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In Swift, a closure marked with @escaping means the closure can outlive the scope of the block of code it is passed into.

In a sense, @escaping tells the closure to “stay up even after the surrounding function is gone”.

But why is this useful?

Let’s say you are performing an (asynchronous) network request such that:

Function f() fetches information from a web page.

After completion, a closure function g() runs inside f().

The only way to make this possible is to pass the function g() as an escaping closure into the function f().

This tells the Swift compiler after calling f() to keep g() alive to wait for the network request to complete and the response to arrive.

In this guide, we are going to take a look at non-escaping closures and escaping closures in detail.

Also, you are going to see a demonstrative example to support understanding.

Closures in Swift

In Swift, closures are non-escaping by default.

If you pass a non-escaping closure into a function, it is called right away.

The life of the non-escaping closure ends when the function call finishes.

As a result, there will be no trace of that closure.

This is the default behavior.

Let’s see a simple example of a non-escaping closure and its behavior.

Please repeat the actions in this guide in your own code editor to truly understand what happens.

Let’s write a function add that takes:

Integers a and b

A closure called action

Right after summing the numbers up, the function add calls the action.

var sum = a + b action(sum) }

Let’s call this function with integers 10 and 20 and a closure that prints the result into the console:

add(a: 10, b: 20, { res in print(res) })



As you can see, the result of adding 10 and 20 gets printed to the console right away.

However, this default behavior of a closure can be an issue when dealing with asynchronous code.

In an asynchronous function, you might not want to execute the closure right away. Instead, you want to wait for the asynchronous task to complete before calling a function.

This is where escaping comes in handy.

@escaping: Escaping Closures in Swift

Escaping makes it possible for the closure to outlive the function surrounding it.

This means the closure can stay around to catch a network request-response that arrives later.

To make a closure escaping, use the @escaping keyword modifier in front of the closure type.

Let’s see an example of how using a non-escaping closure fails in the context of an async function, and how to fix it using an escaping closure.


Say you are using an app to message your friend and you ask them “How are you?”.

As you know, it always takes a while to get a response.

For the sake of demonstration, let’s say it takes 2 seconds for your friend to reply back.

Let’s simulate this conversation in Swift code.

To mimic this behavior, you need a function that takes a response handler closure as a parameter.

This response handler is simply responsible for taking care of handling your friend’s response that arrives 2 seconds later.

Here is how it looks in code:

print(“Hey, how are you?”)

DispatchQueue.main.asyncAfter(deadline: .now() + 2, execute: { responseHandler(“Hi, I’m doing really good.”) })

print(“Responding takes a while…”) }

howAreYou({ friendResponse in print(friendResponse) })

But this code causes a compiler error.

This is because you are trying to execute the responseHandler function 2 seconds after the howAreYou function has executed.

This is a problem because 2 seconds after calling the howAreYou, there is no trace of that function or the responseHandler closure.

Thus the responseHandler never has a chance to handle your friend’s response. It gets destroyed before the response even arrives.

The Swift compiler is clever. It knows this is about to happen. Thus it shows you an error and does not run the code.

To make it work, you should make the responseHandler closure outlive the context of the howAreYou function to see the response arrive.

To do this, you need to turn it into an escaping closure:

print(“Hey, how are you?”)

DispatchQueue.main.asyncAfter(deadline: .now() + 2, execute: { responseHandler(“Hi, I’m doing really good.”) })

print(“Responding takes a while…”) }

howAreYou({ friendResponse in print(friendResponse) })

This way you tell the program that the responseHandler is an asynchronous closure and you want it to stay alive even after howAreYou call completes.

Now the compiler error is gone and the code works as intended.

It is as simple as that!

Now your responseHandler closure is able to show your friend’s response as it can out-live the howAreYou function’s scope!

But this example is quite arbitrary.

The next logical question is when is an escaping closure actually useful.

Let’s take a look.

When Are Escaping Closures Actually Useful

Escaping closures are useful whenever you want the closure to be able to outlive the function’s scope from where you are calling it.

A great example is when dealing with network requests.

When fetching data over a server, it takes a while for a network request to complete, and thus, the response to arrive.

To perform actions on the response, you need to wait for it to arrive.

To make a closure wait for the data, it has to be able to outlive the function’s scope where it is called.

To do this, you need to use an escaping closure.

