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Timesessions

holidays_2010to2015Library "holidays_2010to2015" This dataset is part of my "MarketHolidays" library. The datasets were split into different libraries to overcome compiling limitations, streamline the process of removing specific time frames if not needed, and to enhance code execution speed. The timestamps are generated using a custom Python script that employs the 'pandas_market_calendars' library. To build your own set of arrays, you can find the script and instructions at github.com holidays(_country)   Parameters:      _country (string)
Perpustakaan Pine Script™
oleh Protervus
holidays_2005to2010Library "holidays_2005to2010" This dataset is part of my "MarketHolidays" library. The datasets were split into different libraries to overcome compiling limitations, streamline the process of removing specific time frames if not needed, and to enhance code execution speed. The timestamps are generated using a custom Python script that employs the 'pandas_market_calendars' library. To build your own set of arrays, you can find the script and instructions at github.com holidays(_country)   Parameters:      _country (string)
Perpustakaan Pine Script™
oleh Protervus
holidays_2000to2005Library "holidays_2000to2005" This dataset is part of my "MarketHolidays" library. The datasets were split into different libraries to overcome compiling limitations, streamline the process of removing specific time frames if not needed, and to enhance code execution speed. The timestamps are generated using a custom Python script that employs the 'pandas_market_calendars' library. To build your own set of arrays, you can find the script and instructions at github.com holidays(_country)   Parameters:      _country (string)
Perpustakaan Pine Script™
oleh Protervus
holidays_1990to2000Library "holidays_1990to2000" This dataset is part of my "MarketHolidays" library. The datasets were split into different libraries to overcome compiling limitations, streamline the process of removing specific time frames if not needed, and to enhance code execution speed. The timestamps are generated using a custom Python script that employs the 'pandas_market_calendars' library. To build your own set of arrays, you can find the script and instructions at github.com holidays(_country)   Parameters:      _country (string)
Perpustakaan Pine Script™
oleh Protervus
holidays_1980to1990Library "holidays_1980to1990" This dataset is part of my "MarketHolidays" library. The datasets were split into different libraries to overcome compiling limitations, streamline the process of removing specific time frames if not needed, and to enhance code execution speed. The timestamps are generated using a custom Python script that employs the 'pandas_market_calendars' library. To build your own set of arrays, you can find the script and instructions at github.com holidays(_country)   Parameters:      _country (string)
Perpustakaan Pine Script™
oleh Protervus
holidays_1970to1980Library "holidays_1970to1980" This dataset is part of my "MarketHolidays" library. The datasets were split into different libraries to overcome compiling limitations, streamline the process of removing specific time frames if not needed, and to enhance code execution speed. The timestamps are generated using a custom Python script that employs the 'pandas_market_calendars' library. To build your own set of arrays, you can find the script and instructions at github.com holidays(_country)   Parameters:      _country (string)
Perpustakaan Pine Script™
oleh Protervus
holidays_1962to1970Library "holidays_1962to1970" This dataset is part of my "MarketHolidays" library. The datasets were split into different libraries to overcome compiling limitations, streamline the process of removing specific time frames if not needed, and to enhance code execution speed. The timestamps are generated using a custom Python script that employs the 'pandas_market_calendars' library. To build your own set of arrays, you can find the script and instructions at github.com holidays(_country)   Parameters:      _country (string)
Perpustakaan Pine Script™
oleh Protervus
