Development of a system for daily assessment of the hard currency exchange rate for the distribution of proceeds by type of content in the game "Haunted Urban Adventures"

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Аннотация

Компания «Webgames» уже выпустила множество мобильных игр, одной из таких игр является «Ghost Town Adventures». Каждый день более 30 000 человек играют в «Ghost Town Adventures», а более 7 миллионов человек уже знакомы с этой игрой. «Webgames» получает более 1 миллиарда внутриигровых сообщений в месяц, включая траты и получение внутриигровой валюты и информацию о платежах. Также игра «Ghost Town Adventures» насчитывает более 150 единиц игрового контента и продолжает выпускать новый. В этой статье мы описываем систему для данной игры, которая позволит оценить стоимость игрового контента.

Company «Webgames» has already produced many mobile games, one of such games is «Ghost Town Adventures». Every day more than 30,000 people play the «Ghost Town Adventures», and more than 7 million people already played. «Web-games» receives more than 1 billion in-game messages per month, including spending and receiving hard currency and user payments. Also game «Ghost Town Adventures» has more than 150 units of game content, and continues to release new. In this paper, we describe a system for this game, which allows estimating the cost of in-game content.

The text of this report divides by several parts. Firstly, we define a problem. Then we cover some theoretical parts which is used in this work. Then we consider the results of the study. And finally, discuss the main results and plans.

Key Words: mobile games, hard currency, exchange rate, content monetization



Basic definitions, means, and abbreviations

free-to-play - monetization system and method of distribution of computer or mobile games. Allows the user to play without a mandatory deposit.

hard currency - in-game currency which player buy for real money and can spend for in-game content.

microtransaction - а business model for distributing downloadable content or accessing services at low prices (typically around $ 10).

monetization - turning any assets into money through an exchange, in the case of a game dev, it implies the exchange of in-game content/resources

LTV (lifetime-value) - represents the entire value brought by the player to the game before it stops playing.

AWS (Amazon web services) - services provided by Amazon

CLD (casual loop diagram) - is a causal diagram which visualizes how different variables in a system are connected. [12]

Churn player - a special game term for players who have not played a certain number of days and most likely will not return to the game. For the game "Ghost Tales" is the number of days calculated empirically and is 10 days

Docker - is software for automating the deployment and management of applications in containerized environments. Allows you to "package" an application with all its environment and dependencies into a container that can be moved to any Linux system and provides an environment for container management.



Content

Introduction

1. Problem Relevance

1.1 Background

2. Literature review

2.1 Hard Currency features

2.2 DAG-Processes

2.3 Jobs

2.4 Amazon Web Services

3. Analytics research

3.1 Gather Information

3.2 Choosing methods

3.3 Define the new system's requirements

4. Implementation

4.1 Preliminary implementation plan

4.2 Develop Data Layer

4.3 Develop Program layer

5. Conclusion

5.1 Discussion and Conclusion

Reference

Appendix



Introduction

Purpose of this work is to develop a system tor daily valuation hard currency exchange rate to share the revenue by type of content in the game «Ghost Town Adventures». To achieve this it is necessary:

Explore different ways to estimate the value of a hard currency

Calculate the hard currency exchange rate, using a stack of technologies already applicable in the company

Distribute revenue for in-game content.

The object of research: the problem of calculating hard currency cost

The subject of the study: the system of estimation and distribution of hard currency



1. Problem Relevance

In game design, it is very important to quickly recognize and find the reasons for the decline in income. Most often this is due to technical problems, the churn of paying players, the decrease in the number of installations, the accumulation of in-game currency and the loss of interest in the content. While the first three reasons to trace quite simply, that the assessment of the last two are the problem.

Why is it important to evaluate the quality of content? To answer this question, let's look at the casual loop diagram (Pic. 1). The final goal of the company is to increase its profit. In this scheme, the final goal is presented through LTV, a metric that shows how much players pay for their lifetime in the game, because it describes the profitability of the game well. Entities represented in it:

Players: people playing game content

Payers: people buying Hard Currency in the game. In games, usually with an increase in the number of players playing, the number of payers also increases, while the percentage can vary and greatly depends on the traffic coming into the game.

Payments: money paid by players to receive Hard Currency.

Purchased Hard Currency: Purchased Hard Currency for passing content.

