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MLExtensionsLibrary "MLExtensions" A set of extension methods for a novel implementation of a Approximate Nearest Neighbors (ANN) algorithm in Lorentzian space. normalizeDeriv(src, quadraticMeanLength)   Returns the smoothed hyperbolic tangent of the input series.   Parameters:      src (float) : The input series (i.e., the first-order derivative for price).      quadraticMeanLength (int) : The length of the quadratic mean (RMS).   Returns: nDeriv The normalized derivative of the input series. normalize(src, min, max)   Rescales a source value with an unbounded range to a target range.   Parameters:      src (float) : The input series      min (float) : The minimum value of the unbounded range      max (float) : The maximum value of the unbounded range   Returns: The normalized series rescale(src, oldMin, oldMax, newMin, newMax)   Rescales a source value with a bounded range to anther bounded range   Parameters:      src (float) : The input series      oldMin (float) : The minimum value of the range to rescale from      oldMax (float) : The maximum value of the range to rescale from      newMin (float) : The minimum value of the range to rescale to      newMax (float) : The maximum value of the range to rescale to   Returns: The rescaled series getColorShades(color)   Creates an array of colors with varying shades of the input color   Parameters:      color (color) : The color to create shades of   Returns: An array of colors with varying shades of the input color getPredictionColor(prediction, neighborsCount, shadesArr)   Determines the color shade based on prediction percentile   Parameters:      prediction (float) : Value of the prediction      neighborsCount (int) : The number of neighbors used in a nearest neighbors classification      shadesArr (array) : An array of colors with varying shades of the input color   Returns: shade Color shade based on prediction percentile color_green(prediction)   Assigns varying shades of the color green based on the KNN classification   Parameters:      prediction (float) : Value (int|float) of the prediction   Returns: color color_red(prediction)   Assigns varying shades of the color red based on the KNN classification   Parameters:      prediction (float) : Value of the prediction   Returns: color tanh(src)   Returns the the hyperbolic tangent of the input series. The sigmoid-like hyperbolic tangent function is used to compress the input to a value between -1 and 1.   Parameters:      src (float) : The input series (i.e., the normalized derivative).   Returns: tanh The hyperbolic tangent of the input series. dualPoleFilter(src, lookback)   Returns the smoothed hyperbolic tangent of the input series.   Parameters:      src (float) : The input series (i.e., the hyperbolic tangent).      lookback (int) : The lookback window for the smoothing.   Returns: filter The smoothed hyperbolic tangent of the input series. tanhTransform(src, smoothingFrequency, quadraticMeanLength)   Returns the tanh transform of the input series.   Parameters:      src (float) : The input series (i.e., the result of the tanh calculation).      smoothingFrequency (int)      quadraticMeanLength (int)   Returns: signal The smoothed hyperbolic tangent transform of the input series. n_rsi(src, n1, n2)   Returns the normalized RSI ideal for use in ML algorithms.   Parameters:      src (float) : The input series (i.e., the result of the RSI calculation).      n1 (simple int) : The length of the RSI.      n2 (simple int) : The smoothing length of the RSI.   Returns: signal The normalized RSI. n_cci(src, n1, n2)   Returns the normalized CCI ideal for use in ML algorithms.   Parameters:      src (float) : The input series (i.e., the result of the CCI calculation).      n1 (simple int) : The length of the CCI.      n2 (simple int) : The smoothing length of the CCI.   Returns: signal The normalized CCI. n_wt(src, n1, n2)   Returns the normalized WaveTrend Classic series ideal for use in ML algorithms.   Parameters:      src (float) : The input series (i.e., the result of the WaveTrend Classic calculation).      n1 (simple int)      n2 (simple int)   Returns: signal The normalized WaveTrend Classic series. n_adx(highSrc, lowSrc, closeSrc, n1)   Returns the normalized ADX ideal for use in ML algorithms.   Parameters:      highSrc (float) : The input series for the high price.      lowSrc (float) : The input series for the low price.      closeSrc (float) : The input series for the close price.      n1 (simple int) : The length of the ADX. regime_filter(src, threshold, useRegimeFilter)   Parameters:      src (float)      threshold (float)      useRegimeFilter (bool) filter_adx(src, length, adxThreshold, useAdxFilter)   filter_adx   Parameters:      src (float) : The source series.      length (simple int) : The length of the ADX.      adxThreshold (int) : The ADX threshold.      useAdxFilter (bool) : Whether to use the ADX filter.   Returns: The ADX. filter_volatility(minLength, maxLength, useVolatilityFilter)   filter_volatility   Parameters:      minLength (simple int) : The minimum length of the ATR.      maxLength (simple int) : The maximum length of the ATR.      useVolatilityFilter (bool) : Whether to use the volatility filter.   Returns: Boolean indicating whether or not to let the signal pass through the filter. backtest(high, low, open, startLongTrade, endLongTrade, startShortTrade, endShortTrade, isEarlySignalFlip, maxBarsBackIndex, thisBarIndex, src, useWorstCase)   Performs a basic backtest using the specified parameters and conditions.   Parameters:      high (float) : The input series for the high price.      low (float) : The input series for the low price.      open (float) : The input series for the open price.      startLongTrade (bool) : The series of conditions that indicate the start of a long trade.      endLongTrade (bool) : The series of conditions that indicate the end of a long trade.      startShortTrade (bool) : The series of conditions that indicate the start of a short trade.      endShortTrade (bool) : The series of conditions that indicate the end of a short trade.      isEarlySignalFlip (bool) : Whether or not the signal flip is early.      maxBarsBackIndex (int) : The maximum number of bars to go back in the backtest.      thisBarIndex (int) : The current bar index.      src (float) : The source series.      useWorstCase (bool) : Whether to use the worst case scenario for the backtest.   Returns: A tuple containing backtest values init_table()   init_table()   Returns: tbl The backtest results. update_table(tbl, tradeStatsHeader, totalTrades, totalWins, totalLosses, winLossRatio, winrate, earlySignalFlips)   update_table(tbl, tradeStats)   Parameters:      tbl (table) : The backtest results table.      tradeStatsHeader (string) : The trade stats header.      totalTrades (float) : The total number of trades.      totalWins (float) : The total number of wins.      totalLosses (float) : The total number of losses.      winLossRatio (float) : The win loss ratio.      winrate (float) : The winrate.      earlySignalFlips (float) : The total number of early signal flips.   Returns: Updated backtest results table.
مكتبة Pine Script®
من ‎TFU_Tradewise‎
RSI Forecast Colorful [DiFlip]RSI Forecast Colorful Introducing one of the most complete RSI indicators available — a highly customizable analytical tool that integrates advanced prediction capabilities. RSI Forecast Colorful is an evolution of the classic RSI, designed to anticipate potential future RSI movements using linear regression. Instead of simply reacting to historical data, this indicator provides a statistical projection of the RSI’s future behavior, offering a forward-looking view of market conditions. ⯁ Real-Time RSI Forecasting For the first time, a public RSI indicator integrates linear regression (least squares method) to forecast the RSI’s future behavior. This innovative approach allows traders to anticipate market movements based on historical trends. By applying Linear Regression to the RSI, the indicator displays a projected trendline n periods ahead, helping traders make more informed buy or sell decisions. ⯁ Highly Customizable The indicator is fully adaptable to any trading style. Dozens of parameters can be optimized to match your system. All 28 long and short entry conditions are selectable and configurable, allowing the construction of quantitative, statistical, and automated trading models. Full control over signals ensures precise alignment with your strategy. ⯁ Innovative and Science-Based This is the first public RSI indicator to apply least-squares predictive modeling to RSI calculations. Technically, it incorporates machine-learning logic into a classic indicator. Using Linear Regression embeds strong statistical foundations into RSI forecasting, making this tool especially valuable for traders seeking quantitative and analytical advantages. ⯁ Scientific Foundation: Linear Regression Linear regression is a fundamental statistical method that models the relationship between a dependent variable y and one or more independent variables x. The general formula for simple linear regression is: y = β₀ + β₁x + ε where: y  = predicted variable (e.g., future RSI value) x  = explanatory variable (e.g., bar index or time) β₀ = intercept (value of y when x = 0) β₁ = slope (rate of change of y relative to x) ε   = random error term The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the RSI projection based on recent trends. ⯁ Least Squares Estimation To minimize the error between predicted and observed values, we use the formulas: β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²) β₀ = ȳ - β₁x̄ Σ denotes summation; x̄ and ȳ are the means of x and y; and i ranges from 1 to n (number of observations). These equations produce the best linear unbiased estimator under the Gauss–Markov assumptions — constant variance (homoscedasticity) and a linear relationship between variables. ⯁ Linear Regression in Machine Learning Linear regression is a foundational component of supervised learning. Its simplicity and precision in numerical prediction make it essential in AI, predictive algorithms, and time-series forecasting. Applying regression to RSI is akin to embedding artificial intelligence inside a classic indicator, adding a new analytical dimension. ⯁ Visual Interpretation Imagine a time series of RSI values like this: Time → RSI    → The regression line smooths these historical values and projects itself n periods forward, creating a predictive trajectory. This projected RSI line can cross the actual RSI, generating sophisticated entry and exit signals. In summary, the RSI Forecast Colorful indicator provides both the current RSI and the forecasted RSI, allowing comparison between past and future trend behavior. ⯁ Summary of Scientific Concepts Used Linear Regression: Models relationships between variables using a straight line. Least Squares: Minimizes squared prediction errors for optimal fit. Time-Series Forecasting: Predicts future values from historical patterns. Supervised Learning: Predictive modeling based on known output values. Statistical Smoothing: Reduces noise to highlight underlying trends. ⯁ Why This Indicator Is Revolutionary Scientifically grounded: Built on statistical and mathematical theory. First of its kind: The first public RSI with least-squares predictive modeling. Intelligent: Incorporates machine-learning logic into RSI interpretation. Forward-looking: Generates predictive, not just reactive, signals. Customizable: Exceptionally flexible for any strategic framework. ⯁ Conclusion By combining RSI and linear regression, the RSI Forecast Colorful allows traders to predict market momentum rather than simply follow it. It's not just another indicator: it's a scientific advancement in technical analysis technology. Offering 28 configurable entry conditions and advanced signals, this open-source indicator paves the way for innovative quantitative systems. ⯁ Example of simple linear regression with one independent variable This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables. ⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x) This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component. ⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models. ⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model. ⯁ Result of fitting a set of data points with a quadratic function This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line. ⯁ What Is RSI? The RSI (Relative Strength Index) is a technical indicator developed by J. Welles Wilder. It measures the velocity and magnitude of recent price movements to identify overbought and oversold conditions. The RSI ranges from 0 to 100 and is commonly used to identify potential reversals and evaluate trend strength. ⯁ How RSI Works RSI is calculated from average gains and losses over a set period (commonly 14 bars) and plotted on a 0–100 scale. It consists of three key zones: Overbought: RSI above 70 may signal an overbought market. Oversold: RSI below 30 may signal an oversold market. Neutral Zone: RSI between 30 and 70, indicating no extreme condition. These zones help identify potential price reversals and confirm trend strength. ⯁ Entry Conditions All conditions below are fully customizable and allow detailed control over entry signal creation. 📈 BUY 🧲 Signal Validity: Signal remains valid for X bars. 🧲 Signal Logic: Configurable using AND or OR. 🧲 RSI > Upper 🧲 RSI < Upper 🧲 RSI > Lower 🧲 RSI < Lower 🧲 RSI > Middle 🧲 RSI < Middle 🧲 RSI > MA 🧲 RSI < MA 🧲 MA > Upper 🧲 MA < Upper 🧲 MA > Lower 🧲 MA < Lower 🧲 RSI (Crossover) Upper 🧲 RSI (Crossunder) Upper 🧲 RSI (Crossover) Lower 🧲 RSI (Crossunder) Lower 🧲 RSI (Crossover) Middle 🧲 RSI (Crossunder) Middle 🧲 RSI (Crossover) MA 🧲 RSI (Crossunder) MA 🧲 MA (Crossover)Upper 🧲 MA (Crossunder)Upper 🧲 MA (Crossover) Lower 🧲 MA (Crossunder) Lower 🧲 RSI Bullish Divergence 🧲 RSI Bearish Divergence 🔮 RSI (Crossover) Forecast MA 🔮 RSI (Crossunder) Forecast MA 📉 SELL 🧲 Signal Validity: Signal remains valid for X bars. 🧲 Signal Logic: Configurable using AND or OR. 🧲 RSI > Upper 🧲 RSI < Upper 🧲 RSI > Lower 🧲 RSI < Lower 🧲 RSI > Middle 🧲 RSI < Middle 🧲 RSI > MA 🧲 RSI < MA 🧲 MA > Upper 🧲 MA < Upper 🧲 MA > Lower 🧲 MA < Lower 🧲 RSI (Crossover) Upper 🧲 RSI (Crossunder) Upper 🧲 RSI (Crossover) Lower 🧲 RSI (Crossunder) Lower 🧲 RSI (Crossover) Middle 🧲 RSI (Crossunder) Middle 🧲 RSI (Crossover) MA 🧲 RSI (Crossunder) MA 🧲 MA (Crossover)Upper 🧲 MA (Crossunder)Upper 🧲 MA (Crossover) Lower 🧲 MA (Crossunder) Lower 🧲 RSI Bullish Divergence 🧲 RSI Bearish Divergence 🔮 RSI (Crossover) Forecast MA 🔮 RSI (Crossunder) Forecast MA 🤖 Automation All BUY and SELL conditions can be automated using TradingView alerts. Every configurable condition can trigger alerts suitable for fully automated or semi-automated strategies. ⯁ Unique Features Linear Regression Forecast Signal Validity: Keep signals active for X bars Signal Logic: AND/OR configuration Condition Table: BUY/SELL Condition Labels: BUY/SELL Chart Labels: BUY/SELL markers above price Automation & Alerts: BUY/SELL Background Colors: bgcolor Fill Colors: fill Linear Regression Forecast Signal Validity: Keep signals active for X bars Signal Logic: AND/OR configuration Condition Table: BUY/SELL Condition Labels: BUY/SELL Chart Labels: BUY/SELL markers above price Automation & Alerts: BUY/SELL Background Colors: bgcolor Fill Colors: fill
مؤشر Pine Script®
من ‎DiFlip‎
2
Candle Size MonitorCandle Size Monitor – Description Update 27.10.25 Objective Volatility Assessment The Candle Size Monitor helps traders assess actual market movement—regardless of whether candles appear visually large or small on the chart. It supports evaluating whether your planned trade structure (e.g., stop-loss, take-profit) aligns with current volatility. Key Features Volatility Analysis: Calculates the average candle size (difference between high and low) over a user-defined number of candles. Identifies the largest candle in the selected period. Displays results in a compact table on the chart. Exchange Rate Integration (optional): Shows the current USD-EUR exchange rate (formatted with German-style comma and four decimal places). Useful for traders in USD-denominated markets who apply EUR-based risk management rules. Customizable Display: Text Size: Small, medium, or large. Colors: Customizable text and background colors. Table Position: Top/bottom left/right. Number of Candles: User-defined (default: 20). Dynamic Updates: The table updates with each new bar. The exchange rate is fetched in real-time from OANDA:EURUSD. Settings and Translations Settings Anzahl Kerzen → Number of Candles (Number of candles for calculation, default: 20). Textgröße → Text Size (Text size in the table: small, medium, large). Textfarbe → Text Color (Text color, default: white). Hintergrundfarbe → Background Color (Background color of the table, default: black). Position → Position (Table position: Top Left, Top Right, Bottom Left, Bottom Right). Wechselkurs anzeigen (USD → EUR) → Show Exchange Rate (USD → EUR) (Option to display the exchange rate). Table Contents and Translations The table displays the following information (with German formatting): Ø Größe (N): English: "Avg Size (N): " (Average candle size over the last N candles). Example: "Ø Größe (20): 15,3" → "Avg Size (20): 15.3". Größte Kerze: English: "Largest Candle: " (Largest candle size in the selected period). Example: "Größte Kerze: 42,7" → "Largest Candle: 42.7". 1 USD = € (only if enabled) English: "1 USD = EUR" (Current USD-EUR exchange rate, formatted with a comma). Example: "1 USD = 0,9234 €" → "1 USD = 0.9234 EUR".
مؤشر Pine Script®
من ‎kp_eu_de‎
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.
