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Techindicator

LibPvotLibrary "LibPvot" This is a library for advanced technical analysis, specializing in two core areas: the detection of price-oscillator divergences and the analysis of market structure. It provides a back-end engine for signal detection and a toolkit for indicator plotting. Key Features: 1. **Complete Divergence Suite (Class A, B, C):** The engine detects all three major types of divergences, providing a full spectrum of analytical signals: - **Regular (A):** For potential trend reversals. - **Hidden (B):** For potential trend continuations. - **Exaggerated (C):** For identifying weakness at double tops/bottoms. 2. **Advanced Signal Filtering:** The detection logic uses a percentage-based price tolerance (`prcTol`). This feature enables the practical detection of Exaggerated divergences (which rarely occur at the exact same price) and creates a "dead zone" to filter insignificant noise from triggering Regular divergences. 3. **Pivot Synchronization:** A bar tolerance (`barTol`) is used to reliably match price and oscillator pivots that do not align perfectly on the same bar, preventing missed signals. 4. **Signal Invalidation Logic:** Features two built-in invalidation rules: - An optional `invalidate` parameter automatically terminates active divergences if the price or the oscillator breaks the level of the confirming pivot. - The engine also discards 'half-pivots' (e.g., a price pivot) if a corresponding oscillator pivot does not appear within the `barTol` window. 5. **Stateful Plotting Helpers:** Provides helper functions (`bullDivPos` and `bearDivPos`) that abstract away the state management issues of visualizing persistent signals. They generate gap-free, accurately anchored data series ready to be used in `plotshape` functions, simplifying indicator-side code. 6. **Rich Data Output:** The core detection functions (`bullDiv`, `bearDiv`) return a comprehensive 9-field data tuple. This includes the boolean flags for each divergence type and the precise coordinates (price, oscillator value, bar index) of both the starting and the confirming pivots. 7. **Market Structure & Trend Analysis:** Includes a `marketStructure` function to automatically identify pivot highs/lows, classify their relationship (HH, LH, LL, HL), detect structure breaks, and determine the current trend state (Up, Down, Neutral) based on pivot sequences. --- **DISCLAIMER** This library is provided "AS IS" and for informational and educational purposes only. It does not constitute financial, investment, or trading advice. The author assumes no liability for any errors, inaccuracies, or omissions in the code. Using this library to build trading indicators or strategies is entirely at your own risk. As a developer using this library, you are solely responsible for the rigorous testing, validation, and performance of any scripts you create based on these functions. The author shall not be held liable for any financial losses incurred directly or indirectly from the use of this library or any scripts derived from it. bullDiv(priceSrc, oscSrc, leftLen, rightLen, depth, barTol, prcTol, persist, invalidate)   Detects bullish divergences (Regular, Hidden, Exaggerated) based on pivot lows.   Parameters:      priceSrc (float) : series float Price series to check for pivots (e.g., `low`).      oscSrc (float) : series float Oscillator series to check for pivots.      leftLen (int) : series int Number of bars to the left of a pivot (default 5).      rightLen (int) : series int Number of bars to the right of a pivot (default 5).      depth (int) : series int Maximum number of stored pivot pairs to check against (default 2).      barTol (int) : series int Maximum bar distance allowed between the price pivot and the oscillator pivot (default 3).      prcTol (float) : series float The percentage tolerance for comparing pivot prices. Used to detect Exaggerated divergences and filter out market noise (default 0.05%).      persist (bool) : series bool If `true` (default), the divergence flag stays active for the entire duration of the signal. If `false`, it returns a single-bar pulse on detection.      invalidate (bool) : series bool If `true` (default), terminates an active divergence if price or oscillator break below the confirming pivot low.   Returns: A tuple containing comprehensive data for a detected bullish divergence. regBull series bool `true` if a Regular bullish divergence (Class A) is active. hidBull series bool `true` if a Hidden bullish divergence (Class B) is active. exgBull series bool `true` if an Exaggerated bullish divergence (Class C) is active. initPivotPrc series float Price value of the initial (older) pivot low. initPivotOsz series float Oscillator value of the initial pivot low. initPivotBar series int Bar index of the initial pivot low. lastPivotPrc series float Price value of the last (confirming) pivot low. lastPivotOsz series float Oscillator value of the last pivot low. lastPivotBar series int Bar index of the last pivot low. bearDiv(priceSrc, oscSrc, leftLen, rightLen, depth, barTol, prcTol, persist, invalidate)   Detects bearish divergences (Regular, Hidden, Exaggerated) based on pivot highs.   Parameters:      priceSrc (float) : series float Price series to check for pivots (e.g., `high`).      oscSrc (float) : series float Oscillator series to check for pivots.      leftLen (int) : series int Number of bars to the left of a pivot (default 5).      rightLen (int) : series int Number of bars to the right of a pivot (default 5).      depth (int) : series int Maximum number of stored pivot pairs to check against (default 2).      barTol (int) : series int Maximum bar distance allowed between the price pivot and the oscillator pivot (default 3).      prcTol (float) : series float The percentage tolerance for comparing pivot prices. Used to detect Exaggerated divergences and filter out market noise (default 0.05%).      persist (bool) : series bool If `true` (default), the divergence flag stays active for the entire duration of the signal. If `false`, it returns a single-bar pulse on detection.      invalidate (bool) : series bool If `true` (default), terminates an active divergence if price or oscillator break above the confirming pivot high.   Returns: A tuple containing comprehensive data for a detected bearish divergence. regBear series bool `true` if a Regular bearish divergence (Class A) is active. hidBear series bool `true` if a Hidden bearish divergence (Class B) is active. exgBear series bool `true` if an Exaggerated bearish divergence (Class C) is active. initPivotPrc series float Price value of the initial (older) pivot high. initPivotOsz series float Oscillator value of the initial pivot high. initPivotBar series int Bar index of the initial pivot high. lastPivotPrc series float Price value of the last (confirming) pivot high. lastPivotOsz series float Oscillator value of the last pivot high. lastPivotBar series int Bar index of the last pivot high. bullDivPos(regBull, hidBull, exgBull, rightLen, yPos)   Calculates the plottable data series for bullish divergences. It manages the complex state of a persistent signal's plotting window to ensure gap-free and accurately anchored visualization.   Parameters:      regBull (bool) : series bool The regular bullish divergence flag from `bullDiv`.      hidBull (bool) : series bool The hidden bullish divergence flag from `bullDiv`.      exgBull (bool) : series bool The exaggerated bullish divergence flag from `bullDiv`.      rightLen (int) : series int The same `rightLen` value used in `bullDiv` for correct timing.      yPos (float) : series float The series providing the base Y-coordinate for the shapes (e.g., `low`).   Returns: A tuple of three `series float` for plotting bullish divergences. regBullPosY series float Contains the static anchor Y-value for Regular divergences where a shape should be plotted; `na` otherwise. hidBullPosY series float Contains the static anchor Y-value for Hidden divergences where a shape should be plotted; `na` otherwise. exgBullPosY series float Contains the static anchor Y-value for Exaggerated divergences where a shape should be plotted; `na` otherwise. bearDivPos(regBear, hidBear, exgBear, rightLen, yPos)   Calculates the plottable data series for bearish divergences. It manages the complex state of a persistent signal's plotting window to ensure gap-free and accurately anchored visualization.   Parameters:      regBear (bool) : series bool The regular bearish divergence flag from `bearDiv`.      hidBear (bool) : series bool The hidden bearish divergence flag from `bearDiv`.      exgBear (bool) : series bool The exaggerated bearish divergence flag from `bearDiv`.      rightLen (int) : series int The same `rightLen` value used in `bearDiv` for correct timing.      yPos (float) : series float The series providing the base Y-coordinate for the shapes (e.g., `high`).   Returns: A tuple of three `series float` for plotting bearish divergences. regBearPosY series float Contains the static anchor Y-value for Regular divergences where a shape should be plotted; `na` otherwise. hidBearPosY series float Contains the static anchor Y-value for Hidden divergences where a shape should be plotted; `na` otherwise. exgBearPosY series float Contains the static anchor Y-value for Exaggerated divergences where a shape should be plotted; `na` otherwise. marketStructure(highSrc, lowSrc, leftLen, rightLen, srcTol)   Analyzes the market structure by identifying pivot points, classifying their sequence (e.g., Higher Highs, Lower Lows), and determining the prevailing trend state.   Parameters:      highSrc (float) : series float Price series for pivot high detection (e.g., `high`).      lowSrc (float) : series float Price series for pivot low detection (e.g., `low`).      leftLen (int) : series int Number of bars to the left of a pivot (default 5).      rightLen (int) : series int Number of bars to the right of a pivot (default 5).      srcTol (float) : series float Percentage tolerance to consider two pivots as 'equal' (default 0.05%).   Returns: A tuple containing detailed market structure information. pivType series PivType The type of the most recently formed pivot (e.g., `hh`, `ll`). lastPivHi series float The price level of the last confirmed pivot high. lastPivLo series float The price level of the last confirmed pivot low. lastPiv series float The price level of the last confirmed pivot (either high or low). pivHiBroken series bool `true` if the price has broken above the last pivot high. pivLoBroken series bool `true` if the price has broken below the last pivot low. trendState series TrendState The current trend state (`up`, `down`, or `neutral`).
