7(1), pp. 115-125.
Sanders, D.R. and Irwin, S.H. (2011) The impact of index funds in commodity futures markets: A systems approach, Journal of Alternative Investments, 14(1), pp. 40-49.
Books and Practitioner Resources:
Murphy, J.J. (1999) Technical Analysis of the Financial Markets: A Guide to Trading Methods and Applications. New York: New York Institute of Finance.
Pring, M.J. (2002) Technical Analysis Explained: The Investor's Guide to Spotting Investment Trends and Turning Points. 4th edn. New York: McGraw-Hill.
Federal Reserve and Research Institution Publications:
Federal Reserve Banks regularly publish working papers examining commodity markets, futures positioning, and price discovery mechanisms. The Federal Reserve Bank of San Francisco and Federal Reserve Bank of Kansas City maintain active research programs in this area.
Online Resources:
The CFTC website provides free access to current and historical COT reports, explanatory materials, and regulatory documentation.
Barchart offers enhanced COT data visualization and screening tools.
TradingView's community library contains numerous published scripts and educational materials exploring different approaches to positioning analysis.
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Smart MACD Volume Trader# Smart MACD Volume Trader
## Overview
Smart MACD Volume Trader is an enhanced momentum indicator that combines the classic MACD (Moving Average Convergence Divergence) oscillator with an intelligent high-volume filter. This combination significantly reduces false signals by ensuring that trading signals are only generated when price momentum is confirmed by substantial volume activity.
The indicator supports over 24 different instruments including major and exotic forex pairs, precious metals (gold and silver), energy commodities (crude oil, natural gas), and industrial metals (copper). For forex and commodity traders, the indicator automatically maps to CME and COMEX futures contracts to provide accurate institutional-grade volume data.
## Originality and Core Concept
Traditional MACD indicators generate signals based solely on price momentum, which can result in numerous false signals during low-activity periods or ranging markets. This indicator addresses this critical weakness by introducing a volume confirmation layer with automatic institutional volume integration.
**What makes this approach original:**
- Signals are triggered only when MACD crossovers coincide with elevated volume activity
- Implements a lookback mechanism to detect volume spikes within recent bars
- Automatically detects and maps 24+ forex pairs and commodities to their corresponding CME and COMEX futures contracts
- Provides real institutional volume data for forex pairs where spot volume is unreliable
- Combines two independent market dimensions (price momentum and volume) into a single, actionable signal
- Includes intelligent asset detection that works across multiple exchanges and ticker formats
**The underlying principle:** Volume validates price movement. When institutional money enters the market, it creates volume signatures. By requiring high volume confirmation and using actual institutional volume data from futures markets, this indicator filters out weak price movements and focuses on trades backed by genuine market participation. The automatic futures mapping ensures that forex and commodity traders always have access to the most accurate volume data available, without manual configuration.
## How It Works
### MACD Component
The indicator calculates MACD using standard methodology:
1. **Fast EMA (default: 12 periods)** - Tracks short-term price momentum
2. **Slow EMA (default: 26 periods)** - Tracks longer-term price momentum
3. **MACD Line** - Difference between Fast EMA and Slow EMA
4. **Signal Line (default: 9-period SMA)** - Smoothed average of MACD line
**Crossover signals:**
- **Bullish:** MACD line crosses above Signal line (momentum turning positive)
- **Bearish:** MACD line crosses below Signal line (momentum turning negative)
### Volume Filter Component
The volume filter adds an essential confirmation layer:
1. **Volume Moving Average** - Calculates exponential MA of volume (default: 20 periods)
2. **High Volume Threshold** - Multiplies MA by ratio (default: 2.0x or 200%)
3. **Volume Detection** - Identifies bars where current volume exceeds threshold
4. **Lookback Period** - Checks if high volume occurred in recent bars (default: 5 bars)
**Signal logic:**
- Buy/Sell signals only trigger when BOTH conditions are met:
- MACD crossover/crossunder occurs
- High volume detected within lookback period
### Automatic CME Futures Integration
For forex traders, spot FX volume data can be unreliable or non-existent. This indicator solves this problem by automatically detecting forex pairs and mapping them to corresponding CME futures contracts with real institutional volume data.
**Supported Major Forex Pairs (7):**
- EURUSD → CME:6E1! (Euro FX Futures)
- GBPUSD → CME:6B1! (British Pound Futures)
- AUDUSD → CME:6A1! (Australian Dollar Futures)
- USDJPY → CME:6J1! (Japanese Yen Futures)
- USDCAD → CME:6C1! (Canadian Dollar Futures)
- USDCHF → CME:6S1! (Swiss Franc Futures)
- NZDUSD → CME:6N1! (New Zealand Dollar Futures)
**Supported Exotic Forex Pairs (4):**
- USDMXN → CME:6M1! (Mexican Peso Futures)
- USDRUB → CME:6R1! (Russian Ruble Futures)
- USDBRL → CME:6L1! (Brazilian Real Futures)
- USDZAR → CME:6Z1! (South African Rand Futures)
**Supported Cross Pairs (6):**
- EURJPY → CME:6E1! (Uses Euro Futures)
- GBPJPY → CME:6B1! (Uses British Pound Futures)
- EURGBP → CME:6E1! (Uses Euro Futures)
- AUDJPY → CME:6A1! (Uses Australian Dollar Futures)
- EURAUD → CME:6E1! (Uses Euro Futures)
- GBPAUD → CME:6B1! (Uses British Pound Futures)
**Supported Precious Metals (2):**
- Gold (XAUUSD, GOLD) → COMEX:GC1! (Gold Futures)
- Silver (XAGUSD, SILVER) → COMEX:SI1! (Silver Futures)
**Supported Energy Commodities (3):**
- WTI Crude Oil (USOIL, WTIUSD) → NYMEX:CL1! (Crude Oil Futures)
- Brent Oil (UKOIL) → NYMEX:BZ1! (Brent Crude Futures)
- Natural Gas (NATGAS) → NYMEX:NG1! (Natural Gas Futures)
**Supported Industrial Metals (1):**
- Copper (COPPER) → COMEX:HG1! (Copper Futures)
**How the automatic detection works:**
The indicator intelligently identifies the asset type by analyzing:
1. Exchange name (FX, OANDA, TVC, COMEX, NYMEX, etc.)
2. Currency pair pattern (6-letter codes like EURUSD, GBPUSD)
3. Commodity identifiers (XAU for gold, XAG for silver, OIL for crude)
When a supported instrument is detected, the indicator automatically switches to the corresponding futures contract for volume analysis. For stocks, cryptocurrencies, and other assets, the indicator uses the native volume data from the current chart.
**Visual feedback:**
An information table appears in the top-right corner of the MACD pane showing:
- Current chart symbol
- Exchange name
- Currency pair or asset name
- Volume source being used (highlighted in orange for futures, yellow for native volume)
- Current high volume status
This provides complete transparency about which data source the indicator is using for its volume analysis.
## How to Use
### Basic Setup
1. Add the indicator to your chart
2. The indicator displays in a separate pane (MACD) and overlay (signals/volume bars)
3. Default settings work well for most assets, but can be customized
### Signal Interpretation
### Visual Signals
**Visual Signals:**
- **Green "BUY" label** - Bullish MACD crossover confirmed by high volume
- **Red "SELL" label** - Bearish MACD crossunder confirmed by high volume
- **Green/Red candles** - Highlight bars with volume exceeding the threshold
- **Light green/red background** - Emphasizes signal bars on the chart
**Information Table:**
A detailed information table appears in the top-right corner of the MACD pane, providing real-time transparency about the indicator's operation:
- **Chart:** Current symbol being analyzed
- **Exchange:** The exchange or data feed being used
- **Pair:** The currency pair or asset name extracted from the ticker
- **Volume From:** The actual symbol used for volume analysis
- Orange color indicates CME or COMEX futures are being used (automatic institutional volume)
- Yellow color indicates native volume from the chart symbol is being used
- Hover tooltip shows whether automatic futures mapping is active
- **High Volume:** Current status showing YES (green) when volume exceeds threshold, NO (gray) otherwise
This table ensures complete transparency and allows you to verify that the correct volume source is being used for your analysis.
**Volume Analysis:**
- Gray histogram bars = Normal volume
- Red histogram bars = High volume (exceeds threshold)
- Green line = Volume moving average baseline
**MACD Analysis:**
- Blue line = MACD line (momentum indicator)
- Orange line = Signal line (trend confirmation)
- Gray dotted line = Zero line (bullish above, bearish below)
### Parameter Customization
**MACD Parameters:**
- Adjust Fast/Slow EMA lengths for different sensitivities
- Shorter periods = More signals, faster response
- Longer periods = Fewer signals, less noise
**Volume Parameters:**
- **Volume MA Period:** Higher values smooth volume analysis
- **High Volume Ratio:** Lower values (1.5x) = More signals; Higher values (3.0x) = Fewer, stronger signals
- **Volume Lookback Bars:** Controls how recent the volume spike must be
**Direction Filters:**
- **Only Buy Signals:** Enables long-only strategy mode
- **Only Sell Signals:** Enables short-only strategy mode
### Alert Configuration
The indicator includes three alert types:
1. **Buy Signal Alert** - Triggers when bullish signal appears
2. **Sell Signal Alert** - Triggers when bearish signal appears
3. **High Volume Alert** - Triggers when volume exceeds threshold
To set up alerts:
1. Click the indicator name → "Add alert on Smart MACD Volume Trader"
2. Select desired alert condition
3. Configure notification method (popup, email, webhook, etc.)
## Trading Strategy Guidelines
### Best Practices
**Recommended markets:**
- Liquid stocks (large-cap, high daily volume)
- Major forex pairs (EURUSD, GBPUSD, USDJPY, AUDUSD, USDCAD, USDCHF, NZDUSD)
- Exotic forex pairs (USDMXN, USDRUB, USDBRL, USDZAR)
- Cross pairs (EURJPY, GBPJPY, EURGBP, AUDJPY, EURAUD, GBPAUD)
- Precious metals (Gold, Silver with automatic COMEX futures mapping)
- Energy commodities (Crude Oil, Natural Gas with automatic NYMEX futures mapping)
- Industrial metals (Copper with automatic COMEX futures mapping)
- Major cryptocurrency pairs
- Index futures and ETFs
**Timeframe recommendations:**
- **Day trading:** 5-minute to 15-minute charts
- **Swing trading:** 1-hour to 4-hour charts
- **Position trading:** Daily charts
**Risk management:**
- Use signals as entry confirmation, not standalone strategy
- Combine with support/resistance levels
- Consider overall market trend direction
- Always use stop-loss orders
### Strategy Examples
**Trend Following Strategy:**
1. Identify overall trend using higher timeframe (e.g., daily chart)
2. Trade only in trend direction
3. Use "Only Buy" filter in uptrends, "Only Sell" in downtrends
4. Enter on signal, exit on opposite signal or at resistance/support
**Volume Breakout Strategy:**
1. Wait for consolidation period (low volume, tight MACD range)
2. Enter when signal appears with high volume (confirms breakout)
3. Target previous swing highs/lows
4. Stop loss below/above recent consolidation
**Forex Scalping Strategy (with automatic CME futures):**
1. The indicator automatically detects forex pairs and uses CME futures volume
2. Trade during active sessions only (use session filter)
3. Focus on quick profits (10-20 pips)
4. Exit at opposite signal or profit target
**Commodities Trading Strategy (Gold, Silver, Oil):**
1. The indicator automatically maps to COMEX and NYMEX futures contracts
2. Trade during high-liquidity sessions (overlap of major markets)
3. Use the high volume confirmation to identify institutional entry points
4. Combine with key support and resistance levels for entries
5. Monitor the information table to confirm futures volume is being used (orange color)
6. Exit on opposite MACD signal or at predefined profit targets
## Why This Combination Works
### The Volume Advantage
Studies consistently show that price movements accompanied by high volume are more likely to continue, while low-volume movements often reverse. This indicator leverages this principle by requiring volume confirmation.
**Key benefits:**
1. **Reduced False Signals:** Eliminates MACD whipsaws during low-volume consolidation
2. **Confirmation Bias:** Two independent indicators (price momentum + volume) agreeing
3. **Institutional Alignment:** High volume often indicates institutional participation
4. **Trend Validation:** Volume confirms that price momentum has "conviction"
### Statistical Edge
By combining two uncorrelated signals (MACD crossovers and volume spikes), the indicator creates a higher-probability setup than either signal alone. The lookback mechanism ensures signals aren't missed if volume spike slightly precedes the MACD cross.
## Supported Exchanges and Automatic Detection
The indicator includes intelligent asset detection that works across multiple exchanges and ticker formats:
**Forex Exchanges (Automatic CME Mapping):**
- FX (TradingView forex feed)
- OANDA
- FXCM
- SAXO
- FOREXCOM
- PEPPERSTONE
- EASYMARKETS
- FX_IDC
**Commodity Exchanges (Automatic COMEX/NYMEX Mapping):**
- TVC (TradingView commodity feed)
- COMEX (directly)
- NYMEX (directly)
- ICEUS
**Other Asset Classes (Native Volume):**
- Stock exchanges (NASDAQ, NYSE, AMEX, etc.)
- Cryptocurrency exchanges (BINANCE, COINBASE, KRAKEN, etc.)
- Index providers (SP, DJ, etc.)
The detection algorithm analyzes three factors:
1. Exchange prefix in the ticker symbol
2. Pattern matching for currency pairs (6-letter codes)
3. Commodity identifiers in the symbol name
This ensures accurate automatic detection regardless of which data feed or exchange you use for charting. The information table in the top-right corner always displays which volume source is being used, providing complete transparency.
## Technical Details
**Calculations:**
- MACD Fast MA: EMA(close, fastLength)
- MACD Slow MA: EMA(close, slowLength)
- MACD Line: Fast MA - Slow MA
- Signal Line: SMA(MACD Line, signalLength)
- Volume MA: Exponential MA of volume
- High Volume: Current volume >= Volume MA × Ratio
**Signal logic:**
```
Buy Signal = (MACD crosses above Signal) AND (High volume in last N bars)
Sell Signal = (MACD crosses below Signal) AND (High volume in last N bars)
```
## Parameters Reference
| Parameter | Default | Description |
|-----------|---------|-------------|
| Volume Symbol | Blank | Manual override for volume source (leave blank for automatic detection) |
| Use CME Futures | False | Legacy option (automatic detection is now built-in) |
| Alert Session | 1530-2200 | Active session time range for alerts |
| Timezone | UTC+1 | Timezone for alert sessions |
| Volume MA Period | 20 | Number of periods for volume moving average |
| High Volume Ratio | 2.0 | Volume threshold multiplier (2.0 = 200% of average) |
| Volume Lookback | 5 | Number of bars to check for high volume confirmation |
| MACD Fast Length | 12 | Fast EMA period for MACD calculation |
| MACD Slow Length | 26 | Slow EMA period for MACD calculation |
| MACD Signal Length | 9 | Signal line SMA period |
| Only Buy | False | Filter to show only bullish signals |
| Only Sell | False | Filter to show only bearish signals |
| Show Signals | True | Display buy and sell labels on chart |
## Optimization Tips
**For volatile markets (crypto, small caps):**
- Increase High Volume Ratio to 2.5-3.0
- Reduce Volume Lookback to 3-4 bars
- Consider faster MACD settings (8, 17, 9)
**For stable markets (large-cap stocks, bonds):**
- Decrease High Volume Ratio to 1.5-1.8
- Increase Volume MA Period to 30-50
- Use standard MACD settings
**For forex (with automatic CME futures):**
- The indicator automatically uses CME futures when forex pairs are detected
- Set appropriate trading session based on your timezone
- Use Volume Lookback of 5-7 bars
- Consider session-based alerts only
- Monitor the information table to verify correct futures mapping
**For commodities (Gold, Silver, Oil, Copper):**
- The indicator automatically maps to COMEX and NYMEX futures
- Increase High Volume Ratio to 2.0-2.5 for metals
- Use slightly higher Volume MA Period (25-30) for smoother analysis
- Trade during active market hours for best volume data
- The information table will show the futures contract being used (orange highlight)
## Limitations and Considerations
**What this indicator does NOT do:**
- Does not predict future price direction
- Does not guarantee profitable trades
- Does not replace proper risk management
- Does not work well in extremely low-volume conditions
**Market conditions to avoid:**
- Pre-market and after-hours sessions (low volume)
- Major news events (volatile, unpredictable volume)
- Holidays and low-liquidity periods
- Extremely low float stocks
## Conclusion
Smart MACD Volume Trader represents a significant evolution of the traditional MACD indicator by combining volume confirmation with automatic institutional volume integration. This dual-confirmation approach significantly improves signal quality by filtering out low-conviction price movements and ensuring traders work with accurate volume data.
The indicator's automatic detection and mapping system supports over 24 instruments across forex, commodities, and metals markets. By intelligently switching to CME and COMEX futures contracts when appropriate, the indicator provides forex and commodity traders with the same quality of volume data that stock traders naturally have access to.
This indicator is particularly valuable for traders who want to:
- Align their entries with institutional money flow
- Avoid getting trapped in false breakouts
- Trade forex pairs with reliable volume data
- Access accurate volume information for gold, silver, and energy commodities
- Combine momentum and volume analysis in a single, streamlined tool
Whether you are day trading stocks, swing trading forex pairs, or positioning in commodities markets, this indicator provides a robust framework for identifying high-probability momentum trades backed by genuine institutional participation. The automatic futures mapping works seamlessly across all supported instruments, requiring no manual configuration or expertise in futures markets.
---
## Support and Updates
This indicator is actively maintained and updated based on user feedback and market conditions. For questions about implementation or custom modifications, please use the comments section below.
**Disclaimer:** This indicator is for educational and informational purposes only. Past performance does not guarantee future results. Always conduct your own analysis and risk management before trading.
Contrarian Period High & LowContrarian Period High & Low
This indicator pairs nicely with the Contrarian 100 MA and can be located here:
Overview
The "Contrarian Period High & Low" indicator is a powerful technical analysis tool designed for traders seeking to identify key support and resistance levels and capitalize on contrarian trading opportunities. By tracking the highest highs and lowest lows over user-defined periods (Daily, Weekly, or Monthly), this indicator plots historical levels and generates buy and sell signals when price breaks these levels in a contrarian manner. A unique blue dot counter and action table enhance decision-making, making it ideal for swing traders, trend followers, and those trading forex, stocks, or cryptocurrencies. Optimized for daily charts, it can be adapted to other timeframes with proper testing.
How It Works
The indicator identifies the highest high and lowest low within a specified period (e.g., daily, weekly, or monthly) and draws horizontal lines for the previous period’s extremes on the chart. These levels act as dynamic support and resistance zones. Contrarian signals are generated when the price crosses below the previous period’s low (buy signal) or above the previous period’s high (sell signal), indicating potential reversals. A blue dot counter tracks consecutive buy signals, and a table displays the count and recommended action, helping traders decide whether to hold or flip positions.
Key Components
Period High/Low Levels: Tracks the highest high and lowest low for each period, plotting red lines for highs and green lines for lows from the bar where they occurred, extending for a user-defined length (default: 200 bars).
