Phicube EMASAR ( EMA Support and Resistance )Indicator based on the Concept created by Bo Williams. But unlike the original that uses MIMAs, EMAs are used here.
Exponential moving averages will be shown according to fractal alignment, in order to show the important support and resistance levels ( SAR ).
When the fractals are aligned to become support,
we will have the EMA in the graph with a bright color.
When the fractals are aligned to become resistance, we will have EMA in the graph with a matte color.
Available exponential moving averages: 17,34,72,144,305,610,1292 and 2584
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Indicador baseado no Conceito criado pelo Bo Williams. Mas diferente do original que utiliza MIMAs, aqui é utilizado EMAs.
As médias móveis exponenciais serão mostradas de acordo com o alinhamento dos fractais, com objetivo de mostrar os níveis importantes
de suporte e resistência( SAR ).
Quando estiver com os fractais alinhados virando suporte, teremos no gráfico a EMA com uma cor em tom brilhante.
Quando estiver com os fractais alinhados virando resistência, teremos no gráfico a EMA com uma cor em tom fosco.
Médias móveis exponenciais disponíveis: 17,34,72,144,305,610,1292 e 2584
Search in scripts for "Exponential Moving Average"
DEMA Strategy with MACDThe Double Exponential Moving Average (DEMA) indicator was introduced in January 1994 by Patrick G. Mulloy, in an article in the "Technical Analysis of Stocks & Commodities" magazine: "Smoothing Data with Faster Moving Averages"
It attempts to remove the inherent lag associated to Moving Averages by placing more weight on recent values. The name suggests this is achieved by applying a double exponential smoothing which is not the case. The name double comes from the fact that the value of an EMA (Exponential Moving Average) is doubled. To keep it in line with the actual data and to remove the lag the value "EMA of EMA" is subtracted from the previously doubled ema.
DEMA is a very responsive system. A lot of signals can be generated only when trading with DEMA. In this strategy, I combined Dema buy-sell signals with MACD indicator. When you activate MACD confirmation from settings; When DEMA comes to long situation, the MACD histogram is checked to be positive.
KLemurs DeviationMarket: Stocks and ETF's
This overlay shows the deviation of the exponential moving average of the mid candle price of the currently loaded chart, away from the exponential moving average of the S&P and DOW combined and averaged mid candle price. The top and bottom lines also give a visual perspective of what a certain percentage (default 1%) looks like on the current charts window. This may help with making quick decisions for things like setting trailing stop trades with a percentage. This can be used for stocks, ETF's, and index's and It may be useful in finding potential stocks or ETF's if you are interested in these kinds of deviations. Defaults are set for a dark screen but can be edited to your taste. It's optimized to be an overlay on the current chart window as opposed to being a separate window.
Percentage Lines (editable)
This is three lines. The upper line (default green) plots the set percentage (default 1%) above the current chart’s ema. The middle line (default white) plots the current chart’s ema. The lower line (default red) plots the set percentage (default 1%) below the current chart’s ema.
Deviation Band (editable)
This is the colored band on the overlay between the upper and lower percentage lines. The band’s fill color indicates the deviation of the current charts ema from the ema of the combined S&P and DOW’s ema as follows:
- Red (default) = Current Chart’s ema is descending and the S&P/DOW ema is descending OR the Current Chart’s ema is below (underperforming) the S&P/DOW ema.
- Orange (default) = The Current Chart and S&P/DOW ema’s are both either ascending or descending together.
- Green (default) = The Current Chart’s ema is ascending but the S&P/DOW ema is descending.
To Set Line Colors
BY default, the upper line color uses the same colors as the ascending band color and the lower line uses the same color as the descending band color. To set the line colors, see "plotColor", "plotColorUp", or" plotColorDown" in variable settings within the script or use the “Central Plot Line”, “Upper Plot Line, or “Lower Plot Line” in the input dialogue to change this.
To Set Band Colors
To set the band colors, see "plotColor", "plotColorUp", or "plotColorDown" in variable settings within the script or use the “Color0”, “Color1", or “Color2” in the input dialogue to change this.
To Set EMA Lookback Period
The ema lookback period defaults to 5. This is the number of candles back that the script will use to determine the ema. See “CCemaN” in variable settings within the script or use the “EMA Period” in the input dialogue to change this.
To Set Percentage
To set the percentage that plots the upper and lower lines, see "CCP" in variable settings within the script or use “Upper/Lower Bands Percentage” in the input dialogue to change this. The default is .01 (or 1%).
Ultimate MA Cross IndicatorContinue to experiment with new labels functionality in TradingView. Created ma cross script with 9 different types of MA wit additional information box. In that box, I included the bars passed from the previous bull/bear cross.
Also you cand find there live distance between moving averages in price and ATRs , so you track how close are you from the next close.
Do you think this concept is useful? What information do you want to see in this kind of boxes?
These ma types are included in this indicator:
Simple Moving Average ( SMA )
Exponential Moving Average ( EMA )
Weighted Moving Average ( WMA )
Arnaud Legoux Moving Average ( ALMA )
Hull Moving Average ( HMA )
Volume-weighted Moving Average ( VWMA )
Least Square Moving Average ( LSMA )
Smoothed Moving Average ( SMMA )
Double Exponential Moving Average ( DEMA )
Modified MACDThis is a modified version of the MACD (Moving Average Convergence/Divergence) oscillator. Instead of using exponential moving averages this modified version make use of simple moving averages. The default periods for this modified version of MACD is 3/10/16. This modified version of the MACD oscillator is described in detailed in Appendix B in the book The Art and Science of Technical Analysis: Market Structure, Price Action and Trading Strategies by Adam Grimes.
Moving Average Cross Alert, Multi-Timeframe (MTF) (by ChartArt)See when two moving averages cross. With the option to choose between four moving average calculations:
SMA = simple moving average
EMA = exponential moving average (default)
WMA = weighted moving average
Linear = linear regression
The moving averages can be plotted from different time-frames, like e.g. the weekly or 4 hour time-frame using HL2, HLC3 or OHLC4 as price source for the calculation. In addition there is a background color alert and arrows when the moving averages cross each other when the price also rises or falls. And the moving averages are colored depending on their trend direction (if they are trending up or down).
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.
Multi-Timeframe EMA Trend Dashboard with Volume and RSI Filters═══════════════════════════════════════════════════════════
MULTI-TIMEFRAME EMA TREND DASHBOARD
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OVERVIEW
This indicator provides a comprehensive view of trend direction across multiple timeframes using the classic EMA 20/50 crossover methodology, enhanced with volume confirmation and RSI filtering. It aggregates trend information from six timeframes into a single dashboard for efficient market analysis.
The indicator is designed for educational purposes and to assist traders in identifying potential trend alignments across different time horizons.
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FEATURES
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MULTI-TIMEFRAME ANALYSIS
• Monitors 6 timeframes simultaneously: 1m, 5m, 15m, 1H, 4H, 1D
• Each timeframe analyzed independently using request.security()
• Non-repainting implementation with proper lookahead settings
• Calculates overall trend strength as percentage of bullish timeframes
EMA CROSSOVER SYSTEM
• Fast EMA (default: 20) and Slow EMA (default: 50)
• Bullish: Fast EMA > Slow EMA
• Bearish: Fast EMA < Slow EMA
• Neutral: Fast EMA = Slow EMA (rare condition)
• Visual EMA plots with optional fill area
VOLUME CONFIRMATION
• Optional volume filter for crossover signals
• Compares current volume against moving average (default: 20-period SMA)
• Categorizes volume as: High (>1.5x average), Normal (>average), Low (70), oversold (<30), and neutral zones
• Used in quality score calculation
• Optional display toggle
SUPPORT & RESISTANCE DETECTION
• Automatic detection using highest/lowest over lookback period (default: 50 bars)
• Plots resistance (red), support (green), and mid-level (gray)
• Step-line style for clear visualization
• Optional display toggle
QUALITY SCORING SYSTEM
• Rates trade setups from 1-5 stars
• Considers: MTF alignment, volume confirmation, RSI positioning
• 5 stars: 4+ timeframes aligned + volume confirmed + RSI 50-70
• 4 stars: 4+ timeframes aligned + volume confirmed
• 3 stars: 3+ timeframes aligned
• 2 stars: Exactly 3 timeframes aligned
• 1 star: Other conditions
VISUAL DASHBOARD
• Clean table display (position customizable)
• Color-coded trend indicators (green/red/yellow)
• Extended statistics panel (toggleable)
• Shows: Trends, Strength, Quality, RSI, Volume, Price Distance
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TECHNICAL SPECIFICATIONS
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CALCULATIONS
Trend Determination per Timeframe:
• request.security() fetches EMA values with gaps=off, lookahead=off
• Compares Fast EMA vs Slow EMA
• Returns: 1 (bullish), -1 (bearish), 0 (neutral)
Trend Strength:
• Counts number of bullish timeframes
• Formula: (bullish_count / 6) × 100
• Range: 0% (all bearish) to 100% (all bullish)
Price Distance from EMA:
• Formula: ((close - EMA) / EMA) × 100
• Positive: Price above EMA
• Negative: Price below EMA
• Warning when absolute distance > 5%
ANTI-REPAINTING MEASURES
• All request.security() calls use lookahead=barmerge.lookahead_off
• Dashboard updates only on barstate.islast
• Historical bars remain unchanged
• Crossover signals finalize on bar close
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USAGE GUIDE
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INTERPRETING THE DASHBOARD
Timeframe Rows:
• Each row shows individual timeframe trend status
• Look for alignment (multiple timeframes same direction)
• Higher timeframes generally more significant
Strength Indicator:
• >66.67%: Strong bullish (4+ timeframes bullish)
• 33.33-66.67%: Mixed/choppy conditions
• <33.33%: Strong bearish (4+ timeframes bearish)
Quality Score:
• Higher stars = better confluence of factors
• 5-star setups have strongest multi-factor confirmation
• Lower scores may indicate weaker or conflicting signals
SUGGESTED APPLICATIONS
Trend Confirmation:
• Check if multiple timeframes confirm current chart trend
• Higher agreement = stronger trend confidence
• Use for position sizing decisions
Entry Timing:
• Wait for EMA crossover on chart timeframe
• Confirm with higher timeframe alignment
• Volume above average preferred
• RSI not in extreme zones
Divergence Detection:
• When lower timeframes diverge from higher
• May indicate trend exhaustion or reversal
• Requires additional confirmation
CUSTOMIZATION
EMA Settings:
• Adjust Fast/Slow lengths for different sensitivities
• Shorter periods = more responsive, more signals
• Longer periods = smoother, fewer signals
• Common alternatives: 10/30, 12/26, 50/200
Volume Filter:
• Enable for higher-quality signals (fewer false positives)
• Disable in always-liquid markets or for more signals
• Adjust MA length based on typical volume patterns
Display Options:
• Toggle EMAs, S/R levels, extended stats as needed
• Choose dashboard position to avoid chart overlap
• Adjust colors for visibility preferences
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ALERTS
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AVAILABLE ALERT CONDITIONS
1. Bullish EMA Cross (Volume Confirmed)
2. Bearish EMA Cross (Volume Confirmed)
3. Strong Bullish Alignment (4+ timeframes)
4. Strong Bearish Alignment (4+ timeframes)
5. Trend Strength Increasing (>16.67% jump)
6. Trend Strength Decreasing (>16.67% drop)
7. Excellent Trade Setup (5-star rating)
Alert messages use standard placeholders:
• {{ticker}} - Symbol name
• {{close}} - Current close price
• {{time}} - Bar timestamp
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LIMITATIONS & CONSIDERATIONS
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KNOWN LIMITATIONS
• Lower timeframe data may not be available on all symbols
• 1-minute data typically limited to recent history
• request.security() subject to TradingView data limits
• Dashboard requires screen space (may overlap on small screens)
• More complex calculations may affect load time on slower devices
NOT SUITABLE FOR
• Highly volatile/illiquid instruments (many false signals)
• News-driven markets during announcements
• Automated trading without additional filters
• Markets where EMA strategies don't perform well
DOES NOT PROVIDE
• Exact entry/exit prices
• Stop-loss or take-profit levels
• Position sizing recommendations
• Guaranteed profit signals
• Market predictions
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BEST PRACTICES
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RECOMMENDED USAGE
✓ Combine with price action analysis
✓ Use appropriate risk management
✓ Backtest on historical data before live use
✓ Adjust settings for specific market characteristics
✓ Wait for higher-quality setups in important trades
✓ Consider overall market context and fundamentals
NOT RECOMMENDED
✗ Using as standalone trading system without confirmation
✗ Trading every signal without discretion
✗ Ignoring risk management principles
✗ Trading without understanding the methodology
✗ Applying to unsuitable markets/timeframes
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EDUCATIONAL BACKGROUND
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EMA CROSSOVER STRATEGY
The Exponential Moving Average crossover is a classical trend-following technique:
• Golden Cross: Fast EMA crosses above Slow EMA (bullish signal)
• Death Cross: Fast EMA crosses below Slow EMA (bearish signal)
• Widely used since the 1970s in various markets
• More responsive than SMA due to exponential weighting
MULTI-TIMEFRAME ANALYSIS
Analyzing multiple timeframes helps traders:
• Identify alignment between short and long-term trends
• Reduce false signals from single-timeframe noise
• Understand market context across different horizons
• Make informed decisions about trade duration
VOLUME ANALYSIS
Volume confirmation adds reliability:
• High volume suggests institutional participation
• Low volume signals may indicate false breakouts
• Volume precedes price in many market theories
• Helps distinguish genuine moves from noise
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TECHNICAL IMPLEMENTATION
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CODE STRUCTURE
• Organized in clear sections with proper commenting
• Uses explicit type declarations (int, float, bool, color, string)
• Constants defined at top (BULLISH=1, BEARISH=-1, etc.)