مؤشر Pine Script®
من ‎EdgeTools‎
14
Smart MACD Volume Trader# Smart MACD Volume Trader ## Overview Smart MACD Volume Trader is an enhanced momentum indicator that combines the classic MACD (Moving Average Convergence Divergence) oscillator with an intelligent high-volume filter. This combination significantly reduces false signals by ensuring that trading signals are only generated when price momentum is confirmed by substantial volume activity. The indicator supports over 24 different instruments including major and exotic forex pairs, precious metals (gold and silver), energy commodities (crude oil, natural gas), and industrial metals (copper). For forex and commodity traders, the indicator automatically maps to CME and COMEX futures contracts to provide accurate institutional-grade volume data. ## Originality and Core Concept Traditional MACD indicators generate signals based solely on price momentum, which can result in numerous false signals during low-activity periods or ranging markets. This indicator addresses this critical weakness by introducing a volume confirmation layer with automatic institutional volume integration. **What makes this approach original:** - Signals are triggered only when MACD crossovers coincide with elevated volume activity - Implements a lookback mechanism to detect volume spikes within recent bars - Automatically detects and maps 24+ forex pairs and commodities to their corresponding CME and COMEX futures contracts - Provides real institutional volume data for forex pairs where spot volume is unreliable - Combines two independent market dimensions (price momentum and volume) into a single, actionable signal - Includes intelligent asset detection that works across multiple exchanges and ticker formats **The underlying principle:** Volume validates price movement. When institutional money enters the market, it creates volume signatures. By requiring high volume confirmation and using actual institutional volume data from futures markets, this indicator filters out weak price movements and focuses on trades backed by genuine market participation. The automatic futures mapping ensures that forex and commodity traders always have access to the most accurate volume data available, without manual configuration. ## How It Works ### MACD Component The indicator calculates MACD using standard methodology: 1. **Fast EMA (default: 12 periods)** - Tracks short-term price momentum 2. **Slow EMA (default: 26 periods)** - Tracks longer-term price momentum 3. **MACD Line** - Difference between Fast EMA and Slow EMA 4. **Signal Line (default: 9-period SMA)** - Smoothed average of MACD line **Crossover signals:** - **Bullish:** MACD line crosses above Signal line (momentum turning positive) - **Bearish:** MACD line crosses below Signal line (momentum turning negative) ### Volume Filter Component The volume filter adds an essential confirmation layer: 1. **Volume Moving Average** - Calculates exponential MA of volume (default: 20 periods) 2. **High Volume Threshold** - Multiplies MA by ratio (default: 2.0x or 200%) 3. **Volume Detection** - Identifies bars where current volume exceeds threshold 4. **Lookback Period** - Checks if high volume occurred in recent bars (default: 5 bars) **Signal logic:** - Buy/Sell signals only trigger when BOTH conditions are met: - MACD crossover/crossunder occurs - High volume detected within lookback period ### Automatic CME Futures Integration For forex traders, spot FX volume data can be unreliable or non-existent. This indicator solves this problem by automatically detecting forex pairs and mapping them to corresponding CME futures contracts with real institutional volume data. **Supported Major Forex Pairs (7):** - EURUSD → CME:6E1! (Euro FX Futures) - GBPUSD → CME:6B1! (British Pound Futures) - AUDUSD → CME:6A1! (Australian Dollar Futures) - USDJPY → CME:6J1! (Japanese Yen Futures) - USDCAD → CME:6C1! (Canadian Dollar Futures) - USDCHF → CME:6S1! (Swiss Franc Futures) - NZDUSD → CME:6N1! (New Zealand Dollar Futures) **Supported Exotic Forex Pairs (4):** - USDMXN → CME:6M1! (Mexican Peso Futures) - USDRUB → CME:6R1! (Russian Ruble Futures) - USDBRL → CME:6L1! (Brazilian Real Futures) - USDZAR → CME:6Z1! (South African Rand Futures) **Supported Cross Pairs (6):** - EURJPY → CME:6E1! (Uses Euro Futures) - GBPJPY → CME:6B1! (Uses British Pound Futures) - EURGBP → CME:6E1! (Uses Euro Futures) - AUDJPY → CME:6A1! (Uses Australian Dollar Futures) - EURAUD → CME:6E1! (Uses Euro Futures) - GBPAUD → CME:6B1! (Uses British Pound Futures) **Supported Precious Metals (2):** - Gold (XAUUSD, GOLD) → COMEX:GC1! (Gold Futures) - Silver (XAGUSD, SILVER) → COMEX:SI1! (Silver Futures) **Supported Energy Commodities (3):** - WTI Crude Oil (USOIL, WTIUSD) → NYMEX:CL1! (Crude Oil Futures) - Brent Oil (UKOIL) → NYMEX:BZ1! (Brent Crude Futures) - Natural Gas (NATGAS) → NYMEX:NG1! (Natural Gas Futures) **Supported Industrial Metals (1):** - Copper (COPPER) → COMEX:HG1! (Copper Futures) **How the automatic detection works:** The indicator intelligently identifies the asset type by analyzing: 1. Exchange name (FX, OANDA, TVC, COMEX, NYMEX, etc.) 2. Currency pair pattern (6-letter codes like EURUSD, GBPUSD) 3. Commodity identifiers (XAU for gold, XAG for silver, OIL for crude) When a supported instrument is detected, the indicator automatically switches to the corresponding futures contract for volume analysis. For stocks, cryptocurrencies, and other assets, the indicator uses the native volume data from the current chart. **Visual feedback:** An information table appears in the top-right corner of the MACD pane showing: - Current chart symbol - Exchange name - Currency pair or asset name - Volume source being used (highlighted in orange for futures, yellow for native volume) - Current high volume status This provides complete transparency about which data source the indicator is using for its volume analysis. ## How to Use ### Basic Setup 1. Add the indicator to your chart 2. The indicator displays in a separate pane (MACD) and overlay (signals/volume bars) 3. Default settings work well for most assets, but can be customized ### Signal Interpretation ### Visual Signals **Visual Signals:** - **Green "BUY" label** - Bullish MACD crossover confirmed by high volume - **Red "SELL" label** - Bearish MACD crossunder confirmed by high volume - **Green/Red candles** - Highlight bars with volume exceeding the threshold - **Light green/red background** - Emphasizes signal bars on the chart **Information Table:** A detailed information table appears in the top-right corner of the MACD pane, providing real-time transparency about the indicator's operation: - **Chart:** Current symbol being analyzed - **Exchange:** The exchange or data feed being used - **Pair:** The currency pair or asset name extracted from the ticker - **Volume From:** The actual symbol used for volume analysis - Orange color indicates CME or COMEX futures are being used (automatic institutional volume) - Yellow color indicates native volume from the chart symbol is being used - Hover tooltip shows whether automatic futures mapping is active - **High Volume:** Current status showing YES (green) when volume exceeds threshold, NO (gray) otherwise This table ensures complete transparency and allows you to verify that the correct volume source is being used for your analysis. **Volume Analysis:** - Gray histogram bars = Normal volume - Red histogram bars = High volume (exceeds threshold) - Green line = Volume moving average baseline **MACD Analysis:** - Blue line = MACD line (momentum indicator) - Orange line = Signal line (trend confirmation) - Gray dotted line = Zero line (bullish above, bearish below) ### Parameter Customization **MACD Parameters:** - Adjust Fast/Slow EMA lengths for different sensitivities - Shorter periods = More signals, faster response - Longer periods = Fewer signals, less noise **Volume Parameters:** - **Volume MA Period:** Higher values smooth volume analysis - **High Volume Ratio:** Lower values (1.5x) = More signals; Higher values (3.0x) = Fewer, stronger signals - **Volume Lookback Bars:** Controls how recent the volume spike must be **Direction Filters:** - **Only Buy Signals:** Enables long-only strategy mode - **Only Sell Signals:** Enables short-only strategy mode ### Alert Configuration The indicator includes three alert types: 1. **Buy Signal Alert** - Triggers when bullish signal appears 2. **Sell Signal Alert** - Triggers when bearish signal appears 3. **High Volume Alert** - Triggers when volume exceeds threshold To set up alerts: 1. Click the indicator name → "Add alert on Smart MACD Volume Trader" 2. Select desired alert condition 3. Configure notification method (popup, email, webhook, etc.) ## Trading Strategy Guidelines ### Best Practices **Recommended markets:** - Liquid stocks (large-cap, high daily volume) - Major forex pairs (EURUSD, GBPUSD, USDJPY, AUDUSD, USDCAD, USDCHF, NZDUSD) - Exotic forex pairs (USDMXN, USDRUB, USDBRL, USDZAR) - Cross pairs (EURJPY, GBPJPY, EURGBP, AUDJPY, EURAUD, GBPAUD) - Precious metals (Gold, Silver with automatic COMEX futures mapping) - Energy commodities (Crude Oil, Natural Gas with automatic NYMEX futures mapping) - Industrial metals (Copper with automatic COMEX futures mapping) - Major cryptocurrency pairs - Index futures and ETFs **Timeframe recommendations:** - **Day trading:** 5-minute to 15-minute charts - **Swing trading:** 1-hour to 4-hour charts - **Position trading:** Daily charts **Risk management:** - Use signals as entry confirmation, not standalone strategy - Combine with support/resistance levels - Consider overall market trend direction - Always use stop-loss orders ### Strategy Examples **Trend Following Strategy:** 1. Identify overall trend using higher timeframe (e.g., daily chart) 2. Trade only in trend direction 3. Use "Only Buy" filter in uptrends, "Only Sell" in downtrends 4. Enter on signal, exit on opposite signal or at resistance/support **Volume Breakout Strategy:** 1. Wait for consolidation period (low volume, tight MACD range) 2. Enter when signal appears with high volume (confirms breakout) 3. Target previous swing highs/lows 4. Stop loss below/above recent consolidation **Forex Scalping Strategy (with automatic CME futures):** 1. The indicator automatically detects forex pairs and uses CME futures volume 2. Trade during active sessions only (use session filter) 3. Focus on quick profits (10-20 pips) 4. Exit at opposite signal or profit target **Commodities Trading Strategy (Gold, Silver, Oil):** 1. The indicator automatically maps to COMEX and NYMEX futures contracts 2. Trade during high-liquidity sessions (overlap of major markets) 3. Use the high volume confirmation to identify institutional entry points 4. Combine with key support and resistance levels for entries 5. Monitor the information table to confirm futures volume is being used (orange color) 6. Exit on opposite MACD signal or at predefined profit targets ## Why This Combination Works ### The Volume Advantage Studies consistently show that price movements accompanied by high volume are more likely to continue, while low-volume movements often reverse. This indicator leverages this principle by requiring volume confirmation. **Key benefits:** 1. **Reduced False Signals:** Eliminates MACD whipsaws during low-volume consolidation 2. **Confirmation Bias:** Two independent indicators (price momentum + volume) agreeing 3. **Institutional Alignment:** High volume often indicates institutional participation 4. **Trend Validation:** Volume confirms that price momentum has "conviction" ### Statistical Edge By combining two uncorrelated signals (MACD crossovers and volume spikes), the indicator creates a higher-probability setup than either signal alone. The lookback mechanism ensures signals aren't missed if volume spike slightly precedes the MACD cross. ## Supported Exchanges and Automatic Detection The indicator includes intelligent asset detection that works across multiple exchanges and ticker formats: **Forex Exchanges (Automatic CME Mapping):** - FX (TradingView forex feed) - OANDA - FXCM - SAXO - FOREXCOM - PEPPERSTONE - EASYMARKETS - FX_IDC **Commodity Exchanges (Automatic COMEX/NYMEX Mapping):** - TVC (TradingView commodity feed) - COMEX (directly) - NYMEX (directly) - ICEUS **Other Asset Classes (Native Volume):** - Stock exchanges (NASDAQ, NYSE, AMEX, etc.) - Cryptocurrency exchanges (BINANCE, COINBASE, KRAKEN, etc.) - Index providers (SP, DJ, etc.) The detection algorithm analyzes three factors: 1. Exchange prefix in the ticker symbol 2. Pattern matching for currency pairs (6-letter codes) 3. Commodity identifiers in the symbol name This ensures accurate automatic detection regardless of which data feed or exchange you use for charting. The information table in the top-right corner always displays which volume source is being used, providing complete transparency. ## Technical Details **Calculations:** - MACD Fast MA: EMA(close, fastLength) - MACD Slow MA: EMA(close, slowLength) - MACD Line: Fast MA - Slow MA - Signal Line: SMA(MACD Line, signalLength) - Volume MA: Exponential MA of volume - High Volume: Current volume >= Volume MA × Ratio **Signal logic:** ``` Buy Signal = (MACD crosses above Signal) AND (High volume in last N bars) Sell Signal = (MACD crosses below Signal) AND (High volume in last N bars) ``` ## Parameters Reference | Parameter | Default | Description | |-----------|---------|-------------| | Volume Symbol | Blank | Manual override for volume source (leave blank for automatic detection) | | Use CME Futures | False | Legacy option (automatic detection is now built-in) | | Alert Session | 1530-2200 | Active session time range for alerts | | Timezone | UTC+1 | Timezone for alert sessions | | Volume MA Period | 20 | Number of periods for volume moving average | | High Volume Ratio | 2.0 | Volume threshold multiplier (2.0 = 200% of average) | | Volume Lookback | 5 | Number of bars to check for high volume confirmation | | MACD Fast Length | 12 | Fast EMA period for MACD calculation | | MACD Slow Length | 26 | Slow EMA period for MACD calculation | | MACD Signal Length | 9 | Signal line SMA period | | Only Buy | False | Filter to show only bullish signals | | Only Sell | False | Filter to show only bearish signals | | Show Signals | True | Display buy and sell labels on chart | ## Optimization Tips **For volatile markets (crypto, small caps):** - Increase High Volume Ratio to 2.5-3.0 - Reduce Volume Lookback to 3-4 bars - Consider faster MACD settings (8, 17, 9) **For stable markets (large-cap stocks, bonds):** - Decrease High Volume Ratio to 1.5-1.8 - Increase Volume MA Period to 30-50 - Use standard MACD settings **For forex (with automatic CME futures):** - The indicator automatically uses CME futures when forex pairs are detected - Set appropriate trading session based on your timezone - Use Volume Lookback of 5-7 bars - Consider session-based alerts only - Monitor the information table to verify correct futures mapping **For commodities (Gold, Silver, Oil, Copper):** - The indicator automatically maps to COMEX and NYMEX futures - Increase High Volume Ratio to 2.0-2.5 for metals - Use slightly higher Volume MA Period (25-30) for smoother analysis - Trade during active market hours for best volume data - The information table will show the futures contract being used (orange highlight) ## Limitations and Considerations **What this indicator does NOT do:** - Does not predict future price direction - Does not guarantee profitable trades - Does not replace proper risk management - Does not work well in extremely low-volume conditions **Market conditions to avoid:** - Pre-market and after-hours sessions (low volume) - Major news events (volatile, unpredictable volume) - Holidays and low-liquidity periods - Extremely low float stocks ## Conclusion Smart MACD Volume Trader represents a significant evolution of the traditional MACD indicator by combining volume confirmation with automatic institutional volume integration. This dual-confirmation approach significantly improves signal quality by filtering out low-conviction price movements and ensuring traders work with accurate volume data. The indicator's automatic detection and mapping system supports over 24 instruments across forex, commodities, and metals markets. By intelligently switching to CME and COMEX futures contracts when appropriate, the indicator provides forex and commodity traders with the same quality of volume data that stock traders naturally have access to. This indicator is particularly valuable for traders who want to: - Align their entries with institutional money flow - Avoid getting trapped in false breakouts - Trade forex pairs with reliable volume data - Access accurate volume information for gold, silver, and energy commodities - Combine momentum and volume analysis in a single, streamlined tool Whether you are day trading stocks, swing trading forex pairs, or positioning in commodities markets, this indicator provides a robust framework for identifying high-probability momentum trades backed by genuine institutional participation. The automatic futures mapping works seamlessly across all supported instruments, requiring no manual configuration or expertise in futures markets. --- ## Support and Updates This indicator is actively maintained and updated based on user feedback and market conditions. For questions about implementation or custom modifications, please use the comments section below. **Disclaimer:** This indicator is for educational and informational purposes only. Past performance does not guarantee future results. Always conduct your own analysis and risk management before trading.
مؤشر Pine Script®
من ‎Smart-Day-Trader‎
Contrarian Period High & LowContrarian Period High & Low This indicator pairs nicely with the Contrarian 100 MA and can be located here: Overview The "Contrarian Period High & Low" indicator is a powerful technical analysis tool designed for traders seeking to identify key support and resistance levels and capitalize on contrarian trading opportunities. By tracking the highest highs and lowest lows over user-defined periods (Daily, Weekly, or Monthly), this indicator plots historical levels and generates buy and sell signals when price breaks these levels in a contrarian manner. A unique blue dot counter and action table enhance decision-making, making it ideal for swing traders, trend followers, and those trading forex, stocks, or cryptocurrencies. Optimized for daily charts, it can be adapted to other timeframes with proper testing. How It Works The indicator identifies the highest high and lowest low within a specified period (e.g., daily, weekly, or monthly) and draws horizontal lines for the previous period’s extremes on the chart. These levels act as dynamic support and resistance zones. Contrarian signals are generated when the price crosses below the previous period’s low (buy signal) or above the previous period’s high (sell signal), indicating potential reversals. A blue dot counter tracks consecutive buy signals, and a table displays the count and recommended action, helping traders decide whether to hold or flip positions. Key Components Period High/Low Levels: Tracks the highest high and lowest low for each period, plotting red lines for highs and green lines for lows from the bar where they occurred, extending for a user-defined length (default: 200 bars). Contrarian Signals: Generates buy signals (blue circles) when price crosses below the previous period’s low and sell signals (white circles) when price crosses above the previous period’s high, designed to capture potential reversals. Blue Dot Tracker: Counts consecutive buy signals (“blue dots”). If three or more occur, it suggests a stronger trend, with the table recommending whether to “Hold Investment” or “Flip Investment.” Action Table: A 2x2 table in the bottom-right corner displays the blue dot count and action (“Hold Investment” if count ≥ 4, else “Flip Investment”) for quick reference. Mathematical Concepts Period Detection: Uses an approximate bar count to define periods (1 bar for Daily, 5 bars for Weekly, 20 bars for Monthly on a daily chart). When a new period starts, the previous period’s high/low is finalized and plotted. High/Low Tracking: Highest high (periodHigh) and lowest low (periodLow) are updated within the period. Lines are drawn at these levels when the period ends, starting from the bar where the extreme occurred (periodHighBar, periodLowBar). Signal Logic: Buy signal: ta.crossunder(close , prevPeriodLow) and not lowBroken and barstate.isconfirmed Sell signal: ta.crossover(close , prevPeriodHigh) and not highBroken and barstate.isconfirmed Flags (highBroken, lowBroken) prevent multiple signals for the same level within a period. Blue Dot Counter: Increments on each buy signal, resets on a sell signal or if price exceeds the entry price after three or more buy signals. Entry and Exit Rules Buy Signal (Blue Circle): Triggered when the price crosses below the previous period’s low, suggesting a potential oversold condition and buying opportunity. The signal appears as a blue circle below the price bar. Sell Signal (White Circle): Triggered when the price crosses above the previous period’s high, indicating a potential overbought condition and selling opportunity. The signal appears as a white circle above the price bar. Blue Dot Tracker: Increments blueDotCount on each buy signal and sets an entryPrice on the first buy. Resets on a sell signal or if price exceeds entryPrice after three or more buy signals. If blueDotCount >= 3, the table suggests holding; if >= 4, it reinforces “Hold Investment.” Exit Rules: Exit a buy position on a sell signal or when price exceeds the entry price after three or more buy signals. Combine with other tools (e.g., trendlines, support/resistance) for additional confirmation. Always apply proper risk management. Recommended Usage The "Contrarian Period High & Low" indicator is optimized for daily charts but can be adapted to other timeframes (e.g., 1H, 4H) with adjustments to the period bar count. It excels in markets with clear support/resistance levels and potential reversal zones. Traders should: Backtest the indicator on their chosen asset and timeframe to validate signal reliability. Combine with other technical tools (e.g., moving averages, Fibonacci levels) for stronger trade confirmation. Adjust barsPerPeriod (e.g., ~120 bars for Weekly on hourly charts) based on the chart timeframe and market volatility. Monitor the action table to guide position management based on blue dot counts. Customization Options Period Type: Choose between Daily, Weekly, or Monthly periods (default: Monthly). Line Length: Set the length of high/low lines in bars (default: 200). Show Highs/Lows: Toggle visibility of period high (red) and low (green) lines. Max Lines to Keep: Limit the number of historical lines displayed (default: 10). Hide Signals: Toggle buy/sell signal visibility for a cleaner chart. Table Display: A fixed table in the bottom-right corner shows the blue dot count and action, with yellow (Hold) or green (Flip) backgrounds based on the count. Why Use This Indicator? The "Contrarian Period High & Low" indicator offers a unique blend of support/resistance visualization and contrarian signal generation, making it a versatile tool for identifying potential reversals. Its clear visual cues (lines and signals), blue dot tracker, and actionable table provide traders with an intuitive way to monitor market structure and manage trades. Whether you’re a beginner or an experienced trader, this indicator enhances your ability to spot key levels and time entries/exits effectively. Tips for Users Test the indicator thoroughly on your chosen market and timeframe to optimize settings (e.g., adjust barsPerPeriod for non-daily charts). Use in conjunction with price action or other indicators for stronger trade setups. Monitor the action table to decide whether to hold or flip positions based on blue dot counts. Ensure your chart timeframe aligns with the selected period type (e.g., daily chart for Monthly periods). Apply strict risk management to protect against false breakouts. Happy trading with the Contrarian Period High & Low indicator! Share your feedback and strategies in the TradingView community!
