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Techindicator

LibVPrfLibrary "LibVPrf" This library provides an object-oriented framework for volume profile analysis in Pine Script®. It is built around the `VProf` User-Defined Type (UDT), which encapsulates all data, settings, and statistical metrics for a single profile, enabling stateful analysis with on-demand calculations. Key Features: 1. **Object-Oriented Design (UDT):** The library is built around the `VProf` UDT. This object encapsulates all profile data and provides methods for its full lifecycle management, including creation, cloning, clearing, and merging of profiles. 2. **Volume Allocation (`AllotMode`):** Offers two methods for allocating a bar's volume: - **Classic:** Assigns the entire bar's volume to the close price bucket. - **PDF:** Distributes volume across the bar's range using a statistical price distribution model from the `LibBrSt` library. 3. **Buy/Sell Volume Splitting (`SplitMode`):** Provides methods for classifying volume into buying and selling pressure: - **Classic:** Classifies volume based on the bar's color (Close vs. Open). - **Dynamic:** A specific model that analyzes candle structure (body vs. wicks) and a short-term trend factor to estimate the buy/sell share at each price level. 4. **Statistical Analysis (On-Demand):** Offers a suite of statistical metrics calculated using a "Lazy Evaluation" pattern (computed only when requested via `get...` methods): - **Central Tendency:** Point of Control (POC), VWAP, and Median. - **Dispersion:** Value Area (VA) and Population Standard Deviation. - **Shape:** Skewness and Excess Kurtosis. - **Delta:** Cumulative Volume Delta, including its historical high/low watermarks. 5. **Structural Analysis:** Includes a parameter-free method (`getSegments`) to decompose a profile into its fundamental unimodal segments, allowing for modality detection (e.g., identifying bimodal profiles). 6. **Dynamic Profile Management:** - **Auto-Fitting:** Profiles set to `dynamic = true` will automatically expand their price range to fit new data. - **Manipulation:** The resolution, price range, and Value Area of a dynamic profile can be changed at any time. This triggers a resampling process that uses a **linear interpolation model** to re-bucket existing volume. - **Assumption:** Non-dynamic profiles are fixed and will throw a `runtime.error` if `addBar` is called with data outside their initial range. 7. **Bucket-Level Access:** Provides getter methods for direct iteration and analysis of the raw buy/sell volume and price boundaries of each individual price bucket. --- **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. create(buckets, rangeUp, rangeLo, dynamic, valueArea, allot, estimator, cdfSteps, split, trendLen)   Construct a new `VProf` object with fixed bucket count & range.   Parameters:      buckets (int) : series int number of price buckets ≥ 1      rangeUp (float) : series float upper price bound (absolute)      rangeLo (float) : series float lower price bound (absolute)      dynamic (bool) : series bool Flag for dynamic adaption of profile ranges      valueArea (int) : series int Percentage of total volume to include in the Value Area (1..100)      allot (series AllotMode) : series AllotMode Allocation mode `classic` or `pdf` (default `classic`)      estimator (series PriceEst enum from AustrianTradingMachine/LibBrSt/1) : series LibBrSt.PriceEst PDF model when `model == PDF`. (deflault = 'uniform')      cdfSteps (int) : series int even #sub-intervals for Simpson rule (default 20)      split (series SplitMode) : series SplitMode Buy/Sell determination (default `classic`)      trendLen (int) : series int Look‑back bars for trend factor (default 3)   Returns: VProf freshly initialised profile method clone(self)   Create a deep copy of the volume profile.   Namespace types: VProf   Parameters:      self (VProf) : VProf Profile object to copy   Returns: VProf A new, independent copy of the profile method clear(self)   Reset all bucket tallies while keeping configuration intact.   Namespace types: VProf   Parameters:      self (VProf) : VProf profile object   Returns: VProf cleared profile (chaining) method merge(self, srcABuy, srcASell, srcRangeUp, srcRangeLo, srcCvd, srcCvdHi, srcCvdLo)   Merges volume data from a source profile into the current profile. If resizing is needed, it performs a high-fidelity re-bucketing of existing volume using a linear interpolation model inferred from neighboring buckets, preventing aliasing artifacts and ensuring accurate volume preservation.   