To perform actions on the response, you need to


Today you learned what is @escaping in Swift.

In short, @escaping is a keyword modifier that turns a closure into an escaping closure.

An escaping closure is useful in asynchronous tasks when the closure has to wait for the function to complete.

When you use an escaping closure, the closure outlives the function in which it is called. In other words, when the function completes, the closure stays up and keeps waiting for the response.

Thanks for reading. Happy coding!

Further Reading

50 Swift Interview Questions

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What Is Exception In Python?

An exception is an event, which occurs during the execution of a program that disrupts the normal flow of the program’s instructions. In general, when a Python script encounters a situation that it cannot cope with, it raises an exception. An exception is a Python object that represents an error.

When a Python script raises an exception, it must either handle the exception immediately otherwise it terminates and quits.

Handling an exception

If you have some suspicious code that may raise an exception, you can defend your program by placing the suspicious code in a try: block. After the try: block, include an except: statement, followed by a block of code which handles the problem as elegantly as possible.


Here is simple syntaxof try….except…else blocks −

try:    You do your operations here;    ...................... except ExceptionI:    If there is ExceptionI, then execute this block. except ExceptionII:    If there is ExceptionII, then execute this block.    ...................... else:    If there is no exception then execute this block.

Here are few important points about the above-mentioned syntax −

A single try statement can have multiple except statements. This is useful when the try block contains statements that may throw different types of exceptions.

You can also provide a generic except clause, which handles any exception.

After the except clause(s), you can include an else-clause. The code in the else-block executes if the code in the try: block does not raise an exception.

The else-block is a good place for code that does not need the try: block’s protection.


This example opens a file, writes content in the, file and comes out gracefully because there is no problem at all −

 Live Demo

#!/usr/bin/python try:    fh = open("testfile", "w")    fh.write("This is my test file for exception handling!!") except IOError:    print "Error: can't find file or read data" else:    print "Written content in the file successfully"    fh.close() Output

This produces the following result −

Written content in the file successfully Example

This example tries to open a file where you do not have write permission, so it raises an exception −

 Live Demo

#!/usr/bin/python try:    fh = open("testfile", "r")    fh.write("This is my test file for exception handling!!") except IOError:    print "Error: can't find file or read data" else:    print "Written content in the file successfully" Output

This produces the following result −

Error: can't find file or read data The except Clause with No Exceptions

You can also use the except statement with no exceptions defined as follows −

try:    You do your operations here;    ...................... except:    If there is any exception, then execute this block.    ...................... else:    If there is no exception then execute this block.

This kind of a try-except statement catches all the exceptions that occur. Using this kind of try-except statement is not considered a good programming practice though, because it catches all exceptions but does not make the programmer identify the root cause of the problem that may occur.

The except Clause with Multiple Exceptions

You can also use the same except statement to handle multiple exceptions as follows −

try:    You do your operations here;    ...................... except(Exception1[, Exception2[,...ExceptionN]]]):    If there is any exception from the given exception list,    then execute this block.    ...................... else:    If there is no exception then execute this block. The try-finally Clause

You can use a finally: block along with a try: block. The finally block is a place to put any code that must execute, whether the try-block raised an exception or not. The syntax of the try-finally statement is this −

try:    You do your operations here;    ......................    Due to any exception, this may be skipped. finally:    This would always be executed.    ......................

You cannot use else clause as well along with a finally clause.


 Live Demo

#!/usr/bin/python try:    fh = open("testfile", "w")    fh.write("This is my test file for exception handling!!") finally:    print "Error: can't find file or read data" Output

If you do not have permission to open the file in writing mode, then this will produce the following result −

Error: can't find file or read data

Same example can be written more cleanly as follows −


 Live Demo

#!/usr/bin/python try:    fh = open("testfile", "w")    try:       fh.write("This is my test file for exception handling!!")    finally:       print "Going to close the file"       fh.close() except IOError:    print "Error: can't find file or read data"

When an exception is thrown in the try block, the execution immediately passes to the finally block. After all the statements in the finally block are executed, the exception is raised again and is handled in the except statements if present in the next higher layer of the try-except statement.

What Is Orm In Digital Marketing?

Object-relational mapping (ORM) is a technique that allows developers to easily interact with databases and map objects from the application layer to data stored in a database. ORM provides an abstraction layer between application code and the underlying database technology, allowing for easier manipulation of data without writing complex SQL queries.

