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least_squares_regressionLibrary "least_squares_regression" least_squares_regression: Least squares regression algorithm to find the optimal price interval for a given time period basic_lsr(series, series, series) basic_lsr: Basic least squares regression algorithm   Parameters:      series : int t: time scale value array corresponding to price      series : float p: price scale value array corresponding to time      series : int array_size: the length of regression array   Returns: reg_slop, reg_intercept, reg_level, reg_stdev trend_line_lsr(series, series, series, string, series, series) top_trend_line_lsr: Trend line fitting based on least square algorithm   Parameters:      series : int t: time scale value array corresponding to price      series : float p: price scale value array corresponding to time      series : int array_size: the length of regression array      string : reg_type: regression type in 'top' and 'bottom'      series : int max_iter: maximum fitting iterations      series : int min_points: the threshold of regression point numbers   Returns: reg_slop, reg_intercept, reg_level, reg_stdev, reg_point_num
Perpustakaan Pine Script®
oleh dandrideng
simple_squares_regressionLibrary "simple_squares_regression" simple_squares_regression: simple squares regression algorithm to find the optimal price interval for a given time period basic_ssr(series, series, series) basic_ssr: Basic simple squares regression algorithm   Parameters:      series : float src: the regression source such as close      series : int region_forward: number of candle lines at the right end of the regression region from the current candle line      series : int region_len: the length of regression region   Returns: left_loc, right_loc, reg_val, reg_std, reg_max_offset search_ssr(series, series, series, series) search_ssr: simple squares regression region search algorithm   Parameters:      series : float src: the regression source such as close      series : int max_forward: max number of candle lines at the right end of the regression region from the current candle line      series : int region_lower: the lower length of regression region      series : int region_upper: the upper length of regression region   Returns: left_loc, right_loc, reg_val, reg_level, reg_std_err, reg_max_offset
Perpustakaan Pine Script®
oleh dandrideng
2
on_balance_volumeLibrary "on_balance_volume" on_balance_volume: custom on balance volume obv_diff(string, simple) obv_diff: custom on balance volume diff version   Parameters:      string : type: the moving average type of on balance volume      simple : int len: the moving average length of on balance volume   Returns: obv_diff: custom on balance volume diff value obv_diff_norm(string, simple) obv_diff_norm: custom normalized on balance volume diff version   Parameters:      string : type: the moving average type of on balance volume      simple : int len: the moving average length of on balance volume   Returns: obv_diff: custom normalized on balance volume diff value
Perpustakaan Pine Script®
oleh dandrideng
NormalizedOscillatorsLibrary "NormalizedOscillators" Collection of some common Oscillators. All are zero-mean and normalized to fit in the -1..1 range. Some are modified, so that the internal smoothing function could be configurable (for example, to enable Hann Windowing, that John F. Ehlers uses frequently). Some are modified for other reasons (see comments in the code), but never without a reason. This collection is neither encyclopaedic, nor reference, however I try to find the most correct implementation. Suggestions are welcome. rsi2(upper, lower) RSI - second step   Parameters:      upper : Upwards momentum      lower : Downwards momentum   Returns: Oscillator value Modified by Ehlers from Wilder's implementation to have a zero mean (oscillator from -1 to +1) Originally: 100.0 - (100.0 / (1.0 + upper / lower)) Ignoring the 100 scale factor, we get: upper / (upper + lower) Multiplying by two and subtracting 1, we get: (2 * upper) / (upper + lower) - 1 = (upper - lower) / (upper + lower) rms(src, len) Root mean square (RMS)   Parameters:      src : Source series      len : Lookback period Based on by John F. Ehlers implementation ift(src) Inverse Fisher Transform   Parameters:      src : Source series   Returns: Normalized series Based on by John F. Ehlers implementation The input values have been multiplied by 2 (was "2*src", now "4*src") to force expansion - not compression The inputs may be further modified, if needed stoch(src, len) Stochastic   Parameters:      src : Source series      len : Lookback period   Returns: Oscillator series ssstoch(src, len) Super Smooth Stochastic (part of MESA Stochastic) by John F. Ehlers   Parameters:      src : Source series      len : Lookback period   Returns: Oscillator series Introduced in the January 2014 issue of Stocks and Commodities This is not an implementation of MESA Stochastic, as it is based on Highpass filter not present in the function (but you can construct it) This implementation is scaled by 0.95, so that Super Smoother does not exceed 1/-1 I do not know, if this the right way to fix this issue, but it works for now netKendall(src, len) Noise Elimination Technology by John F. Ehlers   Parameters:      src : Source series      len : Lookback period   Returns: Oscillator series Introduced in the December 2020 issue of Stocks and Commodities Uses simplified Kendall correlation algorithm Implementation by @QuantTherapy: rsi(src, len, smooth) RSI   Parameters:      src : Source series      len : Lookback period      smooth : Internal smoothing algorithm   Returns: Oscillator series vrsi(src, len, smooth) Volume-scaled RSI   Parameters:      src : Source series      len : Lookback period      smooth : Internal smoothing algorithm   Returns: Oscillator series This is my own version of RSI. It scales price movements by the proportion of RMS of volume mrsi(src, len, smooth) Momentum RSI   Parameters:      src : Source series      len : Lookback period      smooth : Internal smoothing algorithm   Returns: Oscillator series