BySq - Market PsychologyThe script I provided is a Market Psychology Index indicator for TradingView, which focuses on three key psychological market phases:
FOMO (Fear of Missing Out)
Panic Selling
Reversal
This indicator uses volume, price changes, and specific time periods to gauge market sentiment. Let me break it down:
1. Input Parameters:
FOMO Period: Defines how many bars (candles) the FOMO index will consider for its calculation.
Panic Period: Defines the period to evaluate Panic Selling.
Reversal Period: Defines the period to evaluate potential price reversals.
You can adjust these periods based on your analysis preferences. The default for each period is 14.
2. FOMO Index:
The FOMO Index aims to capture the "fear of missing out" behavior in the market.
It uses volume and price change:
Volume is compared to the Simple Moving Average (SMA) of volume over the specified period.
Price change is calculated as the percentage change in price compared to the previous bar.
If both volume and price change indicate strong upward movement, the FOMO index spikes.
3. Panic Selling Index:
The Panic Selling Index captures when traders are selling out of fear, often in a rapid or irrational way.
Similar to the FOMO Index, it considers volume and price change:
It uses volume and compares it to the SMA of volume for the panic period.
Price change is negative, meaning it considers only price drops.
When there is high volume coupled with significant price drops, it signals panic selling.
4. Reversal Index:
The Reversal Index aims to detect potential trend reversals in the market.
This index also considers volume and price change:
It focuses on upward price movement and compares volume to its SMA.
If there’s strong upward price movement along with increasing volume, it signals the possibility of a price reversal.
5. Graphical Output:
Histograms are drawn on the chart for each of the three indices:
FOMO is shown in green (indicating the presence of FOMO) and red (when the index is low).
Panic Selling is shown in orange.
Reversal is shown in purple.
The Zero Line (horizontal dotted line) helps identify when any of the indices is positive or negative.
6. Labels:
Labels for each index are shown on the chart at the relevant bar when the index spikes.
FOMO is labeled "FOMO" in green when it spikes.
Panic Selling is labeled "Panic Selling" in orange when it spikes.
Reversal is labeled "Reversal" in purple when it spikes.
Additionally, period labels show above the chart, indicating the specific periods (FOMO, Panic, and Reversal periods) currently being applied. This provides clarity on what time frame each index is analyzing.
7. How to Use:
FOMO: High values may indicate that traders are buying out of fear of missing out on a rally, suggesting a potentially overheated market.
Panic Selling: High values could suggest irrational selling behavior or capitulation, potentially marking the bottom of a downtrend.
Reversal: High values signal the potential for a market reversal, where the price could change direction due to increased volume and upward movement.
8. Visual Appearance:
The indicator’s histograms change colors based on the level of market sentiment detected. The color-coded approach provides an easy-to-read visual representation of different psychological phases in the market.
The horizontal zero line allows easy differentiation between positive and negative values.
Summary:
This script combines the psychology of the market (FOMO, Panic Selling, and Reversal) into a set of indicators that help traders identify potential turning points or emotional states in the market. By focusing on volume and price change, the script attempts to give a clear picture of market sentiment and possible future movements.
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TrendingNowTrendingNow Indicator - An Experimental Study
Introduction:
The TrendingNow indicator is an experimental study designed to identify trending market conditions and potential trading opportunities. It combines various technical analysis tools and parameters to provide insights into trend direction, momentum, volume, and price reversals.
Methodology:
The TrendingNow indicator is calculated based on the following parameters and calculations:
Moving Average: A simple moving average (SMA) is calculated using the specified length parameter. It helps smooth out price fluctuations and identify the overall trend direction.
Upper and Lower Bands: The upper and lower bands are derived from the moving average by adding and subtracting a deviation calculated using the multiplier parameter. These bands provide dynamic levels for potential trend reversals.
Price Reversals: The indicator detects price reversals by identifying when the price crosses above or below the upper or lower bands. These reversals suggest potential entry or exit points in the market.
Trend Confirmation: The indicator uses a moving average of the closing prices over the confirmation length parameter to confirm the overall trend direction. It helps filter out false signals and validates the presence of a trend.
Momentum Oscillator: The indicator calculates the relative strength index (RSI) over the momentum length parameter. The RSI measures the speed and change of price movements, indicating potential overbought and oversold conditions.
Volume Trend Confirmation: The study compares the current volume with the average volume over the specified length. If the current volume is above the volume threshold, it suggests increasing volume activity and potential confirmation of the trend.
Volatility Filter: The indicator incorporates an average true range (ATR) calculation to assess market volatility. The volatility threshold is derived by multiplying the ATR by the volatility multiplier parameter. It helps filter out signals during periods of low volatility.
Experimental Study:
The TrendingNow indicator aims to experiment with the combination of these technical analysis tools to identify trending market conditions and potential trading opportunities. By monitoring the price reversals, trend confirmation, momentum, volume trends, and volatility, traders can potentially identify high-probability trade setups.
The study involves observing the indicator's signals and assessing their effectiveness in different market conditions. Traders can experiment with different parameter values, timeframes, and asset classes to optimize the indicator's performance.
Usage and Interpretation:
When using the TrendingNow indicator, traders can consider the following guidelines:
Trend Identification: A bullish trend is indicated when the price is above the upper band, the moving average is rising, and the trend confirmation is positive. A bearish trend is indicated when the price is below the lower band, the moving average is declining, and the trend confirmation is negative.
Price Reversals: Price crossing above the upper band may suggest a potential selling opportunity, while price crossing below the lower band may indicate a potential buying opportunity. These reversals should be confirmed by other indicators and market conditions.
Momentum and Volume Confirmation: Traders can pay attention to the RSI levels to assess overbought and oversold conditions. High volume activity in line with the trend can provide additional confirmation.
Volatility Consideration: Traders may choose to adjust the volatility multiplier parameter based on the current market conditions. Higher values may be more suitable during periods of higher volatility, while lower values may be preferred during low volatility.
Conclusion:
The TrendingNow indicator offers an experimental approach to identifying trending market conditions and potential trading opportunities. Traders can customize the indicator parameters and combine it with other analysis techniques to suit their trading strategies. It is important to conduct thorough testing and validation before incorporating the indicator into live trading.
Disclaimer:
The information provided in this document, including the TrendingNow indicator and the accompanying experimental study, is for educational and experimental purposes only. It should not be considered as financial advice or a recommendation to engage in any trading or investment activities. Trading and investing in financial markets carry inherent risks, and past performance is not indicative of future results.
Before making any trading decisions, it is essential to conduct your own research, evaluate your risk tolerance, and consider your financial situation. The TrendingNow indicator is based on historical price data and technical analysis tools. However, it is important to understand that market conditions can change rapidly, and the indicator may not accurately predict future market movements or generate profitable trades in all situations.
The experimental study aims to explore the effectiveness of the TrendingNow indicator under different market conditions. However, the results obtained from the study are specific to historical data and may not necessarily be indicative of real-time market performance. It is recommended to exercise caution and use the indicator in conjunction with other analysis techniques and risk management strategies.
The TrendingNow indicator's parameters, such as length, multiplier, confirmation length, momentum length, overbought level, oversold level, volume threshold, and volatility multiplier, are adjustable inputs. Traders should carefully consider and test different parameter settings to suit their trading style and market conditions. Furthermore, it is important to regularly review and update the indicator's parameters as market dynamics change.
Trading in financial markets involves the potential for financial loss, and individuals should only trade with funds they can afford to lose. It is strongly advised to seek the guidance of a qualified financial professional or advisor before making any investment decisions.
By using the TrendingNow indicator and conducting the experimental study, you acknowledge that you are solely responsible for any trading decisions you make, and you agree to hold harmless the authors, developers, and distributors of this indicator for any losses, damages, or liabilities incurred as a result of your trading activities.
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.
ORB Pro w/ Filters + Debug Overlay Update with Reason box fixThis indicator is designed to highlight high-probability reversal setups for intraday traders.
It focuses on the cleanest, most reliable candlestick reversal patterns and combines them with trend, VWAP/EMA confluence, and a time-based filter to reduce noise.
🛠️ How It Works
The script scans each bar for well-known reversal signals:
Doji Reversal – small body, long wicks showing indecision.
Hammer / Shooting Star – long wick ≥ 2× body, showing exhaustion.
Engulfing Reversal – full body engulf of the prior candle.
Additional filters include:
✅ VWAP/EMA Confluence (optional) – confirms reversals near key intraday levels.
✅ Time Window (default 9:30–10:30 NY) – avoids false signals later in the session.
✅ Trend Exhaustion Check – requires a short-term directional push before reversal.
✅ Signal Cooldown – limits to one clean signal per move.
When conditions align, the script plots:
🟢 “Bull Rev” label below the bar for bullish reversals.
🔴 “Bear Rev” label above the bar for bearish reversals.
⚙️ Recommended Settings
For the tightest, most reliable signals:
Doji Body % → 25–30
Hammer Wick Multiple → 2.0
Confluence Tolerance % → 0.2–0.3
Time Filter → ON (9:30–10:30 NY)
VWAP/EMA Filter → ON
Cooldown Bars → 10–15
These settings minimize false positives and focus on the strongest reversals.
📈 Use Case
This tool is best for:
Intraday traders (stocks, ETFs, futures, crypto).
Traders who use Opening Range Breakout (ORB) or similar systems but want a secondary tool for catching reversals.
Anyone looking to filter out weak reversal patterns and focus on textbook setups.
⚠️ Disclaimer
This script is for educational purposes only and should not be considered financial advice. Always test in simulation/paper trading before applying live
🚀 Catch textbook reversals with confidence.
This indicator filters out noise and only plots high-probability reversal signals based on proven candlestick patterns + VWAP/EMA confluence.
🔥 Key Features:
✅ Detects Doji, Hammer/Shooting Star, and Engulfing Reversals
✅ VWAP & EMA confluence filter (optional)
✅ Time window filter (default 9:30–10:30 NY for max edge)
✅ Signal cooldown to avoid clutter
✅ Clean chart labels + alert conditions
🎯 Who’s It For?
Day traders who want precision reversal entries
ORB traders looking for secondary setups
Intraday scalpers who value quality over quantity
👉 Designed for traders who want fewer, cleaner, higher-probability signals.
⚠️ Not financial advice. For educational use only
_____
🎯 ORB SET-UP DESCRIPTIONS:
🔧 Exact settings I’d recommend (to avoid that mess):
requireClose = true
requireRetest = true with retestPct = 0.2%
minRangePct = 0.3%, maxRangePct = 1.5%
volumeFilter = true, volumeLength = 20
trendFilter = true, emaLength = 20
cooldownBars = 6 (on 5m chart → 30 minutes)
🔑 ORB Range Settings
Default sweet spot: 0.2% – 0.3%
→ This usually balances enough signals with reduced false breakouts.
High volatility days (CPI, FOMC, big gaps): 0.3% – 0.5%
→ Prevents fake outs.