Contrarian Signals: Generates buy signals (blue circles) when price crosses below the previous period’s low and sell signals (white circles) when price crosses above the previous period’s high, designed to capture potential reversals.
Blue Dot Tracker: Counts consecutive buy signals (“blue dots”). If three or more occur, it suggests a stronger trend, with the table recommending whether to “Hold Investment” or “Flip Investment.”
Action Table: A 2x2 table in the bottom-right corner displays the blue dot count and action (“Hold Investment” if count ≥ 4, else “Flip Investment”) for quick reference.
Mathematical Concepts
Period Detection: Uses an approximate bar count to define periods (1 bar for Daily, 5 bars for Weekly, 20 bars for Monthly on a daily chart). When a new period starts, the previous period’s high/low is finalized and plotted.
High/Low Tracking:
Highest high (periodHigh) and lowest low (periodLow) are updated within the period.
Lines are drawn at these levels when the period ends, starting from the bar where the extreme occurred (periodHighBar, periodLowBar).
Signal Logic:
Buy signal: ta.crossunder(close , prevPeriodLow) and not lowBroken and barstate.isconfirmed
Sell signal: ta.crossover(close , prevPeriodHigh) and not highBroken and barstate.isconfirmed
Flags (highBroken, lowBroken) prevent multiple signals for the same level within a period.
Blue Dot Counter: Increments on each buy signal, resets on a sell signal or if price exceeds the entry price after three or more buy signals.
Entry and Exit Rules
Buy Signal (Blue Circle): Triggered when the price crosses below the previous period’s low, suggesting a potential oversold condition and buying opportunity. The signal appears as a blue circle below the price bar.
Sell Signal (White Circle): Triggered when the price crosses above the previous period’s high, indicating a potential overbought condition and selling opportunity. The signal appears as a white circle above the price bar.
Blue Dot Tracker:
Increments blueDotCount on each buy signal and sets an entryPrice on the first buy.
Resets on a sell signal or if price exceeds entryPrice after three or more buy signals.
If blueDotCount >= 3, the table suggests holding; if >= 4, it reinforces “Hold Investment.”
Exit Rules: Exit a buy position on a sell signal or when price exceeds the entry price after three or more buy signals. Combine with other tools (e.g., trendlines, support/resistance) for additional confirmation. Always apply proper risk management.
Recommended Usage
The "Contrarian Period High & Low" indicator is optimized for daily charts but can be adapted to other timeframes (e.g., 1H, 4H) with adjustments to the period bar count. It excels in markets with clear support/resistance levels and potential reversal zones. Traders should:
Backtest the indicator on their chosen asset and timeframe to validate signal reliability.
Combine with other technical tools (e.g., moving averages, Fibonacci levels) for stronger trade confirmation.
Adjust barsPerPeriod (e.g., ~120 bars for Weekly on hourly charts) based on the chart timeframe and market volatility.
Monitor the action table to guide position management based on blue dot counts.
Customization Options
Period Type: Choose between Daily, Weekly, or Monthly periods (default: Monthly).
Line Length: Set the length of high/low lines in bars (default: 200).
Show Highs/Lows: Toggle visibility of period high (red) and low (green) lines.
Max Lines to Keep: Limit the number of historical lines displayed (default: 10).
Hide Signals: Toggle buy/sell signal visibility for a cleaner chart.
Table Display: A fixed table in the bottom-right corner shows the blue dot count and action, with yellow (Hold) or green (Flip) backgrounds based on the count.
Why Use This Indicator?
The "Contrarian Period High & Low" indicator offers a unique blend of support/resistance visualization and contrarian signal generation, making it a versatile tool for identifying potential reversals. Its clear visual cues (lines and signals), blue dot tracker, and actionable table provide traders with an intuitive way to monitor market structure and manage trades. Whether you’re a beginner or an experienced trader, this indicator enhances your ability to spot key levels and time entries/exits effectively.
Tips for Users
Test the indicator thoroughly on your chosen market and timeframe to optimize settings (e.g., adjust barsPerPeriod for non-daily charts).
Use in conjunction with price action or other indicators for stronger trade setups.
Monitor the action table to decide whether to hold or flip positions based on blue dot counts.
Ensure your chart timeframe aligns with the selected period type (e.g., daily chart for Monthly periods).
Apply strict risk management to protect against false breakouts.
Happy trading with the Contrarian Period High & Low indicator! Share your feedback and strategies in the TradingView community!
ATAI Volume analysis with price action V 1.00ATAI Volume Analysis with Price Action
1. Introduction
1.1 Overview
ATAI Volume Analysis with Price Action is a composite indicator designed for TradingView. It combines per‑side volume data —that is, how much buying and selling occurs during each bar—with standard price‑structure elements such as swings, trend lines and support/resistance. By blending these elements the script aims to help a trader understand which side is in control, whether a breakout is genuine, when markets are potentially exhausted and where liquidity providers might be active.
The indicator is built around TradingView’s up/down volume feed accessed via the TradingView/ta/10 library. The following excerpt from the script illustrates how this feed is configured:
import TradingView/ta/10 as tvta
// Determine lower timeframe string based on user choice and chart resolution
string lower_tf_breakout = use_custom_tf_input ? custom_tf_input :
timeframe.isseconds ? "1S" :
timeframe.isintraday ? "1" :
timeframe.isdaily ? "5" : "60"
// Request up/down volume (both positive)
= tvta.requestUpAndDownVolume(lower_tf_breakout)
Lower‑timeframe selection. If you do not specify a custom lower timeframe, the script chooses a default based on your chart resolution: 1 second for second charts, 1 minute for intraday charts, 5 minutes for daily charts and 60 minutes for anything longer. Smaller intervals provide a more precise view of buyer and seller flow but cover fewer bars. Larger intervals cover more history at the cost of granularity.
Tick vs. time bars. Many trading platforms offer a tick / intrabar calculation mode that updates an indicator on every trade rather than only on bar close. Turning on one‑tick calculation will give the most accurate split between buy and sell volume on the current bar, but it typically reduces the amount of historical data available. For the highest fidelity in live trading you can enable this mode; for studying longer histories you might prefer to disable it. When volume data is completely unavailable (some instruments and crypto pairs), all modules that rely on it will remain silent and only the price‑structure backbone will operate.
Figure caption, Each panel shows the indicator’s info table for a different volume sampling interval. In the left chart, the parentheses “(5)” beside the buy‑volume figure denote that the script is aggregating volume over five‑minute bars; the center chart uses “(1)” for one‑minute bars; and the right chart uses “(1T)” for a one‑tick interval. These notations tell you which lower timeframe is driving the volume calculations. Shorter intervals such as 1 minute or 1 tick provide finer detail on buyer and seller flow, but they cover fewer bars; longer intervals like five‑minute bars smooth the data and give more history.
Figure caption, The values in parentheses inside the info table come directly from the Breakout — Settings. The first row shows the custom lower-timeframe used for volume calculations (e.g., “(1)”, “(5)”, or “(1T)”)
2. Price‑Structure Backbone
Even without volume, the indicator draws structural features that underpin all other modules. These features are always on and serve as the reference levels for subsequent calculations.
2.1 What it draws
• Pivots: Swing highs and lows are detected using the pivot_left_input and pivot_right_input settings. A pivot high is identified when the high recorded pivot_right_input bars ago exceeds the highs of the preceding pivot_left_input bars and is also higher than (or equal to) the highs of the subsequent pivot_right_input bars; pivot lows follow the inverse logic. The indicator retains only a fixed number of such pivot points per side, as defined by point_count_input, discarding the oldest ones when the limit is exceeded.
• Trend lines: For each side, the indicator connects the earliest stored pivot and the most recent pivot (oldest high to newest high, and oldest low to newest low). When a new pivot is added or an old one drops out of the lookback window, the line’s endpoints—and therefore its slope—are recalculated accordingly.
• Horizontal support/resistance: The highest high and lowest low within the lookback window defined by length_input are plotted as horizontal dashed lines. These serve as short‑term support and resistance levels.
• Ranked labels: If showPivotLabels is enabled the indicator prints labels such as “HH1”, “HH2”, “LL1” and “LL2” near each pivot. The ranking is determined by comparing the price of each stored pivot: HH1 is the highest high, HH2 is the second highest, and so on; LL1 is the lowest low, LL2 is the second lowest. In the case of equal prices the newer pivot gets the better rank. Labels are offset from price using ½ × ATR × label_atr_multiplier, with the ATR length defined by label_atr_len_input. A dotted connector links each label to the candle’s wick.
2.2 Key settings
• length_input: Window length for finding the highest and lowest values and for determining trend line endpoints. A larger value considers more history and will generate longer trend lines and S/R levels.
• pivot_left_input, pivot_right_input: Strictness of swing confirmation. Higher values require more bars on either side to form a pivot; lower values create more pivots but may include minor swings.
• point_count_input: How many pivots are kept in memory on each side. When new pivots exceed this number the oldest ones are discarded.
• label_atr_len_input and label_atr_multiplier: Determine how far pivot labels are offset from the bar using ATR. Increasing the multiplier moves labels further away from price.
• Styling inputs for trend lines, horizontal lines and labels (color, width and line style).
Figure caption, The chart illustrates how the indicator’s price‑structure backbone operates. In this daily example, the script scans for bars where the high (or low) pivot_right_input bars back is higher (or lower) than the preceding pivot_left_input bars and higher or lower than the subsequent pivot_right_input bars; only those bars are marked as pivots.
These pivot points are stored and ranked: the highest high is labelled “HH1”, the second‑highest “HH2”, and so on, while lows are marked “LL1”, “LL2”, etc. Each label is offset from the price by half of an ATR‑based distance to keep the chart clear, and a dotted connector links the label to the actual candle.
The red diagonal line connects the earliest and latest stored high pivots, and the green line does the same for low pivots; when a new pivot is added or an old one drops out of the lookback window, the end‑points and slopes adjust accordingly. Dashed horizontal lines mark the highest high and lowest low within the current lookback window, providing visual support and resistance levels. Together, these elements form the structural backbone that other modules reference, even when volume data is unavailable.
3. Breakout Module
3.1 Concept
This module confirms that a price break beyond a recent high or low is supported by a genuine shift in buying or selling pressure. It requires price to clear the highest high (“HH1”) or lowest low (“LL1”) and, simultaneously, that the winning side shows a significant volume spike, dominance and ranking. Only when all volume and price conditions pass is a breakout labelled.
3.2 Inputs
• lookback_break_input : This controls the number of bars used to compute moving averages and percentiles for volume. A larger value smooths the averages and percentiles but makes the indicator respond more slowly.
• vol_mult_input : The “spike” multiplier; the current buy or sell volume must be at least this multiple of its moving average over the lookback window to qualify as a breakout.
• rank_threshold_input (0–100) : Defines a volume percentile cutoff: the current buyer/seller volume must be in the top (100−threshold)%(100−threshold)% of all volumes within the lookback window. For example, if set to 80, the current volume must be in the top 20 % of the lookback distribution.
• ratio_threshold_input (0–1) : Specifies the minimum share of total volume that the buyer (for a bullish breakout) or seller (for bearish) must hold on the current bar; the code also requires that the cumulative buyer volume over the lookback window exceeds the seller volume (and vice versa for bearish cases).
• use_custom_tf_input / custom_tf_input : When enabled, these inputs override the automatic choice of lower timeframe for up/down volume; otherwise the script selects a sensible default based on the chart’s timeframe.
• Label appearance settings : Separate options control the ATR-based offset length, offset multiplier, label size and colors for bullish and bearish breakout labels, as well as the connector style and width.
3.3 Detection logic
1. Data preparation : Retrieve per‑side volume from the lower timeframe and take absolute values. Build rolling arrays of the last lookback_break_input values to compute simple moving averages (SMAs), cumulative sums and percentile ranks for buy and sell volume.
2. Volume spike: A spike is flagged when the current buy (or, in the bearish case, sell) volume is at least vol_mult_input times its SMA over the lookback window.
3. Dominance test: The buyer’s (or seller’s) share of total volume on the current bar must meet or exceed ratio_threshold_input. In addition, the cumulative sum of buyer volume over the window must exceed the cumulative sum of seller volume for a bullish breakout (and vice versa for bearish). A separate requirement checks the sign of delta: for bullish breakouts delta_breakout must be non‑negative; for bearish breakouts it must be non‑positive.
4. Percentile rank: The current volume must fall within the top (100 – rank_threshold_input) percent of the lookback distribution—ensuring that the spike is unusually large relative to recent history.
5. Price test: For a bullish signal, the closing price must close above the highest pivot (HH1); for a bearish signal, the close must be below the lowest pivot (LL1).
6. Labeling: When all conditions above are satisfied, the indicator prints “Breakout ↑” above the bar (bullish) or “Breakout ↓” below the bar (bearish). Labels are offset using half of an ATR‑based distance and linked to the candle with a dotted connector.
Figure caption, (Breakout ↑ example) , On this daily chart, price pushes above the red trendline and the highest prior pivot (HH1). The indicator recognizes this as a valid breakout because the buyer‑side volume on the lower timeframe spikes above its recent moving average and buyers dominate the volume statistics over the lookback period; when combined with a close above HH1, this satisfies the breakout conditions. The “Breakout ↑” label appears above the candle, and the info table highlights that up‑volume is elevated relative to its 11‑bar average, buyer share exceeds the dominance threshold and money‑flow metrics support the move.
Figure caption, In this daily example, price breaks below the lowest pivot (LL1) and the lower green trendline. The indicator identifies this as a bearish breakout because sell‑side volume is sharply elevated—about twice its 11‑bar average—and sellers dominate both the bar and the lookback window. With the close falling below LL1, the script triggers a Breakout ↓ label and marks the corresponding row in the info table, which shows strong down volume, negative delta and a seller share comfortably above the dominance threshold.
4. Market Phase Module (Volume Only)
4.1 Concept
Not all markets trend; many cycle between periods of accumulation (buying pressure building up), distribution (selling pressure dominating) and neutral behavior. This module classifies the current bar into one of these phases without using ATR , relying solely on buyer and seller volume statistics. It looks at net flows, ratio changes and an OBV‑like cumulative line with dual‑reference (1‑ and 2‑bar) trends. The result is displayed both as on‑chart labels and in a dedicated row of the info table.
4.2 Inputs
• phase_period_len: Number of bars over which to compute sums and ratios for phase detection.
• phase_ratio_thresh : Minimum buyer share (for accumulation) or minimum seller share (for distribution, derived as 1 − phase_ratio_thresh) of the total volume.
• strict_mode: When enabled, both the 1‑bar and 2‑bar changes in each statistic must agree on the direction (strict confirmation); when disabled, only one of the two references needs to agree (looser confirmation).
• Color customisation for info table cells and label styling for accumulation and distribution phases, including ATR length, multiplier, label size, colors and connector styles.
• show_phase_module: Toggles the entire phase detection subsystem.
• show_phase_labels: Controls whether on‑chart labels are drawn when accumulation or distribution is detected.
4.3 Detection logic
The module computes three families of statistics over the volume window defined by phase_period_len:
1. Net sum (buyers minus sellers): net_sum_phase = Σ(buy) − Σ(sell). A positive value indicates a predominance of buyers. The code also computes the differences between the current value and the values 1 and 2 bars ago (d_net_1, d_net_2) to derive up/down trends.
2. Buyer ratio: The instantaneous ratio TF_buy_breakout / TF_tot_breakout and the window ratio Σ(buy) / Σ(total). The current ratio must exceed phase_ratio_thresh for accumulation or fall below 1 − phase_ratio_thresh for distribution. The first and second differences of the window ratio (d_ratio_1, d_ratio_2) determine trend direction.
3. OBV‑like cumulative net flow: An on‑balance volume analogue obv_net_phase increments by TF_buy_breakout − TF_sell_breakout each bar. Its differences over the last 1 and 2 bars (d_obv_1, d_obv_2) provide trend clues.
The algorithm then combines these signals:
• For strict mode , accumulation requires: (a) current ratio ≥ threshold, (b) cumulative ratio ≥ threshold, (c) both ratio differences ≥ 0, (d) net sum differences ≥ 0, and (e) OBV differences ≥ 0. Distribution is the mirror case.
• For loose mode , it relaxes the directional tests: either the 1‑ or the 2‑bar difference needs to agree in each category.
If all conditions for accumulation are satisfied, the phase is labelled “Accumulation” ; if all conditions for distribution are satisfied, it’s labelled “Distribution” ; otherwise the phase is “Neutral” .
4.4 Outputs
• Info table row : Row 8 displays “Market Phase (Vol)” on the left and the detected phase (Accumulation, Distribution or Neutral) on the right. The text colour of both cells matches a user‑selectable palette (typically green for accumulation, red for distribution and grey for neutral).
• On‑chart labels : When show_phase_labels is enabled and a phase persists for at least one bar, the module prints a label above the bar ( “Accum” ) or below the bar ( “Dist” ) with a dashed or dotted connector. The label is offset using ATR based on phase_label_atr_len_input and phase_label_multiplier and is styled according to user preferences.
Figure caption, The chart displays a red “Dist” label above a particular bar, indicating that the accumulation/distribution module identified a distribution phase at that point. The detection is based on seller dominance: during that bar, the net buyer-minus-seller flow and the OBV‑style cumulative flow were trending down, and the buyer ratio had dropped below the preset threshold. These conditions satisfy the distribution criteria in strict mode. The label is placed above the bar using an ATR‑based offset and a dashed connector. By the time of the current bar in the screenshot, the phase indicator shows “Neutral” in the info table—signaling that neither accumulation nor distribution conditions are currently met—yet the historical “Dist” label remains to mark where the prior distribution phase began.
Figure caption, In this example the market phase module has signaled an Accumulation phase. Three bars before the current candle, the algorithm detected a shift toward buyers: up‑volume exceeded its moving average, down‑volume was below average, and the buyer share of total volume climbed above the threshold while the on‑balance net flow and cumulative ratios were trending upwards. The blue “Accum” label anchored below that bar marks the start of the phase; it remains on the chart because successive bars continue to satisfy the accumulation conditions. The info table confirms this: the “Market Phase (Vol)” row still reads Accumulation, and the ratio and sum rows show buyers dominating both on the current bar and across the lookback window.
5. OB/OS Spike Module
5.1 What overbought/oversold means here
In many markets, a rapid extension up or down is often followed by a period of consolidation or reversal. The indicator interprets overbought (OB) conditions as abnormally strong selling risk at or after a price rally and oversold (OS) conditions as unusually strong buying risk after a decline. Importantly, these are not direct trade signals; rather they flag areas where caution or contrarian setups may be appropriate.
5.2 Inputs
• minHits_obos (1–7): Minimum number of oscillators that must agree on an overbought or oversold condition for a label to print.
• syncWin_obos: Length of a small sliding window over which oscillator votes are smoothed by taking the maximum count observed. This helps filter out choppy signals.
• Volume spike criteria: kVolRatio_obos (ratio of current volume to its SMA) and zVolThr_obos (Z‑score threshold) across volLen_obos. Either threshold can trigger a spike.
