KR102650137B1 - Social trading service providing system and method performing thereof - Google Patents

Abstract

The social trading service providing server according to an embodiment of the present invention includes a dealer information collection unit that receives transaction information for each asset dealer from a plurality of asset trading platform servers and stores it in a dealer information database, and dealer action information among the transaction information for each asset dealer. Based on this, the asset dealer's investment period group is determined according to the period from the purchase to the sale, the rate of return is determined by comparing the market price at the time of purchase and the market price at the time of sale for each investment period group, and grouped by investment period group. A dealer classification department that classifies asset dealers into subgroups according to their rate of return, and transaction information of asset dealers classified into subcategories for each investment period group, extracts trading patterns and predicts the timing of purchases and sales. It includes a learning model generation unit for each dealer that generates a learning model.

Description

Social trading service provision system and method of implementation {SOCIAL TRADING SERVICE PROVIDING SYSTEM AND METHOD PERFORMING THEREOF}

The present invention relates to a system for providing social trading services and a method of implementing the same. More specifically, the present invention relates to a system for providing a social trading service and a method for implementing the same. More specifically, the present invention relates to a system for providing a social trading service and a method of implementing the same. More specifically, the performance of each dealer is estimated using transaction information for each dealer, and the transaction information of any dealer is provided to the user or related to the transaction of any dealer. It relates to a social trading service provision system that manages users' assets and its execution method.

If the asset exchange does not provide a reasonable quote that stably responds to volatility, general consumers may be harmed by purchasing assets at high prices and selling them at low prices. The Liquidity Providing system is a system first adopted in Europe and concerns the obligation to maintain quotes corresponding to recent transaction prices in order to create a healthy market environment on asset exchanges.

The LP system was introduced and is in operation in the KOSPI and KOSDAQ markets in the late 2000s, and while asset exchanges or securities listing foundations are responsible for providing liquidity to maintain stable quotes even for stocks with relatively low trading volume, virtual asset exchanges Additionally, for virtual asset listed foundations, the obligation to provide liquidity to maintain stable prices is not legally stipulated.

Meanwhile, since virtual assets with higher trading volume are ranked higher on virtual asset exchanges, virtual asset listing foundations often increase trading volume through side trading or false trading for marketing purposes. If prices rise or fall sharply through cross-trading or false transactions, general consumers may suffer significant losses.

In other words, in coin exchanges where volatility is relatively greater than that of assets and institutional safety measures are not in place, there is a risk of financial loss to general consumers if the price is not maintained stably.

The purpose of the present invention is to provide a social trading service providing system and method of implementing the same that provide users with transaction information of any dealer with good performance by estimating each dealer's performance using transaction information for each dealer.

In addition, the present invention is a social trading service providing system and the same that learns transaction information of any dealer and creates and provides a trading bot that learns the dealer's trading pattern, so that users can receive advice on when to buy and sell when trading. The purpose is to provide an implementation method.

In addition, the purpose of the present invention is to provide a social trading service provision system and method of implementing the same, which provides transaction information of dealers with good performance to users as a subscription service so that users can refer to it when trading with the dealer.

The objects of the present invention are not limited to the objects mentioned above, and other objects and advantages of the present invention that are not mentioned can be understood by the following description and will be more clearly understood by the examples of the present invention. Additionally, it will be readily apparent that the objects and advantages of the present invention can be realized by the means and combinations thereof indicated in the patent claims.

To achieve this purpose, the social trading service provision server includes a dealer information collection unit that receives transaction information for each asset dealer from multiple asset trading platform servers and stores it in a dealer information database, based on dealer action information among the transaction information for each asset dealer. According to the period from the time of purchase to the time of sale, the asset dealer's investment period group is determined, the rate of return is determined by comparing the market price at the time of purchase and the market price at the time of sale for each investment period group, and the assets grouped by investment period group A dealer classification unit that once again classifies dealers into small groups according to their rate of return, and the trading information of asset dealers classified into sub-groups for each investment period group, extracts trading patterns, and predicts the buying and selling times. It includes a learning model generation unit for each dealer that generates a model.