Low volatility days (tight overnight range, slow open): 0.15% – 0.2%
→ Keeps you from sitting on hands all day.
📌 Filters you already added help you avoid noise
EMA alignment
Volume confirmation
Optional stop/target logic
This means you don’t have to shrink the box to 0.1% — the filters will keep you in higher-probability trades
✅ Why You Might NOT See a Signal
Check box for reason signal to turn it off, updated coloring so that candles are more visable.
ORB Box Too Wide
If the opening range is large, price has to move much further to trigger a clean breakout.
Wide box = fewer signals (but higher quality).
No Clean Break + Hold
Script waits for a candle to break above/below ORB and close strong enough.
A wick poke doesn’t count.
VWAP / EMA Filter Not Aligned
If price breaks but VWAP/EMA trend filter disagrees → no signal.
Keeps you out of fake moves against the trend.
Confirmation Candle Missing (if enabled)
Even if price breaks, the script may want the next bar to confirm direction before signaling.
Cooldown / One-Signal-Per-Break Rule
Some filters prevent back-to-back spam signals.
Only the first clean setup is alerted.
Kairos BarakahTrade with precision during high-probability windows using this advanced Pine Script indicator, designed specifically for Indian Standard Time (IST). The tool identifies key reversal opportunities within a user-defined trading session, combining time-based reference levels, sequence-validated signals, and multi-factor win probability analysis for confident decision-making.
Key Features
1. Time-Based Reference Levels
Automatically sets high/low reference levels at a customizable start time (default: 19:00 IST).
Active trading window with adjustable duration (default: 135 minutes).
Clear visual reference lines for easy tracking.
2. Intelligent Signal Generation
Initial Signals:
Buy (B): Triggered when price closes above the reference high.
Sell (S): Triggered when price closes below the reference low.
Reversal Signals (R):
Valid only after an initial signal, ensuring proper sequence.
Buy Reversal: Price closes above reference high (after a Sell signal).
Sell Reversal: Price closes below reference low (after a Buy signal).
3. Multi-Dimensional Win Probability
Body Strength: Measures candle conviction (body size / total range).
Volume Confirmation: Compares current volume to 20-period average.
Trend Alignment: Uses EMA crosses (9/21) and RSI (14) for momentum.
Composite Score: Weighted blend of all factors, color-coded for quick interpretation:
🟢 >70%: High-confidence signal.
🟠 40-69%: Moderate confidence.
🔴 <40%: Weak signal.
4. Professional Visualization
Clean labels (B/S/R) at signal points.
Real-time reference table showing levels, active signal, and probabilities.
Customizable alerts for all signal types.
Why Use This Indicator?
IST-Optimized: Tailored for Indian market hours.
Rules-Based Reversals: Avoids false signals with strict sequence checks.
Data-Driven Confidence: Win probability metrics reduce guesswork.
Flexible Setup: Adjust time windows and parameters to fit your strategy.
Probability Grid [LuxAlgo]The Probability Grid tool allows traders to see the probability of where and when the next reversal would occur, it displays a 10x10 grid and/or dashboard with the probability of the next reversal occurring beyond each cell or within each cell.
🔶 USAGE
By default, the tool displays deciles (percentiles from 0 to 90), users can enable, disable and modify each percentile, but two of them must always be enabled or the tool will display an error message alerting of it.
The use of the tool is quite simple, as shown in the chart above, the further the price moves on the grid, the higher the probability of a reversal.
In this case, the reversal took place on the cell with a probability of 9%, which means that there is a probability of 91% within the square defined by the last reversal and this cell.
🔹 Grid vs Dashboard
The tool can display a grid starting from the last reversal and/or a dashboard at three predefined locations, as shown in the chart above.
🔶 DETAILS
🔹 Raw Data vs Normalized Data
By default the tool displays the normalized data, this means that instead of using the raw data (price delta between reversals) it uses the returns between each reversal, this is useful to make an apples to apples comparison of all the data in the dataset.
This can be seen in the left side of the chart above (BTCUSD Daily chart) where normalize data is disabled, the percentiles from 0 to 40 overlap and are indistinguishable from each other because the tool uses the raw price delta over the entire bitcoin history, with normalize data enabled as we can see in the right side of the chart we can have a fair comparison of the data over the entire history.
🔹 Probability Beyond or Within Each Cell
Two different probability modes are available, the default mode is Probability Beyond Each Cell, the number displayed in each cell is the probability of the next reversal to be located in the area beyond the cell, for example, if the cell displays 20%, it means that in the area formed by the square starting from the last reversal and ending at the cell, there is an 80% probability and outside that square there is a 20% probability for the location of the next reversal.
The second probability mode is the probability within each cell, this outlines the chance that the next reversal will be within the cell, as we can see on the right chart above, when using deciles as percentiles (default settings), each cell has the same 1% probability for the 10x10 grid.
🔶 SETTINGS
Swing Length: The maximum length in bars used to identify a swing
Maximum Reversals: Maximum number of reversals included in calculations
Normalize Data: Use returns between swings instead of raw price
Probability: Choose between two different probability modes: beyond and inside each cell
Percentiles: Enable/disable each of the ten percentiles and select the percentile number and line style
🔹 Dashboard
Show Dashboard: Enable or disable the dashboard
Position: Choose dashboard location
Size: Choose dashboard size
🔹 Style
Show Grid: Enable or disable the grid
Size: Choose grid text size
Colors: Choose grid background colors
Show Marks: Enable/disable reversal markers
Volume Weighted RSI (VW RSI)The Volume Weighted RSI (VW RSI) is a momentum oscillator designed for TradingView, implemented in Pine Script v6, that enhances the traditional Relative Strength Index (RSI) by incorporating trading volume into its calculation. Unlike the standard RSI, which measures the speed and change of price movements based solely on price data, the VW RSI weights its analysis by volume, emphasizing price movements backed by significant trading activity. This makes the VW RSI particularly effective for identifying bullish or bearish momentum, overbought/oversold conditions, and potential trend reversals in markets where volume plays a critical role, such as stocks, forex, and cryptocurrencies.
Key Features
Volume-Weighted Momentum Calculation:
The VW RSI calculates momentum by comparing the volume associated with upward price movements (up-volume) to the volume associated with downward price movements (down-volume).
Up-volume is the volume on bars where the closing price is higher than the previous close, while down-volume is the volume on bars where the closing price is lower than the previous close.
These volumes are smoothed over a user-defined period (default: 14 bars) using a Running Moving Average (RMA), and the VW RSI is computed using the formula:
\text{VW RSI} = 100 - \frac{100}{1 + \text{VoRS}}
where
\text{VoRS} = \frac{\text{Average Up-Volume}}{\text{Average Down-Volume}}
.
Oscillator Range and Interpretation:
The VW RSI oscillates between 0 and 100, with a centerline at 50.
Above 50: Indicates bullish volume momentum, suggesting that volume on up bars dominates, which may signal buying pressure and a potential uptrend.
Below 50: Indicates bearish volume momentum, suggesting that volume on down bars dominates, which may signal selling pressure and a potential downtrend.
Overbought/Oversold Levels: User-defined thresholds (default: 70 for overbought, 30 for oversold) help identify potential reversal points:
VW RSI > 70: Overbought, indicating a possible pullback or reversal.
VW RSI < 30: Oversold, indicating a possible bounce or reversal.
Visual Elements:
VW RSI Line: Plotted in a separate pane below the price chart, colored dynamically based on its value:
Green when above 50 (bullish momentum).
Red when below 50 (bearish momentum).
Gray when at 50 (neutral).
Centerline: A dashed line at 50, optionally displayed, serving as the neutral threshold between bullish and bearish momentum.
Overbought/Oversold Lines: Dashed lines at the user-defined overbought (default: 70) and oversold (default: 30) levels, optionally displayed, to highlight extreme conditions.
Background Coloring: The background of the VW RSI pane is shaded red when the indicator is in overbought territory and green when in oversold territory, providing a quick visual cue of potential reversal zones.
Alerts:
Built-in alerts for key events:
Bullish Momentum: Triggered when the VW RSI crosses above 50, indicating a shift to bullish volume momentum.
Bearish Momentum: Triggered when the VW RSI crosses below 50, indicating a shift to bearish volume momentum.
Overbought Condition: Triggered when the VW RSI crosses above the overbought threshold (default: 70), signaling a potential pullback.
Oversold Condition: Triggered when the VW RSI crosses below the oversold threshold (default: 30), signaling a potential bounce.
Input Parameters
VW RSI Length (default: 14): The period over which the up-volume and down-volume are smoothed to calculate the VW RSI. A longer period results in smoother signals, while a shorter period increases sensitivity.
Overbought Level (default: 70): The threshold above which the VW RSI is considered overbought, indicating a potential reversal or pullback.
Oversold Level (default: 30): The threshold below which the VW RSI is considered oversold, indicating a potential reversal or bounce.
Show Centerline (default: true): Toggles the display of the 50 centerline, which separates bullish and bearish momentum zones.
Show Overbought/Oversold Lines (default: true): Toggles the display of the overbought and oversold threshold lines.
How It Works
Volume Classification:
For each bar, the indicator determines whether the price movement is upward or downward:
If the current close is higher than the previous close, the bar’s volume is classified as up-volume.
If the current close is lower than the previous close, the bar’s volume is classified as down-volume.
If the close is unchanged, both up-volume and down-volume are set to 0 for that bar.
Smoothing:
The up-volume and down-volume are smoothed using a Running Moving Average (RMA) over the specified period (default: 14 bars) to reduce noise and provide a more stable measure of volume momentum.
VW RSI Calculation:
The Volume Relative Strength (VoRS) is calculated as the ratio of smoothed up-volume to smoothed down-volume.
The VW RSI is then computed using the standard RSI formula, but with volume data instead of price changes, resulting in a value between 0 and 100.
Visualization and Alerts:
The VW RSI is plotted with dynamic coloring to reflect its momentum direction, and optional lines are drawn for the centerline and overbought/oversold levels.
Background coloring highlights overbought and oversold conditions, and alerts notify the trader of significant crossings.
Usage
Timeframe: The VW RSI can be used on any timeframe, but it is particularly effective on intraday charts (e.g., 1-hour, 4-hour) or daily charts where volume data is reliable. Shorter timeframes may require a shorter length for increased sensitivity, while longer timeframes may benefit from a longer length for smoother signals.
Markets: Best suited for markets with significant and reliable volume data, such as stocks, forex, and cryptocurrencies. It may be less effective in markets with low or inconsistent volume, such as certain futures contracts.
Trading Strategies:
Trend Confirmation:
Use the VW RSI to confirm the direction of a trend. For example, in an uptrend, look for the VW RSI to remain above 50, indicating sustained bullish volume momentum, and consider buying on pullbacks when the VW RSI dips but stays above 50.
In a downtrend, look for the VW RSI to remain below 50, indicating sustained bearish volume momentum, and consider selling on rallies when the VW RSI rises but stays below 50.