Pine Script® ライブラリ
AustrianTradingMachineの投稿
LibVolmLibrary "LibVolm" This library provides a collection of core functions for volume and money flow analysis. It offers implementations of several classic volume-based indicators, with a focus on flexibility for applications like multi-timeframe and session-based analysis. Key Features: 1. **Suite of Classic Volume Indicators:** Includes standard implementations of several foundational indicators: - **On Balance Volume (`obv`):** A momentum indicator that accumulates volume based on price direction. - **Accumulation/Distribution Line (`adLine`):** Measures cumulative money flow using the close's position within the bar's range. - **Chaikin Money Flow (`cmf`):** An oscillator version of the ADL that measures money flow over a specified lookback period. 2. **Anchored/Resettable Indicators:** The library includes flexible, resettable indicators ideal for cyclical analysis: - **Anchored VWAP (`vwap`):** Calculates a Volume Weighted Average Price that can be reset on any user-defined `reset` condition. It returns both the VWAP and the number of bars (`prdBars`) in the current period. - **Resettable CVD (`cvd`):** Computes a Cumulative Volume Delta that can be reset on a custom `reset` anchor. The function also tracks and returns the highest (`hi`) and lowest (`lo`) delta values reached within the current period. (Note: The delta sign is determined by a specific logic: it first checks close vs. open, then close vs. prior close, and persists the last non-zero sign). 3. **Volume Sanitization:** All functions that use the built-in `volume` variable automatically sanitize it via an internal function. This process replaces `na` values with 0 and ensures no negative volume values are used, providing stable calculations. --- **DISCLAIMER** This library is provided "AS IS" and for informational and educational purposes only. It does not constitute financial, investment, or trading advice. The author assumes no liability for any errors, inaccuracies, or omissions in the code. Using this library to build trading indicators or strategies is entirely at your own risk. As a developer using this library, you are solely responsible for the rigorous testing, validation, and performance of any scripts you create based on these functions. The author shall not be held liable for any financial losses incurred directly or indirectly from the use of this library or any scripts derived from it. obv(price)   Calculates the On Balance Volume (OBV) cumulative indicator.   Parameters:      price (float) : series float Source price series, typically the close.   Returns: series float Cumulative OBV value. adLine()   Computes the Accumulation/Distribution Line (AD Line).   Returns: series float Cumulative AD Line value. cmf(length)   Computes Chaikin Money Flow (CMF).   Parameters:      length (int) : series int Lookback length for the CMF calculation.   Returns: series float CMF value. vwap(price, reset)   Calculates an anchored Volume Weighted Average Price (VWAP).   Parameters:      price (float) : series float Source price series (usually *close*).      reset (bool) : series bool A signal that is *true* on the bar where the accumulation should be reset.   Returns: vwap series float The calculated Volume Weighted Average Price for the current period. prdBars series int The number of bars that have passed since the last reset. cvd(reset)   Calculates a resettable, cumulative Volume Delta (CVD). It accumulates volume delta and tracks its high/low range. The accumulation is reset to zero whenever the `reset` condition is true. This is useful for session-based analysis, intra-bar calculations, or any other custom-anchored accumulation.   Parameters:      reset (bool) : series bool A signal that is *true* on the bar where the accumulation should be reset.   Returns: cum series float The current cumulative volume delta. hi series float The highest peak the cumulative delta has reached in the current period. lo series float The lowest trough the cumulative delta has reached in the current period.
Pine Script® ライブラリ
AustrianTradingMachineの投稿
LibMvAvLibrary "LibMvAv" This library provides a unified interface for calculating a wide variety of moving averages. It is designed to simplify indicator development by consolidating numerous MA calculations into a single function and integrating the weighting capabilities from the `LibWght` library. Key Features: 1. **All-in-One MA Function:** The core of the library is the `ma()` function. Users can select the desired calculation method via the `MAType` enum, which helps create cleaner and more maintainable code compared to using many different `ta.*` or custom functions. 2. **Comprehensive Selection of MA Types:** It provides a selection of 12 different moving averages, covering common Pine Script built-ins and their weighted counterparts: - **Standard MAs:** SMA, EMA, WMA, RMA (Wilder's), HMA (Hull), and LSMA (Least Squares / Linear Regression). - **Weighted MAs:** Weight-enhanced versions of the above (WSMA, WEMA, WWMA, WRMA, WHMA, WLSMA). 3. **Integrated Weighting:** The library provides weighted versions for each of its standard MA types (e.g., `wsma` alongside `sma`). By acting as a dispatcher, the `ma()` function allows these weighted calculations to be called using the optional `weight` parameter, which are then processed by the `LibWght` library. 4. **Simple API:** The library internally handles the logic of choosing the correct function based on the selected `MAType`. The user only needs to provide the source, length, and optional weight, simplifying the development process. --- **DISCLAIMER** This library is provided "AS IS" and for informational and educational purposes only. It does not constitute financial, investment, or trading advice. The author assumes no liability for any errors, inaccuracies, or omissions in the code. Using this library to build trading indicators or strategies is entirely at your own risk. As a developer using this library, you are solely responsible for the rigorous testing, validation, and performance of any scripts you create based on these functions. The author shall not be held liable for any financial losses incurred directly or indirectly from the use of this library or any scripts derived from it. ma(maType, source, length, weight)   Returns the requested moving average.   Parameters:      maType (simple MAType) : simple MAType Desired type (see enum above).      source (float) : series float Data series to smooth.      length (simple int) : simple int Look-back / period length.      weight (float) : series float Weight series (default = na)   Returns: series float Moving-average value.
Pine Script® ライブラリ
AustrianTradingMachineの投稿
LibWghtLibrary "LibWght" This is a library of mathematical and statistical functions designed for quantitative analysis in Pine Script. Its core principle is the integration of a custom weighting series (e.g., volume) into a wide array of standard technical analysis calculations. Key Capabilities: 1. **Universal Weighting:** All exported functions accept a `weight` parameter. This allows standard calculations (like moving averages, RSI, and standard deviation) to be influenced by an external data series, such as volume or tick count. 2. **Weighted Averages and Indicators:** Includes a comprehensive collection of weighted functions: - **Moving Averages:** `wSma`, `wEma`, `wWma`, `wRma` (Wilder's), `wHma` (Hull), and `wLSma` (Least Squares / Linear Regression). - **Oscillators & Ranges:** `wRsi`, `wAtr` (Average True Range), `wTr` (True Range), and `wR` (High-Low Range). 3. **Volatility Decomposition:** Provides functions to decompose total variance into distinct components for market analysis. - **Two-Way Decomposition (`wTotVar`):** Separates variance into **between-bar** (directional) and **within-bar** (noise) components. - **Three-Way Decomposition (`wLRTotVar`):** Decomposes variance relative to a linear regression into **Trend** (explained by the LR slope), **Residual** (mean-reversion around the LR line), and **Within-Bar** (noise) components. - **Local Volatility (`wLRLocTotStdDev`):** Measures the total "noise" (within-bar + residual) around the trend line. 4. **Weighted Statistics and Regression:** Provides a robust function for Weighted Linear Regression (`wLinReg`) and a full suite of related statistical measures: - **Between-Bar Stats:** `wBtwVar`, `wBtwStdDev`, `wBtwStdErr`. - **Residual Stats:** `wResVar`, `wResStdDev`, `wResStdErr`. 5. **Fallback Mechanism:** All functions are designed for reliability. If the total weight over the lookback period is zero (e.g., in a no-volume period), the algorithms automatically fall back to their unweighted, uniform-weight equivalents (e.g., `wSma` becomes a standard `ta.sma`), preventing errors and ensuring continuous calculation. --- **DISCLAIMER** This library is provided "AS IS" and for informational and educational purposes only. It does not constitute financial, investment, or trading advice. The author assumes no liability for any errors, inaccuracies, or omissions in the code. Using this library to build trading indicators or strategies is entirely at your own risk. As a developer using this library, you are solely responsible for the rigorous testing, validation, and performance of any scripts you create based on these functions. The author shall not be held liable for any financial losses incurred directly or indirectly from the use of this library or any scripts derived from it. wSma(source, weight, length)   Weighted Simple Moving Average (linear kernel).   Parameters:      source (float) : series float Data to average.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 1.   Returns: series float Linear-kernel weighted mean; falls back to the arithmetic mean if Σweight = 0. wEma(source, weight, length)   Weighted EMA (exponential kernel).   Parameters:      source (float) : series float Data to average.      weight (float) : series float Weight series.      length (simple int) : simple int Look-back length ≥ 1.   Returns: series float Exponential-kernel weighted mean; falls back to classic EMA if Σweight = 0. wWma(source, weight, length)   Weighted WMA (linear kernel).   Parameters:      source (float) : series float Data to average.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 1.   Returns: series float Linear-kernel weighted mean; falls back to classic WMA if Σweight = 0. wRma(source, weight, length)   Weighted RMA (Wilder kernel, α = 1/len).   Parameters:      source (float) : series float Data to average.      weight (float) : series float Weight series.      length (simple int) : simple int Look-back length ≥ 1.   