In one embodiment, when the social trading service providing server receives an asset investment consultation request message from the user terminal, it determines a dealer matching the user based on the asset investment consultation request message and makes purchases using a learning model associated with the dealer. It may include a social trading bot provider that predicts the timing and selling timing and provides the prediction to the user terminal.

In addition, a social trading service provision method executed on a social trading service provision server to achieve this purpose includes receiving transaction information for each asset dealer from a plurality of asset trading platform servers and storing the transaction information for each asset dealer in a dealer information database. Determining the asset dealer's investment period group according to the period from the purchase to the sale based on the dealer action information; determining the rate of return by comparing the market price at the time of purchase and the market price at the time of sale for each investment period group; A step of classifying the asset dealers grouped by investment period group into subgroups again according to the rate of return, and analyzing the transaction information of the asset dealers classified into the subgroups of each investment period group to extract transaction patterns, and then determine the timing of purchase and sale. It includes the step of creating a learning model that predicts the point in time.

In one embodiment, a method of providing a social trading service includes, upon receiving an asset investment consultation request message from a user terminal, determining a dealer matching the user based on the asset investment consultation request message and using a learning model associated with the dealer. It includes predicting the purchase time and selling time and providing the information to the user terminal.

According to the present invention as described above, there is an advantage in that transaction information of dealers with good performance can be provided to users by estimating each dealer's performance using transaction information for each dealer.

In addition, according to the present invention, there is an advantage that users can receive consultation on when to buy and sell when trading by creating and providing a trading bot that learns the dealer's trading patterns by learning the transaction information of a dealer with good performance.

In addition, according to the present invention, there is an advantage in that transaction information of dealers with good performance is provided to users as a subscription service, so that users can refer to it when making their own transactions.

Figure 1 is a network configuration diagram for explaining a social trading service providing system according to an embodiment of the present invention.
Figure 2 is a block diagram for explaining the internal structure of a social trading service providing server according to the present invention.
Figure 3 is a flow chart to explain an embodiment of a method for providing social trading services according to the present invention.
Figure 4 is a flow chart to explain another embodiment of a method for providing social trading services according to the present invention.

The above-mentioned objects, features, and advantages will be described in detail later with reference to the attached drawings, so that those skilled in the art will be able to easily implement the technical idea of the present invention. In describing the present invention, if it is determined that a detailed description of known technologies related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description will be omitted. Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the attached drawings. In the drawings, identical reference numerals are used to indicate identical or similar components.

1 is a network configuration diagram for explaining a social trading providing system according to an embodiment of the present invention.

Referring to FIG. 1, the social trading providing system includes an asset trading platform server (100_1 to 100_N), a social trading service providing server 200, and a user terminal 300.

The multiple asset trading platform servers (100_1 to 100_N) provide a platform for brokering transactions of assets listed on the exchange and can broker asset transactions according to purchase or sale requests from the user terminal 300.

Multiple asset trading platform servers (100_1~100_N) provide not only services related to asset trading, but also bill payment such as loans, credit card fees, and communication fees, hotel rooms, game tickets, itinerary tours, restaurant reservations, etc. It offers various types of full services for investors, including a reservation service for investors and a donation service to transfer donations to charity accounts.

The plurality of asset trading platform servers (100_1 to 100_N) provide an account registration request message to the user terminal 300 to register the investor's account (A), and in response to the account registration request message from the user terminal 300, When investor information is received, an account is created using the investor information.

Afterwards, the multiple asset trading platform servers (100_1 to 100_N) register an account with the investor's real name. For this purpose, the asset trading platform server (100_1~100_N) verifies the investor's identity via e-mail or SMS message.