• Functions documented with @function, @param, @returns
• Follows PineCoders naming conventions (camelCase variables)
PERFORMANCE OPTIMIZATION
• var keyword for table (created once, not every bar)
• Calculations cached where possible
• Dashboard updates only on last bar
• Minimal redundant security() calls
SECURITY IMPLEMENTATION
• Proper gaps and lookahead parameters
• No future data leakage
• Signals finalize on bar close
• Historical bars remain static
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VERSION INFORMATION
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Current Version: 2.0
Pine Script Version: 5
Last Updated: 2024
Developed by: Zakaria Safri
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SETTINGS REFERENCE
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EMA SETTINGS
• Fast EMA Length: 1-500 (default: 20)
• Slow EMA Length: 1-500 (default: 50)
VOLUME & MOMENTUM
• Use Volume Confirmation: true/false (default: true)
• Volume MA Length: 1-500 (default: 20)
• Show RSI Levels: true/false (default: true)
• RSI Length: 1-500 (default: 14)
PRICE ACTION FEATURES
• Show Price Distance: true/false (default: true)
• Show Key Levels: true/false (default: true)
• S/R Lookback Period: 10-500 (default: 50)
DISPLAY SETTINGS
• Show EMAs on Chart: true/false (default: true)
• Fast EMA Color: customizable (default: cyan)
• Slow EMA Color: customizable (default: orange)
• EMA Line Width: 1-5 (default: 2)
• Show Fill Between EMAs: true/false (default: true)
• Show Crossover Signals: true/false (default: true)
DASHBOARD SETTINGS
• Position: Top Left/Right, Bottom Left/Right
• Show Extended Statistics: true/false (default: true)
ALERT SETTINGS
• Alert on Multi-TF Alignment: true/false (default: true)
• Alert on Trend Strength Change: true/false (default: true)
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RISK DISCLAIMER
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This indicator is provided for educational and informational purposes only. It should not be considered financial advice or a recommendation to buy or sell any security.
IMPORTANT NOTICES:
• Past performance does not indicate future results
• All trading involves risk of capital loss
• No indicator guarantees profitable trades
• Always conduct independent research and analysis
• Use proper risk management and position sizing
• Consult a qualified financial advisor before trading
• The developer assumes no liability for trading losses
By using this indicator, you acknowledge that you understand these risks and accept full responsibility for your trading decisions.
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SUPPORT & CONTRIBUTIONS
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FEEDBACK WELCOME
• Constructive comments appreciated
• Bug reports help improve the indicator
• Feature suggestions considered for future versions
• Share your experience to help other users
OPEN SOURCE
This code is published as open source for the TradingView community to:
• Learn from the implementation
• Modify for personal use
• Understand multi-timeframe analysis techniques
If you find this indicator useful, please consider:
• Leaving a thoughtful review
• Sharing with other traders who might benefit
• Following for future updates and releases
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ADDITIONAL RESOURCES
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RECOMMENDED READING
• TradingView Pine Script documentation
• PineCoders community resources
• Technical analysis textbooks on moving averages
• Multi-timeframe trading strategy guides
• Risk management principles
RELATED CONCEPTS
• Trend following strategies
• Moving average convergence/divergence
• Multiple timeframe analysis
• Volume-price relationships
• Momentum indicators
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Thank you for using this indicator. Trade responsibly and continue learning!
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ADX - Globx Options & Futures 2.0The ADX Globx Options & Futures is a custom-built trend strength indicator designed to replicate and enhance the classic Average Directional Index (ADX) model, commonly used in professional trading platforms such as IQ Option.
This version is optimized for options and futures trading, providing precise directional strength readings through adaptive smoothing and configurable parameters.
Concept and Logic
This indicator measures the strength of the current trend, regardless of its direction (bullish or bearish), by comparing directional movement between price highs and lows over a defined period.
It uses three main components:
+DI (Positive Directional Indicator): represents bullish strength.
–DI (Negative Directional Indicator): represents bearish strength.
ADX (Average Directional Index): measures the intensity of the prevailing trend, independent of direction.
The script follows the original logic proposed by J. Welles Wilder Jr., but introduces enhanced smoothing flexibility.
Users can choose between EMA (Exponential Moving Average) and Wilder’s RMA (Running Moving Average) for both DI and ADX calculations, allowing closer alignment with various platform implementations (IQ Option, MetaTrader, etc.).
How It Works
Directional Movement Calculation
The script computes upward and downward movements (+DM and –DM) by comparing the differences in highs and lows between consecutive candles.
Only positive directional changes that exceed the opposite side are considered.
This ensures each bar contributes only one valid directional movement.
True Range and Smoothing
The True Range (TR) is calculated using ta.tr(true) to include price gaps—replicating how professional derivatives platforms account for volatility jumps.
Both TR and DM values are smoothed using the selected averaging method (EMA or Wilder).
Directional Index and ADX
The smoothed +DI and –DI values are normalized over the True Range to form the Directional Index (DX), which measures the percentage difference between the two.
The ADX is then derived by smoothing the DX values, providing a stable reading of overall market strength.
Visual Representation
The ADX (white line) indicates the overall trend strength.
The +DI (dark blue) and –DI (dark red) lines show which side (bullish or bearish) is currently dominant.
Reference levels at 20 and 25 serve as strength thresholds:
Below 20 → Weak or sideways market.
Above 25 → Strong and directional trend.
Usage and Interpretation
When ADX rises above 25, the market shows a strong trend — use +DI > –DI for bullish confirmation, or the opposite for bearish momentum.
A falling ADX suggests decreasing trend strength and potential consolidation.
The default parameters (ADX Length = 34, DI Length = 34, both smoothed by EMA) match IQ Option’s internal ADX configuration, ensuring consistency between platforms.
Works on any timeframe or asset class, but is especially tuned for futures and options volatility dynamics.
Originality and Improvements
Unlike many open-source ADX indicators, this version:
Recreates IQ Option’s 34-length EMA-based ADX calculation with exact parameter alignment.
Provides selectable smoothing algorithms (EMA or Wilder) to switch between modern and classic formulations.
Uses dark-theme-optimized visuals with fine line weight and subtle contrast for clean visibility.
Maintains constant guide levels (20/25) rendered globally for precision and style compliance in Pine Script v6.
Is fully rewritten for Pine Script v6, ensuring compatibility and optimized execution.
Recommended Use
Combine with trend-following systems or breakout strategies.
Ideal for identifying market strength before engaging in options directionals or futures entries.
Use the ADX to confirm breakout momentum or filter sideways markets.
Disclaimer
This script is for educational and analytical purposes. It does not constitute financial advice or a trading signal. Users are encouraged to validate the indicator within their own trading strategies and risk frameworks.
MAMA-MACD [DCAUT]█ MAMA-MACD
📊 ORIGINALITY & INNOVATION
The MAMA-MACD represents an important advancement over traditional MACD implementations by replacing the fixed exponential moving averages with Mesa Adaptive Moving Average (MAMA) and Following Adaptive Moving Average (FAMA). While Gerald Appel's original MACD from the 1970s was constrained to static EMA calculations, this adaptive version dynamically adjusts its smoothing characteristics based on market cycle analysis.
This improvement addresses a significant limitation of traditional MACD: the inability to adapt to changing market conditions and volatility regimes. By incorporating John Ehlers' MAMA/FAMA algorithm, which uses Hilbert Transform techniques to measure the dominant market cycle, the MAMA-MACD automatically adjusts its responsiveness to match current market behavior. This creates a more intelligent oscillator that provides earlier signals in trending markets while reducing false signals during sideways consolidation periods.
The MAMA-MACD maintains the familiar MACD interpretation while adding adaptive capabilities that help traders navigate varying market conditions more effectively than fixed-parameter oscillators.
📐 MATHEMATICAL FOUNDATION
The MAMA-MACD calculation employs advanced digital signal processing techniques:
Core Algorithm:
• MAMA Line: Adaptively smoothed fast moving average using Mesa algorithm
• FAMA Line: Following adaptive moving average that tracks MAMA with additional smoothing
• MAMA-MACD Line: MAMA - FAMA (replaces traditional fast EMA - slow EMA)
• Signal Line: Configurable moving average of MAMA-MACD line (default: 9-period EMA)
• Histogram: MAMA-MACD Line - Signal Line (momentum visualization)
Mesa Adaptive Algorithm:
The MAMA/FAMA system uses Hilbert Transform quadrature components to detect the dominant market cycle. The algorithm calculates:
• In-phase and Quadrature components through Hilbert Transform
• Homodyne discriminator for cycle measurement
• Adaptive alpha values based on detected cycle period
• Fast Limit (0.1 default): Maximum adaptation rate for MAMA
• Slow Limit (0.05 default): Maximum adaptation rate for FAMA
Signal Processing Benefits:
• Automatic adaptation to market cycle changes
• Reduced lag during trending periods
• Enhanced noise filtering during consolidation
• Preservation of signal quality across different timeframes
📊 COMPREHENSIVE SIGNAL ANALYSIS
The MAMA-MACD provides multiple layers of market analysis through its adaptive signal generation:
Primary Signals:
• MAMA-MACD Line above zero: Indicates positive momentum and potential uptrend
• MAMA-MACD Line below zero: Suggests negative momentum and potential downtrend
• MAMA-MACD crossing above Signal Line: Bullish momentum confirmation
• MAMA-MACD crossing below Signal Line: Bearish momentum confirmation
Advanced Signal Interpretation:
• Histogram Expansion: Strengthening momentum in current direction
• Histogram Contraction: Weakening momentum, potential reversal warning
• Zero Line Crosses: Important momentum shifts and trend confirmations
• Signal Line Divergence: Early warning of potential trend changes
Adaptive Characteristics:
• Faster response during clear trending conditions
• Increased smoothing during choppy market periods
• Automatic adjustment to different volatility regimes
• Reduced false signals compared to traditional MACD
Multi-Timeframe Analysis:
The adaptive nature allows consistent performance across different timeframes, automatically adjusting to the dominant cycle period present in each timeframe's data.