Overbought/Oversold Conditions:
When the VW RSI crosses above 70, the market may be overbought, suggesting a potential pullback or reversal. Consider taking profits on long positions or preparing for a short entry, but confirm with price action or other indicators.
When the VW RSI crosses below 30, the market may be oversold, suggesting a potential bounce or reversal. Consider entering long positions or covering shorts, but confirm with additional signals.
Divergences:
Look for divergences between the VW RSI and price to spot potential reversals. For example, if the price makes a higher high but the VW RSI makes a lower high, this bearish divergence may signal an impending downtrend.
Conversely, if the price makes a lower low but the VW RSI makes a higher low, this bullish divergence may signal an impending uptrend.
Momentum Shifts:
A crossover above 50 can signal the start of bullish momentum, making it a potential entry point for long trades.
A crossunder below 50 can signal the start of bearish momentum, making it a potential entry point for short trades or an exit for long positions.
Example
On a 4-hour SOLUSDT chart:
During an uptrend, the VW RSI might rise above 50 and stay there, confirming bullish volume momentum. If it approaches 70, it may indicate overbought conditions, as seen near a price peak of 145.08, suggesting a potential pullback.
During a downtrend, the VW RSI might fall below 50, confirming bearish volume momentum. If it drops below 30 near a price low of 141.82, it may indicate oversold conditions, suggesting a potential bounce, as seen in a slight recovery afterward.
A bullish divergence might occur if the price makes a lower low during the downtrend, but the VW RSI makes a higher low, signaling a potential reversal.
Limitations
Lagging Nature: Like the traditional RSI, the VW RSI is a lagging indicator because it relies on smoothed data (RMA). It may not react quickly to sudden price reversals, potentially missing the start of new trends.
False Signals in Ranging Markets: In choppy or ranging markets, the VW RSI may oscillate around 50, generating frequent crossovers that lead to false signals. Combining it with a trend filter (e.g., ADX) can help mitigate this.
Volume Data Dependency: The VW RSI relies on accurate volume data, which may be inconsistent or unavailable in some markets (e.g., certain forex pairs or futures contracts). In such cases, the indicator’s effectiveness may be reduced.
Overbought/Oversold in Strong Trends: During strong trends, the VW RSI can remain in overbought or oversold territory for extended periods, leading to premature exit signals. Use additional confirmation to avoid exiting too early.
Potential Improvements
Smoothing Options: Add options to use different smoothing methods (e.g., EMA, SMA) instead of RMA for the up/down volume calculations, allowing users to adjust the indicator’s responsiveness.
Divergence Detection: Include logic to detect and plot bullish/bearish divergences between the VW RSI and price, providing visual cues for potential reversals.
Customizable Colors: Allow users to customize the colors of the VW RSI line, centerline, overbought/oversold lines, and background shading.
Trend Filter: Integrate a trend strength filter (e.g., ADX > 25) to ensure signals are generated only during strong trends, reducing false signals in ranging markets.
The Volume Weighted RSI (VW RSI) is a powerful tool for traders seeking to incorporate volume into their momentum analysis, offering a unique perspective on market dynamics by emphasizing price movements backed by significant trading activity. It is best used in conjunction with other indicators and price action analysis to confirm signals and improve trading decisions.
Multi-Timeframe Stochastic OverviewPurpose of the Multi-Timeframe Stochastic Indicator:
The Multi-Timeframe Stochastic Indicator provides a consolidated view of market conditions across multiple timeframes (M1, M5, M15, H1) based on the Stochastic Oscillator, a popular technical analysis tool. The main objective is to allow traders to quickly assess momentum and potential trend reversals across different timeframes on a single chart, helping to make informed trading decisions.
---
General Purpose of Stochastic Oscillator:
The Stochastic Oscillator measures the relationship between a security's closing price and its price range over a given period, aiming to identify momentum, overbought/oversold levels, and potential reversal points. It works on the assumption that:
1. In uptrends, prices tend to close near their highs.
2. In downtrends, prices tend to close near their lows.
It consists of two lines:
%K (fast line): Represents the raw Stochastic value.
%D (slow line): A moving average of %K, used to smooth the data for better signals.
The indicator is generally used to:
Identify Overbought (price above 80% threshold) and Oversold (price below 20% threshold) conditions.
Spot Bullish and Bearish divergences for potential trend reversals.
Evaluate momentum strength within a trend.
---
How This Multi-Timeframe Indicator Enhances Stochastic's Utility:
1. Multi-Timeframe Overview:
The indicator calculates Stochastic values for multiple timeframes (1-minute, 5-minute, 15-minute, and 1-hour) and displays their market conditions (e.g., Bullish, Bearish, Overbought, Oversold, or Indecision) in an organized table format.
This gives traders a broad perspective on short-term, mid-term, and long-term trends simultaneously.
2. Market Condition Summary:
Bullish: Indicates upward momentum (both %K and %D > 50%).
Bearish: Indicates downward momentum (both %K and %D < 50%).
Overbought: Suggests potential trend exhaustion (both %K and %D > 80%).
Oversold: Suggests a potential reversal to the upside (both %K and %D < 20%).
Indecision: Highlights uncertainty when %K and %D are on opposite sides of the 50% level.
3. Quick Decision-Making:
The color-coded table (green for Bullish/Overbought, red for Bearish/Oversold, orange for Indecision) allows traders to quickly identify dominant conditions and momentum alignment across timeframes, helping in trade confirmation.
4. Trend Analysis:
By observing alignment or divergence in market conditions across timeframes, traders can gauge the strength of a trend or anticipate reversals. For example:
If all timeframes show "Bullish," it suggests strong momentum.
If smaller timeframes are "Overbought" while larger ones are "Bearish," it warns of a possible pullback.
5. Customizable Parameters:
The indicator allows customization of Stochastic K, D, smoothing values, and overbought/oversold levels, enabling users to tailor the analysis to specific trading styles or market conditions.
---
Use Cases:
1. Scalping:
A scalper can use lower timeframes (e.g., M1, M5) to find overbought/oversold zones for quick trades.
2. Swing Trading:
Swing traders can align smaller timeframes with higher ones (e.g., M15 and H1) to confirm momentum before entering a trade.
3. Trend Reversals:
Overbought or oversold conditions across all timeframes may indicate a major reversal point, helping traders plan exits or countertrend entries.
4. Trend Continuation:
Consistent bullish or bearish conditions across all timeframes confirm the continuation of a trend, providing confidence to hold positions.
---
Summary:
This indicator enhances the traditional Stochastic Oscillator by giving a multi-timeframe snapshot of market momentum, overbought/oversold conditions, and trend direction. It enables traders to quickly assess the overall market state, spot opportunities, and make more informed trading decisions.
GL Gann Swing IndicatorIntroduction
The GL Gann Swing Indicator is a versatile tool designed to help traders identify market trends, support and resistance areas, and potential reversals. This indicator applies the principles of Gann Swing Charts, a technique developed by W.D. Gann, which focuses on market swings to determine the overall direction and turning points of price action. Gann Swing Charts are a time-tested method of technical analysis that simplifies price action by focusing on significant highs and lows, thereby eliminating market noise and providing a clearer view of the trend.
By analyzing price action and determining swing directions and turning points, the indicator filters out market noise using four distinct bar types:
Up Bar: Higher High, Higher Low
Down Bar: Lower High, Lower Low
Inside Bar: Lower High, Higher Low
Outside Bar: Higher High, Lower Low
This approach helps traders to:
Identify the primary trend direction.
Determine key support and resistance levels.
Recognize potential reversal points.
Filter out minor price fluctuations that do not affect the overall trend.
Features
Bar Types: Display bar types by checking the Show Bar Type box in the indicator's settings. Up bars appear as green upward-pointing triangles, down bars as red downward-pointing triangles, inside bars as grey circles, and outside bars as blue diamonds. These visual aids help traders quickly identify the type of bar and its significance.
Break Lines: These lines highlight when the price rises above a previous swing high or falls below a prior swing low. Green lines indicate breaks of swing highs, while red lines indicate breaks of swing lows. Break lines are enabled by default but can be turned off in the indicator's settings. Break lines provide visual confirmation of trend continuation or reversal.
Bar Count: Bar counts help determine if a swing is overextended and if a reversal is likely. This feature is off by default but can be enabled in the indicator's settings. Users can set a minimum bar count to focus on significant swings. Analyzing the number of bars in a swing can help traders gauge the strength and potential exhaustion of a trend.
Swing MA (Moving Averages): This feature plots the average of a user-defined number of previous swing highs and lows. Options are available to add two moving averages, allowing for both fast and slow averages. Swing MAs can be enabled in the indicator's settings. These moving averages smooth out the price data, making it easier to identify the underlying trend direction.
Why This Indicator is Useful
The GL Gann Swing Indicator is particularly useful for several reasons:
Trend Identification: By focusing on significant price swings, the indicator helps traders identify the primary trend direction, making it easier to align trades with the overall market movement.
Noise Reduction: The indicator filters out minor price fluctuations, allowing traders to focus on meaningful market movements and avoid being misled by short-term volatility.
Support and Resistance Levels: By highlighting key swing highs and lows, the indicator helps traders identify crucial support and resistance levels, which are essential for making informed trading decisions.
Potential Reversals: The indicator's ability to identify overextended swings and potential reversal points can help traders anticipate market turning points and adjust their strategies accordingly.
Customizability: With options to display bar types, break lines, bar counts, and swing moving averages, traders can customize the indicator to suit their specific trading style and preferences.
By incorporating Gann Swing principles, the GL Gann Swing Indicator offers traders a powerful tool to enhance their technical analysis, improve their trading decisions, and ultimately achieve better trading outcomes.
price action reversion bands - [SigmaStreet]█ OVERVIEW
The "Price Action Reversion Bands" is designed to help traders identify potential reversal zones through the integration of polynomial regression, fractal analysis, and pinbar detection. This tool overlays directly onto the price chart, providing dynamic visual cues and signals for market reversals. Its unique synthesis of these methodologies offers traders a powerful, multifaceted approach to market analysis.
█ CONCEPTS
Polynomial Regression Bands:
What It Does:
Models the main trend using a polynomial equation to create a middle trend line with dynamic support and resistance bands.
How It Works:
Calculates polynomial coefficients to plot a regression line and adjusts the bands according to market volatility and conditions.
Fibonacci Retracement Levels:
What It Does:
Provides additional lines inside the regression bands at key Fibonacci ratios to identify potential support and resistance areas.
How It Works:
Calculates retracement levels by identifying high and low points over the same period used to calculate the regression bands, applying Fibonacci ratios to these points.
Fractal Analysis:
What It Does: Identifies natural resistance and support levels, indicating potential reversal zones.
How It Works: Detects fractals based on a specific pattern of price action, using Williams Fractal methodology.
Pinbar Detection:
What It Does: Signals potential price reversals through pinbar candlestick patterns.
How It Works: Analyzes
candlesticks to identify pinbars which show a rejection of prices, suggesting possible reversals.
