Mulakan

Cari dalam skrip untuk "如何用wind搜索股票的发行价和份数"

RSI Impact Heat Map [Trendoscope]Here is a simple tool to measure and display outcome of certain RSI event over heat map. 🎲 Process 🎯Event Event can be either Crossover or Crossunder of RSI on certain value. 🎯Measuring Impact Impact of the event after N number of bars is measured in terms of highest and lowest displacement from the last close price. Impact can be collected as either number of times of ATR or percentage of price. Impact for each trigger is recorded separately and stored in array of custom type. 🎯Plotting Heat Map Heat map is displayed using pine tables. Users can select heat map size - which can vary from 10 to 90. Selecting optimal size is important in order to get right interpretation of data. Having higher number of cells can give more granular data. But, chart may not fit into the window. Having lower size means, stats are combined together to get less granular data which may not give right picture of the results. Default value for size is 50 - meaning data is displayed in 51X51 cells. Range of the heat map is adjusted automatically based on min and max value of the displacement. In order to filter out or merge extreme values, range is calculated based on certain percentile of the values. This will avoid displaying lots of empty cells which can obscure the actual impact. 🎲 Settings Settings allow users to define their event, impact duration and reference, and few display related properties. The description of these parameters are as below: 🎲 Use Cases In this script, we have taken RSI as an example to measure impact. But, we can do this for any event. This can be price crossing over/under upper/lower bollinger bands, moving average crossovers or even complex entry or exit conditions. Overall, we can use this to plot and evaluate our trade criteria. 🎲 Interpretation Q1 - If more coloured dots appear on the top right corner of the table, then the event is considered to trigger high volatility and high risk environment. Q2 - If more coloured dots appear on the top left corner, then the events are considered to trigger bearish environment. Q3 - If more coloured dots appear on the bottom left corner of the chart, then the events are considered insignificant as they neither generate higher displacement in positive or negative side. You can further alter outlier percentage to reduce the bracket and hence have higher distribution move towards Q4 - If more coloured dots appear on the bottom right corner, then the events are considered to trigger bullish environment. Will also look forward to implement this as library so that any conditions or events can be plugged into it.
Penunjuk Pine Script®
oleh Trendoscope
23
Harmonic Patterns Based Trend FollowerEarlier this week, published an idea on how harmonic patterns can be used for trend following. This script is an attempt to implement the same. 🎲 Process 🎯 Derive Zigzag and scan harmonic patterns for last 5 confirmed pivots 🎯 If a pattern is found, highest point of pattern will become the bullish zone and lower point of the pattern will become bearish zone. 🎯 Since it is trend following method, when price reaches bullish zone, then the trend is considered as bullish and when price reaches bearish zone, the trend is considered as bearish. 🎯 If price does not touch both regions, then trend remains unchanged. 🎯 Bullish and bearish zone will change as and when new patterns are formed. 🎲 Note Patterns are not created on latest pivot as last pivot will be unconfirmed and moving. Due to this, patterns appear after certain delay - patterns will not be real time. But, this is expected and does not impact the overall process. When new pattern formed When price breaks over the zones 🎲 Output 🎯 Patterns formed are drawn in blue coloured lines. Due to pine limitation of max 500 lines, older patterns automatically get deleted when new ones come. 🎯 Bullish Zone and Bearish Zone are plotted in green and red colours and the zone will change whenever new pattern comes along. 🎯 Bar colors are changed according to calculated trend. Trend value can be 1 or -1 based on the current trend. You can also find the value in data window. 🎯 For simplicity purpose, input option for selection of specific patterns are not provided and also pattern names are not displayed on the chart.
