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FunctionLinearRegression Library "FunctionLinearRegression" Method for Linear Regression using array sample points. linreg(sample_x, sample_y) Performs Linear Regression over the provided sample points. Parameters: sample_x : float array, sample points X value. sample_y : float array, sample points Y value. Returns: tuple with: _predictions: Array with adjusted Y values. _max_dev: Max deviation from the mean. _min_dev: Min deviation from the mean. _stdev/_sizeX: Average deviation from the mean. draw(sample_x, sample_y, extend, mid_color, mid_style, mid_width, std_color, std_style, std_width, max_color, max_style, max_width) Method for drawing the Linear Regression into chart. Parameters: sample_x : float array, sample point X value. sample_y : float array, sample point Y value. extend : string, default=extend.none, extend lines. mid_color : color, default=color.blue, middle line color. mid_style : string, default=line.style_solid, middle line style. mid_width : int, default=2, middle line width. std_color : color, default=color.aqua, standard deviation line color. std_style : string, default=line.style_dashed, standard deviation line style. std_width : int, default=1, standard deviation line width. max_color : color, default=color.purple, max range line color. max_style : string, default=line.style_dotted, max line style. max_width : int, default=1, max line width. Returns: line array.

pandas_ta Library "pandas_ta" Level: 3 Background Today is the first day of 2022 and happy new year every tradingviewers! May health and wealth go along with you all the time. I use this chance to publish my 1st PINE v5 lib : pandas_ta This is not a piece of cake like thing, which cost me a lot of time and efforts to build this lib. Beyond 300 versions of this script was iterated in draft. Function Library "pandas_ta" PINE v5 Counterpart of Pandas TA - A Technical Analysis Library in Python 3 at github.com The Original Pandas Technical Analysis (Pandas TA) is an easy to use library that leverages the Pandas package with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns. I realized most of indicators except Candlestick Patterns because tradingview built-in Candlestick Patterns are even more powerful! I use this to verify pandas_ta python version indicators for myself, but I realize that maybe many may need similar lib for pine v5 as well. Function Brief Descriptions (Pls find details in script comments) bton --> Binary to number wcp --> Weighted Closing Price (WCP) counter --> Condition counter xbt --> Between ebsw --> Even Better SineWave (EBSW) ao --> Awesome Oscillator (AO) apo --> Absolute Price Oscillator (APO) xrf --> Dynamic shifted values bias --> Bias (BIAS) bop --> Balance of Power (BOP) brar --> BRAR (BRAR) cci --> Commodity Channel Index (CCI) cfo --> Chande Forcast Oscillator (CFO) cg --> Center of Gravity (CG) cmo --> Chande Momentum Oscillator (CMO) coppock --> Coppock Curve (COPC) cti --> Correlation Trend Indicator (CTI) dmi --> Directional Movement Index(DMI) er --> Efficiency Ratio (ER) eri --> Elder Ray Index (ERI) fisher --> Fisher Transform (FISHT) inertia --> Inertia (INERTIA) kdj --> KDJ (KDJ) kst --> 'Know Sure Thing' (KST) macd --> Moving Average Convergence Divergence (MACD) mom --> Momentum (MOM) pgo --> Pretty Good Oscillator (PGO) ppo --> Percentage Price Oscillator (PPO) psl --> Psychological Line (PSL) pvo --> Percentage Volume Oscillator (PVO) qqe --> Quantitative Qualitative Estimation (QQE) roc --> Rate of Change (ROC) rsi --> Relative Strength Index (RSI) rsx --> Relative Strength Xtra (rsx) rvgi --> Relative Vigor Index (RVGI) slope --> Slope smi --> SMI Ergodic Indicator (SMI) sqz* --> Squeeze (SQZ) * NOTE: code sufferred from very strange error, code was commented. sqz_pro --> Squeeze PRO(SQZPRO) xfl --> Condition