█ ORIGINALITY AND USEFULNESS
The price action reversion bands distinguishes itself through its innovative integration of several advanced analytical methods, providing traders with a holistic view of potential market reversals:
Unique Combination:
While many tools use these techniques in isolation, this indicator synergistically combines polynomial regression, Fibonacci retracement levels, fractal analysis, and pinbar detection. This multi-faceted approach allows traders to assess strength, potential reversal zones, and price rejection more effectively than using traditional single-method indicators.
Advanced Polynomial Regression Application:
Unlike standard regression tools that offer static insights, this indicator dynamically adjusts its regression bands based on real-time market volatility, providing a more accurate reflection of market conditions.
Enhanced Signal Reliability:
By using fractals and pinbars in conjunction to validate each other, the indicator significantly increases the reliability of its reversal signals. This dual-validation method filters out less probable signals, focusing on high-probability trading opportunities.
Customization and Flexibility:
It offers unprecedented customization options, allowing traders to fine-tune the tool according to their trading style and market conditions. Traders can adjust the polynomial degree, the sensitivity of the Fibonacci retracements, and even the definition of what constitutes a significant pinbar, making it highly adaptable to various trading scenarios.
Educational Value:
The indicator not only aids in trading but also serves as an educational tool that helps traders understand the interaction between different types of market analysis techniques. This contributes to a deeper knowledge base and better trading decisions over time.
These distinctive features make the "Price Action Reversion Bands - " not just another indicator but a comprehensive trading tool that enhances decision-making through a well-rounded analysis of market dynamics.
█ HOW TO USE
Installation and Setup:
Apply the indicator to your TradingView chart from the "Indicators" menu.
Select either polynomial regression or Fibonacci retracement as the basis for the bands through the indicator settings.
Reading the Indicator:
Monitor the approach of price to the upper and lower bands which indicate potential reversal zones.
Look for fractal and pinbar formations near these bands for additional signal confirmation.
Customization:
Adjust settings such as the polynomial degree, data window length, and engagement zones to tailor the bands to your trading style.
Modify visual aspects like color and line type for better clarity and personal preference.
█ FEATURES
Dynamic Adjustment:
Bands adjust in real-time based on incoming price data and selected settings.
Multiple Analysis Techniques: Combines several analytical techniques to provide a comprehensive view of potential market movements. The integration of polynomial regression with Fibonacci levels, supplemented by fractal and pinbar analysis, marks this tool as particularly innovative, offering a level of synthesis that enhances predictive accuracy and usability.
User-Friendly Customization: Allows for extensive customization to suit individual trading strategies and preferences.
█ LIMITATIONS
Market Dependency:
Performance may vary significantly across different markets and conditions.
Parameter Sensitivity: Requires fine-tuning of parameters to ensure optimal performance, which might demand a steep learning curve for new users.
█ NOTES
For best results, combine this tool with other forms of analysis, such as fundamental analysis and other technical indicators, to confirm signals and enhance decision-making.
█ THANKS
Special thanks to the PineCoders community the Pine Coders themselves for their foundational contributions to the concepts used in this script. Their pioneering work in the fields of technical analysis and Pine Script development has been invaluable. This script is a testament to the collaborative spirit of the TradingView developer community, integrating analytical techniques with innovative approaches to offer a tool that is both modern and cutting-edge.
Opening Score with DivergenceOverview
The Opening Score Indicator is a versatile tool designed to help traders assess market sentiment, trend direction, and potential reversals. By combining Opening Range Breakout (ORB), VWAP, Trend, Volatility, and Divergence Detection, this indicator provides a composite score that adapts to different market conditions.
This version includes divergence detection between the Opening Score and price, which highlights potential trend reversals or continuations before they happen. When a regular divergence occurs, the histogram bar turns orange, signaling an increased probability of a trend change.
Best for Both Intraday & Longer-Term Charts
📊 Optimized for intraday trading → Works well on 1m to 30m timeframes for short-term strategies.
📈 Also effective on longer-term charts → Can be used on 1-hour, 4-hour, daily, or weekly charts to identify macro trends and momentum shifts.
🕰️ Adapts to different market conditions → Whether you’re a day trader, swing trader, or position trader, the Opening Score helps you track trend health and reversals.
How It Works
📊 Composite Opening Score Calculation
• ORB Signal → Detects bullish/bearish breakouts based on the opening range.
• VWAP Signal → Measures price positioning relative to VWAP for trend confirmation.
• Trend Signal → Uses a moving average to determine market direction.
• Volatility Signal → Tracks ATR changes to assess market strength.
• Divergence Detection → Identifies regular and hidden divergences for potential reversals or trend continuation.
🔹 Reversal Alerts with Color-Coded Histogram
• Green Bars → Normal bullish Opening Score.
• Red Bars → Normal bearish Opening Score.
• Orange Bars → Warning! Regular Divergence detected → Possible trend reversal.
🔹 Hidden & Regular Divergence Detection
• Regular Divergence (Reversal Signals)
• 📉 Bearish Regular Divergence → Price makes a Higher High, but Opening Score makes a Lower High → 🔻 Possible Downtrend Reversal.
• 📈 Bullish Regular Divergence → Price makes a Lower Low, but Opening Score makes a Higher Low → 🔼 Possible Uptrend Reversal.
• Hidden Divergence (Trend Continuation Signals)
• 📉 Bearish Hidden Divergence → Price makes a Lower High, but Opening Score makes a Higher High → 🔻 Trend Likely to Continue Down.
• 📈 Bullish Hidden Divergence → Price makes a Higher Low, but Opening Score makes a Lower Low → 🔼 Trend Likely to Continue Up.
How to Use It
✅ Watch for Reversal Alerts (Orange Bars) → These highlight potential market turning points.
✅ Use the Zero Line as a Trend Filter → A score above 0 suggests bullish conditions, while below 0 signals bearish conditions.
✅ Combine with Market Structure & Volume Profile → Works well when paired with support/resistance levels, liquidity zones, and order flow data.
✅ Adjust settings based on timeframe → Increase moving average length & lookback periods for longer-term analysis.
Why Use This Indicator?
🚀 Works for both short-term and long-term traders → Adapts to intraday and higher timeframes.
📊 Multi-Factor Analysis → Combines multiple key market indicators for better accuracy.
🎯 Customizable Weighting → Adjust the influence of each signal to suit your trading style.
✅ No Clutter – Only the Opening Score is plotted → Keeps your chart clean & efficient.
🔔 Recommended for Intraday Trading (1m – 30m) AND Longer-Term Analysis (1H – Weekly) → Use this indicator to enhance your trend detection & reversal strategy! 🚀
Bollinger Bands + RSI [Uncle Sam Trading]The Bollinger Bands + RSI indicator combines two popular technical analysis tools, Bollinger Bands (BB) and the Relative Strength Index (RSI), into a unified framework designed to assess both market volatility and momentum. This indicator provides both visual signals on the chart, and allows you to set alerts. It is intended to help traders identify potential overbought/oversold conditions, trend reversals, and to refine trade entry and exit points.
Key Features:
Bollinger Bands: The indicator plots Bollinger Bands, which consist of a basis line (typically a 20-period Simple Moving Average), an upper band (basis + 2 standard deviations), and a lower band (basis - 2 standard deviations). The bands dynamically adjust to market volatility, widening during periods of increased volatility and contracting during periods of decreased volatility.
Relative Strength Index (RSI): The RSI, a momentum oscillator, is plotted in a separate pane below the price chart. It measures the magnitude of recent price changes to evaluate overbought or oversold conditions in the price of a stock or other asset. Traditional interpretation uses 70 and 30 as overbought and oversold levels, respectively.
Overbought/Oversold Zones Highlighting: This indicator uniquely highlights overbought and oversold zones directly on the price chart based on the RSI values. When the RSI is above the overbought level (default 70), a red-shaded area is displayed. When the RSI is below the oversold level (default 30), a green-shaded area is displayed. These visual cues enhance the identification of potential trend reversals.
Buy and Sell Signals: The indicator generates buy signals when the price crosses above the lower Bollinger Band and the RSI is below the oversold level (if the RSI filter is enabled). Sell signals are generated when the price crosses below the upper Bollinger Band and the RSI is above the overbought level (if the RSI filter is enabled). These signals are plotted as green upward-pointing triangles (buy) and red downward-pointing triangles (sell) on the chart.
Customizable Parameters: Users can adjust various settings, including:
Bollinger Bands Length: The number of periods used to calculate the moving average and standard deviation.
Bollinger Bands Standard Deviation: The multiplier used to determine the distance of the upper and lower bands from the basis.
RSI Length: The number of periods used to calculate the RSI.
RSI Overbought/Oversold Levels: The threshold values that define overbought and oversold conditions for the RSI.
Use RSI Filter for Signals: Enable/disable the RSI filter for buy and sell signals.
Colors: The colors of the Bollinger Bands, RSI, overbought/oversold levels, and zone highlights can be customized to suit user preferences.
Alerts: The indicator supports customizable alerts for various conditions, including:
Buy Signal: Triggered when a buy signal is generated.
Sell Signal: Triggered when a sell signal is generated.
Price Crossed Upper BB: Triggered when the price crosses above the upper Bollinger Band.
Price Crossed Lower BB: Triggered when the price crosses below the lower Bollinger Band.
RSI Overbought: Triggered when the RSI crosses above the overbought level.
RSI Oversold: Triggered when the RSI crosses below the oversold level.
How to Use:
The Bollinger Bands + RSI indicator can be used in various ways, including:
Identifying Potential Trend Reversals: Price crosses above the lower band coupled with an oversold RSI (and highlighted zone) may signal a bullish reversal. Conversely, a price cross below the upper band with an overbought RSI (and highlighted zone) may indicate a bearish reversal.
Confirming Trend Strength: In an uptrend, the price may "ride" the upper band, while in a downtrend, it may "ride" the lower band.
Exit Signals: Crossing the opposite band while in a trade, particularly with confirming RSI signals, is often used to identify potential exit points.
Combined with Other Analysis: This indicator works well in conjunction with other technical analysis tools, such as trend lines, support/resistance levels, chart patterns, and moving average-based strategies.
Disclaimer:
This indicator is for educational and informational purposes only and should not be considered as financial advice. Trading involves risk, and past performance is not indicative of future results. Always conduct thorough research and consider your risk tolerance before making any trading decisions.
GMO (Gyroscopic Momentum Oscillator) GMO
Overview
This indicator fuses multiple advanced concepts to give traders a comprehensive view of market momentum, volatility, and potential turning points. It leverages the Gyroscopic Momentum Oscillator (GMO) foundation and layers on IQR-based bands, dynamic ATR-adjusted OB/OS levels, torque filtering, and divergence detection. The outcome is a versatile tool that can assist in identifying both short-term squeezes and long-term reversal zones while detecting subtle shifts in momentum acceleration.
Key Components:
Gyroscopic Momentum Oscillator (GMO) – A physics-inspired metric capturing trend stability and momentum by treating price dynamics as “angle,” “angular velocity,” and “inertia.”