• Oscillator toggles and periods: Each of RSI, Stochastic (K and D), Williams %R, CCI, MFI, DeMarker and Stochastic RSI can be independently enabled; their periods are adjustable.
• Label appearance: ATR‑based offset, size, colors for OB and OS labels, plus connector style and width.
5.3 Detection logic
1. Directional volume spikes: Volume spikes are computed separately for buyer and seller volumes. A sell volume spike (sellVolSpike) flags a potential OverBought bar, while a buy volume spike (buyVolSpike) flags a potential OverSold bar. A spike occurs when the respective volume exceeds kVolRatio_obos times its simple moving average over the window or when its Z‑score exceeds zVolThr_obos.
2. Oscillator votes: For each enabled oscillator, calculate its overbought and oversold state using standard thresholds (e.g., RSI ≥ 70 for OB and ≤ 30 for OS; Stochastic %K/%D ≥ 80 for OB and ≤ 20 for OS; etc.). Count how many oscillators vote for OB and how many vote for OS.
3. Minimum hits: Apply the smoothing window syncWin_obos to the vote counts using a maximum‑of‑last‑N approach. A candidate bar is only considered if the smoothed OB hit count ≥ minHits_obos (for OverBought) or the smoothed OS hit count ≥ minHits_obos (for OverSold).
4. Tie‑breaking: If both OverBought and OverSold spike conditions are present on the same bar, compare the smoothed hit counts: the side with the higher count is selected; ties default to OverBought.
5. Label printing: When conditions are met, the bar is labelled as “OverBought X/7” above the candle or “OverSold X/7” below it. “X” is the number of oscillators confirming, and the bracket lists the abbreviations of contributing oscillators. Labels are offset from price using half of an ATR‑scaled distance and can optionally include a dotted or dashed connector line.
Figure caption, In this chart the overbought/oversold module has flagged an OverSold signal. A sell‑off from the prior highs brought price down to the lower trend‑line, where the bar marked “OverSold 3/7 DeM” appears. This label indicates that on that bar the module detected a buy‑side volume spike and that at least three of the seven enabled oscillators—in this case including the DeMarker—were in oversold territory. The label is printed below the candle with a dotted connector, signaling that the market may be temporarily exhausted on the downside. After this oversold print, price begins to rebound towards the upper red trend‑line and higher pivot levels.
Figure caption, This example shows the overbought/oversold module in action. In the left‑hand panel you can see the OB/OS settings where each oscillator (RSI, Stochastic, Williams %R, CCI, MFI, DeMarker and Stochastic RSI) can be enabled or disabled, and the ATR length and label offset multiplier adjusted. On the chart itself, price has pushed up to the descending red trendline and triggered an “OverBought 3/7” label. That means the sell‑side volume spiked relative to its average and three out of the seven enabled oscillators were in overbought territory. The label is offset above the candle by half of an ATR and connected with a dashed line, signaling that upside momentum may be overextended and a pause or pullback could follow.
6. Buyer/Seller Trap Module
6.1 Concept
A bull trap occurs when price appears to break above resistance, attracting buyers, but fails to sustain the move and quickly reverses, leaving a long upper wick and trapping late entrants. A bear trap is the opposite: price breaks below support, lures in sellers, then snaps back, leaving a long lower wick and trapping shorts. This module detects such traps by looking for price structure sweeps, order‑flow mismatches and dominance reversals. It uses a scoring system to differentiate risk from confirmed traps.
6.2 Inputs
• trap_lookback_len: Window length used to rank extremes and detect sweeps.
• trap_wick_threshold: Minimum proportion of a bar’s range that must be wick (upper for bull traps, lower for bear traps) to qualify as a sweep.
• trap_score_risk: Minimum aggregated score required to flag a trap risk. (The code defines a trap_score_confirm input, but confirmation is actually based on price reversal rather than a separate score threshold.)
• trap_confirm_bars: Maximum number of bars allowed for price to reverse and confirm the trap. If price does not reverse in this window, the risk label will expire or remain unconfirmed.
• Label settings: ATR length and multiplier for offsetting, size, colours for risk and confirmed labels, and connector style and width. Separate settings exist for bull and bear traps.
• Toggle inputs: show_trap_module and show_trap_labels enable the module and control whether labels are drawn on the chart.
6.3 Scoring logic
The module assigns points to several conditions and sums them to determine whether a trap risk is present. For bull traps, the score is built from the following (bear traps mirror the logic with highs and lows swapped):
1. Sweep (2 points): Price trades above the high pivot (HH1) but fails to close above it and leaves a long upper wick at least trap_wick_threshold × range. For bear traps, price dips below the low pivot (LL1), fails to close below and leaves a long lower wick.
2. Close break (1 point): Price closes beyond HH1 or LL1 without leaving a long wick.
3. Candle/delta mismatch (2 points): The candle closes bullish yet the order flow delta is negative or the seller ratio exceeds 50%, indicating hidden supply. Conversely, a bearish close with positive delta or buyer dominance suggests hidden demand.
4. Dominance inversion (2 points): The current bar’s buyer volume has the highest rank in the lookback window while cumulative sums favor sellers, or vice versa.
5. Low‑volume break (1 point): Price crosses the pivot but total volume is below its moving average.
The total score for each side is compared to trap_score_risk. If the score is high enough, a “Bull Trap Risk” or “Bear Trap Risk” label is drawn, offset from the candle by half of an ATR‑scaled distance using a dashed outline. If, within trap_confirm_bars, price reverses beyond the opposite level—drops back below the high pivot for bull traps or rises above the low pivot for bear traps—the label is upgraded to a solid “Bull Trap” or “Bear Trap” . In this version of the code, there is no separate score threshold for confirmation: the variable trap_score_confirm is unused; confirmation depends solely on a successful price reversal within the specified number of bars.
Figure caption, In this example the trap module has flagged a Bear Trap Risk. Price initially breaks below the most recent low pivot (LL1), but the bar closes back above that level and leaves a long lower wick, suggesting a failed push lower. Combined with a mismatch between the candle direction and the order flow (buyers regain control) and a reversal in volume dominance, the aggregate score exceeds the risk threshold, so a dashed “Bear Trap Risk” label prints beneath the bar. The green and red trend lines mark the current low and high pivot trajectories, while the horizontal dashed lines show the highest and lowest values in the lookback window. If, within the next few bars, price closes decisively above the support, the risk label would upgrade to a solid “Bear Trap” label.
Figure caption, In this example the trap module has identified both ends of a price range. Near the highs, price briefly pushes above the descending red trendline and the recent pivot high, but fails to close there and leaves a noticeable upper wick. That combination of a sweep above resistance and order‑flow mismatch generates a Bull Trap Risk label with a dashed outline, warning that the upside break may not hold. At the opposite extreme, price later dips below the green trendline and the labelled low pivot, then quickly snaps back and closes higher. The long lower wick and subsequent price reversal upgrade the previous bear‑trap risk into a confirmed Bear Trap (solid label), indicating that sellers were caught on a false breakdown. Horizontal dashed lines mark the highest high and lowest low of the lookback window, while the red and green diagonals connect the earliest and latest pivot highs and lows to visualize the range.
7. Sharp Move Module
7.1 Concept
Markets sometimes display absorption or climax behavior—periods when one side steadily gains the upper hand before price breaks out with a sharp move. This module evaluates several order‑flow and volume conditions to anticipate such moves. Users can choose how many conditions must be met to flag a risk and how many (plus a price break) are required for confirmation.
7.2 Inputs
• sharp Lookback: Number of bars in the window used to compute moving averages, sums, percentile ranks and reference levels.
• sharpPercentile: Minimum percentile rank for the current side’s volume; the current buy (or sell) volume must be greater than or equal to this percentile of historical volumes over the lookback window.
• sharpVolMult: Multiplier used in the volume climax check. The current side’s volume must exceed this multiple of its average to count as a climax.
• sharpRatioThr: Minimum dominance ratio (current side’s volume relative to the opposite side) used in both the instant and cumulative dominance checks.
• sharpChurnThr: Maximum ratio of a bar’s range to its ATR for absorption/churn detection; lower values indicate more absorption (large volume in a small range).
• sharpScoreRisk: Minimum number of conditions that must be true to print a risk label.
• sharpScoreConfirm: Minimum number of conditions plus a price break required for confirmation.
• sharpCvdThr: Threshold for cumulative delta divergence versus price change (positive for bullish accumulation, negative for bearish distribution).
• Label settings: ATR length (sharpATRlen) and multiplier (sharpLabelMult) for positioning labels, label size, colors and connector styles for bullish and bearish sharp moves.
• Toggles: enableSharp activates the module; show_sharp_labels controls whether labels are drawn.
7.3 Conditions (six per side)
For each side, the indicator computes six boolean conditions and sums them to form a score:
1. Dominance (instant and cumulative):
– Instant dominance: current buy volume ≥ sharpRatioThr × current sell volume.
– Cumulative dominance: sum of buy volumes over the window ≥ sharpRatioThr × sum of sell volumes (and vice versa for bearish checks).
2. Accumulation/Distribution divergence: Over the lookback window, cumulative delta rises by at least sharpCvdThr while price fails to rise (bullish), or cumulative delta falls by at least sharpCvdThr while price fails to fall (bearish).
3. Volume climax: The current side’s volume is ≥ sharpVolMult × its average and the product of volume and bar range is the highest in the lookback window.
4. Absorption/Churn: The current side’s volume divided by the bar’s range equals the highest value in the window and the bar’s range divided by ATR ≤ sharpChurnThr (indicating large volume within a small range).
5. Percentile rank: The current side’s volume percentile rank is ≥ sharp Percentile.
6. Mirror logic for sellers: The above checks are repeated with buyer and seller roles swapped and the price break levels reversed.
Each condition that passes contributes one point to the corresponding side’s score (0 or 1). Risk and confirmation thresholds are then applied to these scores.
7.4 Scoring and labels
• Risk: If scoreBull ≥ sharpScoreRisk, a “Sharp ↑ Risk” label is drawn above the bar. If scoreBear ≥ sharpScoreRisk, a “Sharp ↓ Risk” label is drawn below the bar.
• Confirmation: A risk label is upgraded to “Sharp ↑” when scoreBull ≥ sharpScoreConfirm and the bar closes above the highest recent pivot (HH1); for bearish cases, confirmation requires scoreBear ≥ sharpScoreConfirm and a close below the lowest pivot (LL1).
• Label positioning: Labels are offset from the candle by ATR × sharpLabelMult (full ATR times multiplier), not half, and may include a dashed or dotted connector line if enabled.
Figure caption, In this chart both bullish and bearish sharp‑move setups have been flagged. Earlier in the range, a “Sharp ↓ Risk” label appears beneath a candle: the sell‑side score met the risk threshold, signaling that the combination of strong sell volume, dominance and absorption within a narrow range suggested a potential sharp decline. The price did not close below the lower pivot, so this label remains a “risk” and no confirmation occurred. Later, as the market recovered and volume shifted back to the buy side, a “Sharp ↑ Risk” label prints above a candle near the top of the channel. Here, buy‑side dominance, cumulative delta divergence and a volume climax aligned, but price has not yet closed above the upper pivot (HH1), so the alert is still a risk rather than a confirmed sharp‑up move.
Figure caption, In this chart a Sharp ↑ label is displayed above a candle, indicating that the sharp move module has confirmed a bullish breakout. Prior bars satisfied the risk threshold — showing buy‑side dominance, positive cumulative delta divergence, a volume climax and strong absorption in a narrow range — and this candle closes above the highest recent pivot, upgrading the earlier “Sharp ↑ Risk” alert to a full Sharp ↑ signal. The green label is offset from the candle with a dashed connector, while the red and green trend lines trace the high and low pivot trajectories and the dashed horizontals mark the highest and lowest values of the lookback window.
8. Market‑Maker / Spread‑Capture Module
8.1 Concept
Liquidity providers often “capture the spread” by buying and selling in almost equal amounts within a very narrow price range. These bars can signal temporary congestion before a move or reflect algorithmic activity. This module flags bars where both buyer and seller volumes are high, the price range is only a few ticks and the buy/sell split remains close to 50%. It helps traders spot potential liquidity pockets.
8.2 Inputs
• scalpLookback: Window length used to compute volume averages.
• scalpVolMult: Multiplier applied to each side’s average volume; both buy and sell volumes must exceed this multiple.
• scalpTickCount: Maximum allowed number of ticks in a bar’s range (calculated as (high − low) / minTick). A value of 1 or 2 captures ultra‑small bars; increasing it relaxes the range requirement.
• scalpDeltaRatio: Maximum deviation from a perfect 50/50 split. For example, 0.05 means the buyer share must be between 45% and 55%.
• Label settings: ATR length, multiplier, size, colors, connector style and width.
• Toggles : show_scalp_module and show_scalp_labels to enable the module and its labels.
8.3 Signal
When, on the current bar, both TF_buy_breakout and TF_sell_breakout exceed scalpVolMult times their respective averages and (high − low)/minTick ≤ scalpTickCount and the buyer share is within scalpDeltaRatio of 50%, the module prints a “Spread ↔” label above the bar. The label uses the same ATR offset logic as other modules and draws a connector if enabled.
Figure caption, In this chart the spread‑capture module has identified a potential liquidity pocket. Buyer and seller volumes both spiked above their recent averages, yet the candle’s range measured only a couple of ticks and the buy/sell split stayed close to 50 %. This combination met the module’s criteria, so it printed a grey “Spread ↔” label above the bar. The red and green trend lines link the earliest and latest high and low pivots, and the dashed horizontals mark the highest high and lowest low within the current lookback window.
9. Money Flow Module
9.1 Concept
To translate volume into a monetary measure, this module multiplies each side’s volume by the closing price. It tracks buying and selling system money default currency on a per-bar basis and sums them over a chosen period. The difference between buy and sell currencies (Δ$) shows net inflow or outflow.
9.2 Inputs
• mf_period_len_mf: Number of bars used for summing buy and sell dollars.
• Label appearance settings: ATR length, multiplier, size, colors for up/down labels, and connector style and width.
• Toggles: Use enableMoneyFlowLabel_mf and showMFLabels to control whether the module and its labels are displayed.
9.3 Calculations
• Per-bar money: Buy $ = TF_buy_breakout × close; Sell $ = TF_sell_breakout × close. Their difference is Δ$ = Buy $ − Sell $.
• Summations: Over mf_period_len_mf bars, compute Σ Buy $, Σ Sell $ and ΣΔ$ using math.sum().
• Info table entries: Rows 9–13 display these values as texts like “↑ USD 1234 (1M)” or “ΣΔ USD −5678 (14)”, with colors reflecting whether buyers or sellers dominate.
• Money flow status: If Δ$ is positive the bar is marked “Money flow in” ; if negative, “Money flow out” ; if zero, “Neutral”. The cumulative status is similarly derived from ΣΔ.Labels print at the bar that changes the sign of ΣΔ, offset using ATR × label multiplier and styled per user preferences.
Figure caption, The chart illustrates a steady rise toward the highest recent pivot (HH1) with price riding between a rising green trend‑line and a red trend‑line drawn through earlier pivot highs. A green Money flow in label appears above the bar near the top of the channel, signaling that net dollar flow turned positive on this bar: buy‑side dollar volume exceeded sell‑side dollar volume, pushing the cumulative sum ΣΔ$ above zero. In the info table, the “Money flow (bar)” and “Money flow Σ” rows both read In, confirming that the indicator’s money‑flow module has detected an inflow at both bar and aggregate levels, while other modules (pivots, trend lines and support/resistance) remain active to provide structural context.
In this example the Money Flow module signals a net outflow. Price has been trending downward: successive high pivots form a falling red trend‑line and the low pivots form a descending green support line. When the latest bar broke below the previous low pivot (LL1), both the bar‑level and cumulative net dollar flow turned negative—selling volume at the close exceeded buying volume and pushed the cumulative Δ$ below zero. The module reacts by printing a red “Money flow out” label beneath the candle; the info table confirms that the “Money flow (bar)” and “Money flow Σ” rows both show Out, indicating sustained dominance of sellers in this period.
10. Info Table
10.1 Purpose
When enabled, the Info Table appears in the lower right of your chart. It summarises key values computed by the indicator—such as buy and sell volume, delta, total volume, breakout status, market phase, and money flow—so you can see at a glance which side is dominant and which signals are active.
10.2 Symbols
• ↑ / ↓ — Up (↑) denotes buy volume or money; down (↓) denotes sell volume or money.
• MA — Moving average. In the table it shows the average value of a series over the lookback period.
• Σ (Sigma) — Cumulative sum over the chosen lookback period.
• Δ (Delta) — Difference between buy and sell values.
• B / S — Buyer and seller share of total volume, expressed as percentages.
• Ref. Price — Reference price for breakout calculations, based on the latest pivot.
• Status — Indicates whether a breakout condition is currently active (True) or has failed.
10.3 Row definitions
1. Up volume / MA up volume – Displays current buy volume on the lower timeframe and its moving average over the lookback period.
2. Down volume / MA down volume – Shows current sell volume and its moving average; sell values are formatted in red for clarity.
3. Δ / ΣΔ – Lists the difference between buy and sell volume for the current bar and the cumulative delta volume over the lookback period.
4. Σ / MA Σ (Vol/MA) – Total volume (buy + sell) for the bar, with the ratio of this volume to its moving average; the right cell shows the average total volume.
5. B/S ratio – Buy and sell share of the total volume: current bar percentages and the average percentages across the lookback period.
6. Buyer Rank / Seller Rank – Ranks the bar’s buy and sell volumes among the last (n) bars; lower rank numbers indicate higher relative volume.
7. Σ Buy / Σ Sell – Sum of buy and sell volumes over the lookback window, indicating which side has traded more.
8. Breakout UP / DOWN – Shows the breakout thresholds (Ref. Price) and whether the breakout condition is active (True) or has failed.
9. Market Phase (Vol) – Reports the current volume‑only phase: Accumulation, Distribution or Neutral.
10. Money Flow – The final rows display dollar amounts and status:
– ↑ USD / Σ↑ USD – Buy dollars for the current bar and the cumulative sum over the money‑flow period.
– ↓ USD / Σ↓ USD – Sell dollars and their cumulative sum.
– Δ USD / ΣΔ USD – Net dollar difference (buy minus sell) for the bar and cumulatively.
– Money flow (bar) – Indicates whether the bar’s net dollar flow is positive (In), negative (Out) or neutral.
– Money flow Σ – Shows whether the cumulative net dollar flow across the chosen period is positive, negative or neutral.
The chart above shows a sequence of different signals from the indicator. A Bull Trap Risk appears after price briefly pushes above resistance but fails to hold, then a green Accum label identifies an accumulation phase. An upward breakout follows, confirmed by a Money flow in print. Later, a Sharp ↓ Risk warns of a possible sharp downturn; after price dips below support but quickly recovers, a Bear Trap label marks a false breakdown. The highlighted info table in the center summarizes key metrics at that moment, including current and average buy/sell volumes, net delta, total volume versus its moving average, breakout status (up and down), market phase (volume), and bar‑level and cumulative money flow (In/Out).