مؤشر Pine Script®
من ‎chuckaschultz‎
Cumulative Returns by Session [BackQuant]Cumulative Returns by Session What this is This tool breaks the trading day into three user-defined sessions and tracks how much each session contributes to return, volatility, and volume. It then aggregates results over a rolling window so you can see which session has been pulling its weight, how streaky each session has been, and how sessions relate to one another through a compact correlation heatmap. We’ve also given the functionality for the user to use a simplified table, just by switching off all settings they are not interested in. How it works 1) Session segmentation You define APAC, EU, and US sessions with explicit hours and time zones. The script detects when each session starts and ends on every intraday bar and records its open, intraday high and low, close, and summed volume. 2) Per-session math At each session end the script computes: Return — either Percent: (Close−Open)÷Open×100(Close − Open) ÷ Open × 100(Close−Open)÷Open×100 or Points: (Close−Open)(Close − Open)(Close−Open), based on your selection. Volatility — either Range: (High−Low)÷Open×100(High − Low) ÷ Open × 100(High−Low)÷Open×100 or ATR scaled by price: ATR÷Open×100ATR ÷ Open × 100ATR÷Open×100. Volume — total volume transacted during that session. 3) Storage and lookback Each day’s three session stats are stored as a row. You choose how many recent sessions to keep in memory. The script then: Builds cumulative returns for APAC, EU, US across the lookback. Computes averages, win rates, and a Sharpe-like ratio avgreturn÷avgvolatilityavg return ÷ avg volatilityavgreturn÷avgvolatility per session. Tracks streaks of positive or negative sessions to show momentum. Tracks drawdowns on cumulative returns to show worst runs from peak. Computes rolling means over a short window for short-term drift. 4) Correlation heatmap Using the stored arrays of session returns, the script calculates Pearson correlations between APAC–EU, APAC–US, and EU–US, and colors the matrix by strength and sign so you can spot coupling or decoupling at a glance. What it plots Three lines: cumulative return for APAC, EU, US over the chosen lookback. Zero reference line for orientation. A statistics table with cumulative %, average %, positive session rate, and optional columns for volatility, average volume, max drawdown, current streak, return-to-vol ratio, and rolling average. A small correlation heatmap table showing APAC, EU, US cross-session correlations. How to use it Pick the asset — leave Custom Instrument empty to use the chart symbol, or point to another symbol for cross-asset studies. Set your sessions and time zones — defaults approximate APAC, EU, and US hours, but you can align them to exchange times or your workflow. Choose calculation modes — Percent vs Points for return, Range vs ATR for volatility. Points are convenient for futures and fixed-tick assets, Percent is comparable across symbols. Decide the lookback — more sessions smooths lines and stats; fewer sessions makes the tool more reactive. Toggle analytics — add volatility, volume, drawdown, streaks, Sharpe-like ratio, rolling averages, and the correlation table as needed. Why session attribution helps Different sessions are driven by different flows. Asia often sets the overnight tone, Europe adds liquidity and direction changes, and the US session can dominate range expansion. Separating contributions by session helps you: Identify which session has been the main driver of net trend. Measure whether volatility or volume is concentrated in a specific window. See if one session’s gains are consistently given back in another. Adapt tactics: fade during a mean-reverting session, press during a trending session. Reading the tables Cumulative % — sum of session returns over the lookback. The sign and slope tell you who is carrying the move. Avg Return % and Positive Sessions % — direction and hit rate. A low average but high hit rate implies many small moves; the reverse implies occasional big swings. Avg Volatility % — typical intrabars range for that session. Compare with Avg Return to judge efficiency. Return/Vol Ratio — return per unit of volatility. Higher is better for stability. Max Drawdown % — worst cumulative give-back within the lookback. A quick way to spot riskiness by session. Current Streak — consecutive up or down sessions. Useful for mean-reversion or regime awareness. Rolling Avg % — short-window drift indicator to catch recent turnarounds. Correlation matrix — green clusters indicate sessions tending to move together; red indicates offsetting behavior. Settings overview Basic Number of Sessions — how many recent days to include. Custom Instrument — analyze another ticker while staying on your current chart. Session Configuration and Times Enable or hide APAC, EU, US rows. Set hours per session and the specific time zone for each. Calculation Methods Return Calculation — Percent or Points. Volatility Calculation — Range or ATR; ATR Length when applicable. Advanced Analytics Correlation, Drawdown, Momentum, Sharpe-like ratio, Rolling Statistics, Rolling Period. Display Options and Colors Show Statistics Table and its position. Toggle columns for Volatility and Volume. Pick individual colors for each session line and row accents. Common applications Session bias mapping — find which window tends to trend in your market and plan exposure accordingly. Strategy scheduling — allocate attention or risk to the session with the best return-to-vol ratio. News and macro awareness — see if correlation rises around central bank cycles or major data releases. Cross-asset monitoring — set the Custom Instrument to a driver (index future, DXY, yields) to see if your symbol reacts in a particular session. Notes This indicator works on intraday charts, since sessions are defined within a day. If you change session clocks or time zones, give the script a few bars to accumulate fresh rows. Percent vs Points and Range vs ATR choices affect comparability across assets, so be consistent when comparing symbols. Session context is one of the simplest ways to explain a messy tape. By separating the day into three windows and scoring each one on return, volatility, and consistency, this tool shows not just where price ended up but when and how it got there. Use the cumulative lines to spot the steady driver, read the table to judge quality and risk, and glance at the heatmap to learn whether the sessions are amplifying or canceling one another. Adjust the hours to your market and let the data tell you which session deserves your focus.
مؤشر Pine Script®
من ‎BackQuant‎
ATAI Volume analysis with price action V 1.00ATAI Volume Analysis with Price Action 1. Introduction 1.1 Overview ATAI Volume Analysis with Price Action is a composite indicator designed for TradingView. It combines per‑side volume data —that is, how much buying and selling occurs during each bar—with standard price‑structure elements such as swings, trend lines and support/resistance. By blending these elements the script aims to help a trader understand which side is in control, whether a breakout is genuine, when markets are potentially exhausted and where liquidity providers might be active. The indicator is built around TradingView’s up/down volume feed accessed via the TradingView/ta/10 library. The following excerpt from the script illustrates how this feed is configured: import TradingView/ta/10 as tvta // Determine lower timeframe string based on user choice and chart resolution string lower_tf_breakout = use_custom_tf_input ? custom_tf_input : timeframe.isseconds ? "1S" : timeframe.isintraday ? "1" : timeframe.isdaily ? "5" : "60" // Request up/down volume (both positive) = tvta.requestUpAndDownVolume(lower_tf_breakout) Lower‑timeframe selection. If you do not specify a custom lower timeframe, the script chooses a default based on your chart resolution: 1 second for second charts, 1 minute for intraday charts, 5 minutes for daily charts and 60 minutes for anything longer. Smaller intervals provide a more precise view of buyer and seller flow but cover fewer bars. Larger intervals cover more history at the cost of granularity. Tick vs. time bars. Many trading platforms offer a tick / intrabar calculation mode that updates an indicator on every trade rather than only on bar close. Turning on one‑tick calculation will give the most accurate split between buy and sell volume on the current bar, but it typically reduces the amount of historical data available. For the highest fidelity in live trading you can enable this mode; for studying longer histories you might prefer to disable it. When volume data is completely unavailable (some instruments and crypto pairs), all modules that rely on it will remain silent and only the price‑structure backbone will operate. Figure caption, Each panel shows the indicator’s info table for a different volume sampling interval. In the left chart, the parentheses “(5)” beside the buy‑volume figure denote that the script is aggregating volume over five‑minute bars; the center chart uses “(1)” for one‑minute bars; and the right chart uses “(1T)” for a one‑tick interval. These notations tell you which lower timeframe is driving the volume calculations. Shorter intervals such as 1 minute or 1 tick provide finer detail on buyer and seller flow, but they cover fewer bars; longer intervals like five‑minute bars smooth the data and give more history. Figure caption, The values in parentheses inside the info table come directly from the Breakout — Settings. The first row shows the custom lower-timeframe used for volume calculations (e.g., “(1)”, “(5)”, or “(1T)”) 2. Price‑Structure Backbone Even without volume, the indicator draws structural features that underpin all other modules. These features are always on and serve as the reference levels for subsequent calculations. 2.1 What it draws • Pivots: Swing highs and lows are detected using the pivot_left_input and pivot_right_input settings. A pivot high is identified when the high recorded pivot_right_input bars ago exceeds the highs of the preceding pivot_left_input bars and is also higher than (or equal to) the highs of the subsequent pivot_right_input bars; pivot lows follow the inverse logic. The indicator retains only a fixed number of such pivot points per side, as defined by point_count_input, discarding the oldest ones when the limit is exceeded. • Trend lines: For each side, the indicator connects the earliest stored pivot and the most recent pivot (oldest high to newest high, and oldest low to newest low). When a new pivot is added or an old one drops out of the lookback window, the line’s endpoints—and therefore its slope—are recalculated accordingly. • Horizontal support/resistance: The highest high and lowest low within the lookback window defined by length_input are plotted as horizontal dashed lines. These serve as short‑term support and resistance levels. • Ranked labels: If showPivotLabels is enabled the indicator prints labels such as “HH1”, “HH2”, “LL1” and “LL2” near each pivot. The ranking is determined by comparing the price of each stored pivot: HH1 is the highest high, HH2 is the second highest, and so on; LL1 is the lowest low, LL2 is the second lowest. In the case of equal prices the newer pivot gets the better rank. Labels are offset from price using ½ × ATR × label_atr_multiplier, with the ATR length defined by label_atr_len_input. A dotted connector links each label to the candle’s wick. 2.2 Key settings • length_input: Window length for finding the highest and lowest values and for determining trend line endpoints. A larger value considers more history and will generate longer trend lines and S/R levels. • pivot_left_input, pivot_right_input: Strictness of swing confirmation. Higher values require more bars on either side to form a pivot; lower values create more pivots but may include minor swings. • point_count_input: How many pivots are kept in memory on each side. When new pivots exceed this number the oldest ones are discarded. • label_atr_len_input and label_atr_multiplier: Determine how far pivot labels are offset from the bar using ATR. Increasing the multiplier moves labels further away from price. • Styling inputs for trend lines, horizontal lines and labels (color, width and line style). Figure caption, The chart illustrates how the indicator’s price‑structure backbone operates. In this daily example, the script scans for bars where the high (or low) pivot_right_input bars back is higher (or lower) than the preceding pivot_left_input bars and higher or lower than the subsequent pivot_right_input bars; only those bars are marked as pivots. These pivot points are stored and ranked: the highest high is labelled “HH1”, the second‑highest “HH2”, and so on, while lows are marked “LL1”, “LL2”, etc. Each label is offset from the price by half of an ATR‑based distance to keep the chart clear, and a dotted connector links the label to the actual candle. The red diagonal line connects the earliest and latest stored high pivots, and the green line does the same for low pivots; when a new pivot is added or an old one drops out of the lookback window, the end‑points and slopes adjust accordingly. Dashed horizontal lines mark the highest high and lowest low within the current lookback window, providing visual support and resistance levels. Together, these elements form the structural backbone that other modules reference, even when volume data is unavailable. 3. Breakout Module 3.1 Concept This module confirms that a price break beyond a recent high or low is supported by a genuine shift in buying or selling pressure. It requires price to clear the highest high (“HH1”) or lowest low (“LL1”) and, simultaneously, that the winning side shows a significant volume spike, dominance and ranking. Only when all volume and price conditions pass is a breakout labelled. 3.2 Inputs • lookback_break_input : This controls the number of bars used to compute moving averages and percentiles for volume. A larger value smooths the averages and percentiles but makes the indicator respond more slowly. • vol_mult_input : The “spike” multiplier; the current buy or sell volume must be at least this multiple of its moving average over the lookback window to qualify as a breakout. • rank_threshold_input (0–100) : Defines a volume percentile cutoff: the current buyer/seller volume must be in the top (100−threshold)%(100−threshold)% of all volumes within the lookback window. For example, if set to 80, the current volume must be in the top 20 % of the lookback distribution. • ratio_threshold_input (0–1) : Specifies the minimum share of total volume that the buyer (for a bullish breakout) or seller (for bearish) must hold on the current bar; the code also requires that the cumulative buyer volume over the lookback window exceeds the seller volume (and vice versa for bearish cases). • use_custom_tf_input / custom_tf_input : When enabled, these inputs override the automatic choice of lower timeframe for up/down volume; otherwise the script selects a sensible default based on the chart’s timeframe. • Label appearance settings : Separate options control the ATR-based offset length, offset multiplier, label size and colors for bullish and bearish breakout labels, as well as the connector style and width. 3.3 Detection logic 1. Data preparation : Retrieve per‑side volume from the lower timeframe and take absolute values. Build rolling arrays of the last lookback_break_input values to compute simple moving averages (SMAs), cumulative sums and percentile ranks for buy and sell volume. 2. Volume spike: A spike is flagged when the current buy (or, in the bearish case, sell) volume is at least vol_mult_input times its SMA over the lookback window. 3. Dominance test: The buyer’s (or seller’s) share of total volume on the current bar must meet or exceed ratio_threshold_input. In addition, the cumulative sum of buyer volume over the window must exceed the cumulative sum of seller volume for a bullish breakout (and vice versa for bearish). A separate requirement checks the sign of delta: for bullish breakouts delta_breakout must be non‑negative; for bearish breakouts it must be non‑positive. 4. Percentile rank: The current volume must fall within the top (100 – rank_threshold_input) percent of the lookback distribution—ensuring that the spike is unusually large relative to recent history. 5. Price test: For a bullish signal, the closing price must close above the highest pivot (HH1); for a bearish signal, the close must be below the lowest pivot (LL1). 6. Labeling: When all conditions above are satisfied, the indicator prints “Breakout ↑” above the bar (bullish) or “Breakout ↓” below the bar (bearish). Labels are offset using half of an ATR‑based distance and linked to the candle with a dotted connector. Figure caption, (Breakout ↑ example) , On this daily chart, price pushes above the red trendline and the highest prior pivot (HH1). The indicator recognizes this as a valid breakout because the buyer‑side volume on the lower timeframe spikes above its recent moving average and buyers dominate the volume statistics over the lookback period; when combined with a close above HH1, this satisfies the breakout conditions. The “Breakout ↑” label appears above the candle, and the info table highlights that up‑volume is elevated relative to its 11‑bar average, buyer share exceeds the dominance threshold and money‑flow metrics support the move. Figure caption, In this daily example, price breaks below the lowest pivot (LL1) and the lower green trendline. The indicator identifies this as a bearish breakout because sell‑side volume is sharply elevated—about twice its 11‑bar average—and sellers dominate both the bar and the lookback window. With the close falling below LL1, the script triggers a Breakout ↓ label and marks the corresponding row in the info table, which shows strong down volume, negative delta and a seller share comfortably above the dominance threshold. 4. Market Phase Module (Volume Only) 4.1 Concept Not all markets trend; many cycle between periods of accumulation (buying pressure building up), distribution (selling pressure dominating) and neutral behavior. This module classifies the current bar into one of these phases without using ATR , relying solely on buyer and seller volume statistics. It looks at net flows, ratio changes and an OBV‑like cumulative line with dual‑reference (1‑ and 2‑bar) trends. The result is displayed both as on‑chart labels and in a dedicated row of the info table. 4.2 Inputs • phase_period_len: Number of bars over which to compute sums and ratios for phase detection. • phase_ratio_thresh : Minimum buyer share (for accumulation) or minimum seller share (for distribution, derived as 1 − phase_ratio_thresh) of the total volume. • strict_mode: When enabled, both the 1‑bar and 2‑bar changes in each statistic must agree on the direction (strict confirmation); when disabled, only one of the two references needs to agree (looser confirmation). • Color customisation for info table cells and label styling for accumulation and distribution phases, including ATR length, multiplier, label size, colors and connector styles. • show_phase_module: Toggles the entire phase detection subsystem. • show_phase_labels: Controls whether on‑chart labels are drawn when accumulation or distribution is detected. 4.3 Detection logic The module computes three families of statistics over the volume window defined by phase_period_len: 1. Net sum (buyers minus sellers): net_sum_phase = Σ(buy) − Σ(sell). A positive value indicates a predominance of buyers. The code also computes the differences between the current value and the values 1 and 2 bars ago (d_net_1, d_net_2) to derive up/down trends. 2. Buyer ratio: The instantaneous ratio TF_buy_breakout / TF_tot_breakout and the window ratio Σ(buy) / Σ(total). The current ratio must exceed phase_ratio_thresh for accumulation or fall below 1 − phase_ratio_thresh for distribution. The first and second differences of the window ratio (d_ratio_1, d_ratio_2) determine trend direction. 3. OBV‑like cumulative net flow: An on‑balance volume analogue obv_net_phase increments by TF_buy_breakout − TF_sell_breakout each bar. Its differences over the last 1 and 2 bars (d_obv_1, d_obv_2) provide trend clues. The algorithm then combines these signals: • For strict mode , accumulation requires: (a) current ratio ≥ threshold, (b) cumulative ratio ≥ threshold, (c) both ratio differences ≥ 0, (d) net sum differences ≥ 0, and (e) OBV differences ≥ 0. Distribution is the mirror case. • For loose mode , it relaxes the directional tests: either the 1‑ or the 2‑bar difference needs to agree in each category. If all conditions for accumulation are satisfied, the phase is labelled “Accumulation” ; if all conditions for distribution are satisfied, it’s labelled “Distribution” ; otherwise the phase is “Neutral” . 4.4 Outputs • Info table row : Row 8 displays “Market Phase (Vol)” on the left and the detected phase (Accumulation, Distribution or Neutral) on the right. The text colour of both cells matches a user‑selectable palette (typically green for accumulation, red for distribution and grey for neutral). • On‑chart labels : When show_phase_labels is enabled and a phase persists for at least one bar, the module prints a label above the bar ( “Accum” ) or below the bar ( “Dist” ) with a dashed or dotted connector. The label is offset using ATR based on phase_label_atr_len_input and phase_label_multiplier and is styled according to user preferences. Figure caption, The chart displays a red “Dist” label above a particular bar, indicating that the accumulation/distribution module identified a distribution phase at that point. The detection is based on seller dominance: during that bar, the net buyer-minus-seller flow and the OBV‑style cumulative flow were trending down, and the buyer ratio had dropped below the preset threshold. These conditions satisfy the distribution criteria in strict mode. The label is placed above the bar using an ATR‑based offset and a dashed connector. By the time of the current bar in the screenshot, the phase indicator shows “Neutral” in the info table—signaling that neither accumulation nor distribution conditions are currently met—yet the historical “Dist” label remains to mark where the prior distribution phase began. Figure caption, In this example the market phase module has signaled an Accumulation phase. Three bars before the current candle, the algorithm detected a shift toward buyers: up‑volume exceeded its moving average, down‑volume was below average, and the buyer share of total volume climbed above the threshold while the on‑balance net flow and cumulative ratios were trending upwards. The blue “Accum” label anchored below that bar marks the start of the phase; it remains on the chart because successive bars continue to satisfy the accumulation conditions. The info table confirms this: the “Market Phase (Vol)” row still reads Accumulation, and the ratio and sum rows show buyers dominating both on the current bar and across the lookback window. 5. OB/OS Spike Module 5.1 What overbought/oversold means here In many markets, a rapid extension up or down is often followed by a period of consolidation or reversal. The indicator interprets overbought (OB) conditions as abnormally strong selling risk at or after a price rally and oversold (OS) conditions as unusually strong buying risk after a decline. Importantly, these are not direct trade signals; rather they flag areas where caution or contrarian setups may be appropriate. 