IQR Bands – Highlight statistically typical oscillation ranges, providing insight into short-term squeezes and potential near-term trend shifts.
ATR-Adjusted OB/OS Levels – Dynamic thresholds for overbought/oversold conditions, adapting to volatility, aiding in identifying long-term potential reversal zones.
Torque Filtering & Scaling – Smooths and thresholds torque (the rate of change of momentum) and visually scales it for clarity, indicating sudden force changes that may precede volatility adjustments.
Divergence Detection – Highlights potential reversal cues by comparing oscillator swings against price swings, revealing regular and hidden bullish/bearish divergences.
Conceptual Insights
IQR Bands (Short-Term Squeeze & Trend Direction):
Short-Term Momentum and Squeeze: The IQR (Interquartile Range) bands show where the oscillator tends to “live” statistically. When the GMO line hovers within compressed IQR bands, it can signal a momentum squeeze phase. Exiting these tight ranges often correlates with short-term breakout opportunities.
Trend Reversals: If the oscillator pushes beyond these IQR ranges, it may indicate an emerging short-term trend change. Traders can watch for GMO escaping the IQR “comfort zone” to anticipate a new directional move.
Dynamic OB/OS Levels (Long-Term Reversal Zones):
ATR-Based Adaptive Thresholds: Instead of static overbought/oversold lines, this tool uses ATR to adjust OB/OS boundaries. In calm markets, these lines remain closer to ±90. As volatility rises, they approach ±100, reflecting greater permissible swings.
Long-Term Trend Reversal Potential: If GMO hits these dynamically adjusted OB/OS extremes, it suggests conditions ripe for possible long-term trend reversals. Traders seeking major inflection points may find these adaptive levels more reliable than fixed thresholds.
Torque (Sudden Force & Directional Shifts):
Momentum Acceleration Insight: Torque represents the second derivative of momentum, highlighting how quickly momentum is changing. High positive torque suggests a rapidly strengthening bullish force, while high negative torque warns of sudden bearish pressure.
Early Warning & Stability/Volatility Adjustments: By monitoring torque spikes, traders can anticipate momentum shifts before price fully confirms them. This can signal imminent changes in stability or increased volatility phases.
Indicator Parameters and Usage
GMO-Related Inputs:
lenPivot (Default 100): Length for calculating the pivot line (slow market axis).
lenSmoothAngle (Default 200): Smooths the angle measure, reducing noise.
lenATR (Default 14): ATR period for scaling factor, linking price changes to volatility.
useVolatility (Default true): If true, volatility (ATR) influences inertia, adjusting momentum calculations.
useVolume (Default false): If true, volume affects inertia, adding a liquidity dimension to momentum.
lenVolSmoothing (Default 50): Smooths volume calculations if useVolume is enabled.
lenMomentumSmooth (Default 20): EMA smoothing of GMO for a cleaner oscillator line.
normalizeRange (Default true): Normalizes GMO to a fixed range for consistent interpretation.
lenNorm (Default 100): Length for normalization window, ensuring GMO’s scale adapts to recent extremes.
IQR Bands Settings:
iqrLength (Default 14): Period to compute the oscillator’s statistical IQR.
iqrMult (Default 1.5): Multiplier to define the upper and lower IQR-based bands.
ATR-Adjusted OB/OS Settings:
baseOBLevel (Fixed at 90) and baseOSLevel (Fixed at 90): Base lines for OB/OS.
atrPeriodForOBOS (Default 50): ATR length for adjusting OB/OS thresholds dynamically.
atrScaling (Default 0.2): Controls how strongly volatility affects OB/OS lines.
Torque Filtering & Visualization:
torqueSmoothLength (Default 10): EMA length to smooth raw torque values.
atrPeriodForTorque (Default 14): ATR period to determine torque threshold.
atrTorqueScaling (Default 0.5): Scales ATR for determining torque’s “significant” threshold.
torqueScaleFactor (Default 10.0): Multiplies the torque values for better visual prominence on the chart.
Divergence Inputs:
showDivergences (Default true): Toggles divergence signals.
lbR, lbL (Defaults 5): Pivot lookback periods to identify swing highs and lows.
rangeUpper, rangeLower: Bar constraints to validate potential divergences.
plotBull, plotHiddenBull, plotBear, plotHiddenBear: Toggles for each divergence type.
Visual Elements on the Chart
GMO Line (Blue) & Zero Line (Gray):
GMO line oscillates around zero. Positive territory hints bullish momentum, negative suggests bearish.
IQR Bands (Teal Lines & Yellow Fill):
Upper/lower bands form a statistical “normal range” for GMO. The median line (purple) provides a central reference. Contraction near these bands indicates a short-term squeeze, expansions beyond them can signal emerging short-term trend changes.
Dynamic OB/OS (Red & Green Lines):
Red line near +90 to +100: Overbought zone (dynamic).
Green line near -90 to -100: Oversold zone (dynamic).
Movement into these zones may mark significant, longer-term reversal potential.
Torque Histogram (Colored Bars):
Plotted below GMO. Green bars = torque above positive threshold (bullish acceleration).
Red bars = torque below negative threshold (bearish acceleration).
Gray bars = neutral range.
This provides early warnings of momentum shifts before price responds fully.
Precession (Orange Line):
Scaled for visibility, adds context to long-term angular shifts in the oscillator.
Divergence Signals (Shapes):
Circles and offset lines highlight regular or hidden bullish/bearish divergences, offering potential reversal signals.
Practical Interpretation & Strategy
Short-Term Opportunities (IQR Focus):
If GMO compresses within IQR bands, the market might be “winding up.” A break above/below these bands can signal a short-term trade opportunity.
Long-Term Reversal Zones (Dynamic OB/OS):
When GMO approaches these dynamically adjusted extremes, conditions may be ripe for a major trend shift. This is particularly useful for swing or position traders looking for significant turnarounds.
Monitoring Torque for Acceleration Cues:
Torque spikes can precede price action, serving as an early catalyst signal. If torque turns strongly positive, anticipate bullish acceleration; strongly negative torque may warn of upcoming bearish pressure.
Confirm with Divergences:
Divergences between price and GMO reinforce potential reversal or continuation signals identified by IQR, OB/OS, or torque. Use them to increase confidence in setups.
Tips and Best Practices
Combine with Price & Volume Action:
While the indicator is powerful, always confirm signals with actual price structure, volume patterns, or other trend-following tools.
Adjust Lengths & Periods as Needed:
Shorter lengths = more responsiveness but more noise. Longer lengths = smoother signals but greater lag. Tune parameters to match your trading style and timeframe.
Use ATR and Volume Settings Wisely:
If markets are highly volatile, consider useVolatility to refine momentum readings. If liquidity is key, enable useVolume.
Scaling Torque:
If torque bars are hard to read, increase torqueScaleFactor further. The scaling doesn’t affect logic—only visibility.
Conclusion
The “GMO + IQR Bands + ATR-Adjusted OB/OS + Torque Filtering (Scaled)” indicator presents a holistic framework for understanding market momentum across multiple timescales and conditions. By interpreting short-term squeezes via IQR bands, long-term reversal zones via adaptive OB/OS, and subtle acceleration changes through torque, traders can gain advanced insights into when to anticipate breakouts, manage risk around potential reversals, and fine-tune timing for entries and exits.
This integrated approach helps navigate complex market dynamics, making it a valuable addition to any technical analysis toolkit.
Custom Moving Average Ribbon with EMA Table & Text ColorComprehensive Description of the Custom Moving Average Ribbon with EMA Table & Text Color
The Custom Moving Average Ribbon with EMA Table & Text Color is a highly flexible and customizable indicator designed for traders who use multiple moving averages to assess trends, strength, and potential market reversals. It plots up to 8 moving averages (either SMA, EMA, WMA, or VWMA) on the price chart and displays a table summarizing the moving averages’ values, periods, and colors. The table also allows for the customization of the text color, making it easier to align with your chart’s theme or preference.
Key Features:
Multiple Moving Averages: You can display up to 8 moving averages (MA), each of which can be customized in terms of:
Type: SMA (Simple Moving Average), EMA (Exponential Moving Average), WMA (Weighted Moving Average), or VWMA (Volume-Weighted Moving Average).
Period: Each moving average has a user-defined period, which allows for flexibility depending on your trading style (short-term, medium-term, or long-term).
Enable/Disable: Each moving average can be independently enabled or disabled based on your preference.
Moving Average Ribbon: The indicator visualizes multiple moving averages as a ribbon, giving traders insight into the market's underlying trend. The interaction between these moving averages provides essential signals:
Uptrend: Shorter-term MAs above longer-term MAs, all sloping upward.
Downtrend: Shorter-term MAs below longer-term MAs, sloping downward.
Consolidation: MAs tightly packed, indicating low volatility or a sideways market.
Customizable Table: The indicator includes a table that displays:
The Name of each moving average (e.g., MA 1, MA 2, etc.).
The Period used for each moving average.
The Current Value of each moving average.
Color Coding for easier visual identification on the chart.
Text Color Customization: You can change the text color in the table to match your chart style or to ensure high visibility.
Responsive Design: This indicator works on any time frame, whether you're a day trader, swing trader, or long-term investor, and the table adjusts dynamically as new data comes in.
How to Use the Indicator
a) Trend Identification
The Custom Moving Average Ribbon helps in identifying trends and their strength. Here’s how you can interpret the plotted moving averages:
Uptrend (Bullish):
If the shorter-term moving averages (e.g., 5-period, 10-period) are above the longer-term moving averages (e.g., 50-period, 200-period), and all the MAs are sloping upward, it suggests a strong bullish trend.
The greater the separation between the moving averages, the stronger the uptrend.
Use the table to quickly verify the current value of each MA and confirm that the price is staying above most or all of the MAs.
Downtrend (Bearish):
When shorter-term moving averages are below the longer-term moving averages and all MAs are sloping downward, this indicates a bearish trend.
Greater separation between MAs indicates a stronger downtrend.
Neutral/Consolidating Market:
If the MAs are tightly packed and frequently crossing each other, the market is likely consolidating, and a strong trend is not in play.
In these situations, it’s better to wait for a clearer signal before taking any positions.
b) Reversal Signals
Golden Cross: When a short-term moving average (e.g., 50-period) crosses above a long-term moving average (e.g., 200-period), this is considered a bullish signal, suggesting a possible upward trend.
Death Cross: When a short-term moving average crosses below a long-term moving average, it’s considered a bearish signal, indicating a potential downward trend.
c) Using the Table for Quick Reference
The table allows you to monitor:
The current price value relative to each moving average. If the price is above most MAs, the market is likely in an uptrend, and if below, in a downtrend.
Changes in MA values: If you see values of shorter-term MAs moving closer to or crossing longer-term MAs, this could indicate a weakening trend or a potential reversal.
How to Combine this Indicator with Other Indicators for a Solid Strategy
The Custom Moving Average Ribbon is powerful on its own but can be enhanced when combined with other technical indicators to form a comprehensive trading strategy.