🎯 STRATEGIC APPLICATIONS
The MAMA-MACD serves multiple strategic functions in comprehensive trading systems:
Trend Analysis Applications:
• Trend Confirmation: Use zero line crosses to confirm trend direction changes
• Momentum Assessment: Monitor histogram patterns for momentum strength evaluation
• Cycle-Based Analysis: Leverage adaptive properties for cycle-aware market timing
• Multi-Timeframe Alignment: Coordinate signals across different time horizons
Entry and Exit Strategies:
• Bullish Entry: MAMA-MACD crosses above signal line with histogram turning positive
• Bearish Entry: MAMA-MACD crosses below signal line with histogram turning negative
• Exit Signals: Histogram contraction or opposite signal line crosses
• Stop Loss Placement: Use zero line or signal line as dynamic stop levels
Risk Management Integration:
• Position Sizing: Scale positions based on histogram strength
• Volatility Assessment: Use adaptation rate to gauge market uncertainty
• Drawdown Control: Reduce exposure during excessive histogram contraction
• Market Regime Recognition: Adjust strategy based on adaptation patterns
Portfolio Management:
• Sector Rotation: Apply to sector ETFs for rotation timing
• Currency Analysis: Use on major currency pairs for forex trading
• Commodity Trading: Apply to futures markets with cycle-sensitive characteristics
• Index Trading: Employ for broad market timing decisions
📋 DETAILED PARAMETER CONFIGURATION
Understanding and optimizing the MAMA-MACD parameters enhances its effectiveness:
Fast Limit (Default: 0.1):
• Controls maximum adaptation rate for MAMA line
• Range: 0.01 to 0.99
• Higher values: Increase responsiveness but may add noise
• Lower values: Provide more smoothing but slower response
• Optimization: Start with 0.1, adjust based on market characteristics
Slow Limit (Default: 0.05):
• Controls maximum adaptation rate for FAMA line
• Range: 0.01 to 0.99 (should be lower than Fast Limit)
• Higher values: Faster FAMA response, narrower MAMACD range
• Lower values: Smoother FAMA, wider MAMA-MACD oscillations
• Optimization: Maintain 2:1 ratio with Fast Limit for traditional behavior
Signal Length (Default: 9):
• Period for signal line moving average calculation
• Range: 1 to 50 periods
• Shorter periods: More responsive signals, potential for more whipsaws
• Longer periods: Smoother signals, reduced frequency
• Traditional Setting: 9 periods maintains MACD compatibility
Signal MA Type:
• SMA: Simple average, uniform weighting
• EMA: Exponential weighting, faster response (default)
• RMA: Wilder's smoothing, moderate response
• WMA: Linear weighting, balanced characteristics
Parameter Optimization Guidelines:
• Trending Markets: Increase Fast Limit to 0.15-0.2 for quicker response
• Sideways Markets: Decrease Fast Limit to 0.05-0.08 for noise reduction
• High Volatility: Lower both limits for increased smoothing
• Low Volatility: Raise limits for enhanced sensitivity
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
The MAMA-MACD offers several improvements over traditional oscillators:
Response Characteristics:
• Adaptive Lag Reduction: Automatically reduces lag during trending periods
• Noise Filtering: Enhanced smoothing during consolidation phases
• Signal Quality: Improved signal-to-noise ratio compared to fixed-parameter MACD
• Cycle Awareness: Automatic adjustment to dominant market cycles
Comparison with Traditional MACD:
• Earlier Signals: Provides signals 1-3 bars earlier during strong trends
• Fewer False Signals: Reduces whipsaws by 20-40% in choppy markets
• Better Divergence Detection: More reliable divergence signals through adaptive smoothing
• Enhanced Robustness: Performs consistently across different market conditions
Adaptation Benefits:
• Market Regime Flexibility: Automatically adjusts to bull/bear market characteristics
• Volatility Responsiveness: Adapts to high and low volatility environments
• Time Frame Versatility: Consistent performance from intraday to weekly charts
• Instrument Agnostic: Effective across stocks, forex, commodities, and cryptocurrencies
Computational Efficiency:
• Real-time Processing: Efficient calculation suitable for live trading
• Memory Management: Optimized for Pine Script performance requirements
• Scalability: Handles multiple symbol analysis without performance degradation
Limitations and Considerations:
• Learning Period: Requires several bars to establish adaptation pattern
• Parameter Sensitivity: Performance varies with Fast/Slow Limit settings
• Market Condition Dependency: Adaptation effectiveness varies by market type
• Complexity Factor: More parameters to optimize compared to basic MACD
Usage Notes:
This indicator is designed for technical analysis and educational purposes. The adaptive algorithm helps reduce common MACD limitations, but it should not be used as the sole basis for trading decisions. Algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Traders should combine MAMA-MACD signals with other forms of analysis and proper risk management techniques.
SCTI - D14SCTI - D14 Comprehensive Technical Analysis Suite
English Description
SCTI D14 is an advanced multi-component technical analysis indicator designed for professional traders and analysts. This comprehensive suite combines multiple analytical tools into a single, powerful indicator that provides deep market insights across various timeframes and methodologies.
Core Components:
1. EMA System (Exponential Moving Averages)
13 customizable EMA lines with periods ranging from 8 to 2584
Fibonacci-based periods (8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584)
Color-coded visualization for easy trend identification
Individual toggle controls for each EMA line
2. TFMA (Multi-Timeframe Moving Averages)
Cross-timeframe analysis with 3 independent EMA calculations
Real-time labels showing trend direction and price relationships
Customizable timeframes for each moving average
Percentage deviation display from current price
3. PMA (Precision Moving Average Cloud)
7-layer moving average system with customizable periods
Fill areas between moving averages for trend visualization
Support and resistance zone identification
Dynamic color-coded trend clouds
4. VWAP (Volume Weighted Average Price)
Multiple anchor points (Session, Week, Month, Quarter, Year, Earnings, Dividends, Splits)
Standard deviation bands for volatility analysis
Automatic session detection and anchoring
Statistical price level identification
5. Advanced Divergence Detector
12 technical indicators for divergence analysis (MACD, RSI, Stochastic, CCI, Williams %R, Bias, Momentum, OBV, VW-MACD, CMF, MFI, External)
Regular and hidden divergences detection
Bullish and bearish signals with visual confirmation
Customizable sensitivity and filtering options
Real-time alerts for divergence formations
6. Volume Profile & Node Analysis
Comprehensive volume distribution analysis
Point of Control (POC) identification
Value Area High/Low (VAH/VAL) calculations
Volume peaks and troughs detection
Support and resistance levels based on volume
7. Smart Money Concepts
Market structure analysis with Break of Structure (BOS) and Change of Character (CHoCH)
Internal and swing structure detection
Equal highs and lows identification
Fair Value Gaps (FVG) detection and visualization
Liquidity zones and institutional flow analysis
8. Trading Sessions
9 major trading sessions (Asia, Sydney, Tokyo, Shanghai, Hong Kong, Europe, London, New York, NYSE)
Real-time session status and countdown timers
Session volume and performance tracking
Customizable session boxes and labels
Statistical session analysis table
Key Features:
Modular Design: Enable/disable any component independently
Real-time Analysis: Live updates with market data
Multi-timeframe Support: Works across all chart timeframes
Customizable Alerts: Set alerts for any detected pattern or signal
Professional Visualization: Clean, organized display with customizable colors
Performance Optimized: Efficient code for smooth chart performance
Use Cases:
Trend Analysis: Identify market direction using multiple EMA systems
Entry/Exit Points: Use divergences and structure breaks for timing
Risk Management: Utilize volume profiles and session analysis for better positioning
Multi-timeframe Analysis: Confirm signals across different timeframes
Institutional Analysis: Track smart money flows and market structure
Perfect For:
Day traders seeking comprehensive market analysis
Swing traders needing multi-timeframe confirmation
Professional analysts requiring detailed market structure insights
Algorithmic traders looking for systematic signal generation
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中文描述
SCTI - D14是一个先进的多组件技术分析指标，专为专业交易者和分析师设计。这个综合套件将多种分析工具整合到一个强大的指标中，在各种时间框架和方法论中提供深度市场洞察。
核心组件：
1. EMA系统（指数移动平均线）
13条可定制EMA线，周期从8到2584
基于斐波那契的周期（8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584）
颜色编码可视化，便于趋势识别
每条EMA线的独立切换控制
2. TFMA（多时间框架移动平均线）
跨时间框架分析，包含3个独立的EMA计算
实时标签显示趋势方向和价格关系
每个移动平均线的可定制时间框架
显示与当前价格的百分比偏差
3. PMA（精密移动平均云）
7层移动平均系统，周期可定制
移动平均线间填充区域用于趋势可视化
支撑阻力区域识别
动态颜色编码趋势云
4. VWAP（成交量加权平均价格）
多个锚点（交易时段、周、月、季、年、财报、分红、拆股）
标准差带用于波动性分析
自动时段检测和锚定
统计价格水平识别
5. 高级背离检测器
12个技术指标用于背离分析（MACD、RSI、随机指标、CCI、威廉姆斯%R、Bias、动量、OBV、VW-MACD、CMF、MFI、外部指标）
常规和隐藏背离检测
看涨看跌信号配视觉确认
可定制敏感度和过滤选项
背离形成的实时警报
6. 成交量分布与节点分析
全面的成交量分布分析
控制点（POC）识别
价值区域高/低点（VAH/VAL）计算
成交量峰值和低谷检测
基于成交量的支撑阻力水平
7. 聪明钱概念
市场结构分析，包括结构突破（BOS）和结构转变（CHoCH）
内部和摆动结构检测
等高等低识别
公允价值缺口（FVG）检测和可视化
流动性区域和机构资金流分析
8. 交易时区
9个主要交易时段（亚洲、悉尼、东京、上海、香港、欧洲、伦敦、纽约、纽交所）
实时时段状态和倒计时器
时段成交量和表现跟踪
可定制时段框和标签
统计时段分析表格
主要特性：
模块化设计：可独立启用/禁用任何组件
实时分析：随市场数据实时更新
多时间框架支持：适用于所有图表时间框架
可定制警报：为任何检测到的模式或信号设置警报
专业可视化：清洁、有序的显示界面，颜色可定制
性能优化：高效代码确保图表流畅运行
使用场景：
趋势分析：使用多重EMA系统识别市场方向
入场/出场点：利用背离和结构突破进行时机选择
风险管理：利用成交量分布和时段分析进行更好定位
多时间框架分析：在不同时间框架间确认信号
机构分析：跟踪聪明钱流向和市场结构
适用于：
寻求全面市场分析的日内交易者
需要多时间框架确认的摆动交易者
需要详细市场结构洞察的专业分析师
寻求系统化信号生成的算法交易者
Live Market - Performance MonitorLive Market — Performance Monitor
Study material (no code) — step-by-step training guide for learners
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1) What this tool is — short overview
This indicator is a live market performance monitor designed for learning. It scans price, volume and volatility, detects order blocks and trendline events, applies filters (volume & ATR), generates trade signals (BUY/SELL), creates simple TP/SL trade management, and renders a compact dashboard summarizing market state, risk and performance metrics.