5.2 Inputs • minHits_obos (1–7): Minimum number of oscillators that must agree on an overbought or oversold condition for a label to print. • syncWin_obos: Length of a small sliding window over which oscillator votes are smoothed by taking the maximum count observed. This helps filter out choppy signals. • Volume spike criteria: kVolRatio_obos (ratio of current volume to its SMA) and zVolThr_obos (Z‑score threshold) across volLen_obos. Either threshold can trigger a spike. • Oscillator toggles and periods: Each of RSI, Stochastic (K and D), Williams %R, CCI, MFI, DeMarker and Stochastic RSI can be independently enabled; their periods are adjustable. • Label appearance: ATR‑based offset, size, colors for OB and OS labels, plus connector style and width. 5.3 Detection logic 1. Directional volume spikes: Volume spikes are computed separately for buyer and seller volumes. A sell volume spike (sellVolSpike) flags a potential OverBought bar, while a buy volume spike (buyVolSpike) flags a potential OverSold bar. A spike occurs when the respective volume exceeds kVolRatio_obos times its simple moving average over the window or when its Z‑score exceeds zVolThr_obos. 2. Oscillator votes: For each enabled oscillator, calculate its overbought and oversold state using standard thresholds (e.g., RSI ≥ 70 for OB and ≤ 30 for OS; Stochastic %K/%D ≥ 80 for OB and ≤ 20 for OS; etc.). Count how many oscillators vote for OB and how many vote for OS. 3. Minimum hits: Apply the smoothing window syncWin_obos to the vote counts using a maximum‑of‑last‑N approach. A candidate bar is only considered if the smoothed OB hit count ≥ minHits_obos (for OverBought) or the smoothed OS hit count ≥ minHits_obos (for OverSold). 4. Tie‑breaking: If both OverBought and OverSold spike conditions are present on the same bar, compare the smoothed hit counts: the side with the higher count is selected; ties default to OverBought. 5. Label printing: When conditions are met, the bar is labelled as “OverBought X/7” above the candle or “OverSold X/7” below it. “X” is the number of oscillators confirming, and the bracket lists the abbreviations of contributing oscillators. Labels are offset from price using half of an ATR‑scaled distance and can optionally include a dotted or dashed connector line. Figure caption, In this chart the overbought/oversold module has flagged an OverSold signal. A sell‑off from the prior highs brought price down to the lower trend‑line, where the bar marked “OverSold 3/7 DeM” appears. This label indicates that on that bar the module detected a buy‑side volume spike and that at least three of the seven enabled oscillators—in this case including the DeMarker—were in oversold territory. The label is printed below the candle with a dotted connector, signaling that the market may be temporarily exhausted on the downside. After this oversold print, price begins to rebound towards the upper red trend‑line and higher pivot levels. Figure caption, This example shows the overbought/oversold module in action. In the left‑hand panel you can see the OB/OS settings where each oscillator (RSI, Stochastic, Williams %R, CCI, MFI, DeMarker and Stochastic RSI) can be enabled or disabled, and the ATR length and label offset multiplier adjusted. On the chart itself, price has pushed up to the descending red trendline and triggered an “OverBought 3/7” label. That means the sell‑side volume spiked relative to its average and three out of the seven enabled oscillators were in overbought territory. The label is offset above the candle by half of an ATR and connected with a dashed line, signaling that upside momentum may be overextended and a pause or pullback could follow. 6. Buyer/Seller Trap Module 6.1 Concept A bull trap occurs when price appears to break above resistance, attracting buyers, but fails to sustain the move and quickly reverses, leaving a long upper wick and trapping late entrants. A bear trap is the opposite: price breaks below support, lures in sellers, then snaps back, leaving a long lower wick and trapping shorts. This module detects such traps by looking for price structure sweeps, order‑flow mismatches and dominance reversals. It uses a scoring system to differentiate risk from confirmed traps. 6.2 Inputs • trap_lookback_len: Window length used to rank extremes and detect sweeps. • trap_wick_threshold: Minimum proportion of a bar’s range that must be wick (upper for bull traps, lower for bear traps) to qualify as a sweep. • trap_score_risk: Minimum aggregated score required to flag a trap risk. (The code defines a trap_score_confirm input, but confirmation is actually based on price reversal rather than a separate score threshold.) • trap_confirm_bars: Maximum number of bars allowed for price to reverse and confirm the trap. If price does not reverse in this window, the risk label will expire or remain unconfirmed. • Label settings: ATR length and multiplier for offsetting, size, colours for risk and confirmed labels, and connector style and width. Separate settings exist for bull and bear traps. • Toggle inputs: show_trap_module and show_trap_labels enable the module and control whether labels are drawn on the chart. 6.3 Scoring logic The module assigns points to several conditions and sums them to determine whether a trap risk is present. For bull traps, the score is built from the following (bear traps mirror the logic with highs and lows swapped): 1. Sweep (2 points): Price trades above the high pivot (HH1) but fails to close above it and leaves a long upper wick at least trap_wick_threshold × range. For bear traps, price dips below the low pivot (LL1), fails to close below and leaves a long lower wick. 2. Close break (1 point): Price closes beyond HH1 or LL1 without leaving a long wick. 3. Candle/delta mismatch (2 points): The candle closes bullish yet the order flow delta is negative or the seller ratio exceeds 50%, indicating hidden supply. Conversely, a bearish close with positive delta or buyer dominance suggests hidden demand. 4. Dominance inversion (2 points): The current bar’s buyer volume has the highest rank in the lookback window while cumulative sums favor sellers, or vice versa. 5. Low‑volume break (1 point): Price crosses the pivot but total volume is below its moving average. The total score for each side is compared to trap_score_risk. If the score is high enough, a “Bull Trap Risk” or “Bear Trap Risk” label is drawn, offset from the candle by half of an ATR‑scaled distance using a dashed outline. If, within trap_confirm_bars, price reverses beyond the opposite level—drops back below the high pivot for bull traps or rises above the low pivot for bear traps—the label is upgraded to a solid “Bull Trap” or “Bear Trap” . In this version of the code, there is no separate score threshold for confirmation: the variable trap_score_confirm is unused; confirmation depends solely on a successful price reversal within the specified number of bars. Figure caption, In this example the trap module has flagged a Bear Trap Risk. Price initially breaks below the most recent low pivot (LL1), but the bar closes back above that level and leaves a long lower wick, suggesting a failed push lower. Combined with a mismatch between the candle direction and the order flow (buyers regain control) and a reversal in volume dominance, the aggregate score exceeds the risk threshold, so a dashed “Bear Trap Risk” label prints beneath the bar. The green and red trend lines mark the current low and high pivot trajectories, while the horizontal dashed lines show the highest and lowest values in the lookback window. If, within the next few bars, price closes decisively above the support, the risk label would upgrade to a solid “Bear Trap” label. Figure caption, In this example the trap module has identified both ends of a price range. Near the highs, price briefly pushes above the descending red trendline and the recent pivot high, but fails to close there and leaves a noticeable upper wick. That combination of a sweep above resistance and order‑flow mismatch generates a Bull Trap Risk label with a dashed outline, warning that the upside break may not hold. At the opposite extreme, price later dips below the green trendline and the labelled low pivot, then quickly snaps back and closes higher. The long lower wick and subsequent price reversal upgrade the previous bear‑trap risk into a confirmed Bear Trap (solid label), indicating that sellers were caught on a false breakdown. Horizontal dashed lines mark the highest high and lowest low of the lookback window, while the red and green diagonals connect the earliest and latest pivot highs and lows to visualize the range. 7. Sharp Move Module 7.1 Concept Markets sometimes display absorption or climax behavior—periods when one side steadily gains the upper hand before price breaks out with a sharp move. This module evaluates several order‑flow and volume conditions to anticipate such moves. Users can choose how many conditions must be met to flag a risk and how many (plus a price break) are required for confirmation. 7.2 Inputs • sharp Lookback: Number of bars in the window used to compute moving averages, sums, percentile ranks and reference levels. • sharpPercentile: Minimum percentile rank for the current side’s volume; the current buy (or sell) volume must be greater than or equal to this percentile of historical volumes over the lookback window. • sharpVolMult: Multiplier used in the volume climax check. The current side’s volume must exceed this multiple of its average to count as a climax. • sharpRatioThr: Minimum dominance ratio (current side’s volume relative to the opposite side) used in both the instant and cumulative dominance checks. • sharpChurnThr: Maximum ratio of a bar’s range to its ATR for absorption/churn detection; lower values indicate more absorption (large volume in a small range). • sharpScoreRisk: Minimum number of conditions that must be true to print a risk label. • sharpScoreConfirm: Minimum number of conditions plus a price break required for confirmation. • sharpCvdThr: Threshold for cumulative delta divergence versus price change (positive for bullish accumulation, negative for bearish distribution). • Label settings: ATR length (sharpATRlen) and multiplier (sharpLabelMult) for positioning labels, label size, colors and connector styles for bullish and bearish sharp moves. • Toggles: enableSharp activates the module; show_sharp_labels controls whether labels are drawn. 7.3 Conditions (six per side) For each side, the indicator computes six boolean conditions and sums them to form a score: 1. Dominance (instant and cumulative): – Instant dominance: current buy volume ≥ sharpRatioThr × current sell volume. – Cumulative dominance: sum of buy volumes over the window ≥ sharpRatioThr × sum of sell volumes (and vice versa for bearish checks). 2. Accumulation/Distribution divergence: Over the lookback window, cumulative delta rises by at least sharpCvdThr while price fails to rise (bullish), or cumulative delta falls by at least sharpCvdThr while price fails to fall (bearish). 3. Volume climax: The current side’s volume is ≥ sharpVolMult × its average and the product of volume and bar range is the highest in the lookback window. 4. Absorption/Churn: The current side’s volume divided by the bar’s range equals the highest value in the window and the bar’s range divided by ATR ≤ sharpChurnThr (indicating large volume within a small range). 5. Percentile rank: The current side’s volume percentile rank is ≥ sharp Percentile. 6. Mirror logic for sellers: The above checks are repeated with buyer and seller roles swapped and the price break levels reversed. Each condition that passes contributes one point to the corresponding side’s score (0 or 1). Risk and confirmation thresholds are then applied to these scores. 7.4 Scoring and labels • Risk: If scoreBull ≥ sharpScoreRisk, a “Sharp ↑ Risk” label is drawn above the bar. If scoreBear ≥ sharpScoreRisk, a “Sharp ↓ Risk” label is drawn below the bar. • Confirmation: A risk label is upgraded to “Sharp ↑” when scoreBull ≥ sharpScoreConfirm and the bar closes above the highest recent pivot (HH1); for bearish cases, confirmation requires scoreBear ≥ sharpScoreConfirm and a close below the lowest pivot (LL1). • Label positioning: Labels are offset from the candle by ATR × sharpLabelMult (full ATR times multiplier), not half, and may include a dashed or dotted connector line if enabled. Figure caption, In this chart both bullish and bearish sharp‑move setups have been flagged. Earlier in the range, a “Sharp ↓ Risk” label appears beneath a candle: the sell‑side score met the risk threshold, signaling that the combination of strong sell volume, dominance and absorption within a narrow range suggested a potential sharp decline. The price did not close below the lower pivot, so this label remains a “risk” and no confirmation occurred. Later, as the market recovered and volume shifted back to the buy side, a “Sharp ↑ Risk” label prints above a candle near the top of the channel. Here, buy‑side dominance, cumulative delta divergence and a volume climax aligned, but price has not yet closed above the upper pivot (HH1), so the alert is still a risk rather than a confirmed sharp‑up move. Figure caption, In this chart a Sharp ↑ label is displayed above a candle, indicating that the sharp move module has confirmed a bullish breakout. Prior bars satisfied the risk threshold — showing buy‑side dominance, positive cumulative delta divergence, a volume climax and strong absorption in a narrow range — and this candle closes above the highest recent pivot, upgrading the earlier “Sharp ↑ Risk” alert to a full Sharp ↑ signal. The green label is offset from the candle with a dashed connector, while the red and green trend lines trace the high and low pivot trajectories and the dashed horizontals mark the highest and lowest values of the lookback window. 8. Market‑Maker / Spread‑Capture Module 8.1 Concept Liquidity providers often “capture the spread” by buying and selling in almost equal amounts within a very narrow price range. These bars can signal temporary congestion before a move or reflect algorithmic activity. This module flags bars where both buyer and seller volumes are high, the price range is only a few ticks and the buy/sell split remains close to 50%. It helps traders spot potential liquidity pockets. 8.2 Inputs • scalpLookback: Window length used to compute volume averages. • scalpVolMult: Multiplier applied to each side’s average volume; both buy and sell volumes must exceed this multiple. • scalpTickCount: Maximum allowed number of ticks in a bar’s range (calculated as (high − low) / minTick). A value of 1 or 2 captures ultra‑small bars; increasing it relaxes the range requirement. • scalpDeltaRatio: Maximum deviation from a perfect 50/50 split. For example, 0.05 means the buyer share must be between 45% and 55%. • Label settings: ATR length, multiplier, size, colors, connector style and width. • Toggles : show_scalp_module and show_scalp_labels to enable the module and its labels. 8.3 Signal When, on the current bar, both TF_buy_breakout and TF_sell_breakout exceed scalpVolMult times their respective averages and (high − low)/minTick ≤ scalpTickCount and the buyer share is within scalpDeltaRatio of 50%, the module prints a “Spread ↔” label above the bar. The label uses the same ATR offset logic as other modules and draws a connector if enabled. Figure caption, In this chart the spread‑capture module has identified a potential liquidity pocket. Buyer and seller volumes both spiked above their recent averages, yet the candle’s range measured only a couple of ticks and the buy/sell split stayed close to 50 %. This combination met the module’s criteria, so it printed a grey “Spread ↔” label above the bar. The red and green trend lines link the earliest and latest high and low pivots, and the dashed horizontals mark the highest high and lowest low within the current lookback window. 9. Money Flow Module 9.1 Concept To translate volume into a monetary measure, this module multiplies each side’s volume by the closing price. It tracks buying and selling system money default currency on a per-bar basis and sums them over a chosen period. The difference between buy and sell currencies (Δ$) shows net inflow or outflow. 9.2 Inputs • mf_period_len_mf: Number of bars used for summing buy and sell dollars. • Label appearance settings: ATR length, multiplier, size, colors for up/down labels, and connector style and width. • Toggles: Use enableMoneyFlowLabel_mf and showMFLabels to control whether the module and its labels are displayed. 9.3 Calculations • Per-bar money: Buy $ = TF_buy_breakout × close; Sell $ = TF_sell_breakout × close. Their difference is Δ$ = Buy $ − Sell $. • Summations: Over mf_period_len_mf bars, compute Σ Buy $, Σ Sell $ and ΣΔ$ using math.sum(). • Info table entries: Rows 9–13 display these values as texts like “↑ USD 1234 (1M)” or “ΣΔ USD −5678 (14)”, with colors reflecting whether buyers or sellers dominate. • Money flow status: If Δ$ is positive the bar is marked “Money flow in” ; if negative, “Money flow out” ; if zero, “Neutral”. The cumulative status is similarly derived from ΣΔ.Labels print at the bar that changes the sign of ΣΔ, offset using ATR × label multiplier and styled per user preferences. Figure caption, The chart illustrates a steady rise toward the highest recent pivot (HH1) with price riding between a rising green trend‑line and a red trend‑line drawn through earlier pivot highs. A green Money flow in label appears above the bar near the top of the channel, signaling that net dollar flow turned positive on this bar: buy‑side dollar volume exceeded sell‑side dollar volume, pushing the cumulative sum ΣΔ$ above zero. In the info table, the “Money flow (bar)” and “Money flow Σ” rows both read In, confirming that the indicator’s money‑flow module has detected an inflow at both bar and aggregate levels, while other modules (pivots, trend lines and support/resistance) remain active to provide structural context. In this example the Money Flow module signals a net outflow. Price has been trending downward: successive high pivots form a falling red trend‑line and the low pivots form a descending green support line. When the latest bar broke below the previous low pivot (LL1), both the bar‑level and cumulative net dollar flow turned negative—selling volume at the close exceeded buying volume and pushed the cumulative Δ$ below zero. The module reacts by printing a red “Money flow out” label beneath the candle; the info table confirms that the “Money flow (bar)” and “Money flow Σ” rows both show Out, indicating sustained dominance of sellers in this period. 10. Info Table 10.1 Purpose When enabled, the Info Table appears in the lower right of your chart. It summarises key values computed by the indicator—such as buy and sell volume, delta, total volume, breakout status, market phase, and money flow—so you can see at a glance which side is dominant and which signals are active. 10.2 Symbols • ↑ / ↓ — Up (↑) denotes buy volume or money; down (↓) denotes sell volume or money. • MA — Moving average. In the table it shows the average value of a series over the lookback period. • Σ (Sigma) — Cumulative sum over the chosen lookback period. • Δ (Delta) — Difference between buy and sell values. • B / S — Buyer and seller share of total volume, expressed as percentages. • Ref. Price — Reference price for breakout calculations, based on the latest pivot. • Status — Indicates whether a breakout condition is currently active (True) or has failed. 10.3 Row definitions 1. Up volume / MA up volume – Displays current buy volume on the lower timeframe and its moving average over the lookback period. 2. Down volume / MA down volume – Shows current sell volume and its moving average; sell values are formatted in red for clarity. 3. Δ / ΣΔ – Lists the difference between buy and sell volume for the current bar and the cumulative delta volume over the lookback period. 4. Σ / MA Σ (Vol/MA) – Total volume (buy + sell) for the bar, with the ratio of this volume to its moving average; the right cell shows the average total volume. 5. B/S ratio – Buy and sell share of the total volume: current bar percentages and the average percentages across the lookback period. 6. Buyer Rank / Seller Rank – Ranks the bar’s buy and sell volumes among the last (n) bars; lower rank numbers indicate higher relative volume. 7. Σ Buy / Σ Sell – Sum of buy and sell volumes over the lookback window, indicating which side has traded more. 8. Breakout UP / DOWN – Shows the breakout thresholds (Ref. Price) and whether the breakout condition is active (True) or has failed. 9. Market Phase (Vol) – Reports the current volume‑only phase: Accumulation, Distribution or Neutral. 10. Money Flow – The final rows display dollar amounts and status: – ↑ USD / Σ↑ USD – Buy dollars for the current bar and the cumulative sum over the money‑flow period. – ↓ USD / Σ↓ USD – Sell dollars and their cumulative sum. – Δ USD / ΣΔ USD – Net dollar difference (buy minus sell) for the bar and cumulatively. – Money flow (bar) – Indicates whether the bar’s net dollar flow is positive (In), negative (Out) or neutral. – Money flow Σ – Shows whether the cumulative net dollar flow across the chosen period is positive, negative or neutral. The chart above shows a sequence of different signals from the indicator. A Bull Trap Risk appears after price briefly pushes above resistance but fails to hold, then a green Accum label identifies an accumulation phase. An upward breakout follows, confirmed by a Money flow in print. Later, a Sharp ↓ Risk warns of a possible sharp downturn; after price dips below support but quickly recovers, a Bear Trap label marks a false breakdown. The highlighted info table in the center summarizes key metrics at that moment, including current and average buy/sell volumes, net delta, total volume versus its moving average, breakout status (up and down), market phase (volume), and bar‑level and cumulative money flow (In/Out). 11. Conclusion & Final Remarks This indicator was developed as a holistic study of market structure and order flow. It brings together several well‑known concepts from technical analysis—breakouts, accumulation and distribution phases, overbought and oversold extremes, bull and bear traps, sharp directional moves, market‑maker spread bars and money flow—into a single Pine Script tool. Each module is based on widely recognized trading ideas and was implemented after consulting reference materials and example strategies, so you can see in real time how these concepts interact on your chart. A distinctive feature of this indicator is its reliance on per‑side volume: instead of tallying only total volume, it separately measures buy and sell transactions on a lower time frame. This approach gives a clearer view of who is in control—buyers or sellers—and helps filter breakouts, detect phases of accumulation or distribution, recognize potential traps, anticipate sharp moves and gauge whether liquidity providers are active. The money‑flow module extends this analysis by converting volume into currency values and tracking net inflow or outflow across a chosen window. Although comprehensive, this indicator is intended solely as a guide. It highlights conditions and statistics that many traders find useful, but it does not generate trading signals or guarantee results. Ultimately, you remain responsible for your positions. Use the information presented here to inform your analysis, combine it with other tools and risk‑management techniques, and always make your own decisions when trading.
