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.quandl() retrieves information from NASDAQ Data Link (formerly Quandl).
• request.financial() retrieves financial data from FactSet.
• request.economic() retrieves economic and industry data.
• request.seed() retrieves data from a user-maintained GitHub repository.

Note

Throughout this page, and in other parts of our documentation that discuss request.*() functions, we often use the term “context” to describe the ticker ID, timeframe, and any modifications (price adjustments, session settings, non-standard chart types, etc.) that apply to a chart or the data retrieved by a script.

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

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

request.security_lower_tf(symbol, timeframe, expression, ignore_invalid_symbol, currency, ignore_invalid_timeframe) → 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.quandl(ticker, gaps, index, ignore_invalid_symbol) → 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.seed(source, symbol, expression, ignore_invalid_symbol) → series <type>

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

Note

Users can also allow compatible scripts to evaluate their scopes in other contexts without requiring request.*() functions by using the timeframe parameter of the indicator() declaration statement.

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.

Usage

All request.*() functions return “series” results, which means they can produce different values on every bar. However, most request.*() function parameters require “const”, “input”, or “simple” arguments.

In essence, Pine Script™ must determine the values of most arguments passed into a request.*() function upon compilation of the script or on the first chart bar, depending on the qualified type that each parameter accepts, and these values cannot change throughout the execution of the script. The only exception is the expression parameter in request.security(), request.security_lower_tf(), and request.seed(), which accepts “series” arguments.

Calls to request.*() functions execute on every chart bar, and scripts cannot selectively deactivate them throughout their execution. Scripts cannot call request.*() functions within the local scopes of conditional structures, loops, or functions and methods exported by Libraries, but they can use such function calls within the bodies of non-exported user-defined functions and methods.

When using any request.*() functions within a script, runtime performance is an important consideration. These functions can have a sizable impact on script performance. While scripts can contain a maximum of 40 calls to the request.*() namespace, users should strive to minimize the number of calls in their scripts to keep resource consumption as low as possible. For more information on the limitations of these functions, see this section of our User Manual’s page on Pine’s limitations.

`gaps`

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

Note

When using the indicator() function to evaluate a script in another context, the timeframe_gaps parameter specifies how it handles nonexistent values. The parameter is similar to the gaps parameter for request.*() functions.

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

When the gaps parameter uses barmerge.gaps_on, the function will return na results on all chart bars where new data isn’t yet confirmed from the requested context. Otherwise, when the parameter uses barmerge.gaps_off, the function will fill 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=5
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 don’t 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 will handle 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 doesn’t exist for a country_code.

A request.*() function call will produce a runtime error and halt 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 will return 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 price and currency. 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 to visualize the marketCap history:

//@version=5
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)

//@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 will only return values on valid instruments with total shares outstanding data, such as the one we’ve selected for this example. It will return a market cap value of na on others that don’t have financial data, including forex, crypto, and derivatives.
• Not all issuing companies publish quarterly financial reports. If the symbol’s issuing company doesn’t 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’ve used format.volume in the indicator() and str.tostring() calls, which 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.
• This script creates a table and initializes its cell on the first chart bar, then updates the cell’s text on subsequent bars. To learn more about working with tables, see the Tables page of our User Manual.

`currency`

The currency parameter of a request.*() function allows users to specify the currency of the requested data. When this parameter’s value differs from the syminfo.currency of the requested context, the function will convert the requested values to express them in the specified currency. This parameter can accept a built-in variable from the currency.* namespace, such as currency.JPY, or a “string” representing the ISO 4217 currency code (e.g., “JPY”).

The conversion rate between the syminfo.currency of the requested data and the specified currency depends on the corresponding “FX_IDC” daily rate from the previous day. If no available instrument provides the conversion rate directly, the function will use the value from a spread symbol to derive the rate.

Note

Not all request.*() function calls return values expressed as a currency amount. Therefore, currency conversion is not always necessary. For example, some series returned by request.financial() are expressed in units other than currency, such as the “PIOTROSKI_F_SCORE” and “NUMBER_OF_EMPLOYEES” metrics. It is up to programmers to determine when currency conversion is appropriate in their data requests.

`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 function can request values from times beyond those of the historical bars it executes on. In other words, the lookahead value 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) on each chart bar.