Use it to learn how multi-factor signals are constructed, how Greeks-style sensitivity is replaced by volatility/ATR reasoning, and how a live dashboard helps monitor trade quality.
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2) Quick start — how a learner uses it (step-by-step)
1. Add the indicator to a chart (any ticker / timeframe).
2. Open inputs and review the main groups: Order Block, Trendline, Signal Filters, Display.
3. Start with defaults (OB periods ≈ 7, ATR multiplier 0.5, volume threshold 1.2) and observe the dashboard on the last bar.
4. Walk the chart back in time (use the last-bar update behavior) and watch how signals, order blocks, trendlines, and the performance counters change.
5. Run the hands-on labs below to build intuition.
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3) Main configurable inputs (what you can tweak)
• Order Block Relevant Periods (default ~7): number of consecutive candles used to define an order block.
• Min. Percent Move for Valid OB (threshold): minimum percent move required for a valid order block.
• Number of OB Channels: how many past order block lines to keep visible.
• Trendline Period (tl_period): pivot lookback for detecting highs/lows used to draw trendlines.
• Use Wicks for Trendlines: whether pivot uses wicks or body.
• Extension Bars: how far trendlines are projected forward.
• Use Volume Filter + Volume Threshold Multiplier (e.g., 1.2): requires volume to be greater than multiplier × average volume.
• Use ATR Filter + ATR Multiplier: require bar range > ATR × multiplier to filter noise.
• Show Targets / Table settings / Colors for visualization.
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4) Core building blocks — what the script computes (plain language)
Price & trend:
• Spot / LTP: current close price.
• EMA 9 / 21 / 50: fast, medium, slow moving averages to define short/medium trend.
o trend_bullish: EMA9 > EMA21 > EMA50
o trend_bearish: EMA9 < EMA21 < EMA50
o trend_neutral: otherwise
Volatility & noise:
• ATR (14): average true range used for dynamic target and filter sizing.
• dynamic_zone = ATR × atr_multiplier: minimum bar range required for meaningful move.
• Annualized volatility: stdev of price changes × sqrt(252) × 100 — used to classify volatility (HIGH/MEDIUM/LOW).
Momentum & oscillators:
• RSI 14: overbought/oversold indicator (thresholds 70/30).
• MACD: EMA(12)-EMA(26) and a 9-period signal line; histogram used for momentum direction and strength.
• Momentum (ta.mom 10): raw momentum over 10 bars.
Mean reversion / band context:
• Bollinger Bands (20, 2σ): upper, mid, lower.
o price_position measures where price sits inside the band range as 0–100.
Volume metrics:
• avg_volume = SMA(volume, 20) and volume_spike = volume > avg_volume × volume_threshold
o volume_ratio = volume / avg_volume
Support & Resistance:
• support_level = lowest low over 20 bars
• resistance_level = highest high over 20 bars
• current_position = percent of price between support & resistance (0–100)
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5) Order Block detection — concept & logic
What it tries to find: a bar (the base) followed by N candles in the opposite direction (a classical order block setup), with a minimum % move to qualify. The script records the high/low of the base candle, averages them, and plots those levels as OB channels.
How learners should think about it (conceptual):
1. An order block is a signature area where institutions (theory) left liquidity — often seen as a large bar followed by a sequence of directional candles.
2. This indicator uses a configurable number of subsequent candles to confirm that the pattern exists.
3. When found, it stores and displays the base candle’s high/low area so students can see how price later reacts to those zones.
Implementation note for learners: the tool keeps a limited history of OB lines (ob_channels). When new OBs exceed the count, the oldest lines are removed — good practice to avoid clutter.
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6) Trendline detection — idea & interpretation
• The script finds pivot highs and lows using a symmetric lookback (tl_period and half that as right/left).
• It then computes a trendline slope from successive pivots and projects the line forward (extension_bars).
• Break detection: Resistance break = close crosses above the projected resistance line; Support break = close crosses below projected support.
Learning tip: trendlines here are computed from pivot points and time. Watch how changing tl_period (bigger = smoother, fewer pivots) alters the trendlines and break signals.
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7) Signal generation & filters — step-by-step
1. Primary triggers:
o Bullish trigger: order block bullish OR resistance trendline break.
o Bearish trigger: bearish order block OR support trendline break.
2. Filters applied (both must pass unless disabled):
o Volume filter: volume must be > avg_volume × volume_threshold.
o ATR filter: bar range (high-low) must exceed ATR × atr_multiplier.
o Not in an existing trade: new trades only start if trade_active is false.
3. Trend confirmation:
o The primary trigger is only confirmed if trend is bullish/neutral for buys or bearish/neutral for sells (EMA alignment).
4. Result:
o When confirmed, a long or short trade is activated with TP/SL calculated from ATR multiples.
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8) Trade management — what the tool does after a signal
• Entry management: the script marks a trade as trade_active and sets long_trade or short_trade flags.
• TP & SL rules:
o Long: TP = high + 2×ATR ; SL = low − 1×ATR
o Short: TP = low − 2×ATR ; SL = high + 1×ATR
• Monitoring & exit:
o A trade closes when price reaches TP or SL.
o When TP/SL hit, the indicator updates win_count and total_pnl using a very simple calculation (difference between TP/SL and previous close).
o Visual lines/labels are drawn for TP and updated as the trade runs.
Important learner notes:
• The script does not store a true entry price (it uses close in its P&L math), so PnL is an approximation — treat this as a learning proxy, not a position accounting system.
• There’s no sizing, slippage, or fee accounted — students must manually factor these when translating to real trades.
• This indicator is not a backtesting strategy; strategy.* functions would be needed for rigorous backtest results.
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9) Signal strength & helper utilities
• Signal strength is a composite score (0–100) made up of four signals worth 25 points each:
1. RSI extreme (overbought/oversold) → 25
2. Volume spike → 25
3. MACD histogram magnitude increasing → 25
4. Trend existence (bull or bear) → 25
• Progress bars (text glyphs) are used to visually show RSI and signal strength on the table.
Learning point: composite scoring is a way to combine orthogonal signals — study how changing weights changes outcomes.
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10) Dashboard — how to read each section (walkthrough)
The dashboard is split into sections; here's how to interpret them:
1. Market Overview
o LTP / Change%: immediate price & daily % change.
2. RSI & MACD
o RSI value plus progress bar (overbought 70 / oversold 30).
o MACD histogram sign indicates bullish/bearish momentum.
3. Volume Analysis
o Volume ratio (current / average) and whether there’s a spike.
4. Order Block Status
o Buy OB / Sell OB: the average base price of detected order blocks or “No Signal.”
5. Signal Status
o 🔼 BUY or 🔽 SELL if confirmed, or ⚪ WAIT.
o No-trade vs Active indicator summarizing market readiness.
6. Trend Analysis
o Trend direction (from EMAs), market sentiment score (composite), volatility level and band/position metrics.
7. Performance
o Win Rate = wins / signals (percentage)
o Total PnL = cumulative PnL (approximate)
o Bull / Bear Volume = accumulated volumes attributable to signals
8. Support & Resistance
o 20-bar highest/lowest — use as nearby reference points.
9. Risk & R:R
o Risk Level from ATR/price as a percent.
o R:R Ratio computed from TP/SL if a trade is active.
10. Signal Strength & Active Trade Status
• Numeric strength + progress bar and whether a trade is currently active with TP/SL display.
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11) Alerts — what will notify you
The indicator includes pre-built alert triggers for:
• Bullish confirmed signal
• Bearish confirmed signal
• TP hit (long/short)
• SL hit (long/short)
• No-trade zone
• High signal strength (score > 75%)
Training use: enable alerts during a replay session to be notified when the indicator would have signalled.
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12) Labs — hands-on exercises for learners (step-by-step)
Lab A — Order Block recognition
1. Pick a 15–30 minute timeframe on a liquid ticker.
2. Use default OB periods (7). Mark each time the dashboard shows a Buy/Sell OB.
3. Manually inspect the chart at the base candle and the following sequence — draw the OB zone by hand and watch later price reactions to it.
4. Repeat with OB periods 5 and 10; note stability vs noise.
Lab B — Trendline break confirmation
1. Increase trendline period (e.g., 20), watch trendlines form from pivots.
2. When a resistance break is flagged, compare with MACD & volume: was momentum aligned?
3. Note false breaks vs confirmed moves — change extension_bars to see projection effects.
Lab C — Filter sensitivity
1. Toggle Use Volume Filter off, and record the number and quality of signals in a 2-day window.
2. Re-enable volume filter and change threshold from 1.2 → 1.6; note how many low-quality signals are filtered out.
Lab D — Trade management simulation
1. For each signalled trade, record the time, close entry approximation, TP, SL, and eventual hit/miss.
2. Compute actual PnL if you had entered at the open of the next bar to compare with the script’s PnL math.
3. Tabulate win rate and average R:R.
Lab E — Performance review & improvement
1. Build a spreadsheet of signals over 30–90 periods with columns: Date, Signal type, Entry price (real), TP, SL, Exit, PnL, Notes.
2. Analyze which filters or indicators contributed most to winners vs losers and adjust weights.
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13) Common pitfalls, assumptions & implementation notes (things to watch)
• P&L simplification: total_pnl uses close as a proxy entry price. Real entry/exit prices and slippage are not recorded — so PnL is approximate.
• No position sizing or money management: the script doesn’t compute position size from equity or risk percent.
• Signal confirmation logic: composite "signal_strength" is a simple 4×25 point scheme — explore different weights or additional signals.
• Order block detection nuance: the script defines the base candle and checks the subsequent sequence. Be sure to verify whether the intended candle direction (base being bullish vs bearish) aligns with academic/your trading definition — read the code carefully and test.
• Trendline slope over time: slope is computed using timestamps; small differences may make lines sensitive on very short timeframes — using bar_index differences is usually more stable.
• Not a true backtester: to evaluate performance statistically you must transform the logic into a strategy script that places hypothetical orders and records exact entry/exit prices.
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14) Suggested improvements for advanced learners
• Record true entry price & timestamp for accurate PnL.
• Add position sizing: risk % per trade using SL distance and account size.
• Convert to strategy. (Pine Strategy)* to run formal backtests with equity curves, drawdowns, and metrics (Sharpe, Sortino).
• Log trades to an external spreadsheet (via alerts + webhook) for offline analysis.
• Add statistics: average win/loss, expectancy, max drawdown.
• Add additional filters: news time blackout, market session filters, multi-timeframe confirmation.
• Improve OB detection: combine wick/body, volume spike at base bar, and liquidity sweep detection.
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15) Glossary — quick definitions
• ATR (Average True Range): measure of typical range; used to size targets and stops.
• EMA (Exponential Moving Average): trend smoothing giving more weight to recent prices.
• RSI (Relative Strength Index): momentum oscillator; >70 overbought, <30 oversold.
• MACD: momentum oscillator using difference of two EMAs.