Inspired by RocketRSI by John F. Ehlers (Stocks and Commodities, May 2018) rrsi(src, len, smooth) Rocket RSI   Parameters:      src : Source series      len : Lookback period      smooth : Internal smoothing algorithm   Returns: Oscillator series Inspired by RocketRSI by John F. Ehlers (Stocks and Commodities, May 2018) Does not include Fisher Transform of the original implementation, as the output must be normalized Does not include momentum smoothing length configuration, so always assumes half the lookback length mfi(src, len, smooth) Money Flow Index   Parameters:      src : Source series      len : Lookback period      smooth : Internal smoothing algorithm   Returns: Oscillator series lrsi(src, in_gamma, len) Laguerre RSI by John F. Ehlers   Parameters:      src : Source series      in_gamma : Damping factor (default is -1 to generate from len)      len : Lookback period (alternatively, if gamma is not set)   Returns: Oscillator series The original implementation is with gamma. As it is impossible to collect gamma in my system, where the only user input is length, an alternative calculation is included, where gamma is set by dividing len by 30. Maybe different calculation would be better? fe(len) Choppiness Index or Fractal Energy   Parameters:      len : Lookback period   Returns: Oscillator series The Choppiness Index (CHOP) was created by E. W. Dreiss This indicator is sometimes called Fractal Energy er(src, len) Efficiency ratio   Parameters:      src : Source series      len : Lookback period   Returns: Oscillator series Based on Kaufman Adaptive Moving Average calculation This is the correct Efficiency ratio calculation, and most other implementations are wrong: the number of bar differences is 1 less than the length, otherwise we are adding the change outside of the measured range! For reference, see Stocks and Commodities June 1995 dmi(len, smooth) Directional Movement Index   Parameters:      len : Lookback period      smooth : Internal smoothing algorithm   Returns: Oscillator series Based on the original Tradingview algorithm Modified with inspiration from John F. Ehlers DMH (but not implementing the DMH algorithm!) Only ADX is returned Rescaled to fit -1 to +1 Unlike most oscillators, there is no src parameter as DMI works directly with high and low values fdmi(len, smooth) Fast Directional Movement Index   Parameters:      len : Lookback period      smooth : Internal smoothing algorithm   Returns: Oscillator series Same as DMI, but without secondary smoothing. Can be smoothed later. Instead, +DM and -DM smoothing can be configured doOsc(type, src, len, smooth) Execute a particular Oscillator from the list   Parameters:      type : Oscillator type to use      src : Source series      len : Lookback period      smooth : Internal smoothing algorithm   Returns: Oscillator series Chande Momentum Oscillator (CMO) is RSI without smoothing. No idea, why some authors use different calculations LRSI with Fractal Energy is a combo oscillator that uses Fractal Energy to tune LRSI gamma, as seen here: www.prorealcode.com doPostfilter(type, src, len) Execute a particular Oscillator Postfilter from the list   Parameters:      type : Oscillator type to use      src : Source series      len : Lookback period   Returns: Oscillator series
Perpustakaan Pine Script®
oleh lastguru
CommonFiltersLibrary "CommonFilters" Collection of some common Filters and Moving Averages. This collection is not encyclopaedic, but to declutter my other scripts. Suggestions are welcome, though. Many filters here are based on the work of John F. Ehlers sma(src, len) Simple Moving Average   Parameters:      src : Series to use      len : Filtering length   Returns: Filtered series ema(src, len) Exponential Moving Average   Parameters:      src : Series to use      len : Filtering length   Returns: Filtered series rma(src, len) Wilder's Smoothing (Running Moving Average)   Parameters:      src : Series to use      len : Filtering length   Returns: Filtered series hma(src, len) Hull Moving Average   Parameters:      src : Series to use      len : Filtering length   Returns: Filtered series vwma(src, len) Volume Weighted Moving Average   Parameters:      src : Series to use      len : Filtering length   Returns: Filtered series hp2(src) Simple denoiser   Parameters:      src : Series to use   Returns: Filtered series fir2(src) Zero at 2 bar cycle period by John F. Ehlers   Parameters:      src : Series to use   Returns: Filtered series fir3(src) Zero at 3 bar cycle period by John F. Ehlers   Parameters:      src : Series to use   Returns: Filtered series fir23(src) Zero at 2 bar and 3 bar cycle periods by John F. Ehlers   Parameters:      src : Series to use   Returns: Filtered series fir234(src) Zero at 2, 3 and 4 bar cycle periods by John F. Ehlers   Parameters:      src : Series to use   Returns: Filtered series hp(src, len) High Pass Filter for cyclic components shorter than langth. Part of Roofing Filter by John F. Ehlers   Parameters:      src : Series to use      len : Filtering length   Returns: Filtered series supers2(src, len) 2-pole Super Smoother by John F. Ehlers   Parameters:      src : Series to use      len : Filtering length   Returns: Filtered series filt11(src, len) Filt11 is a variant of 2-pole Super Smoother with error averaging for zero-lag response by John F. Ehlers   Parameters:      src : Series to use      len : Filtering length   Returns: Filtered series supers3(src, len) 3-pole Super Smoother by John F. Ehlers   Parameters:      src : Series to use      len : Filtering length   Returns: Filtered series hannFIR(src, len) Hann Window Filter by John F. Ehlers   Parameters:      src : Series to use      len : Filtering length   Returns: Filtered series hammingFIR(src, len) Hamming Window Filter (inspired by John F. Ehlers). Simplified implementation as Pedestal input parameter cannot be supplied, so I calculate it from the supplied length   Parameters:      src : Series to use      len : Filtering length   Returns: Filtered series triangleFIR(src, len) Triangle Window Filter by John F. Ehlers   Parameters:      src : Series to use      len : Filtering length   Returns: Filtered series doPrefilter(type, src) Execute a particular Prefilter from the list   Parameters:      type : Prefilter type to use      src : Series to use   Returns: Filtered series doMA(type, src, len) Execute a particular MA from the list   Parameters:      type : MA type to use      src : Series to use      len : Filtering length   Returns: Filtered series