Real Value: money received by the company from purchases of players

Player Lifetime: The time a player spends in a game before he stops playing it.

Churned Players: Players who stop playing the game.

Content Quality: A feature of the game that shows how good the content is. In this task, we believe that one content is better than another if it is more conducive to achieving the goals of the company. Most often, this characteristic is evaluated by experienced game designers. But this is a subjective assessment and it does not always contribute to the achievement of company goals. And when creating new content, it is not always possible to rely on previous experience.

Therefore, evaluating Content Quality is very important. For this, indirect metrics are used. As you can see from the diagram, we expect that high-quality content reduces the number of churned players, helps to increase the number of paying players, and purchases of crystals. These metrics help well, but the problem is that these metrics are not tied to specific content, which means that if there are several of them coming out at the same time, then it is difficult to evaluate the quality of the content with their help.

This is where metrics tied to specific content help. Such as wasting crystals on content.

So the company is faced with the question of how to evaluate the value of content. At the same time, it is not convenient for people producing content to see the value of their content in virtual currency, it is easier for them to operate with real money. To cope with this, you can convert hard currency into real money. This will help to clarify which categories of expenditure game players earn more. [8]

1.1 Background

Now the global gaming market is developing very rapidly. More and more people play games and bring money to their creators. In 2019, the gaming market grew by 9% to $ 152.1 billion, 45% of which are mobile games. Now more than ever, games are the foundation of the entertainment business. The way consumers interact with and through games is constantly changing. This not only leads to wider involvement but also leads to the emergence of completely new types of games. Now there are more than 2.5 billion players in the world. [3] That is why the development of games and analytics for them is a very relevant area of activity in the modern world.

Mobile games - a vivid example of successful and complex mobile applications. Mobile applications are an outstanding example of the alliance between electronic communications, the media and the entertainment industry. This is especially true for mobile games. The attractiveness and opportunities of this market are attracting innovators, entrepreneurs and many old and new industry players. Recently, there has also been some academic interest in the field of mobile games. However, the available literature covers mainly either social networks or the prospects of game design. Recently, there has been an academic interest in mobile platforms, in the development of mobile applications on these platforms, and the video game industry as a whole.

There is still a lack of knowledge about the most successful business models for mobile games. New platforms require more flexible business models. Game publishers are transferring existing business models of the gaming software industry to the mobile domain: retail (pay-as-you-go), premium retail, freemium model (the game with basic functionality is free) and a subscription (mainly for online games ) Advertising is also a common complement to familiar business models. Every year the picture changes and one of its most successful models is the free-to-play model. In this model, players are guaranteed to gain access to the full functionality of the game, but they purchase additional content through microtransactions or viewing ads.

Most games are now based on the free-to-play model, and it is the fastest-growing.

The main business models are described below:

Free to Play (F2P): these games are free to download and play, but contain microtransactions.

Pay to play (P2P) or Premium: in these types of models, users must pay before the game.

Freemium: they are part of F2P and are based on the combination of “free” and “Premium”.

Direct monetization: for example, in-app purchases (IAP), it sells VirtualGoods inside games.

Indirect monetization: from advertising

Paynium: this model is similar to freemium, but the difference is that it requires an advance payment upon initial purchase, then the game is free. [4]

Game developers aim to create a really good game with the optimal monetization mechanism to maximize profits. And the game economy helps in this.

A game economy is a virtual economy that sets up all game cycles in a game (currencies, time cycles, levels, prices, etc.). Different game economies will structure the behaviour of different players in the same game.

A well-calculated game economy is the foundation of the game's reward structure and is indispensable for its success. F2P(free-to-play) games based on microtransactions often use a two-currency mechanism, which allows the user to develop in the game using both the currency bought for real money (hard currency) and the virtual currency received in the game (soft currency). In the game "Ghost Town Adventures" crystals (hard currency) can also be obtained in-game. Also, there are ways to buy other in-game resources (soft currency) that players usually get in the game or exchange crystals for them. Therefore it is necessary to approach the problem more complex.