Returns: series float Wilder-kernel weighted mean; falls back to classic RMA if Σweight = 0. wHma(source, weight, length)   Weighted HMA (linear kernel).   Parameters:      source (float) : series float Data to average.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 1.   Returns: series float Linear-kernel weighted mean; falls back to classic HMA if Σweight = 0. wRsi(source, weight, length)   Weighted Relative Strength Index.   Parameters:      source (float) : series float Price series.      weight (float) : series float Weight series.      length (simple int) : simple int Look-back length ≥ 1.   Returns: series float Weighted RSI; uniform if Σw = 0. wAtr(tr, weight, length)   Weighted ATR (Average True Range). Implemented as WRMA on *true range*.   Parameters:      tr (float) : series float True Range series.      weight (float) : series float Weight series.      length (simple int) : simple int Look-back length ≥ 1.   Returns: series float Weighted ATR; uniform weights if Σw = 0. wTr(tr, weight, length)   Weighted True Range over a window.   Parameters:      tr (float) : series float True Range series.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 1.   Returns: series float Weighted mean of TR; uniform if Σw = 0. wR(r, weight, length)   Weighted High-Low Range over a window.   Parameters:      r (float) : series float High-Low per bar.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 1.   Returns: series float Weighted mean of range; uniform if Σw = 0. wBtwVar(source, weight, length, biased)   Weighted Between Variance (biased/unbiased).   Parameters:      source (float) : series float Data series.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population (biased); false → sample.   Returns: variance series float The calculated between-bar variance (σ²btw), either biased or unbiased. sumW series float The sum of weights over the lookback period (Σw). sumW2 series float The sum of squared weights over the lookback period (Σw²). wBtwStdDev(source, weight, length, biased)   Weighted Between Standard Deviation.   Parameters:      source (float) : series float Data series.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population (biased); false → sample.   Returns: series float σbtw uniform if Σw = 0. wBtwStdErr(source, weight, length, biased)   Weighted Between Standard Error.   Parameters:      source (float) : series float Data series.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population (biased); false → sample.   Returns: series float √(σ²btw / N_eff) uniform if Σw = 0. wTotVar(mu, sigma, weight, length, biased)   Weighted Total Variance (= between-group + within-group). Useful when each bar represents an aggregate with its own mean* and pre-estimated σ (e.g., second-level ranges inside a 1-minute bar). Assumes the *weight* series applies to both the group means and their σ estimates.   Parameters:      mu (float) : series float Group means (e.g., HL2 of 1-second bars).      sigma (float) : series float Pre-estimated σ of each group (same basis).      weight (float) : series float Weight series (volume, ticks, …).      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population (biased); false → sample.   Returns: varBtw series float The between-bar variance component (σ²btw). varWtn series float The within-bar variance component (σ²wtn). sumW series float The sum of weights over the lookback period (Σw). sumW2 series float The sum of squared weights over the lookback period (Σw²). wTotStdDev(mu, sigma, weight, length, biased)   Weighted Total Standard Deviation.   Parameters:      mu (float) : series float Group means (e.g., HL2 of 1-second bars).      sigma (float) : series float Pre-estimated σ of each group (same basis).      weight (float) : series float Weight series (volume, ticks, …).      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population (biased); false → sample.   Returns: series float σtot. wTotStdErr(mu, sigma, weight, length, biased)   Weighted Total Standard Error. SE = √( total variance / N_eff ) with the same effective sample size logic as `wster()`.   Parameters:      mu (float) : series float Group means (e.g., HL2 of 1-second bars).      sigma (float) : series float Pre-estimated σ of each group (same basis).      weight (float) : series float Weight series (volume, ticks, …).      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population (biased); false → sample.   Returns: series float √(σ²tot / N_eff). wLinReg(source, weight, length)   Weighted Linear Regression.   Parameters:      source (float) : series float Data series.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 2.   Returns: mid series float The estimated value of the regression line at the most recent bar. slope series float The slope of the regression line. intercept series float The intercept of the regression line. wResVar(source, weight, midLine, slope, length, biased)   Weighted Residual Variance. linear regression – optionally biased (population) or unbiased (sample).   Parameters:      source (float) : series float Data series.      weight (float) : series float Weighting series (volume, etc.).      midLine (float) : series float Regression value at the last bar.      slope (float) : series float Slope per bar.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population variance (σ²_P), denominator ≈ N_eff. false → sample variance (σ²_S), denominator ≈ N_eff - 2. (Adjusts for 2 degrees of freedom lost to the regression).   Returns: variance series float The calculated residual variance (σ²res), either biased or unbiased. sumW series float The sum of weights over the lookback period (Σw). sumW2 series float The sum of squared weights over the lookback period (Σw²). wResStdDev(source, weight, midLine, slope, length, biased)   Weighted Residual Standard Deviation.   Parameters:      source (float) : series float Data series.      weight (float) : series float Weight series.      midLine (float) : series float Regression value at the last bar.      slope (float) : series float Slope per bar.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population (biased); false → sample.   Returns: series float σres; uniform if Σw = 0. wResStdErr(source, weight, midLine, slope, length, biased)   Weighted Residual Standard Error.   Parameters:      source (float) : series float Data series.      weight (float) : series float Weight series.      midLine (float) : series float Regression value at the last bar.      slope (float) : series float Slope per bar.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population (biased); false → sample.   Returns: series float √(σ²res / N_eff); uniform if Σw = 0. wLRTotVar(mu, sigma, weight, midLine, slope, length, biased)   Weighted Linear-Regression Total Variance **around the window’s weighted mean μ**. σ²_tot = E_w ⟶ *within-group variance* + Var_w ⟶ *residual variance* + Var_w ⟶ *trend variance* where each bar i in the look-back window contributes m_i = *mean* (e.g. 1-sec HL2) σ_i = *sigma* (pre-estimated intrabar σ) w_i = *weight* (volume, ticks, …) ŷ_i = b₀ + b₁·x (value of the weighted LR line) r_i = m_i − ŷ_i (orthogonal residual)   Parameters:      mu (float) : series float Per-bar mean m_i.      sigma (float) : series float Pre-estimated σ_i of each bar.      weight (float) : series float Weight series w_i (≥ 0).      midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).      slope (float) : series float Slope b₁ of the regression line.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population; false → sample.   Returns: varRes series float The residual variance component (σ²res). varWtn series float The within-bar variance component (σ²wtn). varTrd series float The trend variance component (σ²trd), explained by the linear regression. sumW series float The sum of weights over the lookback period (Σw). sumW2 series float The sum of squared weights over the lookback period (Σw²). wLRTotStdDev(mu, sigma, weight, midLine, slope, length, biased)   Weighted Linear-Regression Total Standard Deviation.   Parameters:      mu (float) : series float Per-bar mean m_i.      sigma (float) : series float Pre-estimated σ_i of each bar.      weight (float) : series float Weight series w_i (≥ 0).      midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).      slope (float) : series float Slope b₁ of the regression line.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population; false → sample.   Returns: series float √(σ²tot). wLRTotStdErr(mu, sigma, weight, midLine, slope, length, biased)   Weighted Linear-Regression Total Standard Error. SE = √( σ²_tot / N_eff ) with N_eff = Σw² / Σw² (like in wster()).   Parameters:      mu (float) : series float Per-bar mean m_i.      sigma (float) : series float Pre-estimated σ_i of each bar.      weight (float) : series float Weight series w_i (≥ 0).      midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).      slope (float) : series float Slope b₁ of the regression line.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population; false → sample.   Returns: series float √((σ²res, σ²wtn, σ²trd) / N_eff). wLRLocTotStdDev(mu, sigma, weight, midLine, slope, length, biased)   Weighted Linear-Regression Local Total Standard Deviation. Measures the total "noise" (within-bar + residual) around the trend.   Parameters:      mu (float) : series float Per-bar mean m_i.      sigma (float) : series float Pre-estimated σ_i of each bar.      weight (float) : series float Weight series w_i (≥ 0).      midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).      slope (float) : series float Slope b₁ of the regression line.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population; false → sample.   Returns: series float √(σ²wtn + σ²res). wLRLocTotStdErr(mu, sigma, weight, midLine, slope, length, biased)   Weighted Linear-Regression Local Total Standard Error.   Parameters:      mu (float) : series float Per-bar mean m_i.      sigma (float) : series float Pre-estimated σ_i of each bar.      weight (float) : series float Weight series w_i (≥ 0).      midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).      slope (float) : series float Slope b₁ of the regression line.      length (int) : series int Look-back length ≥ 2.      biased (bool) : series bool true → population; false → sample.   Returns: series float √((σ²wtn + σ²res) / N_eff). wLSma(source, weight, length)   Weighted Least Square Moving Average.   Parameters:      source (float) : series float Data series.      weight (float) : series float Weight series.      length (int) : series int Look-back length ≥ 2.   Returns: series float Least square weighted mean. Falls back to unweighted regression if Σw = 0.