Penunjuk Pine Script®
oleh Trendoscope
35
Band Based Trend FilterSimilar to RelativeBandwidthFilter , this script is also a simple trend filter which can be used to define your trading zone. 🎲 Concept On contrary to reversal mindset, we define trend when price hits either side of the band. If close price hits upper band then it is considered as bullish and if close price hits lower band, then it is considered bearish. Further, trend strength is measured in terms of how many times the price hits one side of the band without hitting other side. Hit is counted only if price has touched middle line in between the touches. This way price walks on the bands are considered as just one hit. 🎲 Settings Settings are minimal and details can be found in the tooltips against each parameters 🎲 Usage This can be used with your own strategy to filter your trading/non-trading zones based on trend . Script plots a variable called "Trend" - which is not shown on chart pane. But, it is available in the data window. This can be used in another script as external input and apply logic. Trend values can be 1 : Allow only Long -1 : Allow only short 0 : Do not allow any trades
Penunjuk Pine Script®
oleh Trendoscope
10
Root Mean Square (RMS)The Root Mean Square (RMS) is a statistical measure of the magnitude of a set of numbers. It is a type of mean, or average, that is calculated by taking the square root of the sum of the squares of a set of numbers, divided by the number of items in the set. The RMS is often used to measure the magnitude of a time-varying signal, such as a waveform or a time series data. The indicator takes in two input parameters: the source data and the length of the RMS window. The source data can be any time series data, such as the closing price of a security, and the length parameter determines the number of data points used in the RMS calculation. The script begins by declaring the RMS indicator function and specifying that it should be plotted as an overlay on the chart. The function takes in two parameters: source and length. The source parameter is the time series data that will be used in the RMS calculation, and the length parameter determines the number of data points to include in the calculation. Next, the script defines the RMS function using a single line of code. The function calculates the RMS by taking the square root of the sum of the squares of the source data, divided by the length. This is done using the built-in math.sqrt, math.sum, and math.pow functions, which respectively calculate the square root, sum, and power of a set of numbers. Finally, the script defines the source and length input parameters using the input.source and input.int functions. The source parameter is defined as the closing price of the security, and the length parameter is defined as an integer with a default value of 20. The RMS indicator implemented in this script can be used to measure the magnitude of a time-varying signal. By adjusting the length parameter, users can control the number of data points included in the RMS calculation and fine-tune the indicator to their specific needs.
Penunjuk Pine Script®
oleh The_Peaceful_Lizard
1
Minervini QualifierThe Minervini Qualifier indicator calculates the qualifying conditions from Mark Minervini’s book “Trade like a Stock Market Wizard”. The condition matching is been shown as fill color inside an SMA 20day envelope curve. If the envelope color is red, current close price is below the SMA20 and when blue, current close price is above the SMA20. The fill color can be transparent (not matching qualifying conditions), yellow (matching all conditions except close is still below SMA50), green (all conditions match, SMA200 trending for at least one month up) or blue (all conditions match, SMA200 trending up for at least 5 months) As I wanted also to see which of the qualifying conditions match over time, I’ve added add. lines, each representing one conditions. If it matches, line color is blue, or red if not. Use the data windows (right side), so you know what line represents which condition. Can be turned on/off (default:on) In addition, a relative strength is been calculated, to compare the stock to a reference index. It is just one possible way to calculate it, might be different to what Mark Minervini is using. If the shown value (top right) is above 100, stock performs better compared to reference index (can be set in settings), when below 100, stock performs worse compared to reference index. Can be turned on/off (default:on) How to use it: For more details, read Mark’s book and watch his videos. Limitations: It gives only useful information on daily timeframe (No financial advise, for testing purposes only)
Penunjuk Pine Script®
oleh Lantzwat
21
Volume DeltaThis script is meant to only show you the most significant volume moves. The way it works is it takes the cumulative sum of the delta of the volume. You can go from current all the way to ten bars back in your delta window. Review on what volume is: The Volume indicator measures how much of a given financial asset has traded in a specific period of time. Volume is measured by shares traded for stocks, whereas for futures, it is based on the number of contracts.