As mentioned above, most F2P games are based on a two-currency mechanism and most of the leading profitable games use more than one currency. This is because using only one type of currency limits the flexibility of the user's expenses. If you use only hard currency in your game, you force the user to pay for everything that he wants to get for himself in the game, reducing his interest in the game process. And only about 2-7 per cent (2% of DAU for «Ghost Town Adventures») of users are willing to spend money in the game. Having realized more than one opportunity to monetize your game and allowing users to spend more than one type of currency, your paying users will continue to pay, which will allow them to get a significant advantage. When implementing both hard and soft currencies and several sources (using game achievements, user actions and in-game advertising), the game provides users with more opportunities to spend time.

A necessary key to building a good gaming economy is a balance. You must make sure that the currency sources (purchase + user earnings) are in optimal accordance with the user's expenses, and that the purchase/earnings ratio is also optimal. Thus, you can focus on your earnings, rather than on sales. [5]

The model below can help explain how the free-to-play model can sell virtual items. Despite its simplicity, the model has some interesting implications. To use the free-to-play model, you need to make sure that the opportunity costs of the consumer and marginal costs remain low. When opportunity costs exceed a certain threshold level, players stop paying. Therefore, if someone wants to sell virtual items through the “free-to-play” model, he must ensure that consumers will face low opportunity costs. Also, for games that require players to learn a lot about how to use controls and take a long look at them, the "free-to-play" model may not be applicable. To receive higher incomes, the game must reduce the consumer costs associated with the game. Ways to reduce opportunity costs include making the game easy to access, lowering startup costs (making it easier to learn how to play the game), and increasing the entertainment value of the game. [6]

Here are some game designs for efficient place payment models based on items and resources in online games [7]:

The balance between items that can be bought for cash, and items that can only be obtained through the gameplay. Create synergies between them.

Items that are bought exclusively for decorative purposes should remain forever.

Items with functions designed to assist in the gameplay should be different depending on the amount paid.

Do not disclose evidence of how an item can enhance ability; use descriptive text.

As a rule, virtual objects are developed by artists and designers, and the process rarely involves economic analysis, which is not correct. The selection of attributes of virtual items must be made strategically to maximize profits.



2. Literature review

2.1 Hard Currency features

Features of hard currency in mobile games are not widely investigated. Nevertheless, we can highlight the main points. [1]

Players interact with content in different ways.

Some players will never buy hard currency, but they are also important for the game. currency game income dashboard

Most often, players buy hard currency to immediately spend it. But thanks to offers they can buy more than they need.

Some players buy hard currency to support the company producing the game.

2.2 DAG-Processes

The company «WebGames» uses an interface called the orchestra, one of its submodules is called DAG (from the directed acyclic graph) and is designed to work with DAG processes.

DAG is the process for running the script execution chain. The process is described using an XML file. The dependencies and parameters blocks are optional, and the rest of the blocks must be present in the process description. Block job is the simplest element of the process (one script in python or another language). Block jobTree is a flow of the related job. Block dependency is responsible for linking individual jobs, in an acyclic graph. Block parameters responsible for the transfer of parameters in process and in between jobs.

Workflow example:

A process template is created.

A set of parameters is set for the process (using a template-generated parameter input form).

Sets the time of the process using cron and the type of process startup (one-time or regular).

At the specified time, DAG launches a process instance on the Docker, the description of which is generated by the template and parameters.

The process instance on the Docker according to the process description starts the work on the Docker.

If successful, the corresponding status is displayed.

When the process hangs on any work, a notification is sent and the process branch, which depends on this task, goes to the waiting state of the task data.

2.3 Jobs

Main types of jobs in DAG processes are:

Cluster activator - a special type of job that creates an instance of the cluster with the specified parameters. The cluster is lifted by the java cluster activation application which is available in Amazon s3.

Docker task - type for launching shell-scripts by the link in parameters.

Python script - type for launching python-scripts by the link in parameters.

2.4 Amazon Web Services

Amazon Simple Storage Service (Amazon S3) is an object storage service called file hosting. [10]

The basic unit of storage Amazon S3 objects is organized in buckets. Each object is identified by a unique, user-assigned key. You can manage buckets and objects using the Amazon S3 console, the AWS SDK, or the Amazon S3 REST application programming interface (API).

Features that you need to know:

* S3 buckets are not limited in size and number of files.

* Manually deleting files is worth the money.

* Downloading files to S3 is free, it costs money to download them to a local machine or any server outside Amazon.

Amazon Redshift is a fast, Internet hosting service and scalable data warehouse that makes it easier to analyze the data in your storage.