Pine Script® ライブラリ
AustrianTradingMachineの投稿
mysourcetypesncsLibrary "mysourcetypes" Libreria personale per sorgenti estese (Close, Open, High, Low, Median, Typical, Weighted, Average, Average Median Body, Trend Biased, Trend Biased Extreme, Volume Body, Momentum Biased, Volatility Adjusted, Body Dominance, Shadow Biased, Gap Aware, Rejection Biased, Range Position, Adaptive Trend, Pressure Balanced, Impulse Wave) rclose()   Regular Close   Returns: Close price ropen()   Regular Open   Returns: Open price rhigh()   Regular High   Returns: High price rlow()   Regular Low   Returns: Low price rmedian()   Regular Median (HL2)   Returns: (High + Low) / 2 rtypical()   Regular Typical (HLC3)   Returns: (High + Low + Close) / 3 rweighted()   Regular Weighted (HLCC4)   Returns: (High + Low + Close + Close) / 4 raverage()   Regular Average (OHLC4)   Returns: (Open + High + Low + Close) / 4 ravemedbody()   Average Median Body   Returns: (Open + Close) / 2 rtrendb()   Trend Biased Regular   Returns: Trend-weighted price rtrendbext()   Trend Biased Extreme   Returns: Extreme trend-weighted price rvolbody()   Volume Weighted Body   Returns: Body midpoint weighted by volume intensity rmomentum()   Momentum Biased   Returns: Price biased towards momentum direction rvolatility()   Volatility Adjusted   Returns: Price adjusted by candle's volatility rbodydominance()   Body Dominance   Returns: Emphasizes body over wicks rshadowbias()   Shadow Biased   Returns: Price biased by shadow length rgapaware()   Gap Aware   Returns: Considers gap between candles rrejection()   Rejection Biased   Returns: Emphasizes price rejection levels rrangeposition()   Range Position   Returns: Where close sits within the candle range (0-100%) radaptivetrend()   Adaptive Trend   Returns: Adapts based on recent trend strength rpressure()   Pressure Balanced   Returns: Balances buying/selling pressure within candle rimpulse()   Impulse Wave   Returns: Detects impulsive moves vs corrections
Pine Script® ライブラリ
stefanoncsの投稿
BossExoticMAs A next-generation moving average and smoothing library by TheStopLossBoss, featuring premium adaptive, exotic, and DSP-inspired filters — optimized for Pine Script® v6 and designed for Traders who demand precision and beauty. > BossExoticMAs is a complete moving average and signal-processing toolkit built for Pine Script v6. It combines the essential trend filters (SMA, EMA, WMA, etc.) with advanced, high-performance exotic types used by quants, algo designers, and adaptive systems. Each function is precision-tuned for stability, speed, and visual clarity — perfect for building custom baselines, volatility filters, dynamic ribbons, or hybrid signal engines. Includes built-in color gradient theming powered by the exclusive BossGradient — //Key Features ✅ Full Moving Average Set SMA, EMA, ZEMA, WMA, HMA, WWMA, SMMA DEMA, TEMA, T3 (Tillson) ALMA, KAMA, LSMA VMA, VAMA, FRAMA ✅ Signal Filters One-Euro Filter (Crispin/Casiez implementation) ATR-bounded Range Filter ✅ Color Engine lerpColor() safe blending using color.from_gradient Thematic gradient palettes: STOPLOSS, VAPORWAVE, ROYAL FLAME, MATRIX FLOW Exclusive: BOSS GRADIENT ✅ Helper Functions Clamping, normalization, slope detection, tick delta Slope-based dynamic color control via slopeThemeColor() 🧠 Usage Example //@version=6 indicator("Boss Exotic MA Demo", overlay=true) import TheStopLossBoss/BossExoticMAs/1 as boss len = input.int(50, "Length") atype = input.string("T3", "MA Type", options= ) t3factor = input.float(0.7, "T3 β", step=0.05) smoothColor = boss.slopeThemeColor(close, "BOSS GRADIENT", 0.001)ma = boss.maSelect(close, len, atype, t3factor, 0.85, 14) plot(ma, "Boss Exotic MA", color=smoothColor, linewidth=2) --- 🔑 Notes Built exclusively for Pine Script® v6 Library designed for import use — all exports are prefixed cleanly (boss.functionName()) Some functions maintain internal state (var-based). Warnings are safe to ignore — adaptive design choice. Each MA output is non-repainting and mathematically stable. --- 📜 Author TheStopLossBoss Designer of precision trading systems and custom adaptive algorithms. Follow for exclusive releases, educational material, and full-stack trend solutions. movingaverage, trend, adaptive, filter, volatility, smoothing, quant, technicalanalysis, bossgradient, t3, alma, frama, vma
Pine Script® ライブラリ
TheStopLossBossの投稿
SequencerLibraryLibrary "SequencerLibrary" SequencerLibrary v1 is a Pine Script™ library for identifying, tracking, and visualizing sequential bullish and bearish patterns on price charts. It provides a complete framework for building sequence-based trading systems, including: • Automatic detection and counting of setup and countdown phases. • Real-time tracking of completion states, perfected setups, and exhaustion signals. • Dynamic support and resistance thresholds derived from recent price structure. • Customizable visual highlighting for both setup and countdown sequences. method doSequence(s, src, config, condition)   Updates the sequence state based on the source value, and user configuration.   Namespace types: Sequence   Parameters:      s (Sequence) : The sequence object containing bullish and bearish setups.      src (float) : The source value (e.g., close price) used for evaluating sequence conditions.      config (SequenceInputs) : The user-defined settings for sequence analysis.      condition (bool) : When true, executes the sequence logic.   Returns: highlight(s, css, condition)   Highlights the bullish and bearish sequence setups and countdowns on the chart.   Parameters:      s (Sequence) : The sequence object containing bullish and bearish sequence states.      css (SequenceCSS) : The styling configuration for customizing label appearances.      condition (bool) : When true, the function creates and displays labels for setups and countdowns.   Returns: SequenceState   A type representing the configuration and state of a sequence setup.   Fields:      setup (series int) : Current count of the setup phase (e.g., how many bars have met the setup criteria).      countdown (series int) : Current count of the countdown phase (e.g., bars meeting countdown criteria).      threshold (series float) : The price threshold level used as support/resistance for the sequence.      priceWhenCompleted (series float) : The closing price when the setup or countdown phase is completed.      indicatorWhenCompleted (series float) : The indicator value when the setup or countdown phase is completed.      setupCompleted (series bool) : Indicates if the setup phase has been completed (i.e., reached the required count).      countdownCompleted (series bool) : Indicates if the countdown phase has been completed (i.e., reached exhaustion).      perfected (series bool) : Indicates if the setup meets the "perfected" condition (e.g., aligns with strict criteria).      highlightSetup (series bool) : Determines whether the setup phase should be visually highlighted on the chart.      highlightCountdown (series bool) : Determines whether the countdown phase should be visually highlighted on the chart. Sequence   A type containing bullish and bearish sequence setups.   Fields:      bullish (SequenceState) : Configuration and state for bullish sequences.      bearish (SequenceState) : Configuration and state for bearish sequences. SequenceInputs   A type for user-configurable input settings for sequence-based analysis.   Fields:      showSetup (series bool) : Enables or disables the display of setup sequences.      showCountdown (series bool) : Enables or disables the display of countdown sequences.      setupFilter (series string) : A comma‐separated string containing setup sequence counts to be highlighted (e.g., "1,2,3,4,5,6,7,8,9").      countdownFilter (series string) : A comma‐separated string containing countdown sequence counts to be highlighted (e.g., "1,2,3,4,5,6,7,8,9,10,11,12,13").      lookbackSetup (series int) : Defines the lookback period for evaluating setup conditions (default: 4 bars).      lookbackCountdown (series int) : Defines the lookback period for evaluating countdown conditions (default: 2 bars).      lookbackSetupPerfected (series int) : Defines the lookback period to determine a perfected setup condition (default: 6 bars).      maxSetup (series int) : The maximum count required to complete a setup phase (default: 9).      maxCountdown (series int) : The maximum count required to complete a countdown phase (default: 13). SequenceCSS   A type defining the visual styling options for sequence labels.   Fields:      bullish (series color) : Color used for bullish sequence labels.      bearish (series color) : Color used for bearish sequence labels.      imperfect (series color) : Color used for labels representing imperfect sequences.