Perpustakaan Pine Script®
oleh lastguru
3
WaddahAttarExplosionLibrary "WaddahAttarExplosion" wae(sensitivity, macdFastEMALength, macdSlowEMALength, bbChannelLength, bbStdevMultiplier, refHigh, refLow, refClose) Returns the Waddah Attar Uptrend, Downtrend, Explosion Line and Dead zone   Parameters:      sensitivity : simple float multiplicator for trend line calculation from macd      macdFastEMALength : simple int length for macd fast line      macdSlowEMALength : simple int length for macd slow line      bbChannelLength : simple int length for calculating the bb channel (for std deviation)      bbStdevMultiplier : simple float multiplier to increace bb std deviation      refHigh : series float optional if you want to use another timeframe or symbol, pass it's 'high' series here      refLow : series float optional if you want to use another timeframe or symbol, pass it's 'low' series here      refClose : series float optional if you want to use another timeframe or symbol, pass it's 'close' series here   Returns:
Perpustakaan Pine Script®
oleh robbatt
1
ATRStopLossFinderLibrary "ATRStopLossFinder" Average True Range Stop Loss Finder credits to www.tradingview.com for the initial version stopLossFinder(length, smoothing, multiplier, refHigh, refLow, refClose) Returns the stop losses for an entry on this candle, depending on the ATR   Parameters:      length : simple int optional to select the lookback amount of candles      smoothing : string optional to select the averaging method, options=      multiplier : simple float optional if you want to tweak the speed the trend changes.      refHigh : series float optional if you want to use another timeframe or symbol, pass it's 'high' series here      refLow : series float optional if you want to use another timeframe or symbol, pass it's 'low' series here      refClose : series float optional if you want to use another timeframe or symbol, pass it's 'close' series here   Returns: series float stopLossLong, series float stopLossShort, series float atr
Perpustakaan Pine Script®
oleh robbatt
AbdulLibraryLibrary "AbdulLibrary" The library consists of three sections: Technical Analysis Functions - A collection of tools commonly used by day traders Trading Setup Filters Functions - A number of filters that help day traders to screen trading signals Candlestick Pattern Detection Functions - To detect different candlestick patterns that are used in day trading setups Note that this would have been possible without the help of @ZenAndTheArtOfTrading as I build-up this library after completing his pine script mastery course so big thanks to him The content of the library are:- fibLevels(preDayClose, preDayHigh, preDayLow) Calculates Daily Pivot Point and Fibonacci Key Levels   Parameters:      preDayClose : The previous day candle close      preDayHigh : The previous day candle high      preDayLow : The previous day candle low   Returns: Returns Daily Pivot Point and Fibonacci Key Levels as a tuple bullishFib(canHigh, canLow, fibLevel) Calculates Fibonacci Levels in Bullish move   Parameters:      canHigh : The high of the move      canLow : The low of the move      fibLevel : The Fib level as % you want to calculate   Returns: Returns The Fib level for the Bullish move bearishFib(canHigh, canLow, fibLevel) Calculates Fibonacci Levels in Bearish move   Parameters:      canHigh : The high of the move      canLow : The low of the move      fibLevel : The Fib level as % you want to calculate   Returns: Returns The Fib level for the Bearish move getCandleSize() Calculates the size of candle (high - low) in points   Returns: Returns candle size in points getCandleBodySize() Calculates the size of candle (close - open) in points   Returns: Returns candle body size in points getHighWickSize() Calculates the high wick size of candle in points   Returns: Returns The high wick size of candle in points getLowWickSize() Calculates the low wick size of candle in points   Returns: Returns The low wick size of candle in points getBodyPercentage() Calculates the candle body size as % of overall candle size   Returns: Returns The candle body size as % of overall candle size isSwingHigh(period) Checks if the price has created new swing high over a period of time   Parameters:      period : The lookback time we want to check for swing high   Returns: Returns True if the current candle or the previous candle is a swing high isSwingLow(period) Checks if the price has created new swing low over a period of time   Parameters:      period : The lookback time we want to check for swing low   Returns: Returns True if the current candle or the previous candle is a swing low isDojiSwingHigh(period) Checks if a doji is a swing high over a period of time   Parameters:      period : The lookback time we want to check for swing high   Returns: Returns True if the doji is a swing high isDojiSwingLow(period) Checks if a doji is a swing low over a period of time   Parameters:      period : The lookback time we want to check for swing low   Returns: Returns True if the doji is a swing low isBigBody(atrFilter, atr, candleBodySize, multiplier) Checks if a candle has big body compared to ATR   Parameters:      atrFilter : Check if user wants to use ATR to filter candle-setup signals      atr : The ATR value to be used to compare candle body size      candleBodySize : The candle body size      multiplier : The multiplier to be used to compare candle body size   Returns: Returns Boolean true if the candle setup is big isSmallBody(atrFilter, atr, candleBodySize, multiplier) Checks if a candle has small body compared to ATR   Parameters:      atrFilter : Check if user wants to use ATR to filter candle-setup signals      atr : The ATR value to be used to compare candle body