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อินดิเคเตอร์และกลยุทธ์

RiskMetrics█   OVERVIEW This library is a tool for Pine programmers that provides functions for calculating risk-adjusted performance metrics on periodic price returns. The calculations used by this library's functions closely mirror those the Broker Emulator uses to calculate strategy performance metrics (e.g., Sharpe and Sortino ratios) without depending on strategy-specific functionality. █   CONCEPTS Returns, risk, and volatility The return on an investment is the relative gain or loss over a period, often expressed as a percentage. Investment returns can originate from several sources, including capital gains, dividends, and interest income. Many investors seek the highest returns possible in the quest for profit. However, prudent investing and trading entails evaluating such returns against the associated risks (i.e., the uncertainty of returns and the potential for financial losses) for a clearer perspective on overall performance and sustainability. One way investors and analysts assess the risk of an investment is by analyzing its volatility , i.e., the statistical dispersion of historical returns. Investors often use volatility in risk estimation because it provides a quantifiable way to gauge the expected extent of fluctuation in returns. Elevated volatility implies heightened uncertainty in the market, which suggests higher expected risk. Conversely, low volatility implies relatively stable returns with relatively minimal fluctuations, thus suggesting lower expected risk. Several risk-adjusted performance metrics utilize volatility in their calculations for this reason. Risk-free rate The  risk-free rate  represents the rate of return on a hypothetical investment carrying no risk of financial loss. This theoretical rate provides a benchmark for comparing the returns on a risky investment and evaluating whether its excess returns justify the risks. If an investment's returns are at or below the theoretical risk-free rate or the risk premium is below a desired amount, it may suggest that the returns do not compensate for the extra risk, which might be a call to reassess the investment. Since the risk-free rate is a theoretical concept, investors often utilize proxies for the rate in practice, such as Treasury bills and other government bonds. Conventionally, analysts consider such instruments "risk-free" for a domestic holder, as they are a form of government obligation with a low perceived likelihood of default. The average yield on short-term Treasury bills, influenced by economic conditions, monetary policies, and inflation expectations, has historically hovered around 2-3% over the long term. This range also aligns with central banks' inflation targets. As such, one may interpret a value within this range as a minimum proxy for the risk-free rate, as it may correspond to the minimum rate required to maintain purchasing power over time. The built-in Sharpe and Sortino ratios that strategies calculate and display in the Performance Summary tab use a default risk-free rate of 2%, and the metrics in this library's example code use the same default rate. Users can adjust this value to fit their analysis needs. Risk-adjusted performance Risk-adjusted performance metrics gauge the effectiveness of an investment by considering its returns relative to the perceived risk. They aim to provide a more well-rounded picture of performance by factoring in the level of risk taken to achieve returns. Investors can utilize such metrics to help determine whether the returns from an investment justify the risks and make informed decisions. The two most commonly used risk-adjusted performance metrics are the Sharpe ratio and the Sortino ratio.   1. Sharpe ratio   The Sharpe ratio , developed by Nobel laureate William F. Sharpe, measures the performance of an investment compared to a theoretically risk-free asset, adjusted for the investment risk. The ratio uses the following formula:   Sharpe Ratio = (𝑅𝑎 − 𝑅𝑓) / 𝜎𝑎   Where:    • 𝑅𝑎 = Average return of the investment    • 𝑅𝑓 = Theoretical risk-free rate of return    • 𝜎𝑎 = Standard deviation of the investment's returns (volatility)   A higher Sharpe ratio indicates a more favorable risk-adjusted return, as it signifies that the investment produced higher excess returns per unit of increase in total perceived risk.   2. Sortino ratio   The Sortino ratio is a modified form of the Sharpe ratio that only considers downside volatility , i.e., the volatility of returns below the theoretical risk-free benchmark. Although it shares close similarities with the Sharpe ratio, it can produce very different values, especially when the returns do not have a symmetrical distribution, since it does not penalize upside and downside volatility equally. The ratio uses the following formula:   Sortino Ratio = (𝑅𝑎 − 𝑅𝑓) / 𝜎𝑑   Where:    • 𝑅𝑎 = Average return of the investment    • 𝑅𝑓 = Theoretical risk-free rate of return    • 𝜎𝑑 = Downside deviation (standard deviation of negative excess returns, or downside volatility)   The Sortino ratio offers an alternative perspective on an investment's return-generating efficiency since it does not consider upside volatility in its calculation. A higher Sortino ratio signifies that the investment produced higher excess returns per unit of increase in perceived downside risk. █   CALCULATIONS Return period detection Calculating risk-adjusted performance metrics requires collecting returns across several periods of a given size. Analysts may use different period sizes based on the context and their preferences. However, two widely used standards are monthly or daily periods, depending on the available data and the investment's duration. The built-in ratios displayed in the Strategy Tester utilize returns from either monthly or daily periods in their calculations based on the following logic:  • Use monthly returns if the history of closed trades spans at least two months.  • Use daily returns if the trades span at least two days but less than two months.  • Do not calculate the ratios if the trade data spans fewer than two days. This library's `detectPeriod()` function applies related logic to available chart data rather than trade data to determine which period is appropriate:  • It returns true if the chart's data spans at least two months, indicating that it's sufficient to use monthly periods.  • It returns false if the chart's data spans at least two days but not two months, suggesting the use of daily periods.  • It returns na if the length of the chart's data covers less than two days, signifying that the data is insufficient for meaningful ratio calculations. It's important to note that programmers should only call `detectPeriod()` from a script's global scope or within the outermost scope of a function called from the global scope, as it requires the time value from the first bar to accurately measure the amount of time covered by the chart's data. Collecting periodic returns This library's `getPeriodicReturns()` function tracks price return data within monthly or daily periods and stores the periodic values in an array . It uses a `detectPeriod()` call as the condition to determine whether each element in the array represents the return over a monthly or daily period. The `getPeriodicReturns()` function has two overloads. The first overload requires two arguments and outputs an array of monthly or daily returns for use in the `sharpe()` and `sortino()` methods. To calculate these returns:  1. The `percentChange` argument should be a series that represents percentage gains or losses. The values can be bar-to-bar return percentages on the chart timeframe or percentages requested from a higher timeframe.  2. The function compounds all non-na `percentChange` values within each monthly or daily period to calculate the period's total return percentage. When the `percentChange` represents returns from a higher timeframe, ensure the requested data includes gaps to avoid compounding redundant values.  3. After a period ends, the function queues the compounded return into the array , removing the oldest element from the array when its size exceeds the `maxPeriods` argument. The resulting array represents the sequence of closed returns over up to `maxPeriods` months or days, depending on the available data. The second overload of the function includes an additional `benchmark` parameter. Unlike the first overload, this version tracks and collects differences between the `percentChange` and the specified `benchmark` values. The resulting array represents the sequence of excess returns over up to `maxPeriods` months or days. Passing this array to the `sharpe()` and `sortino()` methods calculates generalized Information ratios , which represent the risk-adjustment performance of a sequence of returns compared to a risky benchmark instead of a risk-free rate. For consistency, ensure the non-na times of the `benchmark` values align with the times of the `percentChange` values. Ratio methods This library's `sharpe()` and `sortino()` methods respectively calculate the Sharpe and Sortino ratios based on an array of returns compared to a specified annual benchmark. Both methods adjust the annual benchmark based on the number of periods per year to suit the frequency of the returns:  • If the method call does not include a `periodsPerYear` argument, it uses `detectPeriod()` to determine whether the returns represent monthly or daily values based on the chart's history. If monthly, the method divides the `annualBenchmark` value by 12. If daily, it divides the value by 365.  • If the method call does specify a `periodsPerYear` argument, the argument's value supersedes the automatic calculation, facilitating custom benchmark adjustments, such as dividing by 252 when analyzing collected daily stock returns. When the array passed to these methods represents a sequence of excess returns , such as the result from the second overload of `getPeriodicReturns()`, use an `annualBenchmark` value of 0 to avoid comparing those excess returns to a separate rate. By default, these methods only calculate the ratios on the last available bar to minimize their resource usage. Users can override this behavior with the `forceCalc` parameter. When the value is true , the method calculates the ratio on each call if sufficient data is available, regardless of the bar index. Look first. Then leap. █   FUNCTIONS & METHODS This library contains the following functions: detectPeriod()   Determines whether the chart data has sufficient coverage to use monthly or daily returns for risk metric calculations.   Returns: (bool) `true` if the period spans more than two months, `false` if it otherwise spans more than two days, and `na` if the data is insufficient. getPeriodicReturns(percentChange, maxPeriods)   (Overload 1 of 2) Tracks periodic return percentages and queues them into an array for ratio calculations. The span of the chart's historical data determines whether the function uses daily or monthly periods in its calculations. If the chart spans more than two months, it uses "1M" periods. Otherwise, if the chart spans more than two days, it uses "1D" periods. If the chart covers less than two days, it does not store changes.   Parameters:      percentChange (float) : (series float) The change percentage. The function compounds non-na values from each chart bar within monthly or daily periods to calculate the periodic changes.      maxPeriods (simple int) : (simple int) The maximum number of periodic returns to store in the returned array.   Returns: (array) An array containing the overall percentage changes for each period, limited to the maximum specified by `maxPeriods`. getPeriodicReturns(percentChange, benchmark, maxPeriods)   (Overload 2 of 2) Tracks periodic excess return percentages and queues the values into an array. The span of the chart's historical data determines whether the function uses daily or monthly periods in its calculations. If the chart spans more than two months, it uses "1M" periods. Otherwise, if the chart spans more than two days, it uses "1D" periods. If the chart covers less than two days, it does not store changes.   Parameters:      percentChange (float) : (series float) The change percentage. The function compounds non-na values from each chart bar within monthly or daily periods to calculate the periodic changes.      benchmark (float) : (series float) The benchmark percentage to compare against `percentChange` values. The function compounds non-na values from each bar within monthly or daily periods and subtracts the results from the compounded `percentChange` values to calculate the excess returns. For consistency, ensure this series has a similar history length to the `percentChange` with aligned non-na value times.      maxPeriods (simple int) : (simple int) The maximum number of periodic excess returns to store in the returned array.   Returns: (array) An array containing monthly or daily excess returns, limited to the maximum specified by `maxPeriods`. method sharpeRatio(returnsArray, annualBenchmark, forceCalc, periodsPerYear)   Calculates the Sharpe ratio for an array of periodic returns. Callable as a method or a function.   Namespace types: array   Parameters:      returnsArray (array) : (array) An array of periodic return percentages, e.g., returns over monthly or daily periods.      annualBenchmark (float) : (series float) The annual rate of return to compare against `returnsArray` values. When `periodsPerYear` is `na`, the function divides this value by 12 to calculate a monthly benchmark if the chart's data spans at least two months or 365 for a daily benchmark if the data otherwise spans at least two days. If `periodsPerYear` has a specified value, the function divides the rate by that value instead.      forceCalc (bool) : (series bool) If `true`, calculates the ratio on every call. Otherwise, ratio calculation only occurs on the last available bar. Optional. The default is `false`.      periodsPerYear (simple int) : (simple int) If specified, divides the annual rate by this value instead of the value determined by the time span of the chart's data.   Returns: (float) The Sharpe ratio, which estimates the excess return per unit of total volatility. method sortinoRatio(returnsArray, annualBenchmark, forceCalc, periodsPerYear)   Calculates the Sortino ratio for an array of periodic returns. Callable as a method or a function.   Namespace types: array   Parameters:      returnsArray (array) : (array) An array of periodic return percentages, e.g., returns over monthly or daily periods.      annualBenchmark (float) : (series float) The annual rate of return to compare against `returnsArray` values. When `periodsPerYear` is `na`, the function divides this value by 12 to calculate a monthly benchmark if the chart's data spans at least two months or 365 for a daily benchmark if the data otherwise spans at least two days. If `periodsPerYear` has a specified value, the function divides the rate by that value instead.      forceCalc (bool) : (series bool) If `true`, calculates the ratio on every call. Otherwise, ratio calculation only occurs on the last available bar. Optional. The default is `false`.      periodsPerYear (simple int) : (simple int) If specified, divides the annual rate by this value instead of the value determined by the time span of the chart's data.   Returns: (float) The Sortino ratio, which estimates the excess return per unit of downside volatility.
ไลบรารี Pine Script®
โดย TradingView
2
Simple Decesion Matrix Classification Algorithm [SS]Hello everyone, It has been a while since I posted an indicator, so thought I would share this project I did for fun. This indicator is an attempt to develop a pseudo Random Forest classification decision matrix model for Pinescript. This is not a full, robust Random Forest model by any stretch of the imagination, but it is a good way to showcase how decision matrices can be applied to trading and within Pinescript. As to not market this as something it is not, I am simply calling it the "Simple Decision Matrix Classification Algorithm". However, I have stolen most of the aspects of this machine learning algo from concepts of Random Forest modelling. How it works: With models like Support Vector Machines (SVM), Random Forest (RF) and Gradient Boosted Machine Learning (GBM), which are commonly used in Machine Learning Classification Tasks (MLCTs), this model operates similarity to the basic concepts shared amongst those modelling types. While it is not very similar to SVM, it is very similar to RF and GBM, in that it uses a "voting" system. What do I mean by voting system? How most classification MLAs work is by feeding an input dataset to an algorithm. The algorithm sorts this data, categorizes it, then introduces something called a confusion matrix (essentially sorting the data in no apparently order as to prevent over-fitting and introduce "confusion" to the algorithm to ensure that it is not just following a trend). From there, the data is called upon based on current data inputs (so say we are using RSI and Z-Score, the current RSI and Z-Score is compared against other RSI's and Z-Scores that the model has saved). The model will process this information and each "tree" or "node" will vote. Then a cumulative overall vote is casted. How does this MLA work? This model accepts 2 independent variables. In order to keep things simple, this model was kept as a three node model. This means that there are 3 separate votes that go in to get the result. A vote is casted for each of the two independent variables and then a cumulative vote is casted for the overall verdict (the result of the model's prediction). The model actually displays this system diagrammatically and it will likely be easier to understand if we look at the diagram to ground the example: In the diagram, at the very top we have the classification variable that we are trying to predict. In this case, we are trying to predict whether there will be a breakout/breakdown outside of the normal ATR range (this is either yes or no question, hence a classification task). So the question forms the basis of the input. The model will track at which points the ATR range is exceeded to the upside or downside, as well as the other variables that we wish to use to predict these exceedences. The ATR range forms the basis of all the data flowing into the model. Then, at the second level, you will see we are using Z-Score and RSI to predict these breaks. The circle will change colour according to "feature importance". Feature importance basically just means that the indicator has a strong impact on the outcome. The stronger the importance, the more green it will be, the weaker, the more red it will be. We can see both RSI and Z-Score are green and thus we can say they are strong options for predicting a breakout/breakdown. So then we move down to the actual voting mechanisms. You will see the 2 pink boxes. These are the first lines of voting. What is happening here is the model is identifying the instances that are most similar and whether the classification task we have assigned (remember out ATR exceedance classifier) was either true or false based on RSI and Z-Score. These are our 2 nodes. They both cast an individual vote. You will see in this case, both cast a vote of 1. The options are either 1 or 0. A vote of 1 means "Yes" or "Breakout likely". However, this is not the only voting the model does. The model does one final vote based on the 2 votes. This is shown in the purple box. We can see the final vote and result at the end with the orange circle. It is 1 which means a range exceedance is anticipated and the most likely outcome. The Data Table Component The model has many moving parts. I have tried to represent the pivotal functions diagrammatically, but some other important aspects and background information must be obtained from the companion data table. If we bring back our diagram from above: We can see the data table to the left. The data table contains 2 sections, one for each independent variable. In this case, our independent variables are RSI and Z-Score. The data table will provide you with specifics about the independent variables, as well as about the model accuracy and outcome. If we take a look at the first row, it simply indicates which independent variable it is looking at. If we go down to the next row where it reads "Weighted Impact", we can see a corresponding percent. The "weighted impact" is the amount of representation each independent variable has within the voting scheme. So in this case, we can see its pretty equal, 45% and 55%, This tells us that there is a slight higher representation of z-score than RSI but nothing to worry about. If there was a major over-respresentation of greater than 30 or 40%, then the model would risk being skewed and voting too heavily in favour of 1 variable over the other. If we move down from there we will see the next row reads "independent accuracy". The voting of each independent variable's accuracy is considered separately. This is one way we can determine feature importance, by seeing how well one feature augments the accuracy. In this case, we can see that RSI has the greatest importance, with an accuracy of around 87% at predicting breakouts. That makes sense as RSI is a momentum based oscillator. Then if we move down one more, we will see what each independent feature (node) has voted for. In this case, both RSI and Z-Score voted for 1 (Breakout in our case). You can weigh these in collaboration, but its always important to look at the final verdict of the model, which if we move down, we can see the "Model prediction" which is "Bullish". If you are using the ATR breakout, the model cannot distinguish between "Bullish" or "Bearish", must that a "Breakout" is likely, either bearish or bullish. However, for the other classification tasks this model can do, the results are either Bullish or Bearish. Using the Function: Okay so now that all that technical stuff is out of the way, let's get into using the function. First of all this function innately provides you with 3 possible classification tasks. These include: 1. Predicting Red or Green Candle 2. Predicting Bullish / Bearish ATR 3. Predicting a Breakout from the ATR range The possible independent variables include: 1. Stochastics, 2. MFI, 3. RSI, 4. Z-Score, 5. EMAs, 6. SMAs, 7. Volume The model can only accept 2 independent variables, to operate within the computation time limits for pine execution. Let's quickly go over what the numbers in the diagram mean: The numbers being pointed at with the yellow arrows represent the cases the model is sorting and voting on. These are the most identical cases and are serving as the voting foundation for the model. The numbers being pointed at with the pink candle is the voting results. Extrapolating the functions (For Pine Developers: So this is more of a feature application, so feel free to customize it to your liking and add additional inputs. But here are some key important considerations if you wish to apply this within your own code: 1. This is a BINARY classification task. The prediction must either be 0 or 1. 2. The function consists of 3 separate functions, the 2 first functions serve to build the confusion matrix and then the final "random_forest" function serves to perform the computations. You will need all 3 functions for implementation. 3. The model can only accept 2 independent variables. I believe that is the function. Hopefully this wasn't too confusing, it is very statsy, but its a fun function for me! I use Random Forest excessively in R and always like to try to convert R things to Pinescript. Hope you enjoy! Safe trades everyone!