Pine Script® ライブラリ
tkarolakの投稿
AlertSenderLibrary_TradingFinderLibrary "AlertSenderLibrary_TradingFinder" TODO: add library description here AlertSender(Condition, Alert, AlertName, AlertType, DetectionType, SetupData, Frequncy, UTC, MoreInfo, Message, o, h, l, c, Entry, TP, SL, Distal, Proximal)   Parameters:      Condition (bool)      Alert (string)      AlertName (string)      AlertType (string)      DetectionType (string)      SetupData (string)      Frequncy (string)      UTC (string)      MoreInfo (string)      Message (string)      o (float)      h (float)      l (float)      c (float)      Entry (float)      TP (float)      SL (float)      Distal (float)      Proximal (float)
Pine Script® ライブラリ
MKUltratraderの投稿
TA█ TA Library 📊 OVERVIEW TA is a Pine Script technical analysis library. This library provides 25+ moving averages and smoothing filters , from classic SMA/EMA to Kalman Filters and adaptive algorithms, implemented based on academic research. 🎯 Core Features Academic Based - Algorithms follow original papers and formulas Performance Optimized - Pre-calculated constants for faster response Unified Interface - Consistent function design Research Based - Integrates technical analysis research 🎯 CONCEPTS Library Design Philosophy This technical analysis library focuses on providing: Academic Foundation Algorithms based on published research papers and academic standards Implementations that follow original mathematical formulations Clear documentation with research references Developer Experience Unified interface design for consistent usage patterns Pre-calculated constants for optimal performance Comprehensive function collection to reduce development time Single import statement for immediate access to all functions Each indicator encapsulated as a simple function call - one line of code simplifies complexity Technical Excellence 25+ carefully implemented moving averages and filters Support for advanced algorithms like Kalman Filter and MAMA/FAMA Optimized code structure for maintainability and reliability Regular updates incorporating latest research developments 🚀 USING THIS LIBRARY Import Library //@version=6 import DCAUT/TA/1 as dta indicator("Advanced Technical Analysis", overlay=true) Basic Usage Example // Classic moving average combination ema20 = ta.ema(close, 20) kama20 = dta.kama(close, 20) plot(ema20, "EMA20", color.red, 2) plot(kama20, "KAMA20", color.green, 2) Advanced Trading System // Adaptive moving average system kama = dta.kama(close, 20, 2, 30) = dta.mamaFama(close, 0.5, 0.05) // Trend confirmation and entry signals bullTrend = kama > kama and mamaValue > famaValue bearTrend = kama < kama and mamaValue < famaValue longSignal = ta.crossover(close, kama) and bullTrend shortSignal = ta.crossunder(close, kama) and bearTrend plot(kama, "KAMA", color.blue, 3) plot(mamaValue, "MAMA", color.orange, 2) plot(famaValue, "FAMA", color.purple, 2) plotshape(longSignal, "Buy", shape.triangleup, location.belowbar, color.green) plotshape(shortSignal, "Sell", shape.triangledown, location.abovebar, color.red) 📋 FUNCTIONS REFERENCE ewma(source, alpha) Calculates the Exponentially Weighted Moving Average with dynamic alpha parameter. Parameters: source (series float) : Series of values to process. alpha (series float) : The smoothing parameter of the filter. Returns: (float) The exponentially weighted moving average value. dema(source, length) Calculates the Double Exponential Moving Average (DEMA) of a given data series. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the moving average calculation. Returns: (float) The calculated Double Exponential Moving Average value. tema(source, length) Calculates the Triple Exponential Moving Average (TEMA) of a given data series. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the moving average calculation. Returns: (float) The calculated Triple Exponential Moving Average value. zlema(source, length) Calculates the Zero-Lag Exponential Moving Average (ZLEMA) of a given data series. This indicator attempts to eliminate the lag inherent in all moving averages. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the moving average calculation. Returns: (float) The calculated Zero-Lag Exponential Moving Average value. tma(source, length) Calculates the Triangular Moving Average (TMA) of a given data series. TMA is a double-smoothed simple moving average that reduces noise. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the moving average calculation. Returns: (float) The calculated Triangular Moving Average value. frama(source, length) Calculates the Fractal Adaptive Moving Average (FRAMA) of a given data series. FRAMA adapts its smoothing factor based on fractal geometry to reduce lag. Developed by John Ehlers. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the moving average calculation. Returns: (float) The calculated Fractal Adaptive Moving Average value. kama(source, length, fastLength, slowLength) Calculates Kaufman's Adaptive Moving Average (KAMA) of a given data series. KAMA adjusts its smoothing based on market efficiency ratio. Developed by Perry J. Kaufman. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the efficiency calculation. fastLength (simple int) : Fast EMA length for smoothing calculation. Optional. Default is 2. slowLength (simple int) : Slow EMA length for smoothing calculation. Optional. Default is 30. Returns: (float) The calculated Kaufman's Adaptive Moving Average value. t3(source, length, volumeFactor) Calculates the Tilson Moving Average (T3) of a given data series. T3 is a triple-smoothed exponential moving average with improved lag characteristics. Developed by Tim Tillson. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the moving average calculation. volumeFactor (simple float) : Volume factor affecting responsiveness. Optional. Default is 0.7. Returns: (float) The calculated Tilson Moving Average value. ultimateSmoother(source, length) Calculates the Ultimate Smoother of a given data series. Uses advanced filtering techniques to reduce noise while maintaining responsiveness. Based on digital signal processing principles by John Ehlers. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the smoothing calculation. Returns: (float) The calculated Ultimate Smoother value. kalmanFilter(source, processNoise, measurementNoise) Calculates the Kalman Filter of a given data series. Optimal estimation algorithm that estimates true value from noisy observations. Based on the Kalman Filter algorithm developed by Rudolf Kalman (1960). Parameters: source (series float) : Series of values to process. processNoise (simple float) : Process noise variance (Q). Controls adaptation speed. Optional. Default is 0.05. measurementNoise (simple float) : Measurement noise variance (R). Controls smoothing. Optional. Default is 1.0. Returns: (float) The calculated Kalman Filter value. mcginleyDynamic(source, length) Calculates the McGinley Dynamic of a given data series. McGinley Dynamic is an adaptive moving average that adjusts to market speed changes. Developed by John R. McGinley Jr. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the dynamic calculation. Returns: (float) The calculated McGinley Dynamic value. mama(source, fastLimit, slowLimit) Calculates the Mesa Adaptive Moving Average (MAMA) of a given data series. MAMA uses Hilbert Transform Discriminator to adapt to market cycles dynamically. Developed by John F. Ehlers. Parameters: source (series float) : Series of values to process. fastLimit (simple float) : Maximum alpha (responsiveness). Optional. Default is 0.5. slowLimit (simple float) : Minimum alpha (smoothing). Optional. Default is 0.05. Returns: (float) The calculated Mesa Adaptive Moving Average value. fama(source, fastLimit, slowLimit) Calculates the Following Adaptive Moving Average (FAMA) of a given data series. FAMA follows MAMA with reduced responsiveness for crossover signals. Developed by John F. Ehlers. Parameters: source (series float) : Series of values to process. fastLimit (simple float) : Maximum alpha (responsiveness). Optional. Default is 0.5. slowLimit (simple float) : Minimum alpha (smoothing). Optional. Default is 0.05. Returns: (float) The calculated Following Adaptive Moving Average value. mamaFama(source, fastLimit, slowLimit) Calculates Mesa Adaptive Moving Average (MAMA) and Following Adaptive Moving Average (FAMA). Parameters: source (series float) : Series of values to process. fastLimit (simple float) : Maximum alpha (responsiveness). Optional. Default is 0.5. slowLimit (simple float) : Minimum alpha (smoothing). Optional. Default is 0.05. Returns: ( ) Tuple containing values. laguerreFilter(source, length, gamma, order) Calculates the standard N-order Laguerre Filter of a given data series. Standard Laguerre Filter uses uniform weighting across all polynomial terms. Developed by John F. Ehlers. Parameters: source (series float) : Series of values to process. length (simple int) : Length for UltimateSmoother preprocessing. gamma (simple float) : Feedback coefficient (0-1). Lower values reduce lag. Optional. Default is 0.8. order (simple int) : The order of the Laguerre filter (1-10). Higher order increases lag. Optional. Default is 8. Returns: (float) The calculated standard Laguerre Filter value. laguerreBinomialFilter(source, length, gamma) Calculates the Laguerre Binomial Filter of a given data series. Uses 6-pole feedback with binomial weighting coefficients. Developed by John F. Ehlers. Parameters: source (series float) : Series of values to process. length (simple int) : Length for UltimateSmoother preprocessing. gamma (simple float) : Feedback coefficient (0-1). Lower values reduce lag. Optional. Default is 0.5. Returns: (float) The calculated Laguerre Binomial Filter value. superSmoother(source, length) Calculates the Super Smoother of a given data series. SuperSmoother is a second-order Butterworth filter from aerospace technology. Developed by John F. Ehlers. Parameters: source (series float) : Series of values to process. length (simple int) : Period for the filter calculation. Returns: (float) The calculated Super Smoother value. rangeFilter(source, length, multiplier) Calculates the Range Filter of a given data series. Range Filter reduces noise by filtering price movements within a dynamic range. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the average range calculation. multiplier (simple float) : Multiplier for the smooth range. Higher values increase filtering. Optional. Default is 2.618. Returns: ( ) Tuple containing filtered value, trend direction, upper band, and lower band. qqe(source, rsiLength, rsiSmooth, qqeFactor) Calculates the Quantitative Qualitative Estimation (QQE) of a given data series. QQE is an improved RSI that reduces noise and provides smoother signals. Developed by Igor Livshin. Parameters: source (series float) : Series of values to process. rsiLength (simple int) : Number of bars for the RSI calculation. Optional. Default is 14. rsiSmooth (simple int) : Number of bars for smoothing the RSI. Optional. Default is 5. qqeFactor (simple float) : QQE factor for volatility band width. Optional. Default is 4.236. Returns: ( ) Tuple containing smoothed RSI and QQE trend line. sslChannel(source, length) Calculates the Semaphore Signal Level (SSL) Channel of a given data series. SSL Channel provides clear trend signals using moving averages of high and low prices. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the moving average calculation. Returns: ( ) Tuple containing SSL Up and SSL Down lines. ma(source, length, maType) Calculates a Moving Average based on the specified type. Universal interface supporting all moving average algorithms. Parameters: source (series float) : Series of values to process. length (simple int) : Number of bars for the moving average calculation. maType (simple MaType) : Type of moving average to calculate. Optional. Default is SMA. Returns: (float) The calculated moving average value based on the specified type. atr(length, maType) Calculates the Average True Range (ATR) using the specified moving average type. Developed by J. Welles Wilder Jr. Parameters: length (simple int) : Number of bars for the ATR calculation. maType (simple MaType) : Type of moving average to use for smoothing. Optional. Default is RMA. Returns: (float) The calculated Average True Range value. macd(source, fastLength, slowLength, signalLength, maType, signalMaType) Calculates the Moving Average Convergence Divergence (MACD) with customizable MA types. Developed by Gerald Appel. Parameters: source (series float) : Series of values to process. fastLength (simple int) : Period for the fast moving average. slowLength (simple int) : Period for the slow moving average. signalLength (simple int) : Period for the signal line moving average. maType (simple MaType) : Type of moving average for main MACD calculation. Optional. Default is EMA. signalMaType (simple MaType) : Type of moving average for signal line calculation. Optional. Default is EMA. Returns: ( ) Tuple containing MACD line, signal line, and histogram values. dmao(source, fastLength, slowLength, maType) Calculates the Dual Moving Average Oscillator (DMAO) of a given data series. Uses the same algorithm as the Percentage Price Oscillator (PPO), but can be applied to any data series. Parameters: source (series float) : Series of values to process. fastLength (simple int) : Period for the fast moving average. slowLength (simple int) : Period for the slow moving average. maType (simple MaType) : Type of moving average to use for both calculations. Optional. Default is EMA. Returns: (float) The calculated Dual Moving Average Oscillator value as a percentage. continuationIndex(source, length, gamma, order) Calculates the Continuation Index of a given data series. The index represents the Inverse Fisher Transform of the normalized difference between an UltimateSmoother and an N-order Laguerre filter. Developed by John F. Ehlers, published in TASC 2025.09. Parameters: source (series float) : Series of values to process. length (simple int) : The calculation length. gamma (simple float) : Controls the phase response of the Laguerre filter. Optional. Default is 0.8. order (simple int) : The order of the Laguerre filter (1-10). Optional. Default is 8. Returns: (float) The calculated Continuation Index value. 📚 RELEASE NOTES v1.0 (2025.09.24) ✅ 25+ technical analysis functions ✅ Complete adaptive moving average series (KAMA, FRAMA, MAMA/FAMA) ✅ Advanced signal processing filters (Kalman, Laguerre, SuperSmoother, UltimateSmoother) ✅ Performance optimized with pre-calculated constants and efficient algorithms ✅ Unified function interface design following TradingView best practices ✅ Comprehensive moving average collection (DEMA, TEMA, ZLEMA, T3, etc.) ✅ Volatility and trend detection tools (QQE, SSL Channel, Range Filter) ✅ Continuation Index - Latest research from TASC 2025.09 ✅ MACD and ATR calculations supporting multiple moving average types ✅ Dual Moving Average Oscillator (DMAO) for arbitrary data series analysis
Pine Script® ライブラリ
DCAUTの投稿
Adaptive FoS LibraryThis library provides Adaptive Functions that I use in my scripts. For calculations, I use the max_bars_back function with a fixed length of 200 bars to prevent errors when a script tries to access data beyond its available history. This is a key difference from most other adaptive libraries — if you don’t need it, you don’t have to use it. Some of the adaptive length functions are normalized. In addition to the adaptive length functions, this library includes various methods for calculating moving averages, normalized differences between fast and slow MA's, as well as several normalized oscillators.