1. Combining with RSI (Relative Strength Index)
How It Works: RSI is a momentum oscillator that measures the speed and change of price movements, typically over 14 periods. It ranges from 0 to 100, with readings above 70 considered overbought and below 30 considered oversold.
Strategy:
Overbought in an Uptrend: If the moving average ribbon indicates an uptrend but the RSI shows the market is overbought (RSI > 70), it could signal a pullback or correction is imminent.
Oversold in a Downtrend: If the moving average ribbon indicates a downtrend but the RSI shows oversold conditions (RSI < 30), a bounce or reversal may be on the horizon.
2. Combining with MACD (Moving Average Convergence Divergence)
How It Works: MACD tracks the difference between two exponential moving averages, typically the 12-period and 26-period EMAs. It generates buy and sell signals based on crossovers and divergences.
Strategy:
Trend Confirmation: Use the MACD to confirm the direction and momentum of the trend indicated by the moving average ribbon. For example, if the MACD line crosses above the signal line while the shorter-term MAs are above the longer-term MAs, it confirms strong bullish momentum.
Divergences: Watch for divergences between price action and MACD. If price is making higher highs but MACD is making lower highs, it could signal a weakening trend, which you can verify using the moving averages.
3. Combining with Bollinger Bands
How It Works: Bollinger Bands plot two standard deviations above and below a moving average, typically the 20-period SMA. The bands widen during periods of high volatility and contract during periods of low volatility.
Strategy:
Breakout or Reversal: If price action moves above the upper Bollinger Band while the shorter-term MAs are crossing above the longer-term MAs, it confirms a strong breakout. Conversely, if price touches or falls below the lower Bollinger Band and the shorter MAs start crossing below the longer-term MAs, it indicates a potential breakdown.
Mean Reversion: In sideways markets, when the moving averages are tightly packed, Bollinger Bands can help spot mean reversion opportunities (buy near the lower band, sell near the upper band).
4. Combining with Volume Indicators
How It Works: Volume is a crucial confirmation indicator for any trend or breakout. Combining volume with the moving average ribbon can enhance your strategy.
Strategy:
Trend Confirmation: If the price breaks above the moving averages and is accompanied by high volume, it confirms a strong breakout. Similarly, if price breaks below the moving averages on high volume, it signals a strong downtrend.
Divergence: If price continues to trend in one direction but volume decreases, it could indicate a weakening trend, helping you prepare for a reversal.
Example Strategies Using the Indicator
Trend-Following Strategy:
Use the moving average ribbon to identify the main trend.
Combine with MACD or RSI for confirmation of momentum.
Enter trades when the shorter-term MAs confirm the trend and the confirmation indicator (MACD or RSI) aligns with the trend.
Exit trades when the moving averages start converging or when your confirmation indicator shows signs of reversal.
Reversal Strategy:
Wait for significant crossovers in the moving averages (Golden Cross or Death Cross).
Confirm the reversal with divergence in MACD or RSI.
Use Bollinger Bands to fine-tune your entry and exit points based on overbought/oversold conditions.
Conclusion
The Custom Moving Average Ribbon with EMA Table & Text Color indicator provides a robust framework for traders looking to use multiple moving averages to gauge trend direction, strength, and potential reversals. By combining it with other technical indicators like RSI, MACD, Bollinger Bands, and volume, you can develop a solid trading strategy that enhances accuracy, reduces false signals, and maximizes profit potential in various market conditions.
This indicator offers high flexibility with customization options, making it suitable for traders of all levels and strategies. Whether you're trend-following, scalping, or swing trading, this tool provides invaluable insights into market movements.
Market Structure Trailing Stop [BigBeluga]The Market Structure Trailing Stop indicator is an advanced tool for identifying market structure shifts, liquidity sweeps, and potential trend reversals using comprehensive volume analysis. This indicator combines the analysis of market structure pivots (CHoCH - Change of Character) with a sophisticated volume-based trailing stop logic. By evaluating delta volume at key structural points, it allows traders to identify high-probability trend continuations or reversals and manage their trades more effectively.
🔵 KEY FEATURES
● Market Structure Analysis
Pivot-Based Market Structure : The indicator identifies high and lows using user-defined periods, allowing traders to spot key market structure shifts.
Change of Character (CHoCH) : The first significant break of a market structure is marked as a CHoCH, indicating a potential trend reversal.
Break of Structure (BoS) : The indicator highlights subsequent breaks of structure after CHoCH, providing traders with crucial insights into trend strength.
● Advanced Volume Analysis
Delta Volume Evaluation : The indicator calculates delta volume (difference between up and down volume) at each ChoCh or BoS market structure point to assess the strength of the move. Identify Delta Volume from break point back to Pivot
● Trailing Stop Logic
Volume-Validated Trailing Stop : The indicator automatically plots a trailing stop if the delta volume at the UP CHoCH is positive and above the defined threshold and vice versa for Down CHoCH , allowing traders to protect their profits while riding the trend.
Trend Weakness Detection : If a subsequent BoS occurs with negative delta volume or lower volume than the input threshold, the trailing stop disappears, indicating potential trend exhaustion or reversal.
Dynamic Stop Placement : The trailing stop is dynamically adjusted based on market structure and volume, providing traders with a more adaptive stop-loss strategy.
Up Trend Trailing Stop:
Down Trend Trailing Stop:
● Liquidity Sweep Detection
Liquidity Sweep (X) Labels : The indicator identifies liquidity sweeps—points where the price temporarily reverses to sweep liquidity above or below a key level—marked with an “X” label.
Potential Reversal Zones : These liquidity sweeps are potential reversal zones, especially when accompanied by significant delta volume changes, providing traders with early warnings of potential trend reversals.
🔵 HOW TO USE
● Identifying Market Structure Shifts
Change of Character (CHoCH) : When a CHoCH occurs, the indicator calculates the total volume from the high point to the break point. If the delta volume is positive and exceeds the input threshold, a trailing stop is plotted, signaling potential trend continuation.
Break of Structure (BoS) : If BoS is enabled, subsequent breaks of structure are highlighted. If these BoS points show weaker volume or negative delta volume, the trailing stop will disappear, indicating that the trend may be losing strength.
● Using the Trailing Stop Feature
Protecting Profits : Once a CHoCH occurs and the delta volume validates the trend, the trailing stop will be plotted below (or above) the price to protect profits while allowing the trend to run.
Trend Reversal Signals : If the trailing stop disappears due to weak volume at subsequent BoS points, it may signal that the trend is losing momentum, and traders may consider closing their positions or tightening their stops manually.
● Liquidity Sweep Interpretation
Spotting Reversal Zones : Liquidity sweeps, marked with an “X” label, indicate zones where the price has swept liquidity. These areas can serve as potential reversal zones, especially when significant delta volume is observed at these points.
Early Reversal Warnings : Traders can use these liquidity sweep labels as early warnings for potential trend reversals, particularly in conjunction with other technical analysis methods.
🔵 CUSTOMIZATION
Highs and Lows Calculation : Customize the number of bars to the left and right for identifying pivots and market structure shifts.
Volume Threshold : Define the volume threshold to filter out weaker moves and focus on significant market structure shifts.
BoS and Liquidity Sweep Labels : Toggle on or off the BoS and Liquidity Sweep labels to tailor the indicator to your trading style.
Trend Color : Enable or disable trend coloring for candles to visually highlight uptrends and downtrends on the chart.
🔵 CONCLUSION
The Market Structure Trailing Stop indicator combines advanced volume analysis with market structure detection to provide traders with a powerful tool for identifying and managing trends. By leveraging delta volume at key structure points, it helps traders validate trend strength and manage their positions with a dynamic trailing stop strategy. The addition of liquidity sweep detection further enhances its utility, offering early warnings of potential trend reversals. This indicator is ideal for traders who want to gain a deeper understanding of market structure while incorporating volume-based insights into their trading strategies.
RSI ATR Range [SS]Hey everyone,
Over the course of the last year I had a bunch of requests to do something with RSI. I did do an RSI expected move plotter, but the requests were to overhaul RSI and make it better I guess.
So here is my attempt!
This is the RSI ATR plotter. Its similar to my RSI expected move plotter, however, it gives you the ATR ranges associated with the current RSI value. This allows you to conceptualize RSI in a different way. Instead of looking for "oversold" over "overbought", you can actually just see the expected high to open range and the expected open to low range based on the current RSI.
This will allow you to determine such things as:
a) Is it likely to be bullish?
b) Is it likely to be bearish?
c) The average move, in a dollar amount, associated with this RSI.
In addition to presenting RSI in terms of ranges as opposed to the actual RSI value, the indicator will also signal likely reversal areas. Whenever there is a huge spike in RSI and range, whether it be up or down, this generally corresponds to an imminent reversal. The indicator is programmed to recognize this and plot little grey circles to notify you of an impending reversal.
Let's take a look at some reversal examples using NVDA:
In the chart above, we can see that the RSI signaled a reversal. As it was part of a downtrend, the reversal was bullish.
Let's look at a top reversal:
The chart above shows a likely downside reversal.
And some little bounce reversals here and there:
In addition to showing you the ATR range and reversals, the indicator will show you the RSI in a bar graph format:
You won't be able to look for RSI divergences, if you are a believer of those. However, you can definitely visualize them in the ATR ranges which are directly affected by the RSI readings.
Aspects of the indicator:
Bull ranges are displayed in green.
Bear ranges are displayed in red.
When green is present we know its entering or currently in a bullish RSI range:
Inversely, when it starts to shift red, we know we are entering a bearish RSI range:
There is a border that circles the range. It will be green when we are in a bullish range and red when we are in a bearish range. In addition to these 2 signals, the RSI bar chart itself will turn green in bullish ranges, and red in bearish ranges.
Here is bullish:
Here is bearish:
Customizability
You can customize the Source input for the RSI (default is close). As well as the length (default is 14).
The ATR length is defaulted to 500. My suggestion is to leave this be. You can increase it but I would not suggest decreasing it as it may omit some of the RSI ranges from its history.
And that is the indicator my friends! Hope you enjoy!
As always, safe trades!
Consecutive Beta with Dynamic Support Resistance [TrendX_]The Consecutive Beta with Dynamic Support Resistance indicator is tailored to harness trend momentum, recognize top & bottom reversals, and leverage dynamic support and resistance levels. This indicator introduces a new approach by combining the concepts of beta, consecutive counting mechanisms, and the supertrend structure, making it a fresh tool for understanding market trends and patterns.
💎 KEY FEATURES
Candle’s Relative Valuation Using Beta: The core of the TrendX indicator lies in using beta to gauge volatility. Beta serves as a measure of how an asset moves relative to the broader market, helping traders understand whether the asset is more or less volatile in different market conditions.
Counting Techniques for Momentum & Reversals: By employing counting techniques to reach a significant threshold, the indicator can measure trend momentum and spot top/bottom reversals.
Dynamic Support & Resistance: This feature relies on consecutive beta counting to dynamically adapt support and resistance levels. These levels are key in predicting potential entry and exit points following the general trend direction.
