Other timeframes and data

Introduction

Pine Script^® allows users to request data from sources and contexts other than those their charts use. The functions we present on this page can fetch data from a variety of alternative sources:

request.security() retrieves data from another symbol, timeframe, or other context.
request.security_lower_tf() retrieves intrabar data, i.e., data from a timeframe lower than the chart timeframe.
request.currency_rate() requests a daily rate to convert a value expressed in one currency to another.
request.dividends(), request.splits(), and request.earnings() respectively retrieve information about an issuing company’s dividends, splits, and earnings.
request.financial() retrieves financial data from FactSet.
request.economic() retrieves economic and industry data.
request.footprint() retrieves volume footprint data.
request.seed() retrieves data from a user-maintained GitHub repository.

These are the signatures of the functions in the request.* namespace:

request.security(symbol, timeframe, expression, gaps, lookahead, ignore_invalid_symbol, currency, calc_bars_count) → series <type>

request.security_lower_tf(symbol, timeframe, expression, ignore_invalid_symbol, currency, ignore_invalid_timeframe, calc_bars_count) → array<type>

request.currency_rate(from, to, ignore_invalid_currency) → series float

request.dividends(ticker, field, gaps, lookahead, ignore_invalid_symbol, currency) → series float

request.splits(ticker, field, gaps, lookahead, ignore_invalid_symbol) → series float

request.earnings(ticker, field, gaps, lookahead, ignore_invalid_symbol, currency) → series float

request.financial(symbol, financial_id, period, gaps, ignore_invalid_symbol, currency) → series float

request.economic(country_code, field, gaps, ignore_invalid_symbol) → series float

request.footprint(ticks_per_row, va_percent, imbalance_percent) → series footprint

request.seed(source, symbol, expression, ignore_invalid_symbol, calc_bars_count) → series <type>

The request.*() family of functions has numerous potential applications. Throughout this page, we discuss in detail these functions and some of their typical use cases.

Common characteristics

Many functions in the request.*() namespace share some common properties and parameters. Before we explore each function in depth, let’s familiarize ourselves with these characteristics.

Behavior

All request.*() functions have similar internal behavior, even though they do not all share the same required parameters. Every unique request.*() call in a script requests a dataset from a defined context (i.e., ticker ID and timeframe) and evaluates an expression across the retrieved data.

The request.security() and request.security_lower_tf() functions allow programmers to specify the context of a request and the expression directly via the symbol, timeframe, and expression parameters, making them suitable for a wide range of data requests.

For example, the request.security() call in this simple script requests daily “AMEX:SPY” data, and it calculates the slope of a 20-bar linear regression line using the retrieved hl2 prices. The first two arguments specify the context of the request, and the third specifies the expression to evaluate across the requested data:

//@version=6 indicator("Behavior of `request.security()` demo") //@variable The 20-bar linear regression slope of `hl2` prices from the "AMEX:SPY" symbol on the "1D" timeframe. float requestedSlope = request.security("AMEX:SPY", "1D", ta.linreg(hl2, 20, 0) - ta.linreg(hl2, 20, 1)) //@variable Is `color.teal` when the `requestedSlope` is positive, and `color.maroon` otherwise. color plotColor = requestedSlope > 0 ? color.teal : color.maroon // Plot the `requestedSlope` with the `plotColor`. plot(requestedSlope, "Requested slope", plotColor, 1, plot.style_area)

Other functions within the request.*() namespace do not allow programmers to directly define the full context of a request or the evaluated expression. Instead, these functions determine some of the necessary information internally because they perform only specific types of requests.

For instance, request.financial() exclusively retrieves periodic financial data. Its required parameters (symbol, financial_id, and period) all define parts of a specific financial ticker ID. The function does not allow specification of the timeframe or expression, as it determines these details internally. The script below demonstrates a simple call to this function that retrieves the annual cost of goods data for the chart symbol’s issuing company:

//@version=6 indicator("Behavior of `request.financial()` demo", format = format.volume) //@variable The annual cost of goods sold by the chart symbol's issuing company. float costOfGoods = request.financial(syminfo.tickerid, "COST_OF_GOODS", "FY") // Plot the `costOfGoods`. plot(costOfGoods, "Cost of goods", color.purple, 3, plot.style_stepline_diamond)

Scripts can perform up to 40 unique requests using any combination of request.*() function calls, or up to 64 if the user has the Ultimate plan. Unique request.*() calls count toward this limit because they are the only calls that fetch new data. By contrast, redundant calls to the same request.*() function with identical arguments do not typically perform new requests. Instead, they reuse the data from the first executed call. See the request.*() calls section of the Limitations page for more information.

`gaps`

When using a request.*() function to retrieve data from another context, the data might not come in on each new bar as it would with the current chart. The gaps parameter of a request.*() function controls how the function responds to nonexistent values in the requested series.

Suppose we have a script that requests hourly data for the chart’s symbol using request.security() executing on a 1-minute chart. The function call returns new values only on the 1-minute bars that cover the opening or closing times of the symbol’s hourly bars. On other chart bars, we can decide whether the function returns na values or the last available values via the gaps parameter.

If the gaps parameter uses barmerge.gaps_on, the function returns na results on all chart bars where new data is not yet confirmed from the requested context. Otherwise, if the parameter uses barmerge.gaps_off, the function fills the gaps in the requested data with the last confirmed values on historical bars and the most recent developing values on realtime bars.

The script below demonstrates the difference in behavior by plotting the results from two request.security() calls that fetch the close price of the current symbol from the hourly timeframe on a 1-minute chart. The first call uses gaps = barmerge.gaps_off and the second uses gaps = barmerge.gaps_on:

//@version=6 indicator("`gaps` demo", overlay = true) //@variable The `close` requested from the hourly timeframe without gaps. float dataWithoutGaps = request.security(syminfo.tickerid, "60", close, gaps = barmerge.gaps_off) //@variable The `close` requested from the hourly timeframe with gaps. float dataWithGaps = request.security(syminfo.tickerid, "60", close, gaps = barmerge.gaps_on) // Plot the requested data. plot(dataWithoutGaps, "Data without gaps", color.blue, 3, plot.style_linebr) plot(dataWithGaps, "Data with gaps", color.purple, 15, plot.style_linebr) // Highlight the background for realtime bars. bgcolor(barstate.isrealtime ? color.new(color.aqua, 70) : na, title = "Realtime bar highlight")

Note that:

barmerge.gaps_off is the default value for the gaps parameter in all applicable request.*() functions.
The script plots the requested series as lines with breaks (plot.style_linebr), which do not bridge over na values as the default style (plot.style_line) does.
When using barmerge.gaps_off, the request.security() function returns the last confirmed close from the hourly timeframe on all historical bars. When running on realtime bars (the bars with the color.aqua background in this example), it returns the symbol’s current close value, regardless of confirmation. For more information, see the Historical and realtime behavior section of this page.

`ignore_invalid_symbol`

The ignore_invalid_symbol parameter of request.*() functions determines how a function handles invalid data requests, e.g.:

Using a request.*() function with a nonexistent ticker ID as the symbol/ticker parameter.
Using request.financial() to retrieve information that does not exist for the specified symbol or period.
Using request.economic() to request a field that does not exist for a country_code.

A request.*() function call produces a runtime error and halts the execution of the script when making an erroneous request if its ignore_invalid_symbol parameter is false. When this parameter’s value is true, the function returns na values in such a case instead of raising an error.

This example uses request.*() calls within a user-defined function to retrieve data for estimating an instrument’s market capitalization (market cap). The user-defined calcMarketCap() function calls request.financial() to retrieve the total shares outstanding for a symbol and request.security() to retrieve a tuple containing the symbol’s close and syminfo.currency values. We’ve included ignore_invalid_symbol = true in both of these request.*() calls to prevent runtime errors for invalid requests.

The script displays a formatted string representing the symbol’s estimated market cap value and currency in a table on the chart and uses a plot() call to visualize the marketCap history:

//@version=6 indicator("`ignore_invalid_symbol` demo", "Market cap estimate", format = format.volume) //@variable The symbol to request data from. string symbol = input.symbol("TSX:SHOP", "Symbol") //@function Estimates the market capitalization of the specified `tickerID` if the data exists. calcMarketCap(simple string tickerID) => //@variable The quarterly total shares outstanding for the `tickerID`. Returns `na` when the data isn't available. float tso = request.financial(tickerID, "TOTAL_SHARES_OUTSTANDING", "FQ", ignore_invalid_symbol = true) //@variable The `close` price and currency for the `tickerID`. Returns `[na, na]` when the `tickerID` is invalid. [price, currency] = request.security( tickerID, timeframe.period, [close, syminfo.currency], ignore_invalid_symbol = true ) // Return a tuple containing the market cap estimate and the quote currency. [tso * price, currency] //@variable A `table` object with a single cell that displays the `marketCap` and `quoteCurrency`. var table infoTable = table.new(position.top_right, 1, 1) // Initialize the table's cell on the first bar. if barstate.isfirst table.cell(infoTable, 0, 0, "", text_color = color.white, text_size = size.huge, bgcolor = color.teal) // Get the market cap estimate and quote currency for the `symbol`. [marketCap, quoteCurrency] = calcMarketCap(symbol) if barstate.islast //@variable The formatted text displayed inside the `infoTable`. string tableText = str.format("Market cap:\n{0} {1}", str.tostring(marketCap, format.volume), quoteCurrency) // Update the `infoTable`. table.cell_set_text(infoTable, 0, 0, tableText) // Plot the `marketCap` value. plot(marketCap, "Market cap", color.new(color.purple, 60), style = plot.style_area)

Note that:

The calcMarketCap() function only returns non-na values on valid instruments with total shares outstanding data, such as the one we selected for this example. It returns na on others that do not have financial data, including forex, crypto, and derivatives.
Not all issuing companies publish quarterly financial reports. If the issuing company of the symbol does not report on a quarterly basis, change the “FQ” value in this script to the company’s minimum reporting period. See the request.financial() section for more information.
We included format.volume in the indicator() and str.tostring() calls to specify that the y-axis of the chart pane represents volume-formatted values and the “string” representation of the marketCap value shows as volume-formatted text.
For efficiency, this script creates a table and initializes its cell on the first chart bar, then updates the cell’s text on the last bar. To learn more about working with tables, see the Tables page.

`currency`

The currency parameter of a request.*() function enables programmers to specify the currency of the requested data. If this parameter’s value differs from the symbol’s syminfo.currency value, the function converts the requested values to express them in the specified currency. The currency parameter accepts a built-in constant from the currency.* namespace, such as currency.JPY, or a string representing a valid currency code (e.g., “JPY”). By default, this parameter accepts a “series” argument that can change across executions. However, if dynamic requests are not enabled, it accepts only a value with the “simple” qualifier or a weaker one.

The conversion rate between the syminfo.currency of the requested data and the specified currency depends on the previous daily value of the corresponding currency pair from the most popular exchange. If no exchange provides the rate directly, the function derives the rate using a spread symbol.

`lookahead`

The lookahead parameter in request.security(), request.dividends(), request.splits(), and request.earnings() specifies the lookahead behavior of the function call. Its default value is barmerge.lookahead_off.

When requesting data from a higher-timeframe (HTF) context, the lookahead value determines whether the request.*() function can return values from times beyond those of the historical bars it executes on. In other words, the lookahead paremeter determines whether the requested data may contain lookahead bias on historical bars.

When requesting data from a lower-timeframe (LTF) context, the lookahead parameter determines whether the function requests values from the first or last intrabar (LTF bar) of each chart-timeframe bar.

Programmers should exercise extreme caution when using lookahead in their requests, especially when requesting data from higher timeframes. When using barmerge.lookahead_on as the lookahead value, ensure that it does not compromise the integrity of the script’s logic by leaking future data into historical chart bars.

The following scenarios are cases where enabling lookahead is acceptable in a request.*() call:

The expression argument in a request.security() call includes a historical offset (e.g., close[1]), which prevents the function from requesting future values that it would not have access to on a realtime basis.
The timeframe argument of the call represents the same timeframe as that of the chart on which the script executes, i.e., timeframe.period.
The function call requests data from an intrabar timeframe, i.e., a timeframe smaller than the timeframe.period. See the Lower-timeframes section for more information.

This example demonstrates how the lookahead parameter affects the behavior of higher-timeframe data requests and why enabling lookahead in request.security() without offsetting the expression is misleading. The script calls request.security() to get the HTF high price for the current chart’s symbol in three different ways and plots the resulting series on the chart for comparison.

The first call uses barmerge.lookahead_off (default), and the others use barmerge.lookahead_on. However, the third request.security() call also offsets its expression using the history-referencing operator [] to avoid leaking future data into the past.

As we see on the chart, the plot of the series requested using barmerge.lookahead_on without an offset (fuchsia line) shows final HTF high prices before they are actually available on historical bars, whereas the other two calls do not:

//@version=6 indicator("`lookahead` demo", overlay = true) //@variable The timeframe to request the data from. string timeframe = input.timeframe("30", "Timeframe") //@variable The requested `high` price from the current symbol on the `timeframe` without lookahead bias. // On realtime bars, it returns the current `high` of the `timeframe`. float lookaheadOff = request.security(syminfo.tickerid, timeframe, high, lookahead = barmerge.lookahead_off) //@variable The requested `high` price from the current symbol on the `timeframe` with lookahead bias. // Returns values that should NOT be accessible yet on historical bars. float lookaheadOn = request.security(syminfo.tickerid, timeframe, high, lookahead = barmerge.lookahead_on) //@variable The requested `high` price from the current symbol on the `timeframe` without lookahead bias or repainting. // Behaves the same on historical and realtime bars. float lookaheadOnOffset = request.security(syminfo.tickerid, timeframe, high[1], lookahead = barmerge.lookahead_on) // Plot the values. plot(lookaheadOff, "High, no lookahead bias", color.new(color.blue, 40), 5) plot(lookaheadOn, "High with lookahead bias", color.fuchsia, 3) plot(lookaheadOnOffset, "High, no lookahead bias or repaint", color.aqua, 3) // Highlight the background on realtime bars. bgcolor(barstate.isrealtime ? color.new(color.orange, 60) : na, title = "Realtime bar highlight")

Note that:

The series requested using barmerge.lookahead_off has a new historical value at the end of each HTF period, and both series requested using barmerge.lookahead_on have new historical data at the start of each period.
On realtime bars, the plot of the series without lookahead (blue) and the series with lookahead and no historical offset (fuchsia) show the same value (i.e., the HTF period’s unconfirmed high price), as no data exists beyond those points to leak into the past. Both of these plots repaint their results after the user reloads the script, because the elapsed realtime bars from the previous run become historical bars in the new run.
The series that uses lookahead and a historical offset (aqua) does not repaint its results, because it always uses the last confirmed value from the higher timeframe. See the Avoiding repainting section of this page for more information.

Dynamic requests

By default, unlike all previous Pine Script versions, request.*() function calls in Pine Script v6 are dynamic.

In contrast to non-dynamic requests, dynamic requests can:

Access data from different data feeds using a single request.*() instance with “series” arguments.
Execute within the local scopes of conditional structures, loops, and exported functions.
Execute nested requests.

Aside from the features listed above, there are insignificant differences in the behavior of dynamic and non-dynamic requests. However, for backward compatibility, programmers can deactivate dynamic requests by specifying dynamic_requests = false in the indicator(), strategy(), or library() declaration statement.

“series” arguments

Scripts without dynamic requests enabled cannot use “series” arguments for most request.*() function parameters, which means the argument values cannot change. The only exception is the expression parameter in request.security(), request.security_lower_tf(), and request.seed(), which always allows “series” values.

In contrast, when a script allows dynamic requests, all request.*() function parameters that define parts of the ticker ID or timeframe of a request accept “series” arguments that can change with each script execution. In other words, with dynamic requests, it’s possible for a single request.*() instance to fetch data from different contexts in different executions. Some other optional parameters, such as ignore_invalid_symbol, can also accept “series” arguments, allowing additional flexibility in request.*() call behaviors.

The following script declares a symbolSeries variable that is assigned four different symbol strings in 20-bar cycles, with its value changing after every five bars. The request.security() call uses this variable as the symbol argument. The script plots the requestedClose values, which therefore represent a different symbol’s close prices for each five-bar period.

//@version=6 indicator("'series' arguments demo") //@variable A "series" that cycles through four different symbol strings. Its value changes every five bars. string symbolSeries = switch int(bar_index / 5) % 4 1 => "NASDAQ:MSFT" 2 => "NASDAQ:AMD" 3 => "NASDAQ:INTC" => "AMEX:SPY" //@variable The requested `close` value from one of the four `symbolSeries` values on the chart's timeframe. float requestedClose = request.security(symbolSeries, timeframe.period, close) // Plot the `requestedClose`. plot(requestedClose, "Requested close", color.purple, 3) // Draw a label displaying the requested symbol each time the `symbolSeries` changes. if symbolSeries != symbolSeries[1] label.new(bar_index, requestedClose, symbolSeries, textcolor = color.white)

Note that:

The script draws a label every time the symbolSeries changes, to signify which symbol’s data the requestedClose currently represents.
Pine v6 scripts enable dynamic requests by default, allowing this script to use a “series string” symbol argument in its request.security() call without error. If the dynamic behavior is disabled by including dynamic_requests = false in the indicator() declaration, then the “series” argument causes a compilation error.

An important limitation is that when using dynamic request.*() calls with “series” arguments or within local scopes, scripts must request all required datasets while executing on historical bars. All request.*() calls on realtime bars can retrieve data from the datasets that the script previously accessed on historical bars, but they cannot request a new context or evaluate a new expression.

To illustrate this limitation, let’s revisit the above script. Notice that it requests close data for all four symbols on the chart’s timeframe during its historical executions. The external datasets for those four contexts are the only ones that any request.*() call on realtime bars can access.

Below, we changed the timeframe argument in the script’s request.security() call to specify that it requests symbolSeries data from the chart’s timeframe on historical bars and the “240” (240 minutes = 4H) timeframe on realtime bars. This version raises a runtime error on the first realtime tick, if it is run on any timeframe other than the 4H timeframe, because it cannot access the 4H data feeds without requesting them on historical bars first:

//@version=6 indicator("Invalid realtime request demo") //@variable A "series" that cycles through four different symbol strings. Its value changes every five bars. string symbolSeries = switch int(bar_index / 5) % 4 1 => "NASDAQ:MSFT" 2 => "NASDAQ:AMD" 3 => "NASDAQ:INTC" => "AMEX:SPY" // Request the `close` of the `symbolSeries` from the chart's timeframe on historical bars and the "240" (4H) timeframe // on realtime bars. Causes a runtime error on the first realtime tick because the script did not previously access // data from the "240" timeframe on any historical bars. float requestedClose = request.security(symbolSeries, barstate.isrealtime ? "240" : timeframe.period, close) // Plot the `requestedClose`. plot(requestedClose, "Requested close", color.purple, 3) // Draw a label displaying the requested symbol each time the `symbolSeries` changes. if symbolSeries != symbolSeries[1] label.new(bar_index, requestedClose, symbolSeries, textcolor = color.white)

In local scopes

When scripts do not allow dynamic requests, all request.*() calls execute once on every bar or realtime tick, which prevents their use within most local scopes. The only exception is for request.*() calls in the scopes of non-exported functions and methods, because the Pine Script compiler extracts such calls into the global scope during translation.

Scripts that allow dynamic requests do not restrict the execution of request.*() calls to the global scope. They can call request.*() functions directly within the scopes of conditional structures and loops, meaning that each request.*() instance in the code can activate zero, one, or several times on each script execution.