filter stc --> Schaff Trend Cycle (STC) stoch --> Stochastic (STOCH) stochrsi --> Stochastic RSI (STOCH RSI) trix --> Trix (TRIX) tsi --> True Strength Index (TSI) uo --> Ultimate Oscillator (UO) willr --> William's Percent R (WILLR) alma --> Arnaud Legoux Moving Average (ALMA) xll --> Dynamic rolling lowest values dema --> Double Exponential Moving Average (DEMA) ema --> Exponential Moving Average (EMA) fwma --> Fibonacci's Weighted Moving Average (FWMA) hilo --> Gann HiLo Activator(HiLo) hma --> Hull Moving Average (HMA) hwma --> HWMA (Holt-Winter Moving Average) ichimoku --> Ichimoku Kinkō Hyō (ichimoku) jma --> Jurik Moving Average Average (JMA) kama --> Kaufman's Adaptive Moving Average (KAMA) linreg --> Linear Regression Moving Average (linreg) mgcd --> McGinley Dynamic Indicator rma --> wildeR's Moving Average (RMA) sinwma --> Sine Weighted Moving Average (SWMA) ssf --> Ehler's Super Smoother Filter (SSF) © 2013 supertrend --> Supertrend (supertrend) xsa --> X simple moving average swma --> Symmetric Weighted Moving Average (SWMA) t3 --> Tim Tillson's T3 Moving Average (T3) tema --> Triple Exponential Moving Average (TEMA) trima --> Triangular Moving Average (TRIMA) vidya --> Variable Index Dynamic Average (VIDYA) vwap --> Volume Weighted Average Price (VWAP) vwma --> Volume Weighted Moving Average (VWMA) wma --> Weighted Moving Average (WMA) zlma --> Zero Lag Moving Average (ZLMA) entropy --> Entropy (ENTP) kurtosis --> Rolling Kurtosis skew --> Rolling Skew xev --> Condition all zscore --> Rolling Z Score adx --> Average Directional Movement (ADX) aroon --> Aroon & Aroon Oscillator (AROON) chop --> Choppiness Index (CHOP) xex --> Condition any cksp --> Chande Kroll Stop (CKSP) dpo --> Detrend Price Oscillator (DPO) long_run --> Long Run psar --> Parabolic Stop and Reverse (psar) short_run --> Short Run vhf --> Vertical Horizontal Filter (VHF) vortex --> Vortex accbands --> Acceleration Bands (ACCBANDS) atr --> Average True Range (ATR) bbands --> Bollinger Bands (BBANDS) donchian --> Donchian Channels (DC) kc --> Keltner Channels (KC) massi --> Mass Index (MASSI) natr --> Normalized Average True Range (NATR) pdist --> Price Distance (PDIST) rvi --> Relative Volatility Index (RVI) thermo --> Elders Thermometer (THERMO) ui --> Ulcer Index (UI) ad --> Accumulation/Distribution (AD) cmf --> Chaikin Money Flow (CMF) efi --> Elder's Force Index (EFI) ecm --> Ease of Movement (EOM) kvo --> Klinger Volume Oscillator (KVO) mfi --> Money Flow Index (MFI) nvi --> Negative Volume Index (NVI) obv --> On Balance Volume (OBV) pvi --> Positive Volume Index (PVI) dvdi --> Dual Volume Divergence Index (DVDI) xhh --> Dynamic rolling highest values pvt --> Price-Volume Trend (PVT) Remarks I also incorporated func descriptions and func test script in commented mode, you can test the functino with the embedded test script and modify them as you wish. This is a Level 3 free and open source indicator library. Feedbacks are appreciated. This is not the end of pandas_ta lib publication, but it is start point with pine v5 lib function and I will add more and more funcs into this lib for my own indicators. Function Name List: bton() wcp() count() xbt() ebsw() ao() apo() xrf() bias() bop() brar() cci() cfo() cg() cmo() coppock() cti() dmi() er() eri() fisher() inertia() kdj() kst() macd() mom() pgo() ppo() psl() pvo() qqe() roc() rsi() rsx() rvgi() slope() smi() sqz_pro() xfl() stc() stoch() stochrsi() trix() tsi() uo() willr() alma() wcx() xll() dema() ema() fwma() hilo() hma() hwma() ichimoku() jma() kama() linreg() mgcd() rma() sinwma() ssf() supertrend() xsa() swma() t3() tema() trima() vidya() vwap() vwma() wma() zlma() entropy() kurtosis() skew() xev() zscore() adx() aroon() chop() xex() cksp() dpo() long_run() psar() short_run() vhf() vortex() accbands() atr() bbands() donchian() kc() massi() natr() pdist() rvi() thermo() ui() ad() cmf() efi() ecm() kvo() mfi() nvi() obv() pvi() dvdi() xhh() pvt()