Programmers should exercise extreme caution when using lookahead in their scripts, namely 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 in request.security() references a series with 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 specified timeframe in the call is the same as the chart the script executes on, i.e., timeframe.period.
• The function call requests data from an intrabar timeframe, i.e., a timeframe smaller than the timeframe.period. See this section for more information.

Note

Using request.security() to leak future data into the past is misleading and not allowed in script publications. While your script’s results on historical bars may look great due to its seemingly “magical” acquisition of prescience (which it will not be able to reproduce on realtime bars), you will be misleading yourself and the users of your script. If you publish your script to share it with others, ensure you do not mislead users by accessing future information on historical bars.

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’re actually available on historical bars, whereas the other two calls do not:

//@version=5
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 will repaint their results after restarting the script’s execution, as realtime bars will become historical bars.
• The series that uses lookahead and a historical offset (aqua) does not repaint its values, as it always references the last confirmed value from the higher timeframe. See the Avoiding repainting section of this page for more information.

Note

In Pine Script™ v1 and v2, the security() function did not include a lookahead parameter, but it behaved as it does in later versions of Pine with lookahead = barmerge.lookahead_on, meaning that it systematically used data from the future HTF context on historical bars. Therefore, users should exercise caution with Pine v1 or v2 scripts that use HTF security() calls unless the function calls contain historical offsets.

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 execution of 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 execution.

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.

Note

As a rule, TradingView does not generate data; it relies on its data providers for the information displayed on charts and accessed by scripts.

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) → 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.

Note

When using the value from an input.source() call in the expression argument and the input references a series from another indicator, request.*() functions calculate that value’s results using the chart’s symbol, regardless of the symbol argument supplied, since they cannot evaluate the scopes required by an external series. We therefore do not recommend attempting to request external source input data from other contexts.

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=5
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 that raises a runtime error when the higherTimeframe input represents a timeframe smaller than the chart timeframe, effectively preventing the script from requesting LTF data:

//@version=5
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 can only return the results from 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=5
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 100,000 intrabars from a lower-timeframe context. See this section of the Limitations page.

Note

While scripts can use request.security() to retrieve the values from a single intrabar on each chart bar, which might provide utility in some unique cases, we recommend using the request.security_lower_tf() function for intrabar analysis when possible, as it returns an array containing data from all available intrabars within a chart bar. See this section to learn more.

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 function calls
• Variables calculated by the script
• Tuples
• Calls to user-defined functions
• Chart points
• Collections
• User-defined types

Note

The request.security() function duplicates the scopes and operations required by the expression to calculate its requested values in another context, which elevates runtime memory consumption. Additionally, the extra scopes produced by each call to request.security() count toward the script’s compilation limits. See the Scope count section of the Limitations page for more information.

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 price from the daily timeframe while on an intraday chart. We can accomplish this 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 from the daily timeframe.

It’s important to note that newcomers to Pine may 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 will return 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 ta.sma(close, length) as its expression argument to return a length-bar SMA from the specified timeframe:

//@version=5
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.

Calculated variables

The expression parameter of request.security() accepts variables declared in the global scope, allowing scripts to evaluate their variables’ calculations from other contexts without redundantly listing the operations in each function call.

For example, one can declare the following variable:

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

and execute the variable’s calculation from another context with request.security():

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

The function call in the line above will return the result of the priceReturn calculation on another symbol’s data as a series adapted to the current chart, which the script can display directly on the chart or utilize in additional operations.

The following example compares the price returns of the current chart’s symbol and another specified symbol. The script declares the priceReturn variable from the chart’s context, then uses that variable in request.security() to evaluate its calculation on another symbol. It then calculates the correlation between the priceReturn and requestedReturn and plots the result on the chart:

//@version=5
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, except it uses the close values fetched from the input symbol.
• The script colors the plot with a gradient based on the correlation value. To learn more about color gradients in Pine, see this section of our User Manual’s page on colors.

Tuples

Tuples in Pine Script™ are comma-separated sets of expressions enclosed in brackets that can hold multiple values of any available type. We use tuples when creating functions or other local blocks that return more than one value.

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.

Note

The combined size of all tuples returned by request.*() calls in a script cannot exceed 127 elements. See this section of the Limitations page for more information.