Penunjuk Pine Script®
oleh The_Peaceful_Lizard
Nadaraya-Watson non repainting [LPWN]// ENGLISH The problem of the wonderfuls Nadaraya-Watson indicators is that they repainting, @jdehorty made an aproximation of the Nadaraya-Watson Estimator using raational Quadratic Kernel so i used this indicator as inspiration i just added the Upper and lower band using ATR with this we get an aproximation of Nadaraya-Watson Envelope without repainting Settings: Bandwidth. This is the number of bars that the indicator will use as a lookback window. Relative Weighting Parameter. The alpha parameter for the Rational Quadratic Kernel function. This is a hyperparameter that controls the smoothness of the curve. A lower value of alpha will result in a smoother, more stretched-out curve, while a lower value will result in a more wiggly curve with a tighter fit to the data. As this parameter approaches 0, the longer time frames will exert more influence on the estimation, and as it approaches infinity, the curve will become identical to the one produced by the Gaussian Kernel. Color Smoothing. Toggles the mechanism for coloring the estimation plot between rate of change and cross over modes. ATR Period. Period to calculate the ATR (upper and lower bands) Multiplier. Separation of the bands // SPANISH El problema de los maravillosos indicadores de Nadaraya-Watson es que repintan, @jdehorty hizo una aproximación delNadaraya-Watson Estimator usando un Kernel cuadrático racional, así que usé este indicador como inspiración y solo agregamos la banda superior e inferior usando ATR con esto obtenemos una aproximación de Nadaraya-Watson Envelope sin volver a pintar Configuración: Banda ancha. Este es el número de barras que el indicador utilizará como ventana retrospectiva. Parámetro de ponderación relativa. El parámetro alfa para la función Rational Quadratic Kernel. Este es un hiperparámetro que controla la suavidad de la curva. Un valor más bajo de alfa dará como resultado una curva más suave y estirada, mientras que un valor más bajo dará como resultado una curva más ondulada con un ajuste más ajustado a los datos. A medida que este parámetro se acerque a 0, los marcos de tiempo más largos ejercerán más influencia en la estimación y, a medida que se acerque al infinito, la curva será idéntica a la que produce el Gaussian Kernel. Suavizado de color. Alterna el mecanismo para colorear el gráfico de estimación entre la tasa de cambio y los modos cruzados. Período ATR. Periodo para calcular el ATR (bandas superior e inferior) Multiplicador. Separación de las bandas
Penunjuk Pine Script®
oleh Lupown
15
Bollinger Bands Width and Bollinger Bands %BThis script shows both the Bollinger Band Width(BBW) and %B on the same indicator window. Both the BBW and %B are introduced by John Bollinger(creator of Bollinger Bands) in 2010. Default Parameter values: Length = 20, Source = Close, Mult = 2 Bollinger Bands Width (BBW): Color = (Default: Green ) - I consider stocks with "BBW >= 4" are at a volatile state and ready for price contraction, but this depends on the parameter values of your choice. Bollinger Bands %B (%B): Color = (Default: Blue ) 1. %B Above 10 = Price is Above the Upper Band 2. %B Equal to 10 = Price is at the Upper Band 3. %B Above 5 = Price is Above the Middle Line 4. %B Below 5 = Price is Below the Middle Line 5. %B Equal to 0 = Price is at the Lower Band 6. %B Below 0 = Price is Below the Lower Band
Penunjuk Pine Script®
oleh polarbearking
Ehlers Linear Extrapolation Predictor [Loxx]Ehlers Linear Extrapolation Predictor is a new indicator by John Ehlers. The translation of this indicator into PineScript™ is a collaborative effort between @cheatcountry and I. The following is an excerpt from "PREDICTION" , by John Ehlers Niels Bohr said “Prediction is very difficult, especially if it’s about the future.”. Actually, prediction is pretty easy in the context of technical analysis. All you have to do is to assume the market will behave in the immediate future just as it has behaved in the immediate past. In this article we will explore several different techniques that put the philosophy into practice. LINEAR EXTRAPOLATION Linear extrapolation takes the philosophical approach quite literally. Linear extrapolation simply takes the difference of the last two bars and adds that difference to the value of the last bar to form the prediction for the next bar. The prediction is extended further into the future by taking the last predicted value as real data and repeating the process of adding the most recent difference to it. The process can be repeated over and over to extend the prediction even further. Linear extrapolation is an FIR filter, meaning it depends only on the data input rather than on a previously computed value. Since the output of an FIR filter depends only on delayed input data, the resulting