Pine Script® ライブラリ
RGtrueの投稿
CoreJuice001Library "CoreJuice001" DrawingData   Fields:      kzone1Boxes (array)      kzone2Boxes (array)      kzone3Boxes (array)      kzone4Boxes (array)      kzone5Boxes (array)      kzone6Boxes (array)      kzone1Labels (array)      kzone2Labels (array)      kzone3Labels (array)      kzone4Labels (array)      kzone5Labels (array)      kzone6Labels (array)      kzone1TrendLines (array)      kzone2TrendLines (array)      kzone3TrendLines (array)      kzone4TrendLines (array)      kzone5TrendLines (array)      kzone6TrendLines (array)      kzone1PriceLabels (array)      kzone2PriceLabels (array)      kzone3PriceLabels (array)      kzone4PriceLabels (array)      kzone5PriceLabels (array)      kzone6PriceLabels (array)
Pine Script® ライブラリ
nuburoの投稿
PivotLibrary222Library "PivotLibrary222" f_determinePivotStrength(_pivotCandidateRelativeIndex, _type, _maxStrength)   Determines the strength of a pivot (low or high).   Parameters:      _pivotCandidateRelativeIndex (int) : The relative bar index of the pivot candidate.      _type (string) : "low" for a pivot low, "high" for a pivot high.      _maxStrength (int) : The maximum number of bars to check on either side for strength.   Returns: An array containing . f_getPlotColorForStrength(_strength)   Gets a plotting color based on pivot strength.   Parameters:      _strength (int) : The calculated pivot strength.   Returns: A color for plotting. f_updateExistingPivotLow(f_pivotLows, f_pivotLowInfoIndex, f_newStrength, f_showLabels)   Updates an existing pivot LOW's strength and its corresponding chart label.   Parameters:      f_pivotLows (array) : The array of pivotLowInfo objects.      f_pivotLowInfoIndex (int) : The index of the pivot to update in the array.      f_newStrength (int) : The new (increased) strength of the pivot.      f_showLabels (bool) : A boolean to control if labels should be updated. f_updateExistingPivotHigh(f_pivotHighs, f_pivotHighInfoIndex, f_newStrength, f_showLabels)   Updates an existing pivot HIGH's strength and its corresponding chart label.   Parameters:      f_pivotHighs (array) : The array of pivotHighInfo objects.      f_pivotHighInfoIndex (int) : The index of the pivot to update in the array.      f_newStrength (int) : The new (increased) strength of the pivot.      f_showLabels (bool) : A boolean to control if labels should be updated. f_findPivotLows(f_pivotLows, f_minStrength, f_maxStrength, f_showLabels)   Finds and processes pivot lows.   Parameters:      f_pivotLows (array) : The array of pivotLowInfo objects to read from and modify.      f_minStrength (int) : Minimum strength required for a new pivot to be recorded.      f_maxStrength (int) : Maximum strength to search for when determining pivot strength.      f_showLabels (bool) : A boolean to control if new labels should be created. f_findPivotHighs(f_pivotHighs, f_minStrength, f_maxStrength, f_showLabels)   Finds and processes pivot highs.   Parameters:      f_pivotHighs (array) : The array of pivotHighInfo objects to read from and modify.      f_minStrength (int) : Minimum strength required for a new pivot to be recorded.      f_maxStrength (int) : Maximum strength to search for when determining pivot strength.      f_showLabels (bool) : A boolean to control if new labels should be created. pivotHighInfo   Represents a detected pivot high.   Fields:      abs_index (series int)      price (series float)      strength (series int)      label_id (series label) pivotLowInfo   Represents a detected pivot low.   Fields:      abs_index (series int)      price (series float)      strength (series int)      label_id (series label)
Pine Script® ライブラリ
pirate118の投稿
1
LiliALHUNTERSystem_v2📚 **Library: LiliALHUNTERSystem_v2** This library provides a powerful target management system for Pine Script developers. It includes advanced calculators for EMA, RMA, and Supertrend, and introduces a central `createTargets()` function to dynamically render target lines and labels based on long/short trade logic. 🛠️ **Main Features:** – Dynamic horizontal & vertical target lines – Dual target configuration (Target 1 & Target 2) – Directional logic via `isLong1`, `isLong2` – Integrated Supertrend validation – Visual dashboard and label display – Works seamlessly with custom indicators 🎯 **Purpose:** The `LiliALHUNTERSystem_v2` Library enables Pine coders to manage and visualize targets consistently across all trading strategies and indicators. It simplifies target logic while maintaining visual clarity and modular usage. ⚠️ **Disclaimer:** This script is intended for educational and analytical purposes only. It does not constitute financial advice. Library "LiliALHUNTERSystem_v2" ema_calc(len, source)   Parameters:      len (simple int)      source (float) rma_calc(len, source)   Parameters:      len (simple int)      source (float) supertrend_calc(length, factor)   Parameters:      length (simple int)      factor (float) createTargets(config, state, source1A, source1B, source2A, source2B)   Parameters:      config (TargetConfig)      state (TargetState)      source1A (float)      source1B (float)      source2A (float)      source2B (float) showDashboard(state, dashLoc, textSize)   Parameters:      state (TargetState)      dashLoc (string)      textSize (string) TargetConfig   Fields:      enableTarget1 (series bool)      enableTarget2 (series bool)      isLong1 (series bool)      isLong2 (series bool)      target1Condition (series string)      target2Condition (series string)      target1Color (series color)      target2Color (series color)      target1Style (series string)      target2Style (series string)      distTarget1 (series float)      distTarget2 (series float)      distOptions1 (series string)      distOptions2 (series string)      showLabels (series bool)      showDash (series bool) TargetState   Fields:      target1LineV (series line)      target1LineH (series line)      target2LineV (series line)      target2LineH (series line)      target1Lbl (series label)      target2Lbl (series label)      target1Active (series bool)      target2Active (series bool)      target1Value (series float)      target2Value (series float)      countTargets1 (series int)      countTgReached1 (series int)      countTargets2 (series int)      countTgReached2 (series int)
Pine Script® ライブラリ
CYRPTOLILIの投稿
FastMetrixLibrary "FastMetrix" This is a library I've been tweaking and working with for a while and I find it useful to get valuable technical analysis metrics faster (why its called FastMetrix). A lot of is personal to my trading style, so sorry if it does not have everything you want. The way I get my variables from library to script is by copying the return function into my new script. TODO: Volatility and short term price analysis functions slope(source, smoothing)   Parameters:      source (float)      smoothing (int) integral(topfunction, bottomfunction, start, end)   Parameters:      topfunction (float)      bottomfunction (float)      start (int)      end (int) deviation(x, y)   Parameters:      x (float)      y (float) getema(len)   TODO: return important exponential long term moving averages and derivatives/variables   Parameters:      len (simple int) getsma(len)   TODO: return requested sma   Parameters:      len (int) kc(mult, len)   TODO: Return Keltner Channels variables and calculations   Parameters:      mult (simple float)      len (simple int) bollinger(len, mult)   TODO: returns bollinger bands with optimal settings   Parameters:      len (int)      mult (simple float) volatility(atrlen, smoothing)   TODO: Returns volatility indicators based on atr   Parameters:      atrlen (simple int)      smoothing (int) premarketfib() countinday(xcondition)   Parameters:      xcondition (bool) countinsession(condition, n)   Parameters:      condition (bool)      n (int)
Pine Script® ライブラリ
Pugh_tradesの投稿
RifleShooterLibLibrary "RifleShooterLib" Provides a collection of helper functions in support of the Rifle Shooter Indicators. Functions support the key components of the Rifle Trade algorithm including * measuring momentum * identifying paraboloic price action (to disable the algorthim during such time) * determine the lookback criteria of X point movement in last N minutes * processing and navigating between the 23/43/73 levels * maintaining a status table of algorithm progress toStrRnd(val, digits)   Parameters:      val (float)      digits (int) _isValidTimeRange(startTimeInput, endTimeInput)   Parameters:      startTimeInput (string)      endTimeInput (string) _normalize(_src, _min, _max)   _normalize Normalizes series with unknown min/max using historical min/max.   Parameters:      _src (float) : Source series to normalize      _min (float) : minimum value of the rescaled series      _max (float) : maximum value of the rescaled series   Returns: The series scaled with values between min and max arrayToSeries(arrayInput)   arrayToSeries Return an array from the provided series.   Parameters:      arrayInput (array) : Source array to convert to a series   Returns: The array as a series datatype f_parabolicFiltering(_activeCount, long, shooterRsi, shooterRsiLongThreshold, shooterRsiShortThreshold, fiveMinuteRsi, fiveMinRsiLongThreshold, fiveMinRsiShortThreshold, shooterRsiRoc, shooterRsiRocLongThreshold, shooterRsiRocShortThreshold, quickChangeLookbackBars, quckChangeThreshold, curBarChangeThreshold, changeFromPrevBarThreshold, maxBarsToholdParabolicMoveActive, generateLabels)   f_parabolicFiltering Return true when price action indicates a parabolic active movement based on the provided inputs and thresholds.   