KR102012373B1 - Method for auto-trading based on searching stock item by the stock trading server - Google Patents

Abstract

The present invention discloses a method for auto-trading based on stock item search performed by a stock trading server. The method comprises the steps of: searching for candidate investment target item using transaction history information of a stock item and stock distribution status information; analyzing the searched at least one candidate investment target item to calculate a target investment item, which is expected to have increase in stock price; and executing stock trading of the target investment item. According to the present invention, the method may search for items, which deviate from the typical relationship between stock price and trading volume, and trade item according to split automatic trading technique.

Description

Automatic trading based on stock searches performed by stock trading servers {METHOD FOR AUTO-TRADING BASED ON SEARCHING STOCK ITEM BY THE STOCK TRADING SERVER}

The present invention relates to an automatic trading technique based on stock search performed by a stock trading server, and more particularly, by a stock trading server that selects stocks through search and analysis, and automatically trades selected stocks. The present invention relates to an automatic trading technique based on the item search performed.

A traditional securities firm visits a securities firm by using a stock trading method to prepare and trade a transaction slip, an ARS telephone call requesting a telephone call, and a home trading system (HTS) transaction and online mobile trading system (MTS). ) Transactions (hereafter referred to as online transactions).

Among the online transactions, the HTS transaction is a transaction through a computer program called HTS program on a PC platform, and the MTS transaction is a transaction through a mobile application called a securities company application on a smartphone platform.

The automated trading program refers to a program that places orders for lions and sellers instead of humans in online stock trading on a PC or smartphone platform. And even in the case of using the automatic trading program may include a part of human judgment.

Even when trading stocks in accordance with the automatic trading program using the automatic trading program, it is entirely up to the investor to choose the type of investment. Existing HTS or MTS provides charting function including various supplementary indicators to help investors select stocks. For example, the Relative Strength Index (RSI) is an index based on the rise and fall of the stock price and shows a strong trend at 50% or above and a weak trend at 50% or below.

The disadvantage of RSI, however, is its low utilization as an indicator to predict the future. To solve this problem, there is an On-Balance Volume (OBV) indicator based on the volume of trading that precedes the stock price. OBV is an indicator created by using the difference in fluctuating trade volume. The downside of OBV is that the OBV indicator may rise in the face of falling stock prices. To solve this problem, there is a Money Flow Index (MFI) indicator that uses both stock price and trading volume, which is made by multiplying the typical price by the trading volume. However, since MFI is based on typical prices, atypical cases where volume does not increase significantly despite share price rises and falls are not suitable for MFI.

According to the automatic trading technique based on the item search according to an embodiment of the present invention, and the stock trading device and server using the same, the user does not dwell on fluctuations in the stock price, but the trade volume does not change even though the stock price fluctuates. It is possible to search candidate stocks with investment momentum with upside reversal momentum, to select target stocks among the searched stocks and to execute trades based on pre-set conditions, thus avoiding brain trading. It is possible to decide whether to place an order or execute an order of additional splitting according to the trend of execution. In this regard, the automatic trading technique based on the item search according to an embodiment of the present invention, and the stock trading device and server using the same, are distinguished from the above-described prior art and disclosed to solve the problems of the prior art.

Korean Unexamined Patent Publication No. 2002-0073864 (2002.09.28.)

The problem to be solved by the present invention is to provide a stock trading device, server and system using the automatic trading technique based on the item search that deviates from the typical relationship between the stock price and the trading volume.

Another object of the present invention is to provide an automatic trading technique based on a stock search showing a collection pattern, and a stock trading device, a server, and a system using the same.

Another object of the present invention is to provide an automatic trading technique based on a search for an item which can be automatically sold and sold to an investment target target item, and a stock trading device, a server, and a system using the same.

According to an embodiment of the present invention, an automatic trading technique based on a stock search includes an investment target candidate search using stock history information and stock distribution status information in the automatic trading technique performed by a stock trading apparatus. Requesting the server; Selecting an investment target target item for which a stock price increase is expected from among items calculated by server analysis of the searched at least one candidate investment item; And requesting stock trading of the investment target target item.

In this case, the requesting of the target candidate search for the server may be performed by requesting a search for the candidate candidate stocks showing a ratio of the trading volume to the stock price that is low compared to other stocks due to the acquisition by a specific buyer. It is characterized by.

In this case, the requesting the server to search for the candidate candidates may include: a transaction amount in a section in which a stock price fluctuates out of a certain range and a volume in a section in which the stock price is conversed within a certain range in a purchase process by a specific buyer. It is characterized by requesting to search for the stocks that are less than other stocks as the ratio indicated by the investment candidate stocks.

The step of requesting the server to search for a candidate candidate item may include using at least one transaction history information among information on a buyer, a purchase period and a buying cycle, a securities firm buying window, and a cumulative purchase amount. .

In this case, the requesting of the target candidate stock search server may include selecting a stock having at least one stock characteristic among a lower market cap, a smaller number of stocks, a high share ratio of major shareholders, and a theme stock issue. It is characterized by requesting a search.

Here, the step of selecting the target investment target, characterized in that for selecting stocks expected to rise due to additional purchases among the stocks showing the pattern of purchase by a particular buyer.

In the selecting of the target investment target item, a weighted score is assigned to the collection pattern and the item characteristic of the candidate investment target item, and the item having the highest score through the sum of the scores is selected. It is characterized in that the selection as a target target investment target.

Here, the step of selecting the target target investment target is analyzed according to a machine learning algorithm based on a learning result of a data set created by using transaction history information of a case in which a stock price has risen through the collection of small stocks. The stocks analyzed as including the stock pattern and the stock features having high reliability are selected as the target stocks for investment.