The procedure for verifying the investor's identity may follow conventional methods related to user authentication. Furthermore, according to an embodiment of the present invention, the plurality of asset trading platform servers (100_1 to 100_N) may authenticate the user's identification card and/or user identification card through the user terminal (300).

For example, when the plurality of asset trading platform servers (100_1 to 100_N) receive a user identification code by scanning the ID and/or user identification card held by the user through the user terminal 300, the user uses the user identification code. Authentication can be performed.

A transparent printing layer is formed in the above user identification card, and a user identification code may be formed in a pattern on the transparent printing layer. At this time, the user identification code value may be formed in the form of a pattern using transparent ink on the transparent printing layer.

Accordingly, the user terminal 300 may scan the user identification card, generate a user identification code using information of light reflected from the user identification card, and provide the user identification code to the plurality of asset trading platform servers 100_1 to 100_N.

Multiple asset trading platform servers (100_1 to 100_N) actively connect to trading financial institutions, such as banks, to verify and confirm the investor's identity. After the identity is verified, the asset trading platform server (100_1~100_N) will take the financial institution account as a confirmed valid account.

Preferably, the verified financial institution account is considered the only financial institution account in the asset trading platform system 100. After confirmation of the financial institution account, the asset trading platform server (100_1~100_N) will establish a connection between the financial institution account and the registered account to enable currency transfer from the financial institution account to the registered account.

So, investors can use the money transferred to their registered account to conduct general asset trading, private asset trading, or general asset trading and private asset trading by the use of the transaction execution module connected to the asset trading platform server (100_1~100_N). there is.

When the asset trading platform server (100_1~100_N) receives a purchase message from an investor or an asset dealer delegated by an investor, it processes the purchase of the asset and then records the asset's item and quantity, purchase price, market, and investment type ( (i.e., personal investments, agent investments, etc.) are stored.

Afterwards, when the asset trading platform server (100_1~100_N) receives a sell message from an investor or an asset dealer delegated by the investor, if the item of the asset requested for sale exists in the investor database, it proceeds with the sale of the asset and then proceeds to sell the asset in the investor database. Store the asset type and quantity, purchase price, sale price, market, and investment type (i.e., personal investment, agent investment, etc.).

The social trading service providing server 200 receives dealer transaction information from each of the multiple asset trading platform servers (100_1 to 100_N), analyzes it, and generates a learning model that predicts the buying and selling times to provide user consultation. It is a server that mediates this.

The social trading service providing server 200 allows investors to have discussions with asset dealers in a convenient way and perform asset trading through text messages or real-time video calls.

For example, the social trading service providing server 200 automatically outputs all of the investor's asset transaction records recorded this month, or instead outputs specific asset transactions of this year upon the investor's manual request. You can.

The social trading service providing server 200 executes a liquidity supply program and receives transaction information for each asset dealer (i.e., asset type, market situation, dealer action information, etc.) from the asset trading platform servers 100_1 to 100_N.

First, the social trading service providing server 200 determines the investment period group of the asset dealer according to the period from purchase to sale based on dealer action information among the asset dealer's transaction information. At this time, the investment period group may include short-term, medium-term, mid- to long-term, and long-term.

In one embodiment, the social trading service providing server 200 determines the investment period group of the asset dealer according to which investment period range the period from purchase to sale belongs to based on dealer action information among the transaction information of the asset dealer. can be decided.

In addition, the social trading service providing server 200 determines the rate of return by comparing the market price at the time of purchase and the market price at the time of sale for each investment period group, and divides the asset dealers grouped by investment period group into small groups again according to the rate of return. Classify.

The reason for this is to classify asset dealers according to their trading patterns and then learn how to decide when to buy and sell.

First, the social trading service providing server 200 extracts the types of assets invested by asset dealers classified into sub-categories of each investment period group.

Afterwards, the social trading service providing server 200 represents the transaction price of the asset type invested by the asset dealer on a graph and analyzes the change in the curve between the purchase point and the sale point.