Namespace types: VProf   Parameters:      self (VProf) : VProf The target profile object to merge into.      srcABuy (array) : array The source profile's buy volume bucket array.      srcASell (array) : array The source profile's sell volume bucket array.      srcRangeUp (float) : series float The upper price bound of the source profile.      srcRangeLo (float) : series float The lower price bound of the source profile.      srcCvd (float) : series float The final Cumulative Volume Delta (CVD) value of the source profile.      srcCvdHi (float) : series float The historical high-water mark of the CVD from the source profile.      srcCvdLo (float) : series float The historical low-water mark of the CVD from the source profile.   Returns: VProf `self` (chaining), now containing the merged data. method addBar(self, offset)   Add current bar’s volume to the profile (call once per realtime bar). classic mode: allocates all volume to the close bucket and classifies by `close >= open`. PDF mode: distributes volume across buckets by the estimator’s CDF mass. For `split = dynamic`, the buy/sell share per price is computed via context-driven piecewise s(u).   Namespace types: VProf   Parameters:      self (VProf) : VProf Profile object      offset (int) : series int To offset the calculated bar   Returns: VProf `self` (method chaining) method setBuckets(self, buckets)   Sets the number of buckets for the volume profile. Behavior depends on the `isDynamic` flag. - If `dynamic = true`: Works on filled profiles by re-bucketing to a new resolution. - If `dynamic = false`: Only works on empty profiles to prevent accidental changes.   Namespace types: VProf   Parameters:      self (VProf) : VProf Profile object      buckets (int) : series int The new number of buckets   Returns: VProf `self` (chaining) method setRanges(self, rangeUp, rangeLo)   Sets the price range for the volume profile. Behavior depends on the `dynamic` flag. - If `dynamic = true`: Works on filled profiles by re-bucketing existing volume. - If `dynamic = false`: Only works on empty profiles to prevent accidental changes.   Namespace types: VProf   Parameters:      self (VProf) : VProf Profile object      rangeUp (float) : series float The new upper price bound      rangeLo (float) : series float The new lower price bound   Returns: VProf `self` (chaining) method setValueArea(self, valueArea)   Set the percentage of volume for the Value Area. If the value changes, the profile is finalized again.   Namespace types: VProf   Parameters:      self (VProf) : VProf Profile object      valueArea (int) : series int The new Value Area percentage (0..100)   Returns: VProf `self` (chaining) method getBktBuyVol(self, idx)   Get Buy volume of a bucket.   Namespace types: VProf   Parameters:      self (VProf) : VProf Profile object      idx (int) : series int Bucket index   Returns: series float Buy volume ≥ 0 method getBktSellVol(self, idx)   Get Sell volume of a bucket.   Namespace types: VProf   Parameters:      self (VProf) : VProf Profile object      idx (int) : series int Bucket index   Returns: series float Sell volume ≥ 0 method getBktBnds(self, idx)   Get Bounds of a bucket.   Namespace types: VProf   Parameters:      self (VProf) : VProf Profile object      idx (int) : series int Bucket index   Returns: up series float The upper price bound of the bucket. lo series float The lower price bound of the bucket. method getPoc(self)   Get POC information.   Namespace types: VProf   Parameters:      self (VProf) : VProf Profile object   Returns: pocIndex series int The index of the Point of Control (POC) bucket. pocPrice. series float The mid-price of the Point of Control (POC) bucket. method getVA(self)   Get Value Area (VA) information.   Namespace types: VProf   Parameters:      self (VProf) : VProf Profile object   Returns: vaUpIndex series int The index of the upper bound bucket of the Value Area. vaUpPrice series float The upper price bound of the Value Area. vaLoIndex series int The index of the lower bound bucket of the Value Area. vaLoPrice series float The lower price bound of the Value Area. method getMedian(self)   Get the profile's median price and its bucket index. Calculates the value on-demand if stale.   Namespace types: VProf   Parameters:      self (VProf) : VProf Profile object.   Returns: medianIndex series int The index of the bucket containing the Median. medianPrice series float The Median price of the profile. method getVwap(self)   Get the profile's VWAP and its bucket index. Calculates the value on-demand if stale.   Namespace types: VProf   Parameters:      self (VProf) : VProf Profile object.   