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

For example, this script calculates the percent rank of the close price over length bars and assigns the expression to the rank variable. It then calls request.security() to request a tuple containing the rank, ta.crossover(rank, 50), and ta.crossunder(rank, 50) values from the specified timeframe. The script plots the requestedRank and uses the crossOver and crossUnder “bool” values within bgcolor() to conditionally highlight the chart pane’s background:

//@version=5
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 execution (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=5
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 reference types that represent coordinates on the chart. Lines, boxes, polylines, and labels use chart.point 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=5
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:

• Since this example is designed specifically for higher timeframes, we’ve included a custom runtime error that the script raises when the timeframe.in_seconds() of the higherTimeframe is smaller than that of the chart’s timeframe.

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

The script creates a data map with “string” keys and “float” values to hold high, low, highest, and lowest price values on each bar, which it uses as the expression in request.security() to calculate an otherData map representing the data from the specified context. It uses the values associated with the “High”, “Low”, “Highest”, and “Lowest” keys of the otherData map to calculate the ratio that it plots in the chart pane:

//@version=5
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 ID.

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=5
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 100,000 intrabars from a lower timeframe. See this section of our Limitations page for more information.

Note

Working with request.security_lower_tf() involves frequent usage of arrays since it always returns array results. We therefore recommend you familiarize yourself with arrays to make the most of this function in your scripts.

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

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

This function only requests data from timeframes less than or equal to the chart’s timeframe. If the timeframe of the request represents a higher timeframe than the chart’s timeframe, the function will either raise a runtime error or return na values depending on the ignore_invalid_timeframe argument in the call. The default value for this parameter is false, meaning it will raise an error and halt the script’s execution when attempting to request HTF data.

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 declared in the script’s main scope. 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.

Note

As with request.security(), request.security_lower_tf() duplicates the scopes and operations required to calculate the expression from another context. The scopes from request.security_lower_tf() increase runtime memory consumption and count toward the script’s compilation limits. See the Scope count section of the Limitations page to learn more.

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 returns its results as arrays. The elements in the returned arrays represent the expression values retrieved from the lower timeframe sorted in ascending order based on each intrabar’s timestamp.

The type template assigned to the returned arrays corresponds to the value 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 of ta.change(close) values from the lowerTimeframe 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, then plots the results on the chart alongside the priceChange for comparison:

//@version=5
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 100,000 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 several arrays of intrabar data with a single request.security_lower_tf() function call.

Note

The combined size of all tuples returned by request.*() calls in a script is limited to 127 elements. See this section of the Limitations page for more information.

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=5
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 may need to request the IDs of collections from an intrabar context. However, unlike request.security(), one cannot pass collections or calls to functions that return them as the expression argument in a request.security_lower_tf() call, as arrays cannot directly reference other collections.

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

Note

The use case described below is advanced and not recommended for beginners. Before exploring this approach, we recommend understanding how user-defined types and collections work in Pine Script™. When possible, we recommend using simpler methods to manage LTF requests, and only using this approach when nothing else will suffice.

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 to hold an `array<float>` instance.
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 call the built-in *.new() function as the expression. For example, this line of code calls Wrapper.new() with array.from(close) as its collection within request.security_lower_tf():

//@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 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 arrays of intrabar data from a lowerTimeframe and uses them to display data from a specific intrabar. 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 an array of 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 assigned to that instance and calls plotcandle() to display its values on the chart:

//@version=5
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 hold an array of price `data`.
type Prices
    array<float> data

//@function Returns 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` 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 from the `intrabarPrices`, or 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 only references a Prices ID if the size of the requestedPrices array is greater than the intrabarIndex, as attempting to use array.get() to get an element that doesn’t exist will result in an out of bounds error.
• The intrabarData variable only references the data field from intrabarPrices if a valid Prices ID exists since a script cannot reference fields of an na value.
• The process used in this example is not necessary to achieve the intended result. We could instead avoid using UDTs and pass an [open, high, low, close] tuple to the expression parameter to retrieve a tuple of arrays for further operations, as explained in the previous section.

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 plots the result as candles on the chart. It also highlights the background when 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=5
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=5
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=5
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=5
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 later in the execution. 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 its execution, what were previously considered realtime bars become historical bars. Therefore, request.security() will only return 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.

Note

It’s often helpful to distinguish historical bars from realtime bars when working with request.*() functions. Scripts can determine whether bars have historical or realtime states via the barstate.ishistory and barstate.isrealtime variables.

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 execution 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=5
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=5
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 execution.