The following example uses a single request.security() instance within a loop to request data from multiple forex data feeds. The script declares an array of symbols on the first chart bar, which it iterates through on all bars using a for…in loop. Each loop iteration calls request.security() to retrieve the volume value for one of the symbols and pushes the result into the requestedData array. After the loop terminates, the script calculates the average, maximum, and minimum values from the requestedData array using built-in methods, then plots the results on the chart:

//@version=6 indicator("In local scopes demo", format = format.volume) //@variable An array of "string" values representing different symbols to request. var array<string> symbols = array.from( "EURUSD", "USDJPY", "GBPUSD", "AUDUSD", "USDCAD", "USDCHF", "NZDUSD", "EURJPY", "GBPJPY", "EURGBP" ) //@variable An array containing the data retrieved for each requested symbol. array<float> requestedData = array.new<float>() // Retrieve `volume` data for each symbol in the `symbols` array and push the results into the `requestedData` array. for symbol in symbols float data = request.security("OANDA:" + symbol, timeframe.period, volume) requestedData.push(data) // Calculate the average, maximum, and minimum tick volume in the `requestedData`. float avgVolume = requestedData.avg() float maxVolume = requestedData.max() float minVolume = requestedData.min() // Plot the `avgVolume`, `maxVolume`, and `minVolume`. plot(avgVolume, "Average volume", color.gray, 3) plot(maxVolume, "Highest volume", color.teal, 3) plot(minVolume, "Lowest volume", color.maroon, 3)

Notice that the expression argument in the above example (volume) is loop-invariant, i.e., it does not change on any loop iteration. When using request.*() calls within a loop, all parameters defining parts of the requested context can accept arguments that depend on variables from the loop’s header or mutable variables that change within the loop’s local scope. However, a request.*() call’s evaluated expression cannot depend on the values of those variables.

Here, we modified the above script to use the second form of the for…in loop statement, which creates a tuple containing the index and value of each element in the symbols array. The request.security() instance in this version uses the index (i) in its expression argument, resulting in a compilation error:

//@version=6 indicator("Loop-dependent expression demo", format = format.volume) //@variable An array of "string" values representing different symbols to request. var array<string> symbols = array.from( "EURUSD", "USDJPY", "GBPUSD", "AUDUSD", "USDCAD", "USDCHF", "NZDUSD", "EURJPY", "GBPJPY", "EURGBP" ) //@variable An array containing the data retrieved for each requested symbol. array<float> requestedData = array.new<float>() // Retrieve `volume` data for each symbol in the `symbols` array, weighted using the element index. // Causes a compilation error because the `expression` in `request.security()` cannot depend on loop variables // or mutable variables that change within the loop's scope. for [i, symbol] in symbols float data = request.security("OANDA:" + symbol, timeframe.period, volume * (10 - i)) requestedData.push(data) // Calculate the average, maximum, and minimum tick volume in the `requestedData`. float avgVolume = requestedData.avg() float maxVolume = requestedData.max() float minVolume = requestedData.min() // Plot the `avgVolume`, `maxVolume`, and `minVolume`. plot(avgVolume, "Average volume", color.gray, 3) plot(maxVolume, "Highest volume", color.teal, 3) plot(minVolume, "Lowest volume", color.maroon, 3)

In libraries

Libraries with dynamic requests enabled can export functions and methods that utilize request.*() calls within their local scopes, provided that the evaluated expressions do not depend on any exported function parameters.

For example, this simple library exports an htfPrices() function that requests a tuple of confirmed open, high, low, and close prices using a specified tickerID and timeframe. If we publish this library, another script can import the function to request higher-timeframe prices without explicitly calling request.security().

//@version=6 library("DynamicRequests") //@function Requests a tuple containing confirmed HTF OHLC data for a specified `tickerID` and `timeframe`. //@param tickerID The ticker identifier to request data for. //@param timeframe The timeframe of the requested data. //@returns A tuple containing the last confirmed `open`, `high`, `low`, and `close` from the requested context. export htfPrices(string tickerID, string timeframe) => if timeframe.in_seconds() >= timeframe.in_seconds(timeframe) runtime.error("The `timeframe` argument of `getHTFPrices()` must be higher than the chart's timeframe.") request.security(tickerID, timeframe, [open[1], high[1], low[1], close[1]], lookahead = barmerge.lookahead_on)

Note that:

The tuple that the request.security() call includes as the expression argument does not depend on the htfPrices() parameters.
The htfPrices() function includes a runtime.error() call that raises a custom runtime error when the timeframe argument does not represent a higher timeframe than the chart’s timeframe. See the higher timeframes section for more information.
The request.security() call uses barmerge.lookahead_on and offsets each item in the tuple by one bar. This is the only recommended method to avoid repainting.

Nested requests

Scripts can use dynamic requests to execute nested requests, i.e., request.*() calls that dynamically evaluate other request.*() calls that their expression arguments depend on.

When a request.security() or request.security_lower_tf() call uses an empty string or syminfo.tickerid for its symbol argument, or if it uses an empty string or timeframe.period for the timeframe argument, the requested ticker ID or timeframe depends on the context where the call executes. This context is normally the ticker ID or timeframe of the chart that the script is running on. However, if such a request.security() or request.security_lower_tf() function call is evaluated by another request.*() call, the nested request inherits that request.*() call’s ticker ID or timeframe information.

For example, the script below contains two request.security() calls and uses Pine Logs to display their results. The first call uses empty strings as its symbol and timeframe arguments, meaning that the requested context depends on where the call executes. It evaluates a concatenated string containing the call’s requested ticker ID and timeframe, and the script assigns its result to the info1 variable.

The second call requests data for a specific symbol and timeframe using the info1 variable as its expression argument. Since the info1 variable depends on the first request.security() call, the second call evaluates the first call within its own context. Therefore, the first call adopts the second call’s ticker ID and timeframe while executing within that context, resulting in a different returned value:

//@version=6 indicator("Nested requests demo") //@variable A concatenated string containing the current `syminfo.tickerid` and `timeframe.period`. string info1 = request.security("", "", syminfo.tickerid + "_" + timeframe.period) //@variable A concatenated string representing the `info1` value calculated within the "NASDAQ:AAPL, 240" context. // This call evaluates the call on line 5 within its context to determine its result because the script // allows dynamic requests. string info2 = request.security("NASDAQ:AAPL", "240", info1) // Log the results from both calls in the Pine Logs pane on the last historical bar. if barstate.islastconfirmedhistory log.info("First request: {0}", info1) log.info("Second request: {0}", info2)

This script allows the execution of the first request.security() call within the context of the second call because Pine v6 scripts enable dynamic request.*() calls by default. We can disable this behavior by including dynamic_requests = false in the indicator() declaration statement. Without dynamic requests enabled, the script evaluates each call independently, passing the first call’s calculated value directly into the second call rather than executing the first call within the second context. Consequently, the second call’s returned value is the same as the first call’s value, as we see below:

//@version=6 indicator("Nested requests demo", dynamic_requests = false) //@variable A concatenated string containing the current `syminfo.tickerid` and `timeframe.period`. string info1 = request.security("", "", syminfo.tickerid + "_" + timeframe.period) //@variable The same value as `info1`. This call does not evalutate the call on line 5 because dynamic requests aren't // allowed. Instead, it only uses the value of `info1`, meaning its result does not change. string info2 = request.security("NASDAQ:AAPL", "240", info1) // Log the results from both calls in the Pine Logs pane on the last historical bar. if barstate.islastconfirmedhistory log.info("First request: {0}", info1) log.info("Second request: {0}", info2)

Data feeds

TradingView’s data providers supply different data feeds that scripts can access to retrieve information about an instrument, including:

Intraday historical data (for timeframes < 1D)
End-of-day (EOD) historical data (for timeframes >= 1D)
Realtime data (which may be delayed, depending on your account type and extra data services)
Extended hours data

Not all of these data feed types exist for every instrument. For example, the symbol “BNC:BLX” only has EOD data available.

For some instruments with intraday and EOD historical feeds, volume data may not be the same since some trades (block trades, OTC trades, etc.) may only be available at the end of the trading day. Consequently, the EOD feed will include this volume data, but the intraday feed will not. Differences between EOD and intraday volume feeds are almost nonexistent for instruments such as cryptocurrencies, but they are commonplace in stocks.

Slight price discrepancies may also occur between EOD and intraday feeds. For example, the high value on one EOD bar may not match any intraday high values supplied by the data provider for that day.

Another distinction between EOD and intraday data feeds is that EOD feeds do not contain information from extended hours.

When retrieving information on realtime bars with request.*() functions, it’s important to note that historical and realtime data reported for an instrument often rely on different data feeds. A broker/exchange may retroactively modify values reported on realtime bars, which the data will only reflect after refreshing the chart or restarting the script.

Another important consideration is that the chart’s data feeds and feeds requested from providers by the script are managed by independent, concurrent processes. Consequently, in some rare cases, it’s possible for races to occur where requested results temporarily fall out of synch with the chart on a realtime bar, which a script retroactively adjusts after restarting its executions.

These points may account for variations in the values retrieved by request.*() functions when requesting data from other contexts. They may also result in discrepancies between data received on realtime bars and historical bars. There are no steadfast rules about the variations one may encounter in their requested data feeds.

When using data feeds requested from other contexts, it’s also crucial to consider the time axis differences between the chart the script executes on and the requested feeds since request.*() functions adapt the returned series to the chart’s time axis. For example, requesting “BTCUSD” data on the “SPY” chart with request.security() will only show new values when the “SPY” chart has new data as well. Since “SPY” is not a 24-hour symbol, the “BTCUSD” data returned will contain gaps that are otherwise not present when viewing its chart directly.

`request.security()`

The request.security() function allows scripts to request data from other contexts than the chart the script executes on, such as:

Other symbols, including spread symbols
Other timeframes (see our User Manual’s page on Timeframes to learn about timeframe specifications in Pine Script)
Custom contexts, including alternative sessions, price adjustments, chart types, etc. using ticker.*() functions

This is the function’s signature:

request.security(symbol, timeframe, expression, gaps, lookahead, ignore_invalid_symbol, currency, calc_bars_count) → series <type>

The symbol value is the ticker identifier representing the symbol to fetch data from. This parameter accepts values in any of the following formats:

A “string” representing a symbol (e.g., “IBM” or “EURUSD”) or an “Exchange:Symbol” pair (e.g., “NYSE:IBM” or “OANDA:EURUSD”). When the value does not contain an exchange prefix, the function selects the exchange automatically. We recommend specifying the exchange prefix when possible for consistent results. Users can also pass an empty string to this parameter, which prompts the function to use the current chart’s symbol.
A “string” representing a spread symbol (e.g., “AMD/INTC”). Note that “Bar Replay” mode does not work with these symbols.
The syminfo.ticker or syminfo.tickerid built-in variables, which return the symbol or the “Exchange:Symbol” pair that the current chart references. We recommend using syminfo.tickerid to avoid ambiguity unless the exchange information does not matter in the data request. For more information on syminfo.* variables, see this section of our Chart information page.
A custom ticker identifier created using ticker.*() functions. Ticker IDs constructed from these functions may contain additional settings for requesting data using non-standard chart calculations, alternative sessions, and other contexts. See the Custom contexts section for more information.

The timeframe value specifies the timeframe of the requested data. This parameter accepts “string” values in our timeframe specification format (e.g., a value of “1D” represents the daily timeframe). To request data from the same timeframe as the chart the script executes on, use the timeframe.period variable or an empty string.

The expression parameter of the request.security() function determines the data it retrieves from the specified context. This versatile parameter accepts “series” values of int, float, bool, color, string, and chart.point types. It can also accept tuples, collections, user-defined types, and the outputs of function and method calls. For more details on the data one can retrieve, see the Requestable data section below.

Timeframes

The request.security() function can request data from any available timeframe, regardless of the chart the script executes on. The timeframe of the data retrieved depends on the timeframe argument in the function call, which may represent a higher timeframe (e.g., using “1D” as the timeframe value while running the script on an intraday chart) or the chart’s timeframe (i.e., using timeframe.period or an empty string as the timeframe argument).

Scripts can also request limited data from lower timeframes with request.security() (e.g., using “1” as the timeframe argument while running the script on a 60-minute chart). However, we don’t typically recommend using this function for LTF data requests. The request.security_lower_tf() function is more optimal for such cases.

Higher timeframes

Most use cases of request.security() involve requesting data from a timeframe higher than or the same as the chart timeframe. For example, this script retrieves the hl2 price from a requested higherTimeframe. It plots the resulting series on the chart alongside the current chart’s hl2 for comparison:

//@version=6 indicator("Higher timeframe security demo", overlay = true) //@variable The higher timeframe to request data from. string higherTimeframe = input.timeframe("240", "Higher timeframe") //@variable The `hl2` value from the `higherTimeframe`. Combines lookahead with an offset to avoid repainting. float htfPrice = request.security(syminfo.tickerid, higherTimeframe, hl2[1], lookahead = barmerge.lookahead_on) // Plot the `hl2` from the chart timeframe and the `higherTimeframe`. plot(hl2, "Current timeframe HL2", color.teal, 2) plot(htfPrice, "Higher timeframe HL2", color.purple, 3)

Note that:

We’ve included an offset to the expression argument and used barmerge.lookahead_on in request.security() to ensure the series returned behaves the same on historical and realtime bars. See the Avoiding repainting section for more information.

Notice that in the above example, it is possible to select a higherTimeframe value that actually represents a lower timeframe than the one the chart uses, as the code does not prevent it. When designing a script to work specifically with higher timeframes, we recommend including conditions to prevent it from accessing lower timeframes, especially if you intend to publish it.

Below, we’ve added an if structure to our previous example. If the higherTimeframe value represents a timeframe that is smaller than the chart’s timeframe, the script calls runtime.error() within the structure’s local block to raise a custom runtime error, effectively preventing the script from requesting LTF data:

//@version=6 indicator("Higher timeframe security demo", overlay = true) //@variable The higher timeframe to request data from. string higherTimeframe = input.timeframe("240", "Higher timeframe") // Raise a runtime error when the `higherTimeframe` is smaller than the chart's timeframe. if timeframe.in_seconds() > timeframe.in_seconds(higherTimeframe) runtime.error("The requested timeframe is smaller than the chart's timeframe. Select a higher timeframe.") //@variable The `hl2` value from the `higherTimeframe`. Combines lookahead with an offset to avoid repainting. float htfPrice = request.security(syminfo.tickerid, higherTimeframe, hl2[1], lookahead = barmerge.lookahead_on) // Plot the `hl2` from the chart timeframe and the `higherTimeframe`. plot(hl2, "Current timeframe HL2", color.teal, 2) plot(htfPrice, "Higher timeframe HL2", color.purple, 3)

Lower timeframes

Although the request.security() function is intended to operate on timeframes greater than or equal to the chart timeframe, it can request data from lower timeframes as well, with limitations. When calling this function to access a lower timeframe, it will evaluate the expression from the LTF context. However, it returns the results from only a single intrabar (LTF bar) on each chart bar.

The intrabar that the function returns data from on each historical chart bar depends on the lookahead value in the function call. When using barmerge.lookahead_on, it will return the first available intrabar from the chart period. When using barmerge.lookahead_off, it will return the last intrabar from the chart period. On realtime bars, it returns the last available value of the expression from the timeframe, regardless of the lookahead value, as the realtime intrabar information retrieved by the function is not yet sorted.

This script retrieves close data from the valid timeframe closest to a fourth of the size of the chart timeframe. It makes two calls to request.security() with different lookahead values. The first call uses barmerge.lookahead_on to access the first intrabar value in each chart bar. The second uses the default lookahead value (barmerge.lookahead_off), which requests the last intrabar value assigned to each chart bar. The script plots the outputs of both calls on the chart to compare the difference:

//@version=6 indicator("Lower timeframe security demo", overlay = true) //@variable The valid timeframe closest to 1/4 the size of the chart timeframe. string lowerTimeframe = timeframe.from_seconds(int(timeframe.in_seconds() / 4)) //@variable The `close` value on the `lowerTimeframe`. Represents the first intrabar value on each chart bar. float firstLTFClose = request.security(syminfo.tickerid, lowerTimeframe, close, lookahead = barmerge.lookahead_on) //@variable The `close` value on the `lowerTimeframe`. Represents the last intrabar value on each chart bar. float lastLTFClose = request.security(syminfo.tickerid, lowerTimeframe, close) // Plot the values. plot(firstLTFClose, "First intrabar close", color.teal, 3) plot(lastLTFClose, "Last intrabar close", color.purple, 3) // Highlight the background on realtime bars. bgcolor(barstate.isrealtime ? color.new(color.orange, 70) : na, title = "Realtime background highlight")

Note that:

The script determines the value of the lowerTimeframe by calculating the number of seconds in the chart timeframe with timeframe.in_seconds(), then dividing by four and converting the result to a valid timeframe string via timeframe.from_seconds().
The plot of the series without lookahead (purple) aligns with the close value on the chart timeframe, as this is the last intrabar value in the chart bar.
Both request.security() calls return the same value (the current close) on each realtime bar, as shown on the bars with the orange background.
Scripts can retrieve up to 200,000 intrabars from a lower-timeframe context. The number of chart bars with available intrabar data varies with the requested lower timeframe, the calc_bars_count value, and the user’s plan. For more information, see this section of the Limitations page.

Requestable data

The request.security() function is quite versatile, as it can retrieve values of any fundamental type (int, float, bool, color, or string). It can also request the IDs of data structures and built-in or user-defined types that reference fundamental types. The data this function requests depends on its expression parameter, which accepts any of the following arguments:

Built-in variables and functions

A frequent use case of request.security() is requesting the output of a built-in variable or function/method call from another symbol or timeframe.

For example, suppose we want to calculate the 20-bar SMA of a symbol’s ohlc4 prices from the daily timeframe while on an intraday chart. We can accomplish this task with a single line of code:

float ma = request.security(syminfo.tickerid, "1D", ta.sma(ohlc4, 20))

The above line calculates the value of ta.sma(ohlc4, 20) on the current symbol’s data from the daily timeframe.

It’s important to note that newcomers to Pine might sometimes confuse the above line of code as being equivalent to the following:

float ma = ta.sma(request.security(syminfo.tickerid, "1D", ohlc4), 20)

However, this line returns an entirely different result. Rather than requesting a 20-bar SMA from the daily timeframe, it requests the ohlc4 price from the daily timeframe and calclates the ta.sma() of the results over 20 chart bars.

In essence, when the intention is to request the results of an expression from other contexts, pass the expression directly to the expression parameter in the request.security() call, as demonstrated in the initial example.

Let’s expand on this concept. The script below calculates a multi-timeframe (MTF) ribbon of moving averages, where each moving average in the ribbon calculates over the same number of bars on its respective timeframe. Each request.security() call uses a ta.sma() call as its expression argument to return a length-bar SMA from the specified timeframe:

//@version=6 indicator("Requesting built-ins demo", "MTF Ribbon", true) //@variable The length of each moving average. int length = input.int(20, "Length", 1) //@variable The number of seconds in the chart timeframe. int chartSeconds = timeframe.in_seconds() // Calculate the higher timeframes closest to 2, 3, and 4 times the size of the chart timeframe. string htf1 = timeframe.from_seconds(chartSeconds * 2) string htf2 = timeframe.from_seconds(chartSeconds * 3) string htf3 = timeframe.from_seconds(chartSeconds * 4) // Calculate the `length`-bar moving averages from each timeframe. float chartAvg = ta.sma(ohlc4, length) float htfAvg1 = request.security(syminfo.tickerid, htf1, ta.sma(ohlc4, length)) float htfAvg2 = request.security(syminfo.tickerid, htf2, ta.sma(ohlc4, length)) float htfAvg3 = request.security(syminfo.tickerid, htf3, ta.sma(ohlc4, length)) // Plot the results. plot(chartAvg, "Chart timeframe SMA", color.red, 3) plot(htfAvg1, "Double timeframe SMA", color.orange, 3) plot(htfAvg2, "Triple timeframe SMA", color.green, 3) plot(htfAvg3, "Quadruple timeframe SMA", color.blue, 3) // Highlight the background on realtime bars. bgcolor(barstate.isrealtime ? color.new(color.aqua, 70) : na, title = "Realtime highlight")

Note that:

The script calculates the ribbon’s higher timeframes by multiplying the chart’s timeframe.in_seconds() value by 2, 3, and 4, then converting each result into a valid timeframe string using timeframe.from_seconds().
Instead of calling ta.sma() within each request.security() call, one could use the chartAvg variable as the expression in each call to achieve the same result. See the next section for more information.
On realtime bars, this script also tracks unconfirmed SMA values from each higher timeframe. See the Historical and realtime behavior section to learn more.

Declared variables

The request.security() function’s expression parameter can accept declared variables that are accessible to the scope from which the function call executes. When using a declared variable as the expression argument, the function call duplicates all preceding code that determines the assigned value or reference. This duplication allows the function to evaluate necessary calculations and logic in the requested context without affecting the original variable.