Returns: vwapIndex series int The index of the bucket containing the VWAP. vwapPrice series float The Volume Weighted Average Price of the profile. method getStdDev(self)   Get the profile's volume-weighted standard deviation. Calculates the value on-demand if stale.   Namespace types: VProf   Parameters:      self (VProf) : VProf Profile object.   Returns: series float The Standard deviation of the profile. method getSkewness(self)   Get the profile's skewness. Calculates the value on-demand if stale.   Namespace types: VProf   Parameters:      self (VProf) : VProf Profile object.   Returns: series float The Skewness of the profile. method getKurtosis(self)   Get the profile's excess kurtosis. Calculates the value on-demand if stale.   Namespace types: VProf   Parameters:      self (VProf) : VProf Profile object.   Returns: series float The Kurtosis of the profile. method getSegments(self)   Get the profile's fundamental unimodal segments. Calculates on-demand if stale. Uses a parameter-free, pivot-based recursive algorithm.   Namespace types: VProf   Parameters:      self (VProf) : VProf The profile object.   Returns: matrix A 2-column matrix where each row is an pair. method getCvd(self)   Cumulative Volume Delta (CVD) like metric over all buckets.   Namespace types: VProf   Parameters:      self (VProf) : VProf Profile object.   Returns: cvd series float The final Cumulative Volume Delta (Total Buy Vol - Total Sell Vol). cvdHi series float The running high-water mark of the CVD as volume was added. cvdLo series float The running low-water mark of the CVD as volume was added. VProf   VProf Bucketed Buy/Sell volume profile plus meta information.   Fields:      buckets (series int) : int Number of price buckets (granularity ≥1)      rangeUp (series float) : float Upper price range (absolute)      rangeLo (series float) : float Lower price range (absolute)      dynamic (series bool) : bool Flag for dynamic adaption of profile ranges      valueArea (series int) : int Percentage of total volume to include in the Value Area (1..100)      allot (series AllotMode) : AllotMode Allocation mode `classic` or `pdf`      estimator (series PriceEst enum from AustrianTradingMachine/LibBrSt/1) : LibBrSt.PriceEst Price density model when `model == PDF`      cdfSteps (series int) : int Simpson integration resolution (even ≥2)      split (series SplitMode) : SplitMode Buy/Sell split strategy per bar      trendLen (series int) : int Look‑back length for trend factor (≥1)      maxBkt (series int) : int User-defined number of buckets (unclamped)      aBuy (array) : array Buy volume per bucket      aSell (array) : array Sell volume per bucket      cvd (series float) : float Final Cumulative Volume Delta (Total Buy Vol - Total Sell Vol).      cvdHi (series float) : float Running high-water mark of the CVD as volume was added.      cvdLo (series float) : float Running low-water mark of the CVD as volume was added.      poc (series int) : int Index of max‑volume bucket (POC). Is `na` until calculated.      vaUp (series int) : int Index of upper Value‑Area bound. Is `na` until calculated.      vaLo (series int) : int Index of lower value‑Area bound. Is `na` until calculated.      median (series float) : float Median price of the volume distribution. Is `na` until calculated.      vwap (series float) : float Profile VWAP (Volume Weighted Average Price). Is `na` until calculated.      stdDev (series float) : float Standard Deviation of volume around the VWAP. Is `na` until calculated.      skewness (series float) : float Skewness of the volume distribution. Is `na` until calculated.      kurtosis (series float) : float Excess Kurtosis of the volume distribution. Is `na` until calculated.      segments (matrix) : matrix A 2-column matrix where each row is an pair. Is `na` until calculated.
Perpustakaan Pine Script®
oleh AustrianTradingMachine
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`).
Perpustakaan Pine Script®
oleh AustrianTradingMachine
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.
Perpustakaan Pine Script®
oleh AustrianTradingMachine
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.
Perpustakaan Pine Script®
oleh AustrianTradingMachine
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.
Perpustakaan Pine Script®
oleh AustrianTradingMachine
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
Perpustakaan Pine Script®
oleh stefanoncs
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
Perpustakaan Pine Script®
oleh TheStopLossBoss
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.