11. Conclusion & Final Remarks
This indicator was developed as a holistic study of market structure and order flow. It brings together several well‑known concepts from technical analysis—breakouts, accumulation and distribution phases, overbought and oversold extremes, bull and bear traps, sharp directional moves, market‑maker spread bars and money flow—into a single Pine Script tool. Each module is based on widely recognized trading ideas and was implemented after consulting reference materials and example strategies, so you can see in real time how these concepts interact on your chart.
A distinctive feature of this indicator is its reliance on per‑side volume: instead of tallying only total volume, it separately measures buy and sell transactions on a lower time frame. This approach gives a clearer view of who is in control—buyers or sellers—and helps filter breakouts, detect phases of accumulation or distribution, recognize potential traps, anticipate sharp moves and gauge whether liquidity providers are active. The money‑flow module extends this analysis by converting volume into currency values and tracking net inflow or outflow across a chosen window.
Although comprehensive, this indicator is intended solely as a guide. It highlights conditions and statistics that many traders find useful, but it does not generate trading signals or guarantee results. Ultimately, you remain responsible for your positions. Use the information presented here to inform your analysis, combine it with other tools and risk‑management techniques, and always make your own decisions when trading.
%ATR + ΔClose HighlightScript Overview
This indicator displays on your chart:
Table of the last N bars that passed the ATR-based range filter:
Columns: Bar #, High, Range (High–Low), Low
Summary row: ATR(N), suggested Stop-Loss (SL = X % of ATR), and the current bar’s range as a percentage of ATR
Red badge on the most recent bar showing ΔClose% (the absolute difference between today’s and yesterday’s close, expressed as % of ATR)
Background highlights:
Blue fill under the most recent bar that met the filter
Yellow fill under bars that failed the filter
Hidden plots of ATR, %ATR, and ΔClose% (for use in strategies or alerts)
All table elements, fills, and plots can be toggled off with a single switch so that only the red ΔClose% badge remains visible.
Inputs
Setting Description Default
Length (bars) Lookback period for ATR and range filter (bars) 5
Upper deviation (%) Upper filter threshold (% of average ATR) 150%
Lower deviation (%) Lower filter threshold (% of average ATR) 50%
SL as % of ATR Stop-loss distance (% of ATR) 10%
Label position Table position relative to bar (“above” or “below”) above
Vertical offset (×ATR) Vertical spacing from the bar in ATR units 2.0
Show table & ATR plots Show or hide table, background highlights, and plots true
How It Works
ATR Calculation & Filtering
Computes average True Range over the last N bars.
Marks bars whose daily range falls within the specified upper/lower deviation band.
Table Construction
Gathers up to N most recent bars that passed the filter (or backfills from the most recent pass).
Formats each bar’s High, Low, and Range into fixed-width columns for neat alignment.
Stop-Loss & Percent Metrics
Calculates a recommended SL distance as a percentage of ATR.
Computes today’s bar range and ΔClose (absolute change in close) as % of ATR.
Chart Display
Table: Shows detailed per-bar data and summary metrics.
Background fills: Blue for the latest valid bar, yellow for invalid bars.
Hidden plots: ATR, %ATR, and ΔClose% (useful for backtesting).
Red badge: Always visible on the right side of the last bar, displaying ΔClose%.
Tips
Disable the table & ATR plots to reduce chart clutter—leave only the red ΔClose% badge for a minimalist volatility alert.
Use the hidden ATR fields (plot outputs) in TradingView Strategies or Alerts to automate volatility-based entries/exits.
Adjust the deviation band to capture “normal” intraday moves vs. outsized volatility spikes.
Load this script on any US market chart (stocks, futures, crypto, etc.) to instantly visualize recent volatility structure, set dynamic SL levels, and highlight today’s price change relative to average true range.
ADR Tracker Version 2Description
The **ADR Tracker** plots a customizable panel on your chart that monitors the Average Daily Range (ADR) and shows how today’s price action compares to that average. It calculates the daily high–low range for each of the past 14 days (can be adjusted) and then takes a simple moving average of those ranges to determine the ADR.
**Features:**
* **Current ADR value:** Shows the 14‑day ADR in price units.
* **ADR status:** Indicates whether today’s range has reached or exceeded the ADR.
* **Ticks remaining:** Calculates how many minimum price ticks remain before the ADR would be met.
* **Real‑time tracking:** Monitors the intraday high and low to update the range continuously.
* **Customizable panel:** Uses TradingView’s table object to display the information. You can set the table’s horizontal and vertical position (top/middle/bottom and left/centre/right) with inputs. The script also lets you change the text and background colours, as well as the width and height of each row. Table cells use explicit width and height percentages, which Pine supports in v6. Each call to `table.cell()` defines the text, colours and dimensions for its cell, so the panel resizes automatically based on your settings.
**Usage:**
Apply the indicator to any chart. For the most accurate real‑time tracking, use it on intraday timeframes (e.g. 5‑min or 1‑hour) so the current day’s range updates as new bars arrive. Adjust the inputs in the settings panel to reposition the list or change its appearance.
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This description explains what the indicator does and highlights its customizable table display, referencing the Pine Script table features used.
Momentum Regression [BackQuant]Momentum Regression
The Momentum Regression is an advanced statistical indicator built to empower quants, strategists, and technically inclined traders with a robust visual and quantitative framework for analyzing momentum effects in financial markets. Unlike traditional momentum indicators that rely on raw price movements or moving averages, this tool leverages a volatility-adjusted linear regression model (y ~ x) to uncover and validate momentum behavior over a user-defined lookback window.
Purpose & Design Philosophy
Momentum is a core anomaly in quantitative finance — an effect where assets that have performed well (or poorly) continue to do so over short to medium-term horizons. However, this effect can be noisy, regime-dependent, and sometimes spurious.
The Momentum Regression is designed as a pre-strategy analytical tool to help you filter and verify whether statistically meaningful and tradable momentum exists in a given asset. Its architecture includes:
Volatility normalization to account for differences in scale and distribution.
Regression analysis to model the relationship between past and present standardized returns.
Deviation bands to highlight overbought/oversold zones around the predicted trendline.
Statistical summary tables to assess the reliability of the detected momentum.
Core Concepts and Calculations
The model uses the following:
Independent variable (x): The volatility-adjusted return over the chosen momentum period.
Dependent variable (y): The 1-bar lagged log return, also adjusted for volatility.
A simple linear regression is performed over a large lookback window (default: 1000 bars), which reveals the slope and intercept of the momentum line. These values are then used to construct:
A predicted momentum trendline across time.
Upper and lower deviation bands , representing ±n standard deviations of the regression residuals (errors).
These visual elements help traders judge how far current returns deviate from the modeled momentum trend, similar to Bollinger Bands but derived from a regression model rather than a moving average.
Key Metrics Provided
On each update, the indicator dynamically displays:
Momentum Slope (β₁): Indicates trend direction and strength. A higher absolute value implies a stronger effect.
Intercept (β₀): The predicted return when x = 0.
Pearson’s R: Correlation coefficient between x and y.
R² (Coefficient of Determination): Indicates how well the regression line explains the variance in y.
Standard Error of Residuals: Measures dispersion around the trendline.
t-Statistic of β₁: Used to evaluate statistical significance of the momentum slope.
These statistics are presented in a top-right summary table for immediate interpretation. A bottom-right signal table also summarizes key takeaways with visual indicators.
Features and Inputs
✅ Volatility-Adjusted Momentum : Reduces distortions from noisy price spikes.
✅ Custom Lookback Control : Set the number of bars to analyze regression.
✅ Extendable Trendlines : For continuous visualization into the future.
✅ Deviation Bands : Optional ±σ multipliers to detect abnormal price action.
✅ Contextual Tables : Help determine strength, direction, and significance of momentum.
✅ Separate Pane Design : Cleanly isolates statistical momentum from price chart.
How It Helps Traders
📉 Quantitative Strategy Validation:
Use the regression results to confirm whether a momentum-based strategy is worth pursuing on a specific asset or timeframe.
🔍 Regime Detection:
Track when momentum breaks down or reverses. Slope changes, drops in R², or weak t-stats can signal regime shifts.
📊 Trade Filtering:
Avoid false positives by entering trades only when momentum is both statistically significant and directionally favorable.
📈 Backtest Preparation:
Before running costly simulations, use this tool to pre-screen assets for exploitable return structures.
When to Use It
Before building or deploying a momentum strategy : Test if momentum exists and is statistically reliable.
During market transitions : Detect early signs of fading strength or reversal.
As part of an edge-stacking framework : Combine with other filters such as volatility compression, volume surges, or macro filters.
Conclusion
The Momentum Regression indicator offers a powerful fusion of statistical analysis and visual interpretation. By combining volatility-adjusted returns with real-time linear regression modeling, it helps quantify and qualify one of the most studied and traded anomalies in finance: momentum.
EMA 200 Monitor - Bybit CoinsEMA 200 Monitor - Bybit Coins
📊 OVERVIEW
The EMA 200 Monitor - Bybit Coins is an advanced indicator that automatically monitors 30 of the top cryptocurrencies traded on Bybit, alerting you when they are close to the 200-period Exponential Moving Average on the 4-hour timeframe.
This indicator was developed especially for traders who use the EMA 200 as a key support/resistance level in their swing trading and position trading strategies.
🎯 WHAT IT'S FOR
Multi-Asset Monitoring: Simultaneous monitoring of 30 cryptocurrencies without having to switch between charts
Opportunity Identification: Detects when coins are approaching the 200 EMA, a crucial technical level
Automated Alerts: Real-time notifications when a coin reaches the configured proximity
Time Efficiency: Eliminates the need to manually check chart collections
⚙️ HOW IT WORKS
Main Functionality
The indicator uses the request.security() function to fetch price data and calculate the 200 EMA of each monitored asset. With each new bar, the script:
Calculates the distance between the current price and the 200 EMA for each coin
Identifies proximity based on the configured percentage (default: 2%)
Displays results in a table organized on the chart
Generates automatic alerts when proximity is detected
Monitored Coins
Major : BTC, ETH, BNB, ADA, XRP, SOL, DOT, DOGE, AVAX
DeFi : UNI, LINK, ATOM, ICP, NEAR, OP, ARB, INJ
Memecoins : SHIB, PEPE, WIF, BONK, FLOKI
Emerging : SUI, TON, APT, POL (ex-MATIC)
📋 AVAILABLE SETTINGS
Adjustable Parameters
EMA Length (Default: 200): Exponential Moving Average Period
Proximity Percentage (Default: 2%): Distance in percentage to consider "close"
Show Table (Default: Active): Show/hide results table
Table Position: Position of the table on the chart (9 options available)
Color System
🔴 Red: Distance ≤ 1% (very close)
🟠 Orange: Distance ≤ 1.5% (close)
🟡 Yellow: Distance ≤ 2% (approaching)
🚀 HOW TO USE
Initial Configuration
Add the indicator to the 4-hour timeframe chart
Set the parameters according to your strategy
Position the table where there is no graphic preference
Setting Alerts
Click "Create Alert" in TradingView
Select the "EMA 200 Monitor" indicator
Set the notification frequency and method
Activate the alert to receive automatic notifications
Results Interpretation
The table shows:
Coin: Asset name (e.g. BTC, ETH)
Price: Current currency quote
EMA 200: Current value of the moving average
Distance: Percentage of proximity to the core code
💡 STRATEGIES TO USE
Reversal Trading
Entry: When price touches or approaches the EMA 200
Stop: Below/above the EMA with a safety margin
Target: Previous resistance/support levels
Breakout Trading
Monitoring: Watch for currencies consolidating near the EMA 200
Entry: When the media is finally broken
Confirmation: Volume and close above/below the EMA
Swing Trading
Identification: Use the monitor to detect setups in formation
Timing: Wait for the EMA 200 to approach for detailed analysis
Management: Use the EMA as a reference for stops dynamics
⚠️ IMPORTANT CONSIDERATIONS
Technical Limitations
Request Bybit data: Access to exchange symbols required
Specific timeframe: Optimized for 4-hour analysis
Minimum delay: Data updated with each new bar
Usage Recommendations
Combine with technical analysis: Use together with other indicators
Confirm the configuration: Check the graphic patterns before trading
Manage risk: Always use stop loss and adequate position sizing
Backtesting: Test your strategy before applying with real capital
Disclaimer
This indicator is a technical analysis tool and does not constitute investment advice. Always do your own analysis and manage detailed information about the risks of your operations.
🔧 TECHNICAL INFORMATION
Pine Script version: v6
Type: Indicator (overlay=true)
Compatibility: All TradingView plans
Resources used: request.security(), arrays, tables
Performance: Optimized for multiple simultaneous queries
📈 COMPETITIVE ADVANTAGES
✅ Simultaneous monitoring of 30 major assets ✅ Clear visual interface with intuitive core system ✅ Customizable alerts for different details ✅ Optimized code for maximum performance ✅ Flexible configuration adaptable to different strategies ✅ Real-time update without the need for manual refresh
Developed for traders who value efficiency and accuracy in identifying market opportunities based on the EMA 20
EMD Trend [InvestorUnknown]EMD Trend is a dynamic trend-following indicator that utilizes Exponential Moving Deviation (EMD) to build adaptive channels around a selected moving average. Designed for traders who value responsive trend signals with built-in volatility sensitivity, this tool highlights directional bias, market regime shifts, and potential breakout opportunities.
How It Works
Instead of using standard deviation, EMD Trend employs the exponential moving average of the absolute deviation from a moving average—producing smoother, faster-reacting upper and lower bounds:
Bullish (Risk-ON Long): Price crosses above the upper EMD band
Bearish (Risk-ON Short): Price crosses below the lower EMD band
Neutral: Price stays within the channel, indicating potential mean reversion or low momentum
Trend direction is defined by price interaction with these bands, and visual cues (color-coded bars and fills) help quickly identify market conditions.
Features
7 Moving Average Types: SMA, EMA, HMA, DEMA, TEMA, RMA, FRAMA
Custom Price Source: Choose close, hl2, ohlc4, or others
EMD Multiplier: Controls the width of the deviation envelope
Bar Coloring: Candles change color based on current trend
Intra-bar Signal Option: Enables faster updates (with optional repainting)
Speculative Zones: Fills highlight aggressive momentum moves beyond EMD bounds
Backtest Mode
Switch to Backtest Mode for performance evaluation over historical data:
Equity Curve Plot: Compare EMD Trend strategy vs. Buy & Hold
Trade Metrics Table: View number of trades, win/loss stats, profits
Performance Metrics Table: Includes CAGR, Sharpe, max drawdown, and more
Custom Start Date: Select from which date the backtest should begin
Trade Sizing: Configure capital and trade percentage per entry
Signal Filters: Choose from Long Only, Short Only, or Both
Alerts
Built-in alerts let you automate entries, exits, and trend transitions:
LONG (EMD Trend) - Trend flips to Long
SHORT (EMD Trend) - Trend flips to Short
RISK-ON LONG - Price crosses above upper EMD band
RISK-OFF LONG - Price crosses back below upper EMD band
RISK-ON SHORT - Price crosses below lower EMD band
RISK-OFF SHORT - Price crosses back above lower EMD band
Use Cases
Trend Confirmation with volatility-sensitive boundaries
Momentum Entry Filtering via breakout zones
Mean Reversion Avoidance in sideways markets
Backtesting & Strategy Building with real-time metrics
Disclaimer
This indicator is intended for informational and educational purposes only. It does not constitute investment advice. Historical performance does not guarantee future results. Always backtest and use in simulation before live trading.
RMSD Trend [InvestorUnknown]RMSD Trend is a trend-following indicator that utilizes Root Mean Square Deviation (RMSD) to dynamically construct a volatility-weighted trend channel around a selected moving average. This indicator is designed to enhance signal clarity, minimize noise, and offer quantitative insights into market momentum, ideal for both discretionary and systematic traders.
How It Works
At its core, RMSD Trend calculates a deviation band around a selected moving average using the Root Mean Square Deviation (similar to standard deviation but with squared errors), capturing the magnitude of price dispersion over a user-defined period. The logic is simple:
When price crosses above the upper deviation band, the market is considered bullish (Risk-ON Long).
When price crosses below the lower deviation band, the market is considered bearish (Risk-ON Short).
If price stays within the band, the market is interpreted as neutral or ranging, offering low-risk decision zones.
The indicator also generates trend flips (Long/Short) based on crossovers and crossunders of the price and the RMSD bands, and colors candles accordingly for enhanced visual feedback.
Features
7 Moving Average Types: Choose between SMA, EMA, HMA, DEMA, TEMA, RMA, and FRAMA for flexibility.
Customizable Source Input: Use price types like close, hl2, ohlc4, etc.
Volatility-Aware Channel: Adjustable RMSD multiplier determines band width based on volatility.
Smart Coloring: Candles and bands adapt their colors to reflect trend direction (green for bullish, red for bearish).
Intra-bar Repainting Toggle: Option to allow more responsive but repaintable signals.
Speculation Fill Zones: When price exceeds the deviation channel, a semi-transparent fill highlights potential momentum surges.
Backtest Mode
Switching to Backtest Mode unlocks a robust suite of simulation features:
Built-in Equity Curve: Visualizes both strategy equity and Buy & Hold performance.
Trade Metrics Table: Displays the number of trades, win rates, gross profits/losses, and long/short breakdowns.
Performance Metrics Table: Includes key stats like CAGR, drawdown, Sharpe ratio, and more.
Custom Date Range: Set a custom start date for your backtest.
Trade Sizing: Simulate results using position sizing and initial capital settings.
Signal Filters: Choose between Long & Short, Long Only, or Short Only strategies.
Alerts
The RMSD Trend includes six built-in alert conditions:
LONG (RMSD Trend) - Trend flips from Short to Long
SHORT (RMSD Trend) - Trend flips from Long to Short
RISK-ON LONG (RMSD Trend) - Price crosses above upper RMSD band
RISK-OFF LONG (RMSD Trend) - Price falls back below upper RMSD band
RISK-ON SHORT (RMSD Trend) - Price crosses below lower RMSD band
RISK-OFF SHORT (RMSD Trend) - Price rises back above lower RMSD band
Use Cases
Trend Confirmation: Confirms directional bias with RMSD-weighted confidence zones.
Breakout Detection: Highlights moments when price breaks free from historical volatility norms.
Mean Reversion Filtering: Avoids false signals by incorporating RMSD’s volatility sensitivity.
Strategy Development: Backtest your signals or integrate with a broader system for alpha generation.
Settings Summary
Display Mode: Overlay (default) or Backtest Mode
Average Type: Choose from SMA, EMA, HMA, DEMA, etc.
Average Length: Lookback window for moving average
RMSD Multiplier: Band width control based on RMS deviation
Source: Input price source (close, hl2, ohlc4, etc.)
Intra-bar Updating: Real-time updates (may repaint)
Color Bars: Toggle bar coloring by trend direction
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice. Past performance, including backtest results, is not indicative of future results. Use with caution and always test thoroughly before live deployment.
OrderFlow Sentiment SwiftEdgeOrderFlow Sentiment SwiftEdge
Overview
OrderFlow Sentiment SwiftEdge is a visual indicator designed to help traders analyze market dynamics through a simulated orderbook and market sentiment display. It breaks down the current candlestick into 10 price bins, estimating buy and sell volumes, and presents this data in an orderbook table alongside a sentiment row showing the buy vs. sell bias. This tool provides a quick and intuitive way to assess orderflow activity and market sentiment directly on your chart.