For instance, this line of code declares a priceReturn variable that holds the current bar’s arithmetic price return:

float priceReturn = (close - close[1]) / close[1]

We can evaluate the priceReturn variable’s calculations in another context by using it as the expression in a request.security() call. The call below duplicates the variable’s calculation and evaluates it across the data from another symbol, returning a separate series adapted to the chart’s time axis:

float requestedReturn = request.security(symbol, timeframe.period, priceReturn)

This example script compares the price returns of the current chart’s symbol and a user-specified symbol. It calculates the value of the priceReturn variable, then uses that variable as the expression in a request.security() call to evaluate the calculation on the input symbol’s data. After the request, the script calculates the correlation between the priceReturn and requestedReturn series using ta.correlation() and plots the result on the chart:

//@version=6 indicator("Requesting calculated variables demo", "Price return correlation") //@variable The symbol to compare to the chart symbol. string symbol = input.symbol("SPY", "Symbol to compare") //@variable The number of bars in the calculation window. int length = input.int(60, "Length", 1) //@variable The close-to-close price return. float priceReturn = (close - close[1]) / close[1] //@variable The close-to-close price return calculated on another `symbol`. float requestedReturn = request.security(symbol, timeframe.period, priceReturn) //@variable The correlation between the `priceReturn` and `requestedReturn` over `length` bars. float correlation = ta.correlation(priceReturn, requestedReturn, length) //@variable The color of the correlation plot. color plotColor = color.from_gradient(correlation, -1, 1, color.purple, color.orange) // Plot the correlation value. plot(correlation, "Correlation", plotColor, style = plot.style_area)

Note that:

The request.security() call executes the same calculation used in the priceReturn declaration, but the request’s calculation operates on the close values from the specified symbol’s data.
The script uses the color.from_gradient() function to calculate the color for the plot of the correlation series on each bar. See this section of the Colors page to learn more about color gradients.

When using a variable as the expression argument of a request.*() call, it’s important to note that the function only duplicates code that affects the variable before the call. It cannot copy any subsequent code following the call. Consequently, if the script reassigns the variable or modifies its referenced data after calling request.security(), the code evaluated on the requested data does not include those additional operations.

For example, the following script declares a counter variable and calls request.security() to evaluate the variable from the same context as the chart. After the call, the script increments the counter value by one with the addition assignment operator (+=), then uses plots and Pine Logs to display the counter and requestedCounter values for comparison.

As shown below, the plots and logs of the two variables display different values. The requestedCounter variable has a consistent value of 0 because the request.security() call evaluates only the initial variable declaration. The request cannot evaluate the addition assignment operation because the script includes that code after the function call:

//@version=6 indicator("Modifying variables after requests demo") //@variable A counter that starts at 0 and increments by 1 on each bar. var int counter = 0 //@variable Holds a consistent value of 0. // `request.security()` cannot evaluate `counter += 1` in its requested context // because that modification occurs *after* the call. int requestedCounter = request.security(syminfo.tickerid, timeframe.period, counter) // Increment the `counter` by 1. This operation is *not* included in the `requestedCounter` calculation. counter += 1 // Plot both variables for comparison. plot(counter, "Original counter", color.purple, 3) plot(requestedCounter, "Requested counter", color.red, 3) // Log the values of both variables in the Pine Logs pane. if barstate.isconfirmed log.info("counter: {0}, requestedCounter: {1}", counter, requestedCounter)

Tuples

Tuples in Pine Script are comma-separated lists of expressions enclosed in square brackets. Programmers often use tuples when creating functions, conditional structures, or loops that return multiple values or references from their local scopes.

The request.security() function can accept a tuple as its expression argument, allowing scripts to request multiple series of different types using a single function call. The expressions within requested tuples can be of any type outlined throughout the Requestable data section of this page, excluding other tuples.

Tuples are particularly helpful when a script needs to retrieve more than one value from a specific context.

For example, the following script calculates the percent rank of the close series over length bars and assigns the result to the rank variable. It then calls request.security() to request a tuple containing the values of rank, ta.crossover(rank, 50), and ta.crossunder(rank, 50) from a specified timeframe. The script plots the requestedRank series in a separate pane, then uses the result of a ternary expression based on the crossOver and crossUnder values within a bgcolor() call to conditionally highlight the pane’s background:

//@version=6 indicator("Requesting tuples demo", "Percent rank cross") //@variable The timeframe of the request. string timeframe = input.timeframe("240", "Timeframe") //@variable The number of bars in the calculation. int length = input.int(20, "Length") //@variable The previous bar's percent rank of the `close` price over `length` bars. float rank = ta.percentrank(close, length)[1] // Request the `rank` value from another `timeframe`, and two "bool" values indicating the `rank` from the `timeframe` // crossed over or under 50. [requestedRank, crossOver, crossUnder] = request.security( syminfo.tickerid, timeframe, [rank, ta.crossover(rank, 50), ta.crossunder(rank, 50)], lookahead = barmerge.lookahead_on ) // Plot the `requestedRank` and create a horizontal line at 50. plot(requestedRank, "Percent Rank", linewidth = 3) hline(50, "Cross line", linewidth = 2) // Highlight the background of all bars where the `timeframe`'s `crossOver` or `crossUnder` value is `true`. bgcolor(crossOver ? color.new(color.green, 50) : crossUnder ? color.new(color.red, 50) : na)

Note that:

We’ve offset the rank variable’s expression by one bar using the history-referencing operator [] and included barmerge.lookahead_on in the request.security() call to ensure the values on realtime bars do not repaint after becoming historical bars. See the Avoiding repainting section for more information.
The request.security() call returns a tuple, so we use a tuple declaration to declare the requestedRank, crossOver, and crossUnder variables. To learn more about using tuples, see this section of our User Manual’s Type system page.

User-defined functions

User-defined functions and methods are custom functions written by users. They allow users to define sequences of operations associated with an identifier that scripts can conveniently call throughout their executions (e.g., myUDF()).

The request.security() function can request the results of user-defined functions and methods whose scopes consist of any types outlined throughout this page’s Requestable data section.

For example, this script contains a user-defined weightedBB() function that calculates Bollinger Bands with the basis average weighted by a specified weight series. The function returns a tuple of custom band values. The script calls the weightedBB() as the expression argument in request.security() to retrieve a tuple of band values calculated on the specified timeframe and plots the results on the chart:

//@version=6 indicator("Requesting user-defined functions demo", "Weighted Bollinger Bands", true) //@variable The timeframe of the request. string timeframe = input.timeframe("480", "Timeframe") //@function Calculates Bollinger Bands with a custom weighted basis. //@param source The series of values to process. //@param length The number of bars in the calculation. //@param mult The standard deviation multiplier. //@param weight The series of weights corresponding to each `source` value. //@returns A tuple containing the basis, upper band, and lower band respectively. weightedBB(float source, int length, float mult = 2.0, float weight = 1.0) => //@variable The basis of the bands. float ma = math.sum(source * weight, length) / math.sum(weight, length) //@variable The standard deviation from the `ma`. float dev = 0.0 // Loop to accumulate squared error. for i = 0 to length - 1 difference = source[i] - ma dev += difference * difference // Divide `dev` by the `length`, take the square root, and multiply by the `mult`. dev := math.sqrt(dev / length) * mult // Return the bands. [ma, ma + dev, ma - dev] // Request weighted bands calculated on the chart symbol's prices over 20 bars from the // last confirmed bar on the `timeframe`. [basis, highBand, lowBand] = request.security( syminfo.tickerid, timeframe, weightedBB(close[1], 20, 2.0, (high - low)[1]), lookahead = barmerge.lookahead_on ) // Plot the values. basisPlot = plot(basis, "Basis", color.orange, 2) upperPlot = plot(highBand, "Upper", color.teal, 2) lowerPlot = plot(lowBand, "Lower", color.maroon, 2) fill(upperPlot, lowerPlot, color.new(color.gray, 90), "Background")

Note that:

We offset the source and weight arguments in the weightedBB() call used as the expression in request.security() and used barmerge.lookahead_on to ensure the requested results reflect the last confirmed values from the timeframe on realtime bars. See this section to learn more.

Chart points

Chart points are objects that represent coordinates on the chart. Lines, boxes, polylines, and labels use these objects to set their display locations.

The request.security() function can use the ID of a chart.point instance in its expression argument, allowing scripts to retrieve chart coordinates from other contexts.

The example below requests a tuple of historical chart points from a higher timeframe and uses them to draw boxes on the chart. The script declares the topLeft and bottomRight variables that reference chart.point IDs from the last confirmed bar. It then uses request.security() to request a tuple containing the IDs of chart points representing the topLeft and bottomRight from a higherTimeframe.

When a new bar starts on the higherTimeframe, the script draws a new box using the time and price coordinates from the requestedTopLeft and requestedBottomRight chart points:

//@version=6 indicator("Requesting chart points demo", "HTF Boxes", true, max_boxes_count = 500) //@variable The timeframe to request data from. string higherTimeframe = input.timeframe("1D", "Timeframe") // Raise a runtime error if the `higherTimeframe` is smaller than the chart's timeframe. if timeframe.in_seconds(higherTimeframe) < timeframe.in_seconds(timeframe.period) runtime.error("The selected timeframe is too small. Choose a higher timeframe.") //@variable A `chart.point` containing top-left coordinates from the last confirmed bar. topLeft = chart.point.now(high)[1] //@variable A `chart.point` containing bottom-right coordinates from the last confirmed bar. bottomRight = chart.point.from_time(time_close, low)[1] // Request the last confirmed `topLeft` and `bottomRight` chart points from the `higherTimeframe`. [requestedTopLeft, requestedBottomRight] = request.security( syminfo.tickerid, higherTimeframe, [topLeft, bottomRight], lookahead = barmerge.lookahead_on ) // Draw a new box when a new `higherTimeframe` bar starts. // The box uses the `time` fields from the `requestedTopLeft` and `requestedBottomRight` as x-coordinates. if timeframe.change(higherTimeframe) box.new( requestedTopLeft, requestedBottomRight, color.purple, 3, xloc = xloc.bar_time, bgcolor = color.new(color.purple, 90) )

Note that:

Because we designed this example to request data from higher timeframes, we’ve included a runtime.error() call that the script executes if the higherTimeframe value represents a lower timeframe than timeframe.period.

Collections

Pine Script collections (arrays, matrices, and maps) are data structures that contain an arbitrary number of elements with specified types. The request.security() function can retrieve the IDs of collections whose elements consist of:

Fundamental types
Chart points
User-defined types that satisfy the criteria listed in the section below

This example below calculates the ratio of a confirmed bar’s high-low range to the range between the highest and lowest prices over 10 bars from a from a specified symbol and timeframe. It uses maps to hold the values used in the calculations.

The script uses a data map with “string” keys and “float” values to store the current bar’s high, low, ta.highest(), and ta.lowest() results. It passes the map as the expression argument in a request.security() call on each bar to retrieve another map containing the values calculated from the specified context, then assigns that map’s reference to the otherData variable. The script uses the “float” values associated with the “High”, “Low”, “Highest”, and “Lowest” keys of the otherData map to calculate the ratio series that it plots in the chart pane:

//@version=6 indicator("Requesting collections demo", "Bar range ratio") //@variable The ticker ID to request data from. string symbol = input.symbol("", "Symbol") //@variable The timeframe of the request. string timeframe = input.timeframe("30", "Timeframe") //@variable A map with "string" keys and "float" values. var map<string, float> data = map.new<string, float>() // Put key-value pairs into the `data` map. map.put(data, "High", high) map.put(data, "Low", low) map.put(data, "Highest", ta.highest(10)) map.put(data, "Lowest", ta.lowest(10)) //@variable A new `map` whose data is calculated from the last confirmed bar of the requested context. map<string, float> otherData = request.security(symbol, timeframe, data[1], lookahead = barmerge.lookahead_on) //@variable The ratio of the context's bar range to the max range over 10 bars. Returns `na` if no data is available. float ratio = na if not na(otherData) ratio := (otherData.get("High") - otherData.get("Low")) / (otherData.get("Highest") - otherData.get("Lowest")) //@variable A gradient color for the plot of the `ratio`. color ratioColor = color.from_gradient(ratio, 0, 1, color.purple, color.orange) // Plot the `ratio`. plot(ratio, "Range Ratio", ratioColor, 3, plot.style_area)

Note that:

The request.security() call in this script can return na if no data is available from the specified context. Since one cannot call methods on a map variable when its value is na, we’ve added an if structure to only calculate a new ratio value when otherData references a valid map instance.

User-defined types

User-defined types (UDTs) are composite types containing an arbitrary number of fields, which can be of any available type, including other user-defined types.

The request.security() function can retrieve the IDs of objects produced by UDTs from other contexts if their fields consist of:

Fundamental types
Chart points
Collections that satisfy the criteria listed in the section above
Other UDTs whose fields consist of any of these types

The following example requests an object ID using a specified symbol and displays its field values on a chart pane.

The script contains a TickerInfo UDT with “string” fields for syminfo.* values, an array field to store recent “float” price data, and an “int” field to hold the requested ticker’s bar_index value. It assigns a new TickerInfo ID to an info variable on every bar and uses the variable as the expression in request.security() to retrieve the ID of an object representing the calculated info from the specified symbol.

The script displays the requestedInfo object’s description, tickerType, currency, and barIndex values in a label and uses plotcandle() to display the values from its prices array:

//@version=6 indicator("Requesting user-defined types demo", "Ticker info") //@variable The symbol to request information from. string symbol = input.symbol("NASDAQ:AAPL", "Symbol") //@type A custom type containing information about a ticker. //@field description The symbol's description. //@field tickerType The type of ticker. //@field currency The symbol's currency. //@field prices An array of the symbol's current prices. //@field barIndex The ticker's `bar_index`. type TickerInfo string description string tickerType string currency array<float> prices int barIndex //@variable A `TickerInfo` object containing current data. info = TickerInfo.new( syminfo.description, syminfo.type, syminfo.currency, array.from(open, high, low, close), bar_index ) //@variable The `info` requested from the specified `symbol`. TickerInfo requestedInfo = request.security(symbol, timeframe.period, info) // Assign a new `TickerInfo` instance to `requestedInfo` if one wasn't retrieved. if na(requestedInfo) requestedInfo := TickerInfo.new(prices = array.new<float>(4)) //@variable A label displaying information from the `requestedInfo` object. var infoLabel = label.new( na, na, "", color = color.purple, style = label.style_label_left, textcolor = color.white, size = size.large ) //@variable The text to display inside the `infoLabel`. string infoText = na(requestedInfo) ? "" : str.format( "{0}\nType: {1}\nCurrency: {2}\nBar Index: {3}", requestedInfo.description, requestedInfo.tickerType, requestedInfo.currency, requestedInfo.barIndex ) // Set the `point` and `text` of the `infoLabel`. label.set_point(infoLabel, chart.point.now(array.last(requestedInfo.prices))) label.set_text(infoLabel, infoText) // Plot candles using the values from the `prices` array of the `requestedInfo`. plotcandle( requestedInfo.prices.get(0), requestedInfo.prices.get(1), requestedInfo.prices.get(2), requestedInfo.prices.get(3), "Requested Prices" )

Note that:

The syminfo.* variables used in this script all return “simple string” qualified types. However, objects in Pine are always qualified as “series”. Consequently, all values assigned to the info object’s fields automatically adopt the “series” qualifier.
It is possible for the request.security() call to return na due to differences between the data requested from the symbol and the main chart. This script assigns a new TickerInfo object to the requestedInfo in that case to prevent runtime errors.

`request.security_lower_tf()`

The request.security_lower_tf() function is an alternative to request.security() designed for reliably requesting information from lower-timeframe (LTF) contexts.

While request.security() can retrieve data from a single intrabar (LTF bar) in each chart bar, request.security_lower_tf() retrieves data from all available intrabars in each chart bar, which the script can access and use in additional calculations. Each request.security_lower_tf() call can retrieve up to 200,000 intrabars from a lower timeframe, depending on the user’s plan. See this section of our Limitations page for more information.

Below is the function’s signature, which is similar to the signature of request.security():

request.security_lower_tf(symbol, timeframe, expression, ignore_invalid_symbol, currency, ignore_invalid_timeframe, calc_bars_count) → array<type>

This function requests data only from timeframes that are lower than or equal to the chart’s timeframe (timeframe.period). If the timeframe argument of the request.security_lower_tf() call represents a higher timeframe, the function raises a runtime error or returns na results, depending on the ignore_invalid_timeframe parameter. The parameter’s default value is false, meaning the function raises an error and halts the script’s executions if the timeframe argument is invalid.

Requesting intrabar data

Intrabar data can provide a script with additional information that may not be obvious or accessible from solely analyzing data sampled on the chart’s timerframe. The request.security_lower_tf() function can retrieve many data types from an intrabar context.

Before you venture further in this section, we recommend exploring the Requestable data portion of the request.security() section above, which provides foundational information about the types of data one can request. The expression parameter in request.security_lower_tf() accepts most of the same arguments discussed in that section, excluding direct references to collections and mutable variables. Although it accepts many of the same types of arguments, this function returns array results, which comes with some differences in interpretation and handling, as explained below.

Intrabar data arrays

Lower timeframes contain more data points than higher timeframes, as new values come in at a higher frequency. For example, when comparing a 1-minute chart to an hourly chart, the 1-minute chart will have up to 60 times the number of bars per hour, depending on the available data.

To address the fact that multiple intrabars exist within a chart bar, request.security_lower_tf() always creates arrays to store the requested data. The elements in the arrays represent the expression values retrieved from the lower timeframe sorted in ascending order based on each intrabar’s timestamp.

The type identifier of the constructed arrays corresponds to the data types passed in the request.security_lower_tf() call. For example, using an “int” as the expression will produce an array<int> instance, a “bool” as the expression will produce an array<bool> instance, etc.

The following script uses intrabar information to decompose the chart’s close-to-close price changes into positive and negative parts. It calls request.security_lower_tf() to fetch a “float” array containing ta.change(close) values from a specified lower timeframe on each chart bar, then accesses all the array’s elements using a for…in loop to accumulate positiveChange and negativeChange sums. The script adds the accumulated values to calculate the netChange value, then plots the results on the chart alongside the priceChange value for comparison:

//@version=6 indicator("Intrabar arrays demo", "Intrabar price changes") //@variable The lower timeframe of the requested data. string lowerTimeframe = input.timeframe("1", "Timeframe") //@variable The close-to-close price change. float priceChange = ta.change(close) //@variable An array of `close` values from available intrabars on the `lowerTimeframe`. array<float> intrabarChanges = request.security_lower_tf(syminfo.tickerid, lowerTimeframe, priceChange) //@variable The total positive intrabar `close` movement on the chart bar. float positiveChange = 0.0 //@variable The total negative intrabar `close` movement on the chart bar. float negativeChange = 0.0 // Loop to calculate totals, starting from the chart bar's first available intrabar. for change in intrabarChanges // Add the `change` to `positiveChange` if its sign is 1, and add to `negativeChange` if its sign is -1. switch math.sign(change) 1 => positiveChange += change -1 => negativeChange += change //@variable The sum of `positiveChange` and `negativeChange`. Equals the `priceChange` on bars with available intrabars. float netChange = positiveChange + negativeChange // Plot the `positiveChange`, `negativeChange`, and `netChange`. plot(positiveChange, "Positive intrabar change", color.teal, style = plot.style_area) plot(negativeChange, "Negative intrabar change", color.maroon, style = plot.style_area) plot(netChange, "Net intrabar change", color.yellow, 5) // Plot the `priceChange` to compare. plot(priceChange, "Chart price change", color.orange, 2)

Note that:

The plots based on intrabar data may not appear on all available chart bars, as request.security_lower_tf() can only access up to the most recent 200,000 intrabars available from the requested context. When executing this function on a chart bar that doesn’t have accessible intrabar data, it will return an empty array.
The number of intrabars per chart bar may vary depending on the data available from the context and the chart the script executes on. For example, a provider’s 1-minute data feed may not include data for every minute within the 60-minute timeframe due to a lack of trading activity over some 1-minute intervals. To check the number of intrabars retrieved for a chart bar, one can use array.size() on the resulting array.
If the lowerTimeframe value is greater than the chart’s timeframe, the script will raise a runtime error, as we have not supplied an ignore_invalid_timeframe argument in the request.security_lower_tf() call.