อินดิเคเตอร์ Pine Script®
โดย Steversteves
10
Tick CVD [Kioseff Trading]Hello! This script "Tick CVD" employs live tick data to calculate CVD and volume delta! No tick chart required. Features Live price ticks are recorded CVD calculated using live ticks Delta calculated using live ticks Tick-based HMA, WMA, EMA, or SMA for CVD and price Key tick levels (S/R CVD & price) are recorded and displayed Price/CVD displayable as candles or lines Polylines are used - data visuals are not limited to 500 points. Efficiency mode - remove all the bells and whistles to capitalize on efficiently calculated/displayed tick CVD and price How it works While historical tick-data isn't available to non-professional subscribers, live tick data is programmatically accessible. Consequently, this indicator records live tick data to calculate CVD, delta, and other metrics for the user! Generally, Pine Scripts use the following rules to calculate volume/price-related metrics: Bullish Volume: When the close price is greater than the open price. Bearish Volume: When the close price is less than the open price. This script, however, improves on that logic by utilizing live ticks. Instead of relying on time-series charts, it records up ticks as buying volume and down ticks as selling volume. This allows the script to create a more accurate CVD, delta, or price tick chart by tracking real-time buying and selling activity. Price can tick fast; therefore, tick aggregation can occur. While tick aggregation isn't necessarily "incorrect", if you prefer speed and efficiency it's advised to enable "efficiency mode" in a fast market. The image above highlights the tick CVD and price tick graph! Green price tick graph = price is greater than its origin point (first script load) Red price tick graph = price is less than its origin point Blue tick CVD graph = CVD, over the calculation period, is greater than 0. Red tick CVD graph = CVD is less than 0 over the calculation period. The image above explains the right-oriented scales. The upper scale is for the price graph and the lower scale for the CVD graph. The image above explains the circles superimposed on the scale lines for the price graph and the CVD graph. The image above explains the "wavy" lines shown by the indicator. The wavy lines correspond to tick delta - whether the recorded tick was an uptick or down tick and whether buy volume or sell volume transpired. The image above explains the blue/red boxes displayed by the indicator. The boxes offer an alternative visualization of tick delta, including the magnitude of buying/selling volume for the recorded tick. Blue boxes = buying volume Red boxes = selling volume Bright blue = high buying volume (relative) Bright red = high selling volume (relative) Dim blue = low buying volume (relative) Dim red = low selling volume (relative) The numbers displayed in the box show the numbered tick and the volume delta recorded for the tick. The image above further explains visuals for the CVD graph. Dotted red lines indicate key CVD peaks, while dotted blue lines indicate key CVD bottoms. The white dotted line reflects the CVD average of your choice: HMA, WMA, EMA, SMA. The image above offers a similar explanation of visuals for the price graph. The image above offers an alternative view for the indicator! The image above shows the indicator when efficiency mode is enabled. When trading a fast market, enabling efficiency mode is advised - the script will perform quicker. Of course, thank you to @RicardoSantos for his awesome library I use in almost every script :D Thank you for checking this out!
อินดิเคเตอร์ Pine Script®
โดย KioseffTrading
6
analytics_tablesLibrary "analytics_tables" 📝 Description This library provides the implementation of several performance-related statistics and metrics, presented in the form of tables. The metrics shown in the afforementioned tables where developed during the past years of my in-depth analalysis of various strategies in an atempt to reason about the performance of each strategy. The visualization and some statistics where inspired by the existing implementations of the "Seasonality" script, and the performance matrix implementations of @QuantNomad and @ZenAndTheArtOfTrading scripts. While this library is meant to be used by my strategy framework "Template Trailing Strategy (Backtester)" script, I wrapped it in a library hoping this can be usefull for other community strategy scripts that will be released in the future. 🤔 How to Guide To use the functionality this library provides in your script you have to import it first! Copy the import statement of the latest release by pressing the copy button below and then paste it into your script. Give a short name to this library so you can refer to it later on. The import statement should look like this: import jason5480/analytics_tables/1 as ant There are three types of tables provided by this library in the initial release. The stats table the metrics table and the seasonality table. Each one shows different kinds of performance statistics. The table UDT shall be initialized once using the `init()` method. They can be updated using the `update()` method where the updated data UDT object shall be passed. The data UDT can also initialized and get updated on demend depending on the use case A code example for the StatsTable is the following: var ant.StatsData statsData = ant.StatsData.new() statsData.update(SideStats.new(), SideStats.new(), 0) if (barstate.islastconfirmedhistory or (barstate.isrealtime and barstate.isconfirmed)) var statsTable = ant.StatsTable.new().init(ant.getTablePos('TOP', 'RIGHT')) statsTable.update(statsData) A code example for the MetricsTable is the following: var ant.StatsData statsData = ant.StatsData.new() statsData.update(ant.SideStats.new(), ant.SideStats.new(), 0) if (barstate.islastconfirmedhistory or (barstate.isrealtime and barstate.isconfirmed)) var metricsTable = ant.MetricsTable.new().init(ant.getTablePos('BOTTOM', 'RIGHT')) metricsTable.update(statsData, 10) A code example for the SeasonalityTable is the following: var ant.SeasonalData seasonalData = ant.SeasonalData.new().init(Seasonality.monthOfYear) seasonalData.update() if (barstate.islastconfirmedhistory or (barstate.isrealtime and barstate.isconfirmed)) var seasonalTable = ant.SeasonalTable.new().init(seasonalData, ant.getTablePos('BOTTOM', 'LEFT')) seasonalTable.update(seasonalData) 🏋️‍♂️ Please refer to the "EXAMPLE" regions of the script for more advanced and up to date code examples! Special thanks to @Mrcrbw for the proposal to develop this library and @DCNeu for the constructive feedback 🏆. getTablePos(ypos, xpos)   Get table position compatible string   Parameters:      ypos (simple string) : The position on y axise      xpos (simple string) : The position on x axise   Returns: The position to be passed to the table method init(this, pos, height, width, positiveTxtColor, negativeTxtColor, neutralTxtColor, positiveBgColor, negativeBgColor, neutralBgColor)   Initialize the stats table object with the given colors in the given position   Namespace types: StatsTable   Parameters:      this (StatsTable) : The stats table object      pos (simple string) : The table position string      height (simple float) : The height of the table as a percentage of the charts height. By default, 0 auto-adjusts the height based on the text inside the cells      width (simple float) : The width of the table as a percentage of the charts height. By default, 0 auto-adjusts the width based on the text inside the cells      positiveTxtColor (simple color) : The text color when positive      negativeTxtColor (simple color) : The text color when negative      neutralTxtColor (simple color) : The text color when neutral      positiveBgColor (simple color) : The background color with transparency when positive      negativeBgColor (simple color) : The background color with transparency when negative      neutralBgColor (simple color) : The background color with transparency when neutral method init(this, pos, height, width, neutralBgColor)   Initialize the metrics table object with the given colors in the given position   Namespace types: MetricsTable   Parameters:      this (MetricsTable) : The metrics table object      pos (simple string) : The table position string      height (simple float) : The height of the table as a percentage of the charts height. By default, 0 auto-adjusts the height based on the text inside the cells      width (simple float) : The width of the table as a percentage of the charts width. By default, 0 auto-adjusts the width based on the text inside the cells      neutralBgColor (simple color) : The background color with transparency when neutral method init(this, seas)   Initialize the seasonal data   Namespace types: SeasonalData   Parameters:      this (SeasonalData) : The seasonal data object      seas (simple Seasonality) : The seasonality of the matrix data method init(this, data, pos, maxNumOfYears, height, width, extended, neutralTxtColor, neutralBgColor)   Initialize the seasonal table object with the given colors in the given position   Namespace types: SeasonalTable   Parameters:      this (SeasonalTable) : The seasonal table object      data (SeasonalData) : The seasonality data of the table      pos (simple string) : The table position string      maxNumOfYears (simple int) : The maximum number of years that fit into the table      height (simple float) : The height of the table as a percentage of the charts height. By default, 0 auto-adjusts the height based on the text inside the cells      width (simple float) : The width of the table as a percentage of the charts width. By default, 0 auto-adjusts the width based on the text inside the cells      extended (simple bool) : The seasonal table with extended columns for performance      neutralTxtColor (simple color) : The text color when neutral      neutralBgColor (simple color) : The background color with transparency when neutral method update(this, wins, losses, numOfInconclusiveExits)   Update the strategy info data of the strategy   Namespace types: StatsData   Parameters:      this (StatsData) : The strategy statistics object      wins (SideStats)      losses (SideStats)      numOfInconclusiveExits (int) : The number of inconclusive trades method update(this, stats, positiveTxtColor, negativeTxtColor, negativeBgColor, neutralBgColor)   Update the stats table object with the given data   Namespace types: StatsTable   Parameters:      this (StatsTable) : The stats table object      stats (StatsData) : The stats data to update the table      positiveTxtColor (simple color) : The text color when positive      negativeTxtColor (simple color) : The text color when negative      negativeBgColor (simple color) : The background color with transparency when negative      neutralBgColor (simple color) : The background color with transparency when neutral method update(this, stats, buyAndHoldPerc, positiveTxtColor, negativeTxtColor, positiveBgColor, negativeBgColor)   Update the metrics table object with the given data   Namespace types: MetricsTable   Parameters:      this (MetricsTable) : The metrics table object      stats (StatsData) : The stats data to update the table      buyAndHoldPerc (float) : The buy and hold percetage      positiveTxtColor (simple color) : The text color when positive      negativeTxtColor (simple color) : The text color when negative      positiveBgColor (simple color) : The background color with transparency when positive      negativeBgColor (simple color) : The background color with transparency when negative method update(this)   Update the seasonal data based on the season and eon timeframe   Namespace types: SeasonalData   Parameters:      this (SeasonalData) : The seasonal data object method update(this, data, positiveTxtColor, negativeTxtColor, neutralTxtColor, positiveBgColor, negativeBgColor, neutralBgColor, timeBgColor)   Update the seasonal table object with the given data   Namespace types: SeasonalTable   Parameters:      this (SeasonalTable) : The seasonal table object      data (SeasonalData) : The seasonal cell data to update the table      positiveTxtColor (simple color) : The text color when positive      negativeTxtColor (simple color) : The text color when negative      neutralTxtColor (simple color) : The text color when neutral      positiveBgColor (simple color) : The background color with transparency when positive      negativeBgColor (simple color) : The background color with transparency when negative      neutralBgColor (simple color) : The background color with transparency when neutral      timeBgColor (simple color) : The background color of the time gradient SideStats   Object that represents the strategy statistics data of one side win or lose   Fields:      numOf (series int)      sumFreeProfit (series float)      freeProfitStDev (series float)      sumProfit (series float)      profitStDev (series float)      sumGain (series float)      gainStDev (series float)      avgQuantityPerc (series float)      avgCapitalRiskPerc (series float)      avgTPExecutedCount (series float)      avgRiskRewardRatio (series float)      maxStreak (series int) StatsTable   Object that represents the stats table   Fields:      table (series table) : The actual table      rows (series int) : The number of rows of the table      columns (series int) : The number of columns of the table StatsData   Object that represents the statistics data of the strategy   Fields:      wins (SideStats)      losses (SideStats)      numOfInconclusiveExits (series int)      avgFreeProfitStr (series string)      freeProfitStDevStr (series string)      lossFreeProfitStDevStr (series string)      avgProfitStr (series string)      profitStDevStr (series string)      lossProfitStDevStr (series string)      avgQuantityStr (series string) MetricsTable   Object that represents the metrics table   Fields:      table (series table) : The actual table      rows (series int) : The number of rows of the table      columns (series int) : The number of columns of the table SeasonalData   Object that represents the seasonal table dynamic data   Fields:      seasonality (series Seasonality)      eonToMatrixRow (map)      numOfEons (series int)      mostRecentMatrixRow (series int)      balances (matrix)      returnPercs (matrix)      maxDDs (matrix)      eonReturnPercs (array)      eonCAGRs (array)      eonMaxDDs (array) SeasonalTable   Object that represents the seasonal table   Fields:      table (series table) : The actual table      headRows (series int) : The number of head rows of the table      headColumns (series int) : The number of head columns of the table      eonRows (series int) : The number of eon rows of the table      seasonColumns (series int) : The number of season columns of the table      statsRows (series int)      statsColumns (series int) : The number of stats columns of the table      rows (series int) : The number of rows of the table      columns (series int) : The number of columns of the table      extended (series bool) : Whether the table has additional performance statistics
ไลบรารี Pine Script®
โดย jason5480
1
Adaptive Trend Classification: Moving Averages [InvestorUnknown]Adaptive Trend Classification: Moving Averages Overview The Adaptive Trend Classification (ATC) Moving Averages indicator is a robust and adaptable investing tool designed to provide dynamic signals based on various types of moving averages and their lengths. This indicator incorporates multiple layers of adaptability to enhance its effectiveness in various market conditions. Key Features Adaptability of Moving Average Types and Lengths: The indicator utilizes different types of moving averages (EMA, HMA, WMA, DEMA, LSMA, KAMA) with customizable lengths to adjust to market conditions. Dynamic Weighting Based on Performance: ] Weights are assigned to each moving average based on the equity they generate, with considerations for a cutout period and decay rate to manage (reduce) the influence of past performances. Exponential Growth Adjustment: The influence of recent performance is enhanced through an adjustable exponential growth factor, ensuring that more recent data has a greater impact on the signal. Calibration Mode: Allows users to fine-tune the indicator settings for specific signal periods and backtesting, ensuring optimized performance. Visualization Options: Multiple customization options for plotting moving averages, color bars, and signal arrows, enhancing the clarity of the visual output. Alerts: Configurable alert settings to notify users based on specific moving average crossovers or the average signal. User Inputs Adaptability Settings λ (Lambda): Specifies the growth rate for exponential growth calculations. Decay (%): Determines the rate of depreciation applied to the equity over time. CutOut Period: Sets the period after which equity calculations start, allowing for a focus on specific time ranges. Robustness Lengths: Defines the range of robustness for equity calculation with options for Narrow, Medium, or Wide adjustments. Long/Short Threshold: Sets thresholds for long and short signals. Calculation Source: The data source used for calculations (e.g., close price). Moving Averages Settings Lengths and Weights: Allows customization of lengths and initial weights for each moving average type (EMA, HMA, WMA, DEMA, LSMA, KAMA). Calibration Mode Calibration Mode: Enables calibration for fine-tuning inputs. Calibrate: Specifies which moving average type to calibrate. Strategy View: Shifts entries and exits by one bar for non-repainting backtesting. Calculation Logic Rate of Change (R): Calculates the rate of change in the price. Set of Moving Averages: Generates multiple moving averages with different lengths for each type. diflen(length) => int L1 = na, int L_1 = na int L2 = na, int L_2 = na int L3 = na, int L_3 = na int L4 = na, int L_4 = na if robustness == "Narrow" L1 := length + 1, L_1 := length - 1 L2 := length + 2, L_2 := length - 2 L3 := length + 3, L_3 := length - 3 L4 := length + 4, L_4 := length - 4 else if robustness == "Medium" L1 := length + 1, L_1 := length - 1 L2 := length + 2, L_2 := length - 2 L3 := length + 4, L_3 := length - 4 L4 := length + 6, L_4 := length - 6 else L1 := length + 1, L_1 := length - 1 L2 := length + 3, L_2 := length - 3 L3 := length + 5, L_3 := length - 5 L4 := length + 7, L_4 := length - 7 // Function to calculate different types of moving averages ma_calculation(source, length, ma_type) => if ma_type == "EMA" ta.ema(source, length) else if ma_type == "HMA" ta.sma(source, length) else if ma_type == "WMA" ta.wma(source, length) else if ma_type == "DEMA" ta.dema(source, length) else if ma_type == "LSMA" lsma(source,length) else if ma_type == "KAMA" kama(source, length) else na // Function to create a set of moving averages with different lengths SetOfMovingAverages(length, source, ma_type) => = diflen(length) MA = ma_calculation(source, length, ma_type) MA1 = ma_calculation(source, L1, ma_type) MA2 = ma_calculation(source, L2, ma_type) MA3 = ma_calculation(source, L3, ma_type) MA4 = ma_calculation(source, L4, ma_type) MA_1 = ma_calculation(source, L_1, ma_type) MA_2 = ma_calculation(source, L_2, ma_type) MA_3 = ma_calculation(source, L_3, ma_type) MA_4 = ma_calculation(source, L_4, ma_type) Exponential Growth Factor: Computes an exponential growth factor based on the current bar index and growth rate. // The function `e(L)` calculates an exponential growth factor based on the current bar index and a given growth rate `L`. e(L) => // Calculate the number of bars elapsed. // If the `bar_index` is 0 (i.e., the very first bar), set `bars` to 1 to avoid division by zero. bars = bar_index == 0 ? 1 : bar_index // Define the cuttime time using the `cutout` parameter, which specifies how many bars will be cut out off the time series. cuttime = time // Initialize the exponential growth factor `x` to 1.0. x = 1.0 // Check if `cuttime` is not `na` and the current time is greater than or equal to `cuttime`. if not na(cuttime) and time >= cuttime // Use the mathematical constant `e` raised to the power of `L * (bar_index - cutout)`. // This represents exponential growth over the number of bars since the `cutout`. x := math.pow(math.e, L * (bar_index - cutout)) x Equity Calculation: Calculates the equity based on starting equity, signals, and the rate of change, incorporating a natural decay rate. pine code // This function calculates the equity based on the starting equity, signals, and rate of change (R). eq(starting_equity, sig, R) => cuttime = time if not na(cuttime) and time >= cuttime // Calculate the rate of return `r` by multiplying the rate of change `R` with the exponential growth factor `e(La)`. r = R * e(La) // Calculate the depreciation factor `d` as 1 minus the depreciation rate `De`. d = 1 - De var float a = 0.0 // If the previous signal `sig ` is positive, set `a` to `r`. if (sig > 0) a := r // If the previous signal `sig ` is negative, set `a` to `-r`. else if (sig < 0) a := -r // Declare the variable `e` to store equity and initialize it to `na`. var float e = na // If `e ` (the previous equity value) is not available (first calculation): if na(e ) e := starting_equity else // Update `e` based on the previous equity value, depreciation factor `d`, and adjustment factor `a`. e := (e * d) * (1 + a) // Ensure `e` does not drop below 0.25. if (e < 0.25) e := 0.25 e else na Signal Generation: Generates signals based on crossovers and computes a weighted signal from multiple moving averages. Main Calculations The indicator calculates different moving averages (EMA, HMA, WMA, DEMA, LSMA, KAMA) and their respective signals, applies exponential growth and decay factors to compute equities, and then derives a final signal by averaging weighted signals from all moving averages. Visualization and Alerts The final signal, along with additional visual aids like color bars and arrows, is plotted on the chart. Users can also set up alerts based on specific conditions to receive notifications for potential trading opportunities. Repainting The indicator does support intra-bar changes of signal but will not repaint once the bar is closed, if you want to get alerts only for signals after bar close, turn on “Strategy View” while setting up the alert. Conclusion The Adaptive Trend Classification: Moving Averages Indicator is a sophisticated tool for investors, offering extensive customization and adaptability to changing market conditions. By integrating multiple moving averages and leveraging dynamic weighting based on performance, it aims to provide reliable and timely investing signals.