مؤشر Pine Script®
من ‎ata_sabanci‎
29
%ATR + ΔClose HighlightScript Overview This indicator displays on your chart: Table of the last N bars that passed the ATR-based range filter: Columns: Bar #, High, Range (High–Low), Low Summary row: ATR(N), suggested Stop-Loss (SL = X % of ATR), and the current bar’s range as a percentage of ATR Red badge on the most recent bar showing ΔClose% (the absolute difference between today’s and yesterday’s close, expressed as % of ATR) Background highlights: Blue fill under the most recent bar that met the filter Yellow fill under bars that failed the filter Hidden plots of ATR, %ATR, and ΔClose% (for use in strategies or alerts) All table elements, fills, and plots can be toggled off with a single switch so that only the red ΔClose% badge remains visible. Inputs Setting Description Default Length (bars) Lookback period for ATR and range filter (bars) 5 Upper deviation (%) Upper filter threshold (% of average ATR) 150% Lower deviation (%) Lower filter threshold (% of average ATR) 50% SL as % of ATR Stop-loss distance (% of ATR) 10% Label position Table position relative to bar (“above” or “below”) above Vertical offset (×ATR) Vertical spacing from the bar in ATR units 2.0 Show table & ATR plots Show or hide table, background highlights, and plots true How It Works ATR Calculation & Filtering Computes average True Range over the last N bars. Marks bars whose daily range falls within the specified upper/lower deviation band. Table Construction Gathers up to N most recent bars that passed the filter (or backfills from the most recent pass). Formats each bar’s High, Low, and Range into fixed-width columns for neat alignment. Stop-Loss & Percent Metrics Calculates a recommended SL distance as a percentage of ATR. Computes today’s bar range and ΔClose (absolute change in close) as % of ATR. Chart Display Table: Shows detailed per-bar data and summary metrics. Background fills: Blue for the latest valid bar, yellow for invalid bars. Hidden plots: ATR, %ATR, and ΔClose% (useful for backtesting). Red badge: Always visible on the right side of the last bar, displaying ΔClose%. Tips Disable the table & ATR plots to reduce chart clutter—leave only the red ΔClose% badge for a minimalist volatility alert. Use the hidden ATR fields (plot outputs) in TradingView Strategies or Alerts to automate volatility-based entries/exits. Adjust the deviation band to capture “normal” intraday moves vs. outsized volatility spikes. Load this script on any US market chart (stocks, futures, crypto, etc.) to instantly visualize recent volatility structure, set dynamic SL levels, and highlight today’s price change relative to average true range.
مؤشر Pine Script®
من ‎artemtsapkin‎
ADR Plots + OverlayADR Plots + Overlay This tool calculates and displays Average Daily Range (ADR) levels on your chart, giving traders a quick visual reference for expected daily price movement. It plots guide levels above and below the daily open and shows how much of the day's typical range has already been covered—all in one interactive table and on-chart overlay. What It Does ADR Calculation: Uses daily high-low differences over a user-defined period (default 14 days), smoothed via RMA, SMA, EMA, or WMA to calculate the average daily range. Projected Levels: Plots four reference levels relative to the current day's open price: +100% ADR: Open + ADR +50% ADR: Open + 50% of ADR −50% ADR: Open − 50% of ADR −100% ADR: Open − ADR Coverage %: Tracks intraday high and low prices to calculate what percentage of the ADR has already been covered for the current session: Coverage % = (High − Low) ÷ ADR × 100 Interactive Table: Shows the ADR value and today's ADR coverage percentage in a customizable table overlay. The table position, colors, border, transparency, and an optional empty top row can all be adjusted via settings. Customization Options Table Settings: Position the table (top/bottom × left/right). Change background color, text color, border color and thickness. Toggle an empty top row for spacing. Line Settings: Choose color, line style (solid/dotted/dashed), and width. Lines automatically reposition each day based on that day's open price and ADR calculation. General Inputs: ADR length (number of days). Smoothing method (RMA, SMA, EMA, WMA). How to Use It for Trading Measure Daily Movement: Instantly know the expected daily price range based on historical volatility. Identify Overextension: Use the coverage % to see if the market has already moved close to or beyond its typical daily range. Plan Entries & Exits: Align trade targets and stops with ADR levels for more objective intraday planning. Visual Reference: Horizontal guide lines and table update automatically as new data comes in, helping traders stay informed without manual calculations. Ideal For Intraday traders tracking daily volatility limits. Swing traders wanting a quick reference for expected price movement per day. Anyone seeking a volatility-based framework for planning targets, stops, or identifying extended market conditions.
مؤشر Pine Script®
من ‎abbadon9‎
ADR Tracker Version 2Description The **ADR Tracker** plots a customizable panel on your chart that monitors the Average Daily Range (ADR) and shows how today’s price action compares to that average. It calculates the daily high–low range for each of the past 14 days (can be adjusted) and then takes a simple moving average of those ranges to determine the ADR. **Features:** * **Current ADR value:** Shows the 14‑day ADR in price units. * **ADR status:** Indicates whether today’s range has reached or exceeded the ADR. * **Ticks remaining:** Calculates how many minimum price ticks remain before the ADR would be met. * **Real‑time tracking:** Monitors the intraday high and low to update the range continuously. * **Customizable panel:** Uses TradingView’s table object to display the information. You can set the table’s horizontal and vertical position (top/middle/bottom and left/centre/right) with inputs. The script also lets you change the text and background colours, as well as the width and height of each row. Table cells use explicit width and height percentages, which Pine supports in v6. Each call to `table.cell()` defines the text, colours and dimensions for its cell, so the panel resizes automatically based on your settings. **Usage:** Apply the indicator to any chart. For the most accurate real‑time tracking, use it on intraday timeframes (e.g. 5‑min or 1‑hour) so the current day’s range updates as new bars arrive. Adjust the inputs in the settings panel to reposition the list or change its appearance. --- This description explains what the indicator does and highlights its customizable table display, referencing the Pine Script table features used.
مؤشر Pine Script®
من ‎davon.jennings1023‎
Orthogonal Projections to Latent Structures (O-PLS)Version 0.1 Orthogonal Projections to Latent Structures (O-PLS) Indicator for TradingView This indicator, named "Orthogonal Projections to Latent Structures (O-PLS)", is designed to help traders understand the relevance or predictive power of various market variables on the future close price of the asset it's applied to. Unlike standard correlation coefficients that show a simple linear relationship, O-PLS aims to separate variables into "predictive" (relevant to Y) and "orthogonal" (irrelevant noise) components. This Pine Script indicator provides a simplified proxy of the relevance score derived from O-PLS principles. Purpose of the Indicator The primary purpose of this indicator is to identify which technical factors (such as price, volume, and other indicators) have the strongest relationship with the future price movement of the current trading instrument. By providing a "relevance score" for each input variable, it helps traders focus on the most influential data points, potentially leading to more informed trading decisions. Inputs The indicator offers the following user-definable inputs: * **Lookback Period:** This integer input (default: 100, min: 10, max: 500) determines the number of past bars used to calculate the relevance scores for each variable. A longer lookback period considers more historical data, which can lead to smoother, less reactive scores but might miss recent shifts in variable importance. * **External Asset Symbol:** This symbol input (default: `BINANCE:BTCUSDT`) allows you to specify an external asset (e.g., `BINANCE:ETHUSDT`, `NASDAQ:TSLA`) whose close price will be included in the analysis as an additional variable. This is useful for cross-market analysis to see how other assets influence the current chart. * **Plot Visibility Checkboxes (e.g., "Plot: Open Price Relevance", "Plot: Volume Relevance", etc.):** These boolean checkboxes allow you to toggle the visibility of individual relevance score plots on the chart, helping to declutter the display and focus on specific variables. Outputs The indicator provides two main types of output: Relevance Score Plots: These are lines plotted in a separate pane below the main price chart. Each line corresponds to a specific market variable (Open Price, Close Price, High Price, Low Price, Volume, various RSIs, SMAs, MFI, and the External Asset Close). The value of each line represents the calculated "relevance score" for that variable, typically scaled between 0 and 10. A higher score indicates a stronger predictive relationship with the future close price. Sorted Relevance Table : A table displayed in the top-right corner of the chart provides a clear, sorted list of all analyzed variables and their corresponding relevance scores. The table is sorted in descending order of relevance, making it easy to identify the most influential factors at a glance. Each variable name in the table is colored according to its plot color, and the external asset's name is dynamically displayed without the "BINANCE:" prefix. How to Use the Indicator 1. **Add to Chart:** Apply the "Orthogonal Projections to Latent Structures (O-PLS)" indicator to your desired trading chart (e.g., ETH/USDT). 2. **Adjust Inputs:** * **Lookback Period:** Experiment with different lookback periods to see how the relevance scores change. A shorter period might highlight recent correlations, while a longer one might show more fundamental relationships. * **External Asset Symbol:** If you trade BTC/USDT, you might add ETH/USDT or SPX as an external asset to see its influence. 3. **Analyze Relevance Scores:** * **Plots:** Observe the individual relevance score plots over time. Are certain variables consistently high? Do scores change before significant price moves? * **Table:** Refer to the sorted table on the latest confirmed bar to quickly identify the top-ranked variables. 4. **Incorporate into Strategy:** Use the insights from the relevance scores to: * Prioritize certain indicators or price actions in your trading strategy. For example, if "Volume" has a high relevance score, it suggests volume confirmation is critical for future price moves. * Understand the influence of inter-market relationships (via the External Asset Close). How the Indicator Works The indicator works by performing the following steps on each bar: 1. **Data Fetching:** It gathers historical data for various price components (open, high, low, close), volume, and calculated technical indicators (SMA, RSI, MFI) for the specified `lookback` period. It also fetches the close price of an `External Asset Symbol` . 2. **Standardization (Z-scoring):** All collected raw data series are standardized by converting them into Z-scores. This involves subtracting the mean of each series and dividing by its standard deviation . Standardization is crucial because it brings all variables to a common scale, preventing variables with larger absolute values from disproportionately influencing the correlation calculations. 3. **Correlation Calculation (Proxy for O-PLS Relevance):** The indicator then calculates a simplified form of correlation between each standardized input variable and the standardized future close price (Y variable) . This correlation is a proxy for the relevance that O-PLS would identify. A high absolute correlation indicates a strong linear relationship. 4. **Relevance Scaling:** The calculated correlation values are then scaled to a range of 0 to 10 to provide an easily interpretable "relevance score" . 5. **Output Display:** The relevance scores are presented both as time-series plots (allowing observation of changes over time) and in a real-time sorted table (for quick identification of top factors on the current bar) . How it Differs from Full O-PLS This indicator provides a *simplified proxy* of O-PLS principles rather than a full, mathematically rigorous O-PLS model. Here's why and how it differs: * **Dimensionality Reduction:** A full O-PLS model would involve complex matrix factorization techniques to decompose the independent variables (X) into components that are predictive of Y and components that are orthogonal (unrelated) to Y but still describe X's variance. Pine Script's array capabilities and computational limits make direct implementation of these matrix operations challenging. * **Orthogonal Components:** A true O-PLS model explicitly identifies and removes orthogonal components (noise) from the X data that are unrelated to Y. This indicator, in its simplified form, primarily focuses on the direct correlation (relevance) between each X variable and Y after standardization, without explicitly modeling and separating these orthogonal variations. * **Predictive Model:** A full O-PLS model is ultimately a predictive model that can be used for regression (predicting Y). This indicator, however, focuses solely on **identifying the relevance/correlation of inputs to Y**, rather than building a predictive model for Y itself. It's more of an analytical tool for feature importance than a direct prediction engine. * **Computational Intensity:** Full O-PLS involves Singular Value Decomposition (SVD) or Partial Least Squares (PLS) algorithms, which are computationally intensive. The indicator uses simpler statistical measures (mean, standard deviation, and direct correlation calculation over a lookback window) that are feasible within Pine Script's execution limits. In essence, this Pine Script indicator serves as a practical tool for gaining insights into variable relevance, inspired by the spirit of O-PLS, but adapted for the constraints and common use cases of a TradingView environment.