Here, the requesting the stock trading may include determining a stock trading time point; A test trading step of placing an order of a split trade for a predetermined number of times for the target investment item at the determined stock trading time point; Reporting on the divided trades concluded in accordance with the test trade and requesting prior approval for the remaining trades other than the test trade; And a main trading step of placing an order for a part or all of the remaining trades according to the pre-approval result, and if there is no feedback on the request, only the divided trades for the preset trades in the test trade It is characterized in that the order, and do not order the remaining split sales other than the test trade.

Here, the determining of the stock trading time point, the automatic trading conditions to determine the point of purchase based on the trading conditions, or to determine the selling point on the basis of the average purchase price of the specific buying entity to buy the target target investment item Determining; And generating a selling signal when the current situation satisfies the automatic selling condition.

According to an embodiment of the present invention, an automatic trading technique based on a stock search, in the automatic trading technique performed by a stock trading server, uses a trading history information and stock distribution status information of a stock item to select a candidate item for investment. Searching; Analyzing the searched at least one candidate investment item to calculate a target investment item for which a stock price increase is expected; And executing the stock trading of the investment target target item.

The searching of the candidate stocks for investment may include searching the stocks for the investment target candidates, which are low in comparison with other stocks due to the collection by a specific buyer, and which show a ratio of the trading volume to the variable stock price. .

The searching of the candidate stocks to be invested may include a difference between a trading volume in a section in which a stock price fluctuates out of a certain range and a trading volume in a section in which the stock price is conserved within a certain range in a purchase process by a specific buying entity. The item having a ratio lower than that of other items may be searched as the candidate item for investment.

The searching for the candidate item for investment may include using at least one transaction history information among information on a buyer, a purchase period and a buying cycle, a securities company buying window, and a cumulative purchase amount.

The searching for the candidate stocks may include: searching for stocks having at least one or more stocks among the market caps, low circulation stocks, high shareholder ratios, and theme stock issues. It features.

Here, the step of calculating the target investment target product, characterized in that to calculate the stock price of the stock price expected to increase due to additional purchases among the stocks showing the buying pattern by the particular buyer.

The calculating of the target investment target item may include assigning a weighted score to a collection pattern and an item characteristic of the candidate investment target item, and obtaining the item having the highest score through the sum of the scores. It is characterized by calculating the target target investment.

Here, the step of calculating the target target investment target, based on the learning results of the data set created by using the transaction history information of the case where the stock price rose through the collection of a small number of distribution stocks, according to the machine learning algorithm, The item analyzed as having a buy pattern and a stock feature having a high reliability is calculated as the target investment item.

Here, the step of executing the stock trading of the target investment target stock, determining a stock trading time point; A test trading step of executing a split trade for a predetermined number of times for the target investment item at the time of the stock trading; Reporting on the split trade concluded in accordance with the test trade and requesting prior approval for the remaining split trades other than the test trade; And a main trading step of executing a part or all of the remaining trades according to the pre-approval result, and if there is no feedback on the request, executes only the divided trades for the preset trades in the test trade. It is characterized in that the remaining splitting trades other than the test trades are not executed.

Here, the determining of the stock trading time point, the automatic trading conditions to determine the point of purchase based on the trading conditions, or to determine the selling point on the basis of the average purchase price of the specific buying entity to buy the target target investment item Determining; And generating a selling signal when the current situation satisfies the automatic selling condition.

Stock trading device based on the stock search according to an embodiment of the present invention, the search module for using the transaction history information and stock distribution status information of the stock item to request the investment target candidate item to the server; A selection module for selecting an investment target target item, which is expected to increase in price, from among items calculated by server analysis of the searched at least one candidate investment item; And a transaction request module for requesting stock trading of the target investment item.

Here, the search module uses at least one or more transaction history information among information on buyer status, purchase period and purchase cycle, securities firm buying window, and cumulative purchase amount, and has a lower market capitalization, a smaller number of circulation stocks than other stocks, The stocks having at least one or more of the characteristics of the major shareholders and the theme stock issues may be searched for as candidate candidates.

The stock trading device based on the stock search includes: an operation module configured to calculate at least one of an average purchase price, an average selling price, a buying amount, and a selling amount of a specific buying entity purchasing the target investment item; A signal generation module for generating a buy signal and a sell signal based on at least one of the average buy price, average sell price, buy amount, and sell amount; And an automatic trading order module for ordering automatic selling of investment target target stocks according to the buying signal and selling signal.

Here, the transaction request module, the automatic trading conditions determination module for determining the stock trading time point in accordance with the automatic trading conditions determination; Automatically placing an order for a test trade before the main trade for the target investment item at the determined stock trading time, reporting the conclusion of the transaction of the test trade, and placing the order for the main trade according to the result of the pre-approval request of the main trade. Including a trading order module, the automatic trade order module, if there is no feedback on the request, the order only the test trade, characterized in that not ordering the main trade other than the test trade.

Stock trading server based on the stock search according to an embodiment of the present invention, the search unit for searching the investment target candidates by using the transaction history information and stock distribution status information of the stock item according to the request of the user terminal; A calculator configured to analyze the searched one or more candidate investment items and calculate a stock price expected to rise; And a transaction execution unit that executes stock trading of the target investment item.

Here, the search unit searches for stocks that have a relatively low trading volume ratio compared to other stocks in the buying process by a particular buying entity as the candidate items for investment, and the stock price is fixed during the buying process by a specific buying entity. The investment target candidates may be searched for stocks having a ratio lower than that of other stocks, in which a difference between the trading volume and the stock price in a section that is conversed within a certain range is out of range.