อินดิเคเตอร์ Pine Script®
โดย InvestorUnknown
6
Statistics • Chi Square • P-value • SignificanceThe Statistics • Chi Square • P-value • Significance publication aims to provide a tool for combining different conditions and checking whether the outcome is significant using the Chi-Square Test and P-value. 🔶 USAGE The basic principle is to compare two or more groups and check the results of a query test, such as asking men and women whether they want to see a romantic or non-romantic movie. ––––––––––––––––––––––––––––––––––––––––––––– | | ROMANTIC | NON-ROMANTIC | ⬅︎ MOVIE | ––––––––––––––––––––––––––––––––––––––––––––– | MEN | 2 | 8 | 10 | ––––––––––––––––––––––––––––––––––––––––––––– | WOMEN | 7 | 3 | 10 | ––––––––––––––––––––––––––––––––––––––––––––– |⬆︎ SEX | 10 | 10 | 20 | ––––––––––––––––––––––––––––––––––––––––––––– We calculate the Chi-Square Formula, which is: Χ² = Σ ( (Observed Value − Expected Value)² / Expected Value ) In this publication, this is: chiSquare = 0. for i = 0 to rows -1 for j = 0 to colums -1 observedValue = aBin.get(i).aFloat.get(j) expectedValue = math.max(1e-12, aBin.get(i).aFloat.get(colums) * aBin.get(rows).aFloat.get(j) / sumT) //Division by 0 protection chiSquare += math.pow(observedValue - expectedValue, 2) / expectedValue Together with the 'Degree of Freedom', which is (rows − 1) × (columns − 1) , the P-value can be calculated. In this case it is P-value: 0.02462 A P-value lower than 0.05 is considered to be significant. Statistically, women tend to choose a romantic movie more, while men prefer a non-romantic one. Users have the option to choose a P-value, calculated from a standard table or through a math.ucla.edu - Javascript-based function (see references below). Note that the population (10 men + 10 women = 20) is small, something to consider. Either way, this principle is applied in the script, where conditions can be chosen like rsi, close, high, ... 🔹 CONDITION Conditions are added to the left column ('CONDITION') For example, previous rsi values (rsi ) between 0-100, divided in separate groups 🔹 CLOSE Then, the movement of the last close is evaluated UP when close is higher then previous close (close ) DOWN when close is lower then previous close EQUAL when close is equal then previous close It is also possible to use only 2 columns by adding EQUAL to UP or DOWN UP DOWN/EQUAL or UP/EQUAL DOWN In other words, when previous rsi value was between 80 and 90, this resulted in: 19 times a current close higher than previous close 14 times a current close lower than previous close 0 times a current close equal than previous close However, the P-value tells us it is not statistical significant. NOTE: Always keep in mind that past behaviour gives no certainty about future behaviour. A vertical line is drawn at the beginning of the chosen population (max 4990) Here, the results seem significant. 🔹 GROUPS It is important to ensure that the groups are formed correctly. All possibilities should be present, and conditions should only be part of 1 group. In the example above, the two top situations are acceptable; close against close can only be higher, lower or equal. The two examples at the bottom, however, are very poorly constructed. Several conditions can be placed in more than 1 group, and some conditions are not integrated into a group. Even if the results are significant, they are useless because of the group formation. A population count is added as an aid to spot errors in group formation. In this example, there is a discrepancy between the population and total count due to the absence of a condition. The results when rsi was between 5-25 are not included, resulting in unreliable results. 🔹 PRACTICAL EXAMPLES In this example, we have specific groups where the condition only applies to that group. For example, the condition rsi > 55 and rsi <= 65 isn't true in another group. Also, every possible rsi value (0 - 100) is present in 1 of the groups. rsi > 15 and rsi <= 25 28 times UP, 19 times DOWN and 2 times EQUAL. P-value: 0.01171 When looking in detail and examining the area 15-25 RSI, we see this: The population is now not representative (only checking for RSI between 15-25; all other RSI values are not included), so we can ignore the P-value in this case. It is merely to check in detail. In this case, the RSI values 23 and 24 seem promising. NOTE: We should check what the close price did without any condition. If, for example, the close price had risen 100 times out of 100, this would make things very relative. In this case (at least two conditions need to be present), we set 1 condition at 'always true' and another at 'always false' so we'll get only the close values without any condition: Changing the population or the conditions will change the P-value. In the following example, the outcome is evaluated when: close value from 1 bar back is higher than the close value from 2 bars back close value from 1 bar back is lower/equal than the close value from 2 bars back Or: close value from 1 bar back is higher than the close value from 2 bars back close value from 1 bar back is equal than the close value from 2 bars back close value from 1 bar back is lower than the close value from 2 bars back In both examples, all possibilities of close against close are included in the calculations. close can only by higher, equal or lower than close Both examples have the results without a condition included (5 = 5 and 5 < 5) so one can compare the direction of current close. 🔶 NOTES • Always keep in mind that: Past behaviour gives no certainty about future behaviour. Everything depends on time, cycles, events, fundamentals, technicals, ... • This test only works for categorical data (data in categories), such as Gender {Men, Women} or color {Red, Yellow, Green, Blue} etc., but not numerical data such as height or weight. One might argue that such tests shouldn't use rsi, close, ... values. • Consider what you're measuring For example rsi of the current bar will always lead to a close higher than the previous close, since this is inherent to the rsi calculations. • Be careful; often, there are na -values at the beginning of the series, which are not included in the calculations! • Always keep in mind considering what the close price did without any condition • The numbers must be large enough. Each entry must be five or more. In other words, it is vital to make the 'population' large enough. • The code can be developed further, for example, by splitting UP, DOWN in close UP 1-2%, close UP 2-3%, close UP 3-4%, ... • rsi can be supplemented with stochRSI, MFI, sma, ema, ... 🔶 SETTINGS 🔹 Population • Choose the population size; in other words, how many bars you want to go back to. If fewer bars are available than set, this will be automatically adjusted. 🔹 Inputs At least two conditions need to be chosen. • Users can add up to 11 conditions, where each condition can contain two different conditions. 🔹 RSI • Length 🔹 Levels • Set the used levels as desired. 🔹 Levels • P-value: P-value retrieved using a standard table method or a function. • Used function, derived from Chi-Square Distribution Function; JavaScript LogGamma(Z) => S = 1 + 76.18009173 / Z - 86.50532033 / (Z+1) + 24.01409822 / (Z+2) - 1.231739516 / (Z+3) + 0.00120858003 / (Z+4) - 0.00000536382 / (Z+5) (Z-.5) * math.log(Z+4.5) - (Z+4.5) + math.log(S * 2.50662827465) Gcf(float X, A) => // Good for X > A +1 A0=0., B0=1., A1=1., B1=X, AOLD=0., N=0 while (math.abs((A1-AOLD)/A1) > .00001) AOLD := A1 N += 1 A0 := A1+(N-A)*A0 B0 := B1+(N-A)*B0 A1 := X*A0+N*A1 B1 := X*B0+N*B1 A0 := A0/B1 B0 := B0/B1 A1 := A1/B1 B1 := 1 Prob = math.exp(A * math.log(X) - X - LogGamma(A)) * A1 1 - Prob Gser(X, A) => // Good for X < A +1 T9 = 1. / A G = T9 I = 1 while (T9 > G* 0.00001) T9 := T9 * X / (A + I) G := G + T9 I += 1 G *= math.exp(A * math.log(X) - X - LogGamma(A)) Gammacdf(x, a) => GI = 0. if (x<=0) GI := 0 else if (x Chisqcdf = Gammacdf(Z/2, DF/2) Chisqcdf := math.round(Chisqcdf * 100000) / 100000 pValue = 1 - Chisqcdf 🔶 REFERENCES mathsisfun.com, Chi-Square Test Chi-Square Distribution Function
อินดิเคเตอร์ Pine Script®
โดย fikira
7
FiniteStateMachine🟩 OVERVIEW A flexible framework for creating, testing and implementing a Finite State Machine (FSM) in your script. FSMs use rules to control how states change in response to events. This is the first Finite State Machine library on TradingView and it's quite a different way to think about your script's logic. Advantages of using this vs hardcoding all your logic include: • Explicit logic : You can see all rules easily side-by-side. • Validation : Tables show your rules and validation results right on the chart. • Dual approach : Simple matrix for straightforward transitions; map implementation for concurrent scenarios. You can combine them for complex needs. • Type safety : Shows how to use enums for robustness while maintaining string compatibility. • Real-world examples : Includes both conceptual (traffic lights) and practical (trading strategy) demonstrations. • Priority control : Explicit control over which rules take precedence when multiple conditions are met. • Wildcard system : Flexible pattern matching for states and events. The library seems complex, but it's not really. Your conditions, events, and their potential interactions are complex. The FSM makes them all explicit, which is some work. However, like all "good" pain in life, this is front-loaded, and *saves* pain later, in the form of unintended interactions and bugs that are very hard to find and fix. 🟩 SIMPLE FSM (MATRIX-BASED) The simple FSM uses a matrix to define transition rules with the structure: state > event > state. We look up the current state, check if the event in that row matches, and if it does, output the resulting state. Each row in the matrix defines one rule, and the first matching row, counting from the top down, is applied. A limitation of this method is that you can supply only ONE event. You can design layered rules using widlcards. Use an empty string "" or the special string "ANY" for any state or event wildcard. The matrix FSM is foruse where you have clear, sequential state transitions triggered by single events. Think traffic lights, or any logic where only one thing can happen at a time. The demo for this FSM is of traffic lights. 🟩 CONCURRENT FSM (MAP-BASED) The map FSM uses a more complex structure where each state is a key in the map, and its value is an array of event rules. Each rule maps a named condition to an output (event or next state). This FSM can handle multiple conditions simultaneously. Rules added first have higher priority. Adding more rules to existing states combines the entries in the map (if you use the supplied helper function) rather than overwriting them. This FSM is for more complex scenarios where multiple conditions can be true simultaneously, and you need to control which takes precedence. Like trading strategies, or any system with concurrent conditions. The demo for this FSM is a trading strategy. 🟩 HOW TO USE Pine Script libraries contain reusable code for importing into indicators. You do not need to copy any code out of here. Just import the library and call the function you want. For example, for version 1 of this library, import it like this: import SimpleCryptoLife/FiniteStateMachine/1 See the EXAMPLE USAGE sections within the library for examples of calling the functions. For more information on libraries and incorporating them into your scripts, see the Libraries section of the Pine Script User Manual. 🟩 TECHNICAL IMPLEMENTATION Both FSM implementations support wildcards using blank strings "" or the special string "ANY". Wildcards match in this priority order: • Exact state + exact event match • Exact state + empty event (event wildcard) • Empty state + exact event (state wildcard) • Empty state + empty event (full wildcard) When multiple rules match the same state + event combination, the FIRST rule encountered takes priority. In the matrix FSM, this means row order determines priority. In the map FSM, it's the order you add rules to each state. The library uses user-defined types for the map FSM: • o_eventRule : Maps a condition name to an output • o_eventRuleWrapper : Wraps an array of rules (since maps can't contain arrays directly) Everything uses strings for maximum library compatibility, though the examples show how to use enums for type safety by converting them to strings. Unlike normal maps where adding a duplicate key overwrites the value, this library's `m_addRuleToEventMap()` method *combines* rules, making it intuitive to build rule sets without breaking them. 🟩 VALIDATION & ERROR HANDLING The library includes comprehensive validation functions that catch common FSM design errors: Error detection: • Empty next states • Invalid states not in the states array • Duplicate rules • Conflicting transitions • Unreachable states (no entry/exit rules) Warning detection: • Redundant wildcards • Empty states/events (potential unintended wildcards) • Duplicate conditions within states You can display validation results in tables on the chart, with tooltips providing detailed explanations. The helper functions to display the tables are exported so you can call them from your own script. 🟩 PRACTICAL EXAMPLES The library includes four comprehensive demos: Traffic Light Demo (Simple FSM) : Uses the matrix FSM to cycle through traffic light states (red → red+amber → green → amber → red) with timer events. Includes pseudo-random "break" events and repair logic to demonstrate wildcards and priority handling. Trading Strategy Demo (Concurrent FSM) : Implements a realistic long-only trading strategy using BOTH FSM types: • Map FSM converts multiple technical conditions (EMA crosses, gaps, fractals, RSI) into prioritised events • Matrix FSM handles state transitions (idle → setup → entry → position → exit → re-entry) • Includes position management, stop losses, and re-entry logic Error Demonstrations : Both FSM types include error demos with intentionally malformed rules to showcase the validation system's capabilities. 🟩 BRING ON THE FUNCTIONS f_printFSMMatrix(_mat_rules, _a_states, _tablePosition)   Prints a table of states and rules to the specified position on the chart. Works only with the matrix-based FSM.   Parameters:      _mat_rules (matrix)      _a_states (array)      _tablePosition (simple string)   Returns: The table of states and rules. method m_loadMatrixRulesFromText(_mat_rules, _rulesText)   Loads rules into a rules matrix from a multiline string where each line is of the form "current state | event | next state" (ignores empty lines and trims whitespace). This is the most human-readable way to define rules because it's a visually aligned, table-like format.   Namespace types: matrix   Parameters:      _mat_rules (matrix)      _rulesText (string)   Returns: No explicit return. The matrix is modified as a side-effect. method m_addRuleToMatrix(_mat_rules, _currentState, _event, _nextState)   Adds a single rule to the rules matrix. This can also be quite readble if you use short variable names and careful spacing.   Namespace types: matrix   Parameters:      _mat_rules (matrix)      _currentState (string)      _event (string)      _nextState (string)   Returns: No explicit return. The matrix is modified as a side-effect. method m_validateRulesMatrix(_mat_rules, _a_states, _showTable, _tablePosition)   Validates a rules matrix and a states array to check that they are well formed. Works only with the matrix-based FSM. Checks: matrix has exactly 3 columns; no empty next states; all states defined in array; no duplicate states; no duplicate rules; all states have entry/exit rules; no conflicting transitions; no redundant wildcards. To avoid slowing down the script unnecessarily, call this method once (perhaps using `barstate.isfirst`), when the rules and states are ready.   Namespace types: matrix   Parameters:      _mat_rules (matrix)      _a_states (array)      _showTable (bool)      _tablePosition (simple string)   Returns: `true` if the rules and states are valid; `false` if errors or warnings exist. method m_getStateFromMatrix(_mat_rules, _currentState, _event, _strictInput, _strictTransitions)   Returns the next state based on the current state and event, or `na` if no matching transition is found. Empty (not na) entries are treated as wildcards if `strictInput` is false. Priority: exact match > event wildcard > state wildcard > full wildcard.   Namespace types: matrix   Parameters:      _mat_rules (matrix)      _currentState (string)      _event (string)      _strictInput (bool)      _strictTransitions (bool)   Returns: The next state or `na`. method m_addRuleToEventMap(_map_eventRules, _state, _condName, _output)   Adds a single event rule to the event rules map. If the state key already exists, appends the new rule to the existing array (if different). If the state key doesn't exist, creates a new entry.   Namespace types: map   Parameters:      _map_eventRules (map)      _state (string)      _condName (string)      _output (string)   Returns: No explicit return. The map is modified as a side-effect. method m_addEventRulesToMapFromText(_map_eventRules, _configText)   Loads event rules from a multiline text string into a map structure. Format: "state | condName > output | condName > output | ..." . Pairs are ordered by priority. You can have multiple rules on the same line for one state. Supports wildcards: Use an empty string ("") or the special string "ANY" for state or condName to create wildcard rules. Examples: " | condName > output" (state wildcard), "state | > output" (condition wildcard), " | > output" (full wildcard). Splits lines by , extracts state as key, creates/appends to array with new o_eventRule(condName, output). Call once, e.g., on barstate.isfirst for best performance.   Namespace types: map   Parameters:      _map_eventRules (map)      _configText (string)   Returns: No explicit return. The map is modified as a side-effect. f_printFSMMap(_map_eventRules, _a_states, _tablePosition)   Prints a table of map-based event rules to the specified position on the chart.   Parameters:      _map_eventRules (map)      _a_states (array)      _tablePosition (simple string)   Returns: The table of map-based event rules. method m_validateEventRulesMap(_map_eventRules, _a_states, _a_validEvents, _showTable, _tablePosition)   Validates an event rules map to check that it's well formed. Checks: map is not empty; wrappers contain non-empty arrays; no duplicate condition names per state; no empty fields in o_eventRule objects; optionally validates outputs against matrix events. NOTE: Both "" and "ANY" are treated identically as wildcards for both states and conditions. To avoid slowing down the script unnecessarily, call this method once (perhaps using `barstate.isfirst`), when the map is ready.   Namespace types: map   Parameters:      _map_eventRules (map)      _a_states (array)      _a_validEvents (array)      _showTable (bool)      _tablePosition (simple string)   Returns: `true` if the event rules map is valid; `false` if errors or warnings exist. method m_getEventFromConditionsMap(_currentState, _a_activeConditions, _map_eventRules)   Returns a single event or state string based on the current state and active conditions. Uses a map of event rules where rules are pre-sorted by implicit priority via load order. Supports wildcards using empty string ("") or "ANY" for flexible rule matching. Priority: exact match > condition wildcard > state wildcard > full wildcard.   Namespace types: series string, simple string, input string, const string   Parameters:      _currentState (string)      _a_activeConditions (array)      _map_eventRules (map)   Returns: The output string (event or state) for the first matching condition, or na if no match found. o_eventRule   o_eventRule defines a condition-to-output mapping for the concurrent FSM.   Fields:      condName (series string) : The name of the condition to check.      output (series string) : The output (event or state) when the condition is true. o_eventRuleWrapper   o_eventRuleWrapper wraps an array of o_eventRule for use as map values (maps cannot contain collections directly).   Fields:      a_rules (array) : Array of o_eventRule objects for a specific state.