Tuples of intrabar data

When passing a tuple or a function call that returns a tuple as the expression argument in request.security_lower_tf(), the result is a tuple of arrays with type templates corresponding to the types within the argument. For example, using a [float, string, color] tuple as the expression will result in [array<float>, array<string>, array<color>] data returned by the function. Using a tuple expression allows a script to fetch the IDs of several arrays containing intrabar data with a single request.security_lower_tf() function call.

The following example requests OHLC data from a lower timeframe and visualizes the current bar’s intrabars on the chart using lines and boxes. The script calls request.security_lower_tf() with the [open, high, low, close] tuple as its expression to retrieve a tuple of arrays representing OHLC information from a calculated lowerTimeframe. It then uses a for loop to set line coordinates with the retrieved data and current bar indices to display the results next to the current chart bar, providing a “magnified view” of the price movement within the latest candle. It also draws a box around the lines to indicate the chart region occupied by intrabar drawings:

//@version=6 indicator("Tuples of intrabar data demo", "Candle magnifier", max_lines_count = 500) //@variable The maximum number of intrabars to display. int maxIntrabars = input.int(20, "Max intrabars", 1, 250) //@variable The width of the drawn candle bodies. int candleWidth = input.int(20, "Candle width", 2) //@variable The largest valid timeframe closest to `maxIntrabars` times smaller than the chart timeframe. string lowerTimeframe = timeframe.from_seconds(math.ceil(timeframe.in_seconds() / maxIntrabars)) //@variable An array of lines to represent intrabar wicks. var array<line> wicks = array.new<line>() //@variable An array of lines to represent intrabar bodies. var array<line> bodies = array.new<line>() //@variable A box that surrounds the displayed intrabars. var box magnifierBox = box.new(na, na, na, na, bgcolor = na) // Fill the `wicks` and `bodies` arrays with blank lines on the first bar. if barstate.isfirst for i = 1 to maxIntrabars array.push(wicks, line.new(na, na, na, na, color = color.gray)) array.push(bodies, line.new(na, na, na, na, width = candleWidth)) //@variable A tuple of "float" arrays containing `open`, `high`, `low`, and `close` prices from the `lowerTimeframe`. [oData, hData, lData, cData] = request.security_lower_tf(syminfo.tickerid, lowerTimeframe, [open, high, low, close]) //@variable The number of intrabars retrieved from the `lowerTimeframe` on the chart bar. int numIntrabars = array.size(oData) if numIntrabars > 0 // Define the start and end bar index values for intrabar display. int startIndex = bar_index + 2 int endIndex = startIndex + numIntrabars // Loop to update lines. for i = 0 to maxIntrabars - 1 line wickLine = array.get(wicks, i) line bodyLine = array.get(bodies, i) if i < numIntrabars //@variable The `bar_index` of the drawing. int candleIndex = startIndex + i // Update the properties of the `wickLine` and `bodyLine`. line.set_xy1(wickLine, startIndex + i, array.get(hData, i)) line.set_xy2(wickLine, startIndex + i, array.get(lData, i)) line.set_xy1(bodyLine, startIndex + i, array.get(oData, i)) line.set_xy2(bodyLine, startIndex + i, array.get(cData, i)) line.set_color(bodyLine, bodyLine.get_y2() > bodyLine.get_y1() ? color.teal : color.maroon) continue // Set the coordinates of the `wickLine` and `bodyLine` to `na` if no intrabar data is available at the index. line.set_xy1(wickLine, na, na) line.set_xy2(wickLine, na, na) line.set_xy1(bodyLine, na, na) line.set_xy2(bodyLine, na, na) // Set the coordinates of the `magnifierBox`. box.set_lefttop(magnifierBox, startIndex - 1, array.max(hData)) box.set_rightbottom(magnifierBox, endIndex, array.min(lData))

Note that:

The script draws each candle using two lines: one to represent wicks and the other to represent the body. Since the script can display up to 500 lines on the chart, we’ve limited the maxIntrabars input to 250.
The lowerTimeframe value is the result of calculating the math.ceil() of the timeframe.in_seconds() divided by the maxIntrabars and converting to a valid timeframe string with timeframe.from_seconds().
The script sets the top of the box drawing using the array.max() of the requested hData array, and it sets the box’s bottom using the array.min() of the requested lData array. As we see on the chart, these values correspond to the high and low of the chart bar.

Requesting collections

In some cases, a script might need to request collections from an intrabar context. However, in contrast to request.security(), scripts cannot use collection references or calls to functions that return them as the expression argument in a request.security_lower_tf() call, because arrays cannot directly store references to other collections.

Despite these limitations, it is possible to request collections from lower timeframes, if needed, with the help of wrapper types.

To make collections requestable with request.security_lower_tf(), we must create a UDT with a field to reference a collection ID. This step is necessary since arrays cannot reference other collections directly but can reference UDTs with collection fields:

//@type A "wrapper" type for storing an `array<float>` reference. type Wrapper array<float> collection

With our Wrapper UDT defined, we can now pass the IDs of objects of the UDT to the expression parameter in request.security_lower_tf().

A straightforward approach is to use a call to the type’s built-in *.new() function as the expression argument. For example, this line of code uses a call to Wrapper.new() with array.from(close) as the collection argument directly within the request.security_lower_tf() call:

//@variable An array of `Wrapper` IDs requested from the 1-minute timeframe. array<Wrapper> wrappers = request.security_lower_tf(syminfo.tickerid, "1", Wrapper.new(array.from(close)))

Alternatively, we can create a user-defined function or method that returns a reference to an object of the UDT and call that function within request.security_lower_tf(). For instance, this code calls a custom newWrapper() function that returns a Wrapper ID as the expression argument:

//@function Creates a new `Wrapper` instance to wrap the specified `collection`. newWrapper(array<float> collection) => Wrapper.new(collection) //@variable An array of `Wrapper` IDs requested from the 1-minute timeframe. array<Wrapper> wrappers = request.security_lower_tf(syminfo.tickerid, "1", newWrapper(array.from(close)))

The result with either of the above is an array containing Wrapper IDs from all available intrabars in the chart bar, which the script can use to reference Wrapper instances from specific intrabars and use their collection fields in additional operations.

The script below utilizes this approach to collect the IDs of arrays containing intrabar data from a lowerTimeframe, then uses those arrays to display data from a specific lower-timeframe bar. Its custom Prices type contains a single data field to reference array<float> instances that hold price data, and the user-defined newPrices() function returns the ID of a Prices object.

The script calls request.security_lower_tf() with a newPrices() call as its expression argument to retrieve the ID of an array containing Prices IDs from each intrabar in the chart bar, then uses array.get() to get the ID from a specified available intrabar, if it exists. Lastly, it uses array.get() on the data array referenced by that instance and calls plotcandle() to display its values on the chart:

//@version=6 indicator("Requesting LTF collections demo", "Intrabar viewer", true) //@variable The timeframe of the LTF data request. string lowerTimeframe = input.timeframe("1", "Timeframe") //@variable The index of the intrabar to show on each chart bar. 0 is the first available intrabar. int intrabarIndex = input.int(0, "Intrabar to show", 0) //@variable A custom type to store an `array<float>` reference. type Prices array<float> data //@function Returns the ID of a new `Prices` instance containing current `open`, `high`, `low`, and `close` prices. newPrices() => Prices.new(array.from(open, high, low, close)) //@variable An array of `Prices` IDs requested from the `lowerTimeframe`. array<Prices> requestedPrices = request.security_lower_tf(syminfo.tickerid, lowerTimeframe, newPrices()) //@variable The `Prices` ID from the `requestedPrices` array at the `intrabarIndex`, or `na` if not available. Prices intrabarPrices = array.size(requestedPrices) > intrabarIndex ? array.get(requestedPrices, intrabarIndex) : na //@variable The `data` array ID from the `intrabarPrices` object, or the ID of an array of `na` values if `intrabarPrices` is `na`. array<float> intrabarData = na(intrabarPrices) ? array.new<float>(4, na) : intrabarPrices.data // Plot the `intrabarData` values as candles. plotcandle(intrabarData.get(0), intrabarData.get(1), intrabarData.get(2), intrabarData.get(3))

Note that:

The intrabarPrices variable references a Prices object only if the size of the requestedPrices array is greater than the intrabarIndex, because attempting to use array.get() to retrieve an element that doesn’t exist causes an out of bounds error.
The intrabarData variable references an array from the intrabarPrices.data field only if the intrabarPrices variable references a Prices object. If intrabarPrices holds na because intrabar data is not available for a bar, the intrabarData variable references an array of na values.
The process used in this example is not necessary to achieve the intended result. Instead of using UDTs, we can use the tuple [open, high, low, close] as the expression argument in the request to retrieve a tuple of arrays for further operations. See the Tuples of intrabar data section above for more information.

Custom contexts

Pine Script includes multiple ticker.*() functions that allow scripts to construct custom ticker IDs that specify additional settings for data requests when used as a symbol argument in request.security() and request.security_lower_tf():

ticker.new() constructs a custom ticker ID from a specified prefix and ticker with additional session and adjustment settings.
ticker.modify() constructs a modified form of a specified tickerid with additional session and adjustment settings.
ticker.heikinashi(), ticker.renko(), ticker.pointfigure(), ticker.kagi(), and ticker.linebreak() construct a modified form a symbol with non-standard chart settings.
ticker.inherit() constructs a new ticker ID for a symbol with additional parameters inherited from the from_tickerid specified in the function call, allowing scripts to request the symbol data with the same modifiers as the from_tickerid, including session, dividend adjustment, currency conversion, non-standard chart type, back-adjustment, settlement-as-close, etc.
ticker.standard() constructs a standard ticker ID representing the symbol without additional modifiers.

Let’s explore some practical examples of applying ticker.*() functions to request data from custom contexts.

Suppose we want to include dividend adjustment in a stock symbol’s prices without enabling the “Adjust data for dividends” option in the “Symbol” section of the chart’s settings. We can achieve this in a script by constructing a custom ticker ID for the instrument using ticker.new() or ticker.modify() with an adjustment value of adjustment.dividends.

This script creates an adjustedTickerID using ticker.modify(), uses that ticker ID as the symbol in request.security() to retrieve a tuple of adjusted price values, then uses plotcandle() to plot the result as candles on the chart. It also highlights the background of bars where the requested prices differ from the prices without dividend adjustment.

As we see on the “NYSE:XOM” chart below, enabling dividend adjustment results in different historical values before the date of the latest dividend:

//@version=6 indicator("Custom contexts demo 1", "Adjusted prices", true) //@variable A custom ticker ID representing the chart's symbol with the dividend adjustment modifier. string adjustedTickerID = ticker.modify(syminfo.tickerid, adjustment = adjustment.dividends) // Request the adjusted prices for the chart's symbol. [o, h, l, c] = request.security(adjustedTickerID, timeframe.period, [open, high, low, close]) //@variable The color of the candles on the chart. color candleColor = c > o ? color.teal : color.maroon // Plot the adjusted prices. plotcandle(o, h, l, c, "Adjusted Prices", candleColor) // Highlight the background when `c` is different from `close`. bgcolor(c != close ? color.new(color.orange, 80) : na)

Note that:

If a modifier included in a constructed ticker ID does not apply to the symbol, the script will ignore that modifier when requesting data. For instance, this script will display the same values as the main chart on forex symbols such as “EURUSD”.

While the example above demonstrates a simple way to modify the chart’s symbol, a more frequent use case for ticker.*() functions is applying custom modifiers to another symbol while requesting data. If a ticker ID referenced in a script already has the modifiers one would like to apply (e.g., adjustment settings, session type, etc.), they can use ticker.inherit() to quickly and efficiently add those modifiers to another symbol.

In the example below, we’ve edited the previous script to request data for a symbolInput using modifiers inherited from the adjustedTickerID. This script calls ticker.inherit() to construct an inheritedTickerID and uses that ticker ID in a request.security() call. It also requests data for the symbolInput without additional modifiers and plots candles for both ticker IDs in a separate chart pane to compare the difference.

As shown on the chart, the data requested using the inheritedTickerID includes dividend adjustment, whereas the data requested using the symbolInput directly does not:

//@version=6 indicator("Custom contexts demo 2", "Inherited adjustment") //@variable The symbol to request data from. string symbolInput = input.symbol("NYSE:PFE", "Symbol") //@variable A custom ticker ID representing the chart's symbol with the dividend adjustment modifier. string adjustedTickerID = ticker.modify(syminfo.tickerid, adjustment = adjustment.dividends) //@variable A custom ticker ID representing the `symbolInput` with modifiers inherited from the `adjustedTickerID`. string inheritedTickerID = ticker.inherit(adjustedTickerID, symbolInput) // Request prices using the `symbolInput`. [o1, h1, l1, c1] = request.security(symbolInput, timeframe.period, [open, high, low, close]) // Request prices using the `inheritedTickerID`. [o2, h2, l2, c2] = request.security(inheritedTickerID, timeframe.period, [open, high, low, close]) //@variable The color of the candles that use the `inheritedTickerID` prices. color candleColor = c2 > o2 ? color.teal : color.maroon // Plot the `symbol` prices. plotcandle(o1, h1, l1, c1, "Symbol", color.gray, color.gray, bordercolor = color.gray) // Plot the `inheritedTickerID` prices. plotcandle(o2, h2, l2, c2, "Symbol With Modifiers", candleColor) // Highlight the background when `c1` is different from `c2`. bgcolor(c1 != c2 ? color.new(color.orange, 80) : na)

Note that:

Since the adjustedTickerID represents a modified form of the syminfo.tickerid, if we modify the chart’s context in other ways, such as changing the chart type or enabling extended trading hours in the chart’s settings, those modifiers will also apply to the adjustedTickerID and inheritedTickerID. However, they will not apply to the symbolInput since it represents a standard ticker ID.

Another frequent use case for requesting custom contexts is retrieving data that uses non-standard chart calculations. For example, suppose we want to use Renko price values to calculate trade signals in a strategy() script. If we simply change the chart type to “Renko” to get the prices, the strategy will also simulate its trades based on those synthetic prices, producing misleading results:

//@version=6 strategy( "Custom contexts demo 3", "Renko strategy", true, default_qty_type = strategy.percent_of_equity, default_qty_value = 2, initial_capital = 50000, slippage = 2, commission_type = strategy.commission.cash_per_contract, commission_value = 1, margin_long = 100, margin_short = 100 ) //@variable When `true`, the strategy places a long market order. bool longEntry = ta.crossover(close, open) //@variable When `true`, the strategy places a short market order. bool shortEntry = ta.crossunder(close, open) if longEntry strategy.entry("Long Entry", strategy.long) if shortEntry strategy.entry("Short Entry", strategy.short)

To ensure our strategy shows results based on actual prices, we can create a Renko ticker ID using ticker.renko() while keeping the chart on a standard type, allowing the script to request and use Renko prices to calculate its signals without calculating the strategy results on them:

//@version=6 strategy( "Custom contexts demo 3", "Renko strategy", true, default_qty_type = strategy.percent_of_equity, default_qty_value = 2, initial_capital = 50000, slippage = 1, commission_type = strategy.commission.cash_per_contract, commission_value = 1, margin_long = 100, margin_short = 100 ) //@variable A Renko ticker ID. string renkoTickerID = ticker.renko(syminfo.tickerid, "ATR", 14) // Request the `open` and `close` prices using the `renkoTickerID`. [renkoOpen, renkoClose] = request.security(renkoTickerID, timeframe.period, [open, close]) //@variable When `true`, the strategy places a long market order. bool longEntry = ta.crossover(renkoClose, renkoOpen) //@variable When `true`, the strategy places a short market order. bool shortEntry = ta.crossunder(renkoClose, renkoOpen) if longEntry strategy.entry("Long Entry", strategy.long) if shortEntry strategy.entry("Short Entry", strategy.short) plot(renkoOpen) plot(renkoClose)

Historical and realtime behavior

Functions in the request.*() namespace can behave differently on historical and realtime bars. This behavior is closely related to Pine’s Execution model.

Consider how a script behaves within the main context. Throughout the chart’s history, the script calculates its required values once and commits them to that bar so their states are accessible on subsequent executions. On an unconfirmed bar, however, the script recalculates its values on each update to the bar’s data to align with realtime changes. Before recalculating the values on that bar, it reverts calculated values to their last committed states, otherwise known as rollback, and it only commits values to that bar once the bar closes.

Now consider the behavior of data requests from other contexts with request.security(). As when evaluating historical bars in the main context, request.security() only returns new historical values when it confirms a bar in its specified context. When executing on realtime bars, it returns recalculated values on each chart bar, similar to how a script recalculates values in the main context on the open chart bar.

However, the function only confirms the requested values when a bar from its context closes. When the script restarts, what were previously realtime bars become historical bars. Therefore, request.security() only returns the values it confirmed on those bars. In essence, this behavior means that requested data may repaint when its values fluctuate on realtime bars without confirmation from the context.

In most circumstances where a script requests data from a broader context, one will typically require confirmed, stable values that do not fluctuate on realtime bars. The section below explains how to achieve such a result and avoid repainting data requests.

Avoiding repainting

Higher-timeframe data

When requesting values from a higher timeframe, they are subject to repainting since realtime bars can contain unconfirmed information from developing HTF bars, and the script may adjust the times that new values come in on historical bars. To avoid repainting HTF data, one must ensure that the function only returns confirmed values with consistent timing on all bars, regardless of bar state.

The most reliable approach to achieve non-repainting results is to use an expression argument that only references past bars (e.g., close[1]) while using barmerge.lookahead_on as the lookahead value.

Using barmerge.lookahead_on with non-offset HTF data requests is discouraged since it prompts request.security() to “look ahead” to the final values of an HTF bar, retrieving confirmed values before they’re actually available in the script’s history. However, if the values used in the expression are offset by at least one bar, the “future” data the function retrieves is no longer from the future. Instead, the data represents confirmed values from established, available HTF bars. In other words, applying an offset to the expression effectively prevents the requested data from repainting when the script restarts its executions and eliminates lookahead bias in the historical series.

The following example demonstrates a repainting HTF data request. The script uses request.security() without offset modifications or additional arguments to retrieve the results of a ta.wma() call from a higher timeframe. It also highlights the background to indicate which bars were in a realtime state during its calculations.

As shown on the chart below, the plot of the requested WMA only changes on historical bars when HTF bars close, whereas it fluctuates on all realtime bars since the data includes unconfirmed values from the higher timeframe:

//@version=6 indicator("Avoiding HTF repainting demo", overlay = true) //@variable The multiplier applied to the chart's timeframe. int tfMultiplier = input.int(10, "Timeframe multiplier", 1) //@variable The number of bars in the moving average. int length = input.int(5, "WMA smoothing length") //@variable The valid timeframe string closest to `tfMultiplier` times larger than the chart timeframe. string timeframe = timeframe.from_seconds(timeframe.in_seconds() * tfMultiplier) //@variable The weighted MA of `close` prices over `length` bars on the `timeframe`. // This request repaints because it includes unconfirmed HTF data on realtime bars and it may offset the // times of its historical results. float requestedWMA = request.security(syminfo.tickerid, timeframe, ta.wma(close, length)) // Plot the requested series. plot(requestedWMA, "HTF WMA", color.purple, 3) // Highlight the background on realtime bars. bgcolor(barstate.isrealtime ? color.new(color.orange, 70) : na, title = "Realtime bar highlight")

To avoid repainting in this script, we can add lookahead = barmerge.lookahead_on to the request.security() call and offset the call history of ta.wma() by one bar with the history-referencing operator [], ensuring the request always retrieves the last confirmed HTF bar’s WMA at the start of each new timeframe. Unlike the previous script, this version has consistent behavior on historical and realtime bar states, as we see below:

//@version=6 indicator("Avoiding HTF repainting demo", overlay = true) //@variable The multiplier applied to the chart's timeframe. int tfMultiplier = input.int(10, "Timeframe multiplier", 1) //@variable The number of bars in the moving average. int length = input.int(5, "WMA smoothing length") //@variable The valid timeframe string closest to `tfMultiplier` times larger than the chart timeframe. string timeframe = timeframe.from_seconds(timeframe.in_seconds() * tfMultiplier) //@variable The weighted MA of `close` prices over `length` bars on the `timeframe`. // This request does not repaint, as it always references the last confirmed WMA value on all bars. float requestedWMA = request.security( syminfo.tickerid, timeframe, ta.wma(close, length)[1], lookahead = barmerge.lookahead_on ) // Plot the requested value. plot(requestedWMA, "HTF WMA", color.purple, 3) // Highlight the background on realtime bars. bgcolor(barstate.isrealtime ? color.new(color.orange, 70) : na, title = "Realtime bar highlight")

Lower-timeframe data

The request.security() and request.security_lower_tf() functions can retrieve data from lower-timeframe contexts. The request.security() function can only retrieve data from a single intrabar in each chart bar, and request.security_lower_tf() retrieves data from all available intrabars.