Perpustakaan Pine Script®
oleh tkarolak
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)
Perpustakaan Pine Script®
oleh MKUltratrader
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
Perpustakaan Pine Script®
oleh DCAUT
TAUtilityLibLibrary "TAUtilityLib" Technical Analysis Utility Library - Collection of functions for market analysis, smoothing, scaling, and structure detection log_snapshot(label1, val1, label2, val2, label3, val3, label4, val4, label5, val5)   Creates formatted log snapshot with 5 labeled values   Parameters:      label1 (string)      val1 (float)      label2 (string)      val2 (float)      label3 (string)      val3 (float)      label4 (string)      val4 (float)      label5 (string)      val5 (float)   Returns: void (logs to console) f_get_next_tf(tf, steps)   Gets next higher timeframe(s) from current   Parameters:      tf (string) : Current timeframe string      steps (string) : "1 TF Higher" for next TF, any other value for 2 TFs higher   Returns: Next timeframe string or na if at maximum f_get_prev_tf(tf)   Gets previous lower timeframe from current   Parameters:      tf (string) : Current timeframe string   Returns: Previous timeframe string or na if at minimum supersmoother(_src, _length)   Ehler's SuperSmoother - low-lag smoothing filter   Parameters:      _src (float) : Source series to smooth      _length (simple int) : Smoothing period   Returns: Smoothed series butter_smooth(src, len)   Butterworth filter for ultra-smooth price filtering   Parameters:      src (float) : Source series      len (simple int) : Filter period   Returns: Butterworth smoothed series f_dynamic_ema(source, dynamic_length)   Dynamic EMA with variable length   Parameters:      source (float) : Source series      dynamic_length (float) : Dynamic period (can vary bar to bar)   Returns: Dynamically adjusted EMA dema(source, length)   Double Exponential Moving Average (DEMA)   Parameters:      source (float) : Source series      length (simple int) : Period for DEMA calculation   Returns: DEMA value f_scale_percentile(primary_line, secondary_line, x)   Scales secondary line to match primary line using percentile ranges   Parameters:      primary_line (float) : Reference series for target scale      secondary_line (float) : Series to be scaled      x (int) : Lookback bars for percentile calculation   Returns: Scaled version of secondary_line calculate_correlation_scaling(demamom_range, demamom_min, correlation_range, correlation_min)   Calculates scaling factors for correlation alignment   Parameters:      demamom_range (float) : Range of primary series      demamom_min (float) : Minimum of primary series      correlation_range (float) : Range of secondary series      correlation_min (float) : Minimum of secondary series   Returns: tuple for alignment getBB(src, length, mult, chartlevel)   Calculates Bollinger Bands with chart level offset   Parameters:      src (float) : Source series      length (simple int) : MA period      mult (simple float) : Standard deviation multiplier      chartlevel (simple float) : Vertical offset for plotting   Returns: tuple get_mrc(source, length, mult, mult2, gradsize)   Mean Reversion Channel with multiple bands and conditions   Parameters:      source (float) : Price source      length (simple int) : Channel period      mult (simple float) : First band multiplier      mult2 (simple float) : Second band multiplier      gradsize (simple float) : Gradient size for zone detection   Returns: analyzeMarketStructure(highFractalBars, highFractalPrices, lowFractalBars, lowFractalPrices, trendDirection)   Analyzes market structure for ChoCH and BOS patterns   Parameters:      highFractalBars (array) : Array of high fractal bar indices      highFractalPrices (array) : Array of high fractal prices      lowFractalBars (array) : Array of low fractal bar indices      lowFractalPrices (array) : Array of low fractal prices      trendDirection (int) : Current trend (1=up, -1=down, 0=neutral)   Returns: - change signals and new trend direction
Perpustakaan Pine Script®
oleh cybermediaboy
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.
Perpustakaan Pine Script®
oleh RGtrue
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)
Perpustakaan Pine Script®
oleh nuburo
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)
Perpustakaan Pine Script®
oleh pirate118
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)
Perpustakaan Pine Script®
oleh CYRPTOLILI
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)
Perpustakaan Pine Script®
oleh Pugh_trades
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)
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