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 retrieved by the function on realtime bars are unsorted until restarting the script’s execution:

//@version=5
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=5
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 100,000 intrabars from an LTF context. 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 “FX_IDC” data, 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 in the ISO 4217 format (e.g., “USD”) or any built-in currency.* variable (e.g., currency.USD).

When the function cannot calculate a valid conversion rate between the from and to currencies supplied in the call, one can decide whether it will raise a runtime error or return na via the ignore_invalid_currency parameter. The default value is false, meaning the function will raise a runtime error and halt the script’s execution.

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 “FX_IDC” 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=5
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()` since no direct "FX_IDC" rate exists.
simple string spreadSymbol = str.format("FX_IDC:{0}{2} * FX_IDC:{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 paramter.

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.

Note

Scripts can also retrieve information about upcoming earnings and dividends for an instrument via the earnings.future_* and dividends.future_* built-in variables.

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=5
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.quandl()`

TradingView forms partnerships with many fintech companies to provide users access to extensive information on financial instruments, economic data, and more. One of our many partners is Nasdaq Data Link (formerly Quandl), which provides multiple external data feeds that scripts can access via the request.quandl() function.

Here is the function’s signature:

request.quandl(ticker, gaps, index, ignore_invalid_symbol) → series float

The ticker parameter accepts a “simple string” representing the ID of the database published on Nasdaq Data Link and its time series code, separated by the “/” delimiter. For example, the code “FRED/DFF” represents the “Effective Federal Funds Rate” time series from the “Federal Reserve Economic Data” database.

The index parameter accepts a “simple int” representing the column index of the requested data, where 0 is the first available column. Consult the database’s documentaion on Nasdaq Data Link’s website to see available columns.

For details on the gaps and ignore_invalid_symbol parameters, see the Common characteristics section of this page.

Note

The request.quandl() function can only request free data from Nasdaq Data Link. No data that requires a paid subscription to their services is accessible with this function. Nasdaq Data Link may change the data it provides over time, and they may not update available datasets regularly. Therefore, it’s up to programmers to research the supported data available for request and review the documentation provided for each dataset. You can search for free data here.

This script requests Bitcoin hash rate (“HRATE”) information from the “Bitcoin Data Insights” (“BCHAIN”) database and plots the retrieved time series data on the chart. It uses color.from_gradient() to color the area plot based on the distance from the current hash rate to its all-time high:

//@version=5
indicator("Quandl demo", "BTC hash rate")

//@variable The estimated hash rate for the Bitcoin network.
float hashRate = request.quandl("BCHAIN/HRATE", barmerge.gaps_off, 0)
//@variable The percentage threshold from the all-time highest `hashRate`.
float dropThreshold = input.int(40, "Drop threshold", 0, 100)

//@variable The all-time highest `hashRate`.
float maxHashRate = ta.max(hashRate)
//@variable The value `dropThreshold` percent below the `maxHashRate`.
float minHashRate = maxHashRate * (100 - dropThreshold) / 100
//@variable The color of the plot based on the `minHashRate` and `maxHashRate`.
color plotColor = color.from_gradient(hashRate, minHashRate, maxHashRate, color.orange, color.blue)

// Plot the `hashRate`.
plot(hashRate, "Hash Rate Estimate", plotColor, style = plot.style_area)

`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 “simple string” 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 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=5
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=5
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:

• Income statements
• Balance sheet
• Cash flow
• Statistics

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().

Note

The tables in these sections are quite lengthy, as there are many financial_id arguments available. Use the “Click to show/hide” option above each table to toggle its visibility.

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 “simple string” 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 specifies the metric the function will request. 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=5
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.

Note

The tables in the sections below are rather large, as there are numerous country_code and field arguments available. Use the “Click to show/hide” option above each table to toggle its visibility.

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.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.

Note

This section contains only a brief overview of Pine Seeds. For in-depth information about Pine Seeds functionality, setting up a repo, data formats, and more, consult the documentation here.

To retrieve data from a Pine Seeds data feed within a script, one can use the request.seed() function.

Below is the function’s signature:

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

The source parameter specifies the unique name of the user-maintained GitHub repository that contains the data feed. For details on creating a repo, see this page.

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 expressions used as 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.

Note

As with request.security() and request.security_lower_tf(), request.seed() duplicates the scopes necessary to evaluate its expression in another context, which contributes toward compilation limits and script memory demands. See the Limitations page’s section on scope count limits for more information.

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

//@version=5
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 repo 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 will always return 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.