⚙️ USAGES
Initial Start and Distance: Customize the initial start point and distance for better control over trading strategies. For instance, starting at 1 and using an even distance of 2 will yield odd consecutive counting series;
Phase 1 Completion for Reversal Strategies: This initial phase focuses on identifying short-term reversals;
Phase 2 Completion for Support/Resistance: A support level forms after completing two bullish phases, while a resistance level forms after completing two bearish phases. This structure helps in clarifying trend directions when breakout these key levels.
🔎 BREAKDOWN
Phase 1:
The indicator counts consecutive candles that show a higher Beta than in previous periods over a given length. The completion of countings only succeed when the whole series is uninterruptedly counted, or else countings will be canceled. This strict adherence to consecutive counts serves to ensure that only strong, sustained momentum is recognized and also helps filter out noise, weak signals and establish the initial direction catalyst, setting up for further trend analysis.
Phase 2:
After Phase 1 ends, the Phase 2 counting mechanism begins. This phase focuses on bottom reversals through consecutive higher beta candles, and top reversals by counting lower beta candles. At this stage, interuptions will not cancel the counting process. The ability to continue counting in Phase 2 allows for a broader perspective on market behavior. Even if individual candles do not consistently meet the criteria for consecutive counts, the cumulative effect of higher or lower beta readings over time provides valuable insights into market sentiment and trend direction.
Dynamic Support & Resistance:
After Phase 2 completion, if the average of high, low, and close surpasses both recent support and resistance levels from Phase 2, an uptrend is confirmed, which the support level is displayed. If it drops below these levels, a downtrend is indicated, where resistance is displayed instead of support. The result is displayed through a colored supertrend-line (teal for uptrend, red for downtrend).
DISCLAIMER
This indicator is not financial advice, it can only help traders make better decisions. There are many factors and uncertainties that can affect the outcome of any endeavor, and no one can guarantee or predict with certainty what will occur. Therefore, one should always exercise caution and judgment when making decisions based on past performance.
Xtrender and TSI FusionXtrender and TSI Fusion Indicator
I created this indicator for myself. I was inspired by the indicators created by Bjorgum, Duyck and QuantTherapy and decided to create multiple indicators that either work well combined with their indicators or something new that applies some of their indicator concepts. I decided to share all of the indicator I have created because I believe in learning and earing together as a community. If you guys have any questions or suggestions write them.
Overview: The Xtrender and TSI Fusion Indicator is a powerful tool designed to help traders analyze market momentum, trends, and potential reversals. By combining Xtrender with the True Strength Index (TSI), this indicator provides a comprehensive view of market dynamics, making it easier to identify trading opportunities.
Image: Timeframe is set to daily
Features:
1.Xtrender Analysis:
Short-Term Xtrender: Visualizes short-term momentum using RSI-based calculations on EMA differences. This helps in identifying immediate market trends and pullbacks.
Image above: showcases Short-Term Xtrender
Xtrender T3: A smoothed version of the Xtrender that reduces noise and highlights significant trend changes.
Image above: showcases Xtrender T3 with Xtrender T3 color
2.TSI (True Strength Index):
TSI Value: Measures momentum by comparing price changes over two time periods, offering a clear view of trend strength.
TSI Signal Line: A smoothed version of the TSI value, used to generate buy and sell signals when crossed by the TSI.
Image: showcases TSI Value with TSI Signal Line
TSI Histogram: Shows the difference between the TSI and its signal line, highlighting potential reversals and trend continuations.
Image: showcases TSI Histogram
3.Color Coding and Visual Cues:
Trend Colors: The indicator uses dynamic colors to represent bullish or bearish conditions, making it easy to interpret market sentiment.
Background Color : The background changes color based on TSI signals, further aiding in visual trend analysis.
Image: showcases Background color and Zero line
How to Use
1.Xtrender Analysis:
Short-Term Xtrender: The short-term Xtrender is plotted as columns, changing color based on its direction and value. Green or lime indicates positive momentum, while red or maroon indicates negative momentum.
Xtrender T3: The Xtrender T3 line (black) represents a smoothed version of the short-term Xtrender, providing a clearer picture of the overall trend. The color of this line changes based on the Xtrender's value, helping you spot potential trend changes.
2.TSI (True Strength Index):
TSI Value and Signal Line: The TSI value is plotted as a line, with its color changing based on its relationship to the signal line. A crossover of the TSI above the signal line suggests a potential bullish move, while a crossover below indicates a bearish trend.
TSI Histogram: The histogram represents the difference between the TSI and its signal line. Positive values indicate bullish momentum, while negative values suggest bearish momentum.
3.Background Color:
The background color changes based on the TSI signal, with a greenish hue indicating bullish conditions and a reddish hue indicating bearish conditions. This provides a quick visual reference for market sentiment.
4.Zero Line:
A horizontal gray dotted line at the zero level helps you easily identify when the Xtrender or TSI crosses into positive or negative territory, signaling potential trend shifts.
Image above: Timeframe on daily with the individual elements combined
Example of Use:
•Trend Confirmation: Use the Xtrender and Xtrender T3 to confirm the direction of the trend. If both are aligned with the same color and direction, it increases the probability of a strong trend.
•Momentum Reversals: Watch for TSI crosses and histogram shifts to identify potential reversals. For example, a TSI crossover above its signal line with a corresponding change in the histogram from negative to positive could signal a buying opportunity.
•Pullbacks: Identify pullbacks within a trend by observing temporary shifts in the short-term Xtrender or TSI histogram. Use these signals to enter trades in the direction of the overall trend.
Image above: Showcases, Trend confirmation, reversal and pullbacks on daily timeframe.
Customization:
•TSI Speed: Choose between "Fast" and "Slow" TSI settings based on your trading style. Fast settings are more responsive to price changes, while slow settings offer smoother signals.
•Color Settings: Customize the colors for bullish, bearish, and neutral TSI conditions to match your personal preferences or chart theme.
This indicator is versatile and can be used for various trading strategies, from trend following to momentum trading, making it a valuable tool in any trader's arsenal.
My Scripts/Indicators/Ideas /Systems that I share are only for educational purposes
RVol LabelThis Code is update version of Code Provided by @ssbukam, Here is Link to his original Code and review the Description
Below is Original Description
1. When chart resolution is Daily or Intraday (D, 4H, 1H, 5min, etc), Relative Volume shows value based on DAILY. RVol is measured on daily basis to compare past N number of days.
2. When resolution is changed to Weekly or Monthly, then Relative Volume shows corresponding value. i.e. Weekly shows weekly relative volume of this week compared to past 'N' weeks. Likewise for Monthly. You would see change in label name. Like, Weekly chart shows W_RVol (Weekly Relative Volume). Likewise, Daily & Intraday shows D_RVol. Monthly shows M_RVol (Monthly Relative Volume).
3. Added a plot (by default hidden) for this specific reason: When you move the cursor to focus specific candle, then Indicator Value displays relative volume of that specific candle. This applies to Intraday as well. So if you're in 1HR chart and move the cursor to a specific candle, Indicator Value shows relative volume for that specific candlestick bar.
4. Updating the script so that text size and location can be customized.
Changes to Updated Label by me
1. Added Today's Volume to the Label
2. Added Total Average Volume to the Label
3. Comparison vs Both in Single Line and showing how much volume has traded vs the average volume for that time of the day
4. Aesthetic Look of the Label
How to Use Relative Volume for Trading
Using Relative Volume (RVol) in trading can be a valuable tool to help you identify potential trading opportunities and gain insight into market behavior. Here are some ways to use RVol in your trading strategy:
Identifying High-Volume Breakouts: RVol can help you spot potential breakouts when the volume surges significantly above its average. High RVol during a breakout suggests strong market interest, increasing the probability of a sustained move in the direction of the breakout.
Confirming Trends and Reversals: RVol can act as a confirmation tool for trends and reversals. A trend accompanied by rising RVol indicates a strong and sustainable move. Conversely, a trend with declining RVol might suggest a weakening trend or potential reversal.
Spotting Volume Divergence: When the price is moving in one direction, but RVol is declining or not confirming the move, it may indicate a divergence. This discrepancy could suggest a potential reversal or trend change.
Support and Resistance Confirmation: High RVol near key support or resistance levels can indicate potential price reactions at those levels. This confirmation can be valuable in determining whether a level is likely to hold or break.
Filtering Trade Signals: Incorporate RVol into your existing trading strategy as a filter. For example, you might consider taking trades only if RVol is above a certain threshold, ensuring that you focus on high-impact trading opportunities.
Avoiding Low-Volume Traps: Low RVol can indicate a lack of interest or participation in the market. In such situations, price movements may be erratic and less reliable, so it's often wise to avoid trading during low RVol periods.
Monitoring News Events: Around significant news events or earnings releases, RVol can help you gauge the market's reaction to the information. High RVol during such events can present trading opportunities but be cautious of increased volatility and potential gaps.
Adjusting Trade Size: During periods of extremely high RVol, it might be prudent to adjust your position size to account for higher risk.
Using Relative Volume in Morning Session
If the Volume traded in first 15 minute to 30 Minutes is already at 50% or 100% depending upon the ticker, it means that it is going to have very high Volume vs average by end of the day.
This gives me conviction for Long or Short Trades
Remember that RVol is not a standalone indicator; it works best when used in conjunction with other technical and fundamental analysis tools. Additionally, RVol's effectiveness may vary across different markets and trading strategies. Therefore, backtesting and validating the use of RVol in your trading approach is essential.
Lastly, risk management is crucial in trading. While RVol can provide valuable insights, it cannot guarantee profitable trades. Always use appropriate risk management strategies, such as setting stop-loss levels, and avoid overexposing yourself to the market based solely on RVol readings.
Multi-Timeframe PSAR Indicator ver 1.0Enhance your trend analysis with the Multi-Timeframe Parabolic SAR (MTF PSAR) indicator! This powerful tool displays the Parabolic SAR (Stop and Reverse) from both the current chart's timeframe and a higher timeframe, all in one convenient view. Identify potential trend reversals and set dynamic trailing stops with greater confidence by understanding the broader market context.
Key Features:
Dual Timeframe Analysis: Simultaneously visualize the PSAR on your current chart and a user-defined higher timeframe (e.g., see the Daily PSAR while trading on the 1-hour chart). This helps you align your trades with the dominant trend.
Customizable PSAR Settings: Fine-tune the PSAR calculation with adjustable Start, Increment, and Maximum values. Optimize the indicator's sensitivity to match your trading style and the volatility of the asset.
Independent Timeframe Control: Choose to display either or both the current timeframe PSAR and the higher timeframe PSAR. Focus on the information most relevant to your analysis.
Clear Visual Representation: Distinct colors for the current and higher timeframe PSAR dots make it easy to differentiate between the two. Quickly identify potential entry and exit points.
Configurable Colors You can easily change colors of Current and HTF PSAR.