When using these functions to retrieve intrabar data, it’s important to note that such requests are not immune to repainting behavior. Historical and realtime series often rely on separate data feeds. Data providers may retroactively modify realtime data, and it’s possible for races to occur in realtime data feeds, as explained in the Data feeds section of this page. Either case may result in intrabar data retrieved on realtime bars repainting after the script restarts its executions.

Additionally, a particular case that will cause repainting LTF requests is using request.security() with barmerge.lookahead_on to retrieve data from the first intrabar in each chart bar. While it will generally work as expected on historical bars, it will track only the most recent intrabar on realtime bars, as request.security() does not retain all intrabar information, and the intrabars the function retrieves on realtime bars are unsorted until restarting the script:

//@version=6 indicator("Avoiding LTF repainting demo", overlay = true) //@variable The lower timeframe of the requested data. string lowerTimeframe = input.timeframe("1", "Timeframe") //@variable The first intrabar `close` requested from the `lowerTimeframe` on each bar. // Only works as intended on historical bars. float requestedClose = request.security(syminfo.tickerid, lowerTimeframe, close, lookahead = barmerge.lookahead_on) // Plot the `requestedClose`. plot(requestedClose, "First intrabar close", linewidth = 3) // Highlight the background on realtime bars. bgcolor(barstate.isrealtime ? color.new(color.orange, 60) : na, title = "Realtime bar Highlight")

One can mitigate this behavior and track the values from the first intrabar, or any available intrabar in the chart bar, by using request.security_lower_tf() since it maintains an array of intrabar values ordered by the times they come in. Here, we call array.first() on a requested array of intrabar data to retrieve the close price from the first available intrabar in each chart bar:

//@version=6 indicator("Avoiding LTF repainting demo", overlay = true) //@variable The lower timeframe of the requested data. string lowerTimeframe = input.timeframe("1", "Timeframe") //@variable An array of intrabar `close` values requested from the `lowerTimeframe` on each bar. array<float> requestedCloses = request.security_lower_tf(syminfo.tickerid, lowerTimeframe, close) //@variable The first intrabar `close` on each bar with available data. float firstClose = requestedCloses.size() > 0 ? requestedCloses.first() : na // Plot the `firstClose`. plot(firstClose, "First intrabar close", linewidth = 3) // Highlight the background on realtime bars. bgcolor(barstate.isrealtime ? color.new(color.orange, 60) : na, title = "Realtime bar Highlight")

Note that:

While request.security_lower_tf() is more optimized for handling historical and realtime intrabars, it’s still possible in some cases for minor repainting to occur due to data differences from the provider, as outlined above.
This code may not show intrabar data on all available chart bars, depending on how many intrabars each chart bar contains, as request.*() functions can retrieve up to 200,000 intrabars from an LTF context. The maximum number of requestable intrabars depends on the user’s plan. See this section of the Limitations page for more information.

`request.currency_rate()`

When a script needs to convert values expressed in one currency to another, one can use request.currency_rate(). This function requests a daily rate for currency conversion calculations based on currency pair or spread data from the most popular exchanges, providing a simpler alternative to fetching specific pairs or spreads with request.security().

While one can use request.security() to retrieve daily currency rates, its use case is more involved than request.currency_rate(), as one needs to supply a valid ticker ID for a currency pair or spread to request the rate. Additionally, a historical offset and barmerge.lookahead_on are necessary to prevent the results from repainting, as explained in this section.

The request.currency_rate() function, on the other hand, only requires currency codes. No ticker ID is needed when requesting rates with this function, and it ensures non-repainting results without requiring additional specification.

The function’s signature is as follows:

request.currency_rate(from, to, ignore_invalid_currency) → series float

The from parameter specifies the currency to convert, and the to parameter specifies the target currency. Both parameters accept “string” values representing valid currency codes (e.g., “USD”) or any built-in currency.* variable (e.g., currency.USD).

When the function cannot calculate a valid conversion rate between the specified from and to currencies, programmers can decide whether it raises a runtime error or returns na via the ignore_invalid_currency parameter. The default value is false, meaning the function raises a runtime error and halts the script’s executions.

The following example demonstrates a simple use case for request.currency_rate(). Suppose we want to convert values expressed in Turkish lira (currency.TRY) to South Korean won (currency.KRW) using a daily conversion rate. If we use request.security() to retrieve the rate, we must supply a valid ticker ID and request the last confirmed close from the previous day.

In this case, no valid symbol exists that would allow us to retrieve a conversion rate directly with request.security(). Therefore, we first need a ticker ID for a spread that converts TRY to an intermediate currency, such as USD, then converts the intermediate currency to KRW. We can then use that ticker ID within request.security() with close[1] as the expression and barmerge.lookahead_on as the lookahead value to request a non-repainting daily rate.

Alternatively, we can achieve the same result more simply by calling request.currency_rate(). This function does all the heavy lifting for us, only requiring from and to currency arguments to perform its calculation.

As we see below, both approaches return the same daily rate:

//@version=6 indicator("Requesting currency rates demo") //@variable The currency to convert. simple string fromCurrency = currency.TRY //@variable The resulting currency. simple string toCurrency = currency.KRW //@variable The spread symbol to request. Required in `request.security()` because no direct symbol exists. simple string spreadSymbol = str.format("{0}{2} * {2}{1}", fromCurrency, toCurrency, currency.USD) //@variable The non-repainting conversion rate from `request.security()` using the `spreadSymbol`. float securityRequestedRate = request.security(spreadSymbol, "1D", close[1], lookahead = barmerge.lookahead_on) //@variable The non-repainting conversion rate from `request.currency_rate()`. float nonSecurityRequestedRate = request.currency_rate(fromCurrency, toCurrency) // Plot the requested rates. We can multiply TRY values by these rates to convert them to KRW. plot(securityRequestedRate, "`request.security()` value", color.purple, 5) plot(nonSecurityRequestedRate, "`request.currency_rate()` value", color.yellow, 2)

`request.dividends()`, `request.splits()`, and `request.earnings()`

Analyzing a stock’s earnings data and corporate actions provides helpful insights into its underlying financial strength. Pine Script provides the ability to retrieve essential information about applicable stocks via request.dividends(), request.splits(), and request.earnings().

These are the functions’ signatures:

request.dividends(ticker, field, gaps, lookahead, ignore_invalid_symbol, currency) → series float

request.splits(ticker, field, gaps, lookahead, ignore_invalid_symbol) → series float

request.earnings(ticker, field, gaps, lookahead, ignore_invalid_symbol, currency) → series float

Each function has the same parameters in its signature, with the exception of request.splits(), which doesn’t have a currency parameter.

Note that unlike the symbol parameter in other request.*() functions, the ticker parameter in these functions only accepts an “Exchange:Symbol” pair, such as “NASDAQ:AAPL”. The built-in syminfo.ticker variable does not work with these functions since it does not contain exchange information. Instead, one must use syminfo.tickerid for such cases.

The field parameter determines the data the function will retrieve. Each of these functions accepts different built-in variables as the field argument since each requests different information about a stock:

The request.dividends() function retrieves current dividend information for a stock, i.e., the amount per share the issuing company paid out to investors who purchased shares before the ex-dividend date. Passing the built-in dividends.gross or dividends.net variables to the field parameter specifies whether the returned value represents dividends before or after factoring in expenses the company deducts from its payouts.
The request.splits() function retrieves current split and reverse split information for a stock. A split occurs when a company increases its outstanding shares to promote liquidity. A reverse split occurs when a company consolidates its shares and offers them at a higher price to attract specific investors or maintain their listing on a market that has a minimum per-share price. Companies express their split information as ratios. For example, a 5:1 split means the company issued additional shares to its shareholders so that they have five times the number of shares they had before the split, and the raw price of each share becomes one-fifth of the previous price. Passing splits.numerator or splits.denominator to the field parameter of request.splits() determines whether it returns the numerator or denominator of the split ratio.
The request.earnings() function retrieves the earnings per share (EPS) information for a stock ticker’s issuing company. The EPS value is the ratio of a company’s net income to the number of outstanding stock shares, which investors consider an indicator of the company’s profitability. Passing earnings.actual, earnings.estimate, or earnings.standardized as the field argument in request.earnings() respectively determines whether the function requests the actual, estimated, or standardized EPS value.

For a detailed explanation of the gaps, lookahead, and ignore_invalid_symbol parameters of these functions, see the Common characteristics section at the top of this page.

It’s important to note that the values returned by these functions reflect the data available as it comes in. This behavior differs from financial data originating from a request.financial() call in that the underlying data from such calls becomes available according to a company’s fiscal reporting period.

Here, we’ve included an example that displays a handy table containing the most recent dividend, split, and EPS data. The script calls the request.*() functions discussed in this section to retrieve the data, then converts the values to “strings” with str.*() functions and displays the results in the infoTable with table.cell():

//@version=6 indicator("Dividends, splits, and earnings demo", overlay = true) //@variable The size of the table's text. string tableSize = input.string( size.large, "Table size", [size.auto, size.tiny, size.small, size.normal, size.large, size.huge] ) //@variable The color of the table's text and frame. var color tableColor = chart.fg_color //@variable A `table` displaying the latest dividend, split, and EPS information. var table infoTable = table.new(position.top_right, 3, 4, frame_color = tableColor, frame_width = 1) // Add header cells on the first bar. if barstate.isfirst table.cell(infoTable, 0, 0, "Field", text_color = tableColor, text_size = tableSize) table.cell(infoTable, 1, 0, "Value", text_color = tableColor, text_size = tableSize) table.cell(infoTable, 2, 0, "Date", text_color = tableColor, text_size = tableSize) table.cell(infoTable, 0, 1, "Dividend", text_color = tableColor, text_size = tableSize) table.cell(infoTable, 0, 2, "Split", text_color = tableColor, text_size = tableSize) table.cell(infoTable, 0, 3, "EPS", text_color = tableColor, text_size = tableSize) //@variable The amount of the last reported dividend as of the current bar. float latestDividend = request.dividends(syminfo.tickerid, dividends.gross, barmerge.gaps_on) //@variable The numerator of that last reported split ratio as of the current bar. float latestSplitNum = request.splits(syminfo.tickerid, splits.numerator, barmerge.gaps_on) //@variable The denominator of the last reported split ratio as of the current bar. float latestSplitDen = request.splits(syminfo.tickerid, splits.denominator, barmerge.gaps_on) //@variable The last reported earnings per share as of the current bar. float latestEPS = request.earnings(syminfo.tickerid, earnings.actual, barmerge.gaps_on) // Update the "Value" and "Date" columns when new values come in. if not na(latestDividend) table.cell( infoTable, 1, 1, str.tostring(math.round(latestDividend, 3)), text_color = tableColor, text_size = tableSize ) table.cell(infoTable, 2, 1, str.format_time(time, "yyyy-MM-dd"), text_color = tableColor, text_size = tableSize) if not na(latestSplitNum) table.cell( infoTable, 1, 2, str.format("{0}-for-{1}", latestSplitNum, latestSplitDen), text_color = tableColor, text_size = tableSize ) table.cell(infoTable, 2, 2, str.format_time(time, "yyyy-MM-dd"), text_color = tableColor, text_size = tableSize) if not na(latestEPS) table.cell(infoTable, 1, 3, str.tostring(latestEPS), text_color = tableColor, text_size = tableSize) table.cell(infoTable, 2, 3, str.format_time(time, "yyyy-MM-dd"), text_color = tableColor, text_size = tableSize)

Note that:

We’ve included barmerge.gaps_on in the request.*() calls, so they only return values when new data is available. Otherwise, they return na.
The script assigns a table ID to the infoTable variable on the first chart bar. On subsequent bars, it updates necessary cells with new information whenever data is available.
If no information is available from any of the request.*() calls throughout the chart’s history (e.g., if the ticker has no dividend information), the script does not initialize the corresponding cells since it’s unnecessary.

`request.financial()`

Financial metrics provide investors with insights about a company’s economic and financial health that are not tangible from solely analyzing its stock prices. TradingView offers a wide variety of financial metrics from FactSet that traders can access via the “Financials” tab in the “Indicators” menu of the chart. Scripts can access available metrics for an instrument directly via the request.financial() function.

This is the function’s signature:

request.financial(symbol, financial_id, period, gaps, ignore_invalid_symbol, currency) → series float

As with the first parameter in request.dividends(), request.splits(), and request.earnings(), the symbol parameter in request.financial() requires an “Exchange:Symbol” pair. To request financial information for the chart’s ticker ID, use syminfo.tickerid, as syminfo.ticker will not work.

The financial_id parameter accepts a “string” value representing the ID of the requested financial metric. TradingView has numerous financial metrics to choose from. See the Financial IDs section below for an overview of all accessible metrics and their “string” identifiers.

The period parameter specifies the fiscal period for which new requested data comes in. It accepts one of the following “string” arguments: “FQ” (quarterly), “FH” (semiannual), “FY” (annual), or “TTM” (trailing twelve months). Not all fiscal periods are available for all metrics or instruments. To confirm which periods are available for specific metrics, see the second column of the tables in the Financial IDs section.

See this page’s Common characteristics section for a detailed explanation of this function’s gaps, ignore_invalid_symbol, and currency parameters.

It’s important to note that the data retrieved from this function comes in at a fixed frequency, independent of the precise date on which the data is made available within a fiscal period. For a company’s dividends, splits, and earnings per share (EPS) information, one can request data reported on exact dates via request.dividends(), request.splits(), and request.earnings().

This script uses request.financial() to retrieve information about the income and expenses of a stock’s issuing company and visualize the profitability of its typical business operations. It requests the “OPER_INCOME”, “TOTAL_REVENUE”, and “TOTAL_OPER_EXPENSE” financial IDs for the syminfo.tickerid over the latest fiscalPeriod, then plots the results on the chart:

//@version=6 indicator("Requesting financial data demo", format = format.volume) //@variable The size of the fiscal reporting period. Some options may not be available, depending on the instrument. string fiscalPeriod = input.string("FQ", "Period", ["FQ", "FH", "FY", "TTM"]) //@variable The operating income after expenses reported for the stock's issuing company. float operatingIncome = request.financial(syminfo.tickerid, "OPER_INCOME", fiscalPeriod) //@variable The total revenue reported for the stock's issuing company. float totalRevenue = request.financial(syminfo.tickerid, "TOTAL_REVENUE", fiscalPeriod) //@variable The total operating expenses reported for the stock's issuing company. float totalExpenses = request.financial(syminfo.tickerid, "TOTAL_OPER_EXPENSE", fiscalPeriod) //@variable Is aqua when the `totalRevenue` exceeds the `totalExpenses`, fuchsia otherwise. color incomeColor = operatingIncome > 0 ? color.new(color.aqua, 50) : color.new(color.fuchsia, 50) // Display the requested data. plot(operatingIncome, "Operating income", incomeColor, 1, plot.style_area) plot(totalRevenue, "Total revenue", color.green, 3) plot(totalExpenses, "Total operating expenses", color.red, 3)

Note that:

Not all fiscalPeriod options are available for every ticker ID. For example, companies in the US typically publish quarterly reports, whereas many European companies publish semiannual reports. See this page in our Help Center for more information.

Calculating financial metrics

The request.financial() function can provide scripts with numerous useful financial metrics that don’t require additional calculations. However, some commonly used financial estimates require combining an instrument’s current market price with requested financial data. Such is the case for:

Market Capitalization (market price * total shares outstanding)
Earnings Yield (12-month EPS / market price)
Price-to-Book Ratio (market price / BVPS)
Price-to-Earnings Ratio (market price / EPS)
Price-to-Sales Ratio (market cap / 12-month total revenue)

The following script contains user-defined functions that calculate the above financial metrics for the syminfo.tickerid. We’ve created these functions so users can easily copy them into their scripts. This example uses them within a str.format() call to construct a tooltipText, which it displays in tooltips on the chart using labels. Hovering over any bar’s label will expose the tooltip containing the metrics calculated on that bar:

//@version=6 indicator("Calculating financial metrics demo", overlay = true, max_labels_count = 500) //@function Calculates the market capitalization (market cap) for the chart's symbol. marketCap() => //@variable The most recent number of outstanding shares reported for the symbol. float totalSharesOutstanding = request.financial(syminfo.tickerid, "TOTAL_SHARES_OUTSTANDING", "FQ") // Return the market cap value. totalSharesOutstanding * close //@function Calculates the Earnings Yield for the chart's symbol. earningsYield() => //@variable The most recent 12-month earnings per share reported for the symbol. float eps = request.financial(syminfo.tickerid, "EARNINGS_PER_SHARE", "TTM") //Return the Earnings Yield percentage. 100.0 * eps / close //@function Calculates the Price-to-Book (P/B) ratio for the chart's symbol. priceBookRatio() => //@variable The most recent Book Value Per Share (BVPS) reported for the symbol. float bookValuePerShare = request.financial(syminfo.tickerid, "BOOK_VALUE_PER_SHARE", "FQ") // Return the P/B ratio. close / bookValuePerShare //@function Calculates the Price-to-Earnings (P/E) ratio for the chart's symbol. priceEarningsRatio() => //@variable The most recent 12-month earnings per share reported for the symbol. float eps = request.financial(syminfo.tickerid, "EARNINGS_PER_SHARE", "TTM") // Return the P/E ratio. close / eps //@function Calculates the Price-to-Sales (P/S) ratio for the chart's symbol. priceSalesRatio() => //@variable The most recent number of outstanding shares reported for the symbol. float totalSharesOutstanding = request.financial(syminfo.tickerid, "TOTAL_SHARES_OUTSTANDING", "FQ") //@variable The most recent 12-month total revenue reported for the symbol. float totalRevenue = request.financial(syminfo.tickerid, "TOTAL_REVENUE", "TTM") // Return the P/S ratio. totalSharesOutstanding * close / totalRevenue //@variable The text to display in label tooltips. string tooltipText = str.format( "Market Cap: {0} {1}\nEarnings Yield: {2}%\nP/B Ratio: {3}\nP/E Ratio: {4}\nP/S Ratio: {5}", str.tostring(marketCap(), format.volume), syminfo.currency, earningsYield(), priceBookRatio(), priceEarningsRatio(), priceSalesRatio() ) //@variable Displays a blank label with a tooltip containing the `tooltipText`. label info = label.new(chart.point.now(high), tooltip = tooltipText)

Note that:

Since not all companies publish quarterly financial reports, one may need to change the “FQ” in these functions to match the minimum reporting period for a specific company, as the request.financial() calls will return na when “FQ” data isn’t available.

Financial IDs

Below is an overview of all financial metrics one can request via request.financial(), along with the periods in which reports may be available. We’ve divided this information into four tables corresponding to the categories displayed in the “Financials” section of the “Indicators” menu:

Each table has the following three columns:

The first column contains descriptions of each metric with links to Help Center pages for additional information.
The second column lists the possible period arguments allowed for the metric. Note that all available values may not be compatible with specific ticker IDs, e.g., while “FQ” may be a possible argument, it will not work if the issuing company does not publish quarterly data.
The third column lists the “string” IDs for the financial_id argument in request.financial().

Income statements

This table lists the available metrics that provide information about a company’s income, costs, profits and losses.