Here, the calculation unit is analyzed according to the machine learning algorithm based on the learning result of the data set created using the transaction history information of the case in which the stock price rose through the collection of small stocks, the number of purchase patterns having the highest reliability and The item analyzed as including the item feature is calculated as the target item for investment.

Herein, the transaction execution unit may include an automatic trading condition determination unit determining a stock trading point of time according to the automatic trading condition determination; Executing a test trade before the main trade for the target investment item at the determined stock trading time point, reporting the conclusion of the transaction of the test trade, and executing the main trade according to the result of the pre-approval request of the main trade Including the unit, the automatic trade execution unit, if there is no feedback for the request, characterized in that only running the test trade, not the main trade other than the test trade.

In a stock trading system based on stock search according to an embodiment of the present invention, a system including a user terminal and a server that executes a stock transaction by receiving an order from the user terminal may perform a trading order according to a buy signal or a sell signal. A user terminal; A stock trading server that executes trading in accordance with the trading order; And an exchange server that reflects the results of the execution of the trading by a plurality of stock trading servers, wherein at least one of the user terminal and the stock trading server uses the trading history information and the stock distribution status information of the stock item according to the user's request. A search unit for searching candidate investment items; A selection unit which selects an investment target target item, which is expected to rise in price, through analysis of the searched at least one candidate candidate item; And a stock trading unit trading a stock of the target investment target item, wherein the stock trading unit comprises: an automatic trading condition determination unit determining a stock trading time point according to an automatic trading condition determination; At the time of the determined stock trading, an order of a test trade is placed before the main trade for the target investment item, a transaction of the test trade is reported, and the order of the main trade is made as a result of the request for prior approval of the trade. Including an automatic trading order unit, the automatic trading order unit, if there is no feedback on the request, it is characterized by ordering only the test sales, not ordering this trade other than the test sales.

A non-transitory computer readable storage medium storing a program having stored instructions computed to perform an automatic trading technique based on a stock search according to an embodiment of the present invention, when executed by a computing device, Requesting a server to search for candidate candidate items using transaction history information and stock distribution status information; Selecting an investment target target item for which a stock price increase is expected from among items calculated by server analysis of the searched at least one candidate investment item; And requesting stock trading of the target investment target item, wherein the requesting stock trading comprises: determining a stock trading time point according to an automatic trading condition determination; Placing an order for a test trade before the main trade for the target investment item at the determined stock trading time point, reporting a conclusion of the transaction of the test trade, and placing the order for the main trade according to the result of the pre-approval request of the main trade Including, but if there is no feedback for the request, and ordering only the test trade, characterized in that not ordering the sale other than the test trade.

A non-transitory computer readable storage medium storing a program having stored instructions computed to perform an automatic trading technique based on a stock search according to an embodiment of the present invention, when executed by a computing device, Searching for candidate investment items using transaction history information and stock distribution status information; Analyzing the searched at least one candidate investment item to calculate a target investment item for which a stock price increase is expected; And executing the stock trading of the target investment target item, wherein the executing the stock trading comprises: determining a stock trading time point according to an automatic trading condition determination; Executing a test trade before the main trade for the target investment item at the determined stock trading time point, reporting the conclusion of the transaction of the test trade, and executing the main trade in accordance with a result of the pre-approval request of the main trade However, if there is no feedback for the request, it is characterized in that only the test trade is executed, and the main trade other than the test trade is not executed.

According to the present invention, it is possible to search for stocks that deviate from the typical relationship between stock price and trading volume, and to trade the stocks according to the split automatic trading technique.

In addition, you can search for stocks showing the pattern of buying, trading the stocks according to the split automatic trading technique.

In addition, the stock may be sold by dividing the stock according to the predetermined tightening amount, the closing price, and the fastening mode by using the buy signal or the sell signal.

1 is an illustration of a stock trading system according to an embodiment of the present invention.
2 is a block diagram of a stock trading apparatus according to an embodiment of the present invention.
3 is a block diagram of a stock trading server according to an embodiment of the present invention.
4 is a block diagram of a calculator according to an exemplary embodiment.
5 is a block diagram of a computing device corresponding to a stock trading device and a server according to an embodiment of the present invention.
6 is a flowchart illustrating an automatic trading technique based on item search according to an embodiment of the present invention.
7 is a flowchart illustrating an automatic trading technique based on item search according to an embodiment of the present invention.
8 is an illustration of a quotation window of an online stock trading.
9 is a flowchart illustrating operations S131 or S231 according to an embodiment of the present invention.

Hereinafter, with reference to the accompanying drawings will be described in detail the preferred embodiment of the automatic trading method based on the item search, the apparatus using the same, a server and a system including the same.

Like reference numerals in the drawings denote like elements. In addition, specific structural to functional descriptions of one embodiment of the present invention are only illustrated for the purpose of describing the embodiment according to the present invention, and unless otherwise defined, all terms used herein including technical or scientific terms are used. The terms have the same meaning as commonly understood by one of ordinary skill in the art. Terms such as those defined in the commonly used dictionaries should be construed as having meanings consistent with the meanings in the context of the related art, and are not construed in ideal or excessively formal meanings unless expressly defined herein. It is preferable not to.

Hereinafter, a stock trading system 1000 including a stock trading device 100 and a server 200 based on a stock search according to an exemplary embodiment will be described.

1 is an illustration of a stock trading system according to an embodiment of the present invention.

Referring to FIG. 1, the stock trading system 1000 includes a stock trading device 100, a stock trading server 200, an exchange server 300, and a network 400.

The stock trading device 100 searches for investment target candidates using the automatic trading technique (S100, S300) based on the item search according to the date and time of the present invention, selects a target investment target from the searched results, and generates The stock transaction server 200 requests a stock transaction of the target investment target selected according to the purchased buy signal or sell signal.