It also eliminates tedious manual coding by allowing developers to write less code while still accessing all of the features available within their chosen database system. By using ORM, developers can focus more on building applications quickly and efficiently instead of spending time writing out tedious SQL statements.

Brand Building − A strong SEO strategy allows brands to build trust with their audiences by providing quality content relevant to their target market audience as well as building authority by becoming a trusted source within their industry space. Additionally, effective SEO tactics promote brand consistency across all channels so potential customers know what to expect from interactions.

What Are the Different Types of ORM?

Object-relational mapping (ORM) is a way to map between the data in an object-oriented programming language and the data stored in a relational database. There are several different types of ORM that can be used to facilitate this type of mapping.

Some of these include Active Record, Data Mapper, Table Data Gateway, Unit Of Work, and Identity Map. Active Record is an ORM that keeps track of persistent objects by storing them as rows in a database table. It simplifies tasks such as saving new objects or retrieving existing ones from the database by providing simple accessor methods for each attribute stored on the object itself.

Data Mapper provides loose coupling between domain objects and their underlying representations in different stores like databases or XML files. In this approach, all persistence logic lives outside the domain objects themselves thus allowing them to focus more on modeling business logic rather than managing data storage details.

Table Data Gateway acts as an interface between application layer code and a specific set of tables within a relational database system thus creating another level of abstraction above basic SQL queries making it easier for developers to work with complex databases without having expert knowledge about them.

How to Implement an Effective ORM Strategy?

Once you’ve decided to implement an ORM strategy, the next step is to determine how best to structure it. First and foremost, you need to ensure that all of your data is properly organized for easy retrieval. This includes creating a system of classifications, assigning categories or labels to different pieces of data, and making sure that each piece has its own unique identifier (such as a serial number). Additionally, you’ll need to create rules around how your team will access the information in order for it to be accurately tracked and maintained over time.

It’s important for any new user to understand exactly what they can do with the system before they start using it so there are no surprises down the road when complex queries come into play.

Finally, part of an effective ORM strategy involves regularly reviewing existing policies and procedures in order to make sure they’re still relevant and up-to-date with current standards in terms of accuracy and efficiency levels desired by your team.

Best Practices for ORM in Digital Marketing

In order to effectively implement an ORM strategy, organizations must understand the benefits and challenges associated with this type of system. Benefits include improved data accuracy and consistency, increased efficiency in operations, enhanced customer experience, and cost savings from reducing manual processes.

Challenges include a lack of standardization across different systems and services providers, ensuring compliance with governing regulations such as GDPR or HIPAA, managing user access control policies for security reasons, and maintaining a sound change management process to ensure changes are applied correctly throughout the organization’s IT infrastructure.

To address these challenges organizations should focus on developing clear objectives for their ORM strategy that will enable them to meet their organizational goals while also adhering to industry standards.

Additionally, implementing an effective governance framework is essential in order to ensure the consistent application of rules across all systems within the organization’s IT environment. Finally monitoring performance metrics can help detect potential issues before they become serious problems by helping organizations quickly identify any areas where improvements could be made.

Pros and Cons of ORM in Digital Marketing

Object-relational mapping (ORM) is a powerful digital marketing tool that can help businesses quickly and easily access data from multiple sources, allowing them to create more accurate customer profiles.

ORM enables marketers to identify potential customers who are likely to be interested in their products or services and target their campaigns more effectively. However, there are also some drawbacks associated with using ORM for digital marketing.

Another downside of using an ORM system for digital marketing is that it can be difficult to modify or customize individual campaigns due to its rigid structure.

As such, any changes will need to be made across all campaigns instead of just targeting specific audiences – this could increase complexity costs as well as diminishing returns on investments if not managed carefully.


ORM is an effective tool to ensure that your brand’s online presence remains positive and helps you stay ahead of potential issues. With ORM, businesses can make sure they are represented positively in the eyes of their target audience which will ultimately lead to more customers and higher profits.

What Is& What Is It Used For?

What is chúng tôi & What is it Used for? All about the process and tips to reduce its CPU usage




The UpdateCheck.exe process is generally linked to a third-party app that uses it to scan for newer versions.

Some notable apps that have a process by the same name include, Coupoon, Maxtor Manager, and RAD Studio.