size      candleBodySize : The candle body size      multiplier : The multiplier to be used to compare candle body size   Returns: Returns Boolean true if the candle setup is small isHammer(fibLevel, colorMatch) Checks if a candle is a hammer based on user input parameters and candle conditions   Parameters:      fibLevel : Fib level to base candle body on      colorMatch : Checks if user needs for the candel to be green   Returns: Returns Boolean - True if the candle setup is hammer isShootingStar(fibLevel, colorMatch) Checks if a candle is a shooting star based on user input parameters and candle conditions   Parameters:      fibLevel : Fib level to base candle body on      colorMatch : Checks if user needs for the candel to be red   Returns: Returns Boolean - True if the candle setup is star isBullEngCan(allowance, period) Check if a candle is a bullish engulfing candle   Parameters:      allowance : How many points the candle open is allowed to be off (To allow for gaps)      period : The lookback period for swing low check   Returns: Boolean - True only if the candle is a bullish engulfing candle isBearEngCan(allowance, period) Check if a candle is a bearish engulfing candle   Parameters:      allowance : How many points the candle open is allowed to be off (To allow for gaps)      period : The lookback period for swing high check   Returns: Boolean - True only if the candle is a bearish engulfing candle isBullDoji(maxSize, wickLimit, colorFilter) Check if a candle is a bullish doji candle   Parameters:      maxSize : Maximum candle body size as % of total candle size to be considered as doji      wickLimit : Maximum wick size of one wick compared to the other wick      colorFilter : Checks if the doji is green   Returns: Boolean - True if the candle is a bullish doji isBearDoji(maxSize, wickLimit, colorFilter) Check if a candle is a bearish doji candle   Parameters:      maxSize : Maximum candle body size as % of total candle size to be considered as doji      wickLimit : Maximum wick size of one wick compared to the other wick      colorFilter : Checks if the doji is red   Returns: Boolean - True if the candle is a bearish doji isBullOutBar() Check if a candle is a bullish outside bar   Returns: Boolean - True if the candle is a bullish outside bar isInsideBar() Check if a candle is an inside bar   Returns: Returns Boolean - True if a candle is an inside bar
Perpustakaan Pine Script®
oleh Abdul_Saif
1
PivotsLibrary "Pivots" This Library focuses in functions related to pivot highs and lows and some of their applications (i.e. divergences, zigzag, harmonics, support and resistance...) pivots(srcH, srcL, length) Delivers series of pivot highs, lows and zigzag.   Parameters:      srcH : Source series to look for pivot highs. Stricter applications might source from 'close' prices. Oscillators are also another possible source to look for pivot highs and lows. By default 'high'      srcL : Source series to look for pivot lows. By default 'low'      length : This value represents the minimum number of candles between pivots. The lower the number, the more detailed the pivot profile. The higher the number, the more relevant the pivots. By default 10   Returns: zigzagArray(pivotHigh, pivotLow) Delivers a Zigzag series based on alternating pivots. Ocasionally this line could paint a few consecutive lows or highs without alternating. That happens because it's finding a few consecutive Higher Highs or Lower Lows. If to use lines entities instead of series, that could be easily avoided. But in this one, I'm more interested outputting series rather than painting/deleting line entities.   Parameters:      pivotHigh : Pivot high series      pivotLow : Pivot low series   Returns: zigzagLine(srcH, srcL, colorLine, widthLine) Delivers a Zigzag based on line entities.   Parameters:      srcH : Source series to look for pivot highs. Stricter applications might source from 'close' prices. Oscillators are also another possible source to look for pivot highs and lows. By default 'high'      srcL : Source series to look for pivot lows. By default 'low'      colorLine : Color of the Zigzag Line. By default Fuchsia      widthLine : Width of the Zigzag Line. By default 4   Returns: Zigzag printed on screen divergence(h2, l2, h1, l1, length) Calculates divergences between 2 series   Parameters:      h2 : Series in which to locate divs: Highs      l2 : Series in which to locate divs: Lows      h1 : Series in which to locate pivots: Highs. By default high      l1 : Series in which to locate pivots: Lows. By default low      length : Length used to calculate Pivots: By default 10   Returns:
Perpustakaan Pine Script®
oleh gliderfund
2
Common FunctionsLibrary "CommonFunctions" This Library provides some handy functions commonly used in Pine Script. crosses(source1, offset1, source2, offset2) Checks the existance of crosses between the series   Parameters:      source1 : First series      offset1 : (Optional) Offset of First series. By default 0      source2 : (Optional) Second series. By default 'close' price      offset2 : (Optional) Offset of Second series. By default 0   Returns: marketState(source1, offset1, source2, offset2) Determines Bullish/Bearish state according to the relative position between the series.   Parameters:      source1 : Active series used to determine bullish/bearish states.      offset1 : (Optional) Offset of First series. By default 0.      source2 : (Optional) Second series. By default 'close' price.      offset2 : (Optional) Offset of Second series. By default 0.   Returns: histProfile(source) Histogram profiling   Parameters:      source : Histogram series   Returns: srcSelect(showSrc1, src1, showSrc2, src2) Selects the appropiate source. If multiple sources are activated simultaneously, the order within the switch clause prevails. The first one activated is the one being output.   Parameters:      showSrc1 : Boolean controlling the activation of Source #1.      src1 : Source #1.      showSrc2 : Boolean controlling the activation of Sources #2-10. By default 'false'.      src2 : Sources #2-10. By default 'number'.   Returns: Selected source.