ไลบรารี Pine Script®
โดย SimpleCryptoLife
2
Trading Activity Index (Zeiierman)█ Overview Trading Activity Index (Zeiierman) is a volume-based market activity meter that transforms dollar-volume into a smooth, normalized “activity index.” It highlights when market participation is unusually low or high with a dynamic color gradient: Light Blue → Low Activity (thin participation, low liquidity conditions) Red/Orange → High Activity (active markets, large trades flowing in) Additional percentile bands (20/40/60/80%) give context, helping you see whether the current activity level is in the bottom quintile, mid-range, or near historical extremes. █ How It Works ⚪ Dollar Volume Transformation Each bar, dollar volume is computed: float dlrVol = close * volume float dlrVolAvg = ta.sma(dlrVol, len_form) Dollar volume = price × volume, smoothed by a configurable SMA window. The result is log-transformed, compressing large outliers for a more stable signal. ⚪ Rolling Percentiles & Ranking The log-dollar-volume series is compared to its rolling history (len_hist bars): float p20 = ta.percentile_linear_interpolation(vscale, len_hist, 20) float p40 = ta.percentile_linear_interpolation(vscale, len_hist, 40) float p60 = ta.percentile_linear_interpolation(vscale, len_hist, 60) float p80 = ta.percentile_linear_interpolation(vscale, len_hist, 80) A normalized rank (0–1) is produced to color the main Trading Activity line. █ How to Use ⚪ Detect High-Impact Sessions Quickly see if today’s session is active or quiet relative to its own history — great for filtering setups that need activity. ⚪ Spot Breakouts & Traps Combine with price action: High activity near breakouts = strong follow-through likely. Low activity breakouts = vulnerable to fake-outs. ⚪ Market Regime Context Percentile bands help you assess whether participation is building up, in the middle of the range, or drying out — valuable for timing mean-reversion trades. Above 80th percentile (red/orange) → Market is highly active, breakout trades and trend strategies are favored. Below 20th percentile (light blue) → Market is quiet; fade moves or wait for expansion. Watch transitions from blue → orange as a signal of growing institutional participation. █ Settings Formation Window (bars) – Number of bars used to average dollar volume before log transform. History Window (bars) – Lookback period for percentile calculations and rank normalization. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
อินดิเคเตอร์ Pine Script®
โดย Zeiierman
6
Expected Value Monte CarloI created this indicator after noticing that there was no Expected Value indicator here on TradingView. The EVMC provides statistical Expected Value to what might happen in the future regarding the asset you are analyzing. It uses 2 quantitative methods: Historical Backtest to ground your analysis in long-term, factual data. Monte Carlo Simulation to project a cone of probable future outcomes based on recent market behavior. This gives you a data-driven edge to quantify risk, and make more informed trading decisions. The indicator includes: Dual analysis: Combines historical probability with forward-looking simulation. Quantified projections: Provides the Expected Value ($ and %), Win Rate, and Sharpe Ratio for both methods. Asset-aware: Automatically adjusts its calculations for Stocks (252 trading days) and Crypto (365 days) for mathematical accuracy. The projection cone shows the mean expected path and the +/- 1 standard deviation range of outcomes. No repainting Calculation: 1. Historical Expected Value: This is a systematic backtest over thousands of bars. It calculates the return Rᵢ for N past trades (buy-and-hold). The Historical EV is the simple average of these returns, giving a baseline performance measure. Historical EV % = (Σ Rᵢ) / N 2. Monte Carlo Projection: This projection uses the Geometric Brownian Motion (GBM) model to simulate thousands of future price paths based on the market's recent behavior. It first measures the drift (μ), or recent trend, and volatility (σ), or recent risk, from the Projection Lookback period. It then projects a final return for each simulation using the core GBM formula: Projected Return = exp( (μ - σ²/2)T + σ√T * Z ) - 1 (Where T is the time horizon and Z is a random variable for the simulation.) The purple line on the chart is the average of all simulated outcomes (the Monte Carlo EV). The cone represents one standard deviation of those outcomes. The dashed lines represent one standard deviation (+/- 1σ) from the average, forming a cone of probable outcomes. Roughly 68% of the simulated paths ended within this cone. This projection answers the question: "If the recent trend and volatility continue, where is the price most likely to go?" Here's how to read the indicator Expected Value ($/%): Is my average trade profitable? Win Rate: How often can I expect to be right? Sharpe Ratio: Am I being adequately compensated for the risk I'm taking? User Guide Max trade duration (bars): This is your analysis timeframe. Are you interested in the probable outcome over the next month (21 bars), quarter (63 bars), or year (252 bars)? Position size ($): Set this to your typical trade size to see the Expected Value in real dollar terms. Projection lookback (bars): This is the most important input for the Monte Carlo model. A short lookback (e.g., 50) makes the projection highly sensitive to recent momentum. Use this to identify potential recency bias. A long lookback (e.g., 252) provides a more stable, long-term projection of trend and volatility. Historical Lookback (bars): For the historical backtest, more data is always better. Use the maximum that your TradingView plan allows for the most statistically significant results. Use TP/SL for Historical EV: Check this box to see how the historical performance would have changed if you had used a simple Take Profit and Stop Loss, rather than just holding for the full duration. I hope you find this indicator useful and please let me know if you have any suggestions. 😊
อินดิเคเตอร์ Pine Script®
โดย HenriqueCentieiro
2
Bar Index & TimeLibrary to convert a bar index to a timestamp and vice versa. Utilizes runtime memory to store the 𝚝𝚒𝚖𝚎 and 𝚝𝚒𝚖𝚎_𝚌𝚕𝚘𝚜𝚎 values of every bar on the chart (and optional future bars), with the ability of storing additional custom values for every chart bar. █  PREFACE This library aims to tackle some problems that pine coders (from beginners to advanced) often come across, such as: I'm trying to draw an object with a 𝚋𝚊𝚛_𝚒𝚗𝚍𝚎𝚡 that is more than 10,000 bars into the past, but this causes my script to fail. How can I convert the 𝚋𝚊𝚛_𝚒𝚗𝚍𝚎𝚡 to a UNIX time so that I can draw visuals using xloc.bar_time ? I have a diagonal line drawing and I want to get the "y" value at a specific time, but line.get_price() only accepts a bar index value. How can I convert the timestamp into a bar index value so that I can still use this function? I want to get a previous 𝚘𝚙𝚎𝚗 value that occurred at a specific timestamp. How can I convert the timestamp into a historical offset so that I can use 𝚘𝚙𝚎𝚗 ? I want to reference a very old value for a variable. How can I access a previous value that is older than the maximum historical buffer size of 𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎 ? This library can solve the above problems (and many more) with the addition of a few lines of code, rather than requiring the coder to refactor their script to accommodate the limitations. █  OVERVIEW The core functionality provided is conversion between xloc.bar_index and xloc.bar_time values. The main component of the library is the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object, created via the 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() function which basically stores the 𝚝𝚒𝚖𝚎 and 𝚝𝚒𝚖𝚎_𝚌𝚕𝚘𝚜𝚎 of every bar on the chart, and there are 3 more overloads to this function that allow collecting and storing additional data. Once a 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object is created, use any of the exported methods: Methods to convert a UNIX timestamp into a bar index or bar offset: 𝚝𝚒𝚖𝚎𝚜𝚝𝚊𝚖𝚙𝚃𝚘𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡(), 𝚐𝚎𝚝𝙽𝚞𝚖𝚋𝚎𝚛𝙾𝚏𝙱𝚊𝚛𝚜𝙱𝚊𝚌𝚔() Methods to retrieve the stored data for a bar index: 𝚝𝚒𝚖𝚎𝙰𝚝𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡(), 𝚝𝚒𝚖𝚎𝙲𝚕𝚘𝚜𝚎𝙰𝚝𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡(), 𝚟𝚊𝚕𝚞𝚎𝙰𝚝𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡(), 𝚐𝚎𝚝𝙰𝚕𝚕𝚅𝚊𝚛𝚒𝚊𝚋𝚕𝚎𝚜𝙰𝚝𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡() Methods to retrieve the stored data at a number of bars back (i.e., historical offset): 𝚝𝚒𝚖𝚎(), 𝚝𝚒𝚖𝚎𝙲𝚕𝚘𝚜𝚎(), 𝚟𝚊𝚕𝚞𝚎() Methods to retrieve all the data points from the earliest bar (or latest bar) stored in memory, which can be useful for debugging purposes: 𝚐𝚎𝚝𝙴𝚊𝚛𝚕𝚒𝚎𝚜𝚝𝚂𝚝𝚘𝚛𝚎𝚍𝙳𝚊𝚝𝚊(), 𝚐𝚎𝚝𝙻𝚊𝚝𝚎𝚜𝚝𝚂𝚝𝚘𝚛𝚎𝚍𝙳𝚊𝚝𝚊() Note: the library's strong suit is referencing data from very old bars in the past, which is especially useful for scripts that perform its necessary calculations only on the last bar. █  USAGE Step 1 Import the library. Replace with the latest available version number for this library. //@version=6 indicator("Usage") import n00btraders/ChartData/ Step 2 Create a 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object to collect data on every bar. Do not declare as `var` or `varip`. chartData = ChartData.collectChartData() // call on every bar to accumulate the necessary data Step 3 Call any method(s) on the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object. Do not modify its fields directly. if barstate.islast int firstBarTime = chartData.timeAtBarIndex(0) int lastBarTime = chartData.time(0) log.info("First `time`: " + str.format_time(firstBarTime) + ", Last `time`: " + str.format_time(lastBarTime)) █  EXAMPLES • Collect Future Times The overloaded 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() functions that accept a 𝚋𝚊𝚛𝚜𝙵𝚘𝚛𝚠𝚊𝚛𝚍 argument can additionally store time values for up to 500 bars into the future. //@version=6 indicator("Example `collectChartData(barsForward)`") import n00btraders/ChartData/1 chartData = ChartData.collectChartData(barsForward = 500) var rectangle = box.new(na, na, na, na, xloc = xloc.bar_time, force_overlay = true) if barstate.islast int futureTime = chartData.timeAtBarIndex(bar_index + 100) int lastBarTime = time box.set_lefttop(rectangle, lastBarTime, open) box.set_rightbottom(rectangle, futureTime, close) box.set_text(rectangle, "Extending box 100 bars to the right. Time: " + str.format_time(futureTime)) • Collect Custom Data The overloaded 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() functions that accept a 𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎𝚜 argument can additionally store custom user-specified values for every bar on the chart. //@version=6 indicator("Example `collectChartData(variables)`") import n00btraders/ChartData/1 var map variables = map.new() variables.put("open", open) variables.put("close", close) variables.put("open-close midpoint", (open + close) / 2) variables.put("boolean", open > close ? 1 : 0) chartData = ChartData.collectChartData(variables = variables) var fgColor = chart.fg_color var table1 = table.new(position.top_right, 2, 9, color(na), fgColor, 1, fgColor, 1, true) var table2 = table.new(position.bottom_right, 2, 9, color(na), fgColor, 1, fgColor, 1, true) if barstate.isfirst table.cell(table1, 0, 0, "ChartData.value()", text_color = fgColor) table.cell(table2, 0, 0, "open ", text_color = fgColor) table.merge_cells(table1, 0, 0, 1, 0) table.merge_cells(table2, 0, 0, 1, 0) for i = 1 to 8 table.cell(table1, 0, i, text_color = fgColor, text_halign = text.align_left, text_font_family = font.family_monospace) table.cell(table2, 0, i, text_color = fgColor, text_halign = text.align_left, text_font_family = font.family_monospace) table.cell(table1, 1, i, text_color = fgColor) table.cell(table2, 1, i, text_color = fgColor) if barstate.islast for i = 1 to 8 float open1 = chartData.value("open", 5000 * i) float open2 = i < 3 ? open : -1 table.cell_set_text(table1, 0, i, "chartData.value(\"open\", " + str.tostring(5000 * i) + "): ") table.cell_set_text(table2, 0, i, "open : ") table.cell_set_text(table1, 1, i, str.tostring(open1)) table.cell_set_text(table2, 1, i, open2 >= 0 ? str.tostring(open2) : "Error") • xloc.bar_index → xloc.bar_time The 𝚝𝚒𝚖𝚎 value (or 𝚝𝚒𝚖𝚎_𝚌𝚕𝚘𝚜𝚎 value) can be retrieved for any bar index that is stored in memory by the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object. //@version=6 indicator("Example `timeAtBarIndex()`") import n00btraders/ChartData/1 chartData = ChartData.collectChartData() if barstate.islast int start = bar_index - 15000 int end = bar_index - 100 // line.new(start, close, end, close) // !ERROR - `start` value is too far from current bar index start := chartData.timeAtBarIndex(start) end := chartData.timeAtBarIndex(end) line.new(start, close, end, close, xloc.bar_time, width = 10) • xloc.bar_time → xloc.bar_index Use 𝚝𝚒𝚖𝚎𝚜𝚝𝚊𝚖𝚙𝚃𝚘𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡() to find the bar that a timestamp belongs to. If the timestamp falls in between the close of one bar and the open of the next bar, the 𝚜𝚗𝚊𝚙 parameter can be used to determine which bar to choose: 𝚂𝚗𝚊𝚙.𝙻𝙴𝙵𝚃 - prefer to choose the leftmost bar (typically used for closing times) 𝚂𝚗𝚊𝚙.𝚁𝙸𝙶𝙷𝚃 - prefer to choose the rightmost bar (typically used for opening times) 𝚂𝚗𝚊𝚙.𝙳𝙴𝙵𝙰𝚄𝙻𝚃 (or 𝚗𝚊) - copies the same behavior as xloc.bar_time uses for drawing objects //@version=6 indicator("Example `timestampToBarIndex()`") import n00btraders/ChartData/1 startTimeInput = input.time(timestamp("01 Aug 2025 08:30 -0500"), "Session Start Time") endTimeInput = input.time(timestamp("01 Aug 2025 15:15 -0500"), "Session End Time") chartData = ChartData.collectChartData() if barstate.islastconfirmedhistory int startBarIndex = chartData.timestampToBarIndex(startTimeInput, ChartData.Snap.RIGHT) int endBarIndex = chartData.timestampToBarIndex(endTimeInput, ChartData.Snap.LEFT) line1 = line.new(startBarIndex, 0, startBarIndex, 1, extend = extend.both, color = color.new(color.green, 60), force_overlay = true) line2 = line.new(endBarIndex, 0, endBarIndex, 1, extend = extend.both, color = color.new(color.green, 60), force_overlay = true) linefill.new(line1, line2, color.new(color.green, 90)) // using Snap.DEFAULT to show that it is equivalent to drawing lines using `xloc.bar_time` (i.e., it aligns to the same bars) startBarIndex := chartData.timestampToBarIndex(startTimeInput) endBarIndex := chartData.timestampToBarIndex(endTimeInput) line.new(startBarIndex, 0, startBarIndex, 1, extend = extend.both, color = color.yellow, width = 3) line.new(endBarIndex, 0, endBarIndex, 1, extend = extend.both, color = color.yellow, width = 3) line.new(startTimeInput, 0, startTimeInput, 1, xloc.bar_time, extend.both, color.new(color.blue, 85), width = 11) line.new(endTimeInput, 0, endTimeInput, 1, xloc.bar_time, extend.both, color.new(color.blue, 85), width = 11) • Get Price of Line at Timestamp The pine script built-in function line.get_price() requires working with bar index values. To get the price of a line in terms of a timestamp, convert the timestamp into a bar index or offset. //@version=6 indicator("Example `line.get_price()` at timestamp") import n00btraders/ChartData/1 lineStartInput = input.time(timestamp("01 Aug 2025 08:30 -0500"), "Line Start") chartData = ChartData.collectChartData() var diagonal = line.new(na, na, na, na, force_overlay = true) if time <= lineStartInput line.set_xy1(diagonal, bar_index, open) if barstate.islastconfirmedhistory line.set_xy2(diagonal, bar_index, close) if barstate.islast int timeOneWeekAgo = timenow - (7 * timeframe.in_seconds("1D") * 1000) // Note: could also use `timetampToBarIndex(timeOneWeekAgo, Snap.DEFAULT)` and pass the value directly to `line.get_price()` int barsOneWeekAgo = chartData.getNumberOfBarsBack(timeOneWeekAgo) float price = line.get_price(diagonal, bar_index - barsOneWeekAgo) string formatString = "Time 1 week ago: {0,number,#} - Equivalent to {1} bars ago 𝚕𝚒𝚗𝚎.𝚐𝚎𝚝_𝚙𝚛𝚒𝚌𝚎(): {2,number,#.##}" string labelText = str.format(formatString, timeOneWeekAgo, barsOneWeekAgo, price) label.new(timeOneWeekAgo, price, labelText, xloc.bar_time, style = label.style_label_lower_right, size = 16, textalign = text.align_left, force_overlay = true) █  RUNTIME ERROR MESSAGES This library's functions will generate a custom runtime error message in the following cases: 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() is not called consecutively, or is called more than once on a single bar Invalid 𝚋𝚊𝚛𝚜𝙵𝚘𝚛𝚠𝚊𝚛𝚍 argument in the 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() function Invalid 𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎𝚜 argument in the 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() function Invalid 𝚕𝚎𝚗𝚐𝚝𝚑 argument in any of the functions that accept a number of bars back Note: there is no runtime error generated for an invalid 𝚝𝚒𝚖𝚎𝚜𝚝𝚊𝚖𝚙 or 𝚋𝚊𝚛𝙸𝚗𝚍𝚎𝚡 argument in any of the functions. Instead, the functions will assign 𝚗𝚊 to the returned values. Any other runtime errors are due to incorrect usage of the library. █  NOTES • Function Descriptions The library source code uses Markdown for the exported functions. Hover over a function/method call in the Pine Editor to display formatted, detailed information about the function/method. //@version=6 indicator("Demo Function Tooltip") import n00btraders/ChartData/1 chartData = ChartData.collectChartData() int barIndex = chartData.timestampToBarIndex(timenow) log.info(str.tostring(barIndex)) • Historical vs. Realtime Behavior Under the hood, the data collector for this library is declared as `var`. Because of this, the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object will always reflect the latest available data on realtime updates. Any data that is recorded for historical bars will remain unchanged throughout the execution of a script. //@version=6 indicator("Demo Realtime Behavior") import n00btraders/ChartData/1 var map variables = map.new() variables.put("open", open) variables.put("close", close) chartData = ChartData.collectChartData(variables) if barstate.isrealtime varip float initialOpen = open varip float initialClose = close varip int updateCount = 0 updateCount += 1 float latestOpen = open float latestClose = close float recordedOpen = chartData.valueAtBarIndex("open", bar_index) float recordedClose = chartData.valueAtBarIndex("close", bar_index) string formatString = "# of updates: {0} 𝚘𝚙𝚎𝚗 at update #1: {1,number,#.