In addition, the stock trading device 100 may receive the conclusion information about the conclusion of the trade by the relay of the stock trading server 200 or directly from the exchange server 300. The fastening information includes the fastening amount and fastening amount of the stocks according to the market type. The conclusion information is provided to the stock trading device 100 as a table or as a chart of various time slots.

The stock trading server 200 searches for candidate investment target items according to a request of the stock trading device 100, calculates a target investment item from the searched results, and selects a target investment item selected by the stock trading device 100. Run stock trading.

The stock trading server 200 is a system including a single device or a plurality of devices, and corresponds to a set of securities company trading systems operated by a plurality of subjects.

The exchange server 300 is operated by a stock exchange representing each country and serves to finally settle a stock trade. Exchange server 300 is a system that includes a single device or a plurality of devices in the case of Korea KRX trading signing system corresponds to this.

Network 400 may be a wired and wireless network, such as a local area network (LAN), a wide area network (WAN), the Internet, an intranet and an extranet, and a mobile network, such as It may be any suitable communication network, including cellular, 3G, LTE, WiFi networks, ad hoc networks, and combinations thereof.

Network 400 may include a connection of network elements such as hubs, bridges, routers, switches, and gateways. Network 400 may include one or more connected networks, such as a multi-network environment, including a public network such as the Internet and a private network such as a secure corporate private network. Access to network 400 may be provided through one or more wired or wireless access networks.

According to an embodiment of the present invention, the stock search, the stock selection, and the stock trading performed by the stock trading apparatus 100 may be performed according to the interworking with the stock trading server 200. For example, when the stock trading device 100 performs a single item search, stock calculation and selection, and requests the stock trading server 200 for the stock trading of the selected target investment target stock, the stock trading server 200 is the investment target. Execute stock trading of target stock.

As another example, when the stock trading device 100 performs a stock search, a stock selection through the stock trading server 200, and requests the stock trading server 200 for a stock transaction of the selected target investment target stock, the stock trading server 200. ) Executes the stock trading of the target stock.

In the above process, the stock trading device 100 performs the automatic trading method based on the item search (S100, S300), the stock trading server 200 performs the automatic trading method (S200, S300) based on the item search do.

Hereinafter, a role of the components of the stock trading device 100 and the stock trading server 200 will be described.

2 is a block diagram of a stock trading apparatus according to an embodiment of the present invention.

Referring to FIG. 2, the stock trading device 100 includes a search module 110, a selection module 120, a trading signal module 130, an operation module 131, and a signal generation module 132. , The automatic trade condition determination module 141, the automatic trade order module 142, and the report and approval request module 150.

Each component included in the stock trading device 100 directly performs a corresponding function, or generates a corresponding command to receive a corresponding function, for example, a stock search function and a stock selection function from the stock trading server 200. It is responsible for receiving and displaying the data.

The search module 110 searches for candidate investment items. The search module 110 may be implemented as a search engine that receives a search expression according to a search request of a user, or searches for an item by using a pre-prepared search expression regarding a item requested by the user.

The selection module 120 selects an investment target target item through item analysis among at least one or more candidate investment target items obtained as a result of the search.

The trading signal module 130 generates a buying signal or a selling signal using various indicators and charts. The trading signal module 130 includes a calculation module 131 and a signal generation module 132.

The calculation module 131 calculates an average purchase price, an average selling price, a stock purchase amount, and a stock sale amount of a specific purchase entity that has purchased the target investment item. Here, the average purchase price, the average selling price, the stock purchase amount, and the stock sale amount may be an indicator for calculating the purchase signal generation, the sale signal generation, the sale quantity, and the purchase quantity in relation to the purchase, the sell, and the loss price. For example, the average purchase price is a direct basis for calculating loss value. In addition, the selling price of the user may be determined according to the expected rate of return from the average purchase price. In addition, the investment amount of the user may be determined according to the purchase amount, and the timing at which the user sells the entire stock and terminates the investment may be determined according to the sale amount. We can anticipate when certain so-called buyers will exit.

The signal generation module 132 generates a trading signal, that is, a buying signal and a selling signal when the automatic trading condition is satisfied according to the determination of the automatic trading condition determining module 141. In addition, the signal generation module 132 generates a buy signal and a sell signal based on the average purchase price, average selling price, stock purchase amount, and stock sale amount of the specific buying entity calculated by the calculation module 131.

The transaction request module 140 includes an automatic trading condition determination module 141 and an automatic trading order module 142.

The automatic trading condition determination module 141 determines the automatic trading conditions set in advance for the stock trading of the target item for investment. When the investment target target item is determined by the selection module 120, the automatic trading condition is determined based on this.

The automatic trade order module 142 may order the trade of the stock of the corresponding item into the test trade and the main trade if the automatic trade conditions are satisfied. Here, the test trade and the main trade may be ordered by dividing the circuit again. After the test trade, the automatic trade order module 142 may order the main trade, do not order, or order only the partial trade of the basic round depending on whether the feedback on the approval request of the main trade and the approval disapproval. That is, if there is no feedback, the automatic trade order module 142 may order only a predetermined number of divided trades.

The reporting and approval request module 150 reports on the executed test trade and requests approval for the next round of split trade. That is, the reporting and approval request module 150 requests the user to approve the order for placing the order of the sale. For example, after the first split trade (test trade) is executed according to the order, the report and approval request module 150 requests the user to report on the executed test trade and approve the main trade. If the user approves the request, the main sale is executed through the order by the automatic sale order module 142.

3 is a block diagram of a stock trading server according to an embodiment of the present invention.