Perpustakaan Pine Script®
oleh gliderfund
MovingAveragesLibraryLibrary "MovingAveragesLibrary" This is a library allowing one to select between many different Moving Average formulas to smooth out any float variable. You can use this library to apply a Moving Average function to any series of data as long as your source is a float. The default application would be for applying Moving Averages onto your chart. However, the scope of this library is beyond that. Any indicator or strategy you are building can benefit from this library. You can apply different types of smoothing and moving average functions to your indicators, momentum oscillators, average true range calculations, support and resistance zones, envelope bands, channels, and anything you can think of to attempt to smooth out noise while finding a delicate balance against lag. If you are developing an indicator, you can use the 'ave_func' to allow your users to select any Moving Average for any function or variable by creating an input string with the following structure: var_name = input.string(, , ) Where the types of Moving Average you would like to be provided would be included in options. Example: i_ma_type = input.string(title = "Moving Average Type", defval = "Hull Moving Average", options = ) Where you would add after options the strings I have included for you at the top of the PineScript for your convenience. Then for the output you desire, simply call 'ave_func' like so: ma = ave_func(source, length, i_ma_type) Now the plotted Moving Average will be the same as what you or your users select from the Input. ema(src, len) Exponential Moving Average.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: Float value. sma(src, len) Simple Moving Average.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: Float value. rma(src, len) Relative Moving Average.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: Float value. wma(src, len) Weighted Moving Average.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: Float value. dv2(len) Donchian V2 function.   Parameters:      len : Lookback length to use.   Returns: Open + Close / 2 for the selected length. ModFilt(src, len) Modular Filter smoothing function.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: Float value. EDSMA(src, len) Ehlers Dynamic Smoothed Moving Average.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: EDSMA smoothing. dema(x, t) Double Exponential Moving Average.   Parameters:      x : Series to use ('close' is used if no argument is supplied).      t : Lookback length to use.   Returns: DEMA smoothing. tema(src, len) Triple Exponential Moving Average.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: TEMA smoothing. smma(x, t) Smoothed Moving Average.   Parameters:      x : Series to use ('close' is used if no argument is supplied).      t : Lookback length to use.   Returns: SMMA smoothing. vwma(x, t) Volume Weighted Moving Average.   Parameters:      x : Series to use ('close' is used if no argument is supplied).      t : Lookback length to use.   Returns: VWMA smoothing. hullma(x, t) Hull Moving Average.   Parameters:      x : Series to use ('close' is used if no argument is supplied).      t : Lookback length to use.   Returns: Hull smoothing. covwma(x, t) Coefficient of Variation Weighted Moving Average.   Parameters:      x : Series to use ('close' is used if no argument is supplied).      t : Lookback length to use.   Returns: COVWMA smoothing. frama(x, t) Fractal Reactive Moving Average.   Parameters:      x : Series to use ('close' is used if no argument is supplied).      t : Lookback length to use.   Returns: FRAMA smoothing. kama(x, t) Kaufman's Adaptive Moving Average.   Parameters:      x : Series to use ('close' is used if no argument is supplied).      t : Lookback length to use.   Returns: KAMA smoothing. donchian(len) Donchian Calculation.   Parameters:      len : Lookback length to use.   Returns: Average of the highest price and the lowest price for the specified look-back period. tma(src, len) Triangular Moving Average.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: TMA smoothing. VAMA(src, len) Volatility Adjusted Moving Average.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: VAMA smoothing. Jurik(src, len) Jurik Moving Average.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: JMA smoothing. MCG(src, len) McGinley smoothing.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: McGinley smoothing. zlema(series, length) Zero Lag Exponential Moving Average.   Parameters:      series : Series to use ('close' is used if no argument is supplied).      length : Lookback length to use.   Returns: ZLEMA smoothing. xema(src, len) Optimized Exponential Moving Average.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      len : Lookback length to use.   Returns: XEMA smoothing. EhlersSuperSmoother(src, lower) Ehlers Super Smoother.   Parameters:      src : Series to use ('close' is used if no argument is supplied).      lower : Smoothing value to use.   Returns: Ehlers Super smoothing. EhlersEmaSmoother(sig, smoothK, smoothP) Ehlers EMA Smoother.   Parameters:      sig : Series to use ('close' is used if no argument is supplied).      smoothK : Lookback length to use.      smoothP : Smothing value to use.   Returns: Ehlers EMA smoothing. ave_func(in_src, in_len, in_type) Returns the source after running it through a Moving Average function.   Parameters:      in_src : Series to use ('close' is used if no argument is supplied).      in_len : Lookback period to be used for the Moving Average function.      in_type : Type of Moving Average function to use. Must have a string input to select the options from that MUST match the type-casing in the function below.   Returns: The source as a float after running it through the Moving Average function.
Perpustakaan Pine Script®
oleh CrackingCryptocurrency
7
adx: Configurable ADX (library) Library "adx" Calculate ADX (and its constituent parts +DI, -DI, ATR), using different moving averages and periods. adx(atrMA, diMA, adxMA, atrLen, diLen, adxLen, h, l, c)   Parameters:      atrMA : Moving Average used for calculating the Average True Range. Traditionally RMA, but using SMA here and in adxMA gives good results too.      diMA : Moving Average used for calculating the Directional Index. Traditionally, RMA.      adxMA : Moving Average used for calculating the Average Directional Index. Traditionally RMA, but using SMA here and in atrMA gives good results too.      atrLen : Length of the Average True Range.      diLen : Length of the Directional Index.      adxLen : Length (smoothing) of the Average Directional Index.      h : Candle's high.      l : Candle's low.      c : Candle's close.   Returns:
Perpustakaan Pine Script®
oleh t92a4
