เริ่มกันเลย

Timesessions

MarketHolidaysLibrary "MarketHolidays" The MarketHolidays library compiles market holidays (including historical special market closures) into arrays, which can then be utilized in TradingView indicators and strategies to account for non-trading days. 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 getHolidays(_country)   The getHolidays function aggregates holiday data from different time periods to create a single array with market holidays for a specified country.   Parameters:      _country (string) : The country code for which to retrieve market holidays. Accepts syminfo.country or pre-set country code in ISO 3166-1 alpha-2 format.   Returns: An array of timestamps of market holidays \ non-trading days for the given country.
ไลบรารี Pine Script™
โดย Protervus
3
holidays_2020to2025Library "holidays_2020to2025" 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)
ไลบรารี Pine Script™
โดย Protervus
holidays_2015to2020Library "holidays_2015to2020" 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)
ไลบรารี Pine Script™
โดย Protervus
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)
ไลบรารี Pine Script™
โดย 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)
ไลบรารี Pine Script™
โดย 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)
ไลบรารี Pine Script™
โดย 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)
ไลบรารี Pine Script™
โดย 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)
ไลบรารี Pine Script™
โดย 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)
ไลบรารี Pine Script™
โดย 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)
ไลบรารี Pine Script™
โดย 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!
ไลบรารี Pine Script™
โดย 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)
ไลบรารี Pine Script™
โดย 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:
ไลบรารี Pine Script™
โดย robbatt