Referring to FIG. 3, the stock trading server 200 includes a search unit 210, a calculation unit 220, a trading signal unit 230, an operation unit 231, a signal generator 232, and a transaction execution unit 240. , The automatic trade condition determination unit 241, the automatic trade execution unit 242, and the report and approval request unit 250. The components of the stock trading server 200 are linked with the components of the stock trading apparatus 100 to provide a corresponding function.

The search unit 210 searches for candidate investment items. The search unit 210 may be implemented as a search engine for searching items by using the received search expression.

The calculator 220 calculates an investment target target item among candidate investment target items obtained as a result of the search. The calculation of the target investment target item performed by the calculation unit 220 is a task of finally determining an investment target target item that is expected to rise in price among the retrieved items using past history information and stock distribution status information. Accordingly, in the item calculation, a weighted score is applied to the collection pattern and the item feature among the searched items, and the item having the highest score through the sum of the scores may be calculated as the investment target item.

In addition, the weighted scoring process may be performed through an artificial intelligence algorithm. This will be described later with reference to FIG. 4.

The trading signal unit 230 generates a buying signal and a selling signal according to the determination of the automatic trading condition determining unit 241 and the calculation result of the calculating unit 231. The buy signal and the sell signal are transmitted to the stock trading device 100 corresponding to the user terminal, and the user determines the buy point and the sell point through the buy signal and the sell signal.

The trading signal unit 230 includes a calculator 231 and a signal generator 232.

The calculating unit 231 calculates the average purchase price, average selling price, stock purchase amount, and stock sale amount of the specific purchase entity who purchased the target investment item. In these calculations, various stock price and volume indicators are used. Here, the average purchase price, the average selling price, the stock purchase amount, and the stock sale amount may be an indicator for determining a buy signal and a sell signal in relation to the purchase amount and the sell amount on the user side.

The signal generator 232 generates a buy signal and a sell signal when the automatic selling condition is satisfied according to the determination of the automatic trading condition determining module 241 and the calculation result of the calculating unit. In particular, the signal generation module 232 generates a sell signal based on the average purchase price and the stock purchase amount of the specific buying entity calculated through the calculation module 231.

The transaction execution unit 240 includes an automatic trading condition determining unit 241 and an automatic trading execution unit 242.

The automatic trading condition determining unit 241 determines a pre-set automatic trading conditions for the stock trading of the target target investment. When the target item for investment is determined by the calculator 220, the automatic trading condition is determined based on the target item.

The automatic trading execution unit 242 may divide the trading of the stock of the corresponding item into a test trade and the main trade if the automatic trade conditions are satisfied. Here, the test trade and the main trade may also be divided into a plurality of circuits. After the test trade, the automatic trade execution unit 242 may execute the main trade or not, or may execute only the partial trade of the basic round, depending on whether or not to give feedback on the approval request of the main trade. In other words, when there is no feedback, the automatic trade execution unit 242 may execute only a predetermined number of divided trades.

The reporting and approval request unit 250 requests the approval of the main sale with or after the report on the executed test sale. That is, the report and approval request unit 250 requests the user to approve in advance for placing and executing the order of the sale. For example, after the test trade is executed according to the order, the report and approval request unit 250 requests the user to report on the test trade that has been executed and approve the main trade. If the user approves the request, the automatic trade execution unit 242 executes the main trade.

4 is a block diagram of a calculator according to an exemplary embodiment.

Referring to FIG. 4, the calculator 220 includes a data input module 221, a data preprocessing module 221, a feature engineering module 223, a machine learning model generation module 224, and a machine learning model learning module 225. And machine learning model evaluation module 226.

Without the help of the stock trading server 200, in order for the stock trading apparatus 100 to independently perform functions performed by each component included in the calculator 220, the selection unit 120 also calculates 220 It may include the same components as).

The calculator 220 generates a data set to be used for model learning and evaluation through data input, data preprocessing, and feature engineering. In addition, the calculator 220 generates a machine learning model that calculates a final target investment target item among the searched at least one candidate investment target item. The generated machine learning model learns the data set generated above, and when a certain level of learning is achieved through model evaluation, the target item to be invested is calculated using the learning ability.

The data input module 221 collects raw data necessary for generating a data set of machine learning learning data from the exchange server 300. The raw data includes data on transaction history information and stock distribution status information of stock items.

The data preprocessing module 222 preprocesses the raw data and generates a data set. The data set includes the features of the buying pattern with stocks and the features of stock distribution with stocks.

The feature engineering module 223 performs feature engineering using raw data. Feature engineering is the process of adding information to data using existing variables. It is one of the methodologies that makes existing data more useful without adding new observations or variables.

Among these methods, scaling is used when the unit of a variable is desired, or when the distribution of the variable is biased, and the relationship between the variables is not easily revealed. The most frequently used method is the Log function, and a similar but less frequently used square root.

The binning method is to make continuous variables into categorical variables. For example, if there is a numerical data representing the trading volume (volume / variable price) against the variable price, it is converted into categorical variables in the form of 10 to 99, 100 to 999, and 1000 to 9999. Since binning does not have special principles, it can be binned in creative ways, depending on the analyst's understanding of the business.

The transform method uses a property of an existing variable to pick up another variable. For example, if you have a data figure that represents the volume (trade volume / variable price) of a variable stock price by date, you can convert the data number into a day variable, such as Friday before the weekend starts or Monday after the weekend ends, You can convert data values into variables before, during, and after a chapter.

There are no special principles in transforms, and various variables can be created depending on the analyst's understanding of the business.

The dummy method is used to convert categorical variables into continuous variables as opposed to binning. It is mainly used when necessary in the analytical methodology to be used.