bench Library "bench" A simple banchmark library to analyse script performance and bottlenecks. Very useful if you are developing an overly complex application in Pine Script, or trying to optimise a library / function / algorithm... Supports artificial looping benchmarks (of fast functions) Supports integrated linear benchmarks (of expensive scripts) One important thing to note is that the Pine Script compiler will completely ignore any calculations that do not eventually produce chart output. Therefore, if you are performing an artificial benchmark you will need to use the bench.reference(value) function to ensure the calculations are executed. Please check the examples towards the bottom of the script. Quick Reference (Be warned this uses non-standard space characters to get the line indentation to work in the description!) ``` // Looping benchmark style benchmark = bench.new(samples = 500, loops = 5000) data = array.new_int() if bench.start(benchmark) while bench.loop(benchmark) array.unshift(data, timenow) bench.mark(benchmark) while bench.loop(benchmark) array.unshift(data, timenow) bench.mark(benchmark) while bench.loop(benchmark) array.unshift(data, timenow) bench.stop(benchmark) bench.reference(array.get(data, 0)) bench.report(benchmark, '1x array.unshift()') // Linear benchmark style benchmark = bench.new() data = array.new_int() bench.start(benchmark) for i = 0 to 1000 array.unshift(data, timenow) bench.mark(benchmark) for i = 0 to 1000 array.unshift(data, timenow) bench.stop(benchmark) bench.reference(array.get(data, 0)) bench.report(benchmark,'1000x array.unshift()') ``` Detailed Interface new(samples, loops) Initialises a new benchmark array Parameters: samples : int, the number of bars in which to collect samples loops : int, the number of loops to execute within each sample Returns: int , the benchmark array active(benchmark) Determing if the benchmarks state is active Parameters: benchmark : int , the benchmark array Returns: bool, true only if the state is active start(benchmark) Start recording a benchmark from this point Parameters: benchmark : int , the benchmark array Returns: bool, true only if the benchmark is unfinished loop(benchmark) Returns true until call count exceeds bench.new(loop) variable Parameters: benchmark : int , the benchmark array Returns: bool, true while looping reference(number, string) Add a compiler reference to the chart so the calculations don't get optimised away Parameters: number : float, a numeric value to reference string : string, a string value to reference mark(benchmark, number, string) Marks the end of one recorded interval and the start of the next Parameters: benchmark : int , the benchmark array number : float, a numeric value to reference string : string, a string value to reference stop(benchmark, number, string) Stop the benchmark, ending the final interval Parameters: benchmark : int , the benchmark array number : float, a numeric value to reference string : string, a string value to reference report(Prints, benchmark, title, text_size, position) Parameters: Prints : the benchmarks results to the screen benchmark : int , the benchmark array title : string, add a custom title to the report text_size : string, the text size of the log console (global size vars) position : string, the position of the log console (global position vars) unittest_bench(case) Cache module unit tests, for inclusion in parent script test suite. Usage: bench.unittest_bench(__ASSERTS) Parameters: case : string , the current test case and array of previous unit tests (__ASSERTS) unittest(verbose) Run the bench module unit tests as a stand alone. Usage: bench.unittest() Parameters: verbose : bool, optionally disable the full report to only display failures