Click to show/hide

Financial	`period`	`financial_id`
After tax other income/expense	FQ, FH, FY, TTM	AFTER_TAX_OTHER_INCOME
Average basic shares outstanding	FQ, FH, FY	BASIC_SHARES_OUTSTANDING
Basic earnings per share (Basic EPS)	FQ, FH, FY, TTM	EARNINGS_PER_SHARE_BASIC
Cost of goods sold	FQ, FH, FY, TTM	COST_OF_GOODS
Deprecation and amortization	FQ, FH, FY, TTM	DEP_AMORT_EXP_INCOME_S
Diluted earnings per share (Diluted EPS)	FQ, FH, FY, TTM	EARNINGS_PER_SHARE_DILUTED
Diluted net income available to common stockholders	FQ, FH, FY, TTM	DILUTED_NET_INCOME
Diluted shares outstanding	FQ, FH, FY	DILUTED_SHARES_OUTSTANDING
Dilution adjustment	FQ, FH, FY, TTM	DILUTION_ADJUSTMENT
Discontinued operations	FQ, FH, FY, TTM	DISCONTINUED_OPERATIONS
EBIT	FQ, FH, FY, TTM	EBIT
EBITDA	FQ, FH, FY, TTM	EBITDA
Equity in earnings	FQ, FH, FY, TTM	EQUITY_IN_EARNINGS
Gross profit	FQ, FH, FY, TTM	GROSS_PROFIT
Interest capitalized	FQ, FH, FY, TTM	INTEREST_CAPITALIZED
Interest expense on debt	FQ, FH, FY, TTM	INTEREST_EXPENSE_ON_DEBT
Interest expense, net of interest capitalized	FQ, FH, FY, TTM	NON_OPER_INTEREST_EXP
Miscellaneous non-operating expense	FQ, FH, FY, TTM	OTHER_INCOME
Net income	FQ, FH, FY, TTM	NET_INCOME
Net income before discontinued operations	FQ, FH, FY, TTM	NET_INCOME_BEF_DISC_OPER
Non-controlling/minority interest	FQ, FH, FY, TTM	MINORITY_INTEREST_EXP
Non-operating income, excl. interest expenses	FQ, FH, FY, TTM	NON_OPER_INCOME
Non-operating income, total	FQ, FH, FY, TTM	TOTAL_NON_OPER_INCOME
Non-operating interest income	FQ, FH, FY, TTM	NON_OPER_INTEREST_INCOME
Operating expenses (excl. COGS)	FQ, FH, FY, TTM	OPERATING_EXPENSES
Operating income	FQ, FH, FY, TTM	OPER_INCOME
Other cost of goods sold	FQ, FH, FY, TTM	COST_OF_GOODS_EXCL_DEP_AMORT
Other operating expenses, total	FQ, FH, FY, TTM	OTHER_OPER_EXPENSE_TOTAL
Preferred dividends	FQ, FH, FY, TTM	PREFERRED_DIVIDENDS
Pretax equity in earnings	FQ, FH, FY, TTM	PRETAX_EQUITY_IN_EARNINGS
Pretax income	FQ, FH, FY, TTM	PRETAX_INCOME
Research & development	FQ, FH, FY, TTM	RESEARCH_AND_DEV
Selling/general/admin expenses, other	FQ, FH, FY, TTM	SELL_GEN_ADMIN_EXP_OTHER
Selling/general/admin expenses, total	FQ, FH, FY, TTM	SELL_GEN_ADMIN_EXP_TOTAL
Taxes	FQ, FH, FY, TTM	INCOME_TAX
Total operating expenses	FQ, FH, FY, TTM	TOTAL_OPER_EXPENSE
Total revenue	FQ, FH, FY, TTM	TOTAL_REVENUE
Unusual income/expense	FQ, FH, FY, TTM	UNUSUAL_EXPENSE_INC

Balance sheet

This table lists the metrics that provide information about a company’s capital structure.

Click to show/hide

Financial	`period`	`financial_id`
Accounts payable	FQ, FH, FY	ACCOUNTS_PAYABLE
Accounts receivable - trade, net	FQ, FH, FY	ACCOUNTS_RECEIVABLES_NET
Accrued payroll	FQ, FH, FY	ACCRUED_PAYROLL
Accumulated depreciation, total	FQ, FH, FY	ACCUM_DEPREC_TOTAL
Additional paid-in capital/Capital surplus	FQ, FH, FY	ADDITIONAL_PAID_IN_CAPITAL
Book value per share	FQ, FH, FY	BOOK_VALUE_PER_SHARE
Capital and operating lease obligations	FQ, FH, FY	CAPITAL_OPERATING_LEASE_OBLIGATIONS
Capitalized lease obligations	FQ, FH, FY	CAPITAL_LEASE_OBLIGATIONS
Cash & equivalents	FQ, FH, FY	CASH_N_EQUIVALENTS
Cash and short term investments	FQ, FH, FY	CASH_N_SHORT_TERM_INVEST
Common equity, total	FQ, FH, FY	COMMON_EQUITY_TOTAL
Common stock par/Carrying value	FQ, FH, FY	COMMON_STOCK_PAR
Current portion of LT debt and capital leases	FQ, FH, FY	CURRENT_PORT_DEBT_CAPITAL_LEASES
Deferred income, current	FQ, FH, FY	DEFERRED_INCOME_CURRENT
Deferred income, non-current	FQ, FH, FY	DEFERRED_INCOME_NON_CURRENT
Deferred tax assets	FQ, FH, FY	DEFERRED_TAX_ASSESTS
Deferred tax liabilities	FQ, FH, FY	DEFERRED_TAX_LIABILITIES
Dividends payable	FY	DIVIDENDS_PAYABLE
Goodwill, net	FQ, FH, FY	GOODWILL
Gross property/plant/equipment	FQ, FH, FY	PPE_TOTAL_GROSS
Income tax payable	FQ, FH, FY	INCOME_TAX_PAYABLE
Inventories - finished goods	FQ, FH, FY	INVENTORY_FINISHED_GOODS
Inventories - progress payments & other	FQ, FH, FY	INVENTORY_PROGRESS_PAYMENTS
Inventories - raw materials	FQ, FH, FY	INVENTORY_RAW_MATERIALS
Inventories - work in progress	FQ, FH, FY	INVENTORY_WORK_IN_PROGRESS
Investments in unconsolidated subsidiaries	FQ, FH, FY	INVESTMENTS_IN_UNCONCSOLIDATE
Long term debt	FQ, FH, FY	LONG_TERM_DEBT
Long term debt excl. lease liabilities	FQ, FH, FY	LONG_TERM_DEBT_EXCL_CAPITAL_LEASE
Long term investments	FQ, FH, FY	LONG_TERM_INVESTMENTS
Minority interest	FQ, FH, FY	MINORITY_INTEREST
Net debt	FQ, FH, FY	NET_DEBT
Net intangible assets	FQ, FH, FY	INTANGIBLES_NET
Net property/plant/equipment	FQ, FH, FY	PPE_TOTAL_NET
Note receivable - long term	FQ, FH, FY	LONG_TERM_NOTE_RECEIVABLE
Notes payable	FY	NOTES_PAYABLE_SHORT_TERM_DEBT
Operating lease liabilities	FQ, FH, FY	OPERATING_LEASE_LIABILITIES
Other common equity	FQ, FH, FY	OTHER_COMMON_EQUITY
Other current assets, total	FQ, FH, FY	OTHER_CURRENT_ASSETS_TOTAL
Other current liabilities	FQ, FH, FY	OTHER_CURRENT_LIABILITIES
Other intangibles, net	FQ, FH, FY	OTHER_INTANGIBLES_NET
Other investments	FQ, FH, FY	OTHER_INVESTMENTS
Other long term assets, total	FQ, FH, FY	LONG_TERM_OTHER_ASSETS_TOTAL
Other non-current liabilities, total	FQ, FH, FY	OTHER_LIABILITIES_TOTAL
Other receivables	FQ, FH, FY	OTHER_RECEIVABLES
Other short term debt	FY	OTHER_SHORT_TERM_DEBT
Paid in capital	FQ, FH, FY	PAID_IN_CAPITAL
Preferred stock, carrying value	FQ, FH, FY	PREFERRED_STOCK_CARRYING_VALUE
Prepaid expenses	FQ, FH, FY	PREPAID_EXPENSES
Provision for risks & charge	FQ, FH, FY	PROVISION_F_RISKS
Retained earnings	FQ, FH, FY	RETAINED_EARNINGS
Shareholders’ equity	FQ, FH, FY	SHRHLDRS_EQUITY
Short term debt	FQ, FH, FY	SHORT_TERM_DEBT
Short term debt excl. current portion of LT debt	FQ, FH, FY	SHORT_TERM_DEBT_EXCL_CURRENT_PORT
Short term investments	FQ, FH, FY	SHORT_TERM_INVEST
Tangible book value per share	FQ, FH, FY	BOOK_TANGIBLE_PER_SHARE
Total assets	FQ, FH, FY	TOTAL_ASSETS
Total current assets	FQ, FH, FY	TOTAL_CURRENT_ASSETS
Total current liabilities	FQ, FH, FY	TOTAL_CURRENT_LIABILITIES
Total debt	FQ, FH, FY	TOTAL_DEBT
Total equity	FQ, FH, FY	TOTAL_EQUITY
Total inventory	FQ, FH, FY	TOTAL_INVENTORY
Total liabilities	FQ, FH, FY	TOTAL_LIABILITIES
Total liabilities & shareholders’ equities	FQ, FH, FY	TOTAL_LIABILITIES_SHRHLDRS_EQUITY
Total non-current assets	FQ, FH, FY	TOTAL_NON_CURRENT_ASSETS
Total non-current liabilities	FQ, FH, FY	TOTAL_NON_CURRENT_LIABILITIES
Total receivables, net	FQ, FH, FY	TOTAL_RECEIVABLES_NET
Treasury stock - common	FQ, FH, FY	TREASURY_STOCK_COMMON

Cash flow

This table lists the available metrics that provide information about how cash flows through a company.

Click to show/hide

Financial	`period`	`financial_id`
Amortization	FQ, FH, FY, TTM	AMORTIZATION
Capital expenditures	FQ, FH, FY, TTM	CAPITAL_EXPENDITURES
Capital expenditures - fixed assets	FQ, FH, FY, TTM	CAPITAL_EXPENDITURES_FIXED_ASSETS
Capital expenditures - other assets	FQ, FH, FY, TTM	CAPITAL_EXPENDITURES_OTHER_ASSETS
Cash from financing activities	FQ, FH, FY, TTM	CASH_F_FINANCING_ACTIVITIES
Cash from investing activities	FQ, FH, FY, TTM	CASH_F_INVESTING_ACTIVITIES
Cash from operating activities	FQ, FH, FY, TTM	CASH_F_OPERATING_ACTIVITIES
Change in accounts payable	FQ, FH, FY, TTM	CHANGE_IN_ACCOUNTS_PAYABLE
Change in accounts receivable	FQ, FH, FY, TTM	CHANGE_IN_ACCOUNTS_RECEIVABLE
Change in accrued expenses	FQ, FH, FY, TTM	CHANGE_IN_ACCRUED_EXPENSES
Change in inventories	FQ, FH, FY, TTM	CHANGE_IN_INVENTORIES
Change in other assets/liabilities	FQ, FH, FY, TTM	CHANGE_IN_OTHER_ASSETS
Change in taxes payable	FQ, FH, FY, TTM	CHANGE_IN_TAXES_PAYABLE
Changes in working capital	FQ, FH, FY, TTM	CHANGES_IN_WORKING_CAPITAL
Common dividends paid	FQ, FH, FY, TTM	COMMON_DIVIDENDS_CASH_FLOW
Deferred taxes (cash flow)	FQ, FH, FY, TTM	CASH_FLOW_DEFERRED_TAXES
Depreciation & amortization (cash flow)	FQ, FH, FY, TTM	CASH_FLOW_DEPRECATION_N_AMORTIZATION
Depreciation/depletion	FQ, FH, FY, TTM	DEPRECIATION_DEPLETION
Financing activities - other sources	FQ, FH, FY, TTM	OTHER_FINANCING_CASH_FLOW_SOURCES
Financing activities - other uses	FQ, FH, FY, TTM	OTHER_FINANCING_CASH_FLOW_USES
Free cash flow	FQ, FH, FY, TTM	FREE_CASH_FLOW
Funds from operations	FQ, FH, FY, TTM	FUNDS_F_OPERATIONS
Investing activities - other sources	FQ, FH, FY, TTM	OTHER_INVESTING_CASH_FLOW_SOURCES
Investing activities - other uses	FQ, FH, FY	OTHER_INVESTING_CASH_FLOW_USES
Issuance of long term debt	FQ, FH, FY, TTM	SUPPLYING_OF_LONG_TERM_DEBT
Issuance/retirement of debt, net	FQ, FH, FY, TTM	ISSUANCE_OF_DEBT_NET
Issuance/retirement of long term debt	FQ, FH, FY, TTM	ISSUANCE_OF_LONG_TERM_DEBT
Issuance/retirement of other debt	FQ, FH, FY, TTM	ISSUANCE_OF_OTHER_DEBT
Issuance/retirement of short term debt	FQ, FH, FY, TTM	ISSUANCE_OF_SHORT_TERM_DEBT
Issuance/retirement of stock, net	FQ, FH, FY, TTM	ISSUANCE_OF_STOCK_NET
Net income (cash flow)	FQ, FH, FY, TTM	NET_INCOME_STARTING_LINE
Non-cash items	FQ, FH, FY, TTM	NON_CASH_ITEMS
Other financing cash flow items, total	FQ, FH, FY, TTM	OTHER_FINANCING_CASH_FLOW_ITEMS_TOTAL
Other investing cash flow items, total	FQ, FH, FY	OTHER_INVESTING_CASH_FLOW_ITEMS_TOTAL
Preferred dividends paid	FQ, FH, FY	PREFERRED_DIVIDENDS_CASH_FLOW
Purchase of investments	FQ, FH, FY, TTM	PURCHASE_OF_INVESTMENTS
Purchase/acquisition of business	FQ, FH, FY, TTM	PURCHASE_OF_BUSINESS
Purchase/sale of business, net	FQ, FH, FY	PURCHASE_SALE_BUSINESS
Purchase/sale of investments, net	FQ, FH, FY, TTM	PURCHASE_SALE_INVESTMENTS
Reduction of long term debt	FQ, FH, FY, TTM	REDUCTION_OF_LONG_TERM_DEBT
Repurchase of common & preferred stock	FQ, FH, FY, TTM	PURCHASE_OF_STOCK
Sale of common & preferred stock	FQ, FH, FY, TTM	SALE_OF_STOCK
Sale of fixed assets & businesses	FQ, FH, FY, TTM	SALES_OF_BUSINESS
Sale/maturity of investments	FQ, FH, FY	SALES_OF_INVESTMENTS
Total cash dividends paid	FQ, FH, FY, TTM	TOTAL_CASH_DIVIDENDS_PAID

Statistics

This table contains a variety of statistical metrics, including commonly used financial ratios.

Click to show/hide

Financial	`period`	`financial_id`
Accruals	FQ, FH, FY	ACCRUALS_RATIO
Altman Z-score	FQ, FH, FY	ALTMAN_Z_SCORE
Asset turnover	FQ, FH, FY	ASSET_TURNOVER
Beneish M-score	FQ, FH, FY	BENEISH_M_SCORE
Buyback yield %	FQ, FH, FY	BUYBACK_YIELD
COGS to revenue ratio	FQ, FH, FY	COGS_TO_REVENUE
Cash conversion cycle	FQ, FY	CASH_CONVERSION_CYCLE
Cash to debt ratio	FQ, FH, FY	CASH_TO_DEBT
Current ratio	FQ, FH, FY	CURRENT_RATIO
Days inventory	FQ, FY	DAYS_INVENT
Days payable	FQ, FY	DAYS_PAY
Days sales outstanding	FQ, FY	DAY_SALES_OUT
Debt to EBITDA ratio	FQ, FH, FY	DEBT_TO_EBITDA
Debt to assets ratio	FQ, FH, FY	DEBT_TO_ASSET
Debt to equity ratio	FQ, FH, FY	DEBT_TO_EQUITY
Debt to revenue ratio	FQ, FH, FY	DEBT_TO_REVENUE
Dividend payout ratio %	FQ, FH, FY, TTM	DIVIDEND_PAYOUT_RATIO
Dividend yield %	FQ, FH, FY	DIVIDENDS_YIELD
Dividends per share - common stock primary issue	FQ, FH, FY, TTM	DPS_COMMON_STOCK_PRIM_ISSUE
EBITDA margin %	FQ, FH, FY, TTM	EBITDA_MARGIN
EPS basic one year growth	FQ, FH, FY, TTM	EARNINGS_PER_SHARE_BASIC_ONE_YEAR_GROWTH
EPS diluted one year growth	FQ, FH, FY	EARNINGS_PER_SHARE_DILUTED_ONE_YEAR_GROWTH
EPS estimates	FQ, FH, FY	EARNINGS_ESTIMATE
Effective interest rate on debt %	FQ, FH, FY	EFFECTIVE_INTEREST_RATE_ON_DEBT
Enterprise value	FQ, FH, FY	ENTERPRISE_VALUE
Enterprise value to EBIT ratio	FQ, FH, FY	EV_EBIT
Enterprise value to EBITDA ratio	FQ, FH, FY	ENTERPRISE_VALUE_EBITDA
Enterprise value to revenue ratio	FQ, FH, FY	EV_REVENUE
Equity to assets ratio	FQ, FH, FY	EQUITY_TO_ASSET
Float shares outstanding	FY	FLOAT_SHARES_OUTSTANDING
Free cash flow margin %	FQ, FH, FY	FREE_CASH_FLOW_MARGIN
Fulmer H factor	FQ, FY	FULMER_H_FACTOR
Goodwill to assets ratio	FQ, FH, FY	GOODWILL_TO_ASSET
Graham’s number	FQ, FY	GRAHAM_NUMBERS
Gross margin %	FQ, FH, FY, TTM	GROSS_MARGIN
Gross profit to assets ratio	FQ, FY	GROSS_PROFIT_TO_ASSET
Interest coverage	FQ, FH, FY	INTERST_COVER
Inventory to revenue ratio	FQ, FH, FY	INVENT_TO_REVENUE
Inventory turnover	FQ, FH, FY	INVENT_TURNOVER
KZ index	FY	KZ_INDEX
Long term debt to total assets ratio	FQ, FH, FY	LONG_TERM_DEBT_TO_ASSETS
Net current asset value per share	FQ, FY	NCAVPS_RATIO
Net income per employee	FY	NET_INCOME_PER_EMPLOYEE
Net margin %	FQ, FH, FY, TTM	NET_MARGIN
Number of employees	FY	NUMBER_OF_EMPLOYEES
Operating earnings yield %	FQ, FH, FY	OPERATING_EARNINGS_YIELD
Operating margin %	FQ, FH, FY	OPERATING_MARGIN
PEG ratio	FQ, FY	PEG_RATIO
Piotroski F-score	FQ, FH, FY	PIOTROSKI_F_SCORE
Price earnings ratio forward	FQ, FY	PRICE_EARNINGS_FORWARD
Price sales ratio forward	FQ, FY	PRICE_SALES_FORWARD
Quality ratio	FQ, FH, FY	QUALITY_RATIO
Quick ratio	FQ, FH, FY	QUICK_RATIO
Research & development to revenue ratio	FQ, FH, FY	RESEARCH_AND_DEVELOP_TO_REVENUE
Return on assets %	FQ, FH, FY	RETURN_ON_ASSETS
Return on common equity %	FQ, FH, FY	RETURN_ON_COMMON_EQUITY
Return on equity %	FQ, FH, FY	RETURN_ON_EQUITY
Return on equity adjusted to book value %	FQ, FH, FY	RETURN_ON_EQUITY_ADJUST_TO_BOOK
Return on invested capital %	FQ, FH, FY	RETURN_ON_INVESTED_CAPITAL
Return on tangible assets %	FQ, FH, FY	RETURN_ON_TANG_ASSETS
Return on tangible equity %	FQ, FH, FY	RETURN_ON_TANG_EQUITY
Revenue estimates	FQ, FH, FY	SALES_ESTIMATES
Revenue one year growth	FQ, FH, FY, TTM	REVENUE_ONE_YEAR_GROWTH
Revenue per employee	FY	REVENUE_PER_EMPLOYEE
Shares buyback ratio %	FQ, FH, FY	SHARE_BUYBACK_RATIO
Sloan ratio %	FQ, FH, FY	SLOAN_RATIO
Springate score	FQ, FY	SPRINGATE_SCORE
Sustainable growth rate	FQ, FY	SUSTAINABLE_GROWTH_RATE
Tangible common equity ratio	FQ, FH, FY	TANGIBLE_COMMON_EQUITY_RATIO
Tobin’s Q (approximate)	FQ, FH, FY	TOBIN_Q_RATIO
Total common shares outstanding	FQ, FH, FY	TOTAL_SHARES_OUTSTANDING
Zmijewski score	FQ, FY	ZMIJEWSKI_SCORE

`request.economic()`

The request.economic() function provides scripts with the ability to retrieve economic data for a specified country or region, including information about the state of the economy (GDP, inflation rate, etc.) or of a particular industry (steel production, ICU beds, etc.).