As described above, the feature engineering module 223 according to an embodiment of the present invention performs feature engineering through a scaling, binning, transformation, or dummy method, and through this process, Identify features from raw data. And a data set is created to include the discovered features.

The machine learning model generation module 224 generates a machine learning model to be trained using the preprocessed data set from the raw data. The machine learning model learns stock patterns and the characteristics of stocks for stocks that have been trapped in the box for a while. Here, the pattern of buying that appears mainly refers to the type of buying in which the trading volume does not show a big difference from the trading volume in the box despite the rise and fall of the stock price.

In this case, even if the stock price is vacant and the stock price fluctuates due to the gap rise or fall, the transaction volume does not increase more than a certain ratio.

The machine learning model training module 225 trains the generated machine learning model using a data set including the features according to the feature engineering performance.

The machine learning model evaluation module 226 evaluates the reliability of the learned machine learning model. The learning process of the machine learning model continues until a certain level of reliability is obtained.

So far, the components included in the calculator 220 of the stock trading server 200 have been described.

When the stock trading device 100 of FIG. 2 performs calculation and selection process of an investment target target item independently without interworking with the stock trading server 200, the selection module 120 includes the components of FIG. 4.

Stock trading apparatus 100 according to an embodiment of the present invention may be implemented in the form of a user terminal 100 in a relationship with the stock trading server 200.

The user terminal 100 is a communication device capable of wired and wireless communication capable of performing the functions of the computing device 500. The user terminal 100 is a dedicated device for placing a buy and sell order for stock trading on the stock trading server 200 and receiving information for stock trading, a general purpose computing device, a network terminal, and a wireless device. It includes a terminal (wireless terminal).

Various embodiments of a wireless terminal include a cellular telephone, a smart phone having a wireless communication function, a personal digital assistant (PDA) having a wireless communication function, a wireless modem, a portable computer having a wireless communication function, and a digital camera having a wireless communication function. Portable units or terminals incorporating combinations of such functions as well as photographing devices, gaming devices with wireless communication capabilities, music storage and playback appliances with wireless communication capabilities, internet appliances with wireless internet access and browsing But may include, but are not limited to.

The components of the stock trading device 100 and the stock trading server 200 depicted in FIGS. 2 and 3 may be implemented in a single hardware form, in combination with each other or in combination with different hardware. Each hardware includes a set of executable computer instructions for the automatic trading technique (S100) based on the item search according to an embodiment of the present invention.

Also, the stock trading device 100 and the stock trading server 200 may be implemented in a form in which a computing device 500 including a processor and a memory, an operating system and software for managing and operating files thereof are combined. Can be.

The operating system is a computer operating program for managing file systems, various interfaces, and controlling operations of components of the stock trading device 100 and the server 200. The operating systems of the stock trading device 100 and the server 200 include, by way of example, the operating systems of wired and mobile devices, such as MS Windows, MS Server, Unix, Linux, XOS, Android, iOS operating systems, However, it is not limited thereto.

Each of the components of FIGS. 2 and 3 may correspond to a functional component of software loaded on a memory. Here, the software will be referred to as stock trading application module 10 or stock trading server module 20.

Stock trading apparatus 100 according to an embodiment of the present invention corresponding to the user terminal 100 is encoded in a computer instruction set to perform the automatic trading technique (S100) based on the item search according to an embodiment of the present invention A file containing the stock trading application module 10 may be stored, installed, and executed.

The user terminal 100 may download the stock trading application module 100 that is not installed from the stock trading server 200, the FTP server, a file server, or a NAS, and store, install, and update the user terminal 100 in the user terminal 100.

The stock trading server 200 corresponds to an application server driving the stock trading server module 20 operating in conjunction with the stock trading application module 10 and a web server providing a stock trading service through the web.

When the user terminal 100 is in the online state, the stock trading server 200 receives an order from the user terminal 100 to execute a transaction, even if the user terminal is not in the online state by the user terminal 100. Transactions can be executed according to predefined preferences.

In addition, the stock trading server 200 may function as a database server including various stock information and transaction data provided to the user terminal 100.

Each component of the stock trading device 100 and the stock trading server 200 illustrated in FIGS. 2 and 3 is a functionally divided configuration for convenience of description, and is logically processed by one processor in hardware. It can be configured as a phosphorus function, it does not limit the invention by the presented division.

In addition, although the connection relationship between each configuration is shown as a connection line, or the connection relationship between each other is not expressed individually, communication or transmission for control or data exchange may occur even between the configuration where the connection line is not shown, and only as shown. It does not limit the invention.

The stock trading device 100 and the server 200 according to an embodiment of the present invention may correspond to the computing device 500. Hereinafter, the stock trading device 100 and the server 200 according to an embodiment of the present invention corresponding to the computing device 500 may be described in more detail through other embodiments.

The stock trading device 100 and the server 200 may include a computing device 500 including one or more central processing units (CPUs), memory, mass storage, an input interface device, and an output interface device. The components of computing device 500 may communicate with each other via a bus.

The hardware platform of computing device 500 may be a distributed computing environment, such as a cloud, including a personal computer, handheld or laptop device, a multiprocessor system, a microprocessor based system, a program appliance, and any of the above systems or devices. It can be implemented in many forms, including an underlying computing system.

5 is a block diagram of a computing device corresponding to a stock trading device and a server according to an embodiment of the present invention.

Referring to FIG. 5, the computing device 500 may include an input interface device 510, an output interface device 520, a memory 531, a storage device 532, a power supply 540, a processor 550, and a network interface. Device 560, wireless communication device 570, and bus 580.

The input interface device 510 inputs characters or objects required for stock trading according to a user's input. The input interface device 510 includes, but is not limited to, a keyboard, a touch screen, a mouse, a stylus pen, and a pen tablet.