chrono_utilsLibrary "chrono_utils" 📝 Description Collection of objects and common functions that are related to datetime windows session days and time ranges. The main purpose of this library is to handle time-related functionality and make it easy to reason about a future bar checking if it will be part of a predefined session and/or inside a datetime window. All existing session functionality I found in the documentation e.g. "not na(time(timeframe, session, timezone))" are not suitable for strategy scripts, since the execution of the orders is delayed by one bar, due to the script execution happening at the bar close. Moreover, a history operator with a negative value that looks forward is not allowed in any pinescript expression. So, a prediction for the next bar using the bars_back argument of "time()"" and "time_close()" was necessary. Thus, I created this library to overcome this small but very important limitation. In the meantime, I added useful functionality to handle session-based behavior. An interesting utility that emerged from this development is data anomaly detection where a comparison between the prediction and the actual value is happening. If those two values are different then a data inconsistency happens between the prediction bar and the actual bar (probably due to a holiday, half session day, a timezone change etc..) 🤔 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/chrono_utils/2 as chr To check if a future bar will be inside a window first of all you have to initialize a DateTimeWindow object. A code example is the following: var dateTimeWindow = chr.DateTimeWindow.new().init(fromDateTime = timestamp('01 Jan 2023 00:00'), toDateTime = timestamp('01 Jan 2024 00:00')) Then you have to "ask" the dateTimeWindow if the future bar defined by an offset (default is 1 that corresponds th the next bar), will be inside that window: // Filter bars outside of the datetime window bool dateFilterApproval = dateTimeWindow.is_bar_included() You can visualize the result by drawing the background of the bars that are outside the given window: bgcolor(color = dateFilterApproval ? na : color.new(color.fuchsia, 90), offset = 1, title = 'Datetime Window Filter') In the same way, you can "ask" the Session if the future bar defined by an offset it will be inside that session. First of all, you should initialize a Session object. A code example is the following: var sess = chr.Session.new().from_sess_string(sess = '0800-1700:23456', refTimezone = 'UTC') Then check if the given bar defined by the offset (default is 1 that corresponds th the next bar), will be inside the session like that: // Filter bars outside the sessions bool sessionFilterApproval = view.sess.is_bar_included() You can visualize the result by drawing the background of the bars that are outside the given session: bgcolor(color = sessionFilterApproval ? na : color.new(color.red, 90), offset = 1, title = 'Session Filter') In case you want to visualize multiple session ranges you can create a SessionView object like that: var view = SessionView.new().init(SessionDays.new().from_sess_string('2345'), array.from(SessionTimeRange.new().from_sess_string('0800-1600'), SessionTimeRange.new().from_sess_string('1300-2200')), array.from('London', 'New York'), array.from(color.blue, color.orange)) and then call the draw method of the SessionView object like that: view.draw() 🏋️‍♂️ Please refer to the "EXAMPLE DATETIME WINDOW FILTER" and "EXAMPLE SESSION FILTER" regions of the script for more advanced code examples of how to utilize the full potential of this library, including user input settings and advanced visualization! ⚠️ Caveats As I mentioned in the description there are some cases that the prediction of the next bar is not accurate. A wrong prediction will affect the outcome of the filtering. The main reasons this could happen are the following: Public holidays when the market is closed Half trading days usually before public holidays Change in the daylight saving time (DST) A data anomaly of the chart, where there are missing and/or inconsistent data. A bug in this library (Please report by PM sending the symbol, timeframe, and settings) Special thanks to @robbatt and @skinra for the constructive feedback 🏆. Without them, the exposed API of this library would be very lengthy and complicated to use. Thanks to them, now the user of this library will be able to get the most, with only a few lines of code!