Parameters:      _activeCount (int)      long (bool)      shooterRsi (float)      shooterRsiLongThreshold (float)      shooterRsiShortThreshold (float)      fiveMinuteRsi (float)      fiveMinRsiLongThreshold (float)      fiveMinRsiShortThreshold (float)      shooterRsiRoc (float)      shooterRsiRocLongThreshold (float)      shooterRsiRocShortThreshold (float)      quickChangeLookbackBars (int)      quckChangeThreshold (int)      curBarChangeThreshold (int)      changeFromPrevBarThreshold (int)      maxBarsToholdParabolicMoveActive (int)      generateLabels (bool) rsiValid(rsi, buyThreshold, sellThreshold)   rsiValid Returns true if the provided RSI value is withing the associated threshold. For the unused threshold set it to na   Parameters:      rsi (float)      buyThreshold (float)      sellThreshold (float) squezeBands(source, length)   squezeBands Returns the squeeze bands momentum color of current source series input   Parameters:      source (float)      length (int) f_momentumOscilator(source, length, transperency)   f_momentumOscilator Returns the squeeze pro momentum value and bar color states of the series input   Parameters:      source (float)      length (int)      transperency (int) f_getLookbackExtreme(lowSeries, highSeries, lbBars, long)   f_getLookbackExtreme Return the highest high or lowest low over the look back window   Parameters:      lowSeries (float)      highSeries (float)      lbBars (int)      long (bool) f_getInitialMoveTarget(lbExtreme, priveMoveOffset, long)   f_getInitialMoveTarget Return the point delta required to achieve an initial rifle move (X points over Y lookback)   Parameters:      lbExtreme (float)      priveMoveOffset (int)      long (bool) isSymbolSupported(sym)   isSymbolSupported Return true if provided symbol is one of the supported DOW Rifle Indicator symbols   Parameters:      sym (string) getBasePrice(price)   getBasePrice Returns integer portion of provided float   Parameters:      price (float) getLastTwoDigitsOfPrice(price)   getBasePrice Returns last two integer numerals of provided float value   Parameters:      price (float) getNextLevelDown(price, lowestLevel, middleLevel, highestLevel)   getNextLevelDown Returns the next level above the provided price value   Parameters:      price (float)      lowestLevel (float)      middleLevel (float)      highestLevel (float) getNextLevelUp(price, lowestLevel, middleLevel, highestLevel)   getNextLevelUp Returns the next level below the provided price value   Parameters:      price (float)      lowestLevel (float)      middleLevel (float)      highestLevel (float) isALevel(price, lowestLevel, middleLevel, highestLevel)   isALevel Returns true if the provided price is onve of the specified levels   Parameters:      price (float)      lowestLevel (float)      middleLevel (float)      highestLevel (float) getClosestLevel(price, lowestLevel, middleLevel, highestLevel)   getClosestLevel Returns the level closest to the price value provided   Parameters:      price (float)      lowestLevel (float)      middleLevel (float)      highestLevel (float) f_fillSetupTableCell(_table, _col, _row, _text, _bgcolor, _txtcolor, _text_size)   f_fillSetupTableCell Helper function to fill a setup table celll   Parameters:      _table (table)      _col (int)      _row (int)      _text (string)      _bgcolor (color)      _txtcolor (color)      _text_size (string) f_fillSetupTableRow(_table, _row, _col0Str, _col1Str, _col2Str, _bgcolor, _textColor, _textSize)   f_fillSetupTableRow Helper function to fill a setup table row   Parameters:      _table (table)      _row (int)      _col0Str (string)      _col1Str (string)      _col2Str (string)      _bgcolor (color)      _textColor (color)      _textSize (string) f_addBlankRow(_table, _row)   f_addBlankRow Helper function to fill a setup table row with empty values   Parameters:      _table (table)      _row (int) f_updateVersionTable(versionTable, versionStr, versionDateStr)   f_updateVersionTable Helper function to fill the version table with provided values   Parameters:      versionTable (table)      versionStr (string)      versionDateStr (string) f_updateSetupTable(_table, parabolicMoveActive, initialMoveTargetOffset, initialMoveAchieved, shooterRsi, shooterRsiValid, rsiRocEnterThreshold, shooterRsiRoc, fiveMinuteRsi, fiveMinuteRsiValid, requireValid5MinuteRsiForEntry, stallLevelOffset, stallLevelExceeded, stallTargetOffset, recoverStallLevelValid, curBarChangeValid, volumeRoc, volumeRocThreshold, enableVolumeRocForTrigger, tradeActive, entryPrice, curCloseOffset, curSymCashDelta, djiCashDelta, showDjiDelta, longIndicator, fontSize)   f_updateSetupTable Manages writing current data to the setup table   Parameters:      _table (table)      parabolicMoveActive (bool)      initialMoveTargetOffset (float)      initialMoveAchieved (bool)      shooterRsi (float)      shooterRsiValid (bool)      rsiRocEnterThreshold (float)      shooterRsiRoc (float)      fiveMinuteRsi (float)      fiveMinuteRsiValid (bool)      requireValid5MinuteRsiForEntry (bool)      stallLevelOffset (float)      stallLevelExceeded (bool)      stallTargetOffset (float)      recoverStallLevelValid (bool)      curBarChangeValid (bool)      volumeRoc (float)      volumeRocThreshold (float)      enableVolumeRocForTrigger (bool)      tradeActive (bool)      entryPrice (float)      curCloseOffset (float)      curSymCashDelta (float)      djiCashDelta (float)      showDjiDelta (bool)      longIndicator (bool)      fontSize (string)
Pine Script® ライブラリ
BlueMagicTradingの投稿
LMAsLibrary "LMAs" Credits Thank you to @QuantraSystems for dynamic calculations. Introduction This lightweight library offers dynamic implementations of popular moving averages that adapt their length automatically as new bars are added to the chart. Each function is built on a dynamic length formula: len = math.min(maxLength, bar_index + 1) This approach ensures that calculations begin as early as the first bar, allowing for smoother initialization and more consistent behavior across all timeframes. It’s especially useful in custom scripts that run from bar 0 or when historical data is limited. Usage You can use this library as a drop-in replacement for standard moving averages. It provides more flexibility and stability in live or backtesting environments where fixed-length indicators may delay or fail to initialize properly. Why Use This? • Works from the very first bar • Avoids na values during early bars • Great for real-time indicators, strategies, and bar-replay • Clean and efficient code with dynamic behavior How to Use Import the library into your script and call any of the included functions just like you would with their native counterparts. Summary A lightweight Pine Script™ library offering dynamic moving averages that work seamlessly from the very first bar. Ideal for strategies and indicators requiring robust initialization and adaptive behavior. SMA(sourceData, maxLength)   Dynamic SMA   Parameters:      sourceData (float)      maxLength (int) EMA(src, length)   Dynamic EMA   Parameters:      src (float)      length (int) DEMA(src, length)   Dynamic DEMA   Parameters:      src (float)      length (int) TEMA(src, length)   Dynamic TEMA   Parameters:      src (float)      length (int) WMA(src, length)   Dynamic WMA   Parameters:      src (float)      length (int) HMA(src, length)   Dynamic HMA   Parameters:      src (float)      length (int) VWMA(src, volsrc, length)   Dynamic VWMA   Parameters:      src (float)      volsrc (float)      length (int) SMMA(src, length)   Dynamic SMMA   Parameters:      src (float)      length (int) LSMA(src, length, offset)   Dynamic LSMA   Parameters:      src (float)      length (int)      offset (int) RMA(src, length)   Dynamic RMA   Parameters:      src (float)      length (int) ALMA(src, length, offset_sigma, sigma)   Dynamic ALMA   Parameters:      src (float)      length (int)      offset_sigma (float)      sigma (float) ZLSMA(src, length)   Dynamic ZLSMA   Parameters:      src (float)      length (int) FRAMA(src, length)   Parameters:      src (float)      length (int) KAMA(src, length)   Dynamic KAMA   Parameters:      src (float)      length (int) JMA(src, length, phase)   Dynamic JMA   Parameters:      src (float)      length (int)      phase (float) T3(src, length, volumeFactor)   Dynamic T3   Parameters:      src (float)      length (int)      volumeFactor (float)
Pine Script® ライブラリ
LyroRSの投稿
TAIndicatorsThis library offers a comprehensive suite of enhanced technical indicator functions, building upon TradingView's built-in indicators. The primary advantage of this library is its expanded flexibility, allowing you to select from a wider range of moving average types for calculations and smoothing across various indicators. The core difference between these functions and TradingView's standard ones is the ability to specify different moving average types beyond the default. While a standard ta.rsi() is fixed, the rsi() in this library, for example, can be smoothed by an 'SMMA (RMA)', 'WMA', 'VWMA', or others, giving you greater control over your analysis. █ FEATURES This library provides enhanced versions of the following popular indicators: Moving Average (ma): A versatile MA function that includes optional secondary smoothing and Bollinger Bands. RSI (rsi): Calculate RSI with an optional smoothed signal line using various MA types, plus built-in divergence detection. MACD (macd): A MACD function where you can define the MA type for both the main calculation and the signal line. ATR (atr): An ATR function that allows for different smoothing types. VWAP (vwap): A comprehensive anchored VWAP with multiple configurable bands. ADX (adx): A standard ADX calculation. Cumulative Volume Delta (cvd): Provides CVD data based on a lower timeframe. Bollinger Bands (bb): Create Bollinger Bands with a customizable MA type for the basis line. Keltner Channels (kc): Keltner Channels with selectable MA types and band styles. On-Balance Volume (obv): An OBV indicator with an optional smoothed signal line using various MA types. ... and more to come! This library will be actively maintained, with new useful indicator functions added over time. █ HOW TO USE To use this library in your scripts, import it using its publishing link. You can then call the functions directly. For example, to calculate a Weighted Moving Average (WMA) and then smooth it with a Simple Moving Average (SMA) : import ActiveQuants/TAIndicators/1 as tai // Calculate a 20-period WMA of the close // Then, smooth the result with a 10-period SMA = tai.ma("WMA", close, 20, "SMA", 10) plot(myWma, color = color.blue) plot(smoothedWma, color = color.orange) █ Why Choose This Library? If you're looking for more control and customization than what's offered by the standard built-in functions, this library is for you. By allowing for a variety of smoothing methods across multiple indicators, it enables a more nuanced and personalized approach to technical analysis. Fine-tune your indicators to better fit your trading style and strategies.