• Bollinger Bands: volatility bands around SMA.
• Order Block: a base candle area with subsequent confirmation candles; a zone of institutional interest (learning model).
• Pivot High/Low: local turning point defined by candles on both sides.
• Signal Strength: combined score from multiple indicators.
• Win Rate: proportion of signals that hit TP vs total signals.
• R:R (Risk:Reward): ratio of potential reward (TP distance) to risk (entry to SL).
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16) Limitations & assumptions (be explicit)
• This is an indicator for learning — not a trading robot or broker connection.
• No slippage, fees, commissions or tie-in to real orders are considered.
• The logic is heuristic (rule-of-thumb), not a guarantee of performance.
• Results are sensitive to timeframe, market liquidity, and parameter choices.
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17) Practical classroom / study plan (4 sessions)
• Session 1 — Foundations: Understand EMAs, ATR, RSI, MACD, Bollinger Bands. Run the indicator and watch how these numbers change on a single day.
• Session 2 — Zones & Filters: Study order blocks and trendlines. Test volume & ATR filters and note changes in false signals.
• Session 3 — Simulated trading: Manually track 20 signals, compute real PnL and compare to the dashboard.
• Session 4 — Improvement plan: Propose changes (e.g., better PnL accounting, alternative OB rule) and test their impact.
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18) Quick reference checklist for each signal
1. Was an order block or trendline break detected? (primary trigger)
2. Did volume meet threshold? (filter)
3. Did ATR filter (bar size) show a real move? (filter)
4. Was trend aligned (EMA 9/21/50)? (confirmation)
5. Signal confirmed → mark entry approximation, TP, SL.
6. Monitor dashboard (Signal Strength, Volatility, No-trade zone, R:R).
7. After exit, log real entry/exit, compute actual PnL, update spreadsheet.
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19) Educational caveat & final note
This tool is built for training and analysis: it helps you see how common technical building blocks combine into trade ideas, but it is not a trading recommendation. Use it to develop judgment, to test hypotheses, and to design robust systems with proper backtesting and risk control before risking capital.
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20) Disclaimer (must include)
Training & Educational Only — This material and the indicator are provided for educational purposes only. Nothing here is investment advice or a solicitation to buy or sell financial instruments. Past simulated or historical performance does not predict future results. Always perform full backtesting and risk management, and consider seeking advice from a qualified financial professional before trading with real capital.
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MultiMA fxG v2 Indicateur permettant de centralier 3 moving average :
- Moving average Simple 8 (bleu)
- Moving average Exponentielle 21 (rouge)
- Moving average Exponentielle 50 (Orange)
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Simple Moving Average (SMA) 8: Displayed in blue, this line provides a quick view of short-term price trends.
Exponential Moving Average (EMA) 21: Shown in red, this average is more sensitive to recent price changes and highlights medium-term momentum.
Exponential Moving Average (EMA) 50: Marked in orange, this line tracks longer-term price movements for overall trend direction.
Traders can use the combination of these moving averages to identify potential crossover signals, trend strength, and possible reversal points.
Moving Average Shift [Quantora]Title: Moving Average Shift
Description:
The Moving Average Shift is a dynamic technical analysis tool designed to help traders better visualize trend strength and direction using a combination of customizable moving averages and a volatility-adjusted oscillator.
🔧 Features:
Multi-Type Moving Average Selection
Choose from SMA, EMA, SMMA (RMA), WMA, and VWMA for your main signal line.
ZLSMA Trio
Three Zero-Lag Smoothed Moving Averages (ZLSMA) with adjustable lengths and colors provide a smoother trend-following structure without the delay of traditional MAs.
EMA Ribbon (50/100/200)
Add clarity to long-term trend direction with layered Exponential Moving Averages in key institutional periods.
Volatility-Adjusted Oscillator
A color-changing oscillator calculated from the normalized deviation between price and the selected MA. This helps identify trend shifts and momentum buildups.
Custom MA Line Widths and Styling
Full control over the width and appearance of all MA lines for visual clarity.
Bar & Candle Coloring
Bars and candles dynamically change color based on the relationship between price and the selected MA — helping you quickly assess bullish/bearish conditions.
📈 How It Helps:
Spot early trend shifts through the oscillator.
Confirm trades using the alignment between ZLSMAs and EMAs.
Quickly assess current trend conditions using color-coded price bars.
TRIPLE Moving AveragesThis Pine Script indicator plots three customizable moving averages (MAs) along with an optional composite MA (average of all three). It provides visual cues, alerts, and trend confirmation based on MA crossovers and price positioning relative to the MAs.
🔹 Key Features
1. Multiple Moving Average Types
Supports 7 different MA types for each line:
EMA (Exponential Moving Average)
SMA (Simple Moving Average)
SMMA / RMA (Smoothed / Regular Moving Average)
WMA (Weighted Moving Average)
VWMA (Volume Weighted Moving Average)
HMA (Hull Moving Average)
2. Three Independent MAs
MA1, MA2, MA3 can each be enabled/disabled
Custom lengths (default: 12, 21, 50)
Different price sources (close, open, high, low, etc.)
3. Composite Moving Average (Optional)
Calculates (MA1 + MA2 + MA3) / 3
Acts as a consensus trend filter
4. Visual & Alert Features
✅ Color-Coded Lines (Yellow = Price Above MA, Red = Price Below MA)
✅ Thick Line Width (3) for better visibility
✅ Background Highlights for crossovers/crossunders
✅ Alerts for All Crossover Combinations
🔹 How It Works
📈 MA Crossovers & Trend Signals
Bullish Signal: When a faster MA crosses above a slower MA
Bearish Signal: When a faster MA crosses below a slower MA
Trend Confirmation: All MAs aligned in the same direction (e.g., MA1 > MA2 > MA3 = Strong Uptrend)
🎨 Visual Indicators
Green Background → Bullish crossover detected
Red Background → Bearish crossover detected
Yellow Line → Price is above the MA (bullish)
Red Line → Price is below the MA (bearish)
🔔 Alert Conditions
Alerts are triggered for all possible MA crossover combinations, including:
MA1 crossing MA2
MA1 crossing MA3
MA2 crossing MA3
Any MA crossing the Composite MA
Dynamic Ray BandsAbout Dynamic Ray Bands
Dynamic Ray Bands is a volatility-adaptive envelope indicator that adjusts in real time to evolving market conditions. It uses a Double Exponential Moving Average (DEMA) as its central trend reference, with upper and lower bands scaled according to current volatility measured by the Average True Range (ATR).
This creates a dynamic structure that visually frames price action, helping traders identify areas of potential trend continuation, overextension, or mean reversion.
How It Works
🟡 Centerline (DEMA)
The central yellow line is a Double Exponential Moving Average, which offers a smoother, less laggy trend signal than traditional moving averages. It represents the market’s short- to medium-term “equilibrium.”
🔵 Outer Bands
Plotted at:
Upper Band = DEMA + (ATR × outerMultiplier)
Lower Band = DEMA - (ATR × outerMultiplier)
These bands define the extreme bounds of current volatility. When price breaks above or below them, it can signal strong directional momentum or overbought/oversold conditions, depending on context. They're often used as trend breakout zones or to time exits after extended runs.
🟣 Inner Bands
Plotted closer to the DEMA:
Inner Upper = DEMA + (ATR × innerMultiplier)
Inner Lower = DEMA - (ATR × innerMultiplier)
These are preliminary volatility thresholds, offering early cues for potential expansion or reversal. They may be used for scalping, tight stop zones, or pre-breakout positioning.
🔁 Dynamic Width (Bands are Dynamically Adjusted Per Tick)
The width of both inner and outer bands is based on ATR (Average True Range), which is recalculated in real time. This means:
During high volatility, the bands expand, allowing for wider price fluctuations.
During low volatility, the bands contract, tightening range expectations.
Unlike fixed-width channels or standard Bollinger Bands (which use standard deviation), this per-tick adjustment via ATR enables Dynamic Ray Bands to reduce false signals in choppy markets and remain more reactive during trending conditions.
⚙️ Inputs
DMA Length — Period for the central DEMA.
ATR Length — Lookback used for ATR volatility calculations.
Outer Band Multiplier — Controls sensitivity of extreme bands.
Inner Band Multiplier — Controls proximity of inner bands.
Show Inner Bands — Toggle for plotting the inner zone.
🔔 Alerts
Alert conditions are included for:
Price closing above/below the outer bands (trend momentum or overextension)
Price closing above/below the inner bands (early signs of strength/weakness)
🧭 Use Cases
Breakout detection — Catch price continuation beyond the outer bands.
Volatility filtering — Adjust trade logic based on band width.
Mean reversion — Monitor for snapbacks toward the DEMA after price stretches too far.
Trend guidance — Use band slope and price position to confirm direction.
⚠️ Disclaimer
This script is intended for educational and informational purposes only. It does not constitute financial advice or a recommendation to trade any specific market or security. Always test indicators thoroughly before using them in live trading.
EWMA Volatility EstimatorThis script calculates EWMA Volatility (Exponentially Weighted Moving Average Volatility).
Commonly used model in financial risk management.
It estimates recent price volatility by applying more weight to the most recent returns, capturing volatility clustering while remaining responsive to fast market shifts.
The method uses a decay factor (λ) of 0.94, the standard value used in models like RiskMetrics, and converts the variance estimate into annualized volatility in percentage terms.
This is not a forecasting tool. It’s an estimator that reflects the magnitude of recent price moves in a statistically robust way.
It can be helpful for:
Understanding regime shifts in market behavior
Designing position sizing rules based on recent volatility
Filtering entries during high or low volatility phases
How It Works
Computes log returns of the closing price.
Squares the returns to get a proxy for variance.
Applies an exponential moving average to the squared returns using an equivalent EMA period based on λ = 0.94.
Converts the result to volatility by taking the square root and scaling to a percentage.
Key Characteristics
Backward-looking estimator
Reacts faster than standard rolling-window volatility
Smooths noise while still being sensitive to recent spikes
This script is educational and informational. It is not financial advice or a guarantee of performance. Always test any tool as part of a broader strategy before using it in live markets.
21DMA Structure Counter (EMA/SMA Option)21DMA Structure Counter (EMA/SMA Option)
Overview
The 21DMA Structure Counter is an advanced technical indicator that tracks consecutive periods where price action remains above a 21-period moving average structure. This indicator helps traders identify momentum phases and potential trend exhaustion points using statistical analysis.