lag is somewhat like the delay of water coming out the end of a hose after it supplied at the input. Linear extrapolation has a negative group delay at the longer cycle periods of the spectrum, which means water comes out the end of the hose before it is applied at the input. Of course the analogy breaks down, but it is fun to think of it that way. As shown in Figure 1, the actual group delay varies across the spectrum. For frequency components less than .167 (i.e. a period of 6 bars) the group delay is negative, meaning the filter is predictive. However, the filter has a positive group delay for cycle components whose periods are shorter than 6 bars. Figure 1 Here’s the practical ramification of the group delay: Suppose we are projecting the prediction 5 bars into the future. This is fine as long as the market is continued to trend up in the same direction. But, when we get a reversal, the prediction continues upward for 5 bars after the reversal. That is, the prediction fails just when you need it the most. An interesting phenomenon is that, regardless of how far the extrapolation extends into the future, the prediction will always cross the signal at the same spot along the time axis. The result is that the prediction will have an overshoot. The amplitude of the overshoot is a function of how far the extrapolation has been carried into the future. But the overshoot gives us an opportunity to make a useful prediction at the cyclic turning point of band limited signals (i.e. oscillators having a zero mean). If we reduce the overshoot by reducing the gain of the prediction, we then also move the crossing of the prediction and the original signal into the future. Since the group delay varies across the spectrum, the effect will be less effective for the shorter cycles in the data. Nonetheless, the technique is effective for both discretionary trading and automated trading in the majority of cases. EXPLORING THE CODE Before we predict, we need to create a band limited indicator from which to make the prediction. I have selected a “roofing filter” consisting of a High Pass Filter followed by a Low Pass Filter. The tunable parameter of the High Pass Filter is HPPeriod. Think of it as a “stone wall filter” where cycle period components longer than HPPeriod are completely rejected and cycle period components shorter than HPPeriod are passed without attenuation. If HPPeriod is set to be a large number (e.g. 250) the indicator will tend to look more like a trending indicator. If HPPeriod is set to be a smaller number (e.g. 20) the indicator will look more like a cycling indicator. The Low Pass Filter is a Hann Windowed FIR filter whose tunable parameter is LPPeriod. Think of it as a “stone wall filter” where cycle period components shorter than LPPeriod are completely rejected and cycle period components longer than LPPeriod are passed without attenuation. The purpose of the Low Pass filter is to smooth the signal. Thus, the combination of these two filters forms a “roofing filter”, named Filt, that passes spectrum components between LPPeriod and HPPeriod. Since working into the future is not allowed in EasyLanguage variables, we need to convert the Filt variable to the data array XX . The data array is first filled with real data out to “Length”. I selected Length = 10 simply to have a convenient starting point for the prediction. The next block of code is the prediction into the future. It is easiest to understand if we consider the case where count = 0. Then, in English, the next value of the data array is equal to the current value of the data array plus the difference between the current value and the previous value. That makes the prediction one bar into the future. The process is repeated for each value of count until predictions up to 10 bars in the future are contained in the data array. Next, the selected prediction is converted from the data array to the variable “Prediction”. Filt is plotted in Red and Prediction is plotted in yellow. The Predict Extrapolation indicator is shown above for the Emini S&P Futures contract using the default input parameters. Filt is plotted in red and Predict is plotted in yellow. The crossings of the Predict and Filt lines provide reliable buy and sell timing signals. There is some overshoot for the shorter cycle periods, for example in February and March 2021, but the only effect is a late timing signal. Further reducing the gain and/or reducing the BarsFwd inputs would provide better timing signals during this period. ADDITIONS Loxx's Expanded source types: Library for expanded source types: Explanation for expanded source types: Three different signal types: 1) Prediction/Filter crosses; 2) Prediction middle crosses; and, 3) Filter middle crosses. Bar coloring to color trend. Signals, both Long and Short. Alerts, both Long and Short.