The process triggers high CPU usage for many and, in some cases, affects the Internet speed.



To fix Windows PC system issues, you will need a dedicated tool

Fortect is a tool that does not simply cleans up your PC, but has a repository with several millions of Windows System files stored in their initial version. When your PC encounters a problem, Fortect will fix it for you, by replacing bad files with fresh versions. To fix your current PC issue, here are the steps you need to take:

Download Fortect and install it on your PC.

Start the tool’s scanning process to look for corrupt files that are the source of your problem

Fortect has been downloaded by


readers this month.

Of the many background process running in Windows, most pose no harm or lead to any issues. But a few have been a cause of concern for users, especially the ones triggered by rather unknown third-party apps. One such is the chúng tôi process.

The UpdateCheck process is listed in the Task Manager and often results in high CPU usage. This automatically affects the PC’s performance, slows the Internet speed, and sometimes causes other apps to crash. Keep reading to find out all about the process!

UpdateCheck.exe, as the name suggests, looks for newer versions of the software that installs it. The catch is that several programs have the UpdateCheck process in their directory. The primary ones are:

So, chúng tôi is in no way a critical Windows process, and terminating or disabling it shouldn’t affect the core functioning of the PC, except for the app that triggers it. That, too, would only affect the automatic updates. You can still manually search for and install these.

We have had several instances where hackers disguised malware as a critical process to bypass detection. So, your primary approach should be to identify the file path and verify whether the process is stored in the program’s dedicated directory or in another location.

For the programs discussed earlier, here is the usual storage path:

Coupoon: C:Program Files (x86)Coupoon

Maxtor Manager: C:Program FilesMaxtorOneTouch Status

In case the chúng tôi process is not in the dedicated directory, use a reliable antivirus solution to run a full system scan.

If you find the process in the usual path, relax a bit! Though it doesn’t mean that the process won’t throw any errors. So, here are a few solutions to get things back to normal!

1. Scan for malware

When you are unsure whether chúng tôi is safe or if it’s a malware, a quick way to identify that is to run a virus scan. Though the built-in Windows Security is capable of handling such issues, for a deeper scan, you can use a third-party antivirus software.

2. Uninstall the program behind the process

While you could manually delete the chúng tôi file, there’s a good chance the program would fetch it all over again. So, it’s recommended that you uninstall the app altogether to get rid of the process.

For this, many prefer using an effective uninstaller software to eliminate any leftover files and registry entries.

3. Perform a system restore

If everything else fails to work, as a last resort, you can always restore the PC to a state where the chúng tôi issue didn’t exist. A system restore doesn’t affect the stored files, though some configured settings or installed apps may be removed.

Before you leave, check some quick tips to make Windows faster and get superior performance.

Still experiencing issues?

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What Is Stress Testing In Software Testing?

Stress Testing

Stress Testing is a type of software testing that verifies stability & reliability of software application. The goal of Stress testing is measuring software on its robustness and error handling capabilities under extremely heavy load conditions and ensuring that software doesn’t crash under crunch situations. It even tests beyond normal operating points and evaluates how software works under extreme conditions.

In Software Engineering, Stress Testing is also known as Endurance Testing. Under Stress Testing, AUT is be stressed for a short period of time to know its withstanding capacity. A most prominent use of stress testing is to determine the limit, at which the system or software or hardware breaks. It also checks whether the system demonstrates effective error management under extreme conditions.

The application under testing will be stressed when 5GB data is copied from the website and pasted in notepad. Notepad is under stress and gives ‘Not Responded’ error message.

Need for Stress Testing

Consider the following real-time examples where we can discover the usage of Stress Testing-

During festival time, an online shopping site may witness a spike in traffic, or when it announces a sale.

When a blog is mentioned in a leading newspaper, it experiences a sudden surge in traffic.

It is imperative to perform Stress Testing to accommodate such abnormal traffic spikes. Failure to accommodate this sudden traffic may result in loss of revenue and repute.

Stress testing is also extremely valuable for the following reasons:

To check whether the system works under abnormal conditions.

Displaying appropriate error message when the system is under stress.

System failure under extreme conditions could result in enormous revenue loss

It is better to be prepared for extreme conditions by executing Stress Testing.