An Amazon Redshift data warehouse is a collection of computing resources called nodes, which are organized into a group called a cluster. Each cluster runs an Amazon Redshift engine and contains one or more databases. [13]



3. Analytics research

3.1 Gather Information

To evaluate and determine how to convert hard currency, we used our own producer analytics experience, and the manufacturer of the Ghost Town Adventures game was also interviewed.

The main goal of the company is to make money, which is facilitated by an increase in LTV. Thus, to assess the impact on the company's well-being, we consider LTV. And to increase it is worth increasing the quality of the content. (Pic. 1) As can be seen from the diagram, LTV and the quality of the content are the parts of a reinforced loop, which indicates that an increase in one contributes to an increase in the other. It remains to understand how to evaluate the quality of the content. As you can see, it is associated with the churn of players and payments, but this is a general characteristic that cannot be attributed to specific content.

And for this task, it is necessary to analyze the spending of hard currency on specific content, but since there are various ways to get hard currency, this is not always convenient. Therefore, it was decided to transfer hard currency into real money, which could be analyzed and compared with each other.

3.2 Choosing methods

We have several ways of calculating the hard currency exchange rate, which differs by whether we will take into account the value of the prized currency. We determine the significance of each approach using AHP. [14] The criteria will be:

Need for additional tracking - if not needed, then this is better than if needed. (not very significant criterion, weight 0.1)

Meeting end-user expectations - how much such a calculation corresponds to expectations were evaluated jointly with the producer of the game. (significant criterion, weight 0.3)

Required amount of data - an estimate of the volume of tables needed for calculations. The higher the score, the lower the volume of the tables (significant criterion, weight 0.3)

Easiness of implementation - the complexity of the changes made. The higher the score, the fewer changes need to be made to the existing system. (not very significant criterion, weight 0.1)

Algorithmic complexity - the complexity of the calculations and the time spent on computing. (mean criterion, weight 0.2)

Alternatives:

For all currency - a method in which we will divide all the money received by the player into all received crystals. To do this, you will have to keep the balance of the received currency and the amount of money paid.

Only for purchased currency - a method in which we will divide all the money received by the player only into the purchased crystals. To do this, you will have to keep the balance of the purchased currency and the amount of money paid.

For purchased currency with conversion - a method in which we will divide all the money received by the player only into purchased crystals, but recount when buying new ones. To do this, you will have to keep the balance of the purchased currency and the amount of money paid.

We determine the significance of each method using AHP. [14] The figure (Pic. 3) shows the estimated weights of each criterion. And Table (Tab. 1) shows the normalized estimates for each alternative. Using AHP, we get that the most significant method is «For purchased currency with conversion».

But when using this method, it is necessary to separate the crystals into purchased and prize crystals, which means that you need to choose in what way the player spends them. For this, we also use the AHP method. We use the same criteria and their weights as in Figure (Pic. 4, Tab. 2), and other alternatives:

Write-off of purchased currency at the end - first spend prize crystals, and then purchased ones. To do this, you will need to store both purchased and prize crystals.

Write-off of purchased currency at the beginning - spend purchased crystals first. To do this, you only need to store the number of purchased crystals.

Write-off to receive currency - spend crystals in the order of their receipt. To do this, you have to store the chain of production of crystals.

Using AHP, we get that the most significant method is «Write-off of purchased currency at the beginning».

3.3 Define the new system's requirements

In the game «Ghost Town Adventures" the exchange of in-game content for money between the company and the player passes through the hard currency. It's described on Pic. 1 and table bellow.

	Company Give	Company Gain	Player Spend	Player Gain
1-2	Hard Currency	Money	Money	Hard Currency
3-4	Content	Hard Currency	Hard Currency	Content

In 1-2 phase Player buy hard currency for real money and Company gain this Money for producing new content. In phase 3-4 Company offers new content to the player and he can buy it for his hard currency.

The main problem with this is that Players can buy hard currency at different prices due to promotions and discounts. And in most games, as in "Ghost Town Adventures", players can get hard currency or resource by spending their time on any activities in the game or bonuses for free. Also, gamers are able not to spend all their hard currency on one unit of content, they can only spend part of it, or buy content for currency received from different sources.

In this case, the system needs to evaluate crystals and resources at the time of purchase and at the time of spending.