Penunjuk Pine Script®
oleh loxx
6
SubCandleI created this script as POC to handle specific cases where not having tick data on historical bars create repainting. Happy to share if this serves purpose for other coders. What is the function of this script? Script plots a sub-candle which is remainder of candle after forming the latest peak. Higher body of Sub-candle refers to strong retracement of price from its latest peak. Color of the sub-candle defines the direction of retracement. Higher wick of Sub-candle refers to higher push in the direction of original candle. Meaning, after price reaching its peak, price retraced but could not hold. Here is a screenshot with explanation to visualise the concept: Settings There is only one setting which is number of backtest bars. Lower timeframe resolution which is used for calculating the Sub-candle uses this number to automatically calculate maximum possible lower timeframe so that all the required backtest windows are covered without having any issue. We need to keep in mind that max available lower timeframe bars is 100,000. Hence, with 5000 backtest bars, lower timeframe resolution can be about 20 (100000/5000) times lesser than that of regular chart timeframe. We need to also keep in mind that minimum resolution available as part of security_lower_tf is 1 minute. Hence, it is not advisable to use this script for chart timeframes less than 15 mins. Application I have been facing this issue in pattern recognition scripts where patterns are formed using high/low prices but entry and targets are calculated based on the opposite side (low/high). It becomes tricky during extreme bars to identify entry conditions based on just the opposite peak because, the candle might have originated from it before identifying the pattern and might have never reached same peak after forming the pattern. Due to lack of tick data on historical bars, we cannot use close price to measure such conditions. This leads to repaint and few unexpected results. I am intending to use this method to overcome the issue up-to some extent.
Penunjuk Pine Script®
oleh Trendoscope
9
Growth Stock Arbitrage Indicator [@PierceARK]This indicator takes advantage of the fact that when the 10 and 5 year Treasury Constant Maturity Minus Federal Funds rates (T10YFF/T5YFF) go down sharply, investors tend to rotate into stocks. This arbitrage works great for growth stocks, since growth stocks are higher beta by virtue of their lower market cap and more speculative nature in general. This script identifies the moving-average convergence/divergence of the average of the 10y and 5y treasury rates and then finds the variance of that macd line. By averaging that variance with the macdline's inverse, an analog output of treasury -> stock rotation can be identified. The upper and lower thresholds bring buy and sell windows into focus.
Penunjuk Pine Script®
oleh jamiesonpa
3
STD-Stepped Fast Cosine Transform Moving Average [Loxx]STD-Stepped Fast Cosine Transform Moving Average is an experimental moving average that uses Fast Cosine Transform to calculate a moving average. This indicator has standard deviation stepping in order to smooth the trend by weeding out low volatility movements. What is the Discrete Cosine Transform? A discrete cosine transform (DCT) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. The DCT, first proposed by Nasir Ahmed in 1972, is a widely used transformation technique in signal processing and data compression. It is used in most digital media, including digital images (such as JPEG and HEIF, where small high-frequency components can be discarded), digital video (such as MPEG and H.26x), digital audio (such as Dolby Digital, MP3 and AAC), digital television (such as SDTV, HDTV and VOD), digital radio (such as AAC+ and DAB+), and speech coding (such as AAC-LD, Siren and Opus). DCTs are also important to numerous other applications in science and engineering, such as digital signal processing, telecommunication devices, reducing network bandwidth usage, and spectral methods for the numerical solution of partial differential equations. The use of cosine rather than sine functions is critical for compression, since it turns out (as described below) that fewer cosine functions are needed to approximate a typical signal, whereas for differential equations the cosines express a particular choice of boundary conditions. In particular, a DCT is a Fourier-related transform similar to the discrete Fourier transform (DFT), but using only real numbers. The DCTs are generally related to Fourier Series coefficients