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OscillatorPivotsLibrary "OscillatorPivots" Measures pivots in an oscillator and flags if they are above a configurable size. Uses absolute size rather than just highest/lowest in a candle range. f_osc_Pivots() Uses the total change in the Y axis, instead of a simple Williams pivot over a defined number of bars. In other words, it measures the size of the actual pivot, not just whether it happens to be the highest/lowest value in a range. Measures the absolute, cumulative change both before and after the pivot, to avoid flagging mere kinks in trends. The advantage is that absolute pivot size is, in some cases, precisely what we care about. A disadvantage is that it can take an arbitrary, perhaps long, time to confirm. You can configure the threshold size of the pivot so that it finds large or small pivots. Always returns a pivot high after a pivot low, then another pivot high and so on, in order. It never returns a high followed by a high, which simple indicators based on the ta.pivot() function can do. @param chart_H_1 This must always be set to 1, unless you are using my HighTimeframeTiming library, in which case set it to the output of the function for a _HTF_H of 1. @param chart_H_2 This must always be set to 2, unless you are using my HighTimeframeTiming library, in which case set it to the output of the function for a _HTF_H of 2. @param _osc This is the oscillator float value. @param _oscPivotSize This is the user setting for what counts as a big enough change to be a pivot. @returns Information about the pivot that is likely to be useful in further calculations: confirmPeak, confirmDip - whether the pivot was confirmed this bar peakBarsBack, dipBarsBack - how many bars ago the actual peak or dip was peakPrice, dipPrice - the value of the oscillator at the peak/dip It also returns some internal variables, which are plotted in this library only for an understanding of how the function works. debug_peakStartLevel, debug_dipStartLevel - The level of the currently active peak/dip
Perpustakaan Pine Script™
oleh SimpleCryptoLife
8
ReversalCandlestickPatternWithTrendIndentifierGMLibrary "ReversalCandlestickPatternWithTrendIndentifierGM" Provides functions calculating the all-time high/low of values. reversalCandlestickPatternWithTrendIndentifier(bullishcriteria, bearishcriteria, momentumOscillatorTypeInput) Calculates the Reversal Candlestick Pattern With Trend Indentifier.   Parameters:      bullishcriteria : Stoch RSI/RSI Bullish Criteria. defval=70, minval=60, maxval=100      bearishcriteria : Stoch RSI/RSI Bearish Criteria. defval=30, minval=0, maxval=40      momentumOscillatorTypeInput : Momentum Oscillator Type. options=   Returns: Reversal Candlestick Pattern With Trend Indentifier.
Perpustakaan Pine Script™
oleh gonzalomelov
DominantCycleCollection of Dominant Cycle estimators. Length adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly). This collection may become encyclopaedic, so if you have any working cycle estimator, drop me a line in the comments below. Suggestions are welcome. Currently included estimators are based on the work of John F. Ehlers mamaPeriod(src, dynLow, dynHigh) MESA Adaptation - MAMA Cycle   Parameters:      src : Series to use      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length   Returns: Calculated period Based on MESA Adaptive Moving Average by John F. Ehlers Performs Hilbert Transform Homodyne Discriminator cycle measurement Unlike MAMA Alpha function (in LengthAdaptation library), this does not compute phase rate of change Introduced in the September 2001 issue of Stocks and Commodities Inspired by the @everget implementation: Inspired by the @anoojpatel implementation: paPeriod(src, dynLow, dynHigh, preHP, preSS, preHP) Pearson Autocorrelation   Parameters:      src : Series to use      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length      preHP : Use High Pass prefilter (default)      preSS : Use Super Smoother prefilter (default)      preHP : Use Hann Windowing prefilter   Returns: Calculated period Based on Pearson Autocorrelation Periodogram by John F. Ehlers Introduced in the September 2016 issue of Stocks and Commodities Inspired by the @blackcat1402 implementation: Inspired by the @rumpypumpydumpy implementation: Corrected many errors, and made small speed optimizations, so this could be the best implementation to date (still slow, though, so may revisit in future) High Pass and Super Smoother prefilters are used in the original