High Risk Trading TimeLibrary "HighRiskTradingTime"
Utilities for time range labeling
openTime()
timeMinInDay(t, timezone)
Convert given time to minutes of day
Parameters:
t (int) : Time
timezone (string) : Timezone of the input h:m
@return Minutes of day
All exported functions args should be typified
timeMinInDayManual(h, m)
Convert given hour and minute to minutes of day
Parameters:
h (int) : Hour in a day
m (int) : Minute in a day
isForexHighRiskTime()
Return if current time is High Risk for Forex
Timesessions
TimeFormattingLibraryLibrary "TimeFormattingLibrary"
Time formatting functions: formating functions to make timestrings more human readable friendly (for both fixed time and time-elapsed).
Also functions for last and first instance in month of day of week input.
Also a function for identifying bank holiday Mondays.
timeFormatFxn(showDayOfWeek, showDayOfMonth, showMonth, showYear, showHrMin, _time, _timezone)
converts time into readable format
Parameters:
showDayOfWeek (bool) : if you want to show day of week (i.e. Mon, Tues etc)
showDayOfMonth (bool) : if you want to show day number of month with superscript ordinals (i.e. 1ˢᵗ, 2ⁿᵈ, etc)
showMonth (bool) : if you want to show the month (i.e. Jan, Feb, etc)
showYear (bool) : if you want to show the year (i.e. 2023)
showHrMin (bool) : if you want to show time in 24hr clock format
_time (int) : is the unix time (i.e. time or time_close)
_timezone (string) : the user timezone input as string (e.g. "America/New_York", "UTC-5", "GMT+0530")
Returns: time date string
timeElapsedFxn(timespan)
converts timespan into readable format
Parameters:
timespan (int) : is the length of time in milliseconds to be converted into a human readable string
Returns: timespan string (whether it be a for showing 'time-elapsed' or for showing a 'countdown timer')
isFirstXdayofmonth(_dayofweek)
gives bool result for when first occurence in month of the day-of-week input
Parameters:
_dayofweek (int) : (can be integer 1-7 or can be dayofweek variable; i.e. dayofweek.wednesday)
isLastXdayofmonth(_dayofweek)
gives bool result for when last occurence in month of the day-of-week input
Parameters:
_dayofweek (int) : (can be integer 1-7 or can be dayofweek variable; i.e. dayofweek.wednesday)
wasBankHolidayMonday()
gives bool result for if yesterday was a bank holiday monday. Only for use with with request.security() function, see example code below
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.
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)
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)
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)
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)
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)
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)
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)
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)
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)
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!
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)
CalendarCadLibrary "CalendarCad"
This library provides date and time data of the important events on CAD. Data source is csv exported from www.fxstreet.com and transformed into perfered format by C# script.
HighImpactNews2015To2023()
CAD high impact news date and time from 2015 to 2023
CalendarEurLibrary "CalendarEur"
This library provides date and time data of the important events on EUR. Data source is csv exported from www.fxstreet.com and transformed into perfered format by C# script.
HighImpactNews2015To2019()
EUR high impact news date and time from 2015 to 2019
HighImpactNews2020To2023()
EUR high impact news date and time from 2020 to 2023
CalendarGbpLibrary "CalendarGbp"
This library provides date and time data of the important events on GBP. Data source is csv exported from www.fxstreet.com and transformed into perfered format by C# script.
HighImpactNews2015To2019()
GBP high impact news date and time from 2015 to 2019
HighImpactNews2020To2023()
GBP high impact news date and time from 2020 to 2023
CalendarJpyLibrary "CalendarJpy"
This library provides date and time data of the important events on JPY. Data source is csv exported from www.fxstreet.com and transformed into perfered format by C# script.
HighImpactNews2015To2023()
JPY high impact news date and time from 2015 to 2023
CalendarUsdLibrary "CalendarUsd"
This library provides date and time data of the important events on USD. Data source is csv exported from www.fxstreet.com and transformed into perfered format by C# script.
HighImpactNews2015To2019()
USD high impact news date and time from 2015 to 2019
HighImpactNews2020To2023()
USD high impact news date and time from 2020 to 2023
NewsEventsGbpLibrary "NewsEventsGbp"
This library provides date and time data of the high imact news events on GBP. Data source is csv exported from www.fxstreet.com and transformed into perfered format by C# script.
gbpNews2015To2019()
GBP high imact news date and time from 2015 to 2019
gbpNews2020To2023()
GBP high imact news date and time from 2020 to 2023
NewsEventsEurLibrary "NewsEventsEur"
This library provides date and time data of the high imact news events on EUR. Data source is csv exported from www.fxstreet.com and transformed into perfered format by C# script.
eurNews2015To2019()
EUR high imact news date and time from 2015 to 2019
eurNews2020To2023()
EUR high imact news date and time from 2020 to 2023
NewsEventsJpyLibrary "NewsEventsJpy"
This library provides date and time data of the high imact news events on JPY. Data source is csv exported from www.fxstreet.com and transformed into perfered format by C# script.
jpyNews2015To2023()
JPY high imact news date and time from 2015 to 2023
NewsEventsCadLibrary "NewsEventsCad"
This library provides date and time data of the high imact news events on CAD. Data source is csv exported from www.fxstreet.com and transformed into perfered format by C# script.
cadNews2015To2023()
CAD high imact news date and time from 2015 to 2023