Perpustakaan Pine Script™
oleh jason5480
6
SessionVolumeProfileLibrary "SessionVolumeProfile" Analyzes price & volume during regular trading hours to provide a session volume profile analysis. The primary goal of this library is to provide the developer with three values: the value area high, low and the point of control. The library also provides methods for rendering the value areas and histograms. To learn more about this library and how you can use it, click on the website link in my profile where you will find a blog post with detailed information. debug(vp, position)   Helper function to write some information about the supplied SVP object to the screen in a table.   Parameters:      vp (Object) : The SVP object to debug      position (string) : The position.* to place the table. Defaults to position.bottom_center getLowerTimeframe()   Depending on the timeframe of the chart, determines a lower timeframe to grab volume data from for the analysis   Returns: The timeframe string to fetch volume for get(volumeProfile, lowerTimeframeHigh, lowerTimeframeLow, lowerTimeframeVolume)   Populated the provided SessionVolumeProfile object with vp data on the session.   Parameters:      volumeProfile (Object) : The SessionVolumeProfile object to populate      lowerTimeframeHigh (float ) : The lower timeframe high values      lowerTimeframeLow (float ) : The lower timeframe low values      lowerTimeframeVolume (float ) : The lower timeframe volume values drawPriorValueAreas(todaySessionVolumeProfile, extendYesterdayOverToday, showLabels, labelSize, pocColor, pocStyle, pocWidth, vahlColor, vahlStyle, vahlWidth, vaColor)   Given a SessionVolumeProfile Object, will render the historical value areas for that object.   Parameters:      todaySessionVolumeProfile (Object) : The SessionVolumeProfile Object to draw      extendYesterdayOverToday (bool) : Defaults to true      showLabels (bool) : Defaults to true      labelSize (string) : Defaults to size.small      pocColor (color) : Defaults to #e500a4      pocStyle (string) : Defaults to line.style_solid      pocWidth (int) : Defaults to 1      vahlColor (color) : The color of the value area high/low lines. Defaults to #1592e6      vahlStyle (string) : The style of the value area high/low lines. Defaults to line.style_solid      vahlWidth (int) : The width of the value area high/low lines. Defaults to 1      vaColor (color) : The color of the value area background. Defaults to #00bbf911) drawHistogram(volumeProfile, bgColor, showVolumeOnHistogram)   Given a SessionVolumeProfile object, will render the histogram for that object.   Parameters:      volumeProfile (Object) : The SessionVolumeProfile object to draw      bgColor (color) : The baseline color to use for the histogram. Defaults to #00bbf9      showVolumeOnHistogram (bool) : Show the volume amount on the histogram bars. Defaults to false. Object   Fields:      numberOfRows (series__integer)      valueAreaCoverage (series__integer)      trackDevelopingVa (series__bool)      valueAreaHigh (series__float)      pointOfControl (series__float)      valueAreaLow (series__float)      startTime (series__integer)      endTime (series__integer)      dayHigh (series__float)      dayLow (series__float)      step (series__float)      pointOfControlLevel (series__integer)      valueAreaHighLevel (series__integer)      valueAreaLowLevel (series__integer)      volumeRows (array__float)      priceLevelRows (array__float)      ltfSessionHighs (array__float)      ltfSessionLows (array__float)      ltfSessionVols (array__float)
Perpustakaan Pine Script™
oleh jmosullivan
26
lib_trackingLibrary "lib_tracking" tracking highest and lowest with anchor point to track over dynamic periods, e.g. to track a Session HH/LL live and get the bar/time of the LTF wick that matches the HTF HH/LL // DESIGN DECISION // why anchored replacements for ta.highest / ta.highestbars / ta.lowest / ta.lowestbars: // 1. they require a fixed length/lookback which makes it easier to calculate, but // 2. this prevents us from tracking the HH/LL of a changing timeframe, e.g. live tracking the HH/LL of a running session or unfinished higher timeframe // 3. tracking with anchor/start/reset flag allows to persist values until the next start/reset, so no other external storage is required track_highest(value, reset, track_this_bar)   Parameters:      value (float)      reset (bool) : boolean flag to restart tracking from this point (a.k.a anchor)      track_this_bar (bool) : allows enabling and disabling of tracking, e.g. before a session starts or after it ends, values can be kept until next reset. track_lowest(value, reset, track_this_bar)   Parameters:      value (float)      reset (bool) : boolean flag to restart tracking from this point (a.k.a anchor)      track_this_bar (bool) : allows enabling and disabling of tracking, e.g. before a session starts or after it ends, values can be kept until next reset. track_hl_htf(htf, value_high, value_low)   Parameters:      htf (string) : the higher timeframe in pinescript string notation      value_high (float)      value_low (float)   Returns:
Perpustakaan Pine Script™
oleh robbatt
RelativeValue█  OVERVIEW This library is a Pine Script™ programmer's tool offering the ability to compute relative values, which represent comparisons of current data points, such as volume, price, or custom indicators, with their analogous historical data points from corresponding time offsets. This approach can provide insightful perspectives into the intricate dynamics of relative market behavior over time. █  CONCEPTS Relative values In this library, a relative value is a metric that compares a current data point in a time interval to an average of data points with corresponding time offsets across historical periods. Its purpose is to assess the significance of a value by considering the historical context within past time intervals. For instance, suppose we wanted to calculate relative volume on an hourly chart over five daily periods, and the last chart bar is two hours into the current trading day. In this case, we would compare the current volume to the average of volume in the second hour of trading across five days. We obtain the relative volume value by dividing the current volume by this average. This form of analysis rests on the hypothesis that substantial discrepancies or aberrations in present market activity relative to historical time intervals might help indicate upcoming changes in market trends. Cumulative and non-cumulative values In the context of this library, a cumulative value refers to the cumulative sum of a series since the last occurrence of a specific condition (referred to as `anchor` in the function definitions). Given that relative values depend on time, we use time-based conditions such as the onset of a new hour, day, etc. On the other hand, a non-cumulative value is simply the series value at a specific time without accumulation. Calculating relative values Four main functions coordinate together to compute the relative values: `maintainArray()`, `calcAverageByTime()`, `calcCumulativeSeries()`, and `averageAtTime()`. These functions are underpinned by a `collectedData` user-defined type (UDT), which stores data collected since the last reset of the timeframe along with their corresponding timestamps. The relative values are calculated using the following procedure:  1. The `averageAtTime()` function invokes the process leveraging all four of the methods and acts as the main driver of the calculations. For each bar, this function adds the current bar's source and corresponding time value to a `collectedData` object.  2. Within the `averageAtTime()` function, the `maintainArray()` function is called at the start of each anchor period. It adds a new `collectedData` object to the array and ensures the array size does not exceed the predefined `maxSize` by removing the oldest element when necessary. This method plays an essential role in limiting memory usage and ensuring only relevant data over the desired number of periods is in the calculation window.  3. Next, the `calcAverageByTime()` function calculates the average value of elements within the `data` field for each `collectedData` object that corresponds to the same time offset from each anchor condition. This method accounts for cases where the current index of a `collectedData` object exceeds the last index of any past objects by using the last available values instead.  4. For cumulative calculations, the `averageAtTime()` function utilizes the `isCumulative` boolean parameter. If true, the `calcCumulativeSeries()` function will track the running total of the source data from the last bar where the anchor condition was met, providing a cumulative sum of the source values from one anchor point to the next. To summarize, the `averageAtTime()` function continually stores values with their corresponding times in a `collectedData` object for each bar in the anchor period. When the anchor resets, this object is added to a larger array. The array's size is limited by the specified number of periods to be averaged. To correlate data across these periods, time indexing is employed, enabling the function to compare corresponding points across multiple periods. █  USING THIS LIBRARY The library simplifies the complex process of calculating relative values through its intuitive functions. Follow the steps below to use this library in your scripts. Step 1: Import the library and declare inputs Import the library and declare variables based on the user's input. These can include the timeframe for each period, the number of time intervals to include in the average, and whether the calculation uses cumulative values. For example: //@version=5 import TradingView/RelativeValue/1 as TVrv indicator("Relative Range Demo") string resetTimeInput = input.timeframe("D") int lengthInput = input.int(5, "No. of periods") Step 2: Define the anchor condition With these inputs declared, create a condition to define the start of a new period (anchor). For this, we use the change in the time value from the input timeframe: bool anchor = timeframe.change(resetTimeInput) Step 3: Calculate the average At this point, one can calculate the average of a value's history at the time offset from the anchor over a number of periods using the `averageAtTime()` function. In this example, we use True Range (TR) as the `source` and set `isCumulative` to false: float pastRange = TVrv.averageAtTime(ta.tr, lengthInput, anchor, false) Step 4: Display the data You can visualize the results by plotting the returned series. These lines display the non-cumulative TR alongside the average value over `lengthInput` periods for relative comparison: plot(pastRange, "Past True Range Avg", color.new(chart.bg_color, 70), 1, plot.style_columns) plot(ta.tr, "True Range", close >= open ? color.new(color.teal, 50) : color.new(color.red, 50), 1, plot.style_columns) This example will display two overlapping series of columns. The green and red columns depict the current TR on each bar, and the light gray columns show the average over a defined number of periods, e.g., the default inputs on an hourly chart will show the average value at the hour over the past five days. This comparative analysis aids in determining whether the range of a bar aligns with its typical historical values or if it's an outlier. █  NOTES  • The foundational concept of this library was derived from our initial Relative Volume at Time script. This library's logic significantly boosts its performance. Keep an eye out for a forthcoming updated version of the indicator. The demonstration code included in the library emulates a streamlined version of the indicator utilizing the library functions.  • Key efficiencies in the data management are realized through array.binary_search_leftmost() , which offers a performance improvement in comparison to its loop-dependent counterpart.  • This library's architecture utilizes user-defined types (UDTs) to create custom objects which are the equivalent of variables containing multiple parts, each able to hold independent values of different types . The recently added feature was announced in this blog post.  • To enhance readability, the code substitutes array functions with equivalent methods . Look first. Then leap. █  FUNCTIONS This library contains the following functions: calcCumulativeSeries(source, anchor)   Calculates the cumulative sum of `source` since the last bar where `anchor` was `true`.   Parameters:      source (series float) : Source used for the calculation.      anchor (series bool) : The condition that triggers the reset of the calculation. The calculation is reset when `anchor` evaluates to `true`, and continues using the values accumulated since the previous reset when `anchor` is `false`.   Returns: (float) The cumulative sum of `source`. averageAtTime(source, length, anchor, isCumulative)   Calculates the average of all `source` values that share the same time difference from the `anchor` as the current bar for the most recent `length` bars.   Parameters:      source (series float) : Source used for the calculation.      length (simple int) : The number of reset periods to consider for the average calculation of historical data.      anchor (series bool) : The condition that triggers the reset of the average calculation. The calculation is reset when `anchor` evaluates to `true`, and continues using the values accumulated since the previous reset when `anchor` is `false`.      isCumulative (simple bool) : If `true`, `source` values are accumulated until the next time `anchor` is `true`. Optional. The default is `true`.   Returns: (float) The average of the source series at the specified time difference.
Perpustakaan Pine Script™
oleh TradingView
8
SessionAndTimeFct_publicLibrary "SessionAndTimeFct_public" is_in_session(sessionTime, sessionTimeZone)   : Check if actual bar is in specific session with specific time zone   Parameters:      sessionTime      sessionTimeZone is_session_start(sessionTime, sessionTimeZone)   : Check if actual bar the first bar of a specific session   Parameters:      sessionTime      sessionTimeZone is_new_day(timeZone)   : Check if a new day started   Parameters:      timeZone is_new_week(timeZone)   : Check if a new week started   Parameters:      timeZone is_new_month(timeZone)   : Check if a new month started   Parameters:      timeZone is_element_to_show_with_tf_up(base, value)   : Check if an element is to show compared to a specific timeframe >   Parameters:      base      value is_element_to_show_with_tf_down(base, value)   : Check if an element is to show compared to a specific timeframe <   Parameters:      base      value
Perpustakaan Pine Script™
oleh LudoGH68