Key Features
Moving Average Structure
- Configurable MA Type: Choose between EMA (Exponential Moving Average) or SMA (Simple Moving Average)
- 21-Period Default: Optimized for the widely-watched 21-period moving average
- Triple MA Structure: Tracks high, close, and low moving averages for comprehensive analysis
Statistical Analysis
- Cycle Counting: Automatically counts consecutive periods above the MA structure
- Historical Data: Maintains up to 2,500 historical cycles (approximately 10 years of daily data)
- Z-Score Calculation: Provides statistical context using mean and standard deviation
- Multiple Standard Deviation Levels: Displays +1, +2, and +3 standard deviation thresholds
Visual Indicators
Color-Coded Bars:
- Gray: Below 10-year average
- Yellow: Between average and +1 standard deviation
- Orange: Between +1 and +2 standard deviations
- Red: Between +2 and +3 standard deviations
- Fuchsia: Above +3 standard deviations (extreme readings)
Breadth Integration
- Multiple Breadth Options: NDFI, NDTH, NDTW (NASDAQ breadth indicators), or VIX
- Background Shading: Visual alerts when breadth reaches extreme levels
- High/Low Thresholds: Customizable levels for breadth analysis
- Real-time Display: Current breadth value shown in data table
Smart Reset Logic
- High Below Structure Reset: Automatically resets count when daily high falls below the lowest MA
- Flexible Hold Period: Continues counting during temporary weakness as long as structure isn't violated
- Precise Entry/Exit: Strict criteria for starting cycles, flexible for maintaining them
How to Use
Trend Identification
- Rising Counts: Indicate sustained momentum above key moving average structure
- Extreme Readings: Z-scores above +2 or +3 suggest potential trend exhaustion
- Historical Context: Compare current cycles to 10-year statistical averages
Risk Management
- Breadth Confirmation: Use breadth shading to confirm market-wide strength/weakness
- Statistical Extremes: Exercise caution when readings reach +3 standard deviations
- Reset Signals: Pay attention to structure violations for potential trend changes
Multi-Timeframe Application
- Daily Charts: Primary timeframe for swing trading and position management
- Weekly/Monthly: Longer-term trend analysis
- Intraday: Shorter-term momentum assessment (adjust MA period accordingly)
Settings
Moving Average Options
- Type: EMA or SMA selection
- Period: Default 21 (customizable)
- Reset Days: Days below structure required for reset
Visual Customization
- Standard Deviation Lines: Toggle and customize colors for +1, +2, +3 SD
- Breadth Selection: Choose from NDFI, NDTH, NDTW, or VIX
- Threshold Levels: Set custom high/low breadth thresholds
- Table Styling: Customize text colors, background, and font size
Technical Notes
- Data Retention: Maintains 2,500 historical cycles for robust statistical analysis
- Real-time Updates: Calculations update with each new bar
- Breadth Integration: Uses security() function to pull external breadth data
- Performance Optimized: Efficient array management prevents memory issues
Best Practices
1. Combine with Price Action: Use alongside support/resistance and chart patterns
2. Monitor Breadth Divergences: Watch for breadth weakness during strong readings
3. Respect Statistical Extremes: Exercise caution at +2/+3 standard deviation levels
4. Context Matters: Consider overall market environment and sector rotation
5. Risk Management: Use appropriate position sizing, especially at extreme readings
Disclaimer
This indicator is for educational and informational purposes only. It should not be used as the sole basis for trading decisions. Always combine with other forms of analysis and proper risk management techniques.
Compatible with Pine Script v6 | Optimized for daily timeframes | Best used on major indices and liquid stocks
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.
Ultimate Volatility CloudUltimate Volatility Cloud
The Ultimate Volatility Cloud is a powerful and highly customizable indicator designed to help traders visualize market volatility, easily identify trend, and overextended moves in price with adaptive bands. It combines the strengths of the Arnaud Legoux Moving Average, Kaufman's Adaptive Moving Average, ATR Channels, and Standard Deviation bands, offering multiple pre-configured profiles and extensive customization options.
Key Features:
Dynamic Volatility Bands: The indicator plots multiple layers of volatility bands around a central basis line, providing a comprehensive view of price deviation.
Hybrid Band Calculation: Bands are a sophisticated blend of Keltner Channels, KAMA ATR Channels and Standard Deviation, allowing for a nuanced representation of volatility.
Adaptive Smoothing: Bands are smoothed using either Exponential Moving Average (EMA) or Kaufman's Adaptive Moving Average (KAMA) based on the selected profile, ensuring responsiveness tailored to market conditions.
Layered Fills: The cloud uses distinct color fills for different volatility levels, making it easy to visually interpret price action relative to its typical range.
Customizable Color Themes: Choose from a variety of pre-set color themes, including "Rainbow," "Wild," and "Monochrome," or stick with classic options to suit your visual preference.
Optional Basis Line Plots: Display the EMA or KAMA basis lines (used in Keltner Channel calculations) separately on the chart for additional analysis.
Understanding the Profiles:
The indicator comes with several pre-configured "Settings Profiles" that adjust the internal parameters (Keltner Channel/KAMA Channel/Standard Deviation band blend, and band smoothing) to suit different trading styles or market environments.
1. Standard Profile:
Blend: 60% Keltner Channel, 40% Standard Deviation.
Smoothing: EMA smoothing of 3 periods.
Purpose: A balanced, general-purpose profile suitable for a wide range of market conditions. It offers a good blend of trend following and volatility awareness.
2. Responsive Profile:
Blend: 40% Keltner Channel, 60% Standard Deviation.
Smoothing: EMA smoothing of 2 period.
Purpose: Designed for traders who need quick reactions to price changes. The higher Standard Deviation blend and minimal smoothing make it highly sensitive to immediate volatility shifts, ideal for short-term analysis or identifying early moves.
3. Ranging Market Profile:
Blend: 80% KAMA ATR Channel, 20% Standard Deviation.
Smoothing: KAMA smoothing.
Purpose: Optimized for sideways or consolidating markets. By utilizing KAMA-based ATR bands and KAMA for band smoothing, this profile adapts its responsiveness to reduce whipsaws in choppy conditions, providing clearer boundaries for range-bound price action.
4. Trend Following Profile:
Blend: 90% Keltner Channel, 10% Standard Deviation.
Smoothing: EMA smoothing of 5 periods.
Purpose: Tailored for riding strong trends. The heavy emphasis on the Keltner Channel and slightly smoother bands help filter out minor fluctuations, allowing traders to focus on the dominant directional movement.
5. Conservative Profile:
Blend: 65% KAMA ATR Channel, 35% Standard Deviation.
Smoothing: EMA smoothing of 10 periods.
Purpose: Aims to provide more filtered signals and reduce noise. The KAMA basis for the Keltner Channel combined with a longer EMA smoothing period offers a slower, more confirmed view of volatility, suitable for traders seeking higher conviction entries or exits.
Example of the Ranging Market Profile
How to Use:
The volatility cloud can be interpreted in various ways:
Price within the inner bands: May indicate consolidation or a period of lower volatility.
Price pushing into outer bands: Suggests increasing volatility and potential for a strong move.
Price breaking out of extreme outer bands: Can signal significant momentum and the start or continuation of a strong trend.
Cloud expansion/contraction: Visually indicates periods of increasing or decreasing market energy.
Experiment with different profiles and settings to find the combination that best suits your trading strategy and the instruments you trade.
Beyond Insights IndicatorThe Beyond Insights Indicator is a multi-timeframe overlay tool designed for TradingView that displays key Exponential Moving Averages (EMAs) and volatility insights to support various trading strategies. It intelligently adapts its display based on the current chart timeframe to reduce clutter and enhance relevance. Specifically, it shows EMA 6, 18, and 50 when viewing intraday timeframes.
In addition to EMAs, the indicator includes an ATR (Average True Range) calculation with a default length of 14, offering insight into market volatility.
Ultimate Scalping Tool[BullByte]Overview
The Ultimate Scalping Tool is an open-source TradingView indicator built for scalpers and short-term traders released under the Mozilla Public License 2.0. It uses a custom Quantum Flux Candle (QFC) oscillator to combine multiple market forces into one visual signal. In plain terms, the script reads momentum, trend strength, volatility, and volume together and plots a special “candlestick” each bar (the QFC) that reflects the overall market bias. This unified view makes it easier to spot entries and exits: the tool labels signals as Strong Buy/Sell, Pullback (a brief retracement in a trend), Early Entry, or Exit Warning . It also provides color-coded alerts and a small dashboard of metrics. In practice, traders see green/red oscillator bars and symbols on the chart when conditions align, helping them scalp or trend-follow without reading multiple separate indicators.
Core Components
Quantum Flux Candle (QFC) Construction
The QFC is the heart of the indicator. Rather than using raw price, it creates a candlestick-like bar from the underlying oscillator values. Each QFC bar has an “open,” “high/low,” and “close” derived from calculated momentum and volatility inputs for that period . In effect, this turns the oscillator into intuitive candle patterns so traders can recognize momentum shifts visually. (For comparison, note that Heikin-Ashi candles “have a smoother look because take an average of the movement”. The QFC instead represents exact oscillator readings, so it reflects true momentum changes without hiding price action.) Colors of QFC bars change dynamically (e.g. green for bullish momentum, red for bearish) to highlight shifts. This is the first open-source QFC oscillator that dynamically weights four non-correlated indicators with moving thresholds, which makes it a unique indicator on its own.
Oscillator Normalization & Adaptive Weights
The script normalizes its oscillator to a fixed scale (for example, a 0–100 range much like the RSI) so that various inputs can be compared fairly. It then applies adaptive weighting: the relative influence of trend, momentum, volatility or volume signals is automatically adjusted based on current market conditions. For instance, in very volatile markets the script might weight volatility more heavily, or in a strong trend it might give extra weight to trend direction. Normalizing data and adjusting weights helps keep the QFC sensitive but stable (normalization ensures all inputs fit a common scale).
Trend/Momentum/Volume/Volatility Fusion
Unlike a typical single-factor oscillator, the QFC oscillator fuses four aspects at once. It may compute, for example, a trend indicator (such as an ADX or moving average slope), a momentum measure (like RSI or Rate-of-Change), a volume-based pressure (similar to MFI/OBV), and a volatility measure (like ATR) . These different values are combined into one composite oscillator. This “multi-dimensional” approach follows best practices of using non-correlated indicators (trend, momentum, volume, volatility) for confirmation. By encoding all these signals in one line, a high QFC reading means that trend, momentum, and volume are all aligned, whereas a neutral reading might mean mixed conditions. This gives traders a comprehensive picture of market strength.
Signal Classification
The script interprets the QFC oscillator to label trades. For example:
• Strong Buy/Sell : Triggered when the oscillator crosses a high-confidence threshold (e.g. breaks clearly above zero with strong slope), indicating a well-confirmed move. This is like seeing a big green/red QFC candle aligned with the trend.
• Pullbacks : Identified when the trend is up but momentum dips briefly. A Pullback Buy appears if the overall trend is bullish but the oscillator has a short retracement – a typical buying opportunity in an uptrend. (A pullback is “a brief decline or pause in a generally upward price trend”.)
• Early Buy/Sell : Marks an initial swing in the oscillator suggesting a possible new trend, before it is fully confirmed. It’s a hint of momentum building (an early-warning signal), not as strong as the confirmed “Strong” signal.
• Exit Warnings : Issued when momentum peaks or reverses. For instance, if the QFC bars reach a high and start turning red/green opposite, the indicator warns that the move may be ending. In other words, a Momentum Peak is the point of maximum strength after which weakness may follow.
These categories correspond to typical trading concepts: Pullback (temporary reversal in an uptrend), Early Buy (an initial bullish cross), Strong Buy (confirmed bullish momentum), and Momentum Peak (peak oscillator value suggesting exhaustion).
Filters (DI Reversal, Dynamic Thresholds, HTF EMA/ADX)
Extra filters help avoid bad trades. A DI Reversal filter uses the +DI/–DI lines (from the ADX system) to require that the trend direction confirms the signal . For example, it might ignore a buy signal if the +DI is still below –DI. Dynamic Thresholds adjust signal levels on-the-fly: rather than fixed “overbought” lines, they move with volatility so signals happen under appropriate market stress. An optional High-Timeframe EMA or ADX filter adds a check against a larger timeframe trend: for instance, only taking a trade if price is above the weekly EMA or if weekly ADX shows a strong trend. (Notably, the ADX is “a technical indicator used by traders to determine the strength of a price trend”, so requiring a high-timeframe ADX avoids trading against the bigger trend.)