of a periodically and symmetrically extended sequence whereas DFTs are related to Fourier Series coefficients of only periodically extended sequences. DCTs are equivalent to DFTs of roughly twice the length, operating on real data with even symmetry (since the Fourier transform of a real and even function is real and even), whereas in some variants the input and/or output data are shifted by half a sample. There are eight standard DCT variants, of which four are common. The most common variant of discrete cosine transform is the type-II DCT, which is often called simply "the DCT". This was the original DCT as first proposed by Ahmed. Its inverse, the type-III DCT, is correspondingly often called simply "the inverse DCT" or "the IDCT". Two related transforms are the discrete sine transform (DST), which is equivalent to a DFT of real and odd functions, and the modified discrete cosine transform (MDCT), which is based on a DCT of overlapping data. Multidimensional DCTs (MD DCTs) are developed to extend the concept of DCT to MD signals. There are several algorithms to compute MD DCT. A variety of fast algorithms have been developed to reduce the computational complexity of implementing DCT. One of these is the integer DCT (IntDCT), an integer approximation of the standard DCT, : ix, xiii, 1, 141–304  used in several ISO/IEC and ITU-T international standards. Notable settings windowper = period for calculation, restricted to powers of 2: "16", "32", "64", "128", "256", "512", "1024", "2048", this reason for this is FFT is an algorithm that computes DFT (Discrete Fourier Transform) in a fast way, generally in 𝑂(𝑁⋅log2(𝑁)) instead of 𝑂(𝑁2). To achieve this the input matrix has to be a power of 2 but many FFT algorithm can handle any size of input since the matrix can be zero-padded. For our purposes here, we stick to powers of 2 to keep this fast and neat. read more about this here: Cooley–Tukey FFT algorithm smthper = smoothing count, this smoothing happens after the first FCT regular pass. this zeros out frequencies from the previously calculated values above SS count. the lower this number, the smoother the output, it works opposite from other smoothing periods Included Alerts Signals Loxx's Expanded Source Types Additional reading A Fast Computational Algorithm for the Discrete Cosine Transform by Chen et al. Practical Fast 1-D DCT Algorithms With 11 Multiplications by Loeffler et al. Cooley–Tukey FFT algorithm
Penunjuk Pine Script®
oleh loxx
10
Helme-Nikias Weighted Burg AR-SE Extra. of Price [Loxx]Helme-Nikias Weighted Burg AR-SE Extra. of Price is an indicator that uses an autoregressive spectral estimation called the Weighted Burg Algorithm, but unlike the usual WB algo, this one uses Helme-Nikias weighting. This method is commonly used in speech modeling and speech prediction engines. This is a linear method of forecasting data. You'll notice that this method uses a different weighting calculation vs Weighted Burg method. This new weighting is the following: w = math.pow(array.get(x, i - 1), 2), the squared lag of the source parameter and w += math.pow(array.get(x, i), 2), the sum of the squared source parameter This take place of the rectangular, hamming and parabolic weighting used in the Weighted Burg method Also, this method includes Levinson–Durbin algorithm. as was already discussed previously in the following indicator: Levinson-Durbin Autocorrelation Extrapolation of Price What is Helme-Nikias Weighted Burg Autoregressive Spectral Estimate Extrapolation of price? In this paper a new stable modification of the weighted Burg technique for autoregressive (AR) spectral estimation is introduced based on data-adaptive weights that are proportional to the common power of the forward and backward AR process realizations. It is shown that AR spectra of short length sinusoidal signals generated by the new approach do not exhibit phase dependence or line-splitting. Further, it is demonstrated that improvements in resolution may be so obtained relative to other weighted Burg algorithms. The method suggested here is shown to resolve two closely-spaced peaks of dynamic range 24 dB whereas the modified Burg schemes employing rectangular, Hamming or "optimum" parabolic windows fail. Data inputs Source Settings: -Loxx's Expanded Source Types. You typically use "open" since open has already closed on the current active bar LastBar - bar where to start the prediction PastBars - how many bars back to model LPOrder - order of linear prediction model; 0 to 1 FutBars - how many bars you want to forward predict Things to know Normally, a simple moving average is calculated on source data. I've expanded this to 38 different averaging methods using Loxx's Moving Avreages. This indicator repaints Further reading A high-resolution modified Burg algorithm for spectral estimation Related Indicators Levinson-Durbin Autocorrelation Extrapolation of Price Weighted Burg AR Spectral Estimate Extrapolation of Price