##} 𝚌𝚕𝚘𝚜𝚎 at update #1: {2,number,#.##} " + "𝚘𝚙𝚎𝚗 at update #{0}: {3,number,#.##} 𝚌𝚕𝚘𝚜𝚎 at update #{0}: {4,number,#.##} " + "𝚘𝚙𝚎𝚗 stored in memory: {5,number,#.##} 𝚌𝚕𝚘𝚜𝚎 stored in memory: {6,number,#.##}" string labelText = str.format(formatString, updateCount, initialOpen, initialClose, latestOpen, latestClose, recordedOpen, recordedClose) label.new(bar_index, close, labelText, style = label.style_label_left, force_overlay = true) • Collecting Chart Data for Other Contexts If your use case requires collecting chart data from another context, avoid directly retrieving the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object as this may exceed memory limits . //@version=6 indicator("Demo Return Calculated Results") import n00btraders/ChartData/1 timeInput = input.time(timestamp("01 Sep 2025 08:30 -0500"), "Time") var int oneMinuteBarsAgo = na // !ERROR - Memory Limits Exceeded // chartDataArray = request.security_lower_tf(syminfo.tickerid, "1", ChartData.collectChartData()) // oneMinuteBarsAgo := chartDataArray.last().getNumberOfBarsBack(timeInput) // function that returns calculated results (a single integer value instead of an entire `ChartData` object) getNumberOfBarsBack() => chartData = ChartData.collectChartData() chartData.getNumberOfBarsBack(timeInput) calculatedResultsArray = request.security_lower_tf(syminfo.tickerid, "1", getNumberOfBarsBack()) oneMinuteBarsAgo := calculatedResultsArray.size() > 0 ? calculatedResultsArray.last() : na if barstate.islast string labelText = str.format("The selected timestamp occurs 1-minute bars ago", oneMinuteBarsAgo) label.new(bar_index, hl2, labelText, style = label.style_label_left, size = 16, force_overlay = true) • Memory Usage The library's convenience and ease of use comes at the cost of increased usage of computational resources. For simple scripts, using this library will likely not cause any issues with exceeding memory limits. But for large and complex scripts, you can reduce memory issues by specifying a lower 𝚌𝚊𝚕𝚌_𝚋𝚊𝚛𝚜_𝚌𝚘𝚞𝚗𝚝 amount in the indicator() or strategy() declaration statement. //@version=6 // !ERROR - Memory Limits Exceeded using the default number of bars available (~20,000 bars for Premium plans) //indicator("Demo `calc_bars_count` parameter") // Reduce number of bars using `calc_bars_count` parameter indicator("Demo `calc_bars_count` parameter", calc_bars_count = 15000) import n00btraders/ChartData/1 map variables = map.new() variables.put("open", open) variables.put("close", close) variables.put("weekofyear", weekofyear) variables.put("dayofmonth", dayofmonth) variables.put("hour", hour) variables.put("minute", minute) variables.put("second", second) // simulate large memory usage chartData0 = ChartData.collectChartData(variables) chartData1 = ChartData.collectChartData(variables) chartData2 = ChartData.collectChartData(variables) chartData3 = ChartData.collectChartData(variables) chartData4 = ChartData.collectChartData(variables) chartData5 = ChartData.collectChartData(variables) chartData6 = ChartData.collectChartData(variables) chartData7 = ChartData.collectChartData(variables) chartData8 = ChartData.collectChartData(variables) chartData9 = ChartData.collectChartData(variables) log.info(str.tostring(chartData0.time(0))) log.info(str.tostring(chartData1.time(0))) log.info(str.tostring(chartData2.time(0))) log.info(str.tostring(chartData3.time(0))) log.info(str.tostring(chartData4.time(0))) log.info(str.tostring(chartData5.time(0))) log.info(str.tostring(chartData6.time(0))) log.info(str.tostring(chartData7.time(0))) log.info(str.tostring(chartData8.time(0))) log.info(str.tostring(chartData9.time(0))) if barstate.islast result = table.new(position.middle_right, 1, 1, force_overlay = true) table.cell(result, 0, 0, "Script Execution Successful ✅", text_size = 40) █  EXPORTED ENUMS Snap   Behavior for determining the bar that a timestamp belongs to.   Fields:      LEFT : Snap to the leftmost bar.      RIGHT : Snap to the rightmost bar.      DEFAULT : Default `xloc.bar_time` behavior. Note: this enum is used for the 𝚜𝚗𝚊𝚙 parameter of 𝚝𝚒𝚖𝚎𝚜𝚝𝚊𝚖𝚙𝚃𝚘𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡(). █  EXPORTED TYPES Note: users of the library do not need to worry about directly accessing the fields of these types; all computations are done through method calls on an object of the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 type. Variable   Represents a user-specified variable that can be tracked on every chart bar.   Fields:      name (series string) : Unique identifier for the variable.      values (array) : The array of stored values (one value per chart bar). ChartData   Represents data for all bars on a chart.   Fields:      bars (series int) : Current number of bars on the chart.      timeValues (array) : The `time` values of all chart (and future) bars.      timeCloseValues (array) : The `time_close` values of all chart (and future) bars.      variables (array) : Additional custom values to track on all chart bars. █  EXPORTED FUNCTIONS collectChartData()   Collects and tracks the `time` and `time_close` value of every bar on the chart.   Returns: `ChartData` object to convert between `xloc.bar_index` and `xloc.bar_time`. collectChartData(barsForward)   Collects and tracks the `time` and `time_close` value of every bar on the chart as well as a specified number of future bars.   Parameters:      barsForward (simple int) : Number of future bars to collect data for.   Returns: `ChartData` object to convert between `xloc.bar_index` and `xloc.bar_time`. collectChartData(variables)   Collects and tracks the `time` and `time_close` value of every bar on the chart. Additionally, tracks a custom set of variables for every chart bar.   Parameters:      variables (simple map) : Custom values to collect on every chart bar.   Returns: `ChartData` object to convert between `xloc.bar_index` and `xloc.bar_time`. collectChartData(barsForward, variables)   Collects and tracks the `time` and `time_close` value of every bar on the chart as well as a specified number of future bars. Additionally, tracks a custom set of variables for every chart bar.   Parameters:      barsForward (simple int) : Number of future bars to collect data for.      variables (simple map) : Custom values to collect on every chart bar.   Returns: `ChartData` object to convert between `xloc.bar_index` and `xloc.bar_time`. █  EXPORTED METHODS method timestampToBarIndex(chartData, timestamp, snap)   Converts a UNIX timestamp to a bar index.   Namespace types: ChartData   Parameters:      chartData (series ChartData) : The `ChartData` object.      timestamp (series int) : A UNIX time.      snap (series Snap) : A `Snap` enum value.   Returns: A bar index, or `na` if unable to find the appropriate bar index. method getNumberOfBarsBack(chartData, timestamp)   Converts a UNIX timestamp to a history-referencing length (i.e., number of bars back).   Namespace types: ChartData   Parameters:      chartData (series ChartData) : The `ChartData` object.      timestamp (series int) : A UNIX time.   Returns: A bar offset, or `na` if unable to find a valid number of bars back. method timeAtBarIndex(chartData, barIndex)   Retrieves the `time` value for the specified bar index.   Namespace types: ChartData   Parameters:      chartData (series ChartData) : The `ChartData` object.      barIndex (int) : The bar index.   Returns: The `time` value, or `na` if there is no `time` stored for the bar index. method time(chartData, length)   Retrieves the `time` value of the bar that is `length` bars back relative to the latest bar.   Namespace types: ChartData   Parameters:      chartData (series ChartData) : The `ChartData` object.      length (series int) : Number of bars back.   Returns: The `time` value `length` bars ago, or `na` if there is no `time` stored for that bar. method timeCloseAtBarIndex(chartData, barIndex)   Retrieves the `time_close` value for the specified bar index.   Namespace types: ChartData   Parameters:      chartData (series ChartData) : The `ChartData` object.      barIndex (series int) : The bar index.   Returns: The `time_close` value, or `na` if there is no `time_close` stored for the bar index. method timeClose(chartData, length)   Retrieves the `time_close` value of the bar that is `length` bars back from the latest bar.   Namespace types: ChartData   Parameters:      chartData (series ChartData) : The `ChartData` object.      length (series int) : Number of bars back.   Returns: The `time_close` value `length` bars ago, or `na` if there is none stored. method valueAtBarIndex(chartData, name, barIndex)   Retrieves the value of a custom variable for the specified bar index.   Namespace types: ChartData   Parameters:      chartData (series ChartData) : The `ChartData` object.      name (series string) : The variable name.      barIndex (series int) : The bar index.   Returns: The value of the variable, or `na` if that variable is not stored for the bar index. method value(chartData, name, length)   Retrieves a variable value of the bar that is `length` bars back relative to the latest bar.   Namespace types: ChartData   Parameters:      chartData (series ChartData) : The `ChartData` object.      name (series string) : The variable name.      length (series int) : Number of bars back.   Returns: The value `length` bars ago, or `na` if that variable is not stored for the bar index. method getAllVariablesAtBarIndex(chartData, barIndex)   Retrieves all custom variables for the specified bar index.   Namespace types: ChartData   Parameters:      chartData (series ChartData) : The `ChartData` object.      barIndex (series int) : The bar index.   Returns: Map of all custom variables that are stored for the specified bar index. method getEarliestStoredData(chartData)   Gets all values from the earliest bar data that is currently stored in memory.   Namespace types: ChartData   Parameters:      chartData (series ChartData) : The `ChartData` object.   Returns: A tuple: method getLatestStoredData(chartData, futureData)   Gets all values from the latest bar data that is currently stored in memory.   Namespace types: ChartData   Parameters:      chartData (series ChartData) : The `ChartData` object.      futureData (series bool) : Whether to include the future data that is stored in memory.   Returns: A tuple:
ไลบรารี Pine Script®
โดย n00btraders
4
Liquidity Void Detector (Zeiierman)█ Overview Liquidity Void Detector (Zeiierman) is an oscillator highlighting inefficient price displacements under low participation. It measures the most recent price move (standardized return) and amplifies it only when volume is below its own trend. Positive readings ⇒ strong up-move on low volume → potential Buy-Side Imbalance (void below) that often refills. Negative readings ⇒ strong down-move on low volume → potential Sell-Side Imbalance (void above) that often refills. This tool provides a quantitative “void” proxy: when price travels far with unusually thin volume, the move is flagged as likely inefficient and prone to mean-reversion/mitigation. █ How It Works ⚪ Volume Shock (Participation Filter) Each bar, volume is compared to a rolling baseline. This is then z-scored. // Volume Shock calculation volTrend = ta.sma(volume, L) vs = (volume > 0 and volTrend > 0) ? math.log(volume) - math.log(volTrend) : na vsZ = zScore(vs, vzLen) // z-scored volume shock lowVS = (vsZ <= vzThr) // low-volume condition Bars with VolShock Z ≤ threshold are treated as low-volume (thin). ⚪ Prior Return Extremeness The 1-bar log return is computed and z-scored. // Prior return extremeness r1 = math.log(close / close ) retZ = zScore(r1, rLen) // z-scored prior return This shows whether the latest move is unusually large relative to recent history. ⚪ Void Oscillator The oscillator is: // Oscillator construction weight = lowVS ? 1.0 : fadeNoLow osc = retZ * weight where Weight = 1 when volume is low, otherwise fades toward a user-set factor (0–1). Osc > 0: up-move emphasized under low volume ⇒ Buy-Side Imbalance. Osc < 0: down-move emphasized under low volume ⇒ Sell-Side Imbalance. █ Why Use It ⚪ Targets Inefficient Moves By filtering for low participation, the oscillator focuses on moves most likely driven by thin books/noise trading, which are statistically more likely to retrace. ⚪ Simple, Robust Logic No need for tick data or order-book depth. It derives a practical void proxy from OHLCV, making it portable across assets and timeframes. ⚪ Complements Price-Action Tools Use alongside FVG/imbalance zones, key levels, and volume profile to prioritize voids that carry the highest reversal probability. █ How to Use Sell-Side Imbalance = aggressive sell move (price goes down on low volume) → expect price to move up to fill it. Buy-Side Imbalance = aggressive buy move (price goes up on low volume) → expect price to move down to fill it. █ Settings Volume Baseline Length — Bars for the volume trend used in VolShock. Larger = smoother baseline, fewer low-volume flags. Vol Shock Z-Score Lookback — Bars to standardize VolShock; larger = smoother, fewer extremes. Low-Volume Threshold (VolShock Z ≤) — Defines “thin participation.” Typical: −0.5 to −1.0. Return Z-Score Lookback — Bars to standardize the 1-bar log return; larger = smoother “extremeness” measure. Fade When Volume Not Low (0–1) — Weight applied when volume is not low. 0.00 = ignore non-low-volume bars entirely. 1.00 = treat volume condition as irrelevant (pure return extremeness). Upper Threshold (Osc ≥) — Trigger for Sell-Side Imbalance (void below). Lower Threshold (Osc ≤) — Trigger for Buy-Side Imbalance (void above). ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
อินดิเคเตอร์ Pine Script®
โดย Zeiierman
8
DeltaFlow Volume Profile [BigBeluga]🔵 OVERVIEW The DeltaFlow Volume Profile builds a compact volume profile next to price and enriches every bin with flow context : bullish vs. bearish participation (%), a per-bin Delta % , an optional Delta Heat Map , and a PoC band with the bin’s absolute volume. This lets you see not just where volume clustered, but who (buyers or sellers) dominated inside each price slice. 🔵 CONCEPTS Binned Volume Profile : Price range over a user-defined LookBack is split into Bins ; each bin aggregates traded volume. Bull/Bear Split : Within every bin, volume is separated by candle direction into Bull Volume and Bear Volume , then normalized to % of the bin’s displayed size. Delta % : The difference between Bull % and Bear % for the bin. Positive = buyer dominance; negative = seller dominance. Delta Heat Map : Bin background shading that scales with both total volume strength and delta bias. PoC (Point of Control) : The most significant bin gets a PoC band and a label with its absolute volume. 🔵 FEATURES Profile with Flow : A clean horizontal volume bar per bin plus stacked Bull % and Bear % . Per-Bin Delta Label : A readable “Δ xx%” tag at the start of each bin shows dominance at a glance. Delta Heat Map : Optional gradient that intensifies with higher volume and stronger delta. PoC Highlight : Optional PoC band colored separately, labeled with absolute volume (e.g., “1.23M”). Configurable Inputs : LookBack, number of Bins (10–100), toggles for Delta, Heat Map, Volume Bars, and PoC color. Readable Colors : Separate inputs for bullish (volume +) and bearish (volume –) hues. 🔵 HOW TO USE Set the window : Choose LookBack and Bins to balance detail vs. performance (more bins = finer resolution). Enable “Volume Bars” to display the bull/bear split as two stacked percent bars inside each bin. High Bull % near support → constructive demand. High Bear % near resistance → active supply. Use Δ labels (toggle “Delta”) to quickly spot bins with clear buyer/seller control; combine with price position for confluence. Turn on Delta Heat Map to prioritize areas with both large volume and strong imbalance. Watch the PoC : The PoC band marks the most traded (and often magnet) level; its label shows absolute size for context. Trade ideas : Breakout continuation when Δ stays positive across consecutive upper bins. Reversion risk when price enters a large bearish-Δ cluster below. Manage risk around the PoC; reactions there can be sharp. 🔵 CONCLUSION DeltaFlow Volume Profile upgrades a classic profile with flow intelligence. The bull/bear split, explicit Δ %, heat-weighted backdrop, and PoC volume label make dominant participation and key price shelves obvious. Use it to filter levels, time entries with imbalance, and validate breakouts or fades with objective volume-flow evidence.