مؤشر Pine Script®
من ‎VolatilityRob‎
Momentum Regression [BackQuant]Momentum Regression The Momentum Regression is an advanced statistical indicator built to empower quants, strategists, and technically inclined traders with a robust visual and quantitative framework for analyzing momentum effects in financial markets. Unlike traditional momentum indicators that rely on raw price movements or moving averages, this tool leverages a volatility-adjusted linear regression model (y ~ x) to uncover and validate momentum behavior over a user-defined lookback window. Purpose & Design Philosophy Momentum is a core anomaly in quantitative finance — an effect where assets that have performed well (or poorly) continue to do so over short to medium-term horizons. However, this effect can be noisy, regime-dependent, and sometimes spurious. The Momentum Regression is designed as a pre-strategy analytical tool to help you filter and verify whether statistically meaningful and tradable momentum exists in a given asset. Its architecture includes: Volatility normalization to account for differences in scale and distribution. Regression analysis to model the relationship between past and present standardized returns. Deviation bands to highlight overbought/oversold zones around the predicted trendline. Statistical summary tables to assess the reliability of the detected momentum. Core Concepts and Calculations The model uses the following: Independent variable (x): The volatility-adjusted return over the chosen momentum period. Dependent variable (y): The 1-bar lagged log return, also adjusted for volatility. A simple linear regression is performed over a large lookback window (default: 1000 bars), which reveals the slope and intercept of the momentum line. These values are then used to construct: A predicted momentum trendline across time. Upper and lower deviation bands , representing ±n standard deviations of the regression residuals (errors). These visual elements help traders judge how far current returns deviate from the modeled momentum trend, similar to Bollinger Bands but derived from a regression model rather than a moving average. Key Metrics Provided On each update, the indicator dynamically displays: Momentum Slope (β₁): Indicates trend direction and strength. A higher absolute value implies a stronger effect. Intercept (β₀): The predicted return when x = 0. Pearson’s R: Correlation coefficient between x and y. R² (Coefficient of Determination): Indicates how well the regression line explains the variance in y. Standard Error of Residuals: Measures dispersion around the trendline. t-Statistic of β₁: Used to evaluate statistical significance of the momentum slope. These statistics are presented in a top-right summary table for immediate interpretation. A bottom-right signal table also summarizes key takeaways with visual indicators. Features and Inputs ✅ Volatility-Adjusted Momentum : Reduces distortions from noisy price spikes. ✅ Custom Lookback Control : Set the number of bars to analyze regression. ✅ Extendable Trendlines : For continuous visualization into the future. ✅ Deviation Bands : Optional ±σ multipliers to detect abnormal price action. ✅ Contextual Tables : Help determine strength, direction, and significance of momentum. ✅ Separate Pane Design : Cleanly isolates statistical momentum from price chart. How It Helps Traders 📉 Quantitative Strategy Validation: Use the regression results to confirm whether a momentum-based strategy is worth pursuing on a specific asset or timeframe. 🔍 Regime Detection: Track when momentum breaks down or reverses. Slope changes, drops in R², or weak t-stats can signal regime shifts. 📊 Trade Filtering: Avoid false positives by entering trades only when momentum is both statistically significant and directionally favorable. 📈 Backtest Preparation: Before running costly simulations, use this tool to pre-screen assets for exploitable return structures. When to Use It Before building or deploying a momentum strategy : Test if momentum exists and is statistically reliable. During market transitions : Detect early signs of fading strength or reversal. As part of an edge-stacking framework : Combine with other filters such as volatility compression, volume surges, or macro filters. Conclusion The Momentum Regression indicator offers a powerful fusion of statistical analysis and visual interpretation. By combining volatility-adjusted returns with real-time linear regression modeling, it helps quantify and qualify one of the most studied and traded anomalies in finance: momentum.
مؤشر Pine Script®
من ‎BackQuant‎
3
EMA 200 Monitor - Bybit CoinsEMA 200 Monitor - Bybit Coins 📊 OVERVIEW The EMA 200 Monitor - Bybit Coins is an advanced indicator that automatically monitors 30 of the top cryptocurrencies traded on Bybit, alerting you when they are close to the 200-period Exponential Moving Average on the 4-hour timeframe. This indicator was developed especially for traders who use the EMA 200 as a key support/resistance level in their swing trading and position trading strategies. 🎯 WHAT IT'S FOR Multi-Asset Monitoring: Simultaneous monitoring of 30 cryptocurrencies without having to switch between charts Opportunity Identification: Detects when coins are approaching the 200 EMA, a crucial technical level Automated Alerts: Real-time notifications when a coin reaches the configured proximity Time Efficiency: Eliminates the need to manually check chart collections ⚙️ HOW IT WORKS Main Functionality The indicator uses the request.security() function to fetch price data and calculate the 200 EMA of each monitored asset. With each new bar, the script: Calculates the distance between the current price and the 200 EMA for each coin Identifies proximity based on the configured percentage (default: 2%) Displays results in a table organized on the chart Generates automatic alerts when proximity is detected Monitored Coins Major : BTC, ETH, BNB, ADA, XRP, SOL, DOT, DOGE, AVAX DeFi : UNI, LINK, ATOM, ICP, NEAR, OP, ARB, INJ Memecoins : SHIB, PEPE, WIF, BONK, FLOKI Emerging : SUI, TON, APT, POL (ex-MATIC) 📋 AVAILABLE SETTINGS Adjustable Parameters EMA Length (Default: 200): Exponential Moving Average Period Proximity Percentage (Default: 2%): Distance in percentage to consider "close" Show Table (Default: Active): Show/hide results table Table Position: Position of the table on the chart (9 options available) Color System 🔴 Red: Distance ≤ 1% (very close) 🟠 Orange: Distance ≤ 1.5% (close) 🟡 Yellow: Distance ≤ 2% (approaching) 🚀 HOW TO USE Initial Configuration Add the indicator to the 4-hour timeframe chart Set the parameters according to your strategy Position the table where there is no graphic preference Setting Alerts Click "Create Alert" in TradingView Select the "EMA 200 Monitor" indicator Set the notification frequency and method Activate the alert to receive automatic notifications Results Interpretation The table shows: Coin: Asset name (e.g. BTC, ETH) Price: Current currency quote EMA 200: Current value of the moving average Distance: Percentage of proximity to the core code 💡 STRATEGIES TO USE Reversal Trading Entry: When price touches or approaches the EMA 200 Stop: Below/above the EMA with a safety margin Target: Previous resistance/support levels Breakout Trading Monitoring: Watch for currencies consolidating near the EMA 200 Entry: When the media is finally broken Confirmation: Volume and close above/below the EMA Swing Trading Identification: Use the monitor to detect setups in formation Timing: Wait for the EMA 200 to approach for detailed analysis Management: Use the EMA as a reference for stops dynamics ⚠️ IMPORTANT CONSIDERATIONS Technical Limitations Request Bybit data: Access to exchange symbols required Specific timeframe: Optimized for 4-hour analysis Minimum delay: Data updated with each new bar Usage Recommendations Combine with technical analysis: Use together with other indicators Confirm the configuration: Check the graphic patterns before trading Manage risk: Always use stop loss and adequate position sizing Backtesting: Test your strategy before applying with real capital Disclaimer This indicator is a technical analysis tool and does not constitute investment advice. Always do your own analysis and manage detailed information about the risks of your operations. 🔧 TECHNICAL INFORMATION Pine Script version: v6 Type: Indicator (overlay=true) Compatibility: All TradingView plans Resources used: request.security(), arrays, tables Performance: Optimized for multiple simultaneous queries 📈 COMPETITIVE ADVANTAGES ✅ Simultaneous monitoring of 30 major assets ✅ Clear visual interface with intuitive core system ✅ Customizable alerts for different details ✅ Optimized code for maximum performance ✅ Flexible configuration adaptable to different strategies ✅ Real-time update without the need for manual refresh Developed for traders who value efficiency and accuracy in identifying market opportunities based on the EMA 20
مؤشر Pine Script®
من ‎MestreDoFOMO‎
EMD Trend [InvestorUnknown]EMD Trend is a dynamic trend-following indicator that utilizes Exponential Moving Deviation (EMD) to build adaptive channels around a selected moving average. Designed for traders who value responsive trend signals with built-in volatility sensitivity, this tool highlights directional bias, market regime shifts, and potential breakout opportunities. How It Works Instead of using standard deviation, EMD Trend employs the exponential moving average of the absolute deviation from a moving average—producing smoother, faster-reacting upper and lower bounds: Bullish (Risk-ON Long): Price crosses above the upper EMD band Bearish (Risk-ON Short): Price crosses below the lower EMD band Neutral: Price stays within the channel, indicating potential mean reversion or low momentum Trend direction is defined by price interaction with these bands, and visual cues (color-coded bars and fills) help quickly identify market conditions. Features 7 Moving Average Types: SMA, EMA, HMA, DEMA, TEMA, RMA, FRAMA Custom Price Source: Choose close, hl2, ohlc4, or others EMD Multiplier: Controls the width of the deviation envelope Bar Coloring: Candles change color based on current trend Intra-bar Signal Option: Enables faster updates (with optional repainting) Speculative Zones: Fills highlight aggressive momentum moves beyond EMD bounds Backtest Mode Switch to Backtest Mode for performance evaluation over historical data: Equity Curve Plot: Compare EMD Trend strategy vs. Buy & Hold Trade Metrics Table: View number of trades, win/loss stats, profits Performance Metrics Table: Includes CAGR, Sharpe, max drawdown, and more Custom Start Date: Select from which date the backtest should begin Trade Sizing: Configure capital and trade percentage per entry Signal Filters: Choose from Long Only, Short Only, or Both Alerts Built-in alerts let you automate entries, exits, and trend transitions: LONG (EMD Trend) - Trend flips to Long SHORT (EMD Trend) - Trend flips to Short RISK-ON LONG - Price crosses above upper EMD band RISK-OFF LONG - Price crosses back below upper EMD band RISK-ON SHORT - Price crosses below lower EMD band RISK-OFF SHORT - Price crosses back above lower EMD band Use Cases Trend Confirmation with volatility-sensitive boundaries Momentum Entry Filtering via breakout zones Mean Reversion Avoidance in sideways markets Backtesting & Strategy Building with real-time metrics Disclaimer This indicator is intended for informational and educational purposes only. It does not constitute investment advice. Historical performance does not guarantee future results. Always backtest and use in simulation before live trading.
مؤشر Pine Script®
من ‎InvestorUnknown‎
5
RMSD Trend [InvestorUnknown]RMSD Trend is a trend-following indicator that utilizes Root Mean Square Deviation (RMSD) to dynamically construct a volatility-weighted trend channel around a selected moving average. This indicator is designed to enhance signal clarity, minimize noise, and offer quantitative insights into market momentum, ideal for both discretionary and systematic traders. How It Works At its core, RMSD Trend calculates a deviation band around a selected moving average using the Root Mean Square Deviation (similar to standard deviation but with squared errors), capturing the magnitude of price dispersion over a user-defined period. The logic is simple: When price crosses above the upper deviation band, the market is considered bullish (Risk-ON Long). When price crosses below the lower deviation band, the market is considered bearish (Risk-ON Short). If price stays within the band, the market is interpreted as neutral or ranging, offering low-risk decision zones. The indicator also generates trend flips (Long/Short) based on crossovers and crossunders of the price and the RMSD bands, and colors candles accordingly for enhanced visual feedback. Features 7 Moving Average Types: Choose between SMA, EMA, HMA, DEMA, TEMA, RMA, and FRAMA for flexibility. Customizable Source Input: Use price types like close, hl2, ohlc4, etc. Volatility-Aware Channel: Adjustable RMSD multiplier determines band width based on volatility. Smart Coloring: Candles and bands adapt their colors to reflect trend direction (green for bullish, red for bearish). Intra-bar Repainting Toggle: Option to allow more responsive but repaintable signals. Speculation Fill Zones: When price exceeds the deviation channel, a semi-transparent fill highlights potential momentum surges. Backtest Mode Switching to Backtest Mode unlocks a robust suite of simulation features: Built-in Equity Curve: Visualizes both strategy equity and Buy & Hold performance. Trade Metrics Table: Displays the number of trades, win rates, gross profits/losses, and long/short breakdowns. Performance Metrics Table: Includes key stats like CAGR, drawdown, Sharpe ratio, and more. Custom Date Range: Set a custom start date for your backtest. Trade Sizing: Simulate results using position sizing and initial capital settings. Signal Filters: Choose between Long & Short, Long Only, or Short Only strategies. Alerts The RMSD Trend includes six built-in alert conditions: LONG (RMSD Trend) - Trend flips from Short to Long SHORT (RMSD Trend) - Trend flips from Long to Short RISK-ON LONG (RMSD Trend) - Price crosses above upper RMSD band RISK-OFF LONG (RMSD Trend) - Price falls back below upper RMSD band RISK-ON SHORT (RMSD Trend) - Price crosses below lower RMSD band RISK-OFF SHORT (RMSD Trend) - Price rises back above lower RMSD band Use Cases Trend Confirmation: Confirms directional bias with RMSD-weighted confidence zones. Breakout Detection: Highlights moments when price breaks free from historical volatility norms. Mean Reversion Filtering: Avoids false signals by incorporating RMSD’s volatility sensitivity. Strategy Development: Backtest your signals or integrate with a broader system for alpha generation. Settings Summary Display Mode: Overlay (default) or Backtest Mode Average Type: Choose from SMA, EMA, HMA, DEMA, etc. Average Length: Lookback window for moving average RMSD Multiplier: Band width control based on RMS deviation Source: Input price source (close, hl2, ohlc4, etc.) Intra-bar Updating: Real-time updates (may repaint) Color Bars: Toggle bar coloring by trend direction Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice. Past performance, including backtest results, is not indicative of future results. Use with caution and always test thoroughly before live deployment.
مؤشر Pine Script®
من ‎InvestorUnknown‎
1
OrderFlow Sentiment SwiftEdgeOrderFlow Sentiment SwiftEdge Overview OrderFlow Sentiment SwiftEdge is a visual indicator designed to help traders analyze market dynamics through a simulated orderbook and market sentiment display. It breaks down the current candlestick into 10 price bins, estimating buy and sell volumes, and presents this data in an orderbook table alongside a sentiment row showing the buy vs. sell bias. This tool provides a quick and intuitive way to assess orderflow activity and market sentiment directly on your chart. How It Works The indicator consists of two main components: an Orderbook Table and a Market Sentiment Row. Orderbook Table: Simulates buy and sell volumes for the current candlestick by distributing total volume into 10 price bins based on price movement and proximity to open/close levels. Displays the price bins in a table with columns for Price, Buy Volume, and Sell Volume, sorted from highest to lowest price. Highlights the current price level in orange for easy identification, while buy and sell dominance is indicated with green (buy) or red (sell) backgrounds. Market Sentiment Row: Calculates the overall buy and sell sentiment (as a percentage) for the current candlestick based on the simulated orderflow data. Displays the sentiment above the orderbook table, with the background colored green if buyers dominate or red if sellers dominate. Features Customizable Colors: Choose colors for buy (default: green), sell (default: red), and current price (default: orange) levels. Lot Scaling Factor: Adjust the volume scaling factor (default: 0.1 lots per volume unit) to simulate realistic lot sizes. Table Position: Select the table position on the chart (Top, Middle, or Bottom; default: Middle). Default Properties Positive Color: Green Negative Color: Red Current Price Color: Orange Lot Scaling Factor: 0.1 Table Position: Middle Usage This indicator is ideal for traders who want to visualize orderflow dynamics and market sentiment in real-time. The orderbook table provides a snapshot of buy and sell activity at different price levels within the current candlestick, helping you identify areas of high buying or selling pressure. The sentiment row offers a quick overview of market bias, allowing you to gauge whether buyers or sellers are currently dominating. Use this information to complement your trading decisions, such as identifying potential breakout levels or confirming trend direction. Limitations This indicator simulates orderflow data based on candlestick price movement and volume, as TradingView does not provide tick-by-tick data. The volume distribution is an approximation and should be used as a visual aid rather than a definitive measure of market activity. The indicator operates on the chart's current timeframe and does not incorporate higher timeframe data. The simulated volumes are scaled using a user-defined lot scaling factor, which may not reflect actual market lot sizes. Disclaimer This indicator is for informational purposes only and does not guarantee trading results. Always conduct your own analysis and manage risk appropriately. The simulated orderflow data is an estimation and may not reflect real market conditions.
مؤشر Pine Script®
من ‎SwiftEdge‎
4
ETF Builder & Backtest System [TradeDots]Create, analyze, and monitor your own custom “ETF-like” portfolio directly on TradingView. This script merges up to 10 different assets with user-defined weightings into a single composite chart, allowing you to see how your personalized portfolio would have performed historically. It is an original tool designed to help traders and investors quickly gauge risk and return profiles without leaving the TradingView platform. 📝 HOW IT WORKS 1. Custom Portfolio Construction Multiple Assets : Combine up to 10 different stocks, ETFs, cryptocurrencies, or other symbols. User-Defined Weights : Allocate each asset a percentage weight (e.g., 15% in AAPL, 10% in MSFT, etc.). Single Composite Value : The script calculates a weighted “ETF-style” price, effectively simulating a merged portfolio curve on your chart. 2. Performance Tracking & Return Analysis Automatic History Capture : The indicator records each asset’s starting price when it first appears in your chosen date range. Rolling Updates : As time progresses, all asset prices are continually evaluated and the portfolio value is updated in real time. Buy & Hold Returns : See how each asset—and the overall portfolio—performed from the “start” date to the most recent bar. Annualized Return : Automatically calculates CAGR (Compound Annual Growth Rate) to help visualize performance over varying timescales. 3. Table & Visual Output Performance Table : A comprehensive table displays individual asset returns, annualized returns, and portfolio totals. Normalized Chart Plot : The composite ETF value is scaled to 100 at the start date, making it easy to compare relative growth or decline. Optional Time Filter : You can define a specific date range (Start/End Dates) to focus on a particular period or to limit historical data. ⚙️ KEY FEATURES 1. Flexible Asset Selection Choose any symbols from multiple asset classes. The script will only run calculations when data is available—no need to worry about missing quotes. 2. Dynamic Table Reporting Start Price for each asset Percentage Weight in the portfolio Total Return (%) and Annualized Return (%) 3. Simple Backtesting Logic This script takes a straightforward Buy & Hold perspective. Once the start date is reached, the portfolio remains static until the end date, so you can quickly assess hypothetical growth. 4. Plot Customization Toggle the main “ETF” plot on/off. Alter the visual style for tables and text. Adjust the time filter to limit or extend your performance measurement window. 🚀 HOW TO USE IT 1. Add the Script Search for “ETF Builder & Backtest System ” in the Indicators & Strategies tab or manually add it to your chart after saving it in your Pine Editor. 2. Configure Inputs Enable Time Filter : Choose whether to restrict the analysis to a particular date range. Start & End Date : Define the period you want to measure performance over (e.g., from 2019-12-31 to 2025-01-01). Assets & Weights : Enter each symbol and specify a percentage weight (up to 10 assets). Display Options : Pick where you want the Table to appear and choose background/text colors. 3. Interpret the Table & Plots Asset Rows : Each asset’s ticker, weighting, start price, and performance metrics. ETF Total Row : Summarizes total weighting, composite starting value, and overall returns. Normalized Plot : Tracks growth/decline of the combined portfolio, starting at 100 on the chart. 4. Refine Your Strategy Compare how different weights or a new mix of assets would have performed over the same period. Assess if certain assets contribute disproportionately to your returns or volatility. Use the results to guide allocations in your real trading or paper trading accounts. ❗️LIMITATIONS 1. Buy & Hold Only This script does not handle rebalancing or partial divestments. Once the portfolio starts, weights remain fixed throughout the chosen timeframe. 2. No Reinvestment Tracking Dividends or other distributions are not factored into performance. 3. Data Availability If historical data for a particular asset is unavailable on TradingView, related results may display as “N/A.” 4. Market Regimes & Volatility Past performance does not guarantee similar future behavior. Markets can change rapidly, which may render historical backtests less predictive over time. ⚠️ RISK DISCLAIMER Trading and investing carry significant risk and can result in financial loss. The “ETF Builder & Backtest System ” is provided for informational and educational purposes only. It does not constitute financial advice. Always conduct your own research. Use proper risk management and position sizing. Past performance does not guarantee future results. This script is an original creation by TradeDots, published under the Mozilla Public License 2.0. Use this indicator as part of a broader trading or investment approach—consider fundamental and technical factors, overall market context, and personal risk tolerance. No trading tool can assure profits; exercise caution and responsibility in all financial decisions.