eHarmonicpatternsExtendedLibrary "eHarmonicpatternsExtended" Library provides an alternative method to scan harmonic patterns. This is helpful in reducing iterations. Republishing as new library instead of existing eHarmonicpatterns because I need that copy for existing scripts. scan_xab(bcdRatio, err_min, err_max, patternArray) Checks if bcd ratio is in range of any harmonic pattern   Parameters:      bcdRatio : AB/XA ratio      err_min : minimum error threshold      err_max : maximum error threshold      patternArray : Array containing pattern check flags. Checks are made only if flags are true. Upon check flgs are overwritten. scan_abc_axc(abcRatio, axcRatio, err_min, err_max, patternArray) Checks if abc or axc ratio is in range of any harmonic pattern   Parameters:      abcRatio : BC/AB ratio      axcRatio : XC/AX ratio      err_min : minimum error threshold      err_max : maximum error threshold      patternArray : Array containing pattern check flags. Checks are made only if flags are true. Upon check flgs are overwritten. scan_bcd(bcdRatio, err_min, err_max, patternArray) Checks if bcd ratio is in range of any harmonic pattern   Parameters:      bcdRatio : CD/BC ratio      err_min : minimum error threshold      err_max : maximum error threshold      patternArray : Array containing pattern check flags. Checks are made only if flags are true. Upon check flgs are overwritten. scan_xad_xcd(xadRatio, xcdRatio, err_min, err_max, patternArray) Checks if xad or xcd ratio is in range of any harmonic pattern   Parameters:      xadRatio : AD/XA ratio      xcdRatio : CD/XC ratio      err_min : minimum error threshold      err_max : maximum error threshold      patternArray : Array containing pattern check flags. Checks are made only if flags are true. Upon check flgs are overwritten. isHarmonicPattern(x, a, b, c, d, flags, errorPercent) Checks for harmonic patterns   Parameters:      x : X coordinate value      a : A coordinate value      b : B coordinate value      c : C coordinate value      d : D coordinate value      flags : flags to check patterns. Send empty array to enable all      errorPercent : Error threshold   Returns: Array of boolean values which says whether valid pattern exist and array of corresponding pattern names isHarmonicProjection(x, a, b, c, flags, errorPercent) Checks for harmonic pattern projection   Parameters:      x : X coordinate value      a : A coordinate value      b : B coordinate value      c : C coordinate value      flags : flags to check patterns. Send empty array to enable all      errorPercent : Error threshold   Returns: Array of boolean values which says whether valid pattern exist and array of corresponding pattern names. get_prz_range(x, a, b, c, patternArray, errorPercent, start_adj, end_adj) Provides PRZ range based on BCD and XAD ranges   Parameters:      x : X coordinate value      a : A coordinate value      b : B coordinate value      c : C coordinate value      patternArray : Pattern flags for which PRZ range needs to be calculated      errorPercent : Error threshold      start_adj : - Adjustments for entry levels      end_adj : - Adjustments for stop levels   Returns: Start and end of consolidated PRZ range get_prz_range_xad(x, a, b, c, patternArray, errorPercent, start_adj, end_adj) Provides PRZ range based on XAD range only   Parameters:      x : X coordinate value      a : A coordinate value      b : B coordinate value      c : C coordinate value      patternArray : Pattern flags for which PRZ range needs to be calculated      errorPercent : Error threshold      start_adj : - Adjustments for entry levels      end_adj : - Adjustments for stop levels   Returns: Start and end of consolidated PRZ range
Perpustakaan Pine Script®
oleh Trendoscope
14
Adaptive_LengthLibrary "Adaptive_Length" This library contains functions to calculate Adaptive dynamic length which can be used in Moving Averages and other indicators. Two Exponential Moving Averages (EMA) are plotted. Coloring in plot is derived from Chikou filter and Dynamic length of MA1 is adapted using Signal output from Chikou library. dynamic(para, adapt_Pct, minLength, maxLength) Adaptive dynamic length based on boolean parameter   Parameters:      para : Boolean parameter; if true then length would decrease and would increase if its false      adapt_Pct : Percentage adaption based on parameter      minLength : Minimum allowable length      maxLength : Maximum allowable length   Returns: Adaptive Dynamic Length based on Boolean Parameter auto_alpha(src, a) Adaptive length based on automatic alpha calculations from source input   Parameters:      src : Price source for alpha calculations      a : Input Alpha value   Returns: Adaptive Length calculated from input price Source and Alpha
Perpustakaan Pine Script®
oleh MightyZinger
1
HA_CandlesLibrary "HA_Candles" Heikin Ashi Candles HA_Close() Heikin Ashi Modified Close   Returns: Heikin Ashi Modified Close HA_Open() Heikin Ashi Modified Open   Returns: Heikin Ashi Modified Open HA_High() Heikin Ashi Modified High   Returns: Heikin Ashi Modified High HA_Low() Heikin Ashi Modified Low   Returns: Heikin Ashi Modified Low HA_Delta(Heikin) Heikin Ashi Delta   Parameters:      Heikin : Ashi Close, Heikin Ashi Open   Returns: Heikin Ashi Delta
Perpustakaan Pine Script®
oleh smhoma
1
RVSILibrary "RVSI" This Library contains functions that calculate all types of " Relative Volume Strength Index (MZ RVSI ) " depending upon unique volume oscillator. Achieved RVSI value can be used for divergence detection in volume or to adapt dynamic length in Moving Averages or other functions. rvsi_tfs(vol_src, vol_Len, rvsiLen, _open, _close) Relative Volume Strength Index based on TFS Volume Oscillator   Parameters:      vol_src : Volume Source      vol_Len : Volume Legth for TFS Volume Oscillato      rvsiLen : Period of Relative Volume Strength Index      _open : Ticker Open Value      _close : Ticker Close Value   Returns: Relative Volume Strength Index value based on TFS Volume Oscillator rvsi_obv(vol_src, rvsiLen, _close) Relative Volume Strength Index based on On Balance Volume   Parameters:      vol_src : Volume Source to Calculate On Balance Volume      rvsiLen : Period of Relative Volume Strength Index      _close : Ticker Close Value   Returns: Relative Volume Strength Index value based on On Balance Volume rvsi_kvo(vol_src, FastX, SlowX, rvsiLen, _close) Relative Volume Strength Index based on Klinger Volume Oscillator   Parameters:      vol_src : Volume Source      FastX : Volume Fast Length      SlowX : Volume Slow Length      rvsiLen : Period of Relative Volume Strength Index      _close : Ticker Close Value   Returns: Relative Volume Strength Index value based on Klinger Volume Oscillator rvsi_vzo(vol_src, zLen, rvsiLen, _close) Relative Volume Strength Index based on Volume Zone Oscillator   Parameters:      vol_src : Volume Source      zLen : Volume Legth for Volume Zone Oscillator      rvsiLen : Period of Relative Volume