อินดิเคเตอร์ Pine Script®
โดย BigBeluga
12
Volume by Time [LuxAlgo]The Volume by Time indicator collects volume data for every point in time over the day and displays the average volume of the specific dataset collected at each respective bar. The indicator overlays the current volume and the historical average to allow for better comparisons. 🔶 USAGE Throughout the day, the volume of every bar is stored in groups organized by the time when each bar occurred. Over time, the datasets accumulate, and from that, we can simply determine the average value at each specific time of the day. The display is a histogram style, which consists of hollow bars and solid filled columns. -Hollow bars represent the average volume at that time of the day. -Solid columns display the current volume from the current bar. By default, the entire history of data is used, but if desired, the number of days under analysis can be specified to provide a more relevant point of view. A readout of the number of days being analyzed can be seen in the status bar at any time. Note: Due to partial sessions, it is typical to see this value change throughout the day; this is simply due to the fact that not every trading session has the exact same schedule 100% of the time. The analysis type can also be specified; these can be either Average (Default) or Median. Additionally, a Bi-directional can be toggled for a distinct difference between upwards volume and downwards volume. 🔶 SETTINGS Analysis Type: Choose between Average or Median analysis modes. Length (Days): Set the number of days to use for analysis. Set to 0 for full data (Default 0). Bi-Directional Toggle: Toggle between one-sided or two-sided display.
อินดิเคเตอร์ Pine Script®
โดย LuxAlgo
2
FlowScope [Hapharmonic]FlowScope: Uncover the Market's True Intent 🔬 Ever wished you could look inside the candles and see where the real action is happening? FlowScope is your microscope for the market's flow, designed to give you a powerful edge by revealing the volume distribution that price action alone can't show you. Instead of just looking at the open, high, low, and close, FlowScope lets you dive deeper into the market's auction process. It groups candles together and builds a detailed Volume Profile for that period, showing you exactly where the trading happened and revealing the story behind the price action. Let's explore how you can use it to gain a powerful new edge. 🧐 Core Concept: How It Works At its heart, FlowScope does three key things: It Groups Candles: You decide how many candles to group together. For example, setting " Group Candles " to 4 on a 5-minute chart effectively gives you a detailed 20-minute candle and profile. This helps you see the bigger picture and filter out market noise. It Builds a Volume Profile: For each group, FlowScope analyzes the volume at every single price level. It then displays this as a horizontal histogram (we call this a "footprint" or profile). Longer bars mean more volume was traded at that price, indicating a "fair" price or an area of acceptance. Shorter bars mean price moved through quickly, indicating rejection. It Creates a Custom "Grouped Candle": To summarize the group's overall price action, FlowScope draws a single, custom candle representing the entire group's: Open: The open of the first candle in the group. High: The absolute highest price reached within the group. Low: The absolute lowest price reached within the group. Close: The close of the last candle in the group. This gives you a crystal-clear view of the group's net result, free from the back-and-forth noise of the individual candles inside it. Below are some of the stunning preset color palettes you can choose from to customize your view: 🚀 How to Use: Practical Applications FlowScope isn't just for looking pretty; it's a powerful analysis tool. Here are a few ways to integrate it into your trading: Identify High-Volume Nodes (HVNs): Look for the longest bars in the profile. These are price levels where the market spent the most time and traded the most volume. HVNs often act as powerful "magnets" for price, becoming key areas of support and resistance. Spot Low-Volume Nodes (LVNs): These are areas with very short bars or gaps in the profile. They represent price levels that the market moved through quickly and inefficiently. If price returns to an LVN, it's likely to move through it quickly again. Analyze the Summary Box: This is where the real magic happens! ✨ Total Volume (Σ): The total volume for the entire group. Buy (B) vs. Sell (S) Volume: FlowScope analyzes the lower timeframe action to estimate the buying and selling pressure that made up the total volume. Is a big red candle mostly aggressive selling, or was it just a lack of buyers? The B/S data gives you clues. A high-volume candle with nearly 50/50 buy/sell pressure might indicate absorption or a potential reversal. Use the Grouped Candle for Clarity: Is the market in a clear uptrend, or is it just choppy? The grouped candle can give you a much clearer signal. A series of strong, green grouped candles shows much more conviction than a mix of small green and red candles. ⚙️ Settings & Customization This is where you can truly make FlowScope your own. Let's walk through each setting. Profile Settings Group Candles: The number of standard chart candles you want to combine into a single FlowScope profile. A setting of 1 will analyze every single bar. A higher number gives you a broader market view. When Group Candles is set to 5, the data from the 5 individual candles are combined, and the volume is calculated accordingly. Max Profile Boxes: This setting is more than just a number; it's a smart limit that ensures your profiles are always readable and relevant to the current market conditions. Adaptive Sizing (The Ideal Goal): FlowScope first tries to create the perfect profile by making each volume box's height proportional to the current market volatility. It calculates an "ideal" box height based on the Average True Range ( ATR / 10 ). This is powerful because it automatically adapts: you get smaller, more detailed boxes in quiet, low-volatility markets, and larger, clearer boxes in volatile, fast-moving markets. The Safety Cap (Your Setting): However, what if you group several candles during a massive price move? The price range could be huge! If we only used the small, ATR-based box height, you might end up with hundreds of tiny, unreadable boxes. This is where your Max Profile Boxes setting (defaulting to 50) comes in. It acts as a maximum detail cap . If the adaptive, volatility-based calculation determines that it would need more boxes than your setting (e.g., more than 50), the indicator will override it. It will then simply divide the entire price range of the group into exactly the number of boxes you specified (e.g., 50). In short: You are setting the maximum allowable detail. FlowScope intelligently adapts the profile's granularity below that limit based on market volatility, ensuring you always get a clear and meaningful picture. Style Show Profile BG: A simple toggle to show or hide the faint background color behind the volume bars. Turning it off can create a cleaner look. Color Mode: This dropdown controls how the volume profile text is colored. Custom Gradient: This mode uses the three custom colors you select in the "Profile Colors" section to create a beautiful gradient across the profile. Candle Color: This mode colors the profile based on whether the grouped candle was bullish (green) or bearish (red). The color will be a gradient, with the most intense color applied to the box with the highest volume; the colors of the other boxes will fade out from that point. It's a great way to see the profile's "mood" at a glance. Profile Colors 🎨 Use Preset Palette: This is the master switch! If checked: You can choose from 10 stunning, pre-designed color palettes from the Palette dropdown. The custom color pickers below will be disabled. If unchecked (Default): The Palette dropdown will be disabled, and you can now choose your own three colors for the gradient. Palette: (Only active when "Use Preset Palette" is checked) . Choose from 10 luxurious, eye-catching color schemes like "Solar Flare" or "Deep Space" to instantly change the look and feel of your chart. Low Price / Mid Price / High Price: (Only active when "Use Preset Palette" is unchecked) . These three color pickers allow you to design your own unique gradient for the Custom Gradient color mode. Candle Display These settings control the custom "Grouped Candle" that summarizes the profile. When using the "Show Custom Candle" feature, you should change the chart's candlestick display to Bars for a cleaner view. Show Custom Candle: This is the main toggle. When you check this box, the original chart candles will be hidden, and your custom FlowScope candle will be displayed instead. This custom candle is intentionally small to ensure it does not visually overlap with the volume profile boxes. Show Body: (Only active when "Show Custom Candle" is checked) . Toggles the visibility of the candle's body. Wick Width & Body Width: (Only active when "Show Custom Candle" is checked) . These sliders let you control the thickness of the wick and body lines to match your personal style. Up Color / Down Color: (Only active when "Show Custom Candle" is checked) . Choose the colors for your bullish and bearish custom candles. Experiment with the settings, find a style that works for you, and start seeing the market in a whole new light. Happy trading! 📈😊
อินดิเคเตอร์ Pine Script®
โดย hapharmonic
19
VWAP Price ChannelVWAP Price Channel cuts the crust off of a traditional price channel (Donchian Channel) by anchoring VWAPs at the highs and lows. By doing this, the flat levels, characteristic of traditional Donchian Channels, are no more! Author's Note: This indicator is formed with no inherent use, and serves solely as a thought experiment. > Concept I would be hesitant to call this a "predictive" indicator, however the behavior of it would suggest it could be considered at least partially predictive Essentially, the Anchored VWAPs creates something from otherwise nothing. While the DC upper or lower values are staying flat, the VWAPs improvise based on price and volume to project a level that may be a better representation of where future highs or lows may settle. Visually, this looks like we have cut off the corners of the Donchian Channel. Note: Notice how we are calculating values before the corners are realized. > Implementation While this is only a concept indicator, The specific application I've gone with for this, is a sort of supertrend-ish display (A Trend Flipping Trailing Stop Loss). The script uses basic logic to create a trend direction, and then displays the Anchored VWAPs as a form of trailing stop loss. While "In Trend", the script fills in the area between the VWAP and Price in the direction of trend. When new highs or lows are made while in trend, the opposite VWAP will start to generate at the new highs or lows. These happen on every new high or low, so they are not indicating the trend shift, but could be interpreted as breakout levels for the current trend direction in order for continuation. Note: All values are drawn live, but when using higher timeframes, there is a natural calculation discrepancy when using live data vs. historical. > Technicals In this script, I'm simply detecting new highs or lows from the DC and using those as the anchor frequency on the built-in VWAP function. So each time a new high or low is made based on DC, the VWAP function re-anchors to the high or low of the candle. Past that, I have implemented some logic in order to account for a common occurrence I faced during development. Frequently, the price would outpace the anchored VWAP, so we would end up with the VWAP being further from price than the actual DC upper or lower. Due to this, what I have ended up with was a third value which, rather than switching between raw VWAP values and DC values, it adjusts the value based on the change in the VWAP value. This can be simply thought of as a "Start + Change" type of setup. By doing this, I can use the change values from the actual anchored VWAP, and under normal conditions, this will also be the true VWAP value. However, situationally, I am able to update the start value which we're applying the VWAP change to. In other words, when these situations happen, the VWAP change is added to the new (closer to price) DC value. The specific trend logic being used is nothing fancy at all, we are simply checking if a new high or low is created and setting the trend in that direction. This is in line with some traditional DC Strategies. To those who made it here, Just remember: The chart may be ugly, but it's the fastest analysis of the data you can get. Nicer displays often come at the hidden cost of latency. You have to shoot your shot to make it. Choose 2: Fast, Clean, Useful Enjoy!
อินดิเคเตอร์ Pine Script®
โดย SamRecio
14
Fibonacci Sequence Circles [BigBeluga]🔵 Overview The Fibonacci Sequence Circles is a unique and visually intuitive indicator designed for the TradingView platform. It combines the principles of the Fibonacci sequence with geometric circles to help traders identify potential support and resistance levels, as well as price expansion zones. The indicator dynamically anchors to key price points, such as pivot highs, pivot lows, or timeframe changes (daily, weekly, monthly), and generates Fibonacci-based circles around these anchor points. ⚠️For proper indicators visualization use simple not logarithmic chart 🔵 Key Features Customizable Anchor Points : The indicator can be anchored to Pivot Highs , Pivot Lows , or timeframe changes ( Daily, Weekly, Monthly ), making it adaptable to various trading strategies. Fibonacci Sequence Logic : The circles are generated using the Fibonacci sequence, where the diameter of each circle is the sum of the diameters of the two preceding circles. first = start_val secon = start_val + int(start_val/2) three = first + secon four = secon + three five = three + four six = four + five seven = five + six eight = six + seven nine = seven + eight ten = eight + nine Adjustable Start Value : Traders can modify the starting value of the sequence to scale the circles larger or smaller, ensuring they fit the current price action. Color Customization : Each circle can be individually enabled or disabled, and its color can be customized for better visual clarity. Visual Labels : The diameter of each circle (in bars) is displayed next to the circle, providing additional context for analysis. 🔵 Usage Step 1: Set the Anchor Point - Choose the anchor type ( Pivot High, Pivot Low, Daily, Weekly, Monthly ) to define the center of the Fibonacci circles. Step 2: Adjust the Start Value - Modify the starting value of the Fibonacci sequence to scale the circles according to the price action. Step 3: Customize Circle Colors - Enable or disable specific circles and adjust their colors for better visualization. Step 4: Analyze Price Action - Use the circles to identify potential support/resistance levels, price expansion zones, or trend continuation areas. Step 5: Combine with Other Tools - Enhance your analysis by combining the indicator with other technical tools like trendlines, moving averages, or volume indicators. The Fibonacci Sequence Circles is a powerful and flexible tool for traders who rely on Fibonacci principles and geometric patterns. Its ability to anchor to key price points and dynamically scale based on market conditions makes it suitable for various trading styles and timeframes. Whether you're a day trader or a long-term investor, this indicator can help you visualize and anticipate price movements with greater precision.