The output interface device 520 includes a display for displaying a user interface related to the stock trading application module 10 and the like, and a printer for printing and printing a document. In addition, the output interface device 520 includes a speaker, a head-phone, and a headset for outputting text to speech using a text to speech (TTS) engine.

The processor 550 includes a stock trading application module 10 and a server module 20 related to an automatic trading method according to a recommendation item according to an embodiment of the present invention stored in a memory 531 and a storage device 532. Can execute a computer instruction set. The processor 550 may refer to a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which the methods according to the present invention are performed. The memory 531 and the storage device 532 may be configured of a volatile storage medium and a nonvolatile storage medium. For example, the memory 531 may be configured as read only memory (ROM) and random access memory (RAM).

The wireless communication device 570 includes a device for short range wireless communication, wireless data communication, and wireless voice communication.

Each component included in the computing device 500 is connected by a bus 580 to communicate with each other.

Operation of the computer instruction set included in the above-described stock trading apparatus 100 and the stock trading server 200 included in the components of FIGS. 2 and 3 or the stock trading application module 10 and the stock trading server module 20. With reference to Figures 2 to 9 will be described with respect to the automatic trading technique (S100) based on the item search according to an embodiment of the present invention to execute through.

6 is a flowchart illustrating an automatic trading technique based on item search according to an embodiment of the present invention.

Referring to FIG. 6, the automatic trading technique S100 based on the item search according to an embodiment of the present disclosure may be performed by the stock trading apparatus 100 as a subject.

First, the stock trading device 100 searches for an investment target candidate item through the stock trading server 200 (S110). In detail, there is a search request of the stock trading device 100, and the watch transaction server 200 searches for a candidate investment item.

Herein, the candidate item for investment refers to a stock item that you want to buy. In particular, candidate investment items introduced through an embodiment of the present invention refers to candidate stocks that are expected to be additionally bought for the purpose of boosting share prices.

In other words, the stock trading device 100 may search for a candidate investment item that exhibits a buying pattern for securing a quantity by a specific buying entity through the stock trading server 200.

In numerical terms, the stock trading apparatus 100 may search for stocks that have a relatively low trading volume ratio compared to other stocks as candidates for investment in the stocking process by a specific buying entity.

Herein, other stocks refer to general stocks other than candidate stocks for investment. For example, KOSPI may be a virtual stock having an average of KOSPI 200 or a user-specified reference value.

The trading volume (volume / floating price) of the floating stock price is, for example, if one of the KOSPI 200 stocks is traded for about 100 shares without changing the stock price, and the stock is traded while escaping the stock price and rising 100 won. The ratio of trade volume to (1000/100) * 100 = 1000%. In this case, if the target stock is traded for about 100 shares in the box, without trading the stock price, and 150 shares were traded while escaping the box, the stock price would be (150/100) * 100 = 150%. As a relatively low ratio.

In addition, the acquisition pattern of candidate candidates can be represented by using the difference between the volume of the stock price fluctuation and the trade volume in the sideways interval. In the above example, for one of the KOSPI 200 stocks, the ratio of the difference between the trading volume in the section where the stock price fluctuates out of a certain range and the trading volume in the section where the stock price converses within a certain range is (1000-100) / 100 * 100 = 900% and (150-100) / 100 * 100 = 50% for candidate candidates. In other words, investment candidates are relatively low.

The search module 110 searches for stocks using at least one or more pieces of information on a buyer's status, a purchase period and a buying cycle, a securities firm buying window, and a cumulative purchase amount, in order to search for candidate investment items.

For example, in terms of buyers, distinguish between individuals, institutions, and foreigners, especially public funds. Certain buyers may show signs of buying, especially with a few securities firms. The cumulative number of buyers can be expected based on the cumulative volume.

In addition, the search module 110 may search for an item to be invested as a candidate item, which has at least one feature among market items, a low number of circulation stocks, a high share ratio of major shareholders, and theme stock issues. The “item feature” of the candidate item to be invested may be combined with the feature on the collection pattern to form a feature of the stock item.

Next, the stock trading device 100 selects an investment target target item from among candidate investment items through the stock trading server 200 (S120).

As one example, the stock trading device 100 uses the selection module 120 through the stock trading server 200 to assign a weighted score to patterns and features included in the candidate candidates for investment. The item with the highest score through the sum of the scores can be selected as the target item for investment. Here, the weight may be differently set according to the market cap of the stock, the total stock of the stock of the stock, the number of stocks, the type of stock industry, and the type of trading floor. In this case, reference may be made to historical transaction history data of stocks corresponding to similar cases.

In particular, the selection module 120 judges based on the data learning results of the transaction history through a machine learning algorithm, but selects the candidate candidates with the highest frequency among the cases in which the stock price is boosted due to the accumulation of small stocks. The stock having the combination of the patterns and the respective features may be selected as the target stock to be invested.

That is, the selection module 120 receives raw data including past transaction history data and stock item characteristics of the stock item in a case where the stock price is raised due to the collection of small stocks, and preprocesses the data. The data set is generated by analyzing the features in the correlation between the features on the collection pattern and the item features. Create a machine learning model to select the target target stock, train the model through the data set including the analyzed features, and use the model that has been trained above a certain level through the model evaluation. You can choose.

Next, the stock trading device 100 requests the stock trading server 200 for the stock trading of the target investment target item (S130). Here, S130 includes the step of requesting the stock purchase of the target investment target item (S131) and the request of selling the stock of the target investment target item (S132).

Here, the step (S131) of requesting stock purchase of the target investment target item and the request of stock sale (S132) will be described in detail with reference to step S300 of FIG.