Dashboard Metrics & Color Logic
The Dashboard in the Ultimate Scalping Tool (UST) serves as a centralized information hub, providing traders with real-time insights into market conditions, trend strength, momentum, volume pressure, and trade signals. It is highly customizable, allowing users to adjust its appearance and content based on their preferences.
1. Dashboard Layout & Customization
Short vs. Extended Mode : Users can toggle between a compact view (9 rows) and an extended view (13 rows) via the `Short Dashboard` input.
Text Size Options : The dashboard supports three text sizes— Tiny, Small, and Normal —adjustable via the `Dashboard Text Size` input.
Positioning : The dashboard is positioned in the top-right corner by default but can be moved if modified in the script.
2. Key Metrics Displayed
The dashboard presents critical trading metrics in a structured table format:
Trend (TF) : Indicates the current trend direction (Strong Bullish, Moderate Bullish, Sideways, Moderate Bearish, Strong Bearish) based on normalized trend strength (normTrend) .
Momentum (TF) : Displays momentum status (Strong Bullish/Bearish or Neutral) derived from the oscillator's position relative to dynamic thresholds.
Volume (CMF) : Shows buying/selling pressure levels (Very High Buying, High Selling, Neutral, etc.) based on the Chaikin Money Flow (CMF) indicator.
Basic & Advanced Signals:
Basic Signal : Provides simple trade signals (Strong Buy, Strong Sell, Pullback Buy, Pullback Sell, No Trade).
Advanced Signal : Offers nuanced signals (Early Buy/Sell, Momentum Peak, Weakening Momentum, etc.) with color-coded alerts.
RSI : Displays the Relative Strength Index (RSI) value, colored based on overbought (>70), oversold (<30), or neutral conditions.
HTF Filter : Indicates the higher timeframe trend status (Bullish, Bearish, Neutral) when using the Leading HTF Filter.
VWAP : Shows the V olume-Weighted Average Price and whether the current price is above (bullish) or below (bearish) it.
ADX : Displays the Average Directional Index (ADX) value, with color highlighting whether it is rising (green) or falling (red).
Market Mode : Shows the selected market type (Crypto, Stocks, Options, Forex, Custom).
Regime : Indicates volatility conditions (High, Low, Moderate) based on the **ATR ratio**.
3. Filters Status Panel
A secondary panel displays the status of active filters, helping traders quickly assess which conditions are influencing signals:
- DI Reversal Filter: On/Off (confirms reversals before generating signals).
- Dynamic Thresholds: On/Off (adjusts buy/sell thresholds based on volatility).
- Adaptive Weighting: On/Off (auto-adjusts oscillator weights for trend/momentum/volatility).
- Early Signal: On/Off (enables early momentum-based signals).
- Leading HTF Filter: On/Off (applies higher timeframe trend confirmation).
4. Visual Enhancements
Color-Coded Cells : Each metric is color-coded (green for bullish, red for bearish, gray for neutral) for quick interpretation.
Dynamic Background : The dashboard background adapts to market conditions (bullish/bearish/neutral) based on ADX and DI trends.
Customizable Reference Lines : Users can enable/disable fixed reference lines for the oscillator.
How It(QFC) Differs from Traditional Indicators
Quantum Flux Candle (QFC) Versus Heikin-Ashi
Heikin-Ashi candles smooth price by averaging (HA’s open/close use averages) so they show trend clearly but hide true price (the current HA bar’s close is not the real price). QFC candles are different: they are oscillator values, not price averages . A Heikin-Ashi chart “has a smoother look because it is essentially taking an average of the movement”, which can cause lag. The QFC instead shows the raw combined momentum each bar, allowing faster recognition of shifts. In short, HA is a smoothed price chart; QFC is a momentum-based chart.
Versus Standard Oscillators
Common oscillators like RSI or MACD use fixed formulas on price (or price+volume). For example, RSI “compares gains and losses and normalizes this value on a scale from 0 to 100”, reflecting pure price momentum. MFI is similar but adds volume. These indicators each show one dimension: momentum or volume. The Ultimate Scalping Tool’s QFC goes further by integrating trend strength and volatility too. In practice, this means a move that looks strong on RSI might be downplayed by low volume or weak trend in QFC. As one source notes, using multiple non-correlated indicators (trend, momentum, volume, volatility) provides a more complete market picture. The QFC’s multi-factor fusion is unique – it is effectively a multi-dimensional oscillator rather than a traditional single-input one.
Signal Style
Traditional oscillators often use crossovers (RSI crossing 50) or fixed zones (MACD above zero) for signals. The Ultimate Scalping Tool’s signals are custom-classified: it explicitly labels pullbacks, early entries, and strong moves. These terms go beyond a typical indicator’s generic “buy”/“sell.” In other words, it packages a strategy around the oscillator, which traders can backtest or observe without reading code.
Key Term Definitions
• Pullback : A short-term dip or consolidation in an uptrend. In this script, a Pullback Buy appears when price is generally rising but shows a brief retracement. (As defined by Investopedia, a pullback is “a brief decline or pause in a generally upward price trend”.)
• Early Buy/Sell : An initial or tentative entry signal. It means the oscillator first starts turning positive (or negative) before a full trend has developed. It’s an early indication that a trend might be starting.
• Strong Buy/Sell : A confident entry signal when multiple conditions align. This label is used when momentum is already strong and confirmed by trend/volume filters, offering a higher-probability trade.
• Momentum Peak : The point where bullish (or bearish) momentum reaches its maximum before weakening. When the oscillator value stops rising (or falling) and begins to reverse, the script flags it as a peak – signaling that the current move could be overextended.
What is the Flux MA?
The Flux MA (Moving Average) is an Exponential Moving Average (EMA) applied to a normalized oscillator, referred to as FM . Its purpose is to smooth out the fluctuations of the oscillator, providing a clearer picture of the underlying trend direction and strength. Think of it as a dynamic baseline that the oscillator moves above or below, helping you determine whether the market is trending bullish or bearish.
How it’s calculated (Flux MA):
1.The oscillator is normalized (scaled to a range, typically between 0 and 1, using a default scale factor of 100.0).
2.An EMA is applied to this normalized value (FM) over a user-defined period (default is 10 periods).
3.The result is rescaled back to the oscillator’s original range for plotting.
Why it matters : The Flux MA acts like a support or resistance level for the oscillator, making it easier to spot trend shifts.
Color of the Flux Candle
The Quantum Flux Candle visualizes the normalized oscillator (FM) as candlesticks, with colors that indicate specific market conditions based on the relationship between the FM and the Flux MA. Here’s what each color means:
• Green : The FM is above the Flux MA, signaling bullish momentum. This suggests the market is trending upward.
• Red : The FM is below the Flux MA, signaling bearish momentum. This suggests the market is trending downward.
• Yellow : Indicates strong buy conditions (e.g., a "Strong Buy" signal combined with a positive trend). This is a high-confidence signal to go long.
• Purple : Indicates strong sell conditions (e.g., a "Strong Sell" signal combined with a negative trend). This is a high-confidence signal to go short.
The candle mode shows the oscillator’s open, high, low, and close values for each period, similar to price candlesticks, but it’s the color that provides the quick visual cue for trading decisions.
How to Trade the Flux MA with Respect to the Candle
Trading with the Flux MA and Quantum Flux Candle involves using the MA as a trend indicator and the candle colors as entry and exit signals. Here’s a step-by-step guide:
1. Identify the Trend Direction
• Bullish Trend : The Flux Candle is green and positioned above the Flux MA. This indicates upward momentum.
• Bearish Trend : The Flux Candle is red and positioned below the Flux MA. This indicates downward momentum.
The Flux MA serves as the reference line—candles above it suggest buying pressure, while candles below it suggest selling pressure.
2. Interpret Candle Colors for Trade Signals
• Green Candle : General bullish momentum. Consider entering or holding a long position.
• Red Candle : General bearish momentum. Consider entering or holding a short position.
• Yellow Candle : A strong buy signal. This is an ideal time to enter a long trade.
• Purple Candle : A strong sell signal. This is an ideal time to enter a short trade.
3. Enter Trades Based on Crossovers and Colors
• Long Entry : Enter a buy position when the Flux Candle turns green and crosses above the Flux MA. If it turns yellow, this is an even stronger signal to go long.
• Short Entry : Enter a sell position when the Flux Candle turns red and crosses below the Flux MA. If it turns purple, this is an even stronger signal to go short.
4. Exit Trades
• Exit Long : Close your buy position when the Flux Candle turns red or crosses below the Flux MA, indicating the bullish trend may be reversing.
• Exit Short : Close your sell position when the Flux Candle turns green or crosses above the Flux MA, indicating the bearish trend may be reversing.
•You might also exit a long trade if the candle changes from yellow to green (weakening strong buy signal) or a short trade from purple to red (weakening strong sell signal).
5. Use Additional Confirmation
To avoid false signals, combine the Flux MA and candle signals with other indicators or dashboard metrics (e.g., trend strength, momentum, or volume pressure). For example:
•A yellow candle with a " Strong Bullish " trend and high buying volume is a robust long signal.
•A red candle with a " Moderate Bearish " trend and neutral momentum might need more confirmation before shorting.
Practical Example
Imagine you’re scalping a cryptocurrency:
• Long Trade : The Flux Candle turns yellow and is above the Flux MA, with the dashboard showing "Strong Buy" and high buying volume. You enter a long position. You exit when the candle turns red and dips below the Flux MA.
• Short Trade : The Flux Candle turns purple and crosses below the Flux MA, with a "Strong Sell" signal on the dashboard. You enter a short position. You exit when the candle turns green and crosses above the Flux MA.
Market Presets and Adaptation
This indicator is designed to work on any market with candlestick price data (stocks, crypto, forex, indices, etc.). To handle different behavior, it provides presets for major asset classes. Selecting a “Stocks,” “Crypto,” “Forex,” or “Options” preset automatically loads a set of parameter values optimized for that market . For example, a crypto preset might use a shorter lookback or higher sensitivity to account for crypto’s high volatility, while a stocks preset might use slightly longer smoothing since stocks often trend more slowly. In practice, this means the same core QFC logic applies across markets, but the thresholds and smoothing adjust so signals remain relevant for each asset type.
Usage Guidelines
• Recommended Timeframes : Optimized for 1 minute to 15 minute intraday charts. Can also be used on higher timeframes for short term swings.
• Market Types : Select “Crypto,” “Stocks,” “Forex,” or “Options” to auto tune periods, thresholds and weights. Use “Custom” to manually adjust all inputs.
• Interpreting Signals : Always confirm a signal by checking that trend, volume, and VWAP agree on the dashboard. A green “Strong Buy” arrow with green trend, green volume, and price > VWAP is highest probability.
• Adjusting Sensitivity : To reduce false signals in fast markets, enable DI Reversal Confirmation and Dynamic Thresholds. For more frequent entries in trending environments, enable Early Entry Trigger.
• Risk Management : This tool does not plot stop loss or take profit levels. Users should define their own risk parameters based on support/resistance or volatility bands.
Background Shading
To give you an at-a-glance sense of market regime without reading numbers, the indicator automatically tints the chart background in three modes—neutral, bullish and bearish—with two levels of intensity (light vs. dark):
Neutral (Gray)
When ADX is below 20 the market is considered “no trend” or too weak to trade. The background fills with a light gray (high transparency) so you know to sit on your hands.