Goals of Stress Testing

The goal of stress testing is to analyze the behavior of the system after a failure. For stress testing to be successful, a system should display an appropriate error message while it is under extreme conditions.

To conduct Stress Testing, sometimes, massive data sets may be used which may get lost during Stress Testing. Testers should not lose this security-related data while doing stress testing.

The main purpose of stress testing is to make sure that the system recovers after failure which is called as recoverability.

Load Testing Vs Stress Testing

Load Testing Stress Testing

Load Testing is to test the system behavior under normal workload conditions, and it is just testing or simulating with the actual workload Stress testing is to test the system behavior under extreme conditions and is carried out till the system failure.

Load testing does not break the system stress testing tries to break the system by testing with overwhelming data or resources.

Types of Stress Testing:

Following are the types of stress testing and are explained as follows:

Distributed Stress Testing:

In distributed client-server systems, testing is done across all clients from the server. The role of stress server is to distribute a set of stress tests to all stress clients and track on the status of the client. After the client contacts the server, the server adds the name of the client and starts sending data for testing.

Meanwhile, client machines send signal or heartbeat that it is connected with the server. If the server does not receive any signals from the client machine, it needs to be investigated further for debugging. From the figure, a server can connect with the 2 clients (Client1 and Client2), but it cannot send or receive a signal from Client 3 & 4.

Night run is the best option to run these stress testing scenarios. Large server farms need a more efficient method for determining which computers have had stress failures that need to be investigated.

Application Stress Testing:

This testing concentrate on finding defects related to data locking and blocking, network issues and performance bottlenecks in an application.

Transactional Stress Testing:

It does stress testing on one or more transactions between two or more applications. It is used for fine-tuning & optimizing the system.

Systemic Stress Testing:

This is integrated stress testing which can be tested across multiple systems running on the same server. It is used to find defects where one application data blocks another application.

Exploratory Stress Testing:

This is one of the types of stress testing which is used to test the system with unusual parameters or conditions that are unlikely to occur in a real scenario. It is used to find defects in unexpected scenarios like

A large number of users logged at the same time

If a virus scanner started in all machines simultaneously

If Database has gone offline when it is accessed from a website,

When a large volume of data is inserted to the database simultaneously

How to do Stress Testing?

Stress Testing process can be done in 5 major steps:

Step 1) Planning the Stress Test: Here you gather the system data, analyze the system, define the stress test goals

Step 2) Create Automation Scripts: In this phase, you create the Stress testing automation scripts, generate the test data for the stress scenarios.

Step 3) Script Execution: In this stage, you run the Stress testing automation scripts and store the stress results.

Step 4) Results Analysis: In this stage, you analyze the Stress Test results and identify bottlenecks.

Step 5) Tweaking and Optimization: In this stage, you fine-tune the system, change configurations, optimize the code with goal meet the desired benchmark.

Lastly, you again run the entire cycle to determine that the tweaks have produced the desired results. For example, it’s not unusual to have to 3 to 4 cycles of the Stress Testing process to achieve the performance goals

Tools recommended for Stress Testing

LoadRunner from HP is a widely-used Load Testing tool. Load Test Results shaped by Loadrunner are considered as a benchmark.

Jmeter is an Open Source testing tool. It is a pure Java application for stress and Performance Testing. Jmeter is intended to cover types of tests like load, functional, stress, etc. It needs JDK 5 or higher to function.

Stress Tester

This tool provides extensive analysis of the web application performance, provides results in graphical format, and it is extremely easy to use. No high-level scripting is required and gives a good return on investment.

Neo load

This is a popular tool available in the market to test the web and Mobile applications. This tool can simulate thousands of users in order to evaluate the application performance under load and analyze the response times. It also supports Cloud-integrated – performance, load and stress testing. It is easy to use, cost-effective and provides good scalability.

Metrics for Stress Testing

Metrics help in evaluating a System’s performance and generally studied at the end of Stress Test. Commonly used metrics are –

Measuring Scalability & Performance

Pages per Second: Measures how many pages have been requested / Second

Throughput: Basic Metric – Response data size/Second

Rounds: Number of times test scenarios have been planned Versus Number of times a client has executed

Application Response

Hit time: Average time to retrieve an image or a page

Time to the first byte: Time is taken to return the first byte of data or information

Page Time: Time is taken to retrieve all the information in a page


Failed Connections: Number of failed connections refused by the client (Weak Signal)

Failed Rounds: Number of rounds it gets failed

Failed Hits: Number of failed attempts done by the system (Broken links or unseen images)


Stress testing’s objective is to check the system under extreme conditions. It monitors system resources such as Memory, processor, network etc., and checks the ability of the system to recover back to normal status. It checks whether the system displays appropriate error messages while under stress.