مؤشر Pine Script®
من ‎tradedots‎
1
RSI Full Forecast [Titans_Invest]RSI Full Forecast Get ready to experience the ultimate evolution of RSI-based indicators – the RSI Full Forecast, a boosted and even smarter version of the already powerful: RSI Forecast Now featuring over 40 additional entry conditions (forecasts), this indicator redefines the way you view the market. AI-Powered RSI Forecasting: Using advanced linear regression with the least squares method – a solid foundation for machine learning - the RSI Full Forecast enables you to predict future RSI behavior with impressive accuracy. But that’s not all: this new version also lets you monitor future crossovers between the RSI and the MA RSI, delivering early and strategic signals that go far beyond traditional analysis. You’ll be able to monitor future crossovers up to 20 bars ahead, giving you an even broader and more precise view of market movements. See the Future, Now: • Track upcoming RSI & RSI MA crossovers in advance. • Identify potential reversal zones before price reacts. • Uncover statistical behavior patterns that would normally go unnoticed. 40+ Intelligent Conditions: The new layer of conditions is designed to detect multiple high-probability scenarios based on historical patterns and predictive modeling. Each additional forecast is a window into the price's future, powered by robust mathematics and advanced algorithmic logic. Full Customization: All parameters can be tailored to fit your strategy – from smoothing periods to prediction sensitivity. You have complete control to turn raw data into smart decisions. Innovative, Accurate, Unique: This isn’t just an upgrade. It’s a quantum leap in technical analysis. RSI Full Forecast is the first of its kind: an indicator that blends statistical analysis, machine learning, and visual design to create a true real-time predictive system. ⯁ SCIENTIFIC BASIS LINEAR REGRESSION Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥. The general formula for a simple linear regression is given by: y = β₀ + β₁x + ε β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²) β₀ = ȳ - β₁x̄ Where: y   = is the predicted variable (e.g. future value of RSI) x ​​  = is the explanatory variable (e.g. time or bar index) β0 = is the intercept (value of 𝑦 when 𝑥 = 0) 𝛽1 = is the slope of the line (rate of change) ε   = is the random error term The goal is to estimate the coefficients 𝛽0 and 𝛽1 ​​so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares. ⯁ LEAST SQUARES ESTIMATION To minimize the error between predicted and observed values, we use the following formulas: β₁ = / β₀ = ȳ - β₁x̄ Where: ∑ = sum x̄ = mean of x ȳ = mean of y x_i, y_i = individual values ​​of the variables. Where: x_i and y_i are the means of the independent and dependent variables, respectively. i ranges from 1 to n, the number of observations. These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity. ⯁ LINEAR REGRESSION IN MACHINE LEARNING Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies. By applying this model to the RSI, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis. ⯁ VISUAL INTERPRETATION Imagine an RSI time series like this: Time → RSI    → The regression line will smooth these values ​​and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted RSI, which can be crossed with the actual RSI to generate more intelligent signals. ⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED Linear Regression Models the relationship between variables using a straight line. Least Squares Minimizes the sum of squared errors between prediction and reality. Time Series Forecasting Estimates future values ​​based on historical data. Supervised Learning Trains models to predict outputs from known inputs. Statistical Smoothing Reduces noise and reveals underlying trends. ⯁ WHY THIS INDICATOR IS REVOLUTIONARY Scientifically-based: Based on statistical theory and mathematical inference. Unprecedented: First public RSI with least squares predictive modeling. Intelligent: Built with machine learning logic. Practical: Generates forward-thinking signals. Customizable: Flexible for any trading strategy. ⯁ CONCLUSION By combining RSI with linear regression, this indicator allows a trader to predict market momentum, not just follow it. RSI Full Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology. ⯁ Example of simple linear regression, which has one independent variable: ⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ). ⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values ​​using Matlab: ⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables. ⯁ The result of fitting a set of data points with a quadratic function: _________________________________________________ 🔮 Linear Regression: PineScript Technical Parameters 🔮 _________________________________________________ Forecast Types: • Flat: Assumes prices will remain the same. • Linreg: Makes a 'Linear Regression' forecast for n periods. Technical Information: ta.linreg (built-in function) Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values ​​calculated using the least squares method on the source series. Syntax: • Function: ta.linreg() Parameters: • source: Source price series. • length: Number of bars (period). • offset: Offset. • return: Linear regression curve. This function has been cleverly applied to the RSI, making it capable of projecting future values ​​based on past statistical trends. ______________________________________________________ ______________________________________________________ ⯁ WHAT IS THE RSI❓ The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend. ⯁ HOW TO USE THE RSI❓ The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones:   •  Overbought: When the RSI is above 70, indicating that the asset may be overbought.   •  Oversold: When the RSI is below 30, indicating that the asset may be oversold.   •  Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions. ______________________________________________________ ______________________________________________________ ⯁ ENTRY CONDITIONS The conditions below are fully flexible and allow for complete customization of the signal. ______________________________________________________ ______________________________________________________ 🔹 CONDITIONS TO BUY 📈 ______________________________________________________   •  Signal Validity: The signal will remain valid for X bars .   •  Signal Sequence: Configurable as AND or OR . 📈 RSI Conditions: 🔹 RSI > Upper 🔹 RSI < Upper 🔹 RSI > Lower 🔹 RSI < Lower 🔹 RSI > Middle 🔹 RSI < Middle 🔹 RSI > MA 🔹 RSI < MA 📈 MA Conditions: 🔹 MA > Upper 🔹 MA < Upper 🔹 MA > Lower 🔹 MA < Lower 📈 Crossovers: 🔹 RSI (Crossover) Upper 🔹 RSI (Crossunder) Upper 🔹 RSI (Crossover) Lower 🔹 RSI (Crossunder) Lower 🔹 RSI (Crossover) Middle 🔹 RSI (Crossunder) Middle 🔹 RSI (Crossover) MA 🔹 RSI (Crossunder) MA 🔹 MA (Crossover) Upper 🔹 MA (Crossunder) Upper 🔹 MA (Crossover) Lower 🔹 MA (Crossunder) Lower 📈 RSI Divergences: 🔹 RSI Divergence Bull 🔹 RSI Divergence Bear 📈 RSI Forecast: 🔹 RSI (Crossover) MA Forecast 🔹 RSI (Crossunder) MA Forecast 🔹 RSI Forecast 1 > MA Forecast 1 🔹 RSI Forecast 1 < MA Forecast 1 🔹 RSI Forecast 2 > MA Forecast 2 🔹 RSI Forecast 2 < MA Forecast 2 🔹 RSI Forecast 3 > MA Forecast 3 🔹 RSI Forecast 3 < MA Forecast 3 🔹 RSI Forecast 4 > MA Forecast 4 🔹 RSI Forecast 4 < MA Forecast 4 🔹 RSI Forecast 5 > MA Forecast 5 🔹 RSI Forecast 5 < MA Forecast 5 🔹 RSI Forecast 6 > MA Forecast 6 🔹 RSI Forecast 6 < MA Forecast 6 🔹 RSI Forecast 7 > MA Forecast 7 🔹 RSI Forecast 7 < MA Forecast 7 🔹 RSI Forecast 8 > MA Forecast 8 🔹 RSI Forecast 8 < MA Forecast 8 🔹 RSI Forecast 9 > MA Forecast 9 🔹 RSI Forecast 9 < MA Forecast 9 🔹 RSI Forecast 10 > MA Forecast 10 🔹 RSI Forecast 10 < MA Forecast 10 🔹 RSI Forecast 11 > MA Forecast 11 🔹 RSI Forecast 11 < MA Forecast 11 🔹 RSI Forecast 12 > MA Forecast 12 🔹 RSI Forecast 12 < MA Forecast 12 🔹 RSI Forecast 13 > MA Forecast 13 🔹 RSI Forecast 13 < MA Forecast 13 🔹 RSI Forecast 14 > MA Forecast 14 🔹 RSI Forecast 14 < MA Forecast 14 🔹 RSI Forecast 15 > MA Forecast 15 🔹 RSI Forecast 15 < MA Forecast 15 🔹 RSI Forecast 16 > MA Forecast 16 🔹 RSI Forecast 16 < MA Forecast 16 🔹 RSI Forecast 17 > MA Forecast 17 🔹 RSI Forecast 17 < MA Forecast 17 🔹 RSI Forecast 18 > MA Forecast 18 🔹 RSI Forecast 18 < MA Forecast 18 🔹 RSI Forecast 19 > MA Forecast 19 🔹 RSI Forecast 19 < MA Forecast 19 🔹 RSI Forecast 20 > MA Forecast 20 🔹 RSI Forecast 20 < MA Forecast 20 ______________________________________________________ ______________________________________________________ 🔸 CONDITIONS TO SELL 📉 ______________________________________________________   •  Signal Validity: The signal will remain valid for X bars .   •  Signal Sequence: Configurable as AND or OR . 📉 RSI Conditions: 🔸 RSI > Upper 🔸 RSI < Upper 🔸 RSI > Lower 🔸 RSI < Lower 🔸 RSI > Middle 🔸 RSI < Middle 🔸 RSI > MA 🔸 RSI < MA 📉 MA Conditions: 🔸 MA > Upper 🔸 MA < Upper 🔸 MA > Lower 🔸 MA < Lower 📉 Crossovers: 🔸 RSI (Crossover) Upper 🔸 RSI (Crossunder) Upper 🔸 RSI (Crossover) Lower 🔸 RSI (Crossunder) Lower 🔸 RSI (Crossover) Middle 🔸 RSI (Crossunder) Middle 🔸 RSI (Crossover) MA 🔸 RSI (Crossunder) MA 🔸 MA (Crossover) Upper 🔸 MA (Crossunder) Upper 🔸 MA (Crossover) Lower 🔸 MA (Crossunder) Lower 📉 RSI Divergences: 🔸 RSI Divergence Bull 🔸 RSI Divergence Bear 📉 RSI Forecast: 🔸 RSI (Crossover) MA Forecast 🔸 RSI (Crossunder) MA Forecast 🔸 RSI Forecast 1 > MA Forecast 1 🔸 RSI Forecast 1 < MA Forecast 1 🔸 RSI Forecast 2 > MA Forecast 2 🔸 RSI Forecast 2 < MA Forecast 2 🔸 RSI Forecast 3 > MA Forecast 3 🔸 RSI Forecast 3 < MA Forecast 3 🔸 RSI Forecast 4 > MA Forecast 4 🔸 RSI Forecast 4 < MA Forecast 4 🔸 RSI Forecast 5 > MA Forecast 5 🔸 RSI Forecast 5 < MA Forecast 5 🔸 RSI Forecast 6 > MA Forecast 6 🔸 RSI Forecast 6 < MA Forecast 6 🔸 RSI Forecast 7 > MA Forecast 7 🔸 RSI Forecast 7 < MA Forecast 7 🔸 RSI Forecast 8 > MA Forecast 8 🔸 RSI Forecast 8 < MA Forecast 8 🔸 RSI Forecast 9 > MA Forecast 9 🔸 RSI Forecast 9 < MA Forecast 9 🔸 RSI Forecast 10 > MA Forecast 10 🔸 RSI Forecast 10 < MA Forecast 10 🔸 RSI Forecast 11 > MA Forecast 11 🔸 RSI Forecast 11 < MA Forecast 11 🔸 RSI Forecast 12 > MA Forecast 12 🔸 RSI Forecast 12 < MA Forecast 12 🔸 RSI Forecast 13 > MA Forecast 13 🔸 RSI Forecast 13 < MA Forecast 13 🔸 RSI Forecast 14 > MA Forecast 14 🔸 RSI Forecast 14 < MA Forecast 14 🔸 RSI Forecast 15 > MA Forecast 15 🔸 RSI Forecast 15 < MA Forecast 15 🔸 RSI Forecast 16 > MA Forecast 16 🔸 RSI Forecast 16 < MA Forecast 16 🔸 RSI Forecast 17 > MA Forecast 17 🔸 RSI Forecast 17 < MA Forecast 17 🔸 RSI Forecast 18 > MA Forecast 18 🔸 RSI Forecast 18 < MA Forecast 18 🔸 RSI Forecast 19 > MA Forecast 19 🔸 RSI Forecast 19 < MA Forecast 19 🔸 RSI Forecast 20 > MA Forecast 20 🔸 RSI Forecast 20 < MA Forecast 20 ______________________________________________________ ______________________________________________________ 🤖 AUTOMATION 🤖 • You can automate the BUY and SELL signals of this indicator. ______________________________________________________ ______________________________________________________ ⯁ UNIQUE FEATURES ______________________________________________________ Linear Regression: (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL Linear Regression (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL ______________________________________________________ 📜 SCRIPT : RSI Full Forecast 🎴 Art by  : @Titans_Invest & @DiFlip 👨‍💻 Dev by : @Titans_Invest & @DiFlip 🎑 Titans Invest — The Wizards Without Gloves 🧤 ✨ Enjoy! ______________________________________________________ o Mission 🗺 • Inspire Traders to manifest Magic in the Market. o Vision 𐓏 • To elevate collective Energy 𐓷𐓏
مؤشر Pine Script®
من ‎Titans_Invest‎
RSI Forecast [Titans_Invest]RSI Forecast Introducing one of the most impressive RSI indicators ever created – arguably the best on TradingView, and potentially the best in the world. RSI Forecast is a visionary evolution of the classic RSI, merging powerful customization with groundbreaking predictive capabilities. While preserving the core principles of traditional RSI, it takes analysis to the next level by allowing users to anticipate potential future RSI movements. Real-Time RSI Forecasting: For the first time ever, an RSI indicator integrates linear regression using the least squares method to accurately forecast the future behavior of the RSI. This innovation empowers traders to stay one step ahead of the market with forward-looking insight. Highly Customizable: Easily adapt the indicator to your personal trading style. Fine-tune a variety of parameters to generate signals perfectly aligned with your strategy. Innovative, Unique, and Powerful: This is the world’s first RSI Forecast to apply this predictive approach using least squares linear regression. A truly elite-level tool designed for traders who want a real edge in the market. ⯁ SCIENTIFIC BASIS LINEAR REGRESSION Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥. The general formula for a simple linear regression is given by: y = β₀ + β₁x + ε Where: y   = is the predicted variable (e.g. future value of RSI) x ​​  = is the explanatory variable (e.g. time or bar index) β0 = is the intercept (value of 𝑦 when 𝑥 = 0) 𝛽1 = is the slope of the line (rate of change) ε   = is the random error term The goal is to estimate the coefficients 𝛽0 and 𝛽1 ​​so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares. ⯁ LEAST SQUARES ESTIMATION To minimize the error between predicted and observed values, we use the following formulas: β₁ = / β₀ = ȳ - β₁x̄ Where: ∑ = sum x̄ = mean of x ȳ = mean of y x_i, y_i = individual values ​​of the variables. Where: x_i and y_i are the means of the independent and dependent variables, respectively. i ranges from 1 to n, the number of observations. These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity. ⯁ LINEAR REGRESSION IN MACHINE LEARNING Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies. By applying this model to the RSI, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis. ⯁ VISUAL INTERPRETATION Imagine an RSI time series like this: Time → RSI    → The regression line will smooth these values ​​and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted RSI, which can be crossed with the actual RSI to generate more intelligent signals. ⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED Linear Regression Models the relationship between variables using a straight line. Least Squares Minimizes the sum of squared errors between prediction and reality. Time Series Forecasting Estimates future values ​​based on historical data. Supervised Learning Trains models to predict outputs from known inputs. Statistical Smoothing Reduces noise and reveals underlying trends. ⯁ WHY THIS INDICATOR IS REVOLUTIONARY Scientifically-based: Based on statistical theory and mathematical inference. Unprecedented: First public RSI with least squares predictive modeling. Intelligent: Built with machine learning logic. Practical: Generates forward-thinking signals. Customizable: Flexible for any trading strategy. ⯁ CONCLUSION By combining RSI with linear regression, this indicator allows a trader to predict market momentum, not just follow it. RSI Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology. ⯁ Example of simple linear regression, which has one independent variable: ⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ). ⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values ​​using Matlab: ⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables. ⯁ The result of fitting a set of data points with a quadratic function: _______________________________________________________________________ 🥇 This is the world’s first RSI indicator with: Linear Regression for Forecasting 🥇_______________________________________________________________________ _________________________________________________ 🔮 Linear Regression: PineScript Technical Parameters 🔮 _________________________________________________ Forecast Types: • Flat: Assumes prices will remain the same. • Linreg: Makes a 'Linear Regression' forecast for n periods. Technical Information: ta.linreg (built-in function) Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values ​​calculated using the least squares method on the source series. Syntax: • Function: ta.linreg() Parameters: • source: Source price series. • length: Number of bars (period). • offset: Offset. • return: Linear regression curve. This function has been cleverly applied to the RSI, making it capable of projecting future values ​​based on past statistical trends. ______________________________________________________ ______________________________________________________ ⯁ WHAT IS THE RSI❓ The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend. ⯁ HOW TO USE THE RSI❓ The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones:   •  Overbought: When the RSI is above 70, indicating that the asset may be overbought.   •  Oversold: When the RSI is below 30, indicating that the asset may be oversold.   •  Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions. ______________________________________________________ ______________________________________________________ ⯁ ENTRY CONDITIONS The conditions below are fully flexible and allow for complete customization of the signal. ______________________________________________________ ______________________________________________________ 🔹 CONDITIONS TO BUY 📈 ______________________________________________________   •  Signal Validity: The signal will remain valid for X bars .   •  Signal Sequence: Configurable as AND or OR . 📈 RSI Conditions: 🔹 RSI > Upper 🔹 RSI < Upper 🔹 RSI > Lower 🔹 RSI < Lower 🔹 RSI > Middle 🔹 RSI < Middle 🔹 RSI > MA 🔹 RSI < MA 📈 MA Conditions: 🔹 MA > Upper 🔹 MA < Upper 🔹 MA > Lower 🔹 MA < Lower 📈 Crossovers: 🔹 RSI (Crossover) Upper 🔹 RSI (Crossunder) Upper 🔹 RSI (Crossover) Lower 🔹 RSI (Crossunder) Lower 🔹 RSI (Crossover) Middle 🔹 RSI (Crossunder) Middle 🔹 RSI (Crossover) MA 🔹 RSI (Crossunder) MA 🔹 MA (Crossover) Upper 🔹 MA (Crossunder) Upper 🔹 MA (Crossover) Lower 🔹 MA (Crossunder) Lower 📈 RSI Divergences: 🔹 RSI Divergence Bull 🔹 RSI Divergence Bear 📈 RSI Forecast: 🔮 RSI (Crossover) MA Forecast 🔮 RSI (Crossunder) MA Forecast ______________________________________________________ ______________________________________________________ 🔸 CONDITIONS TO SELL 📉 ______________________________________________________   •  Signal Validity: The signal will remain valid for X bars .   •  Signal Sequence: Configurable as AND or OR . 📉 RSI Conditions: 🔸 RSI > Upper 🔸 RSI < Upper 🔸 RSI > Lower 🔸 RSI < Lower 🔸 RSI > Middle 🔸 RSI < Middle 🔸 RSI > MA 🔸 RSI < MA 📉 MA Conditions: 🔸 MA > Upper 🔸 MA < Upper 🔸 MA > Lower 🔸 MA < Lower 📉 Crossovers: 🔸 RSI (Crossover) Upper 🔸 RSI (Crossunder) Upper 🔸 RSI (Crossover) Lower 🔸 RSI (Crossunder) Lower 🔸 RSI (Crossover) Middle 🔸 RSI (Crossunder) Middle 🔸 RSI (Crossover) MA 🔸 RSI (Crossunder) MA 🔸 MA (Crossover) Upper 🔸 MA (Crossunder) Upper 🔸 MA (Crossover) Lower 🔸 MA (Crossunder) Lower 📉 RSI Divergences: 🔸 RSI Divergence Bull 🔸 RSI Divergence Bear 📉 RSI Forecast: 🔮 RSI (Crossover) MA Forecast 🔮 RSI (Crossunder) MA Forecast ______________________________________________________ ______________________________________________________ 🤖 AUTOMATION 🤖 • You can automate the BUY and SELL signals of this indicator. ______________________________________________________ ______________________________________________________ ⯁ UNIQUE FEATURES ______________________________________________________ Linear Regression: (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL Linear Regression (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL ______________________________________________________ 📜 SCRIPT : RSI Forecast 🎴 Art by  : @Titans_Invest & @DiFlip 👨‍💻 Dev by : @Titans_Invest & @DiFlip 🎑 Titans Invest — The Wizards Without Gloves 🧤 ✨ Enjoy! ______________________________________________________ o Mission 🗺 • Inspire Traders to manifest Magic in the Market. o Vision 𐓏 • To elevate collective Energy 𐓷𐓏