Standard PSAR Logic: Uses the classic Parabolic SAR algorithm, providing a reliable and widely-understood trend-following indicator.
lookahead=barmerge.lookahead_off used in the security function, there is no data leak or repainting.
Benefits:
Improved Trend Identification: Spot potential trend changes earlier by observing divergences between the current and higher timeframe PSAR.
Enhanced Risk Management: Use the PSAR as a dynamic trailing stop-loss to protect profits and limit potential losses.
Greater Trading Confidence: Make more informed decisions by considering the broader market trend.
Reduced Chart Clutter: Avoid the need to switch between multiple charts to analyze different timeframes.
Versatile Application: Suitable for various trading styles (swing trading, day trading, trend following) and markets (stocks, forex, crypto, etc.).
How to Use:
Add to Chart: Add the "Multi-Timeframe PSAR" indicator to your TradingView chart.
Configure Settings:
PSAR Settings: Adjust the Start, Increment, and Maximum values to control the PSAR's sensitivity.
Multi-Timeframe Settings: Select the desired "Higher Timeframe PSAR" resolution (e.g., "D" for Daily). Enable or disable the display of the current and/or higher timeframe PSAR using the checkboxes.
Interpret Signals:
Current Timeframe PSAR: Dots below the price suggest an uptrend; dots above the price suggest a downtrend.
Higher Timeframe PSAR: Provides context for the overall trend. Agreement between the current and higher timeframe PSAR strengthens the trend signal. Divergences may indicate potential reversals.
Trade Management:
Use PSAR dots as dynamic trailing stop.
Example Use Cases:
Confirming Trend Strength: A trader on a 1-hour chart sees the 1-hour PSAR flip bullish (dots below the price). They check the MTF PSAR and see that the Daily PSAR is also bullish, confirming the strength of the uptrend.
Identifying Potential Reversals: A trader sees the current timeframe PSAR flip bearish, but the higher timeframe PSAR remains bullish. This divergence could signal a potential pullback within a larger uptrend, or a warning of a more significant reversal.
Trailing Stops: A trader enters a long position and uses the current timeframe PSAR as a trailing stop, moving their stop-loss up as the PSAR dots rise.
Disclaimer: The Parabolic SAR is a lagging indicator and may produce false signals, especially in ranging markets. It is recommended to use this indicator in conjunction with other technical analysis tools and risk management strategies. Past performance is not indicative of future results.
RSI Divergence + Sweep + Signal + Alerts Toolkit [TrendX_]The RSI Toolkit is a powerful set of tools designed to enhance the functionality of the traditional Relative Strength Index (RSI) indicator. By integrating advanced features such as Moving Averages, Divergences, and Sweeps, it helps traders identify key market dynamics, potential reversals, and newly-approach trading stragies.
The toolkit expands on standard RSI usage by incorporating features from smart money concepts (Just try to be creative 🤣 Hope you like it), providing a deeper understanding of momentum, liquidity sweeps, and trend reversals. It is suitable for RSI traders who want to make more informed and effective trading decisions.
💎 FEATURES
RSI Moving Average
The RSI Moving Average (RSI MA) is the moving average of the RSI itself. It can be customized to use various types of moving averages, including Simple Moving Average (SMA), Exponential Moving Average (EMA), Relative Moving Average (RMA), and Volume-Weighted Moving Average (VWMA).
The RSI MA smooths out the RSI fluctuations, making it easier to identify trends and crossovers. It helps traders spot momentum shifts and potential entry/exit points by observing when the RSI crosses above or below its moving average.
RSI Divergence
RSI Divergence identifies discrepancies between price action and RSI momentum. There are two types of divergences: Regular Divergence - Indicates a potential trend reversal; Hidden Divergence - Suggests the continuation of the current trend.
Divergence is a critical signal for spotting weakness or strength in a trend. Regular divergence highlights potential trend reversals, while hidden divergence confirms trend continuation, offering traders valuable insights into market momentum and possible trade setups.
RSI Sweep
RSI Sweep detects moments when the RSI removes liquidity from a trend structure by sweeping above or below the price at key momentum level crossing. These sweeps are overlaid on the RSI chart for easier visualized.
RSI Sweeps are significant because they indicate potential turning points in the market. When RSI sweeps occur: In an uptrend - they suggest buyers' momentum has peaked, possibly leading to a reversal; In a downtrend - they indicate sellers’ momentum has peaked, also hinting at a reversal.
(Note: This feature incorporates Liquidity Sweep concepts from Smart Money Concepts into RSI analysis, helping RSI traders identify areas where liquidity has been removed, which often precedes a trend reversal)
🔎 BREAKDOWN
RSI Moving Average
How MA created: The RSI value is calculated first using the standard RSI formula. The MA is then applied to the RSI values using the trader’s chosen type of MA (SMA, EMA, RMA, or VWMA). The flexibility to choose the type of MA allows traders to adjust the smoothing effect based on their trading style.
Why use MA: RSI by itself can be noisy and difficult to interpret in volatile markets. Applying moving average would provide a smoother, more reliable view of RSI trends.
RSI Divergence
How Regular Divergence created: Regular Divergence is detected when price forms HIGHER highs while RSI forms LOWER highs (bearish divergence) or when price forms LOWER lows while RSI forms HIGHER lows (bullish divergence).
How Hidden Divergence created: Hidden Divergence is identified when price forms HIGHER lows while RSI forms LOWER lows (bullish hidden divergence) or when price forms LOWER highs while RSI forms HIGHER highs (bearish hidden divergence).
Why use Divergence: Divergences provide early warning signals of a potential trend change. Regular divergence helps traders anticipate reversals, while hidden divergence supports trend continuation, enabling traders to align their trades with market momentum.
RSI Sweep
How Sweep created: Trend Structure Shift are identified based on the RSI crossing key momentum level of 50. To track these sweeps, the indicator pinpoints moments when liquidity is removed from the Trend Structure Shift. This is a direct application of Liquidity Sweep concepts used in Smart Money theories, adapted to RSI.
Why use Sweep: RSI Sweeps are created to help traders detect potential trend reversals. By identifying areas where momentum has exhausted during a certain trend direction, the indicator highlights opportunities for traders to enter trades early in a reversal or continuation phase.
⚙️ USAGES
Divergence + Sweep
This is an example of combining Devergence & Sweep in BTCUSDT (1 hour)
Wait for a divergence (regular or hidden) to form on the RSI. After the divergence is complete, look for a sweep to occur. A potential entry might be formed at the end of the sweep.
Divergences indicate a potential trend change, but confirmation is required to ensure the setup is valid. The RSI Sweep provides that confirmation by signaling a liquidity event, increasing the likelihood of a successful trade.
Sweep + MA Cross
This is an example of combining Devergence & Sweep in BTCUSDT (1 hour)
Wait for an RSI Sweep to form then a potential entry might be formed when the RSI crosses its MA.
The RSI Sweep highlights a potential turning point in the market. The MA cross serves as additional confirmation that momentum has shifted, providing a more reliable and more potential entry signal for trend continuations.
DISCLAIMER
This indicator is not financial advice, it can only help traders make better decisions. There are many factors and uncertainties that can affect the outcome of any endeavor, and no one can guarantee or predict with certainty what will occur. Therefore, one should always exercise caution and judgment when making decisions based on past performance.
Sash Trending Suite NEWWhy
The " Sash Trending Suite " (STS) indicator simplifies trading by highlighting market trends and potential reversals. In a world of complex charts and overwhelming data, STS helps traders quickly understand market direction and make informed decisions.
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How and What
STS combines key technical tools into one easy-to-read indicator, directly showing important signals on the chart:
Macro Trend Detection
How : Uses two EMAs (fast and slow) and the ADX to identify strong bullish or bearish trends.
What to Look For :
Bar Colors :
Green Bars : Indicate a strong upward (bullish) trend.
Red Bars : Indicate a strong downward (bearish) trend.
Benefit : Quickly see the overall market direction.
Alpha Track Line
How : An adaptive EMA that acts as a dynamic support or resistance line.
What to Look For :
Line Colors :
Green Line : Price is above the line (bullish momentum).
Red Line : Price is below the line (bearish momentum).
Benefit : Visualize momentum shifts easily.
Reversal Signals
How : Combines RSI with price action to spot potential market reversals.
What to Look For :
"R" Labels :
Turquoise "R" Below Bar : Potential bullish reversal.
Amber "R" Above Bar : Potential bearish reversal.
Benefit : Identify possible turning points for entry or exit.
Micro Trend Detection
How : Uses shorter EMAs to catch minor trend changes.
What to Look For :
Small Circles :
Green Circle Below Bar : Micro bullish signal.
Red Circle Above Bar : Micro bearish signal.
Benefit : Spot short-term trend shifts promptly.
Alerts
How : Built-in alerts notify you of key events.
What to Expect :
Trend Changes : Alerts when a new bullish or bearish trend starts.
Reversals : Alerts for potential bullish or bearish reversals.
Benefit : Stay updated without constantly watching the chart.
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Summary
The "Sash Trending Suite" provides:
Simplified Analysis : One indicator shows trend direction, momentum, reversals, and micro trends.
Clear Visuals : Color-coded bars and symbols make interpretation easy.
Timely Alerts : Know about important market changes instantly.
By focusing on essential signals and displaying them clearly, STS helps traders navigate the market with confidence and simplicity.
Enhanced Overbought/Oversold IndicatorEnhanced Overbought/Oversold Indicator
Description:
The Enhanced Overbought/Oversold Indicator is a custom technical analysis tool designed to identify potential reversal points in the market by highlighting conditions of overbought and oversold levels on any timeframe. This indicator is based on the Relative Strength Index (RSI), a momentum oscillator that measures the speed and change of price movements.
Features:
Overbought & Oversold Levels:
Overbought (RSI > 70): Indicates that the market is potentially overvalued and might be due for a pullback. The candles are highlighted in Red to signal caution.
Oversold (RSI < 30): Indicates that the market is potentially undervalued and might be due for a bounce. The candles are highlighted in Green to signal potential buying opportunities.
Extreme Conditions:
Extreme Overbought (RSI > 85): Indicates an extremely overbought condition, suggesting a very high likelihood of a reversal or correction. The candles are highlighted in Blue.
Extreme Oversold (RSI < 15): Indicates an extremely oversold condition, suggesting a strong potential for a reversal upwards. The candles are highlighted in Yellow.
Dynamic Highlighting:
The indicator dynamically adjusts the candle colors based on the current RSI value, providing a clear visual representation of market conditions.
Applications:
Trend Reversals: By identifying extreme RSI levels, the indicator helps traders anticipate possible trend reversals.
Entry & Exit Points: Traders can use the highlighted signals to make more informed decisions about entering or exiting trades.
Risk Management: The color-coded signals can be used to manage risk, especially during extreme market conditions.
This indicator is particularly useful for traders looking for a straightforward visual representation of market conditions across different timeframes. By combining standard and extreme RSI levels, it helps identify not just overbought and oversold conditions but also extreme levels where significant reversals are more likely.