อินดิเคเตอร์ Pine Script®
โดย BigBeluga
6
ATAI Volume Pressure Analyzer V 1.0 — Pure Up/DownATAI Volume Pressure Analyzer V 1.0 — Pure Up/Down Overview Volume is a foundational tool for understanding the supply–demand balance. Classic charts show only total volume and don’t tell us what portion came from buying (Up) versus selling (Down). The ATAI Volume Pressure Analyzer fills that gap. Built on Pine Script v6, it scans a lower timeframe to estimate Up/Down volume for each host‑timeframe candle, and presents “volume pressure” in a compact HUD table that’s comparable across symbols and timeframes. 1) Architecture & Global Settings Global Period (P, bars) A single global input P defines the computation window. All measures—host‑TF volume moving averages and the half‑window segment sums—use this length. Default: 55. Timeframe Handling The core of the indicator is estimating Up/Down volume using lower‑timeframe data. You can set a custom lower timeframe, or rely on auto‑selection: ◉ Second charts → 1S ◉ Intraday → 1 minute ◉ Daily → 5 minutes ◉ Otherwise → 60 minutes Lower TFs give more precise estimates but shorter history; higher TFs approximate buy/sell splits but provide longer history. As a rule of thumb, scan thin symbols at 5–15m, and liquid symbols at 1m. 2) Up/Down Volume & Derived Series The script uses TradingView’s library function tvta.requestUpAndDownVolume(lowerTf) to obtain three values: ◉ Up volume (buyers) ◉ Down volume (sellers) ◉ Delta (Up − Down) From these we define: ◉ TF_buy = |Up volume| ◉ TF_sell = |Down volume| ◉ TF_tot = TF_buy + TF_sell ◉ TF_delta = TF_buy − TF_sell A positive TF_delta indicates buyer dominance; a negative value indicates selling pressure. To smooth noise, simple moving averages of TF_buy and TF_sell are computed over P and used as baselines. 3) Key Performance Indicators (KPIs) Half‑window segmentation To track momentum shifts, the P‑bar window is split in half: ◉ C→B: the older half ◉ B→A: the newer half (toward the current bar) For each half, the script sums buy, sell, and delta. Comparing the two halves reveals strengthening/weakening pressure. Example: if AtoB_delta < CtoB_delta, recent buying pressure has faded. [ 4) HUD (Table) Display /i] Colors & Appearance Two main color inputs define the theme: a primary color and a negative color (used when Δ is negative). The panel background uses a translucent version of the primary color; borders use the solid primary color. Text defaults to the primary color and flips to the negative color when a block’s Δ is negative. Layout The HUD is a 4×5 table updated on the last bar of each candle: ◉ Row 1 (Meta): indicator name, P length, lower TF, host TF ◉ Row 2 (Host TF): current ↑Buy, ↓Sell, ΔDelta; plus Σ total and SMA(↑/↓) ◉ Row 3 (Segments): C→B and B→A blocks with ↑/↓/Δ ◉ Rows 4–5: reserved for advanced modules (Wings, α/β, OB/OS, Top 5) Advanced Modules 5.1 Wings “Wings” visualize volume‑driven movement over C→B (left wing) and B→A (right wing) with top/bottom lines and a filled band. Slopes are ATR‑per‑bar normalized for cross‑symbol/TF comparability and converted to angles (degrees). Coloring mirrors HUD sign logic with a near‑zero threshold (default ~3°): ◉ Both lines rising → blue (bullish) ◉ Both falling → red (bearish) ◉ Mixed/near‑zero → gray Left wing reflects the origin of the recent move; right wing reflects the current state. 5.2 α / β at Point B We compute the oriented angle between the two wings at the midpoint B: β is the bottom‑arc angle; α = 360° − β is the top‑arc angle. ◉ Large α (>180°) or small β (<180°) flags meaningful imbalance. ◉ Intuition: large α suggests potential selling pressure; small β implies fragile support. HUD cells highlight these conditions. 5.3 OB/OS Spike OverBought/OverSold (OB/OS) labels appear when directional volume spikes align with a 7‑oscillator vote (RSI, Stoch, %R, CCI, MFI, DeMarker, StochRSI). ◉ OB label (red): unusually high sell volume + enough OB votes ◉ OS label (teal): unusually high buy volume + enough OS votes Minimum votes and sync window are user‑configurable; dotted connectors can link labels to the candle wick. 5.4 Top3 Volume Peaks Within the P window the script ranks the top three BUY peaks (B1–B3) and top three SELL peaks (S1–S3). ◉ B1 and S1 are drawn as horizontal resistance (at B1 High) and support (at S1 Low) zones with adjustable thickness (ticks/percent/ATR). ◉ The HUD dedicates six cells to show ↑/↓/Δ for each rank, and prints the exact High (B1) and Low (S1) inline in their cells. 6) Reading the HUD — A Quick Checklist ◉ Meta: Confirm P and both timeframes (host & lower). ◉ Host TF block: Compare current ↑/↓/Δ against their SMAs. ◉ Segments: Contrast C→B vs B→A deltas to gauge momentum change. ◉ Wings: Right‑wing color/angle = now; left wing = recent origin. ◉ α / β: Look for α > 180° or β < 180° as imbalance cues. ◉ OB/OS: Note labels, color (red/teal), and the vote count. ◉Top3: Keep B1 (resistance) and S1 (support) on your radar. Use these together to sketch scenarios and invalidation levels; never rely on a single signal in isolation. [ 7) Example Highlights (What the table conveys) /i] ◉ Row 1 shows the indicator name, the analysis length P (default 55), and both TFs used for computation and display. ◉ B1 / S1 blocks summarize each side’s peak within the window, with Δ indicating buyer/seller dominance at that peak and inline price (B1 High / S1 Low) for actionable levels. ◉ Angle cells for each wing report the top/bottom line angles vs. the horizontal, reflecting the directional posture. ◉ Ranks B2/B3 and S2/S3 extend context beyond the top peak on each side. ◉ α / β cells quantify the orientation gap at B; changes reflect shifting buyer/seller influence on trend strength. Together these visuals often reveal whether the “wings” resemble a strong, upward‑tilted arm supported by buyer volume—but always corroborate with your broader toolkit 8) Practical Tips & Tuning ◉ Choose P by market structure. For daily charts, 34–89 bars often works well. ◉ Lower TF choice: Thin symbols → 5–15m; liquid symbols → 1m. ◉ Near‑zero angle: In noisy markets, consider 5–7° instead of 3°. ◉ OB/OS votes: Daily charts often work with 3–4 votes; lower TFs may prefer 4–5. ◉ Zone thickness: Tie B1/S1 zone thickness to ATR so it scales with volatility. ◉ Colors: Feel free to theme the primary/negative colors; keep Δ<0 mapped to the negative color for readability. Combine with price action: Use this indicator alongside structure, trendlines, and other tools for stronger decisions. Technical Notes Pine Script v6. ◉ Up/Down split via TradingView/ta library call requestUpAndDownVolume(lowerTf). ◉ HUD‑first design; drawings for Wings/αβ/OBOS/Top3 align with the same sign/threshold logic used in the table. Disclaimer: This indicator is provided solely for educational and analytical purposes. It does not constitute financial advice, nor is it a recommendation to buy or sell any security. Always conduct your own research and use multiple tools before making trading decisions.
อินดิเคเตอร์ Pine Script®
โดย ata_sabanci
9
Market Cap Landscape 3DHello, traders and creators! 👋 Market Cap Landscape 3D. This project is more than just a typical technical analysis tool; it's an exploration into what's possible when code meets artistry on the financial charts. It's a demonstration of how we can transcend flat, two-dimensional lines and step into a vibrant, three-dimensional world of data. This project continues a journey that began with a previous 3D experiment, the T-Virus Sentiment, which you can explore here: The Market Cap Landscape 3D builds on that foundation, visualizing market data—particularly crypto market caps—as a dynamic 3D mountain range. The entire landscape is procedurally generated and rendered in real-time using the powerful drawing capabilities of polyline.new() and line.new() , pushed to their creative limits. This work is intended as a guide and a design example for all developers, born from the spirit of learning and a deep love for understanding the Pine Script™ language. --- 🧐 Core Concept: How It Works The indicator synthesizes multiple layers of information into a single, cohesive 3D scene: The Surface: The mountain range itself is a procedurally generated 3D mesh. Its peaks and valleys create a rich, textured landscape that serves as the canvas for our data. Crypto Data Integration: The core feature is its ability to fetch market cap data for a list of cryptocurrencies you provide. It then sorts them in descending order and strategically places them onto the 3D surface. The Summit: The highest point on the mountain is reserved for the asset with the #1 market cap in your list, visually represented by a flag and a custom emblem. The Mountain Labels: The other assets are distributed across the mountainside, with their rank determining their general elevation. This creates an intuitive visual hierarchy. The Leaderboard Pole: For clarity, a dedicated pole in the back-right corner provides a clean, ranked list of the symbols and their market caps, ensuring the data is always easy to read. --- 🧐 Example of adjusting the view To evoke the feeling of flying over mountains To evoke the feeling of looking at a mountain peak on a low plain 🧐 Example of predefined colors --- 🚀 How to Use Getting started with the Market Cap Landscape 3D: Add to Chart: Apply the "Market Cap Landscape 3D" indicator to your active chart. Open Settings: Double-click anywhere on the 3D landscape or click the "Settings" icon next to the indicator's name. Customize Your Crypto List: The most important setting is in the Crypto Data tab. In the "Symbols" text area, enter a comma-separated list of the crypto tickers you want to visualize (e.g., BTC,ETH,SOL,XRP ). The indicator supports up to 40 unique symbols. > Important Note: This indicator exclusively uses TradingView's `CRYPTOCAP` data source. To find valid symbols, use the main symbol search bar on your chart. Type `CRYPTOCAP:` (including the colon) and you will see a list of available options. For example, typing `CRYPTOCAP:BTC` will confirm that `BTC` is a valid ticker for the indicator's settings. Using symbols that do not exist in the `CRYPTOCAP` index will result in a script error. or, to display other symbols, simply type CRYPTOCAP: (including the colon) and you will see a list of available options. Adjust Your View: Use the settings in the Camera & Projection tab to rotate ( Yaw ), tilt ( Pitch ), and scale the landscape until you find a view you love. Explore & Customize: Play with the color palettes, flag design, and other settings to make the landscape truly your own! --- ⚙️ Settings & Customization This indicator is highly customizable. Here’s a breakdown of what each setting does: #### 🪙 Crypto Data Symbols: Enter the crypto tickers you want to track, separated by commas. The script automatically handles duplicates and case-insensitivity. Show Market Cap on Mountain: When checked, it displays the full market cap value next to the symbol on the mountain. When unchecked, it shows a cleaner look with just the symbol and a colored circle background. #### 📷 Camera & Projection Yaw (°): Rotates the camera view horizontally (side to side). Pitch (°): Tilts the camera view vertically (up and down). Scale X, Y, Z: Stretches or compresses the landscape in width, depth, and height, respectively. Fine-tune these to get the perfect perspective. #### 🏞️ Grid / Surface Grid X/Y resolution: Controls the detail level of the 3D mesh. Higher values create a smoother surface but may use more resources. Fill surface strips: Toggles the beautiful color gradient on the surface. Show wireframe lines: Toggles the visibility of the grid lines. Show nodes (markers): Toggles the small dots at each grid intersection point. #### 🏔️ Peaks / Mountains Fill peaks volume: Draws vertical lines on high peaks, giving them a sense of volume. Fill peaks surface: Draws a cross-hatch pattern on the surface of high peaks. Peak height threshold: Defines the minimum height for a peak to receive the fill effect. Peak fill color/density: Customizes the appearance of the fill lines. #### 🚩 Flags (3D) Show Flag on Summit: A master switch to show or hide the flag and emblem entirely. Flag height, width, etc.: Provides full control over the dimensions and orientation of the flag on the highest peak. #### 🎨 Color Palette Base Gradient Palette: Choose from 13 stunning, pre-designed color themes for the landscape, from the classic SUNSET_WAVE to vibrant themes like NEON_DREAM and OCEANIC . #### 🛡️ Emblem / Badge Controls This section gives you granular control over every element of the custom emblem on the flag. Tweak rotation, offsets, and scale to design your unique logo. --- 👨‍💻 Developer's Corner: Modifying the Core Logic If you're a developer and wish to customize the indicator's core data source, this section is for you. The script is designed to be modular, making it easy to change what data is being ranked and visualized. The heart of the data retrieval and ranking logic is within the f_getSortedCryptoData() function. Here’s how you can modify it: 1. Changing the Data Source (from Market Cap to something else): The current logic uses request.security("CRYPTOCAP:" + syms.get(i), ...) to fetch market capitalization data. To change this, you need to modify this line. Example: Ranking by RSI (14) on the Daily timeframe. First, you'll need a function to calculate RSI. Add this function to the script: f_getRSI(symbol, timeframe, length) => request.security(symbol, timeframe, ta.rsi(close, length)) Then, inside f_getSortedCryptoData() , find the `for` loop that populates the `caps` array and replace the `request.security` call: // OLD LINE: // caps.set(i, request.security("CRYPTOCAP:" + syms.get(i), timeframe.period, close)) // NEW LINE for RSI: // Note: You'll need to decide how to format the symbol name (e.g., "BINANCE:" + syms.get(i) + "USDT") caps.set(i, f_getRSI("BINANCE:" + syms.get(i) + "USDT", "D", 14)) 2. Changing the Data Formatting: The ranking values are formatted for display using the f_fmtCap() function, which currently formats large numbers into "M" (millions), "B" (billions), etc. If you change the data source to something like RSI, you'll want to change the formatting. You can modify f_fmtCap() or create a new formatting function. Example: Formatting for RSI. // Modify f_fmtCap or create f_fmtRSI f_fmtRSI(float v) => str.tostring(v, "#.##") // Simply format to two decimal places Remember to update the calls to this function in the main drawing loop where the labels are created (e.g., str.format("{0}: {1}", crypto.symbol, f_fmtCap(crypto.cap)) ). By modifying these key functions ( f_getSortedCryptoData and f_fmtCap ), you can adapt the Market Cap Landscape 3D to visualize and rank almost any dataset you can imagine, from technical indicators to fundamental data. --- We hope you enjoy using the Market Cap Landscape 3D as much as we enjoyed creating it. Happy charting! ✨
อินดิเคเตอร์ Pine Script®
โดย hapharmonic
13
ZigzagLiteLibrary "ZigzagLite" Lighter version of the Zigzag Library. Without indicators and sub-component divisions method getPrices(pivots)   Gets the array of prices from array of Pivots   Namespace types: Pivot   Parameters:      pivots (Pivot ) : array array of Pivot objects   Returns: array array of pivot prices method getBars(pivots)   Gets the array of bars from array of Pivots   Namespace types: Pivot   Parameters:      pivots (Pivot ) : array array of Pivot objects   Returns: array array of pivot bar indices method getPoints(pivots)   Gets the array of chart.point from array of Pivots   Namespace types: Pivot   Parameters:      pivots (Pivot ) : array array of Pivot objects   Returns: array array of pivot points method getPoints(this)   Namespace types: Zigzag   Parameters:      this (Zigzag) method calculate(this, ohlc, ltfHighTime, ltfLowTime)   Calculate zigzag based on input values and indicator values   Namespace types: Zigzag   Parameters:      this (Zigzag) : Zigzag object      ohlc (float ) : Array containing OHLC values. Can also have custom values for which zigzag to be calculated      ltfHighTime (int) : Used for multi timeframe zigzags when called within request.security. Default value is current timeframe open time.      ltfLowTime (int) : Used for multi timeframe zigzags when called within request.security. Default value is current timeframe open time.   Returns: current Zigzag object method calculate(this)   Calculate zigzag based on properties embedded within Zigzag object   Namespace types: Zigzag   Parameters:      this (Zigzag) : Zigzag object   Returns: current Zigzag object method nextlevel(this)   Namespace types: Zigzag   Parameters:      this (Zigzag) method clear(this)   Clears zigzag drawings array   Namespace types: ZigzagDrawing   Parameters:      this (ZigzagDrawing ) : array   Returns: void method clear(this)   Clears zigzag drawings array   Namespace types: ZigzagDrawingPL   Parameters:      this (ZigzagDrawingPL ) : array   Returns: void method drawplain(this)   draws fresh zigzag based on properties embedded in ZigzagDrawing object without trying to calculate   Namespace types: ZigzagDrawing   Parameters:      this (ZigzagDrawing) : ZigzagDrawing object   Returns: ZigzagDrawing object method drawplain(this)   draws fresh zigzag based on properties embedded in ZigzagDrawingPL object without trying to calculate   Namespace types: ZigzagDrawingPL   Parameters:      this (ZigzagDrawingPL) : ZigzagDrawingPL object   Returns: ZigzagDrawingPL object method drawfresh(this, ohlc)   draws fresh zigzag based on properties embedded in ZigzagDrawing object   Namespace types: ZigzagDrawing   Parameters:      this (ZigzagDrawing) : ZigzagDrawing object      ohlc (float ) : values on which the zigzag needs to be calculated and drawn. If not set will use regular OHLC   Returns: ZigzagDrawing object method drawcontinuous(this, ohlc)   draws zigzag based on the zigzagmatrix input   Namespace types: ZigzagDrawing   Parameters:      this (ZigzagDrawing) : ZigzagDrawing object      ohlc (float ) : values on which the zigzag needs to be calculated and drawn. If not set will use regular OHLC   Returns: PivotCandle   PivotCandle represents data of the candle which forms either pivot High or pivot low or both   Fields:      _high (series float) : High price of candle forming the pivot      _low (series float) : Low price of candle forming the pivot      length (series int) : Pivot length      pHighBar (series int) : represents number of bar back the pivot High occurred.      pLowBar (series int) : represents number of bar back the pivot Low occurred.      pHigh (series float) : Pivot High Price      pLow (series float) : Pivot Low Price Pivot   Pivot refers to zigzag pivot. Each pivot can contain various data   Fields:      point (chart.point) : pivot point coordinates      dir (series int) : direction of the pivot. Valid values are 1, -1, 2, -2      level (series int) : is used for multi level zigzags. For single level, it will always be 0      ratio (series float) : Price Ratio based on previous two pivots      sizeRatio (series float) ZigzagFlags   Flags required for drawing zigzag. Only used internally in zigzag calculation. Should not set the values explicitly   Fields:      newPivot (series bool) : true if the calculation resulted in new pivot      doublePivot (series bool) : true if the calculation resulted in two pivots on same bar      updateLastPivot (series bool) : true if new pivot calculated replaces the old one. Zigzag   Zigzag object which contains whole zigzag calculation parameters and pivots   Fields:      length (series int) : Zigzag length. Default value is 5      numberOfPivots (series int) : max number of pivots to hold in the calculation. Default value is 20      offset (series int) : Bar offset to be considered for calculation of zigzag. Default is 0 - which means calculation is done based on the latest bar.      level (series int) : Zigzag calculation level - used in multi level recursive zigzags      zigzagPivots (Pivot ) : array which holds the last n pivots calculated.      flags (ZigzagFlags) : ZigzagFlags object which is required for continuous drawing of zigzag lines. ZigzagObject   Zigzag Drawing Object   Fields:      zigzagLine (series line) : Line joining two pivots      zigzagLabel (series label) : Label which can be used for drawing the values, ratios, directions etc. ZigzagProperties   Object which holds properties of zigzag drawing. To be used along with ZigzagDrawing   Fields:      lineColor (series color) : Zigzag line color. Default is color.blue      lineWidth (series int) : Zigzag line width. Default is 1      lineStyle (series string) : Zigzag line style. Default is line.style_solid.      showLabel (series bool) : If set, the drawing will show labels on each pivot. Default is false      textColor (series color) : Text color of the labels. Only applicable if showLabel is set to true.      maxObjects (series int) : Max number of zigzag lines to display. Default is 300      xloc (series string) : Time/Bar reference to be used for zigzag drawing. Default is Time - xloc.bar_time.      curved (series bool) : Boolean field to print curved zigzag - used only with polyline implementation ZigzagDrawing   Object which holds complete zigzag drawing objects and properties.   Fields:      zigzag (Zigzag) : Zigzag object which holds the calculations.      properties (ZigzagProperties) : ZigzagProperties object which is used for setting the display styles of zigzag      drawings (ZigzagObject ) : array which contains lines and labels of zigzag drawing. ZigzagDrawingPL   Object which holds complete zigzag drawing objects and properties - polyline version   Fields:      zigzag (Zigzag) : Zigzag object which holds the calculations.      properties (ZigzagProperties) : ZigzagProperties object which is used for setting the display styles of zigzag      zigzagLabels (label )      zigzagLine (series polyline) : polyline object of zigzag lines