Strength Index      _close : Ticker Close Value   Returns: Relative Volume Strength Index value based on Volume Zone Oscillator rvsi_cvo_obv(vol_src, ema1len, ema2len, rvsiLen) Relative Volume Strength Index based on Cumulative Volume Oscillator with On Balance Volume as Calculations Source   Parameters:      vol_src : Volume Source      ema1len : EMA Fast Length      ema2len : EMA Slow Length      rvsiLen : Period of Relative Volume Strength Index   Returns: Relative Volume Strength Index value based on Cumulative Volume Oscillator with On Balance Volume as Calculations Source rvsi_cvo_pvt(vol_src, FastX, SlowX, rvsiLen) Relative Volume Strength Index based on Cumulative Volume Oscillator with Price Volume Trend as Calculations Source   Parameters:      vol_src : Volume Source      FastX : EMA Fast Length      SlowX : EMA Slow Length      rvsiLen : Period of Relative Volume Strength Index   Returns: Relative Volume Strength Index value based on Cumulative Volume Oscillator with Price Volume Trend as Calculations Source rvsi_cvo_cvd(vol_src, FastX, SlowX, rvsiLen, _open, _close, _high, _low) Relative Volume Strength Index based on Cumulative Volume Oscillator with Cumulative Volume Delta as Calculations Source   Parameters:      vol_src : Volume Source      FastX : EMA Fast Length      SlowX : EMA Slow Length      rvsiLen : Period of Relative Volume Strength Index      _open : Ticker Open Value      _close : Ticker Close Value      _high : Ticker High Value      _low : Ticker Low Value   Returns: Relative Volume Strength Index value based on Cumulative Volume Oscillator with Cumulative Volume Delta as Calculations Source
Perpustakaan Pine Script®
oleh MightyZinger
2
JohnEhlersFourierTransformLibrary "JohnEhlersFourierTransform" Fourier Transform for Traders By John Ehlers, slightly modified to allow to inspect other than the 8-50 frequency spectrum. reference: www.mesasoftware.com high_pass_filter(source) Detrended version of the data by High Pass Filtering with a 40 Period cutoff   Parameters:      source : float, data source.   Returns: float. transformed_dft(source, start_frequency, end_frequency) DFT by John Elhers.   Parameters:      source : float, data source.      start_frequency : int, lower bound of the frequency window, must be a positive number >= 0, window must be less than or 30.      end_frequency : int, upper bound of the frequency window, must be a positive number >= 0, window must be less than or 30.   Returns: tuple with float, float array. db_to_rgb(db, transparency) converts the frequency decibels to rgb.   Parameters:      db : float, decibels value.      transparency : float, transparency value.   Returns: color.
Perpustakaan Pine Script®
oleh RicardoSantos
24
windowing_taAll Signals Are the Sum of Sines. When looking at real-world signals, you usually view them as a price changing over time. This is referred to as the time domain. Fourier’s theorem states that any waveform in the time domain can be represented by the weighted sum of sines and cosines. For example, take two sine waves, where one is three times as fast as the other–or the frequency is 1/3 the first signal. When you add them, you can see you get a different signal. Although performing an FFT on a signal can provide great insight, it is important to know the limitations of the FFT and how to improve the signal clarity using windowing. When you use the FFT to measure the frequency component of a signal, you are basing the analysis on a finite set of data. The actual FFT transform assumes that it is a finite data set, a continuous spectrum that is one period of a periodic signal. For the FFT, both the time domain and the frequency domain are circular topologies, so the two endpoints of the time waveform are interpreted as though they were connected together. When the measured signal is periodic and an integer number of periods fill the acquisition time interval, the FFT turns out fine as it matches this assumption. However, many times, the measured signal isn’t an integer number of periods. Therefore, the finiteness of the measured signal may result in a truncated waveform with different characteristics from the original continuous-time signal, and the finiteness can introduce sharp transition changes into the measured signal. The sharp transitions are discontinuities. When the number of periods in the acquisition is not an integer, the endpoints are discontinuous. These artificial discontinuities show up in the FFT as high-frequency components not present in the original signal. These frequencies can be much higher than the Nyquist frequency and are aliased between 0 and half of your sampling rate. The spectrum you get by using a FFT, therefore, is not the actual spectrum of the original signal, but a smeared version. It appears as if energy at one frequency leaks into other frequencies. This phenomenon is known as spectral leakage, which causes the fine spectral lines to spread into wider signals. You can minimize the effects of performing an FFT over a noninteger number of cycles by using a technique called windowing. Windowing reduces the amplitude of the discontinuities at the boundaries of each finite sequence acquired by the digitizer. Windowing consists of multiplying the time record by a finite-length window with an amplitude that varies smoothly and gradually toward zero at the edges. This makes the endpoints of the waveform meet and, therefore, results in a continuous waveform without sharp transitions. This technique is also referred to as applying a window. Here is a windowing_ta library with J.F Ehlers Windowing functions proposed on Sep, 2021. Library "windowing_ta" hann() hamm() fir_sma() fir_triangle()
Perpustakaan Pine Script®
oleh blackcat1402
2
TAExtLibrary "TAExt" Indicator functions can be used in other indicators and strategies. This will be extended by time with indicators I use in my strategies and studies. atrwo(length, stdev_length, stdev_mult) ATR without outliers   Parameters:      length : The length of the ATR      stdev_length : The length of the standard deviation, used for detecting outliers      stdev_mult : The multiplier of the standard deviation, used for detecting outliers   Returns: The ATR value atrwma(src, period, type, atr_length, stdev_length, stdev_mult) ATR without outlier weighted moving average   Parameters:      src : The source of the moving average      period : The period of the moving average      type : The type of the moving average, possible values: SMA, EMA, RMA      atr_length : The length of the ATR      stdev_length : The length of the standard deviation, used for detecting outliers      stdev_mult : The multiplier of the standard deviation, used for detecting outliers   Returns: The moving average value jma(src, period, phase, power) Jurik Moving Average   Parameters:      src : The source of the moving average      