Below is the signature for this function:

request.economic(country_code, field, gaps, ignore_invalid_symbol) → series float

The country_code parameter accepts a “string” value representing the identifier of the country or region to request economic data for (e.g., “US”, “EU”, etc.). See the Country/region codes section for a complete list of codes this function supports. Note that the economic metrics available depend on the country or region specified in the function call.

The field parameter accepts a “string” specifying the metric that the function requests. The Field codes section covers all accessible metrics and the countries/regions they’re available for.

For a detailed explanation on the last two parameters of this function, see the Common characteristics section at the top of this page.

This simple example requests the growth rate of the Gross Domestic Product (“GDPQQ”) for the United States (“US”) using request.economic(), then plots its value on the chart with a gradient color:

//@version=6 indicator("Requesting economic data demo") //@variable The GDP growth rate for the US economy. float gdpqq = request.economic("US", "GDPQQ") //@variable The all-time maximum growth rate. float maxRate = ta.max(gdpqq) //@variable The all-time minimum growth rate. float minRate = ta.min(gdpqq) //@variable The color of the `gdpqq` plot. color rateColor = switch gdpqq >= 0 => color.from_gradient(gdpqq, 0, maxRate, color.purple, color.blue) => color.from_gradient(gdpqq, minRate, 0, color.red, color.purple) // Plot the results. plot(gdpqq, "US GDP Growth Rate", rateColor, style = plot.style_area)

Note that:

This example does not include a gaps argument in the request.economic() call, so the function uses the default barmerge.gaps_off. In other words, it returns the last retrieved value when new data isn’t yet available.

Country/region codes

The table in this section lists all country/region codes available for use with request.economic(). The first column of the table contains the “string” values that represent the country or region code, and the second column contains the corresponding country/region names.

It’s important to note that the value used as the country_code argument determines which field codes are accessible to the function.

Click to show/hide

`country_code`	Country/region name
AF	Afghanistan
AL	Albania
DZ	Algeria
AD	Andorra
AO	Angola
AG	Antigua and Barbuda
AR	Argentina
AM	Armenia
AW	Aruba
AU	Australia
AT	Austria
AZ	Azerbaijan
BS	Bahamas
BH	Bahrain
BD	Bangladesh
BB	Barbados
BY	Belarus
BE	Belgium
BZ	Belize
BJ	Benin
BM	Bermuda
BT	Bhutan
BO	Bolivia
BA	Bosnia and Herzegovina
BW	Botswana
BR	Brazil
BN	Brunei
BG	Bulgaria
BF	Burkina Faso
BI	Burundi
KH	Cambodia
CM	Cameroon
CA	Canada
CV	Cape Verde
KY	Cayman Islands
CF	Central African Republic
TD	Chad
CL	Chile
CN	China
CO	Colombia
KM	Comoros
CG	Congo
CR	Costa Rica
HR	Croatia
CU	Cuba
CY	Cyprus
CZ	Czech Republic
DK	Denmark
DJ	Djibouti
DM	Dominica
DO	Dominican Republic
TL	East Timor
EC	Ecuador
EG	Egypt
SV	El Salvador
GQ	Equatorial Guinea
ER	Eritrea
EE	Estonia
ET	Ethiopia
EU	Euro area
FO	Faroe Islands
FJ	Fiji
FI	Finland
FR	France
GA	Gabon
GM	Gambia
GE	Georgia
DE	Germany
GH	Ghana
GR	Greece
GL	Greenland
GD	Grenada
GT	Guatemala
GN	Guinea
GW	Guinea Bissau
GY	Guyana
HT	Haiti
HN	Honduras
HK	Hong Kong
HU	Hungary
IS	Iceland
IN	India
ID	Indonesia
IR	Iran
IQ	Iraq
IE	Ireland
IM	Isle of Man
IL	Israel
IT	Italy
CI	Ivory Coast
JM	Jamaica
JP	Japan
JO	Jordan
KZ	Kazakhstan
KE	Kenya
KI	Kiribati
XK	Kosovo
KW	Kuwait
KG	Kyrgyzstan
LA	Laos
LV	Latvia
LB	Lebanon
LS	Lesotho
LR	Liberia
LY	Libya
LI	Liechtenstein
LT	Lithuania
LU	Luxembourg
MO	Macau
MK	Macedonia
MG	Madagascar
MW	Malawi
MY	Malaysia
MV	Maldives
ML	Mali
MT	Malta
MR	Mauritania
MU	Mauritius
MX	Mexico
MD	Moldova
MC	Monaco
MN	Mongolia
ME	Montenegro
MA	Morocco
MZ	Mozambique
MM	Myanmar
NA	Namibia
NP	Nepal
NL	Netherlands
NC	New Caledonia
NZ	New Zealand
NI	Nicaragua
NE	Niger
NG	Nigeria
KP	North Korea
NO	Norway
OM	Oman
PK	Pakistan
PS	Palestine
PA	Panama
PG	Papua New Guinea
PY	Paraguay
PE	Peru
PH	Philippines
PL	Poland
PT	Portugal
PR	Puerto Rico
QA	Qatar
CD	Republic of the Congo
RO	Romania
RU	Russia
RW	Rwanda
WS	Samoa
SM	San Marino
ST	Sao Tome and Principe
SA	Saudi Arabia
SN	Senegal
RS	Serbia
SC	Seychelles
SL	Sierra Leone
SG	Singapore
SK	Slovakia
SI	Slovenia
SB	Solomon Islands
SO	Somalia
ZA	South Africa
KR	South Korea
SS	South Sudan
ES	Spain
LK	Sri Lanka
LC	St Lucia
VC	St Vincent and the Grenadines
SD	Sudan
SR	Suriname
SZ	Swaziland
SE	Sweden
CH	Switzerland
SY	Syria
TW	Taiwan
TJ	Tajikistan
TZ	Tanzania
TH	Thailand
TG	Togo
TO	Tonga
TT	Trinidad and Tobago
TN	Tunisia
TR	Turkey
TM	Turkmenistan
UG	Uganda
UA	Ukraine
AE	United Arab Emirates
GB	United Kingdom
US	United States
UY	Uruguay
UZ	Uzbekistan
VU	Vanuatu
VE	Venezuela
VN	Vietnam
YE	Yemen
ZM	Zambia
ZW	Zimbabwe

Field codes

The table in this section lists the field codes available for use with request.economic(). The first column contains the “string” values used as the field argument, and the second column contains names of each metric and links to our Help Center with additional information, including the countries/regions they’re available for.

Click to show/hide

`field`	Metric
AA	Asylum Applications
ACR	API Crude Runs
AE	Auto Exports
AHE	Average Hourly Earnings
AHO	API Heating Oil
AWH	Average Weekly Hours
BBS	Banks Balance Sheet
BCLI	Business Climate Indicator
BCOI	Business Confidence Index
BI	Business Inventories
BLR	Bank Lending Rate
BOI	NFIB Business Optimism Index
BOT	Balance Of Trade
BP	Building Permits
BR	Bankruptcies
CA	Current Account
CAG	Current Account To GDP
CAP	Car Production
CAR	Car Registrations
CBBS	Central Bank Balance Sheet
CCC	Claimant Count Change
CCI	Consumer Confidence Index
CCOS	Cushing Crude Oil Stocks
CCP	Core Consumer Prices
CCPI	Core CPI
CCPT	Consumer Confidence Price Trends
CCR	Consumer Credit
CCS	Credit Card Spending
CEP	Cement Production
CF	Capital Flows
CFNAI	Chicago Fed National Activity Index
CI	API Crude Imports
CIND	Coincident Index
CIR	Core Inflation Rate, YoY
CJC	Continuing Jobless Claims
CN	API Cushing Number
COI	Crude Oil Imports
COIR	Crude Oil Imports from Russia
CONSTS	Construction Spending
COP	Crude Oil Production
COR	Crude Oil Rigs
CORD	Construction Orders, YoY
CORPI	Corruption Index
CORR	Corruption Rank
COSC	Crude Oil Stocks Change
COUT	Construction Output, YoY
CP	Copper Production
CPCEPI	Core PCE Price Index
CPI	Consumer Price Index
CPIHU	CPI Housing Utilities
CPIM	CPI Median
CPIT	CPI Transportation
CPITM	CPI Trimmed Mean
CPMI	Chicago PMI
CPPI	Core Producer Price Index
CPR	Corporate Profits
CRLPI	Cereals Price Index
CRR	Cash Reserve Ratio
CS	Consumer Spending
CSC	API Crude Oil Stock Change
CSHPI	Case Shiller Home Price Index
CSHPIMM	Case Shiller Home Price Index, MoM
CSHPIYY	Case Shiller Home Price Index, YoY
CSS	Chain Store Sales
CTR	Corporate Tax Rate
CU	Capacity Utilization
DFMI	Dallas Fed Manufacturing Index
DFP	Distillate Fuel Production
DFS	Distillate Stocks
DFSI	Dallas Fed Services Index
DFSRI	Dallas Fed Services Revenues Index
DG	Deposit Growth
DGO	Durable Goods Orders
DGOED	Durable Goods Orders Excluding Defense
DGOET	Durable Goods Orders Excluding Transportation
DIR	Deposit Interest Rate
DPI	Disposable Personal Income
DRPI	Dairy Price Index
DS	API Distillate Stocks
DT	CBI Distributive Trades
EC	ADP Employment Change
ED	External Debt
EDBR	Ease Of Doing Business Ranking
EHS	Existing Home Sales
ELP	Electricity Production
EMC	Employment Change
EMCI	Employment Cost Index
EMP	Employed Persons
EMR	Employment Rate
EOI	Economic Optimism Index
EP	Export Prices
ESI	ZEW Economic Sentiment Index
EWS	Economy Watchers Survey
EXP	Exports
EXPYY	Exports, YoY
FAI	Fixed Asset Investment
FBI	Foreign Bond Investment
FDI	Foreign Direct Investment
FE	Fiscal Expenditure
FER	Foreign Exchange Reserves
FI	Food Inflation, YoY
FO	Factory Orders
FOET	Factory Orders Excluding Transportation
FPI	Food Price Index
FSI	Foreign Stock Investment
FTE	Full Time Employment
FYGDPG	Full Year GDP Growth
GASP	Gasoline Prices
GBP	Government Budget
GBV	Government Budget Value
GCI	Competitiveness Index
GCR	Competitiveness Rank
GD	Government Debt
GDG	Government Debt To GDP
GDP	Gross Domestic Product
GDPA	GDP From Agriculture
GDPC	GDP From Construction
GDPCP	GDP Constant Prices
GDPD	GDP Deflator
GDPGA	GDP Growth Annualized
GDPMAN	GDP From Manufacturing
GDPMIN	GDP From Mining
GDPPA	GDP From Public Administration
GDPPC	GDP Per Capita
GDPPCP	GDP Per Capita, PPP
GDPQQ	GDP Growth Rate
GDPS	GDP From Services
GDPSA	GDP Sales
GDPT	GDP From Transport
GDPU	GDP From Utilities
GDPYY	GDP, YoY
GDTPI	Global Dairy Trade Price Index
GFCF	Gross Fixed Capital Formation
GNP	Gross National Product
GP	Gold Production
GPA	Government Payrolls
GPRO	Gasoline Production
GR	Government Revenues
GRES	Gold Reserves
GS	API Gasoline Stocks
GSC	Grain Stocks Corn
GSCH	Gasoline Stocks Change
GSG	Government Spending To GDP
GSP	Government Spending
GSS	Grain Stocks Soy
GSW	Grain Stocks Wheat
GTB	Goods Trade Balance
HB	Hospital Beds
HDG	Households Debt To GDP
HDI	Households Debt To Income
HICP	Harmonised Index of Consumer Prices
HIRMM	Harmonised Inflation Rate, MoM
HIRYY	Harmonised Inflation Rate, YoY
HMI	NAHB Housing Market Index
HOR	Home Ownership Rate
HOS	Heating Oil Stocks
HOSP	Hospitals
HPI	House Price Index
HPIMM	House Price Index, MoM
HPIYY	House Price Index, YoY
HS	Home Loans
HSP	Household Spending
HST	Housing Starts
IC	Changes In Inventories
ICUB	ICU Beds
IE	Inflation Expectations
IFOCC	IFO Assessment Of The Business Situation
IFOE	IFO Business Developments Expectations
IJC	Initial Jobless Claims
IMP	Imports
IMPYY	Imports, YoY
INBR	Interbank Rate
INTR	Interest Rate
IPA	IP Addresses
IPMM	Industrial Production, MoM
IPRI	Import Prices
IPYY	Industrial Production, YoY
IRMM	Inflation Rate, MoM
IRYY	Inflation Rate, YoY
IS	Industrial Sentiment
ISP	Internet Speed
JA	Job Advertisements
JAR	Jobs To Applications Ratio
JC	Challenger Job Cuts
JC4W	Jobless Claims, 4-Week Average
JO	Job Offers
JV	Job Vacancies
KFMI	Kansas Fed Manufacturing Index
LB	Loans To Banks
LC	Labor Costs
LEI	Leading Economic Index
LFPR	Labor Force Participation Rate
LG	Loan Growth, YoY
LIVRR	Liquidity Injections Via Reverse Repo
LMIC	LMI Logistics Managers Index Current
LMICI	LMI Inventory Costs
LMIF	LMI Logistics Managers Index Future
LMITP	LMI Transportation Prices
LMIWP	LMI Warehouse Prices
LPS	Loans To Private Sector
LR	Central Bank Lending Rate
LTUR	Long Term Unemployment Rate
LWF	Living Wage Family
LWI	Living Wage Individual
M0	Money Supply M0
M1	Money Supply M1
M2	Money Supply M2
M3	Money Supply M3
MA	Mortgage Approvals
MAPL	Mortgage Applications
MCE	Michigan Consumer Expectations
MCEC	Michigan Current Economic Conditions
MD	Medical Doctors
ME	Military Expenditure
MGDPYY	Monthly GDP, YoY
MIE1Y	Michigan Inflation Expectations
MIE5Y	Michigan 5 Year Inflation Expectations
MIP	Mining Production, YoY
MMI	MBA Mortgage Market Index
MO	Machinery Orders
MP	Manufacturing Payrolls
MPI	Meat Price Index
MPRMM	Manufacturing Production, MoM
MPRYY	Manufacturing Production, YoY
MR	Mortgage Rate
MRI	MBA Mortgage Refinance Index
MS	Manufacturing Sales
MTO	Machine Tool Orders
MW	Minimum Wages
NDCGOEA	Orders For Non-defense Capital Goods Excluding Aircraft
NEGTB	Goods Trade Deficit With Non-EU Countries
NFP	Nonfarm Payrolls
NGI	Natural Gas Imports
NGIR	Natural Gas Imports from Russia
NGSC	Natural Gas Stocks Change
NHPI	Nationwide House Price Index
NHS	New Home Sales
NHSMM	New Home Sales, MoM
NMPMI	Non-Manufacturing PMI
NO	New Orders
NODXMM	Non-Oil Domestic Exports, MoM
NODXYY	Non-Oil Domestic Exports, YoY
NOE	Non-Oil Exports
NPP	Nonfarm Payrolls Private
NURS	Nurses
NYESMI	NY Empire State Manufacturing Index
OE	Oil Exports
OPI	Oils Price Index
PCEPI	PCE Price Index
PDG	Private Debt To GDP
PFMI	Philadelphia Fed Manufacturing Index
PHSIMM	Pending Home Sales Index, MoM
PHSIYY	Pending Home Sales Index, YoY
PI	Personal Income
PIN	Private Investment
PIND	MBA Purchase Index
PITR	Personal Income Tax Rate
POP	Population
PPI	Producer Price Index
PPII	Producer Price Index Input
PPIMM	Producer Price Inflation, MoM
PPIYY	Producer Prices Index, YoY
PRI	API Product Imports
PROD	Productivity
PS	Personal Savings
PSC	Private Sector Credit
PSP	Personal Spending
PTE	Part Time Employment
PUAC	Pandemic Unemployment Assistance Claims
RAM	Retirement Age Men
RAW	Retirement Age Women
RCR	Refinery Crude Runs
REM	Remittances
RFMI	Richmond Fed Manufacturing Index
RFMSI	Richmond Fed Manufacturing Shipments Index
RFSI	Richmond Fed Services Index
RI	Redbook Index
RIEA	Retail Inventories Excluding Autos
RPI	Retail Price Index
RR	Repo Rate
RRR	Reverse Repo Rate
RSEA	Retail Sales Excluding Autos
RSEF	Retail Sales Excluding Fuel
RSMM	Retail Sales, MoM
RSYY	Retail Sales, YoY
RTI	Reuters Tankan Index
SBSI	Small Business Sentiment Index
SFHP	Single Family Home Prices
SP	Steel Production
SPI	Sugar Price Index
SS	Services Sentiment
SSR	Social Security Rate
SSRC	Social Security Rate For Companies
SSRE	Social Security Rate For Employees
STR	Sales Tax Rate
TA	Tourist Arrivals
TAXR	Tax Revenue
TCB	Treasury Cash Balance
TCPI	Tokyo CPI
TI	Terrorism Index
TII	Tertiary Industry Index
TOT	Terms Of Trade
TR	Tourism Revenues
TVS	Total Vehicle Sales
UC	Unemployment Change
UP	Unemployed Persons
UR	Unemployment Rate
WAG	Wages
WES	Weapons Sales
WG	Wage Growth, YoY
WHS	Wages High Skilled
WI	Wholesale Inventories
WLS	Wages Low Skilled
WM	Wages In Manufacturing
WPI	Wholesale Price Index
WS	Wholesale Sales
YUR	Youth Unemployment Rate
ZCC	ZEW Current Conditions

`request.footprint()`

The request.footprint() function enables scripts to retrieve volume footprint data for the bars in the datasets on which they run. For a given bar, a volume footprint categorizes volume values from lower timeframes as “buy” (upward) or “sell” (downward) based on intrabar price action, then collects the categorized volume data into equally sized rows that cover the bar’s price range. Programmers can use retrieved footprint data to inspect the distribution of “buy”, “sell”, and total volume across the rows for a bar’s range, identify a bar’s Point of Control (POC) and other significant price levels, calculate volume delta information, detect volume imbalances, and more.

The function’s signature is as follows:

request.footprint(ticks_per_row, va_percent, imbalance_percent) → series footprint

The ticks_per_row parameter specifies the size of each row in the calculated volume footprint, in ticks. It requires a positive “simple int” value representing a multiplier for the instrument’s minimum tick size. For example, if the argument is 100, the price range of each row equals the value of 100 * syminfo.mintick. The specified row size affects the total number of rows in each bar’s footprint. Increase the value for fewer rows with a larger size, or decrease the value for the opposite.

The va_percent parameter accepts a “simple float” value specifying the percentage of the footprint’s total volume to use for calculating the bar’s Value Area (VA), where a value of 100 represents 100% of the total volume. Specifying an argument is optional. The default value is 70, meaning that the footprint’s VA includes 70% of the total volume.

The imbalance_percent parameter accepts a “simple float” value specifying the required percentage difference between “buy” and “sell” volume in adjacent footprint rows for detecting volume imbalances:

The footprint considers a row to have a buy imbalance if the row’s “buy” volume exceeds the “sell” volume of the row below it by the specified percentage.
The footprint considers a row to have a sell imbalance if the row’s “sell” volume exceeds the “buy” volume of the row above it by the given percentage.