implementation dftPeriod(src, dynLow, dynHigh, preHP, preSS, preHP) Discrete Fourier Transform   Parameters:      src : Series to use      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length      preHP : Use High Pass prefilter (default)      preSS : Use Super Smoother prefilter (default)      preHP : Use Hann Windowing prefilter   Returns: Calculated period Based on Spectrum from Discrete Fourier Transform by John F. Ehlers Inspired by the @blackcat1402 implementation: High Pass, Super Smoother and Hann Windowing prefilters are used in the original implementation phasePeriod(src, dynLow, dynHigh, preHP, preSS, preHP) Phase Accumulation   Parameters:      src : Series to use      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length      preHP : Use High Pass prefilter (default)      preSS : Use Super Smoother prefilter (default)      preHP : Use Hamm Windowing prefilter   Returns: Calculated period Based on Dominant Cycle from Phase Accumulation by John F. Ehlers High Pass and Super Smoother prefilters are used in the original implementation doAdapt(type, src, len, dynLow, dynHigh, chandeSDLen, chandeSmooth, chandePower, preHP, preSS, preHP) Execute a particular Length Adaptation or Dominant Cycle Estimator from the list   Parameters:      type : Length Adaptation or Dominant Cycle Estimator type to use      src : Series to use      len : Reference lookback length      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length      chandeSDLen : Lookback length of Standard deviation for Chande's Dynamic Length      chandeSmooth : Smoothing length of Standard deviation for Chande's Dynamic Length      chandePower : Exponent of the length adaptation for Chande's Dynamic Length (lower is smaller variation)      preHP : Use High Pass prefilter for the Estimators that support it (default)      preSS : Use Super Smoother prefilter for the Estimators that support it (default)      preHP : Use Hann Windowing prefilter for the Estimators that support it   Returns: Calculated period (float, not limited) doEstimate(type, src, dynLow, dynHigh, preHP, preSS, preHP) Execute a particular Dominant Cycle Estimator from the list   Parameters:      type : Dominant Cycle Estimator type to use      src : Series to use      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length      preHP : Use High Pass prefilter for the Estimators that support it (default)      preSS : Use Super Smoother prefilter for the Estimators that support it (default)      preHP : Use Hann Windowing prefilter for the Estimators that support it   Returns: Calculated period (float, not limited)
Perpustakaan Pine Script™
oleh lastguru
6
LengthAdaptationCollection of dynamic length adaptation algorithms. Mostly from various Adaptive Moving Averages (they are usually just EMA otherwise). Now you can combine Adaptations with any other Moving Averages or Oscillators (see my other libraries), to get something like Deviation Scaled RSI or Fractal Adaptive VWMA. This collection is not encyclopaedic. Suggestions are welcome. chande(src, len, sdlen, smooth, power) Chande's Dynamic Length   Parameters:      src : Series to use      len : Reference lookback length      sdlen : Lookback length of Standard deviation      smooth : Smoothing length of Standard deviation      power : Exponent of the length adaptation (lower is smaller variation)   Returns: Calculated period Taken from Chande's Dynamic Momentum Index (CDMI or DYMOI), which is dynamic RSI with this length Original default power value is 1, but I use 0.5 A variant of this algorithm is also included, where volume is used instead of price vidya(src, len, dynLow) Variable Index Dynamic Average Indicator (VIDYA)   Parameters:      src : Series to use      len : Reference lookback length      dynLow : Lower bound for the dynamic length   Returns: Calculated period Standard VIDYA algorithm. The period oscillates from the Lower Bound up (slow) I took the adaptation part, as it is just an EMA otherwise vidyaRS(src, len, dynHigh) Relative Strength Dynamic Length - VIDYA RS   Parameters:      src : Series to use      len : Reference lookback length      dynHigh : Upper bound for the dynamic length   Returns: Calculated period Based on Vitali Apirine's modification (Stocks and Commodities, January 2022) of VIDYA algorithm. The period oscillates from the Upper Bound down (fast) I took the adaptation part, as it is just an EMA otherwise kaufman(src, len, dynLow, dynHigh) Kaufman Efficiency Scaling   Parameters:      src : Series to use      len : Reference lookback length      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length   Returns: Calculated period Based on Efficiency Ratio calculation orifinally used in Kaufman Adaptive Moving Average developed by Perry J. Kaufman I took the adaptation part, as it is just an EMA otherwise ds(src, len) Deviation Scaling   Parameters:      src : Series to use      len : Reference lookback length   Returns: Calculated period Based on Derivation Scaled Super Smoother (DSSS) by John F. Ehlers Originally used with Super Smoother RMS originally has 50 bar lookback. Changed to 4x length for better flexibility. Could be wrong. maa(src, len, threshold) Median Average Adaptation   Parameters:      src : Series to use      len : Reference lookback length      threshold : Adjustment threshold (lower is smaller length, default: 0.002, min: 0.0001)   Returns: Calculated period Based on Median Average Adaptive Filter by John F. Ehlers Discovered and implemented by @cheatcountry: I took the adaptation part, as it is just an EMA otherwise fra(len, fc, sc) Fractal Adaptation   Parameters:      len : Reference lookback length      fc : Fast constant (default: 1)      sc : Slow constant (default: 200)   Returns: Calculated period Based on FRAMA by John F. Ehlers Modified to allow lower and upper bounds by an unknown author I took the adaptation part, as it is just an EMA otherwise mama(src, dynLow, dynHigh) MESA Adaptation - MAMA Alpha   Parameters:      src : Series to use      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length   Returns: Calculated period Based on MESA Adaptive Moving Average by John F. Ehlers Introduced in the September 2001 issue of Stocks and Commodities Inspired by the @everget implementation: I took the adaptation part, as it is just an EMA otherwise doAdapt(type, src, len, dynLow, dynHigh, chandeSDLen, chandeSmooth, chandePower) Execute a particular Length Adaptation from the list   Parameters:      type : Length Adaptation type to use      src : Series to use      len : Reference lookback length      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length      chandeSDLen : Lookback length of Standard deviation for Chande's Dynamic Length      chandeSmooth : Smoothing length of Standard deviation for Chande's Dynamic Length      chandePower : Exponent of the length adaptation for Chande's Dynamic Length (lower is smaller variation)   Returns: Calculated period (float, not limited) doMA(type, src, len) MA wrapper on wrapper: if DSSS is selected, calculate it here   Parameters:      type : MA type to use      src : Series to use      len : Filtering length   Returns: Filtered series Demonstration of a combined indicator: Deviation Scaled Super Smoother
Perpustakaan Pine Script™
oleh lastguru
2
divergenceLibrary "divergence" divergence: divergence algorithm with top and bottom kline tolerance regular_bull(series, series, simple, simple, simple, simple, simple) regular_bull: regular bull divergence, lower low src but higher low osc   Parameters:      series : float src: the source series      series : float osc: the oscillator index      simple : int lbL: look back left      simple : int lbR: look back right      simple : int rangeL: min look back range      simple : int rangeU: max look back range      simple : int tolerance: the number of tolerant klines   Returns: array: hidden_bull(series, series, simple, simple, simple, simple, simple) hidden_bull: hidden bull divergence, higher low src but lower low osc   Parameters:      series : float src: the source series      series : float osc: the oscillator index      simple : int lbL: look back left      simple : int lbR: look back right      simple : int rangeL: min look back range      simple : int rangeU: max look back range      simple : int tolerance: the number of tolerant klines   Returns: array: regular_bear(series, series, simple, simple, simple, simple, simple) regular_bear: regular bear divergence, higher high src but lower high osc   Parameters:      series : float src: the source series      series : float osc: the oscillator index      simple : int lbL: look back left      simple : int lbR: look back right      simple : int rangeL: min look back range      simple : int rangeU: max look back range      simple : int tolerance: the number of tolerant klines   Returns: array: hidden_bear(series, series, simple, simple, simple, simple, simple) hidden_bear: hidden bear divergence, lower high src but higher high osc   Parameters:      series : float src: the source series      series : float osc: the oscillator index      simple : int lbL: look back left      simple : int lbR: look back right      simple : int rangeL: min look back range      simple : int rangeU: max look back range      simple : int tolerance: the number of tolerant klines   Returns: array:
Perpustakaan Pine Script™
oleh dandrideng
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
InitialiseArraysLibrary "InitialiseArrays" @description: Efficiently create arrays, populating them with an arbitrary number of elements and setting their starting values. Works with a mix of typed and na values. A limitation of the built-in functions to create arrays is that while you can create and populate arrays with a single command, you can only set all elements to the same value in this way. Or, you can create an array from a set of values or variables, but you can't include any na values. Now, it's easy enough to work around this, but I wanted to make something more efficient (because I want to use lots of arrays), hence this library. Calling a function from here is more efficient because you only do it once (assuming it's a var) and don't evaluate an if condition each run, and don't create unnecessary variables. It's also easier to read and takes less space. f_initialiseBoolArray() @function f_initialiseBoolArray(string _values) Creates a boolean array, populated with the values that you specify in the input string. @param _values is a string that contains a comma-separated list of the values that you want to initialise the boolean array with. Values other than true, false, and na are disregarded. Spaces are disregarded. @returns Returns a boolean array of arbitrary length. f_initialiseFloatArray() @function f_initialiseFloatArray(string _values) Creates a float array, populated with the values that you specify in the input string. @param _values is a string that contains a comma-separated list of the values that you want to initialise the float array with. Non-numerical entries are converted to NaN values. Spaces are disregarded. @returns Returns a float array of arbitrary length. f_initialiseIntArray() @function f_initialiseIntArray(string _values) Creates an int array, populated with the values that you specify in the input string. @param _values is a string that contains a comma-separated list of the values that you want to initialise the int array with. Floating-point numbers are rounded down to the nearest integer. Any na values are preserved. Spaces are disregarded. @returns Returns a float array of arbitrary length. V1 includes functions for bools, floats, and ints. I might extend it if people want.
Perpustakaan Pine Script™
oleh SimpleCryptoLife
2
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
math_utilsLibrary "math_utils" Collection of math functions that are not part of the standard math library num_of_non_decimal_digits(number) num_of_non_decimal_digits - The number of the most significant digits on the left of the dot   Parameters:      number : - The floating point number   Returns: number of non digits num_of_decimal_digits(number) num_of_decimal_digits - The number of the most significant digits on the right of the dot   Parameters:      number : - The floating point number   Returns: number of decimal digits floor(number, precision) floor - floor with precision to the given most significant decimal point   Parameters:      number : - The floating point number      precision : - The number of decimal places a.k.a the most significant decimal digit - e.g precision 2 will produce 0.01 minimum change   Returns: number floored to the decimal digits defined by the precision ceil(number, precision) floor - ceil with precision to the given most significant decimal point   Parameters:      number : - The floating point number      precision : - The number of decimal places a.k.a the most significant decimal digit - e.g precision 2 will produce 0.01 minimum change   Returns: number ceiled to the decimal digits defined by the precision clamp(number, lower, higher, precision) clamp - clamp with precision to the given most significant decimal point   Parameters:      number : - The floating point number      lower : - The lowerst number limit to return      higher : - The highest number limit to return      precision : - The number of decimal places a.k.a the most significant decimal digit - e.g precision 2 will produce 0.01 minimum change   Returns: number clamped to the decimal digits defined by the precision
Perpustakaan Pine Script™
oleh jason5480
4