In one embodiment, the social trading service providing server 200 stores the first selling price when the price rises from the time of purchase and the first selling possible point is determined, and then the price falls so that it is not lower than the first selling possible point. However, the time when the purchase price is higher than the above is determined as the second possible sale time.

Afterwards, the social trading service providing server 200 calculates the first average price by applying a weight corresponding to the market situation to the price at the first selling point, then averaging the price, and adjusting the price to the market situation at the second selling point. After applying the corresponding weights, they are averaged to calculate the second average price.

In conclusion, the social trading service providing server 200 calculates the difference price between the first average price and the second average price, compares the difference price and the profit amount, and learns the point when the profit amount increases as the dealer sale prediction point. A learning model can be created.

In addition, the social trading service providing server 200 represents the transaction prices of the asset types invested by the asset dealer on a graph, and analyzes the change in the curve of the transaction price graph during a specific period from before the time of purchase.

In one embodiment, the social trading service providing server 200 stores the first available purchase price when the market price falls on the transaction price graph for a specific period from before the time of purchase and determines the first purchase possible time, and then changes the market price. The point at which the price rises and is higher than the first purchase price by a certain price is determined as the second purchase possibility point.

Afterwards, the social trading service providing server 200 calculates the first average price by applying a weight corresponding to the market situation to the price at the first purchase possible point and averaging the price, and adjusts the price to the market situation at the second possible purchase point. After applying the corresponding weights, they are averaged to calculate the second average price.

In conclusion, the social trading service providing server 200 calculates the difference price between the first average price and the second average price, compares the difference price and the actual purchase amount, and determines the point when the difference price is lower as the dealer purchase prediction point. You can create a learning model by learning.

Additionally, when the social trading service providing server 200 receives an asset investment consultation request message from the user terminal 300, it determines a dealer matching with the user based on the asset investment consultation request message. That is, the social trading service providing server 200 extracts the investment period, rate of return, and asset type from the asset investment consultation request message and then determines the dealer corresponding to the investment period, rate of return, and asset type.

Then, the social trading service providing server 200 uses a learning model associated with the dealer to predict at least one of the user's buying and selling timings and provides the prediction.

In one embodiment, when a user requests consultation on the time to sell after purchasing a specific asset, the social trading service providing server 200 inputs the purchase time and asset type into a learning model associated with the dealer to predict the selling time. After that, it is provided to the user terminal 300.

In another embodiment, when a user requests consultation on the time to purchase a specific asset, the social trading service providing server 200 predicts the purchase time by inputting the asset type into a learning model associated with the dealer, and then sends the user terminal 300 ) is provided.

When the social trading service providing server 200 receives a request message for automatic account operation from the user terminal 300, it determines a dealer matched with the user based on the asset investment consultation request message. That is, the social trading service providing server 200 extracts the investment period, rate of return, and asset type from the asset investment consultation request message and then determines the dealer corresponding to the investment period, rate of return, and asset type.

Then, the social trading service providing server 200 uses a learning model associated with the dealer to predict at least one of the user's buying and selling times and automatically executes buying and selling.

In one embodiment, when a user requests consultation on the purchase time of a specific asset, the social trading service providing server 200 predicts the purchase time by inputting the asset type into a learning model associated with the dealer, and then makes the purchase at the purchase time. It can run automatically.

In another embodiment, the social trading service providing server 200 may input the purchase time and asset type into a learning model associated with the dealer to predict the selling time, and then automatically proceed with the sale at the selling time.

Figure 2 is a block diagram for explaining the internal structure of a social trading service providing server according to the present invention.

Referring to Figure 2, the social trading service providing server 200 includes a dealer information collection unit 210, a dealer information database 220, a dealer classification unit 230, a learning model creation unit for each dealer 240, and a chatbot service provider. It includes a study unit 250, an asset management service provision unit 260, and a control unit 270.