Penunjuk Pine Script®
oleh loxx
Relative Bandwidth FilterThis is a very simple script which can be used as measure to define your trading zones based on volatility. Concept This script tries to identify the area of low and high volatility based on comparison between Bandwidth of higher length and ATR of lower length. Relative Bandwidth = Bandwidth / ATR Bandwidth can be based on either Bollinger Band, Keltner Channel or Donchian Channel. Length of the bandwidth need to be ideally higher. ATR is calculated using built in ATR method and ATR length need to be ideally lower than that used for calculating Bandwidth. Once we got Relative Bandwidth, the next step is to apply Bollinger Band on this to measure how relatively high/low this value is. Overall - If relative bandwidth is higher, then volatility is comparatively low. If relative bandwidth is lower, then volatility is comparatively high. Usage This can be used with your own strategy to filter out your non-trading zones based on volatility. Script plots a variable called "Signal" - which is not shown on chart pane. But, it is available in the data window. This can be used in another script as external input and apply logic. Signal values can be 1 : Allow only Long -1 : Allow only short 0 : Do not allow any trades 2 : Allow both Long and Short
Penunjuk Pine Script®
oleh Trendoscope
19
[blackcat] L1 Vitali Apirine HHs & LLs StochasticsLevel 1 Background This indicator was originally formulated by Vitali Apirine for TASC - February 2016 Traders Tips. Function According to Vitali Apirine, his momentum indicator–based system HHLLS (higher high lower low stochastic) can help to spot emerging trends, define correction periods, and anticipate reversals. As with many indicators, HHLLS signals can also be generated by looking for divergences and crossovers. Because the HHLLS is an oscillator, it can also be used to identify overbought & oversold levels. Remarks I changed EMA or SMA into hanning windowing function to reduce lag issue. colorful area is bearish power. colorful solid thick line is bull power. Feedbacks are appreciated.
Penunjuk Pine Script®
oleh blackcat1402
1
Chart VWAP█   OVERVIEW This indicator displays a ​Volume-Weighted Average Price anchored to the leftmost visible bar of the chart. It dynamically recalculates when the chart's visible bars change because you scroll or zoom your chart. If you are not already familiar with ​VWAP, our Help Center will get you started. The typical ​VWAP is designed to be used on intraday charts, as it resets at the beginning of the day. Our Rolling ​VWAP , instead, resets on a rolling time window. You may also find the VWAP Auto Anchored built-in indicator worth a try. █   HOW TO USE IT Load the indicator on an active chart (see the Help Center if you don't know how). By default, it displays the chart's ​VWAP in orange and a simple average of the chart's visible close values in gray. This average can be used as a companion to the ​VWAP, since both are calculated from the same set of bars. The script's settings allow you to hide it. You may also use the script's settings to enable the display of the chart's OHLC (open, high, low, close) levels and the values of the high and low. These are also calculated from the range of visible bars. You can complement the high and low lines with their price and their distance in percent from the chart's latest visible close . You can use the levels to quickly identify the distances from extreme points in the visible price range, as well as observe the visible chart's beginning and end prices. █   NOTES FOR Pine Script™ CODERS This script showcases three novelties:  • Dynamic recalculation on visible bars  • The VisibleChart library by PineCoders  • The new `anchor` parameter of ta.vwap() Dynamic recalculation on visible bars This script behaves in a novel way made possible by the recent introduction of two new built-in variables: chart.left_visible_bar_time and chart.right_visible_bar_time , which return the opening time of the leftmost and rightmost visible bars on the chart. These are only two of many new built-ins in the `chart.