In this situation, you have several ways of calculating crystals. When gaining crystals you can calculate the cost of every hard currency unit in the moment of gain (1 crystal for «GTA»). You divide the money by the number of crystals. And save data about this crystal. But this is not convenient from data storage, because a lot of people play the game, and there are a lot of operations with crystals. Such a volume of data is difficult to read and then operate on, and that would be redundant. Also player does not think when he spends crystals that he bought this one for $ 2, this one for $ 1, and this one he got for free.

And a good solution for this situation is to combine all the purchased crystals and calculate the average price of the purchased crystal for a player.

Then the system should work like this:

1) For each player, information is collected about currency purchases and their spending, and what they were spent on. Also, calculate the current balance of crystals and money paid for crystals.

2) The player's crystal balance is calculated

3) The number of crystals purchased is calculated

4) The average cost of the purchased crystal is calculated

5) The cost of the content is calculated (the cost of the object in crystals multiplied to the average cost of the crystal).

It should be noted that in this scheme, if the game has purchased crystals, then they are spent earlier than free ones. From the position of the business model, it is believed that the player buys hard currency to purchase the nearest content.



4. Implementation

4.1 Preliminary implementation plan

To implement new tables in an existing database.

Implement scripts for regular updating of data tables with data on the balance of players hard currency.

Integrate these scripts into the existing structure of the dag-process.

Test the received functionality.

4.2 Develop Data Layer

To obtain the final table, data is used that is already collected in the company about payments and transactions with hard currency. This data is presented in the diagram (Pic. 5) as the payments and hard_currency tables; during the dag, these tables are uploaded to the cluster from external storage in Amazon s3 to work with them and update the data. Access to the data must be obtained after updating and before uploading the data back to the external storage. Our goal is to update the final table real_attr_hc, which is also unloaded from external storage to the cluster since the data from it is also used. For connecting real_attr_hc with payments and hard_currency tables with row_hash key md5 function was used for table row fields. To make it more convenient to work, we combine the data from the payments and hard_currency and real_attr_hc tables in the request, receiving data in the format of the temp table on the diagram (Pic. 5).

To do this, we take the fresh spending of crystals from hard_currency table for the selected period or with a positive current balance with the "spend" event, combine with the purchase of crystals from payments table with the "receive" event, and combine with the last spend or receive the event from the real_attr_hc table for payers or players who spend crystals with the corresponding event.



4.3 Develop Program layer

To create the final table, we will use a python script, and not standard SQL scripts, because, for the implementation of the algorithms for calculating the current balance of the currency and the price of the hard currency, cycles are used that are best implemented in the python language. The algorithms used for calculation are presented in the figures (Pic 6, 7).

In first algorithm we calculate current bought crystals balance, for each player, we pass to the method a list of receipts and spends of the player's hard currency, sorted by the time the messages arrived. With a «+» sign for gaining and a sign «-» for spending. And then we change the current balance of the player. We check that the balance is not negative, because when players spend more currency it is mean that they spent prize currency.

In the second algorithm for every user and item calculate the current cost of bought items. For each player and for each operation with currency, we consider: if a player receives a currency, we recalculate the real value of the player's currency, and if a player spends, we leave the previous value.

So the whole script using these algorithms should perform the following operations:

1) Create an array in which receiving hard currency will be a positive number, and writing off the currency negative.

2) For each user and operation, calculate his balance of the purchased currency after the operation.

3) For each currency debit procedure, if the amount of currency spent is greater than the current balance of the purchased currency, we consider the difference in prize currency. Accordingly, the difference in total spending and prize currency is considered a waste of purchased currency.

4) We consider the amount of real money spent in a transaction as the product of the spent amount of the purchased currency and the current currency price for the player.

These script needed to be inserted into the daily dag to collect tables

Current dag looks like (Pic. 8)

Waves run scripts that run the necessary jobs to start and close the cluster and waves that execute SQL scripts that create and update the necessary tables, the python script must be inserted between wave 1 and 2, since it uses tables updated in wave 1 and hive-wave, and creates tables that in the future they will be used in wave 2 for dashboards.



5. Conclusion

5.1 Discussion and Conclusion

As a result of system integration, dashboards for game producers have been improved. Now they see a specific profit in real money from the content they produce and can respond in time to changes in the profitability of the content.