period : The period of the moving average calculation      phase : The phase of jurik MA calculation (-100..100)      power : The power of jurik MA calculation   Returns: The Jurik MA series anyma(src, period, type, offset, sigma, phase, power) Moving Average by type   Parameters:      src : The source of the moving average      period : The period of the moving average calculation      type : The type of the moving average      offset : Used only by ALMA, it is the ALMA offset      sigma : Used only by ALMA, it is the ALMA sigma      phase : The phase of jurik MA calculation (-100..100)      power : The power of jurik MA calculation   Returns: The moving average series wae(macd_src, macd_fast_length, macd_slow_length, macd_sensitivity, bb_base_src, bb_upper_src, bb_lower_src, bb_length, bb_mult, dead_zone_length, dead_zone_mult) Waddah Attar Explosion (WAE)   Parameters:      macd_src : The source series used by MACD      macd_fast_length : The fast MA length of the MACD      macd_slow_length : The slow MA length of the MACD      macd_sensitivity : The MACD diff multiplier      bb_base_src : The source used by stdev      bb_upper_src : The source used by the upper Bollinger Band      bb_lower_src : The source used by the lower Bollinger Band      bb_length : The lenth for Bollinger Bands      bb_mult : The multiplier for Bollinger Bands      dead_zone_length : The ATR length for dead zone calculation      dead_zone_mult : The ATR multiplier for dead zone   Returns: ssl(length, high_src, low_src) Semaphore Signal Level channel (SSL)   Parameters:      length : The length of the moving average      high_src : Source of the high moving average      low_src : Source of the low moving average   Returns: adx(atr_length, di_length, adx_length, high_src, low_src, atr_ma_type, di_ma_type, adx_ma_type) Average Directional Index + Direction Movement Index (ADX + DMI)   Parameters:      atr_length : The length of ATR      di_length : DI plus and minus smoothing length      adx_length : ADX smoothing length      high_src : Source of the high moving average      low_src : Source of the low moving average      atr_ma_type : MA type of the ATR calculation      di_ma_type : MA type of the DI calculation      adx_ma_type : MA type of the ADX calculation   Returns:
Perpustakaan Pine Script®
oleh TheBacktestGuy
9
historicalrangeLibrary "historicalrange" Library provices a method to calculate historical percentile range of series. hpercentrank(source) calculates historical percentrank of the source   Parameters:      source : Source for which historical percentrank needs to be calculated. Source should be ranging between 0-100. If using a source which can beyond 0-100, use short term percentrank to baseline them.   Returns: pArray - percentrank array which contains how many instances of source occurred at different levels. upperPercentile - percentile based on higher value lowerPercentile - percentile based on lower value median - median value of the source max - max value of the source distancefromath(source) returns stats on historical distance from ath in terms of percentage   Parameters:      source : for which stats are calculated   Returns: percentile and related historical stats regarding distance from ath distancefromma(maType, length, source) returns stats on historical distance from moving average in terms of percentage   Parameters:      maType : Moving Average Type : Can be sma, ema, hma, rma, wma, vwma, swma, highlow, linreg, median      length : Moving Average Length      source : for which stats are calculated   Returns: percentile and related historical stats regarding distance from ath bpercentb(source, maType, length, multiplier, sticky) returns percentrank and stats on historical bpercentb levels   Parameters:      source : Moving Average Source      maType : Moving Average Type : Can be sma, ema, hma, rma, wma, vwma, swma, highlow, linreg, median      length : Moving Average Length      multiplier : Standard Deviation multiplier      sticky : - sticky boundaries which will only change when value is outside boundary.   Returns: percentile and related historical stats regarding Bollinger Percent B kpercentk(source, maType, length, multiplier, useTrueRange, sticky) returns percentrank and stats on historical kpercentk levels   Parameters:      source : Moving Average Source      maType : Moving Average Type : Can be sma, ema, hma, rma, wma, vwma, swma, highlow, linreg, median      length : Moving Average Length      multiplier : Standard Deviation multiplier      useTrueRange : - if set to false, uses high-low.      sticky : - sticky boundaries which will only change when value is outside boundary.   Returns: percentile and related historical stats regarding Keltener Percent K dpercentd(useAlternateSource, alternateSource, length, sticky) returns percentrank and stats on historical dpercentd levels   Parameters:      useAlternateSource : - Custom source is used only if useAlternateSource is set to true      alternateSource : - Custom source      length : - donchian channel length      sticky : - sticky boundaries which will only change when value is outside boundary.   Returns: percentile and related historical stats regarding Donchian Percent D oscillator(type, length, shortLength, longLength, source, highSource, lowSource, method, highlowLength, sticky) oscillator - returns Choice of oscillator with custom overbought/oversold range   Parameters:      type : - oscillator type. Valid values : cci, cmo, cog, mfi, roc, rsi, stoch, tsi, wpr      length : - Oscillator length - not used for TSI      shortLength : - shortLength only used for TSI      longLength : - longLength only used for TSI      source : - custom source if required      highSource : - custom high source for stochastic oscillator      lowSource : - custom low source for stochastic oscillator      method : - Valid values for method are : sma, ema, hma, rma, wma, vwma, swma, highlow, linreg, median      highlowLength : - length on which highlow of the oscillator is calculated      sticky : - overbought, oversold levels won't change unless crossed   Returns: percentile and related historical stats regarding oscillator
Perpustakaan Pine Script®
oleh Trendoscope
1
dc_taAdaptive technical indicators are importants in a non stationary market, the ability to adapt to a situation can boost the efficiency of your strategy. A lot of methods have been proposed to make technical indicators "smarters", the dominant cycle tuned indicators are one of them which are based on J.F.Ehlers theory. Here is a collections of algorithms to calculate dominant cycles. ENJOY! Library "dc_ta" bton() EhlersHoDyDC() EhlersPhAcDC() EhlersDuDiDC() EhlersCycPer() EhlersCycPer2() EhlersBPZC() EhlersAutoPer() EhlersHoDyDCE() EhlersPhAcDCE() EhlersDuDiDCE() EhlersDFTDC() EhlersDFTDC2()
Perpustakaan Pine Script®
oleh blackcat1402
5