How It Works
The indicator consists of two main components: an Orderbook Table and a Market Sentiment Row.
Orderbook Table:
Simulates buy and sell volumes for the current candlestick by distributing total volume into 10 price bins based on price movement and proximity to open/close levels.
Displays the price bins in a table with columns for Price, Buy Volume, and Sell Volume, sorted from highest to lowest price.
Highlights the current price level in orange for easy identification, while buy and sell dominance is indicated with green (buy) or red (sell) backgrounds.
Market Sentiment Row:
Calculates the overall buy and sell sentiment (as a percentage) for the current candlestick based on the simulated orderflow data.
Displays the sentiment above the orderbook table, with the background colored green if buyers dominate or red if sellers dominate.
Features
Customizable Colors: Choose colors for buy (default: green), sell (default: red), and current price (default: orange) levels.
Lot Scaling Factor: Adjust the volume scaling factor (default: 0.1 lots per volume unit) to simulate realistic lot sizes.
Table Position: Select the table position on the chart (Top, Middle, or Bottom; default: Middle).
Default Properties
Positive Color: Green
Negative Color: Red
Current Price Color: Orange
Lot Scaling Factor: 0.1
Table Position: Middle
Usage
This indicator is ideal for traders who want to visualize orderflow dynamics and market sentiment in real-time. The orderbook table provides a snapshot of buy and sell activity at different price levels within the current candlestick, helping you identify areas of high buying or selling pressure. The sentiment row offers a quick overview of market bias, allowing you to gauge whether buyers or sellers are currently dominating. Use this information to complement your trading decisions, such as identifying potential breakout levels or confirming trend direction.
Limitations
This indicator simulates orderflow data based on candlestick price movement and volume, as TradingView does not provide tick-by-tick data. The volume distribution is an approximation and should be used as a visual aid rather than a definitive measure of market activity.
The indicator operates on the chart's current timeframe and does not incorporate higher timeframe data.
The simulated volumes are scaled using a user-defined lot scaling factor, which may not reflect actual market lot sizes.
Disclaimer
This indicator is for informational purposes only and does not guarantee trading results. Always conduct your own analysis and manage risk appropriately. The simulated orderflow data is an estimation and may not reflect real market conditions.
RSI Full Forecast [Titans_Invest]RSI Full Forecast
Get ready to experience the ultimate evolution of RSI-based indicators – the RSI Full Forecast, a boosted and even smarter version of the already powerful: RSI Forecast
Now featuring over 40 additional entry conditions (forecasts), this indicator redefines the way you view the market.
AI-Powered RSI Forecasting:
Using advanced linear regression with the least squares method – a solid foundation for machine learning - the RSI Full Forecast enables you to predict future RSI behavior with impressive accuracy.
But that’s not all: this new version also lets you monitor future crossovers between the RSI and the MA RSI, delivering early and strategic signals that go far beyond traditional analysis.
You’ll be able to monitor future crossovers up to 20 bars ahead, giving you an even broader and more precise view of market movements.
See the Future, Now:
• Track upcoming RSI & RSI MA crossovers in advance.
• Identify potential reversal zones before price reacts.
• Uncover statistical behavior patterns that would normally go unnoticed.
40+ Intelligent Conditions:
The new layer of conditions is designed to detect multiple high-probability scenarios based on historical patterns and predictive modeling. Each additional forecast is a window into the price's future, powered by robust mathematics and advanced algorithmic logic.
Full Customization:
All parameters can be tailored to fit your strategy – from smoothing periods to prediction sensitivity. You have complete control to turn raw data into smart decisions.
Innovative, Accurate, Unique:
This isn’t just an upgrade. It’s a quantum leap in technical analysis.
RSI Full Forecast is the first of its kind: an indicator that blends statistical analysis, machine learning, and visual design to create a true real-time predictive system.
⯁ SCIENTIFIC BASIS LINEAR REGRESSION
Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥.
The general formula for a simple linear regression is given by:
y = β₀ + β₁x + ε
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
y = is the predicted variable (e.g. future value of RSI)
x = is the explanatory variable (e.g. time or bar index)
β0 = is the intercept (value of 𝑦 when 𝑥 = 0)
𝛽1 = is the slope of the line (rate of change)
ε = is the random error term
The goal is to estimate the coefficients 𝛽0 and 𝛽1 so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares.
⯁ LEAST SQUARES ESTIMATION
To minimize the error between predicted and observed values, we use the following formulas:
β₁ = /
β₀ = ȳ - β₁x̄
Where:
∑ = sum
x̄ = mean of x
ȳ = mean of y
x_i, y_i = individual values of the variables.
Where:
x_i and y_i are the means of the independent and dependent variables, respectively.
i ranges from 1 to n, the number of observations.
These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity.
⯁ LINEAR REGRESSION IN MACHINE LEARNING
Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies.
By applying this model to the RSI, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis.
⯁ VISUAL INTERPRETATION
Imagine an RSI time series like this:
Time →
RSI →
The regression line will smooth these values and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted RSI, which can be crossed with the actual RSI to generate more intelligent signals.
⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED
Linear Regression Models the relationship between variables using a straight line.
Least Squares Minimizes the sum of squared errors between prediction and reality.
Time Series Forecasting Estimates future values based on historical data.
Supervised Learning Trains models to predict outputs from known inputs.
Statistical Smoothing Reduces noise and reveals underlying trends.
⯁ WHY THIS INDICATOR IS REVOLUTIONARY
Scientifically-based: Based on statistical theory and mathematical inference.
Unprecedented: First public RSI with least squares predictive modeling.
Intelligent: Built with machine learning logic.
Practical: Generates forward-thinking signals.
Customizable: Flexible for any trading strategy.
⯁ CONCLUSION
By combining RSI with linear regression, this indicator allows a trader to predict market momentum, not just follow it.
RSI Full Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology.
⯁ Example of simple linear regression, which has one independent variable:
⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ).
⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values using Matlab:
⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables.
⯁ The result of fitting a set of data points with a quadratic function:
_________________________________________________
🔮 Linear Regression: PineScript Technical Parameters 🔮
_________________________________________________
Forecast Types:
• Flat: Assumes prices will remain the same.
• Linreg: Makes a 'Linear Regression' forecast for n periods.
Technical Information:
ta.linreg (built-in function)
Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values calculated using the least squares method on the source series.
Syntax:
• Function: ta.linreg()
Parameters:
• source: Source price series.
• length: Number of bars (period).
• offset: Offset.
• return: Linear regression curve.
This function has been cleverly applied to the RSI, making it capable of projecting future values based on past statistical trends.
______________________________________________________
______________________________________________________
⯁ WHAT IS THE RSI❓
The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend.
⯁ HOW TO USE THE RSI❓
The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones:
• Overbought: When the RSI is above 70, indicating that the asset may be overbought.
• Oversold: When the RSI is below 30, indicating that the asset may be oversold.
• Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions.
______________________________________________________
______________________________________________________
⯁ ENTRY CONDITIONS
The conditions below are fully flexible and allow for complete customization of the signal.
______________________________________________________
______________________________________________________
🔹 CONDITIONS TO BUY 📈
______________________________________________________
• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
📈 RSI Conditions:
🔹 RSI > Upper
🔹 RSI < Upper
🔹 RSI > Lower
🔹 RSI < Lower
🔹 RSI > Middle
🔹 RSI < Middle
🔹 RSI > MA
🔹 RSI < MA
📈 MA Conditions:
🔹 MA > Upper
🔹 MA < Upper
🔹 MA > Lower
🔹 MA < Lower
📈 Crossovers:
🔹 RSI (Crossover) Upper
🔹 RSI (Crossunder) Upper
🔹 RSI (Crossover) Lower
🔹 RSI (Crossunder) Lower
🔹 RSI (Crossover) Middle
🔹 RSI (Crossunder) Middle
🔹 RSI (Crossover) MA
🔹 RSI (Crossunder) MA
🔹 MA (Crossover) Upper
🔹 MA (Crossunder) Upper
🔹 MA (Crossover) Lower
🔹 MA (Crossunder) Lower
📈 RSI Divergences:
🔹 RSI Divergence Bull
🔹 RSI Divergence Bear
📈 RSI Forecast:
🔹 RSI (Crossover) MA Forecast
🔹 RSI (Crossunder) MA Forecast
🔹 RSI Forecast 1 > MA Forecast 1
🔹 RSI Forecast 1 < MA Forecast 1
🔹 RSI Forecast 2 > MA Forecast 2
🔹 RSI Forecast 2 < MA Forecast 2
🔹 RSI Forecast 3 > MA Forecast 3
🔹 RSI Forecast 3 < MA Forecast 3
🔹 RSI Forecast 4 > MA Forecast 4
🔹 RSI Forecast 4 < MA Forecast 4
🔹 RSI Forecast 5 > MA Forecast 5
🔹 RSI Forecast 5 < MA Forecast 5
🔹 RSI Forecast 6 > MA Forecast 6
🔹 RSI Forecast 6 < MA Forecast 6
🔹 RSI Forecast 7 > MA Forecast 7
🔹 RSI Forecast 7 < MA Forecast 7
🔹 RSI Forecast 8 > MA Forecast 8
🔹 RSI Forecast 8 < MA Forecast 8
🔹 RSI Forecast 9 > MA Forecast 9
🔹 RSI Forecast 9 < MA Forecast 9
🔹 RSI Forecast 10 > MA Forecast 10
🔹 RSI Forecast 10 < MA Forecast 10
🔹 RSI Forecast 11 > MA Forecast 11
🔹 RSI Forecast 11 < MA Forecast 11
🔹 RSI Forecast 12 > MA Forecast 12
🔹 RSI Forecast 12 < MA Forecast 12
🔹 RSI Forecast 13 > MA Forecast 13
🔹 RSI Forecast 13 < MA Forecast 13
🔹 RSI Forecast 14 > MA Forecast 14
🔹 RSI Forecast 14 < MA Forecast 14
🔹 RSI Forecast 15 > MA Forecast 15
🔹 RSI Forecast 15 < MA Forecast 15
🔹 RSI Forecast 16 > MA Forecast 16
🔹 RSI Forecast 16 < MA Forecast 16
🔹 RSI Forecast 17 > MA Forecast 17
🔹 RSI Forecast 17 < MA Forecast 17
🔹 RSI Forecast 18 > MA Forecast 18
🔹 RSI Forecast 18 < MA Forecast 18
🔹 RSI Forecast 19 > MA Forecast 19
🔹 RSI Forecast 19 < MA Forecast 19
🔹 RSI Forecast 20 > MA Forecast 20
🔹 RSI Forecast 20 < MA Forecast 20
______________________________________________________
______________________________________________________
🔸 CONDITIONS TO SELL 📉
______________________________________________________
• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
📉 RSI Conditions:
🔸 RSI > Upper
🔸 RSI < Upper
🔸 RSI > Lower
🔸 RSI < Lower
🔸 RSI > Middle
🔸 RSI < Middle
🔸 RSI > MA
🔸 RSI < MA
📉 MA Conditions:
🔸 MA > Upper
🔸 MA < Upper
🔸 MA > Lower
🔸 MA < Lower
📉 Crossovers:
🔸 RSI (Crossover) Upper
🔸 RSI (Crossunder) Upper
🔸 RSI (Crossover) Lower
🔸 RSI (Crossunder) Lower
🔸 RSI (Crossover) Middle
🔸 RSI (Crossunder) Middle
🔸 RSI (Crossover) MA
🔸 RSI (Crossunder) MA
🔸 MA (Crossover) Upper
🔸 MA (Crossunder) Upper
🔸 MA (Crossover) Lower
🔸 MA (Crossunder) Lower
📉 RSI Divergences:
🔸 RSI Divergence Bull
🔸 RSI Divergence Bear
📉 RSI Forecast:
🔸 RSI (Crossover) MA Forecast
🔸 RSI (Crossunder) MA Forecast
🔸 RSI Forecast 1 > MA Forecast 1
🔸 RSI Forecast 1 < MA Forecast 1
🔸 RSI Forecast 2 > MA Forecast 2
🔸 RSI Forecast 2 < MA Forecast 2
🔸 RSI Forecast 3 > MA Forecast 3
🔸 RSI Forecast 3 < MA Forecast 3
🔸 RSI Forecast 4 > MA Forecast 4
🔸 RSI Forecast 4 < MA Forecast 4
🔸 RSI Forecast 5 > MA Forecast 5
🔸 RSI Forecast 5 < MA Forecast 5
🔸 RSI Forecast 6 > MA Forecast 6
🔸 RSI Forecast 6 < MA Forecast 6
🔸 RSI Forecast 7 > MA Forecast 7
🔸 RSI Forecast 7 < MA Forecast 7
🔸 RSI Forecast 8 > MA Forecast 8
🔸 RSI Forecast 8 < MA Forecast 8
🔸 RSI Forecast 9 > MA Forecast 9
🔸 RSI Forecast 9 < MA Forecast 9
🔸 RSI Forecast 10 > MA Forecast 10
🔸 RSI Forecast 10 < MA Forecast 10
🔸 RSI Forecast 11 > MA Forecast 11
🔸 RSI Forecast 11 < MA Forecast 11
🔸 RSI Forecast 12 > MA Forecast 12
🔸 RSI Forecast 12 < MA Forecast 12
🔸 RSI Forecast 13 > MA Forecast 13
🔸 RSI Forecast 13 < MA Forecast 13
🔸 RSI Forecast 14 > MA Forecast 14
🔸 RSI Forecast 14 < MA Forecast 14
🔸 RSI Forecast 15 > MA Forecast 15
🔸 RSI Forecast 15 < MA Forecast 15
🔸 RSI Forecast 16 > MA Forecast 16
🔸 RSI Forecast 16 < MA Forecast 16
🔸 RSI Forecast 17 > MA Forecast 17
🔸 RSI Forecast 17 < MA Forecast 17
🔸 RSI Forecast 18 > MA Forecast 18
🔸 RSI Forecast 18 < MA Forecast 18
🔸 RSI Forecast 19 > MA Forecast 19
🔸 RSI Forecast 19 < MA Forecast 19
🔸 RSI Forecast 20 > MA Forecast 20
🔸 RSI Forecast 20 < MA Forecast 20
______________________________________________________
______________________________________________________
🤖 AUTOMATION 🤖
• You can automate the BUY and SELL signals of this indicator.
______________________________________________________
______________________________________________________
⯁ UNIQUE FEATURES
______________________________________________________
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
______________________________________________________
📜 SCRIPT : RSI Full Forecast
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
______________________________________________________
o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
RSI Forecast [Titans_Invest]RSI Forecast
Introducing one of the most impressive RSI indicators ever created – arguably the best on TradingView, and potentially the best in the world.
RSI Forecast is a visionary evolution of the classic RSI, merging powerful customization with groundbreaking predictive capabilities. While preserving the core principles of traditional RSI, it takes analysis to the next level by allowing users to anticipate potential future RSI movements.
Real-Time RSI Forecasting:
For the first time ever, an RSI indicator integrates linear regression using the least squares method to accurately forecast the future behavior of the RSI. This innovation empowers traders to stay one step ahead of the market with forward-looking insight.
Highly Customizable:
Easily adapt the indicator to your personal trading style. Fine-tune a variety of parameters to generate signals perfectly aligned with your strategy.
Innovative, Unique, and Powerful:
This is the world’s first RSI Forecast to apply this predictive approach using least squares linear regression. A truly elite-level tool designed for traders who want a real edge in the market.
⯁ SCIENTIFIC BASIS LINEAR REGRESSION
Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥.
The general formula for a simple linear regression is given by:
y = β₀ + β₁x + ε
Where:
y = is the predicted variable (e.g. future value of RSI)
x = is the explanatory variable (e.g. time or bar index)
β0 = is the intercept (value of 𝑦 when 𝑥 = 0)
𝛽1 = is the slope of the line (rate of change)
ε = is the random error term
The goal is to estimate the coefficients 𝛽0 and 𝛽1 so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares.
⯁ LEAST SQUARES ESTIMATION
To minimize the error between predicted and observed values, we use the following formulas:
β₁ = /
β₀ = ȳ - β₁x̄
Where:
∑ = sum
x̄ = mean of x
ȳ = mean of y
x_i, y_i = individual values of the variables.
Where:
x_i and y_i are the means of the independent and dependent variables, respectively.
i ranges from 1 to n, the number of observations.
These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity.
⯁ LINEAR REGRESSION IN MACHINE LEARNING
Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies.
By applying this model to the RSI, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis.
⯁ VISUAL INTERPRETATION
Imagine an RSI time series like this:
Time →
RSI →
The regression line will smooth these values and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted RSI, which can be crossed with the actual RSI to generate more intelligent signals.
⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED
Linear Regression Models the relationship between variables using a straight line.
Least Squares Minimizes the sum of squared errors between prediction and reality.
Time Series Forecasting Estimates future values based on historical data.
Supervised Learning Trains models to predict outputs from known inputs.
Statistical Smoothing Reduces noise and reveals underlying trends.
⯁ WHY THIS INDICATOR IS REVOLUTIONARY
Scientifically-based: Based on statistical theory and mathematical inference.
Unprecedented: First public RSI with least squares predictive modeling.
Intelligent: Built with machine learning logic.
Practical: Generates forward-thinking signals.
Customizable: Flexible for any trading strategy.
⯁ CONCLUSION
By combining RSI with linear regression, this indicator allows a trader to predict market momentum, not just follow it.
RSI Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology.
⯁ Example of simple linear regression, which has one independent variable:
⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ).
⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values using Matlab:
⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables.
⯁ The result of fitting a set of data points with a quadratic function:
_______________________________________________________________________
🥇 This is the world’s first RSI indicator with: Linear Regression for Forecasting 🥇_______________________________________________________________________
_________________________________________________
🔮 Linear Regression: PineScript Technical Parameters 🔮
_________________________________________________
Forecast Types:
• Flat: Assumes prices will remain the same.
• Linreg: Makes a 'Linear Regression' forecast for n periods.
Technical Information:
ta.linreg (built-in function)
Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values calculated using the least squares method on the source series.
Syntax:
• Function: ta.linreg()
Parameters:
• source: Source price series.
• length: Number of bars (period).
• offset: Offset.
• return: Linear regression curve.
This function has been cleverly applied to the RSI, making it capable of projecting future values based on past statistical trends.
______________________________________________________
______________________________________________________
⯁ WHAT IS THE RSI❓
The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend.
⯁ HOW TO USE THE RSI❓
The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones:
• Overbought: When the RSI is above 70, indicating that the asset may be overbought.
• Oversold: When the RSI is below 30, indicating that the asset may be oversold.
• Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions.
______________________________________________________
______________________________________________________
⯁ ENTRY CONDITIONS
The conditions below are fully flexible and allow for complete customization of the signal.