The dealer information collection unit 210 executes a liquidity supply program to receive transaction information for each asset dealer (i.e., asset type, market situation, dealer action information, etc.) from the asset trading platform servers (100_1 to 100_N), and receives the dealer information database ( 220).

The dealer classification unit 230 determines the investment period group of the asset dealer according to the period from purchase to sale based on dealer action information among the asset dealer's transaction information. At this time, the investment period group may include short-term, medium-term, mid- to long-term, and long-term.

In one embodiment, the dealer classification unit 230 determines the investment period group of the asset dealer according to which investment period range the period from purchase to sale belongs to based on dealer action information among the asset dealer's transaction information. You can.

In addition, the dealer classification unit 230 determines the rate of return by comparing the market price at the time of purchase and the market price at the time of sale for each investment period group, and classifies the asset dealers grouped by investment period group into small groups again according to the rate of return. .

The reason for this is to classify asset dealers according to their trading patterns and then learn how to decide when to buy and sell.

The dealer-specific learning model generator 240 extracts the types of assets invested by the asset dealer classified into sub-categories for each investment period group from the dealer information database 220.

Afterwards, the learning model creation unit 240 for each dealer represents the transaction price of the asset type invested by the asset dealer on a graph and analyzes the change in the curve between the purchase time and the sale time.

In one embodiment, the dealer-specific learning model generator 240 stores the first selling price when the market price rises from the time of purchase and the first selling possible point is determined, and then the market price falls to be lower than the first selling possible point. The point in time that is lower but higher than the above purchase price is determined as the second possible selling point.

Thereafter, the learning model generator for each dealer 240 calculates the first average price by applying a weight corresponding to the market situation to the price at the first possible sale point and averaging the price, and calculates the market situation at the second possible sale point. The weight corresponding to is applied and then averaged to calculate the second average price.

In conclusion, the dealer-specific learning model generator 240 calculates the difference price between the first average price and the second average price, compares the difference price and the profit amount, and learns the point when the profit amount increases as the dealer sale prediction point. You can create a learning model by doing this.

In addition, the dealer-specific learning model generator 240 expresses the transaction price of the asset type invested by the asset dealer on a graph and analyzes curve changes in the transaction price graph during a specific period from before the time of purchase.

In one embodiment, the dealer-specific learning model generator 240 stores the first available purchase price when the market price falls on the transaction price graph for a specific period from before the time of purchase and the first available purchase time is determined. The moment when the market price rises and is higher than the first purchase price by a certain price is determined as the second purchase possible point.

Thereafter, the learning model generator for each dealer 240 calculates the first average price by applying a weight corresponding to the market situation to the price at the first purchase possible point and then averaging it, and calculates the first average price by calculating the market situation at the second possible purchase point. The weight corresponding to is applied and then averaged to calculate the second average price.

In conclusion, the dealer-specific learning model generator 240 calculates the difference price between the first average price and the second average price, compares the difference price and the actual purchase amount, and determines the point when the difference price is lowered as the dealer purchase prediction time. You can create a learning model by learning.

When the chatbot service provider 250 receives an asset investment consultation request message from the user terminal 300, it determines a dealer matched with the user based on the asset investment consultation request message. That is, the chatbot service provider 250 extracts the investment period, rate of return, and asset type from the asset investment consultation request message and then determines the dealer corresponding to the investment period, rate of return, and asset type.

Then, the chatbot service provider 250 uses a learning model associated with the dealer to predict at least one of the user's buying and selling times and provides the prediction.

In one embodiment, when a user requests consultation on the time to sell after purchasing a specific asset, the chatbot service provider 250 inputs the purchase time and asset type into a learning model associated with the dealer to predict the selling time. and then provided to the user terminal 300.

In another embodiment, when a user requests consultation on the time to purchase a specific asset, the chatbot service provider 250 predicts the purchase time by inputting the asset type into a learning model associated with the dealer and then sends the user terminal 300 to the user terminal 300. provided to.