مؤشر Pine Script®
من ‎Titans_Invest‎
3
RSI Full [Titans_Invest]RSI Full One of the most complete RSI indicators on the market. While maintaining the classic RSI foundation, our indicator integrates multiple entry conditions to generate more accurate buy and sell signals. All conditions are fully configurable, allowing complete customization to fit your trading strategy. ⯁ WHAT IS THE RSI❓ The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend. ⯁ HOW TO USE THE RSI❓ The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones: Overbought: When the RSI is above 70, indicating that the asset may be overbought. Oversold: When the RSI is below 30, indicating that the asset may be oversold. Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions. ⯁ ENTRY CONDITIONS The conditions below are fully flexible and allow for complete customization of the signal. ______________________________________________________ 🔹 CONDITIONS TO BUY 📈 ______________________________________________________   •  Signal Validity: The signal will remain valid for X bars .   •  Signal Sequence: Configurable as AND/OR . 📈 RSI Conditions: 🔹 RSI > Upper 🔹 RSI < Upper 🔹 RSI > Lower 🔹 RSI < Lower 🔹 RSI > Middle 🔹 RSI < Middle 🔹 RSI > MA 🔹 RSI < MA 📈 MA Conditions: 🔹 MA > Upper 🔹 MA < Upper 🔹 MA > Lower 🔹 MA < Lower 📈 Crossovers: 🔹 RSI (Crossover) Upper 🔹 RSI (Crossunder) Upper 🔹 RSI (Crossover) Lower 🔹 RSI (Crossunder) Lower 🔹 RSI (Crossover) Middle 🔹 RSI (Crossunder) Middle 🔹 RSI (Crossover) MA 🔹 RSI (Crossunder) MA 🔹 MA (Crossover) Upper 🔹 MA (Crossunder) Upper 🔹 MA (Crossover) Lower 🔹 MA (Crossunder) Lower 📈 RSI Divergences: 🔹 RSI Divergence Bull 🔹 RSI Divergence Bear ______________________________________________________ ______________________________________________________ 🔸 CONDITIONS TO SELL 📉 ______________________________________________________   •  Signal Validity: The signal will remain valid for X bars .   •  Signal Sequence: Configurable as AND/OR . 📉 RSI Conditions: 🔸 RSI > Upper 🔸 RSI < Upper 🔸 RSI > Lower 🔸 RSI < Lower 🔸 RSI > Middle 🔸 RSI < Middle 🔸 RSI > MA 🔸 RSI < MA 📉 MA Conditions: 🔸 MA > Upper 🔸 MA < Upper 🔸 MA > Lower 🔸 MA < Lower 📉 Crossovers: 🔸 RSI (Crossover) Upper 🔸 RSI (Crossunder) Upper 🔸 RSI (Crossover) Lower 🔸 RSI (Crossunder) Lower 🔸 RSI (Crossover) Middle 🔸 RSI (Crossunder) Middle 🔸 RSI (Crossover) MA 🔸 RSI (Crossunder) MA 🔸 MA (Crossover) Upper 🔸 MA (Crossunder) Upper 🔸 MA (Crossover) Lower 🔸 MA (Crossunder) Lower 📉 RSI Divergences: 🔸 RSI Divergence Bull 🔸 RSI Divergence Bear ______________________________________________________ ______________________________________________________ 🤖 AUTOMATION 🤖 • You can automate the BUY and SELL signals of this indicator. ______________________________________________________ ______________________________________________________ ⯁ UNIQUE FEATURES ______________________________________________________ Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL ______________________________________________________ 📜 SCRIPT : RSI Full 🎴 Art by  : @Titans_Invest & @DiFlip 👨‍💻 Dev by : @Titans_Invest & @DiFlip 🎑 Titans Invest — The Wizards Without Gloves 🧤 ✨ Enjoy the Spell! ______________________________________________________ o Mission 🗺 • Inspire Traders to manifest Magic in the Market. o Vision 𐓏 • To elevate collective Energy 𐓷𐓏
مؤشر Pine Script®
من ‎Titans_Invest‎
Statistical OHLC Projections [neo|]█  OVERVIEW Statistical OHLC Projections is an indicator designed to offer users a customizable deep-dive on measuring historical price levels for any timeframe. The indicator separates price into two distinct levels, "Manipulation" and "Distribution", where the idea is that for higher timeframe candles, e.g. an up-close candle, the distance from the open to the bottom of the wick would constitute the Manipulation, and the rest would be considered the Distribution. By measuring out these levels, we can gain insight on how far the market may move from higher timeframe opens to their manipulations and distributions, and apply this knowledge to our analysis. IMPORTANT: Since levels are based on the lookback available on your chart, if the levels aren't being displayed this likely means you don't have enough lookback for your selected timeframe. To check this, enable the stat table to see how many values are available for your timeframe, and either reduce the lookback or increase your chart timeframe. █  CONCEPTS The core concept revolves around understanding market behavior through the lens of historical candle structure. The indicator dissects OHLC data to provide statistical boundaries of expected price movement. - Manipulation Levels: These represent the areas typically seen as liquidity grabs or false moves where price extends in one direction before reversing. - Distribution Levels: These highlight where the bulk of directional movement tends to occur, often following the manipulation move. The tool aggregates this data across your selected timeframe to inform you of potential levels associated with it. █  FEATURES Multiple Display Types: Display statistical data through two sleek styles, areas or lines. Where areas represent the area between two customizable lookback values, and lines represent one average value. Adjustable Timeframe Selection: Whether you want to see data based on the 1D chart, or the 1W chart, anything is possible. Simply change the timeframe on the dropdown menu and if there is sufficient lookback the indicator will adjust to your requested timeframe. Customizable Historical Lookback: By default, the indicator will measure the average 60 values of your requested timeframe, however this may be adjusted to be higher or lower based on your preference. If you want to measure recent moves, 10-20 lookback may be better for you, or if you want more data for less volatile instruments, a value of 100 may be better. Historical Display: Prevent historical levels from being removed by unchecking the "Remove Previous Drawings" option, this will allow you to examine how the levels previously interacted with price. NY Midnight Anchoring: By checking the "Use NY Midnight" option, you may see the projection anchored to the New York midnight open time, which is often a significant level on indices. Alerts: You may enable alerts for any of the indicator's provided levels to stay informed, even when off the charts. █  How to use To use the indicator, simply apply it to your chart and modify any of your desired inputs. By default, the indicator will provide levels for the "1D" timeframe, with a desired lookback of 60, on most instruments and plans this can be gotten when you are on the 30 minute timeframe or above. When price reaches or extends beyond a manipulation level, observe how it reacts and whether it rejects from that level, if it does this may be an indication that the candle for the timeframe you selected may be reversing. █  SETTINGS AND OPTIONS Customize the indicator’s behavior, timeframe sources, and visual appearance to fit your analysis style. Each setting has been designed with flexibility in mind, whether you're working on lower or higher timeframes. Display Mode: Switch between different display styles for levels:  - Default: Shows all statistical levels as individual lines.  - Areas: Plots filled zones between two customizable lookbacks to represent the range between them. This is ideal for visually mapping high-probability zones of price activity. Timeframe Settings:  - Show First/Second Timeframe: Choose to show one or both timeframe projections simultaneously.  - First Timeframe / Second Timeframe: Define the higher timeframe candle you want to base calculations on (e.g., 1D, 1W).  - Use NY Midnight: When enabled and using the daily timeframe, the levels will be anchored to the New York Midnight Open (00:00 EST), a key institutional timing reference, especially useful for indices and forex. Calculation Settings:  - Main Lookback Period: The number of historical candles used in the statistical calculations. A lower number focuses on recent price action, while a higher number smooths results across broader history.  - First Lookback / Second Lookback: Used when “Areas” mode is selected to define the range of the shaded zone. For example, an area from 20 to 60 candles creates a band between short- and long-term price behavior averages. Visual Settings:  - Line Style: Set your preferred visual style: Solid, Dashed, or Dotted.  - Remove Previous Drawings: When enabled, only the most recent projection is shown on the chart. Disable to retain previous levels and visually backtest their reactions over time. Color Settings:  Customize each level independently to match your chart theme:  - Manipulation High/Low  - Distribution High/Low  - Open Level  - Label Text Color Premium/Discount Zones:  - Enable Premium/Discount Zones: Overlay price zones above and below equilibrium to visualize potential overbought (premium) and oversold (discount) areas.  - Premium/Discount Colors: Fully customizable zone colors for clarity and emphasis. Table Settings:  - Show Statistics Table: Adds an on-chart table summarizing key levels from your active timeframe(s).  - Table Cell Color: Set the background color of the table cells for visibility.  - Table Position: Choose from preset chart locations to position the table where it works best for your layout. Alerts:  Stay on top of price interactions with key levels even when you're away from the charts.  - Manipulation Hits (High)  - Manipulation Hits (Low)  - Distribution Hits (High)  - Distribution Hits (Low)
مؤشر Pine Script®
من ‎neommxm‎
8
Combined EMA Technical AnalysisThis script is written in Pine Script (version 5) for TradingView and creates a comprehensive technical analysis indicator called "Combined EMA Technical Analysis." It overlays multiple technical indicators on a price chart, including Exponential Moving Averages (EMAs), VWAP, MACD, PSAR, RSI, Bollinger Bands, ADX, and external data from the S&P 500 (SPX) and VIX indices. The script also provides visual cues through colors, shapes, and a customizable table to help traders interpret market conditions. Here’s a breakdown of the script: --- ### **1. Purpose** - The script combines several popular technical indicators to analyze price trends, momentum, volatility, and market sentiment. - It uses color coding (green for bullish, red for bearish, gray/white for neutral) and a table to display key information. --- ### **2. Custom Colors** - Defines custom RGB colors for bullish (`customGreen`), bearish (`customRed`), and neutral (`neutralGray`) signals to enhance visual clarity. --- ### **3. User Inputs** - **EMA Colors**: Users can customize the colors of five EMAs (8, 20, 9, 21, 50 periods). - **MACD Settings**: Adjustable short length (12), long length (26), and signal length (9). - **RSI Settings**: Adjustable length (14). - **Bollinger Bands Settings**: Length (20), multiplier (2), and proximity threshold (0.1% of band width). - **ADX Settings**: Adjustable length (14). - **Table Settings**: Position (e.g., "Bottom Right") and text size (e.g., "Small"). --- ### **4. Indicator Calculations** #### **Exponential Moving Averages (EMAs)** - Calculates five EMAs: 8, 20, 9, 21, and 50 periods based on the closing price. - Used to identify short-term and long-term trends. #### **Volume Weighted Average Price (VWAP)** - Resets daily and calculates the average price weighted by volume. - Color-coded: green if price > VWAP (bullish), red if price < VWAP (bearish), white if neutral. #### **MACD (Moving Average Convergence Divergence)** - Uses short (12) and long (26) EMAs to compute the MACD line, with a 9-period signal line. - Displays "Bullish" (green) if MACD > signal, "Bearish" (red) if MACD < signal. #### **Parabolic SAR (PSAR)** - Calculated with acceleration factors (start: 0.02, increment: 0.02, max: 0.2). - Indicates trend direction: green if price > PSAR (bullish), red if price < PSAR (bearish). #### **Relative Strength Index (RSI)** - Measures momentum over 14 periods. - Highlighted in green if > 70 (overbought), red if < 30 (oversold), white otherwise. #### **Bollinger Bands (BB)** - Uses a 20-period SMA with a 2-standard-deviation multiplier. - Color-coded based on price position: - Green: Above upper band or close to it. - Red: Below lower band or close to it. - Gray: Neutral (within bands). #### **Average Directional Index (ADX)** - Manually calculates ADX to measure trend strength: - Strong trend: ADX > 25. - Very strong trend: ADX > 50. - Direction: Bullish if +DI > -DI, bearish if -DI > +DI. #### **EMA Crosses** - Detects bullish (crossover) and bearish (crossunder) events for: - EMA 9 vs. EMA 21. - EMA 8 vs. EMA 20. - Visualized with green (bullish) or red (bearish) circles. #### **SPX and VIX Data** - Fetches daily closing prices for the S&P 500 (SPX) and VIX (volatility index). - SPX trend: Bullish if EMA 9 > EMA 21, bearish if EMA 9 < EMA 21. - VIX levels: High (> 25, fear), Low (< 15, stability). - VIX color: Green if SPX bullish and VIX low, red if SPX bearish and VIX high, white otherwise. --- ### **5. Visual Outputs** #### **Plots** - EMAs, VWAP, and PSAR are plotted on the chart with their respective colors. - EMA crosses are marked with circles (green for bullish, red for bearish). #### **Table** - Displays a summary of indicators in a customizable position and size. - Indicators shown (if enabled): - EMA 8/20, 9/21, 50: Green dot if bullish, red if bearish. - VWAP: Green if price > VWAP, red if price < VWAP. - MACD: Green if bullish, red if bearish. - MACD Zero: Green if MACD > 0, red if MACD < 0. - PSAR: Green if price > PSAR, red if price < PSAR. - ADX: Arrows for very strong trends (↑/↓), dots for weaker trends, colored by direction. - Bollinger Bands: Arrows (↑/↓) or dots based on price position. - RSI: Numeric value, colored by overbought/oversold levels. - VIX: Numeric value, colored based on SPX trend and VIX level. --- ### **6. Alerts** - Triggers alerts for EMA 8/20 crosses: - Bullish: "EMA 8/20 Bullish Cross on Candle Close!" - Bearish: "EMA 8/20 Bearish Cross on Candle Close!" --- ### **7. Key Features** - **Flexibility**: Users can toggle indicators on/off in the table and adjust parameters. - **Visual Clarity**: Consistent use of green (bullish), red (bearish), and neutral colors. - **Comprehensive**: Combines trend, momentum, volatility, and market sentiment indicators. --- ### **How to Use** 1. Add the script to TradingView. 2. Customize inputs (colors, lengths, table position) as needed. 3. Interpret the chart and table: - Green signals suggest bullish conditions. - Red signals suggest bearish conditions. - Neutral signals indicate indecision or consolidation. 4. Set up alerts for EMA crosses to catch trend changes. This script is ideal for traders who want a multi-indicator dashboard to monitor price action and market conditions efficiently.
مؤشر Pine Script®
من ‎skrillionaire1‎