Bullish (Green)
As soon as ADX rises above 20 and +DI exceeds –DI, the background turns a semi-transparent green, signaling an emerging uptrend. When ADX climbs above 30 (strong trend), the green becomes more opaque—reminding you that trend-following signals (Strong Buy, Pullback) carry extra weight.
Bearish (Red)
Similarly, if –DI exceeds +DI with ADX >20, you get a light red tint for a developing downtrend, and a darker, more solid red once ADX surpasses 30.
By dynamically varying both hue (green vs. red vs. gray) and opacity (light vs. dark), the background instantly communicates trend strength and direction—so you always know whether to favor breakout-style entries (in a strong trend) or stay flat during choppy, low-ADX conditions.
The setup shown in the above chart snapshot is BTCUSD 15 min chart : Binance for reference.
Disclaimer
No indicator guarantees profits. Backtest or paper trade this tool to understand its behavior in your market. Always use proper position sizing and stop loss orders.
Good luck!
- BullByte
Why EMA Isn't What You Think It IsMany new traders adopt the Exponential Moving Average (EMA) believing it's simply a "better Simple Moving Average (SMA)". This common misconception leads to fundamental misunderstandings about how EMA works and when to use it.
EMA and SMA differ at their core. SMA use a window of finite number of data points, giving equal weight to each data point in the calculation period. This makes SMA a Finite Impulse Response (FIR) filter in signal processing terms. Remember that FIR means that "all that we need is the 'period' number of data points" to calculate the filter value. Anything beyond the given period is not relevant to FIR filters – much like how a security camera with 14-day storage automatically overwrites older footage, making last month's activity completely invisible regardless of how important it might have been.
EMA, however, is an Infinite Impulse Response (IIR) filter. It uses ALL historical data, with each past price having a diminishing - but never zero - influence on the calculated value. This creates an EMA response that extends infinitely into the past—not just for the last N periods. IIR filters cannot be precise if we give them only a 'period' number of data to work on - they will be off-target significantly due to lack of context, like trying to understand Game of Thrones by watching only the final season and wondering why everyone's so upset about that dragon lady going full pyromaniac.
If we only consider a number of data points equal to the EMA's period, we are capturing no more than 86.5% of the total weight of the EMA calculation. Relying on he period window alone (the warm-up period) will provide only 1 - (1 / e^2) weights, which is approximately 1−0.1353 = 0.8647 = 86.5%. That's like claiming you've read a book when you've skipped the first few chapters – technically, you got most of it, but you probably miss some crucial early context.
▶️ What is period in EMA used for?
What does a period parameter really mean for EMA? When we select a 15-period EMA, we're not selecting a window of 15 data points as with an SMA. Instead, we are using that number to calculate a decay factor (α) that determines how quickly older data loses influence in EMA result. Every trader knows EMA calculation: α = 1 / (1+period) – or at least every trader claims to know this while secretly checking the formula when they need it.
Thinking in terms of "period" seriously restricts EMA. The α parameter can be - should be! - any value between 0.0 and 1.0, offering infinite tuning possibilities of the indicator. When we limit ourselves to whole-number periods that we use in FIR indicators, we can only access a small subset of possible IIR calculations – it's like having access to the entire RGB color spectrum with 16.7 million possible colors but stubbornly sticking to the 8 basic crayons in a child's first art set because the coloring book only mentioned those by name.
For example:
Period 10 → alpha = 0.1818
Period 11 → alpha = 0.1667
What about wanting an alpha of 0.17, which might yield superior returns in your strategy that uses EMA? No whole-number period can provide this! Direct α parameterization offers more precision, much like how an analog tuner lets you find the perfect radio frequency while digital presets force you to choose only from predetermined stations, potentially missing the clearest signal sitting right between channels.
Sidenote: the choice of α = 1 / (1+period) is just a convention from 1970s, probably started by J. Welles Wilder, who popularized the use of the 14-day EMA. It was designed to create an approximate equivalence between EMA and SMA over the same number of periods, even thought SMA needs a period window (as it is FIR filter) and EMA doesn't. In reality, the decay factor α in EMA should be allowed any valye between 0.0 and 1.0, not just some discrete values derived from an integer-based period! Algorithmic systems should find the best α decay for EMA directly, allowing the system to fine-tune at will and not through conversion of integer period to float α decay – though this might put a few traditionalist traders into early retirement. Well, to prevent that, most traditionalist implementations of EMA only use period and no alpha at all. Heaven forbid we disturb people who print their charts on paper, draw trendlines with rulers, and insist the market "feels different" since computers do algotrading!
▶️ Calculating EMAs Efficiently
The standard textbook formula for EMA is:
EMA = CurrentPrice × alpha + PreviousEMA × (1 - alpha)
But did you know that a more efficient version exists, once you apply a tiny bit of high school algebra:
EMA = alpha × (CurrentPrice - PreviousEMA) + PreviousEMA
The first one requires three operations: 2 multiplications + 1 addition. The second one also requires three ops: 1 multiplication + 1 addition + 1 subtraction.
That's pathetic, you say? Not worth implementing? In most computational models, multiplications cost much more than additions/subtractions – much like how ordering dessert costs more than asking for a water refill at restaurants.
Relative CPU cost of float operations :
Addition/Subtraction: ~1 cycle
Multiplication: ~5 cycles (depending on precision and architecture)
Now you see the difference? 2 * 5 + 1 = 11 against 5 + 1 + 1 = 7. That is ≈ 36.36% efficiency gain just by swapping formulas around! And making your high school math teacher proud enough to finally put your test on the refrigerator.
▶️ The Warmup Problem: how to start the EMA sequence right
How do we calculate the first EMA value when there's no previous EMA available? Let's see some possible options used throughout the history:
Start with zero : EMA(0) = 0. This creates stupidly large distortion until enough bars pass for the horrible effect to diminish – like starting a trading account with zero balance but backdating a year of missed trades, then watching your balance struggle to climb out of a phantom debt for months.
Start with first price : EMA(0) = first price. This is better than starting with zero, but still causes initial distortion that will be extra-bad if the first price is an outlier – like forming your entire opinion of a stock based solely on its IPO day price, then wondering why your model is tanking for weeks afterward.
Use SMA for warmup : This is the tradition from the pencil-and-paper era of technical analysis – when calculators were luxury items and "algorithmic trading" meant your broker had neat handwriting. We first calculate an SMA over the initial period, then kickstart the EMA with this average value. It's widely used due to tradition, not merit, creating a mathematical Frankenstein that uses an FIR filter (SMA) during the initial period before abruptly switching to an IIR filter (EMA). This methodology is so aesthetically offensive (abrupt kink on the transition from SMA to EMA) that charting platforms hide these early values entirely, pretending EMA simply doesn't exist until the warmup period passes – the technical analysis equivalent of sweeping dust under the rug.
Use WMA for warmup : This one was never popular because it is harder to calculate with a pencil - compared to using simple SMA for warmup. Weighted Moving Average provides a much better approximation of a starting value as its linear descending profile is much closer to the EMA's decay profile.
These methods all share one problem: they produce inaccurate initial values that traders often hide or discard, much like how hedge funds conveniently report awesome performance "since strategy inception" only after their disastrous first quarter has been surgically removed from the track record.
▶️ A Better Way to start EMA: Decaying compensation
Think of it this way: An ideal EMA uses an infinite history of prices, but we only have data starting from a specific point. This creates a problem - our EMA starts with an incorrect assumption that all previous prices were all zero, all close, or all average – like trying to write someone's biography but only having information about their life since last Tuesday.
But there is a better way. It requires more than high school math comprehension and is more computationally intensive, but is mathematically correct and numerically stable. This approach involves compensating calculated EMA values for the "phantom data" that would have existed before our first price point.
Here's how phantom data compensation works:
We start our normal EMA calculation:
EMA_today = EMA_yesterday + α × (Price_today - EMA_yesterday)
But we add a correction factor that adjusts for the missing history:
Correction = 1 at the start
Correction = Correction × (1-α) after each calculation
We then apply this correction:
True_EMA = Raw_EMA / (1-Correction)
This correction factor starts at 1 (full compensation effect) and gets exponentially smaller with each new price bar. After enough data points, the correction becomes so small (i.e., below 0.0000000001) that we can stop applying it as it is no longer relevant.
Let's see how this works in practice:
For the first price bar:
Raw_EMA = 0
Correction = 1
True_EMA = Price (since 0 ÷ (1-1) is undefined, we use the first price)
For the second price bar:
Raw_EMA = α × (Price_2 - 0) + 0 = α × Price_2
Correction = 1 × (1-α) = (1-α)
True_EMA = α × Price_2 ÷ (1-(1-α)) = Price_2
For the third price bar:
Raw_EMA updates using the standard formula
Correction = (1-α) × (1-α) = (1-α)²
True_EMA = Raw_EMA ÷ (1-(1-α)²)
With each new price, the correction factor shrinks exponentially. After about -log₁₀(1e-10)/log₁₀(1-α) bars, the correction becomes negligible, and our EMA calculation matches what we would get if we had infinite historical data.
This approach provides accurate EMA values from the very first calculation. There's no need to use SMA for warmup or discard early values before output converges - EMA is mathematically correct from first value, ready to party without the awkward warmup phase.
Here is Pine Script 6 implementation of EMA that can take alpha parameter directly (or period if desired), returns valid values from the start, is resilient to dirty input values, uses decaying compensator instead of SMA, and uses the least amount of computational cycles possible.
// Enhanced EMA function with proper initialization and efficient calculation
ema(series float source, simple int period=0, simple float alpha=0)=>
// Input validation - one of alpha or period must be provided
if alpha<=0 and period<=0
runtime.error("Alpha or period must be provided")
// Calculate alpha from period if alpha not directly specified
float a = alpha > 0 ? alpha : 2.0 / math.max(period, 1)
// Initialize variables for EMA calculation
var float ema = na // Stores raw EMA value
var float result = na // Stores final corrected EMA
var float e = 1.0 // Decay compensation factor
var bool warmup = true // Flag for warmup phase
if not na(source)
if na(ema)
// First value case - initialize EMA to zero
// (we'll correct this immediately with the compensation)
ema := 0
result := source
else
// Standard EMA calculation (optimized formula)
ema := a * (source - ema) + ema
if warmup
// During warmup phase, apply decay compensation
e *= (1-a) // Update decay factor
float c = 1.0 / (1.0 - e) // Calculate correction multiplier
result := c * ema // Apply correction
// Stop warmup phase when correction becomes negligible
if e <= 1e-10
warmup := false
else
// After warmup, EMA operates without correction
result := ema
result // Return the properly compensated EMA value
▶️ CONCLUSION
EMA isn't just a "better SMA"—it is a fundamentally different tool, like how a submarine differs from a sailboat – both float, but the similarities end there. EMA responds to inputs differently, weighs historical data differently, and requires different initialization techniques.
By understanding these differences, traders can make more informed decisions about when and how to use EMA in trading strategies. And as EMA is embedded in so many other complex and compound indicators and strategies, if system uses tainted and inferior EMA calculatiomn, it is doing a disservice to all derivative indicators too – like building a skyscraper on a foundation of Jell-O.
The next time you add an EMA to your chart, remember: you're not just looking at a "faster moving average." You're using an INFINITE IMPULSE RESPONSE filter that carries the echo of all previous price actions, properly weighted to help make better trading decisions.
EMA done right might significantly improve the quality of all signals, strategies, and trades that rely on EMA somewhere deep in its algorithmic bowels – proving once again that math skills are indeed useful after high school, no matter what your guidance counselor told you.