When the asset management service provider 260 receives an automatic account management request message from the user terminal 300, it determines a dealer matched with the user based on the asset investment consultation request message. That is, the asset management service provider 260 extracts the investment period, rate of return, and asset type from the asset investment consultation request message and then determines the dealer corresponding to the investment period, rate of return, and asset type.

Then, the asset management service provider 260 uses a learning model associated with the dealer to predict at least one of the user's buying and selling times and automatically executes buying and selling.

In one embodiment, when a user requests consultation on the time to purchase a specific asset, the asset management service provider 260 inputs the asset type into a learning model associated with the dealer to predict the time to purchase, and then makes the purchase at the time of purchase. It can run automatically.

In another embodiment, the asset management service provider 260 inputs the purchase time and asset type into a learning model associated with the dealer to predict the selling time, and then automatically proceeds with the sale at the selling time.

Figure 3 is a flowchart for explaining an embodiment of a method for providing social trading services according to the present invention.

Referring to FIG. 3, the social trading service providing server 200 receives transaction information for each asset dealer from a plurality of asset trading platform servers and stores it in the dealer information database (step S310).

The social trading service providing server 200 determines the investment period group of the asset dealer according to the period from purchase to sale based on dealer action information among transaction information for each asset dealer (step S320).

The social trading service providing server 200 determines the rate of return by comparing the market price at the time of purchase and the market price at the time of sale for each investment period group, and classifies asset dealers grouped by investment period group into small groups again according to the rate of return. (Step S330).

The social trading service providing server 200 analyzes transaction information of asset dealers classified into subcategories for each investment period group, extracts transaction patterns, and then creates a learning model that predicts purchase and sell times (step S340 ).

First, the social trading service providing server 200 extracts the types of assets invested by asset dealers classified into sub-categories of each investment period group.

Afterwards, the social trading service providing server 200 represents the transaction price of the asset type invested by the asset dealer on a graph and analyzes the change in the curve between the purchase point and the sale point.

In one embodiment, the social trading service providing server 200 stores the first selling price when the price rises from the time of purchase and the first selling possible point is determined, and then the price falls so that it is not lower than the first selling possible point. However, the time when the purchase price is higher than the above is determined as the second possible sale time.

Afterwards, the social trading service providing server 200 calculates the first average price by applying a weight corresponding to the market situation to the price at the first selling point, then averaging the price, and adjusting the price to the market situation at the second selling point. After applying the corresponding weights, they are averaged to calculate the second average price.

In conclusion, the social trading service providing server 200 calculates the difference price between the first average price and the second average price, compares the difference price and the profit amount, and learns the point when the profit amount increases as the dealer sale prediction point. A learning model can be created.

In addition, the social trading service providing server 200 represents the transaction prices of the asset types invested by the asset dealer on a graph, and analyzes the change in the curve of the transaction price graph during a specific period from before the time of purchase.

In one embodiment, the social trading service providing server 200 stores the first available purchase price when the market price falls on the transaction price graph for a specific period from before the time of purchase and determines the first purchase possible time, and then changes the market price. The point at which the price rises and is higher than the first purchase price by a certain price is determined as the second purchase possibility point.

Afterwards, the social trading service providing server 200 calculates the first average price by applying a weight corresponding to the market situation to the price at the first purchase possible point and averaging the price, and adjusts the price to the market situation at the second possible purchase point. After applying the corresponding weights, they are averaged to calculate the second average price.

In conclusion, the social trading service providing server 200 calculates the difference price between the first average price and the second average price, compares the difference price and the actual purchase amount, and determines the point when the difference price is lower as the dealer purchase prediction point. You can create a learning model by learning.

Figure 4 is a flowchart for explaining another embodiment of a method for providing social trading services according to the present invention.

Referring to FIG. 4, when the social trading service providing server 200 receives an automatic account operation request message from the user terminal 300 (step S410), it determines a dealer matching with the user based on the asset investment consultation request message. (Step S420).