In the course of this work, various methods for assessing hard currency were evaluated and the most suitable ones were selected. Currency calculation was integrated into the current analytical process of the company without using third-party services. And the results obtained are embedded in the dashboard used by the company to display content metrics.

With the current number of users, the added script works for about 2 minutes, which is an acceptable time for the company. During the operation of the method, no anomalies were found in the data or violations of the DAG.

The main advantage of the system is that it can be reused for other projects of the company that have the same event messages.

5.2 Future research

In the course of the study, it was noticed that in some games, including other games of the company, a more complex economic system is possible, when between the hard currency and the content, interlayers in the form of another in-game resource are possible. Thus, further research is possible in the development of real value systems for such more complex gaming economies.



Reference

[1] Alha, K., Koskinen, E., Paavilainen, J., & Hamari, J. (2016). Critical acclaim and commercial success in mobile free-to-play games. Proceedings of DiGRA FDC Conference (pp. 1-6). Dundee, Scotland: DIGRA.

[2] Mobile gaming: Industry challenges and policy implications Claudio Feijoo, Josй-Luis Gуmez-Barroso , Juan-Miguel Aguado , Sergio Ramos

[3] Saaty, Thomas L. Decision Making for Leaders: The Analytic Hierarchy Process for Decisions in a Complex World. -- Pittsburgh, Pennsylvania : RWS Publications, 1999

[4] Alomari, Khaled & Soomro, Tariq & Shaalan, Khaled. (2016). Mobile Gaming Trends and Revenue Models. 9799. 671-683. 10.1007/978-3-319-42007-3_58.

[5] Close Screen How To Build A Smart Game Economy Shai Sasson 2:00 am MSK * August 6, 2015

[6] «Virtual Items Pricing in Online Games» Ernie G.S. Teo Division of Economics, Nanyang Technological University Chanbora Ek Division of Economics, Nanyang Technological University April 30, 2010

[7] Oh, G., Ryu, T., 2007, Game Design on Item-selling Based Payment Korean Online Games, In Proceedings of DiGRA 2007

[8] John D.Sterman, Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw Hill/Irwin, 2000.

[9] Shapiro, C., 1983, Optimal pricing of Experience Goods, Bell Journal of Economics vol. 14, no. 2, Autumn 1983, pp. 497-507

[10] Wingfield, N., 2008, Korea's Nexon Bets on Sales of Virtual Gear for Free Online Games, Wall Street Journal (Eastern edition)., May 23, 2008, pg. B.1

[11] Lehdonvirta, V., 2009, Virtual item sales as a revenue model: identifying attributes that drive purchase decisions, Electronic Commerce Research 9(1-2), Springer, Forthcoming



Appendix

Picture 1. CLD of game dependencies

Picture 2. Money roadmap



Picture 3. AHP hierarchy

Picture 4. AHP hierarchy



Picture 5. TABLES DIAGRAM

Picture 6. BALANCE ALGORITHM



Picture 7. cost ALGORITHM

Picture 8. DAG JOBS

Table 1. AHP evaluation

	Need for additional tracking	Meeting end-user expectations	Required amount of data	Easiness of implementation	Algorithmic complexity	Choosing of approach to accounting for deviding money
Write-off of purchased currency at the end	1,0	0,6	0,6	0,6	0,8	B2*0.1+C2*0.3+D2*0.3+E2*0.1+F2*0.2
Write-off of purchased currency at the beginning	1,0	1,0	0,8	0,8	1,0	B3*0.1+C3*0.3+D3*0.3+E3*0.1+F3*0.2
Write-off in order to receive currency	1,0	0,9	0,4	0,4	0,6	B4*0.1+C4*0.3+D4*0.3+E4*0.1+F4*0.2

Table 2. AHP evaluation

	Need for additional tracking	Meeting end-user expectations	Required amount of data	Easiness of implementation	Algorithmic complexity	Choosing of approach to accounting for deviding money
For all currency	1,0	0,4	0,6	1,0	0,8	B2*0.1+C2*0.3+D2*0.3+E2*0.1+F2*0.2
Only for purchased currency	1,0	0,8	0,4	0,4	0,4	B3*0.1+C3*0.3+D3*0.3+E3*0.1+F3*0.2
For purchased currency with conversion	1,0	1,0	0,6	0,6	0,6	B4*0.1+C4*0.3+D4*0.3+E4*0.1+F4*0.2

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