Including an imbalance_percent argument is optional. The default value is 300, meaning that the “buy” or “sell” volume of a footprint row must be three times (300%) larger than the opposing volume of an adjacent row to signify an imbalance.

A call to the request.footprint() function returns either the reference (ID) of a footprint object that contains the volume footprint data for the current bar, or na if no footprint is available for that bar.

Scripts can use any returned footprint ID that is not na in calls to the built-in footprint.*() functions to retrieve data from a bar’s volume footprint.

For example, the following script calls request.footprint() on each bar to request the ID of a footprint object that contains the bar’s volume footprint data. If the requested data is available, the script then uses the returned ID in calls to four footprint.*() functions – footprint.total_volume(), footprint.buy_volume(), footprint.sell_volume(), and footprint.delta() – to retrieve the footprint’s total volume, total “buy” and “sell” volume, and overall volume delta.

The script plots the “buy” volume, the negative “sell” volume, and the volume delta as columns for visual comparison. It also displays a color-coded label at each bar’s high price to indicate whether the bar’s “buy” volume exceeds its “sell” volume or vice versa. Hovering over a label reveals a tooltip that shows the corresponding bar’s total volume and volume delta:

//@version=6 indicator("Requesting volume footprint data demo", max_labels_count = 500) //@variable The number of ticks to use as the price interval for each footprint row. int numTicksInput = input.int(100, "Ticks per footprint row", minval = 1) //@variable References a `footprint` object for the current bar, or holds `na` if no footprint data is available. footprint reqFootprint = request.footprint(numTicksInput) //@variable Is `true` if the requested `footprint` ID is not `na`, and `false` otherwise. bool hasFootprint = not na(reqFootprint) // Retrieve the bar's total, "buy", and "sell" volume sums and overall volume delta from the `footprint` object. float totalVolume = hasFootprint ? footprint.total_volume(reqFootprint) : na float buyVolume = hasFootprint ? footprint.buy_volume(reqFootprint) : na float sellVolume = hasFootprint ? footprint.sell_volume(reqFootprint) : na float deltaVolume = hasFootprint ? footprint.delta(reqFootprint) : na // Plot the total "buy" and "sell" volume as bidirectional columns, where "buy" volume increases in the // positive direction (upward plot), and "sell" volume increases in the negative direction (downward plot). plot(buyVolume, "Total buy volume", #6eb21c, style = plot.style_columns) plot(sellVolume * -1, "Total sell volume", #b21c2b, style = plot.style_columns) // Plot bar's volume delta on top of the bidirectional columns to show the difference between "buy" and "sell" volume. plot(deltaVolume, "Volume delta", #e8a93c, style = plot.style_columns) hline(0, "Zero line", #d6d6d8, hline.style_solid) // Draw a label at the bar's high. The label is green if the volume delta is positive or zero, and red otherwise. // The label includes a tooltip that shows the bar's total volume and volume delta. label.new( bar_index, high, color = deltaVolume >= 0 ? #6eb21c : #b21c2b, size = 14, tooltip = str.format("Total volume: \t{0}\nVolume delta: \t{1}", totalVolume, deltaVolume), force_overlay = true )

Note that:

The id parameter of each footprint.*() function does not allow na arguments. Therefore, this script uses ternary operations that execute footprint.*() calls only if the retrieved footprint ID is not na. If no data is available, the operations return na directly without executing the calls.
On timeframes higher than or equal to “1D”, a footprint’s total volume might differ significantly from the value of the volume variable. Such differences occur for some instruments because EOD data feeds can include data from block trades, OTC trades, and other sources, whereas requested intraday data feeds do not. See the Data feeds section to learn more about the types of data feeds and their differences.

While some of the footprint.*() functions retrieve values representing overall metrics for a requested volume footprint, as shown above, others retrieve the IDs of volume_row objects that contain data for individual rows from the footprint for more detailed analysis. For instance, the footprint.poc() function retrieves the ID of the volume_row object that contains data for a footprint’s Point of Control row (i.e., the row with the highest total volume), and the footprint.rows() function constructs an array containing the volume_row IDs for every row within a footprint.

Scripts can use non-na IDs of the volume_row type in calls to the built-in volume_row.*() functions to retrieve data for a specific footprint row, including the row’s price levels, volume sums, volume delta, and buy or sell imbalances.

The advanced example below retrieves and displays detailed volume footprint information for visible chart bars. On each visible bar, the script requests a footprint ID using request.footprint(). If the ID is not na, the script calls footprint.rows() to create an array containing the volume_row IDs for all rows in the footprint, and uses other footprint.*() calls to retrieve the individual IDs for the footprint’s POC and Value Area rows.

Afterward, the script loops through the array using a for...in loop. It calls multiple volume_row.*() functions within the loop to retrieve price levels, categorized volume values, volume delta, and imbalance states for each row. On each iteration, the script formats the retrieved “buy” and “sell” volume, volume delta, and imbalance information for the current row into a string, and then displays the text in a box drawn at the row’s price range in a separate pane. Each box uses a gradient background color based on the row’s volume delta and its total volume relative to the POC row’s total volume. The text color of each box is the chart’s foreground color if the row is the POC, purple if the row is a VA boundary, and gray otherwise.

The script also plots the retrieved POC levels and the VA boundaries as circles on the main chart pane for visual reference:

//@version=6 indicator("Retrieving footprint row data demo", max_boxes_count = 500) //@variable The size of each footprint row, expressed in ticks. int numTicksInput = input.int(100, "Ticks per footprint row", 1) //@variable The percentage difference in opposing volume between rows for detecting volume imbalances. int imbalanceInput = input.int(300, "Imbalance percentage", 1) //@variable References a `footprint` object for the current bar, or holds `na` if no footprint data is available. footprint reqFootprint = request.footprint(numTicksInput, imbalance_percent = imbalanceInput) // Declare a tuple of variables to hold the values returned by the `if` structure for plotting. // The values are not `na` only if the bar is visible and the `reqFootprint` variable does not hold `na`. [pocHigh, pocLow, vaHigh, vaLow] = if ( time >= chart.left_visible_bar_time and time <= chart.right_visible_bar_time and not na(reqFootprint) ) //@variable References an array containing a `volume_row` ID for each row in the volume footprint. array<volume_row> volumeRowsArray = reqFootprint.rows() // Retrieve `volume_row` IDs for the footprint's Point of Control, Value Area High, and Value Area Low rows. volume_row poc = reqFootprint.poc() volume_row vah = reqFootprint.vah() volume_row val = reqFootprint.val() // Loop through the array. The `row` variable holds a `volume_row` ID, starting with the one for the *lowest* row. for row in volumeRowsArray // Get the upper and lower price levels of the current row. float upPrice = row.up_price(), float dnPrice = row.down_price() // Get the row's "buy" and "sell" volume values and the volume delta. float buyVol = row.buy_volume(), float sellVol = row.sell_volume(), float delta = row.delta() // Get the row's buy and sell imbalance states. bool buyImbalance = row.has_buy_imbalance(), bool sellImbalance = row.has_sell_imbalance() //@variable A string representing the row's buy volume, sell volume, volume delta, and imbalances, respectively. string boxText = str.format( "B: {0} | S: {1} | D: {2} | I: {3}", str.tostring(buyVol, format.volume), str.tostring(sellVol, format.volume), str.tostring(delta, format.volume), buyImbalance and sellImbalance ? "Both" : buyImbalance ? "Buy" : sellImbalance ? "Sell" : "None" ) // Calculate a green (for positive delta) or red (for negative delta) gradient color based on the row's volume // relative to the POC volume. color deltaColor = delta >= 0 ? color.green : color.red color boxColor = color.from_gradient( row.total_volume() / poc.total_volume(), 0, 1, color.new(deltaColor, 100), color.new(deltaColor, 70) ) // Draw a box at the price range of the row, in a separate pane, to display the `boxText` value. box rowBox = box.new( bar_index, upPrice, bar_index + 1, dnPrice, #787b8650, 1, text = boxText, text_color = #787b86, text_halign = text.align_left, bgcolor = boxColor ) // Update the text color and formatting of the box if the current row is a Value Area boundary or the POC. if upPrice == vah.up_price() or upPrice == val.up_price() rowBox.set_text_color(color.purple) rowBox.set_text_formatting(text.format_bold) if upPrice == poc.up_price() rowBox.set_text_color(chart.fg_color) rowBox.set_text_formatting(text.format_bold) // Return the POC and VA levels for use in the global scope. [poc.up_price(), poc.down_price(), vah.up_price(), val.down_price()] // Plot the retrieved POC and VA levels on the main chart pane. plot(pocHigh, "POC top", chart.fg_color, 5, plot.style_circles, force_overlay = true) plot(pocLow, "POC bottom", chart.fg_color, 5, plot.style_circles, force_overlay = true) plot(vaHigh, "VA top", color.purple, 3, plot.style_circles, force_overlay = true) plot(vaLow, "VA bottom", color.purple, 3, plot.style_circles, force_overlay = true)

Note that:

As with the built-in functions for most other reference types, scripts can call footprint.*() and volume_row.*() built-ins as functions or methods. This script calls the built-ins using method syntax.
The array created by footprint.rows() sorts its elements in ascending order by price level, where the first element refers to the volume_row object for the row with the lowest prices, and the last refers to the one for the row with the highest prices.
The results of volume_row.has_buy_imbalance() and volume_row.has_sell_imbalance() calls depend on the imbalance_percent argument of the request.footprint() call that creates the parent footprint object. In this example, the “Imbalance percentage” input controls the argument, and therefore controls the behavior of the script’s volume_row.has_*_imbalance() calls.

To learn more about the footprint and volume_row types, and the available functions in their namespaces, refer to the footprint and volume_row section of the Type system page.

For more information about volume footprints and how they work, refer to the Volume footprint charts article in our Help Center.

Requesting footprints on other datasets

Scripts can request volume footprint data, or the results of calculations that use footprint data, from other datasets by passing an expression that returns or uses IDs of the footprint or volume_row type as the expression argument in a request.security() or request.security_lower_tf() function call. If a call to either of these functions requests the result of an expression that relies on volume footprint data, it calculates footprints on the specified dataset rather than the script’s main dataset.

For example, the following script requests volume footprint data for the last confirmed period on a specified higher timeframe, then uses the data to display boxes across the current period for footprint rows whose total volume equals or exceeds an input percentage of the Point of Control (POC) row’s total volume.

The script uses a request.security() call with request.footprint(ticksInput)[1] as the expression argument and barmerge.lookahead_on as the lookahead argument to fetch the last confirmed footprint ID for the selected timeframe. If the result is not na at the start of the current HTF period, the script compares each row’s volume to the volume of the POC row in a for…in loop, and draws boxes to project the levels of rows whose volume meets the specified threshold. Each box uses a volume-based gradient color, where blue hues correspond to rows with lower volume, and orange hues correspond to rows with higher volume:

//@version=6 indicator("Requesting footprints on other datasets demo", overlay = true, behind_chart = false, max_boxes_count = 500) //@variable The higher timeframe for which to request footprint data. string htfInput = input.timeframe("1D", "Higher timeframe") //@variable The size of each footprint row, expressed in ticks. int ticksInput = input.int(100, "Ticks per row", minval = 1) //@variable The minimum percentage of POC volume required to qualify a footprint row as a high-volume node. float hvInput = input.float(50.0, "High-volume threshold (% of POC)", 0.0, 100.0) // Raise an error if the specified timeframe is not higher than the chart's timeframe. if barstate.isfirst and timeframe.in_seconds(htfInput) <= timeframe.in_seconds() runtime.error("The requested timeframe must be higher than the chart's timeframe.") //@variable The ID of the *last confirmed* `footprint` object from the higher TF, or `na` when new data is not available. // The `request.security()` call includes `barmerge.gaps_on`, because the script requires the data only on the // *first bar* of each new HTF period. footprint htfFootprint = request.security( syminfo.tickerid, htfInput, request.footprint(ticksInput)[1], barmerge.gaps_on, barmerge.lookahead_on ) if not na(htfFootprint) //@variable References the `volume_row` object for the footprint's POC row. volume_row pocRow = htfFootprint.poc() //@variable The POC row's total volume. float pocVol = pocRow.total_volume() //@variable The minimum volume required for a high-volume row. float minVol = pocVol * hvInput / 100 // Loop through all `volume_row` IDs stored in the array created by `htfFootprint.rows()`. for row in htfFootprint.rows() //@variable The current row's total volume. float rowVol = row.total_volume() // Skip the rest of the current iteration if the row's total volume is less than the required volume. if rowVol < minVol continue // Draw a box across the current period at the confirmed row's price levels if its volume meets the threshold, // and color the box using a volume-based gradient. color boxColor = color.from_gradient(rowVol, minVol, pocVol, #2196f3, #ff9800) box.new( time, row.up_price(), time_close(htfInput), row.down_price(), boxColor, xloc = xloc.bar_time, bgcolor = color.new(boxColor, 50), text = str.tostring(rowVol, format.volume), text_color = #000000 )

Note that:

The request.security() call includes barmerge.gaps_on as the gaps argument to return a non-na footprint ID only on the first chart bar of each new HTF period, because the script does not require new footprint data on every bar.
The script calls the time() and time_close() functions to retrieve the expected opening and closing timestamps for the current HTF period. The box drawings use these timestamps as their left and right coordinates.

Any request.security() or request.security_lower_tf() call whose expression depends on footprint objects counts toward the total number of request.footprint() calls, because such requests copy all code required to calculate the result on the retrieved dataset. A script raises a runtime error if its outputs depend on data from more than one unique request.footprint() call, regardless of whether those requests execute on the script’s main dataset or on the datasets fetched by other request.*() calls.

Consider the following script, which defines a calculation that requests volume footprint data for the current dataset, computes a moving average of the POC row’s median price, then declares a pocMA variable to store the average. The script uses the variable as the expression argument in a request.security() call to calculate the average POC level on the “1D” timeframe, then assigns the result to the requestedMA variable. Lastly, the script attempts to plot both the pocMA and requestedMA series on the chart, but fails and raises a runtime error:

//@version=6 indicator("Too many footprint requests demo") //@variable The size of each footprint row, expressed in ticks. int ticksInput = input.int(100, "Ticks per row", 1) //#region // The code in this region is copied into the `request.security()` call's context, because all of it affects // the `pocMA` variable used as the call's `expression` argument. // Request the ID of a `footprint` object. footprint fp = request.footprint(ticksInput) // Get the `volume_row` ID for the POC row if the requested `footprint` ID is not `na`. volume_row poc = na(fp) ? na : fp.poc() // Calculate a moving average of the POC row's midpoint. float pocMA = ta.sma(na(poc) ? na : math.avg(poc.up_price(), poc.down_price()), 5) //#endregion // Retrieve the `pocMA` value calculated on the "1D" timeframe. // This request copies all code in the region above and evaluates it on the requested dataset. // Therefore, it executes its own separate version of the `request.footprint()` call. float requestedMA = request.security(syminfo.tickerid, "1D", pocMA) // Using *both* the `pocMA` and `requestedMA` variables in the script's outputs causes a *runtime error*, // because the variables' values depend on *two* different footprint requests: one for the chart's timeframe, // and the other for the requested timeframe. plot(requestedMA, "Daily POC MA", color.purple, 3) plot(pocMA, "Chart POC MA", color.blue, 3)

The above script raises an error because its outputs (in this case, its plots) depend on both the pocMA and requestedMA variables, and the values of those variables depend on two separate footprint requests – one for the script’s main dataset and the other for the dataset retrieved by the request.security() call. Both of these requests count toward the total number of request.footprint() calls, even though one of those calls is not defined explicitly in the code.

A simple way to resolve this error is to remove one of the script’s two plot() calls. As explained in the Compiled tokens section of the Limitations page, the Pine Script compiler automatically discards code that a script’s outputs do not depend on, including request.*() calls. Therefore, if we remove the plot of the pocMA series, for example, the request.footprint() call defined explicitly in the global scope does not execute. Instead, only the request.footprint() call copied into the request.security() call’s context executes, because that is the only one that the script’s outputs now require:

//@version=6 indicator("Removed footprint request demo") //@variable The size of each footprint row, expressed in ticks. int ticksInput = input.int(100, "Ticks per row", 1) //#region // Although this code is still defined in the global scope, it does *not* execute in this scope, because the script's // outputs no longer depend on the data assigned to the `fp`, `poc`, or `pocMA` variables now that we removed the // `plot(pocMA)` call. footprint fp = request.footprint(ticksInput) volume_row poc = na(fp) ? na : fp.poc() float pocMA = ta.sma(na(poc) ? na : math.avg(poc.up_price(), poc.down_price()), 5) //#endregion // This request still copies all code in the region above to calculate a POC average on the "1D" timeframe. // The compiler does not discard this request, including the `request.footprint()` call that executes in its context, // because the script still uses the `requestedMA` variable in its outputs. float requestedMA = request.security(syminfo.tickerid, "1D", pocMA) // If we plot only the `requestedMA` series, and *not* the `pocMA` series, the "Too many `request.footprint()` calls" // error no longer occurs, because the script no longer requires two separate footprint requests to determine its outputs. // The only footprint request that it requires now is the one copied into the `request.security()` call's context. plot(requestedMA, "Daily POC MA", color.purple, 3)

Note that:

Although the pocMA variable declaration and its dependencies no longer execute directly, the Pine Profiler displays performance details next to that code in the Pine Editor. Those details represent the performance of the copied calculations that execute within the request.security() call’s context in this case, as the profiler cannot display that information elsewhere. See the When requesting other contexts section of the Profiling and optimization page to learn more about this behavior.

`request.seed()`

TradingView aggregates a vast amount of data from its many providers, including price and volume information on tradable instruments, financials, economic data, and more, which users can retrieve in Pine Script using the functions discussed in the sections above, as well as multiple built-in variables.

To further expand the horizons of possible data one can analyze on TradingView, we have Pine Seeds, which allows users to supply custom user-maintained EOD data feeds via GitHub for use on TradingView charts and within Pine Script code.

To retrieve data from a Pine Seeds data feed within a script, use the request.seed() function. Below is the function’s signature:

request.seed(source, symbol, expression, ignore_invalid_symbol, calc_bars_count) → series <type>

The source parameter specifies the unique name of the user-maintained GitHub repository that contains the data feed.

The symbol parameter represents the file name from the “data/” directory of the source repository, excluding the “.csv” file extension. See this page for information about the structure of the data stored in repositories.

The expression parameter is the series to evaluate using data extracted from the requested context. It is similar to the equivalent in request.security() and request.security_lower_tf(). Data feeds stored in user-maintained repos contain time, open, high, low, close, and volume information, meaning the expression argument can use the corresponding built-in variables, including variables derived from them (e.g., bar_index, ohlc4, etc.) to request their values from the context of the custom data.

The script below visualizes sample data from the seed_crypto_santiment demo repository. It uses two calls to request.seed() to retrieve the close values from the repository’s BTC_SENTIMENT_POSITIVE_TOTAL and BTC_SENTIMENT_NEGATIVE_TOTAL data feeds and plots the results on the chart as step lines:

//@version=6 indicator("Pine Seeds demo", format=format.volume) //@variable The total positive sentiment for BTC extracted from the "seed_crypto_santiment" repository. float positiveTotal = request.seed("seed_crypto_santiment", "BTC_SENTIMENT_POSITIVE_TOTAL", close) //@variable The total negative sentiment for BTC extracted from the "seed_crypto_santiment" repository. float negativeTotal = request.seed("seed_crypto_santiment", "BTC_SENTIMENT_NEGATIVE_TOTAL", close) // Plot the data. plot(positiveTotal, "Positive sentiment", color.teal, 2, plot.style_stepline) plot(negativeTotal, "Negative sentiment", color.maroon, 2, plot.style_stepline)

Note that:

This example requests data from the repository highlighted in the Pine Seeds documentation. It exists solely for example purposes, and its data does not update on a regular basis.
Unlike most other request.*() functions, request.seed() does not have a gaps parameter. It always returns na values when no new data exists.
Pine Seeds data is searchable from the chart’s symbol search bar. To load a data feed on the chart, enter the “Repo:File” pair, similar to searching for an “Exchange:Symbol” pair.