______________________________________________________
______________________________________________________
🔹 CONDITIONS TO BUY 📈
______________________________________________________
• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
📈 RSI Conditions:
🔹 RSI > Upper
🔹 RSI < Upper
🔹 RSI > Lower
🔹 RSI < Lower
🔹 RSI > Middle
🔹 RSI < Middle
🔹 RSI > MA
🔹 RSI < MA
📈 MA Conditions:
🔹 MA > Upper
🔹 MA < Upper
🔹 MA > Lower
🔹 MA < Lower
📈 Crossovers:
🔹 RSI (Crossover) Upper
🔹 RSI (Crossunder) Upper
🔹 RSI (Crossover) Lower
🔹 RSI (Crossunder) Lower
🔹 RSI (Crossover) Middle
🔹 RSI (Crossunder) Middle
🔹 RSI (Crossover) MA
🔹 RSI (Crossunder) MA
🔹 MA (Crossover) Upper
🔹 MA (Crossunder) Upper
🔹 MA (Crossover) Lower
🔹 MA (Crossunder) Lower
📈 RSI Divergences:
🔹 RSI Divergence Bull
🔹 RSI Divergence Bear
📈 RSI Forecast:
🔮 RSI (Crossover) MA Forecast
🔮 RSI (Crossunder) MA Forecast
______________________________________________________
______________________________________________________
🔸 CONDITIONS TO SELL 📉
______________________________________________________
• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
📉 RSI Conditions:
🔸 RSI > Upper
🔸 RSI < Upper
🔸 RSI > Lower
🔸 RSI < Lower
🔸 RSI > Middle
🔸 RSI < Middle
🔸 RSI > MA
🔸 RSI < MA
📉 MA Conditions:
🔸 MA > Upper
🔸 MA < Upper
🔸 MA > Lower
🔸 MA < Lower
📉 Crossovers:
🔸 RSI (Crossover) Upper
🔸 RSI (Crossunder) Upper
🔸 RSI (Crossover) Lower
🔸 RSI (Crossunder) Lower
🔸 RSI (Crossover) Middle
🔸 RSI (Crossunder) Middle
🔸 RSI (Crossover) MA
🔸 RSI (Crossunder) MA
🔸 MA (Crossover) Upper
🔸 MA (Crossunder) Upper
🔸 MA (Crossover) Lower
🔸 MA (Crossunder) Lower
📉 RSI Divergences:
🔸 RSI Divergence Bull
🔸 RSI Divergence Bear
📉 RSI Forecast:
🔮 RSI (Crossover) MA Forecast
🔮 RSI (Crossunder) MA Forecast
______________________________________________________
______________________________________________________
🤖 AUTOMATION 🤖
• You can automate the BUY and SELL signals of this indicator.
______________________________________________________
______________________________________________________
⯁ UNIQUE FEATURES
______________________________________________________
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
______________________________________________________
📜 SCRIPT : RSI Forecast
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
______________________________________________________
o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
RSI Full [Titans_Invest]RSI Full
One of the most complete RSI indicators on the market.
While maintaining the classic RSI foundation, our indicator integrates multiple entry conditions to generate more accurate buy and sell signals.
All conditions are fully configurable, allowing complete customization to fit your trading strategy.
⯁ WHAT IS THE RSI❓
The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend.
⯁ HOW TO USE THE RSI❓
The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones:
Overbought: When the RSI is above 70, indicating that the asset may be overbought.
Oversold: When the RSI is below 30, indicating that the asset may be oversold.
Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions.
⯁ ENTRY CONDITIONS
The conditions below are fully flexible and allow for complete customization of the signal.
______________________________________________________
🔹 CONDITIONS TO BUY 📈
______________________________________________________
• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND/OR .
📈 RSI Conditions:
🔹 RSI > Upper
🔹 RSI < Upper
🔹 RSI > Lower
🔹 RSI < Lower
🔹 RSI > Middle
🔹 RSI < Middle
🔹 RSI > MA
🔹 RSI < MA
📈 MA Conditions:
🔹 MA > Upper
🔹 MA < Upper
🔹 MA > Lower
🔹 MA < Lower
📈 Crossovers:
🔹 RSI (Crossover) Upper
🔹 RSI (Crossunder) Upper
🔹 RSI (Crossover) Lower
🔹 RSI (Crossunder) Lower
🔹 RSI (Crossover) Middle
🔹 RSI (Crossunder) Middle
🔹 RSI (Crossover) MA
🔹 RSI (Crossunder) MA
🔹 MA (Crossover) Upper
🔹 MA (Crossunder) Upper
🔹 MA (Crossover) Lower
🔹 MA (Crossunder) Lower
📈 RSI Divergences:
🔹 RSI Divergence Bull
🔹 RSI Divergence Bear
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🔸 CONDITIONS TO SELL 📉
______________________________________________________
• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND/OR .
📉 RSI Conditions:
🔸 RSI > Upper
🔸 RSI < Upper
🔸 RSI > Lower
🔸 RSI < Lower
🔸 RSI > Middle
🔸 RSI < Middle
🔸 RSI > MA
🔸 RSI < MA
📉 MA Conditions:
🔸 MA > Upper
🔸 MA < Upper
🔸 MA > Lower
🔸 MA < Lower
📉 Crossovers:
🔸 RSI (Crossover) Upper
🔸 RSI (Crossunder) Upper
🔸 RSI (Crossover) Lower
🔸 RSI (Crossunder) Lower
🔸 RSI (Crossover) Middle
🔸 RSI (Crossunder) Middle
🔸 RSI (Crossover) MA
🔸 RSI (Crossunder) MA
🔸 MA (Crossover) Upper
🔸 MA (Crossunder) Upper
🔸 MA (Crossover) Lower
🔸 MA (Crossunder) Lower
📉 RSI Divergences:
🔸 RSI Divergence Bull
🔸 RSI Divergence Bear
______________________________________________________
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🤖 AUTOMATION 🤖
• You can automate the BUY and SELL signals of this indicator.
______________________________________________________
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⯁ UNIQUE FEATURES
______________________________________________________
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
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📜 SCRIPT : RSI Full
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy the Spell!
______________________________________________________
o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
Statistical OHLC Projections [neo|]█ OVERVIEW
Statistical OHLC Projections is an indicator designed to offer users a customizable deep-dive on measuring historical price levels for any timeframe. The indicator separates price into two distinct levels, "Manipulation" and "Distribution", where the idea is that for higher timeframe candles, e.g. an up-close candle, the distance from the open to the bottom of the wick would constitute the Manipulation, and the rest would be considered the Distribution. By measuring out these levels, we can gain insight on how far the market may move from higher timeframe opens to their manipulations and distributions, and apply this knowledge to our analysis.
IMPORTANT: Since levels are based on the lookback available on your chart, if the levels aren't being displayed this likely means you don't have enough lookback for your selected timeframe. To check this, enable the stat table to see how many values are available for your timeframe, and either reduce the lookback or increase your chart timeframe.
█ CONCEPTS
The core concept revolves around understanding market behavior through the lens of historical candle structure. The indicator dissects OHLC data to provide statistical boundaries of expected price movement.
- Manipulation Levels: These represent the areas typically seen as liquidity grabs or false moves where price extends in one direction before reversing.
- Distribution Levels: These highlight where the bulk of directional movement tends to occur, often following the manipulation move.
The tool aggregates this data across your selected timeframe to inform you of potential levels associated with it.
█ FEATURES
Multiple Display Types: Display statistical data through two sleek styles, areas or lines. Where areas represent the area between two customizable lookback values, and lines represent one average value.
Adjustable Timeframe Selection: Whether you want to see data based on the 1D chart, or the 1W chart, anything is possible. Simply change the timeframe on the dropdown menu and if there is sufficient lookback the indicator will adjust to your requested timeframe.
Customizable Historical Lookback: By default, the indicator will measure the average 60 values of your requested timeframe, however this may be adjusted to be higher or lower based on your preference. If you want to measure recent moves, 10-20 lookback may be better for you, or if you want more data for less volatile instruments, a value of 100 may be better.
Historical Display: Prevent historical levels from being removed by unchecking the "Remove Previous Drawings" option, this will allow you to examine how the levels previously interacted with price.
NY Midnight Anchoring: By checking the "Use NY Midnight" option, you may see the projection anchored to the New York midnight open time, which is often a significant level on indices.
Alerts: You may enable alerts for any of the indicator's provided levels to stay informed, even when off the charts.
█ How to use
To use the indicator, simply apply it to your chart and modify any of your desired inputs.
By default, the indicator will provide levels for the "1D" timeframe, with a desired lookback of 60, on most instruments and plans this can be gotten when you are on the 30 minute timeframe or above.
When price reaches or extends beyond a manipulation level, observe how it reacts and whether it rejects from that level, if it does this may be an indication that the candle for the timeframe you selected may be reversing.
█ SETTINGS AND OPTIONS
Customize the indicator’s behavior, timeframe sources, and visual appearance to fit your analysis style. Each setting has been designed with flexibility in mind, whether you're working on lower or higher timeframes.
Display Mode: Switch between different display styles for levels: - Default: Shows all statistical levels as individual lines.
- Areas: Plots filled zones between two customizable lookbacks to represent the range between them.
This is ideal for visually mapping high-probability zones of price activity.
Timeframe Settings:
- Show First/Second Timeframe: Choose to show one or both timeframe projections simultaneously.
- First Timeframe / Second Timeframe: Define the higher timeframe candle you want to base calculations on (e.g., 1D, 1W).
- Use NY Midnight: When enabled and using the daily timeframe, the levels will be anchored to the New York Midnight Open (00:00 EST), a key institutional timing reference, especially useful for indices and forex.
Calculation Settings:
- Main Lookback Period: The number of historical candles used in the statistical calculations. A lower number focuses on recent price action, while a higher number smooths results across broader history.
- First Lookback / Second Lookback: Used when “Areas” mode is selected to define the range of the shaded zone. For example, an area from 20 to 60 candles creates a band between short- and long-term price behavior averages.
Visual Settings:
- Line Style: Set your preferred visual style: Solid, Dashed, or Dotted.
- Remove Previous Drawings: When enabled, only the most recent projection is shown on the chart. Disable to retain previous levels and visually backtest their reactions over time.
Color Settings:
Customize each level independently to match your chart theme:
- Manipulation High/Low
- Distribution High/Low
- Open Level
- Label Text Color
Premium/Discount Zones:
- Enable Premium/Discount Zones: Overlay price zones above and below equilibrium to visualize potential overbought (premium) and oversold (discount) areas.
- Premium/Discount Colors: Fully customizable zone colors for clarity and emphasis.
Table Settings:
- Show Statistics Table: Adds an on-chart table summarizing key levels from your active timeframe(s).
- Table Cell Color: Set the background color of the table cells for visibility.
- Table Position: Choose from preset chart locations to position the table where it works best for your layout.
Alerts:
Stay on top of price interactions with key levels even when you're away from the charts.
- Manipulation Hits (High)
- Manipulation Hits (Low)
- Distribution Hits (High)
- Distribution Hits (Low)
Uptrick Signal Density Cloud🟪 Introduction
The Uptrick Signal Density Cloud is designed to track market direction and highlight potential reversals or shifts in momentum. It plots two smoothed lines on the chart and fills the space between them (often called a “cloud”). The bars on the chart change color depending on bullish or bearish conditions, and small triangles appear when certain reversal criteria are met. A metrics table displays real-time values for easy reference.
🟩 Why These Features Have Been Linked Together
1) Dual-Line Structure
Two separate lines represent shorter- and longer-term market tendencies. Linking them in one tool allows traders to view both near-term changes and the broader directional bias in a single glance.
2) Smoothed Averages
The script offers multiple smoothing methods—exponential, simple, hull, and an optimized approach—to reduce noise. Using more than one type of moving average can help balance responsiveness with stability.
3) Density Cloud Concept
Shading the region between the two lines highlights the gap or “thickness.” A wider gap typically signals stronger momentum, while a narrower gap could indicate a weakening trend or potential market indecision. When the cloud is too wide and crosses a certain threshold defined by the user, it indicates a possible reversal. When the cloud is too narrow it may indicate a potential breakout.
🟪 Why Use This Indicator
• Trend Visibility: The color-coded lines and bars make it easier to distinguish bullish from bearish conditions.
• Momentum Tracking: Thicker cloud regions suggest stronger separation between the faster and slower lines, potentially indicating robust momentum.
• Possible Reversal Alerts: Small triangles appear within thick zones when the indicator detects a crossover, drawing attention to key moments of potential trend change.
• Quick Reference Table: A metrics table shows line values, bullish or bearish status, and cloud thickness without needing to hover over chart elements.
🟩 Inputs
1) First Smoothing Length (length1)
Default: 14
Defines the lookback period for the faster line. Lower values make the line respond more quickly to price changes.
2) Second Smoothing Length (length2)
Default: 28
Defines the lookback period for the slower line or one of the moving averages in optimized mode. It generally responds more slowly than the faster line.
3) Extra Smoothing Length (extraLength)
Default: 50
A medium-term period commonly seen in technical analysis. In optimized mode, it helps add broader perspective to the combined lines.
4) Source (source)
Default: close
Specifies the price data (for example, open, high, low, or a custom source) used in the calculations.
5) Cloud Type (cloudType)
Options: Optimized, EMA, SMA, HMA
Determines the smoothing method used for the lines. “Optimized” blends multiple exponential averages at different lengths.
6) Cloud Thickness Threshold (thicknessThreshold)
Default: 0.5
Sets the minimum separation between the two lines to qualify as a “thick” zone, indicating potentially stronger momentum.
🟪 Core Components
1) Faster and Slower Lines
Each line is smoothed according to user preferences or the optimized technique. The faster line typically reacts more quickly, while the slower line provides a broader overview.
2) Filled Density Cloud
The space between the two lines is filled to visualize in which direction the market is trending.
3) Color-Coded Bars
Price bars adopt bullish or bearish colors based on which line is on top, providing an immediate sense of trend direction.
4) Reversal Triangles
When the cloud is thick (exceeding the threshold) and the lines cross in the opposite direction, small triangles appear, signaling a possible market shift.
5) Metrics Table
A compact table shows the current values of both lines, their bullish/bearish statuses, the cloud thickness, and whether the cloud is in a “reversal zone.”
🟩 Calculation Process
1) Raw Averages
Depending on the mode, standard exponential, simple, hull, or “optimized” exponential blends are calculated.
2) Optimized Averages (if selected)
The faster line is the average of three exponential moving averages using length1, length2, and extraLength.
The slower line similarly uses those same lengths multiplied by 1.5, then averages them together for broader smoothing.
3) Difference and Threshold
The absolute gap between the two lines is measured. When it exceeds thicknessThreshold, the cloud is considered thick.
4) Bullish or Bearish Determination
If sma1 (the faster line) is above sma2 (the slower line), conditions are deemed bullish; otherwise, they are bearish. This distinction is reflected in both bar colors and cloud shading.
5) Reversal Markers
In thick zones, a crossover triggers a triangle at the point of potential reversal, alerting traders to a possible trend change.
🟪 Smoothing Methods
1) Exponential (EMA)
Prioritizes recent data for quicker responsiveness.
2) Simple (SMA)
Takes a straightforward average of the chosen period, smoothing price action but often lagging more in volatile markets.
3) Hull (HMA)
Employs a specialized formula to reduce lag while maintaining smoothness.
4) Optimized (Blended Exponential)
Combines multiple EMA calculations to strike a balance between responsiveness and noise reduction.
🟩 Cloud Logic and Reversal Zones
Cloud thickness above the defined threshold typically signals exceeding momentum and can lead to a quick reversal. During these thick periods, if the width exceeds the defined threshold, small triangles mark potential reversal points. In order for the reversal shape to show, the color of the cloud has to be the opposite. So, for example, if the cloud is bearish, and exceeds momentum, defined by the user, a bullish signal appears. The opposite conditions for a bullish signal. This approach can help traders focus on notable changes rather than minor oscillations.
🟪 Bar Coloring and Layered Lines
Bars take on bullish or bearish tints, matching the faster line’s position relative to the slower line. The lines themselves are plotted multiple times with varying opacities, creating a layered, glowing look that enhances visibility without affecting calculations.
🟩 The Metrics Table
Located in the top-right corner of the chart, this table displays:
• SMA1 and SMA2 current values.
• Bullish or bearish alignment for each line.
• Cloud thickness.
• Reversal zone status (in or out of zone).
This numeric readout allows for a quick data check without hovering over the chart.
🟪 Why These Specific Moving Average Lengths Are Used
Default lengths of 14, 28, and 50 are common in technical analysis. Fourteen captures near-term price movement without overreacting. Twenty-eight, roughly double 14, provides a moderate smoothing level. Fifty is widely regarded as a medium-term benchmark. Multiplying each length by 1.5 for the slower line enhances separation when combined with the faster line.
🟩 Originality and Usefulness
• Multi-Layered Smoothing. The user can select from several moving average modes, including a unique “optimized” blend, possibly reducing random fluctuations in the market data.
• Combined Visual and Numeric Clarity. Bars, clouds, and a real-time table merge into a single interface, enabling efficient trend analysis.
• Focus on Significant Shifts. Thick cloud zones and triangles draw attention to potentially stronger momentum changes and plausible reversals.
• Flexible Across Markets. The adjustable lengths and threshold can be tuned to different asset classes (stocks, forex, commodities, crypto) and timeframes.
By integrating multiple technical concepts—cloud-based trend detection, color coding, reversal markers, and an immediate reference table—the Uptrick Signal Density Cloud aims to streamline chart reading and decision-making.
🟪 Additional Considerations
• Timeframes. Intraday, daily, and weekly charts each yield different signals. Adjust the smoothing lengths and threshold to suit specific trading horizons.
• Market Types. Though applicable across asset classes, parameters might need tweaking to address the volatility of commodities, forex pairs, or cryptocurrencies.
• Confirmation Tools. Pairing this indicator with volume studies or support/resistance analysis can improve the reliability of signals.
• Potential Limitations. No indicator is foolproof; sudden market shifts or choppy conditions may reduce accuracy. Cautious position sizing and risk management remain essential.
🟩 Disclaimers
The Uptrick Signal Density Cloud relies on historical price data and may lag sudden moves or provide false positives in ranging conditions. Always combine it with other analytical techniques and sound risk management. This script is offered for educational purposes only and should not be considered financial advice.
🟪 Conclusion
The Uptrick Signal Density Cloud blends trend identification, momentum assessment, and potential reversal alerts in a single, user-friendly tool. With customizable smoothing methods and a focus on cloud thickness, it visually highlights important market conditions. While it cannot guarantee predictive accuracy, it can serve as a comprehensive reference for traders seeking both a quick snapshot of the current trend and deeper insights into market dynamics.
Dynamic Intensity Transition Oscillator (DITO)The Dynamic Intensity Transition Oscillator (DITO) is a comprehensive indicator designed to identify and visualize the slope of price action normalized by volatility, enabling consistent comparisons across different assets. This indicator calculates and categorizes the intensity of price movement into six states—three positive and three negative—while providing visual cues and alerts for state transitions.
Components and Functionality
1. Slope Calculation
- The slope represents the rate of change in price action over a specified period (Slope Calculation Period).
- It is calculated as the difference between the current price and the simple moving average (SMA) of the price, divided by the length of the period.