*` namespace. See this blog post for more information, or look up them up by typing "chart." in the Pine Script™ Reference Manual . Any script using chart.left_visible_bar_time or chart.right_visible_bar_time acquires a unique property, which triggers its recalculation when traders scroll or zoom their chart, causing the range of visible bars to change. This new capability is what makes it possible for this script to calculate its ​VWAP on the chart's visible bars only, and dynamically recalculate if the user scrolls or zooms their chart. This script is just a start to the party; endless uses for indicators that redraw on changes to the chart will no doubt emerge through the hands of our community's Pine Script™ programmers. The VisibleChart library by PineCoders The newly published VisibleChart library is designed to help programmers benefit from the new capabilities made possible by the fact that Pine Script™ code can now tell when it is executing on visible bars. The library's description, functions and example code will help programmers make the most of the new feature. This script uses three of the library's functions:  • `PCvc.vVwap()` calculates a ​VWAP for visible bars. ​​​​ • `PCvc.​avg()` calculates the average of a source value for visible bars only. We use it to calculate the average close (the default source).  • `PCvc.chartXTimePct(25)` calculates a time value corresponding to 25% of the horizontal distance between visible bars, starting from the left. The new `anchor` parameter of ta.vwap() Our script also uses this new `anchor` parameter to reset the ​VWAP at the leftmost visible bar. See how simple the code is for the VisibleChart library's `vVwap()` function. Look first. Then leap.
Penunjuk Pine Script®
oleh TradingView
17
Buying Selling Volume v3Bug fixed from v2. Currently adds up values correctly. Note: To get more accurate readings reduce the time frame. For some reason it isn't counting the last bar and I am not smart enough to figure out why. Builds on Ceyhun's "Buying Selling Volume" indicator. This version allows users to define periods by effortlessly dragging two points or you may define periods by manually entering the start and end times in the settings window. Once the period is defined, both buying and selling volume will be totaled thus displaying the amount of buys and sells in that period. I have found the information provided from the script helps in defining a period of consolidation as either being accumulative or distributive.
Penunjuk Pine Script®
oleh ceevee12312
8
Buying Selling Volume v2Builds on Ceyhun's "Buying Selling Volume" indicator. This version allows users to define periods by effortlessly dragging two points or you may define periods by manually entering the start and end times in the settings window. Once the period is defined, both buying and selling volume will be totaled thus displaying the amount of buys and sells in that period. I have found the information provided from the script helps in defining a period of consolidation as either being accumulative or distributive.
Penunjuk Pine Script®
oleh ceevee12312
4
NVT Ratio: OnchainNVT Ratio Defined as the ratio of market capitalization divided by transacted volume (in USD). Network Value to Transactions Ratio (NVT Ratio) is defined as the ratio of market capitalization divided by transacted volume in the specified window. History NVT first made an appearance as a tweet on Woo Bull account in Feb 2017. In that tweet he promised an explanatory article which came much later in Oct 2017, first debuting on Forbes. In Feb 2018, Dimitry Kalichkin published his work to improve NVT for use as a more responsive indicator, hence Kalichkin NVT Signal. In the same month, Woo Bull applied some trader techniques to NVT Signal and published an article summarising how to use it within a trading environment. Interpretation: NVT Ratio (Network Value to Transactions Ratio) is similar to the PE Ratio used in equity markets. this indicator measures whether the blockchain network is overvalued or not. When Bitcoin`s NVT is high, it indicates that its network valuation is outstripping the value being transmitted on its payment network, this can happen when the network is in high growth and investors are valuing it as a high return investment, or alternatively when the price is in an unsustainable bubble. High: Overvalued Network worth - Bearish Marketcap is too much valued compared to the low ability to transact coins in terms of volume Low : Undervalued Network worth - Bullish Marketcap is undervalued compared to the high ability to transact coins in terms of volume
Penunjuk Pine Script®
oleh cryptoonchain