Example of Stress Testing

E-commerce website announces a festival sale

News website at the time of some major events

Education Board’s result website

Social networking sites or blogs, apps, etc

What Is Netback In Oil And Gas?


Used to assess oil and gas company efficiency and profitability

Written by

CFI Team

Published January 14, 2023

Updated July 7, 2023

What is Netback?

Netback is a calculation used to assess companies specifically in the oil and gas industry. This benchmark considers the revenue generated from the sale of oil and gas, and nets it against specific costs required to bring the product to market. Often this is shown as a per barrel measurement. It essentially shows how much the company retains from the sale of a single barrel of oil or oil byproducts. Netback per barrel can be used to assess company efficiency over time or to compare a company to its competitors.

Quick Summary

Netback is a benchmark used in the oil and gas industry to assess the profitability and efficiency of a company based on the price, production, transportation, and selling of their products

This benchmark is calculated by subtracting royalties, transportation, and other operating costs from revenue

Netback/barrels of oil/byproducts is a useful metric for assessing a company over time and comparing the company to its competitors

This benchmark can be found in the management discussion and analysis section of a company’s annual report

How is Netback Calculated?

Netback is calculated by starting with revenue and subtracting the costs of production, transportation, marketing, and other costs of bringing the oil and gas to market.:

Netback = Oil and Gas Revenue – Realized Loss on Financial Derivatives – Royalties – Operating Expenses – Transportation

Netback is often calculated as a per barrel of oil equivalent (BOE) instead, giving a more useful figure to assess the company by:

Netback/BOE = Price – Realized Loss on Financial Derivatives/BOE – Royalties/BOE – Operating Expense/BOE – Transportation/BOE

Why is Netback Important?

Netback is an important benchmark because it is a very useful measure for assessing a company without the bias of non-operating, financing, or other costs. Calculating this metric essentially tells an analyst how efficient the company is at producing and selling its oil and gas products. Taking this number per unit of barrels of oil equivalent gives a type of efficiency ratio. By monitoring netback/BOE over time, the efficiency of the company’s production, transporting and selling can be evaluated. A falling netback/BOE might indicate issues that should be further looked into.

Netback/BOE is also a very useful measure to look at when comparing different companies. Depending on the value, an analyst will be able to judge whether a company can more efficiently produce and market oil, allowing them to retain more profits from the sale of each barrel of oil. A higher netback/BOE will also show how able a company is at dealing with price volatility in the market. A higher netback/BOE suggests that in times of falling prices, the company would still be able to remain profitable.

Netback, however, is not a standardized equation. Different companies may calculate netback and netback/BOE using various methods and may include or exclude different items. Netback/BOE can still be used to look at changes over time for a specific company, however, when comparing the netback of competitors it is important to adjust the equation to ensure they are comparable values.

Netback – Worked Example

Let us consider an example of calculating the netback of a company. Say a company has oil and gas revenues of $11,000,000. They pay royalties of $300,000, transportation costs of $500,000, and have operating expenses of $3,800,000. If they sold 275,000 barrels of oil equivalents, what is their operating netback in dollars, and in dollars per barrel?

To calculate the operating netback, we start with revenues and subtract the costs of bringing the product to market:

Netback = $11,000,000 – $300,000 – $500,000 – $3,800,000 = $6,400,000

Here we see that the total netback is $6,400,000. To make this number more useful for analysts, netback per barrel can be calculated. Netback per barrels of oil can start with price, and each cost can be thought of as a per barrel cost. However, to calculate netback/BOE we can also simply divide the netback by the number of barrels:

Netback/BOE = $6,400,00/275,000 BOE = $23.27/BOE

The calculation for netback is generally found in the management discussion and analysis of a company’s annual report. This benchmark is most often shown in a table. Below illustrates what would generally be seen for a company:

Additional Resource

Thank you for reading CFI’s article on the netback benchmark. If you would like to learn more about related concepts, check out CFI’s other resources:

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