2. Normalization Using ATR
- To standardize the slope across assets with different price scales and volatilities, the slope is divided by the Average True Range (ATR).
- The ATR ensures that the slope is comparable across assets with varying price levels and volatility.
3. Intensity Levels
- The normalized slope is categorized into six distinct intensity levels:
High Positive: Strong upward momentum.
Medium Positive: Moderate upward momentum.
Low Positive: Weak upward movement or consolidation.
Low Negative: Weak downward movement or consolidation.
Medium Negative: Moderate downward momentum.
High Negative: Strong downward momentum.
4. Visual Representation
- The oscillator is displayed as a histogram, with each intensity level represented by a unique color:
High Positive: Lime green.
Medium Positive: Aqua.
Low Positive: Blue.
Low Negative: Yellow.
Medium Negative: Purple.
High Negative: Fuchsia.
Threshold levels (Low Intensity, Medium Intensity) are plotted as horizontal dotted lines for visual reference, with separate colors for positive and negative thresholds.
5. Intensity Table
- A dynamic table is displayed on the chart to show the current intensity level.
- The table's text color matches the intensity level color for easy interpretation, and its size and position are customizable.
6. Alerts for State Transitions
- The indicator includes a robust alerting system that triggers when the intensity level transitions from one state to another (e.g., from "Medium Positive" to "High Positive").
- The alert includes both the previous and current states for clarity.
Inputs and Customization
The DITO indicator offers a variety of customizable settings:
Indicator Parameters
Slope Calculation Period: Defines the period over which the slope is calculated.
ATR Calculation Period: Defines the period for the ATR used in normalization.
Low Intensity Threshold: Threshold for categorizing weak momentum.
Medium Intensity Threshold: Threshold for categorizing moderate momentum.
Intensity Table Settings
Table Position: Allows you to position the intensity table anywhere on the chart (e.g., "Bottom Right," "Top Left").
Table Size: Enables customization of table text size (e.g., "Small," "Large").
Use Cases
Trend Identification:
- Quickly assess the strength and direction of price movement with color-coded intensity levels.
Cross-Asset Comparisons:
- Use the normalized slope to compare momentum across different assets, regardless of price scale or volatility.
Dynamic Alerts:
- Receive timely alerts when the intensity transitions, helping you act on significant momentum changes.
Consolidation Detection:
- Identify periods of low intensity, signaling potential reversals or breakout opportunities.
How to Use
- Add the indicator to your chart.
- Configure the input parameters to align with your trading strategy.
Observe:
The Oscillator: Use the color-coded histogram to monitor price action intensity.
The Intensity Table: Track the current intensity level dynamically.
Alerts: Respond to state transitions as notified by the alerts.
Final Notes
The Dynamic Intensity Transition Oscillator (DITO) combines trend strength detection, cross-asset comparability, and real-time alerts to offer traders an insightful tool for analyzing market conditions. Its user-friendly visualization and comprehensive alerting make it suitable for both novice and advanced traders.
Disclaimer: This indicator is for educational purposes and is not financial advice. Always perform your own analysis before making trading decisions.
Average Price Range Screener [KFB Quant]Average Price Range Screener
Overview:
The Average Price Range Screener is a technical analysis tool designed to provide insights into the average price volatility across multiple symbols over user-defined time periods. The indicator compares price ranges from different assets and displays them in a visual table and chart for easy reference. This can be especially helpful for traders looking to identify symbols with high or low volatility across various time frames.
Key Features:
Multiple Symbols Supported:
The script allows for analysis of up to 10 symbols, such as major cryptocurrencies and market indices. Symbols can be selected by the user and configured for tracking price volatility.
Dynamic Range Calculation:
The script calculates the average price range of each symbol over three distinct time periods (default are 30, 60, and 90 bars). The price range for each symbol is calculated as a percentage of the bar's high-to-low difference relative to its low value.
Range Visualization:
The results are visually represented using:
- A color-coded table showing the calculated average ranges of each symbol and the current chart symbol.
- A line plot that visually tracks the volatility for each symbol on the chart, with color gradients representing the range intensity from low (red/orange) to high (blue/green).
Customizable Inputs:
- Length Inputs: Users can define the time lengths (default are 30, 60, and 90 bars) for calculating average price ranges for each symbol.
- Symbol Inputs: 10 symbols can be tracked at once, with default values set to popular crypto pairs and indices.
- Color Inputs: Users can customize the color scheme for the range values displayed in the table and chart.
Real-Time Ranking:
The indicator ranks symbols by their average price range, providing a clear view of which assets are exhibiting higher volatility at any given time.
Each symbol's range value is color-coded based on its relative volatility within the selected symbols (using a gradient from low to high range).
Data Table:
The table shows the average range values for each symbol in real-time, allowing users to compare volatility across multiple assets at a glance. The table is dynamically updated as new data comes in.
Interactive Labels:
The indicator adds labels to the chart, showing the average range for each symbol. These labels adjust in real-time as the price range values change, giving users an immediate view of volatility rankings.
How to Use:
Set Time Periods: Adjust the time periods (lengths) to match your trading strategy's timeframe and volatility preference.
Symbol Selection: Add and track the price range for your preferred symbols (cryptocurrencies, stocks, indices).
Monitor Volatility: Use the visual table and plot to identify symbols with higher or lower volatility, and adjust your trading strategy accordingly.
Interpret the Table and Chart: Ranges that are color-coded from red/orange (lower volatility) to blue/green (higher volatility) allow you to quickly gauge which symbols are most volatile.
Disclaimer: This tool is provided for informational and educational purposes only and should not be considered as financial advice. Always conduct your own research and consult with a licensed financial advisor before making any investment decisions.
Burst PowerThe Burst Power indicator is to be used for Indian markets where most stocks have a maximum price band limit of 20%.
This indicator is intended to identify stocks with high potential for significant price movements. By analysing historical price action over a user-defined lookback period, it calculates a Burst Power score that reflects the stock's propensity for rapid and substantial moves. This can be helpful for stock selection in strategies involving momentum bursts, swing trading, or identifying stocks with explosive potential.
Key Components
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Significant Move Counts:
5% Moves: Counts the number of days within the lookback period where the stock had a positive close-to-close move between 5% and 10%.
10% Moves: Counts the number of days with a positive close-to-close move between 10% and 19%.
19% Moves: Counts the number of days with a positive close-to-close move of 19% or more.
Maximum Price Move (%):
Identifies the largest positive close-to-close percentage move within the lookback period, along with the date it occurred.
Burst Power Score:
A composite score calculated using the counts of significant moves: Burst Power =(Count5%/5) +(Count10%/2) + (Count19%/0.5)
The score is then rounded to the nearest whole number.
A higher Burst Power score indicates a higher frequency of significant price bursts.
Visual Indicators:
Table Display: Presents all the calculated data in a customisable table on the chart.
Markers on Chart: Plots markers on the chart where significant moves occurred, aiding visual analysis.
Using the Lookback Period
____________________________
The lookback period determines how much historical data the indicator analyses. Users can select from predefined options:
3 Months
6 Months
1 Year
3 Years
5 Years
A shorter lookback period focuses on recent price action, which may be more relevant for short-term trading strategies. A longer lookback period provides a broader historical context, useful for identifying long-term patterns and behaviors.
Interpreting the Burst Power Score
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High Burst Power Score (≥15):
Indicates the stock frequently experiences significant price moves.
Suitable for traders seeking quick momentum bursts and swing trading opportunities.
Stocks with high scores may be more volatile but offer potential for rapid gains.
Moderate Burst Power Score (10 to 14):
Suggests occasional significant price movements.
May suit traders looking for a balance between volatility and stability.
Low Burst Power Score (<10):
Reflects fewer significant price bursts.
Stocks are more likely to exhibit longer, sustainable, but slower price trends.
May be preferred by traders focusing on steady growth or longer-term investments.
Note: Trading involves uncertainties, and the Burst Power score should be considered as one of many factors in a comprehensive trading strategy. It is essential to incorporate broader market analysis and risk management practices.
Customisation Options
_________________________
The indicator offers several customisation settings to tailor the display and functionality to individual preferences:
Display Mode:
Full Mode: Shows the detailed table with all components, including significant move counts, maximum price move, and the Burst Power score.
Mini Mode: Displays only the Burst Power score and its corresponding indicator (green, orange, or red circle).
Show Latest Date Column:
Toggle the display of the "Latest Date" column in the table, which shows the most recent occurrence of each significant move category.
Theme (Dark Mode):
Switch between Dark Mode and Light Mode for better visual integration with your chart's color scheme.
Table Position and Size:
Position: Place the table at various locations on the chart (top, middle, bottom; left, center, right).
Size: Adjust the table's text size (tiny, small, normal, large, huge, auto) for optimal readability.
Header Size: Customise the font size of the table headers (Small, Medium, Large).
Color Settings:
Disable Colors in Table: Option to display the table without background colors, which can be useful for printing or if colors are distracting.
Bullish Closing Filter:
Another customisation here is to count a move only when the closing for the day is strong. For this, we have an additional filter to see if close is within the chosen % of the range of the day. Closing within the top 1/3, for instance, indicates a way more bullish day tha, say, closing within the bottom 25%.
Move Markers on chart:
The indicator also marks out days with significant moves. You can choose to hide or show the markers on the candles/bars.
Practical Applications
________________________
Momentum Trading: High Burst Power scores can help identify stocks that are likely to experience rapid price movements, suitable for momentum traders.
Swing Trading: Traders looking for short- to medium-term opportunities may focus on stocks with moderate to high Burst Power scores.
Positional Trading: Lower Burst Power scores may indicate steadier stocks that are less prone to volatility, aligning with long-term investment strategies.
Risk Management: Understanding a stock's propensity for significant moves can aid in setting appropriate stop-loss and take-profit levels.
Disclaimer: Trading involves significant risk, and past performance is not indicative of future results. The Burst Power indicator is intended for educational purposes and should not be construed as financial advice. Always conduct thorough research and consult with a qualified financial professional before making investment decisions.
Interest Rate Trading (Manually Added Rate Decisions) [TANHEF]Interest Rate Trading: How Interest Rates Can Guide Your Next Move.
How were interest rate decisions added?
All interest rate decision dates were manually retrieved from the 'Record of Policy Actions' and 'Minutes of Actions' on the Federal Reserve's website due to inconsistent dates from other sources. These were manually added as Pine Script currently only identifies rate changes, not pauses.
█ Simple Explanation:
This script is designed for analyzing and backtesting trading strategies based on U.S. interest rate decisions which occur during Federal Open Market Committee (FOMC) meetings, to make trading decisions. No trading strategy is perfect, and it's important to understand that expectations won't always play out. The script leverages historical interest rate changes, including increases, decreases, and pauses, across multiple economic time periods from 1971 to the present. The tool integrates two key data sources for interest rates—USINTR and FEDFUNDS—to support decision-making around rate-based trades. The focus is on identifying opportunities and tracking trades driven by interest rate movements.
█ Interest Rate Decision Sources:
As noted above, each decision date has been manually added from the 'Record of Policy Actions' and 'Minutes of Actions' documents on the Federal Reserve's website. This includes +50 years of more than 600 rate decisions.
█ Interest Rate Data Sources:
USINTR: Reflects broader U.S. interest rate trends, including Treasury yields and various benchmarks. This is the preferred option as it corresponds well to the rate decision dates.
FEDFUNDS: Tracks the Federal Funds Rate, which is a more specific rate targeted by the Federal Reserve. This does not change on the exact same days as the rate decisions that occur at FOMC meetings.
█ Trade Criteria:
A variety of trading conditions are predefined to suit different trading strategies. These conditions include:
Increase/Decrease: Standard rate increases or decreases.
Double/Triple Increase/Decrease: A series of consecutive changes.
Aggressive Increase/Decrease: Rate changes that exceed recent movements.
Pause: Identification of no changes (pauses) between rate decisions, including double or triple pauses.
Complex Patterns: Combinations of pauses, increases, or decreases, such as "Pause after Increase" or "Pause or Increase."
█ Trade Execution and Exit:
The script allows automated trade execution based on selected criteria:
Auto-Entry: Option to enter trades automatically at the first valid period.
Max Trade Duration: Optional exit of trades after a specified number of bars (candles).
Pause Days: Minimum duration (in days) to validate rate pauses as entry conditions. This is especially useful for earlier periods (prior to the 2000s), where rate decisions often seemed random compared to the consistency we see today.
█ Visualization:
Several visual elements enhance the backtesting experience:
Time Period Highlighting: Economic time periods are visually segmented on the chart, each with a unique color. These periods include historical phases such as "Stagflation (1971-1982)" and "Post-Pandemic Recovery (2021-Present)".
Trade and Holding Results: Displays the profit and loss of trades and holding results directly on the chart.
Interest Rate Plot: Plots the interest rate movements on the chart, allowing for real-time tracking of rate changes.
Trade Status: Highlights active long or short positions on the chart.
█ Statistics and Criteria Display:
Stats Table: Summarizes trade results, including wins, losses, and draw percentages for both long and short trades.
Criteria Table: Lists the selected entry and exit criteria for both long and short positions.
█ Economic Time Periods:
The script organizes interest rate decisions into well-defined economic periods, allowing traders to backtest strategies specific to historical contexts like:
(1971-1982) Stagflation
(1983-1990) Reaganomics and Deregulation
(1991-1994) Early 1990s (Recession and Recovery)
(1995-2001) Dot-Com Bubble
(2001-2006) Housing Boom
(2007-2009) Global Financial Crisis
(2009-2015) Great Recession Recovery
(2015-2019) Normalization Period
(2019-2021) COVID-19 Pandemic
(2021-Present) Post-Pandemic Recovery
█ User-Configurable Inputs:
Rate Source Selection: Choose between USINTR or FEDFUNDS as the primary interest rate source.
Trade Criteria Customization: Users can select the criteria for long and short trades, specifying when to enter or exit based on changes in the interest rate.
Time Period: Select the time period that you want to isolate testing a strategy with.
Auto-Entry and Pause Settings: Options to automatically enter trades and specify the number of days to confirm a rate pause.
Max Trade Duration: Limits how long trades can remain open, defined by the number of bars.
█ Trade Logic:
The script manages entries and exits for both long and short trades. It calculates the profit or loss percentage based on the entry and exit prices. The script tracks ongoing trades, dynamically updating the profit or loss as price changes.
█ Examples:
One of the most popular opinions is that when rate starts begin you should sell, then buy back in when rate cuts stop dropping. However, this can be easily proven to be a difficult task. Predicting the end of a rate cut is very difficult to do with the the exception that assumes rates will not fall below 0.25%.
2001-2009
Trade Result: +29.85%
Holding Result: -27.74%
1971-2024
Trade Result: +533%
Holding Result: +5901%
█ Backtest and Real-Time Use:
This backtester is useful for historical analysis and real-time trading. By setting up various entry and exit rules tied to interest rate movements, traders can test and refine strategies based on real historical data and rate decision trends.
This powerful tool allows traders to customize strategies, backtest them through different economic periods, and get visual feedback on their trading performance, helping to make more informed decisions based on interest rate dynamics. The main goal of this indicator is to challenge the belief that future events must mirror the 2001 and 2007 rate cuts. If everyone expects something to happen, it usually doesn’t.
Ticker Tape█ OVERVIEW
This indicator creates a dynamic, scrolling display of multiple securities' latest prices and daily changes, similar to the ticker tapes on financial news channels and the Ticker Tape Widget . It shows realtime market information for a user-specified list of symbols along the bottom of the main chart pane.
█ CONCEPTS
Ticker tape
Traditionally, a ticker tape was a continuous, narrow strip of paper that displayed stock prices, trade volumes, and other financial and security information. Invented by Edward A. Calahan in 1867, ticker tapes were the earliest method for electronically transmitting live stock market data.
A machine known as a "stock ticker" received stock information via telegraph, printing abbreviated company names, transaction prices, and other information in a linear sequence on the paper as new data came in. The term "ticker" in the name comes from the "tick" sound the machine made as it printed stock information. The printed tape provided a running record of trading activity, allowing market participants to stay informed on recent market conditions without needing to be on the exchange floor.
In modern times, electronic displays have replaced physical ticker tapes. However, the term "ticker" remains persistent in today's financial lexicon. Nowadays, ticker symbols and digital tickers appear on financial news networks, trading platforms, and brokerage/exchange websites, offering live updates on market information. Modern electronic displays, thankfully, do not rely on telegraph updates to operate.
█ FEATURES
Requesting a list of securities
The "Symbol list" text box in the indicator's "Settings/Inputs" tab allows users to list up to 40 symbols or ticker Identifiers. The indicator dynamically requests and displays information for each one. To add symbols to the list, enter their names separated by commas . For example: "BITSTAMP:BTCUSD, TSLA, MSFT".
Each item in the comma-separated list must represent a valid symbol or ticker ID. If the list includes an invalid symbol, the script will raise a runtime error.
To specify a broker/exchange for a symbol, include its name as a prefix with a colon in the "EXCHANGE:SYMBOL" format. If a symbol in the list does not specify an exchange prefix, the indicator selects the most commonly used exchange when requesting the data.
Realtime updates
This indicator requests symbol descriptions, current market prices, daily price changes, and daily change percentages for each ticker from the user-specified list of symbols or ticker identifiers. It receives updated information for each security after new realtime ticks on the current chart.
After a new realtime price update, the indicator updates the values shown in the tape display and their colors.
The color of the percentages in the tape depends on the change in price from the previous day . The text is green when the daily change is positive, red when the value is negative, and gray when the value is 0.
The color of each displayed price depends on the change in value from the last recorded update, not the change over a daily period. For example, if a security's price increases in the latest update, the ticker tape shows that price with green text, even if the current price is below the previous day's closing price. This behavior allows users to monitor realtime directional changes in the requested securities.
NOTE: Pine scripts execute on realtime bars when new ticks are available in the chart's data feed. If no new updates are available from the chart's realtime feed, it may cause a delay in the data the indicator receives.
Ticker motion
This indicator's tape display shows a list of security information that incrementally scrolls horizontally from right to left after new chart updates, providing a dynamic visual stream of current market data. The scrolling effect works by using a counter that increments across successive intervals after realtime ticks to control the offset of each listed security. Users can set the initial scroll offset with the "Offset" input in the "Settings/Inputs" tab.
The scrolling rate of the ticker tape display depends on the realtime ticks available from the chart's data feed. Using the indicator on a chart with frequent realtime updates results in smoother scrolling. If no new realtime ticks are available in the chart's feed, the ticker tape does not move. Users can also deactivate the scrolling feature by toggling the "Running" input in the indicator's settings.
█ FOR Pine Script™ CODERS
• This script utilizes dynamic requests to iteratively fetch information from multiple contexts using a single request.security() instance in the code. Previously, `request.*()` functions were not allowed within the local scopes of loops or conditional structures, and most `request.*()` function parameters, excluding `expression`, required arguments of a simple or weaker qualified type. The new `dynamic_requests` parameter in script declaration statements enables more flexibility in how scripts can use `request.*()` calls. When its value is `true`, all `request.*()` functions can accept series arguments for the parameters that define their requested contexts, and `request.*()` functions can execute within local scopes. See the Dynamic requests section of the Pine Script™ User Manual to learn more.
