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Indicator Functions with Factor and HeikinAshiHello all, This indicator returns below selected indicators values with entered parameters. Also you can add factorization, functions candles, function HeikinAshi and more to the plot. VERSION: Version 1: returns series only source and Length with already defined default values Version 2: returns series with source, Length, p1 and p2 parameters according to the indicator definition (ex: ) PARAMETERS p1 p2 for defining multi arguments (See indicators list) indicator input value usable with verison=V2 selected.. ex: for alma( src , len ,offset=0.85,sigma=6), set source=source, len=length, p1=0.85 an p2=6 FACTOR: Add double triple, Quadruple factors to selected indicator (like converting EMA to 2-DEMA, 3-TEMA, 4-QEMA...) 1-Original 2-Double 3-Triple 4-Quadruple LOG Log: Use log, log10 on function entries PLOTTING: PType: Plotting type of the function on the screen Original :use original values Org. Range (-1,1): usable for indicators between range -1 and 1 Stochastic: Convert indicator values by using stochastic calculation between -1 & 1. (use AT/% length to better view) PercentRank: Convert indicator values by using Percent Rank calculation between -1 & 1. (use AT/% length to better view) ST/%: length for plotting Type for stochastic and Percent Rank options Smooth: Use SWMA for smoothing the function DISPLAY TYPES Plot Candles: Display the selected indicator as candle by implementing values Plot Ind: Display result of indicator with selected source HeikinAshi: Display Selected indicator candles with Heikin Ashi calculation INDICATOR LIST: hide = 'DONT DISPLAY', //Dont display & calculate the indicator. (For my framework usage) alma = 'alma( src , len ,offset=0.85,sigma=6)', // Arnaud Legoux Moving Average ama = 'ama( src , len ,fast=14,slow=100)', //Adjusted Moving Average acdst = 'accdist()', // Accumulation/distribution index. cma = 'cma( src , len )', //Corrective Moving average dema = 'dema( src , len )', // Double EMA (Same as EMA with 2 factor) ema = 'ema( src , len )', // Exponential Moving Average gmma = 'gmma( src , len )', //Geometric Mean Moving Average hghst = 'highest( src , len )', //Highest value for a given number of bars back. hl2ma = 'hl2ma( src , len )', //higest lowest moving average hma = 'hma( src , len )', // Hull Moving Average . lgAdt = 'lagAdapt( src , len ,perclen=5,fperc=50)', //Ehler's Adaptive Laguerre filter lgAdV = 'lagAdaptV( src , len ,perclen=5,fperc=50)', //Ehler's Adaptive Laguerre filter variation lguer = 'laguerre( src , len )', //Ehler's Laguerre filter lsrcp = 'lesrcp( src , len )', //lowest exponential esrcpanding moving line lexp = 'lexp( src , len )', //lowest exponential expanding moving line linrg = 'linreg( src , len ,loffset=1)', // Linear regression lowst = 'lowest( src , len )', //Lovest value for a given number of bars back. pcnl = 'percntl( src , len )', //percentile nearest rank. Calculates percentile using method of Nearest Rank. pcnli = 'percntli( src , len )', //percentile linear interpolation. Calculates percentile using method of linear interpolation between the two nearest ranks. rema = 'rema( src , len )', //Range EMA (REMA) rma = 'rma( src , len )', //Moving average used in RSI . It is the exponentially weighted moving average with alpha = 1 / length. sma = 'sma( src , len )', // Smoothed Moving Average smma = 'smma( src , len )', // Smoothed Moving Average supr2 = 'super2( src , len )', //Ehler's super smoother, 2 pole supr3 = 'super3( src , len )', //Ehler's super smoother, 3 pole strnd = 'supertrend( src , len ,period=3)', //Supertrend indicator swma = 'swma( src , len )', //Sine-Weighted Moving Average tema = 'tema( src , len )', // Triple EMA (Same as EMA with 3 factor) tma = 'tma( src , len )', //Triangular Moving Average vida = 'vida( src , len )', // Variable Index Dynamic Average vwma = 'vwma( src , len )', // Volume Weigted Moving Average wma = 'wma( src , len )', //Weigted Moving Average angle = 'angle( src , len )', //angle of the series (Use its Input as another indicator output) atr = 'atr( src , len )', // average true range . RMA of true range. bbr = 'bbr( src , len ,mult=1)', // bollinger %% bbw = 'bbw( src , len ,mult=2)', // Bollinger Bands Width . The Bollinger Band Width is the difference between the upper and the lower Bollinger Bands divided by the middle band. cci = 'cci( src , len )', // commodity channel index cctbb = 'cctbbo( src , len )', // CCT Bollinger Band Oscilator chng = 'change( src , len )', //Difference between current value and previous, source - source. cmo = 'cmo( src , len )', // Chande Momentum Oscillator . Calculates the difference between the sum of recent gains and the sum of recent losses and then divides the result by the sum of all price movement over the same period. cog = 'cog( src , len )', //The cog (center of gravity ) is an indicator based on statistics and the Fibonacci golden ratio. cpcrv = 'copcurve( src , len )', // Coppock Curve. was originally developed by Edwin "Sedge" Coppock (Barron's Magazine, October 1962). corrl = 'correl( src , len )', // Correlation coefficient . Describes the degree to which two series tend to deviate from their ta. sma values. count = 'count( src , len )', //green avg - red avg dev = 'dev( src , len )', //ta.dev() Measure of difference between the series and it's ta. sma fall = 'falling( src , len )', //ta.falling() Test if the `source` series is now falling for `length` bars long. (Use its Input as another indicator output) kcr = 'kcr( src , len ,mult=2)', // Keltner Channels Range kcw = 'kcw( src , len ,mult=2)', //ta.kcw(). Keltner Channels Width. The Keltner Channels Width is the difference between the upper and the lower Keltner Channels divided by the middle channel. macd = 'macd( src , len )', // macd mfi = 'mfi( src , len )', // Money Flow Index nvi = 'nvi()', // Negative Volume Index obv = 'obv()', // On Balance Volume pvi = 'pvi()', // Positive Volume Index pvt = 'pvt()', // Price Volume Trend rise = 'rising( src , len )', //ta.rising() Test if the `source` series is now rising for `length` bars long. (Use its Input as another indicator output) roc = 'roc( src , len )', // Rate of Change rsi = 'rsi( src , len )', // Relative strength Index smosc = 'smi_osc( src , len ,fast=5, slow=34)', //smi Oscillator smsig = 'smi_sig( src , len ,fast=5, slow=34)', //smi Signal stdev = 'stdev( src , len )', //Standart deviation trix = 'trix( src , len )' , //the rate of change of a triple exponentially smoothed moving average . tsi = 'tsi( src , len )', //True Strength Index vari = 'variance( src , len )', //ta.variance(). Variance is the expectation of the squared deviation of a series from its mean (ta. sma ), and it informally measures how far a set of numbers are spread out from their mean. wilpc = 'willprc( src , len )', // Williams %R wad = 'wad()', // Williams Accumulation/Distribution . wvad = 'wvad()' //Williams Variable Accumulation/Distribution I will update the indicator list when I will update the library Thanks to tradingview, @RodrigoKazuma for their open source indicators
Индикатор Pine Script™
от dturkuler
1
Dow Factor Stoch RSIThe indicator was generated by adding the Dow Factor to the Stochastic Relative Strength Index.( Stoch RSI ) The Dow factor is the effect of the correlation coefficient, which determines the relationship between volume and price, on the existing indicators. With these codes we are able to integrate them numerically into the indicators. For more information on the Dow factor, please see my indicator: This code is open source under the MIT license. ( github.com ) My dow factor updates will continue.We adapted the indicators and saw successful results, now it is time to examine and develop the factor itself. Stay tuned , best regards.
Индикатор Pine Script™
от Noldo
5