ไลบรารี Pine Script®
โดย Trendoscope
14
ZigzagLibrary "Zigzag" Zigzag related user defined types. Depends on DrawingTypes library for basic types method tostring(this, sortKeys, sortOrder, includeKeys)   Converts ZigzagTypes/Pivot object to string representation   Namespace types: Pivot   Parameters:      this (Pivot) : ZigzagTypes/Pivot      sortKeys (bool) : If set to true, string output is sorted by keys.      sortOrder (int) : Applicable only if sortKeys is set to true. Positive number will sort them in ascending order whreas negative numer will sort them in descending order. Passing 0 will not sort the keys      includeKeys (string ) : Array of string containing selective keys. Optional parmaeter. If not provided, all the keys are considered   Returns: string representation of ZigzagTypes/Pivot method tostring(this, sortKeys, sortOrder, includeKeys)   Converts Array of Pivot objects to string representation   Namespace types: Pivot   Parameters:      this (Pivot ) : Pivot object array      sortKeys (bool) : If set to true, string output is sorted by keys.      sortOrder (int) : Applicable only if sortKeys is set to true. Positive number will sort them in ascending order whreas negative numer will sort them in descending order. Passing 0 will not sort the keys      includeKeys (string ) : Array of string containing selective keys. Optional parmaeter. If not provided, all the keys are considered   Returns: string representation of Pivot object array method tostring(this)   Converts ZigzagFlags object to string representation   Namespace types: ZigzagFlags   Parameters:      this (ZigzagFlags) : ZigzagFlags object   Returns: string representation of ZigzagFlags method tostring(this, sortKeys, sortOrder, includeKeys)   Converts ZigzagTypes/Zigzag object to string representation   Namespace types: Zigzag   Parameters:      this (Zigzag) : ZigzagTypes/Zigzagobject      sortKeys (bool) : If set to true, string output is sorted by keys.      sortOrder (int) : Applicable only if sortKeys is set to true. Positive number will sort them in ascending order whreas negative numer will sort them in descending order. Passing 0 will not sort the keys      includeKeys (string ) : Array of string containing selective keys. Optional parmaeter. If not provided, all the keys are considered   Returns: string representation of ZigzagTypes/Zigzag method calculate(this, ohlc, indicators, indicatorNames)   Calculate zigzag based on input values and indicator values   Namespace types: Zigzag   Parameters:      this (Zigzag) : Zigzag object      ohlc (float ) : Array containing OHLC values. Can also have custom values for which zigzag to be calculated      indicators (matrix) : Array of indicator values      indicatorNames (string ) : Array of indicator names for which values are present. Size of indicators array should be equal to that of indicatorNames   Returns: current Zigzag object method calculate(this)   Calculate zigzag based on properties embedded within Zigzag object   Namespace types: Zigzag   Parameters:      this (Zigzag) : Zigzag object   Returns: current Zigzag object method nextlevel(this)   Calculate Next Level Zigzag based on the current calculated zigzag object   Namespace types: Zigzag   Parameters:      this (Zigzag) : Zigzag object   Returns: Next Level Zigzag object method clear(this)   Clears zigzag drawings array   Namespace types: ZigzagDrawing   Parameters:      this (ZigzagDrawing ) : array   Returns: void method drawplain(this)   draws fresh zigzag based on properties embedded in ZigzagDrawing object without trying to calculate   Namespace types: ZigzagDrawing   Parameters:      this (ZigzagDrawing) : ZigzagDrawing object   Returns: ZigzagDrawing object method drawfresh(this, ohlc, indicators, indicatorNames)   draws fresh zigzag based on properties embedded in ZigzagDrawing object   Namespace types: ZigzagDrawing   Parameters:      this (ZigzagDrawing) : ZigzagDrawing object      ohlc (float ) : values on which the zigzag needs to be calculated and drawn. If not set will use regular OHLC      indicators (matrix) : Array of indicator values      indicatorNames (string ) : Array of indicator names for which values are present. Size of indicators array should be equal to that of indicatorNames   Returns: ZigzagDrawing object method drawcontinuous(this, ohlc, indicators, indicatorNames)   draws zigzag based on the zigzagmatrix input   Namespace types: ZigzagDrawing   Parameters:      this (ZigzagDrawing) : ZigzagDrawing object      ohlc (float ) : values on which the zigzag needs to be calculated and drawn. If not set will use regular OHLC      indicators (matrix) : Array of indicator values      indicatorNames (string ) : Array of indicator names for which values are present. Size of indicators array should be equal to that of indicatorNames   Returns: method getPrices(pivots)   Namespace types: Pivot   Parameters:      pivots (Pivot ) method getBars(pivots)   Namespace types: Pivot   Parameters:      pivots (Pivot ) Indicator   Indicator is collection of indicator values applied on high, low and close   Fields:      indicatorHigh (series float) : Indicator Value applied on High      indicatorLow (series float) : Indicator Value applied on Low PivotCandle   PivotCandle represents data of the candle which forms either pivot High or pivot low or both   Fields:      _high (series float) : High price of candle forming the pivot      _low (series float) : Low price of candle forming the pivot      length (series int) : Pivot length      pHighBar (series int) : represents number of bar back the pivot High occurred.      pLowBar (series int) : represents number of bar back the pivot Low occurred.      pHigh (series float) : Pivot High Price      pLow (series float) : Pivot Low Price      indicators (Indicator ) : Array of Indicators - allows to add multiple Pivot   Pivot refers to zigzag pivot. Each pivot can contain various data   Fields:      point (chart.point) : pivot point coordinates      dir (series int) : direction of the pivot. Valid values are 1, -1, 2, -2      level (series int) : is used for multi level zigzags. For single level, it will always be 0      componentIndex (series int) : is the lower level zigzag array index for given pivot. Used only in multi level Zigzag Pivots      subComponents (series int) : is the number of sub waves per each zigzag wave. Only applicable for multi level zigzags      microComponents (series int) : is the number of base zigzag components in a zigzag wave      ratio (series float) : Price Ratio based on previous two pivots      sizeRatio (series float)      subPivots (Pivot )      indicatorNames (string ) : Names of the indicators applied on zigzag      indicatorValues (float ) : Values of the indicators applied on zigzag      indicatorRatios (float ) : Ratios of the indicators applied on zigzag based on previous 2 pivots ZigzagFlags   Flags required for drawing zigzag. Only used internally in zigzag calculation. Should not set the values explicitly   Fields:      newPivot (series bool) : true if the calculation resulted in new pivot      doublePivot (series bool) : true if the calculation resulted in two pivots on same bar      updateLastPivot (series bool) : true if new pivot calculated replaces the old one. Zigzag   Zigzag object which contains whole zigzag calculation parameters and pivots   Fields:      length (series int) : Zigzag length. Default value is 5      numberOfPivots (series int) : max number of pivots to hold in the calculation. Default value is 20      offset (series int) : Bar offset to be considered for calculation of zigzag. Default is 0 - which means calculation is done based on the latest bar.      level (series int) : Zigzag calculation level - used in multi level recursive zigzags      zigzagPivots (Pivot ) : array which holds the last n pivots calculated.      flags (ZigzagFlags) : ZigzagFlags object which is required for continuous drawing of zigzag lines. ZigzagObject   Zigzag Drawing Object   Fields:      zigzagLine (series line) : Line joining two pivots      zigzagLabel (series label) : Label which can be used for drawing the values, ratios, directions etc. ZigzagProperties   Object which holds properties of zigzag drawing. To be used along with ZigzagDrawing   Fields:      lineColor (series color) : Zigzag line color. Default is color.blue      lineWidth (series int) : Zigzag line width. Default is 1      lineStyle (series string) : Zigzag line style. Default is line.style_solid.      showLabel (series bool) : If set, the drawing will show labels on each pivot. Default is false      textColor (series color) : Text color of the labels. Only applicable if showLabel is set to true.      maxObjects (series int) : Max number of zigzag lines to display. Default is 300      xloc (series string) : Time/Bar reference to be used for zigzag drawing. Default is Time - xloc.bar_time. ZigzagDrawing   Object which holds complete zigzag drawing objects and properties.   Fields:      zigzag (Zigzag) : Zigzag object which holds the calculations.      properties (ZigzagProperties) : ZigzagProperties object which is used for setting the display styles of zigzag      drawings (ZigzagObject ) : array which contains lines and labels of zigzag drawing.
ไลบรารี Pine Script®
โดย Trendoscope
7
Angled Volume Profile [Trendoscope]Volume profile is useful tool to understand the demand and supply zones on horizontal level. But, what if you want to measure the volume levels over trend line? In trending markets, the feature to measure volume over angled levels can be very useful for traders who use these measures. Here is an attempt to provide such tool. 🎲 How to use 🎯 Interactive input for selecting starting point and angle. Upon loading the script, you will be prompted to select Start time and price - this is a point which you can select by moving the maroon highlighted label. End price - though this is shown as maroon bullet, this is price only input. Hence, when you click on the bullet, a horizontal line will appear. Users can move the line to use different End price. Start and End price are used for identifying the angle at which volume profile need to be calculated. Whereas start time is used as starting time of the volume profile. Last bar of the chart is considered as ending bar. 🎯 Other settings. From settings, users can select the colour of volume profile and style. Step multiplier defines the distance at which the profile lines needs to be drawn. Higher multiplier leads to less dense profile lines whereas lower multiplier leads to higher density of profile lines. 🎲 Limitations 🎯 Max 500 lines Pinescript only allows max 500 lines on an indicator. Due to this, if we set very low multiplier - this can lead to more than 500 profile lines. Due to this some lines can get removed. On the contrary, if multiplier is too high, then you will see very few lines which may not be meaningful. Hence, it is important to select optimal multiplier based on your timeframe 🎯 No updates on new bar Since the profile can spawn many bars, it is not possible to recalculate the whole volume profile when price creates new bars. Hence, there will not be visual update when new bars are created. But, to update the chart, users only need to make another movement of Start or ending point on interactive input.
อินดิเคเตอร์ Pine Script®
โดย Trendoscope
24
MathEasingFunctionsLibrary "MathEasingFunctions" A collection of Easing functions. Easing functions are commonly used for smoothing actions over time, They are used to smooth out the sharp edges of a function and make it more pleasing to the eye, like for example the motion of a object through time. Easing functions can be used in a variety of applications, including animation, video games, and scientific simulations. They are a powerful tool for creating realistic visual effects and can help to make your work more engaging and enjoyable to the eye. --- Includes functions for ease in, ease out, and, ease in and out, for the following constructs: sine, quadratic, cubic, quartic, quintic, exponential, elastic, circle, back, bounce. --- Reference: easings.net learn.microsoft.com ease_in_sine_unbound(v)   Sinusoidal function, the position over elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_sine(v)   Sinusoidal function, the position over elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_sine_unbound(v)   Sinusoidal function, the position over elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_sine(v)   Sinusoidal function, the position over elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_sine_unbound(v)   Sinusoidal function, the position over elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_sine(v)   Sinusoidal function, the position over elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quad_unbound(v)   Quadratic function, the position equals the square of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quad(v)   Quadratic function, the position equals the square of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quad_unbound(v)   Quadratic function, the position equals the square of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quad(v)   Quadratic function, the position equals the square of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quad_unbound(v)   Quadratic function, the position equals the square of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quad(v)   Quadratic function, the position equals the square of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_cubic_unbound(v)   Cubic function, the position equals the cube of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_cubic(v)   Cubic function, the position equals the cube of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_cubic_unbound(v)   Cubic function, the position equals the cube of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_cubic(v)   Cubic function, the position equals the cube of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_cubic_unbound(v)   Cubic function, the position equals the cube of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_cubic(v)   Cubic function, the position equals the cube of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quart_unbound(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quart(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quart_unbound(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quart(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quart_unbound(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quart(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quint_unbound(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quint(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quint_unbound(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quint(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quint_unbound(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quint(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_expo_unbound(v)   Exponential function, the position equals the exponential formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_expo(v)   Exponential function, the position equals the exponential formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_expo_unbound(v)   Exponential function, the position equals the exponential formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_expo(v)   Exponential function, the position equals the exponential formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_expo_unbound(v)   Exponential function, the position equals the exponential formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_expo(v)   Exponential function, the position equals the exponential formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_circ_unbound(v)   Circular function, the position equals the circular formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_circ(v)   Circular function, the position equals the circular formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_circ_unbound(v)   Circular function, the position equals the circular formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_circ(v)   Circular function, the position equals the circular formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_circ_unbound(v)   Circular function, the position equals the circular formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_circ(v)   Circular function, the position equals the circular formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_back_unbound(v)   Back function, the position retreats a bit before resuming (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_back(v)   Back function, the position retreats a bit before resuming (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_back_unbound(v)   Back function, the position retreats a bit before resuming (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_back(v)   Back function, the position retreats a bit before resuming (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_back_unbound(v)   Back function, the position retreats a bit before resuming (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_back(v)   Back function, the position retreats a bit before resuming (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_elastic_unbound(v)   Elastic function, the position oscilates back and forth like a spring (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_elastic(v)   Elastic function, the position oscilates back and forth like a spring (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_elastic_unbound(v)   Elastic function, the position oscilates back and forth like a spring (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_elastic(v)   Elastic function, the position oscilates back and forth like a spring (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_elastic_unbound(v)   Elastic function, the position oscilates back and forth like a spring (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_elastic(v)   Elastic function, the position oscilates back and forth like a spring (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_bounce_unbound(v)   Bounce function, the position bonces from the boundery (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_bounce(v)   Bounce function, the position bonces from the boundery (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_bounce_unbound(v)   Bounce function, the position bonces from the boundery (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_bounce(v)   Bounce function, the position bonces from the boundery (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_bounce_unbound(v)   Bounce function, the position bonces from the boundery (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_bounce(v)   Bounce function, the position bonces from the boundery (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. select(v, formula, effect, bounded)   Parameters:      v (float)      formula (string)      effect (string)      bounded (bool)
ไลบรารี Pine Script®
โดย RicardoSantos
6
Treasury Yields Heatmap [By MUQWISHI]▋ INTRODUCTION : The “Treasury Yields Heatmap” generates a dynamic heat map table, showing treasury yield bond values corresponding with dates. In the last column, it presents the status of the yield curve, discerning whether it’s in a normal, flat, or inverted configuration, which determined by using Pearson's linear regression coefficient. This tool is built to offer traders essential insights for effectively tracking bond values and monitoring yield curve status, featuring the flexibility to input a starting period, timeframe, and select from a range of major countries' bond data. _______________________ ▋ OVERVIEW: ______________________ ▋ YIELD CURVE: It is determined through Pearson's linear regression coefficient and considered… R ≥ 0.7 → Normal 0.7 > R ≥ 0.35 → Slight Normal 0.35 > R > -0.35 → Flat -0.35 ≥ R > -0.7 → Slight Inverted -0.7 ≥ R → Inverted _______________________ ▋ INDICATOR SETTINGS: #Section One: Table Setting #Section Two: Technical Setting (1) Country: Select country’s treasury yields data (2) Timeframe: Time interval. (3) Fetch By:  (3A) Date: Retrieve data by beginning of date.  (3B) Period: Retrieve data by specifying the number of time series back. Enjoy. Please let me know if you have any questions. Thank you.
อินดิเคเตอร์ Pine Script®
โดย MUQWISHI
2