• Scripts can execute up to 40 unique `request.*()` function calls. A `request.*()` call is unique only if the script does not already call the same function with the same arguments. See this section of the User Manual's Limitations page for more information.
• This script converts a comma-separated "string" list of symbols or ticker IDs into an array . It then loops through this array, dynamically requesting data from each symbol's context and storing the results within a collection of custom `Tape` objects . Each `Tape` instance holds information about a symbol, which the script uses to populate the table that displays the ticker tape.
• This script uses the varip keyword to declare variables and `Tape` fields that update across ticks on unconfirmed bars without rolling back. This behavior allows the script to color the tape's text based on the latest price movements and change the locations of the table cells after realtime updates without reverting. See the `varip` section of the User Manual to learn more about using this keyword.
• Typically, when requesting higher-timeframe data with request.security() using barmerge.lookahead_on as the `lookahead` argument, the `expression` argument should use the history-referencing operator to offset the series, preventing lookahead bias on historical bars. However, the request.security() call in this script uses barmerge.lookahead_on without offsetting the `expression` because the script only displays results for the latest historical bar and all realtime bars, where there is no future information to leak into the past. Instead, using this call on those bars ensures each request fetches the most recent data available from each context.
• The request.security() instance in this script includes a `calc_bars_count` argument to specify that each request retrieves only a minimal number of bars from the end of each symbol's historical data feed. The script does not need to request all the historical data for each symbol because it only shows results on the last chart bar that do not depend on the entire time series. In this case, reducing the retrieved bars in each request helps minimize resource usage without impacting the calculated results.
Look first. Then leap.
Uptrick: TimeFrame Trends: Performance & Sentiment Indicator### **Uptrick: TimeFrame Trends: Performance & Sentiment Indicator (TFT) - In-Depth Explanation**
#### **Overview**
The **Uptrick: TimeFrame Trends: Performance & Sentiment Indicator (TFT)** is a sophisticated trading tool designed to provide traders with a comprehensive view of market trends across multiple timeframes, combined with a sentiment gauge through the Relative Strength Index (RSI). This indicator offers a unique blend of performance analysis, sentiment evaluation, and visual signal generation, making it an invaluable resource for traders who seek to understand both the macro and micro trends within a financial instrument.
#### **Purpose**
The primary purpose of the TFT indicator is to empower traders with the ability to assess the performance of an asset over various timeframes while simultaneously gauging market sentiment through the RSI. By analyzing price changes over periods ranging from one week to one year, and complementing this with sentiment signals, TFT enables traders to make informed decisions based on a well-rounded analysis of historical price performance and current market conditions.
#### **Key Components and Features**
1. **Multi-Timeframe Performance Analysis:**
- **Performance Lookback Periods:**
- The TFT indicator calculates the percentage price change over several predefined timeframes: 7 days (1 week), 14 days (2 weeks), 30 days (1 month), 180 days (6 months), and 365 days (1 year). These timeframes provide a layered view of how an asset has performed over short, medium, and long-term periods.
- **Percentage Change Calculation:**
- The indicator computes the percentage change for each timeframe by comparing the current closing price to the closing price at the start of each period. This gives traders insight into the strength and direction of the trend over different periods, helping them identify consistent trends or potential reversals.
2. **Sentiment Analysis Using RSI:**
- **Relative Strength Index (RSI):**
- RSI is a widely-used momentum oscillator that measures the speed and change of price movements. It oscillates between 0 and 100 and is typically used to identify overbought or oversold conditions. In TFT, the RSI is calculated using a 14-period lookback, which is standard for most RSI implementations.
- **RSI Smoothing with EMA:**
- To refine the RSI signal and reduce noise, TFT applies a 10-period Exponential Moving Average (EMA) to the RSI values. This smoothed RSI is then used to generate buy, sell, and neutral signals based on its position relative to the 50 level:
- **Buy Signal:** Triggered when the smoothed RSI crosses above 50, indicating bullish sentiment.
- **Sell Signal:** Triggered when the smoothed RSI crosses below 50, indicating bearish sentiment.
- **Neutral Signal:** Triggered when the smoothed RSI equals 50, suggesting indecision or a balanced market.
3. **Visual Signal Generation:**
- **Signal Plots:**
- TFT provides clear visual cues directly on the price chart by plotting shapes at the points where buy, sell, or neutral signals are generated. These shapes are color-coded (green for buy, red for sell, yellow for neutral) and are positioned below or above the price bars for easy identification.
- **First Occurrence Trigger:**
- To avoid clutter and focus on significant market shifts, TFT only triggers the first occurrence of each signal type. This feature helps traders concentrate on the most relevant signals without being overwhelmed by repeated alerts.
4. **Customizable Performance & Sentiment Table:**
- **Table Display:**
- The TFT indicator includes a customizable table that displays the calculated percentage changes for each timeframe. This table is positioned on the chart according to user preference (top-left, top-right, bottom-left, bottom-right) and provides a quick reference to the asset’s performance across multiple periods.
- **Dynamic Text Color:**
- To enhance readability and provide immediate visual feedback, the text color in the table changes based on the direction of the percentage change: green for positive (upward movement) and red for negative (downward movement). This color-coding helps traders quickly assess whether the asset is in an uptrend or downtrend for each period.
- **Customizable Font Size:**
- Traders can adjust the font size of the table to fit their chart layout and personal preferences, ensuring that the information is accessible without being intrusive.
5. **Flexibility and Customization:**
- **Lookback Period Customization:**
- While the default lookback periods are set for common trading intervals (7 days, 14 days, etc.), these can be adjusted to match different trading strategies or market conditions. This flexibility allows traders to tailor the indicator to focus on the timeframes most relevant to their analysis.
- **RSI and EMA Settings:**
- The length of the RSI calculation and the smoothing EMA can also be customized. This is particularly useful for traders who prefer shorter or longer periods for their momentum analysis, allowing them to fine-tune the sensitivity of the indicator.
- **Table Position and Appearance:**
- The table’s position on the chart, along with its font size and colors, is fully customizable. This ensures that the indicator can be integrated seamlessly into any chart setup without obstructing key price data.
#### **Use Cases and Applications**
1. **Trend Identification and Confirmation:**
- **Short-Term Traders:**
- Traders focused on short-term movements can use the 7-day and 14-day performance metrics to identify recent trends and momentum shifts. The RSI signals provide additional confirmation, helping traders enter or exit positions based on the latest market sentiment.
- **Swing Traders:**
- For those holding positions over days to weeks, the 30-day and 180-day performance data are particularly useful. These metrics highlight medium-term trends, and when combined with RSI signals, they provide a robust framework for swing trading strategies.
- **Long-Term Investors:**
- Long-term investors can benefit from the 1-year performance data to gauge the overall health and direction of an asset. The indicator’s ability to track performance across different periods helps in identifying long-term trends and potential reversal points.
2. **Sentiment Analysis and Market Timing:**
- **Market Sentiment Tracking:**
- By using RSI in conjunction with performance metrics, TFT provides a clear picture of market sentiment. Traders can use this information to time their entries and exits more effectively, aligning their trades with periods of strong bullish or bearish sentiment.
- **Avoiding False Signals:**
- The smoothing of RSI helps reduce noise and avoid false signals that are common in volatile markets. This makes the TFT indicator a reliable tool for identifying true market trends and avoiding whipsaws that can lead to losses.
3. **Comprehensive Market Analysis:**
- **Multi-Timeframe Analysis:**
- TFT’s ability to analyze multiple timeframes simultaneously makes it an excellent tool for comprehensive market analysis. Traders can compare short-term and long-term performance to understand the broader market context, making it easier to align their trading strategies with the overall trend.
- **Performance Benchmarking:**
- The percentage change metrics provide a clear benchmark for an asset’s performance over time. This information can be used to compare the asset against broader market indices or other assets, helping traders make more informed decisions about where to allocate their capital.
4. **Custom Strategy Development:**
- **Tailoring to Specific Markets:**
- TFT can be customized to suit different markets, whether it’s stocks, forex, commodities, or cryptocurrencies. For instance, traders in volatile markets may opt for shorter lookback periods and more sensitive RSI settings, while those in stable markets may prefer longer periods for a smoother analysis.
- **Integrating with Other Indicators:**
- TFT can be used alongside other technical indicators to create a more comprehensive trading strategy. For example, combining TFT with moving averages, Bollinger Bands, or MACD can provide additional layers of confirmation and reduce the likelihood of false signals.
#### **Best Practices for Using TFT**
- **Regularly Adjust Lookback Periods:**
- Depending on the market conditions and the asset being traded, it’s important to regularly review and adjust the lookback periods for the performance metrics. This ensures that the indicator remains relevant and responsive to current market trends.
- **Combine with Volume Analysis:**
- While TFT provides a solid foundation for trend and sentiment analysis, combining it with volume indicators can further enhance its effectiveness. Volume can confirm the strength of a trend or signal potential reversals when divergences occur.
- **Use RSI with Other Momentum Indicators:**
- Although RSI is a powerful tool on its own, using it alongside other momentum indicators like Stochastic Oscillator or MACD can provide additional confirmation and help refine entry and exit points.
- **Customize Table Settings for Clarity:**
- Ensure that the performance table is positioned and sized appropriately on the chart. It should be easily readable without obstructing important price data. Adjust the text size and colors as needed to maintain clarity.
- **Monitor Multiple Timeframes:**
- Utilize the multi-timeframe analysis feature of TFT to monitor trends across different periods. This helps in identifying the dominant trend and avoiding trades that go against the broader market direction.
#### **Conclusion**
The **Uptrick: TimeFrame Trends: Performance & Sentiment Indicator (TFT)** is a comprehensive and versatile tool that combines the power of multi-timeframe performance analysis with sentiment gauging through RSI. Its ability to customize and adapt to various trading strategies and markets makes it a valuable asset for traders at all levels. By offering a clear visual representation of trends and market sentiment, TFT empowers traders to make more informed and confident trading decisions, whether they are focusing on short-term price movements or long-term investment opportunities. With its deep integration of performance metrics and sentiment analysis, TFT stands out as a must-have indicator for any trader looking to gain a holistic understanding of market dynamics.
Uptrick: Volume-Weighted EMA Signal### **Uptrick: Volume-Weighted EMA Signal (UVES) Indicator - Comprehensive Description**
#### **Overview**
The **Uptrick: Volume-Weighted EMA Signal (UVES)** is an advanced, multifaceted trading indicator meticulously designed to provide traders with a holistic view of market trends by integrating Exponential Moving Averages (EMA) with volume analysis. This indicator not only identifies the direction of market trends through dynamic EMAs but also evaluates the underlying strength of these trends using real-time volume data. UVES is a versatile tool suitable for various trading styles and markets, offering a high degree of customization to meet the specific needs of individual traders.
#### **Purpose**
The UVES indicator aims to enhance traditional trend-following strategies by incorporating a critical yet often overlooked component: volume. Volume is a powerful indicator of market strength, providing insights into the conviction behind price movements. By merging EMA-based trend signals with detailed volume analysis, UVES offers a more nuanced and reliable approach to identifying trading opportunities. This dual-layer analysis allows traders to differentiate between strong trends supported by significant volume and weaker trends that may be prone to reversals.
#### **Key Features and Functions**
1. **Dynamic Exponential Moving Average (EMA):**
- The core of the UVES indicator is its dynamic EMA, calculated over a customizable period. The EMA is a widely used technical indicator that smooths price data to identify the underlying trend. In UVES, the EMA is dynamically colored—green when the current EMA value is above the previous value, indicating an uptrend, and red when below, signaling a downtrend. This visual cue helps traders quickly assess the trend direction without manually calculating or interpreting raw data.
2. **Comprehensive Moving Average Customization:**
- While the EMA is the default moving average in UVES, traders can select from various other moving average types, including Simple Moving Average (SMA), Smoothed Moving Average (SMMA), Weighted Moving Average (WMA), and Volume-Weighted Moving Average (VWMA). Each type offers unique characteristics:
- **SMA:** Provides a simple average of prices over a specified period, suitable for identifying long-term trends.
- **EMA:** Gives more weight to recent prices, making it more responsive to recent market movements.
- **SMMA (RMA):** A slower-moving average that reduces noise, ideal for capturing smoother trends.
- **WMA:** Weighs prices based on their order in the dataset, making recent prices more influential.
- **VWMA:** Integrates volume data, emphasizing price movements that occur with higher volume, making it particularly useful in volume-sensitive markets.
3. **Signal Line for Trend Confirmation:**
- UVES includes an optional signal line, which applies a secondary moving average to the primary EMA. This signal line can be used to smooth out the EMA and confirm trend changes. The signal line’s color changes based on its slope—green for an upward slope and red for a downward slope—providing a clear visual confirmation of trend direction. Traders can adjust the length and type of this signal line, allowing them to tailor the indicator’s responsiveness to their trading strategy.
4. **Buy and Sell Signal Generation:**
- UVES generates explicit buy and sell signals based on the interaction between the EMA and the signal line. A **buy signal** is triggered when the EMA transitions from a red (downtrend) to a green (uptrend), indicating a potential entry point. Conversely, a **sell signal** is triggered when the EMA shifts from green to red, suggesting an exit or shorting opportunity. These signals are displayed directly on the chart as upward or downward arrows, making them easily identifiable even during fast market conditions.
5. **Volume Analysis with Real-Time Buy/Sell Volume Table:**
- One of the standout features of UVES is its integration of volume analysis, which calculates and displays the volume attributed to buying and selling activities. This analysis includes:
- **Buy Volume:** The portion of the total volume associated with price increases (close higher than open).
- **Sell Volume:** The portion of the total volume associated with price decreases (close lower than open).
- **Buy/Sell Ratio:** A ratio of buy volume to sell volume, providing a quick snapshot of market sentiment.
- These metrics are presented in a real-time table positioned in the top-right corner of the chart, with customizable colors and formatting. The table updates with each new bar, offering continuous feedback on the strength and direction of the market trend based on volume data.
6. **Customizable Settings and User Control:**
- **EMA Length and Source:** Traders can specify the lookback period for the EMA, adjusting its sensitivity to price changes. The source for EMA calculations can also be customized, with options such as close, open, high, low, or other custom price series.
- **Signal Line Customization:** The signal line’s length, type, and width can be adjusted to suit different trading strategies, allowing traders to optimize the balance between trend detection and noise reduction.
- **Offset Adjustment:** The offset feature allows users to shift the EMA and signal line forward or backward on the chart. This can help align the indicator with specific price action or adjust for latency in decision-making processes.
- **Volume Table Positioning and Formatting:** The position, size, and color scheme of the volume table are fully customizable, enabling traders to integrate the table seamlessly into their chart setup without cluttering the visual workspace.
7. **Versatility Across Markets and Trading Styles:**
- UVES is designed to be effective across a wide range of financial markets, including Forex, stocks, cryptocurrencies, commodities, and indices. Its adaptability to different markets is supported by its comprehensive customization options and the inclusion of volume analysis, which is particularly valuable in markets where volume plays a crucial role in price movement.
#### **How Different Traders Can Benefit from UVES**
1. **Trend Followers:**
- Trend-following traders will find UVES particularly beneficial for identifying and riding trends. The dynamic EMA and signal line provide clear visual cues for trend direction, while the volume analysis helps confirm the strength of these trends. This combination allows trend followers to stay in profitable trades longer and exit when the trend shows signs of weakening.
2. **Volume-Based Traders:**
- Traders who focus on volume as a key indicator of market strength can leverage the UVES volume table to gain insights into the buying and selling pressure behind price movements. By monitoring the buy/sell ratio, these traders can identify periods of strong conviction (high buy volume) or potential reversals (high sell volume) with greater accuracy.
3. **Scalpers and Day Traders:**
- For traders operating on shorter time frames, UVES provides quick and reliable signals that are essential for making rapid trading decisions. The ability to customize the EMA length and type allows scalpers to fine-tune the indicator for responsiveness, while the volume analysis offers an additional layer of confirmation to avoid false signals.
4. **Swing Traders:**
- Swing traders, who typically hold positions for several days to weeks, can use UVES to identify medium-term trends and potential entry and exit points. The indicator’s ability to filter out market noise through the signal line and volume analysis makes it ideal for capturing significant price movements without being misled by short-term volatility.
5. **Position Traders and Long-Term Investors:**
- Even long-term investors can benefit from UVES by using it to identify major trend reversals or confirm the strength of long-term trends. The flexibility to adjust the EMA and signal line to longer periods ensures that the indicator remains relevant for detecting shifts in market sentiment over extended time frames.
#### **Optimal Settings for Different Markets**
- **Forex Markets:**
- **EMA Length:** 9 to 14 periods.
- **Signal Line:** Use VWMA or WMA for the signal line to incorporate volume data, which is crucial in the highly liquid Forex markets.
- **Best Use:** Short-term trend following, with an emphasis on identifying rapid changes in market sentiment.
- **Stock Markets:**
- **EMA Length:** 20 to 50 periods.
- **Signal Line:** SMA or EMA with a slightly longer length (e.g., 50 periods) to capture broader market trends.
- **Best Use:** Medium to long-term trend identification, with volume analysis confirming the strength of institutional buying or selling.
- **Cryptocurrency Markets:**
- **EMA Length:** 9 to 12 periods, due to the high volatility in crypto markets.
- **Signal Line:** SMMA or EMA for smoothing out extreme price fluctuations.
- **Best Use:** Identifying entry and exit points in volatile markets, with the volume table providing insights into market manipulation or sudden shifts in trader sentiment.
- **Commodity Markets:**
- **EMA Length:** 14 to 21 periods.
- **Signal Line:** WMA or VWMA, considering the impact of trading volume on commodity prices.
- **Best Use:** Capturing medium-term price movements and confirming trend strength with volume data.
#### **Customization for Advanced Users**
- **Advanced Offset Usage:** Traders can experiment with different offset values to see how shifting the EMA and signal line impacts the timing of buy/sell signals. This can be particularly useful in markets with known latency or for strategies that require a delayed confirmation of trend changes.
- **Volume Table Integration:** The position, size, and colors of the volume table can be adjusted to fit seamlessly into any trading setup. For example, a trader might choose to position the table in the bottom-right corner and use a smaller size to keep the focus on price action while still having access to volume data.
- **Signal Filtering:** By combining the signal line with the primary EMA, traders can filter out false signals during periods of low volatility or when the market is range-bound. Adjusting the length of the signal line allows for greater control over the sensitivity of the trend detection.
#### **Conclusion**
The **Uptrick: Volume-Weighted EMA Signal (UVES)** is a powerful and adaptable indicator designed for traders who demand more from their technical analysis tools. By integrating dynamic EMA trend signals with real-time volume analysis, UVES offers a comprehensive view of market conditions, making it an invaluable resource for identifying trends, confirming signals, and understanding market sentiment. Whether you are a day trader, swing trader, or long-term investor, UVES provides the versatility, precision, and customization needed to make more informed and profitable trading decisions. With its ability to adapt to various markets and trading styles, UVES is not just an indicator but a complete trend analysis solution.