That is, the social trading service providing server 200 extracts the investment period, rate of return, and asset type from the asset investment consultation request message and then determines the dealer corresponding to the investment period, rate of return, and asset type.

Then, the social trading service providing server 200 predicts at least one of the user's buying and selling times using a learning model associated with the dealer and provides the buying and selling times to the user terminal 300 (step S430 ).

Although the present invention has been described with reference to limited embodiments and drawings, the present invention is not limited to the above embodiments, and various modifications and variations can be made by those skilled in the art from these descriptions. Accordingly, the spirit of the present invention should be understood only by the scope of the claims set forth below, and all equivalent or equivalent modifications thereof shall fall within the scope of the spirit of the present invention.

100_1~100_N: Asset trading platform server,
200: Social trading service provision server,
210: Dealer Information Collection Department,
220: dealer information database,
230: dealer classification department,
240: Learning model generation unit for each dealer,
250: Chatbot service provision department,
260: control unit
300: User terminal

Claims (4)

a dealer information collection unit that receives transaction information for each asset dealer from a plurality of asset trading platform servers and stores it in a dealer information database;
Based on the dealer action information among the dealer's transaction information, the asset dealer's investment period group is determined depending on which investment period range the period from purchase to sale belongs to, and the market price and sale at the time of purchase for each investment period group. A dealer classification unit that determines the rate of return by comparing market prices at the time and classifies the asset dealers grouped by investment period group into small groups again according to the rate of return;
Extracts the asset types invested by asset dealers classified into sub-categories for each investment period group, expresses the transaction prices of the asset types invested by asset dealers on a graph, and shows the change in curve between the time of purchase and the time of sale. Analyzing, if the market price rises from the point of purchase and the first sale possible point is determined, the first sale possible price is stored, and then the market falls and is lower than the first sale possible point, but the second sale point is higher than the purchase price. Determine the possible point in time, apply the weight corresponding to the market situation to the price at the first possible selling point, calculate the first average price by averaging, and apply the weight corresponding to the market situation to the price at the second possible selling point. A learning model is calculated by averaging to calculate the second average price, calculating the difference price between the first average price and the second average price, comparing the difference price and profit amount, and learning the point when the profit amount increases as the dealer sale prediction point. A learning model generation unit for each dealer that generates a; and
When receiving an asset investment consultation request message from the user terminal, the investment period, rate of return, and asset type are extracted from the asset investment consultation request message, then the dealer corresponding to the investment period, rate of return, and asset type is determined, and the user purchases a specific asset. When requesting consultation at the time of sale later, the chatbot service provider includes a chatbot service provider that inputs the purchase point and asset type into a learning model associated with the dealer to predict the time to sell and provides it to the user terminal. to do
Social trading service provider server. delete In the method of providing social trading services executed on a social trading service provision server,
Receiving transaction information for each asset dealer from a plurality of asset trading platform servers and storing it in a dealer information database;
Determining the investment period group of the asset dealer according to the period from the time of purchase to the time of sale based on dealer action information among the transaction information for each asset dealer;
Determining the rate of return by comparing the market price at the time of purchase and the market price at the time of sale for each investment period group, and classifying the asset dealers grouped by investment period group into small groups again according to the rate of return;
Analyzing transaction information of asset dealers classified into subcategories for each investment period group, extracting transaction patterns, and then generating a learning model that predicts purchase and sale times,
The step of creating the learning model is
Upon receiving an asset investment consultation request message from the user terminal, extracting the investment period, rate of return, and asset type from the asset investment consultation request message, and then determining a dealer corresponding to the investment period, rate of return, and asset type;
When a user requests consultation on the time to sell after purchasing a specific asset, the step of inputting the purchase time and asset type into a learning model associated with the dealer to predict the selling time and providing the information to the user terminal. doing
How to provide social trading services. delete