Pine Script® ライブラリ
ActiveQuantsの投稿
CCO_LibraryLibrary "CCO_Library" Contrarian Crowd Oscillator (CCO) Library - Multi-oscillator consensus indicator for contrarian trading signals @author B3AR_Trades calculate_oscillators(rsi_length, stoch_length, cci_length, williams_length, roc_length, mfi_length, percentile_lookback, use_rsi, use_stochastic, use_williams, use_cci, use_roc, use_mfi)   Calculate normalized oscillator values   Parameters:      rsi_length (simple int) : (int) RSI calculation period      stoch_length (int) : (int) Stochastic calculation period      cci_length (int) : (int) CCI calculation period      williams_length (int) : (int) Williams %R calculation period      roc_length (int) : (int) ROC calculation period      mfi_length (int) : (int) MFI calculation period      percentile_lookback (int) : (int) Lookback period for CCI/ROC percentile ranking      use_rsi (bool) : (bool) Include RSI in calculations      use_stochastic (bool) : (bool) Include Stochastic in calculations      use_williams (bool) : (bool) Include Williams %R in calculations      use_cci (bool) : (bool) Include CCI in calculations      use_roc (bool) : (bool) Include ROC in calculations      use_mfi (bool) : (bool) Include MFI in calculations   Returns: (OscillatorValues) Normalized oscillator values calculate_consensus_score(oscillators, use_rsi, use_stochastic, use_williams, use_cci, use_roc, use_mfi, weight_by_reliability, consensus_smoothing)   Calculate weighted consensus score   Parameters:      oscillators (OscillatorValues) : (OscillatorValues) Individual oscillator values      use_rsi (bool) : (bool) Include RSI in consensus      use_stochastic (bool) : (bool) Include Stochastic in consensus      use_williams (bool) : (bool) Include Williams %R in consensus      use_cci (bool) : (bool) Include CCI in consensus      use_roc (bool) : (bool) Include ROC in consensus      use_mfi (bool) : (bool) Include MFI in consensus      weight_by_reliability (bool) : (bool) Apply reliability-based weights      consensus_smoothing (int) : (int) Smoothing period for consensus   Returns: (float) Weighted consensus score (0-100) calculate_consensus_strength(oscillators, consensus_score, use_rsi, use_stochastic, use_williams, use_cci, use_roc, use_mfi)   Calculate consensus strength (agreement between oscillators)   Parameters:      oscillators (OscillatorValues) : (OscillatorValues) Individual oscillator values      consensus_score (float) : (float) Current consensus score      use_rsi (bool) : (bool) Include RSI in strength calculation      use_stochastic (bool) : (bool) Include Stochastic in strength calculation      use_williams (bool) : (bool) Include Williams %R in strength calculation      use_cci (bool) : (bool) Include CCI in strength calculation      use_roc (bool) : (bool) Include ROC in strength calculation      use_mfi (bool) : (bool) Include MFI in strength calculation   Returns: (float) Consensus strength (0-100) classify_regime(consensus_score)   Classify consensus regime   Parameters:      consensus_score (float) : (float) Current consensus score   Returns: (ConsensusRegime) Regime classification detect_signals(consensus_score, consensus_strength, consensus_momentum, regime)   Detect trading signals   Parameters:      consensus_score (float) : (float) Current consensus score      consensus_strength (float) : (float) Current consensus strength      consensus_momentum (float) : (float) Consensus momentum      regime (ConsensusRegime) : (ConsensusRegime) Current regime classification   Returns: (TradingSignals) Trading signal conditions calculate_cco(rsi_length, stoch_length, cci_length, williams_length, roc_length, mfi_length, consensus_smoothing, percentile_lookback, use_rsi, use_stochastic, use_williams, use_cci, use_roc, use_mfi, weight_by_reliability, detect_momentum)   Calculate complete CCO analysis   Parameters:      rsi_length (simple int) : (int) RSI calculation period      stoch_length (int) : (int) Stochastic calculation period      cci_length (int) : (int) CCI calculation period      williams_length (int) : (int) Williams %R calculation period      roc_length (int) : (int) ROC calculation period      mfi_length (int) : (int) MFI calculation period      consensus_smoothing (int) : (int) Consensus smoothing period      percentile_lookback (int) : (int) Percentile ranking lookback      use_rsi (bool) : (bool) Include RSI      use_stochastic (bool) : (bool) Include Stochastic      use_williams (bool) : (bool) Include Williams %R      use_cci (bool) : (bool) Include CCI      use_roc (bool) : (bool) Include ROC      use_mfi (bool) : (bool) Include MFI      weight_by_reliability (bool) : (bool) Apply reliability weights      detect_momentum (bool) : (bool) Calculate momentum and acceleration   Returns: (CCOResult) Complete CCO analysis results calculate_cco_default()   Calculate CCO with default parameters   Returns: (CCOResult) CCO result with standard settings cco_consensus_score()   Get just the consensus score with default parameters   Returns: (float) Consensus score (0-100) cco_consensus_strength()   Get just the consensus strength with default parameters   Returns: (float) Consensus strength (0-100) is_panic_bottom()   Check if in panic bottom condition   Returns: (bool) True if panic bottom signal active is_euphoric_top()   Check if in euphoric top condition   Returns: (bool) True if euphoric top signal active bullish_consensus_reversal()   Check for bullish consensus reversal   Returns: (bool) True if bullish reversal detected bearish_consensus_reversal()   Check for bearish consensus reversal   Returns: (bool) True if bearish reversal detected bearish_divergence()   Check for bearish divergence   Returns: (bool) True if bearish divergence detected bullish_divergence()   Check for bullish divergence   Returns: (bool) True if bullish divergence detected get_regime_name()   Get current regime name   Returns: (string) Current consensus regime name get_contrarian_signal()   Get contrarian signal   Returns: (string) Current contrarian trading signal get_position_multiplier()   Get position size multiplier   Returns: (float) Recommended position sizing multiplier OscillatorValues   Individual oscillator values   Fields:      rsi (series float) : RSI value (0-100)      stochastic (series float) : Stochastic value (0-100)      williams (series float) : Williams %R value (0-100, normalized)      cci (series float) : CCI percentile value (0-100)      roc (series float) : ROC percentile value (0-100)      mfi (series float) : Money Flow Index value (0-100) ConsensusRegime   Consensus regime classification   Fields:      extreme_bearish (series bool) : Extreme bearish consensus (<= 20)      moderate_bearish (series bool) : Moderate bearish consensus (20-40)      mixed (series bool) : Mixed consensus (40-60)      moderate_bullish (series bool) : Moderate bullish consensus (60-80)      extreme_bullish (series bool) : Extreme bullish consensus (>= 80)      regime_name (series string) : Text description of current regime      contrarian_signal (series string) : Contrarian trading signal TradingSignals   Trading signals   Fields:      panic_bottom_signal (series bool) : Extreme bearish consensus with high strength      euphoric_top_signal (series bool) : Extreme bullish consensus with high strength      consensus_reversal_bullish (series bool) : Bullish consensus reversal      consensus_reversal_bearish (series bool) : Bearish consensus reversal      bearish_divergence (series bool) : Bearish price-consensus divergence      bullish_divergence (series bool) : Bullish price-consensus divergence      strong_consensus (series bool) : High consensus strength signal CCOResult   Complete CCO calculation results   Fields:      consensus_score (series float) : Main consensus score (0-100)      consensus_strength (series float) : Consensus strength (0-100)      consensus_momentum (series float) : Rate of consensus change      consensus_acceleration (series float) : Rate of momentum change      oscillators (OscillatorValues) : Individual oscillator values      regime (ConsensusRegime) : Regime classification      signals (TradingSignals) : Trading signals      position_multiplier (series float) : Recommended position sizing multiplier
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