The subject in the automatic trading technique (S100) based on the item search according to an embodiment of the present invention is the stock trading device (100). On the other hand, the stock trading server 200 as the main subject can be described in the automatic trading technique (S200) based on the item search according to an embodiment of the present invention.

7 is a flowchart illustrating an automatic trading technique based on item search according to an embodiment of the present invention.

Referring to FIG. 7, an automatic trading technique S200 based on a stock search performed by the stock trading server 200 is depicted.

The automatic trading technique (S200) based on the item search performed by the stock trading server 200 includes steps S210 to S232. And each of the S210 to S232 configuration step is characterized in that performed in conjunction with the configuration step of S100 in response to the request of the stock trading device 100 in step S100. Thus, steps S210 to S232 can be described according to the description of steps S110 to S132.

8 is an illustration of a quotation window of an online stock trading.

Referring to FIG. 8, a quotation window of an item regarding item information of a buy signal or a sell signal received by the stock trading device 100 is depicted.

The bid and ask prices are displayed in the middle column with different background colors of blue and red, and the bid and ask prices are indicated in each bid price. When a transaction is concluded between the buyer and the seller, the closing price and the closing amount are displayed. The minimum bid price is the lowest bid price among the bid prices, and the maximum bid price is the highest bid price among the bid prices. The selling price of the bidding bid and the bidding bidding price of the bidding bid are the selling or buying forces exposed, and the undisclosed selling and buying forces are exposed to stocks or cash with no value. Is in. Also, the closing price may occur at the minimum selling price or the maximum buying price depending on the size of the hidden selling force and the buying force.

Hereinafter, the automatic trading steps S131 and S231 among the automatic trading techniques S100 and S200 based on the item search performed by the stock trading apparatus 100 and the stock trading server 200 will be described in more detail.

Hereinafter, a step (S230) and a step (S330) of requesting an investment target target stock trading will be described in detail using the split automatic trading technique (S300).

9 is a flowchart illustrating operations S130 and S230 according to an embodiment of the present invention.

Referring to FIG. 9, an operation (S300) of requesting or executing an automatic sale of an investment target target stock according to a buy signal or sell signal is described in more detail through steps S310 to S360.

The process of placing an order through the stock trading device 100 and the process of executing a transaction according to the order through the stock trading device 100 may be described through each step of S300. However, for convenience of explanation, the description will be made mainly on the stock trading server 200 that executes a transaction according to the order.

In addition, each step of S300 may be applied to both the case of buying the stock of the target target investment, as well as the case of selling the stock of the target target investment.

The stock trading device 100 accesses the stock trading server 200 and presets an environment setting for automatic trading. When an investment target target item is selected through the item search and selection of the stock trading device 100 and the stock trading server 200, step S300 is performed.

When the automatic trading condition is satisfied, the stock trading server 200 executes the trading of the stock of the corresponding item. Finally, the exchange server 300 makes a payment and transmits the payment information to the stock trading server 200 and the stock trading apparatus 100.

6 and 7, the request for stock trading (S130) or the execution of stock trading (230) may include determining the stock trading timing (S310, S311, S320), and the investment target at the stock trading timing. A test trading step (S330) for executing a predetermined number of divided trades for the target item, reporting the divided trades concluded according to the test trade and requesting prior approval for the remaining divided trades other than the test trade (S340) S350) and a main sales step (S360) of executing a part or all of the remaining divided sales based on the pre-approval result. Each step will be described in detail.

Determining the point of purchase includes determining the automatic trading conditions and generating a trading signal.

First, the stock trading server 200 determines whether the current situation corresponds to a preset automatic trading condition through the automatic trading condition determining unit 241 (S310).

Next, when the current situation corresponds to the automatic trading conditions, the stock trading server 200 generates a trading signal through the signal generator 232. Here, the trading signal includes a selling signal as well as a buying signal.

The automatic trading conditions can be established only if the automatic trading application is premised. When automatic trading is applied to the stock trading server 200 by the stock trading apparatus 100, the automatic trading conditions may be provided in the form of a beginner setting and an expert setting for the user's setting convenience. In the case of the beginner setting, the user completes the rest of the settings while the default options are set in advance, and in the case of the expert setting, the user sets from the beginning to the end.

Automated trading conditions include trading conditions and trading conditions. If the target stocks to be invested are calculated and selected among the back stocks to be invested, and the target stocks to be invested are not excluded from the automatic trading setting, the stock conditions are satisfied. When the target stock is selected for investment, trading conditions are determined.

The trade condition is characterized in that at least one of the trade volume and the transaction price is a condition that is equal to or greater than a set value as compared with before the buy signal or sell signal received. For example, when the closing price at the time of receiving the buying signal is 1 million won, it may be conditioned that the automatic trading is executed when the closing price at the time of receiving the buying signal rises in the range of 2% to 5%. Alternatively, when the transaction volume at the time of reception is 100 shares per minute, it may be conditional that the automatic trading is ordered or executed when the subsequent transaction volume increases to 200%, that is, 200 weeks, compared to the transaction volume at the time of receiving the purchase signal.

As a specific embodiment, automatic trading may be performed when a situation in which at least one of the fastening price and the fastening amount exceeds a set value occurs continuously or discontinuously by a predetermined number of times.

The stock trading apparatus 100 may execute a contract price and a contract amount for a predetermined time period, for example, 5 seconds, based on a tick chart based on the contract information received after a buy signal or sell signal reception time, according to a preset transaction condition. It is possible to condition a split trade to be ordered or executed when a situation in which at least one of the conditions exceeds a set value occurs at a predetermined number of times, for example, at least two times continuously or discontinuously.

The trade terms may include the terms relating to the trade aspect. The condition relating to the aspect of the transaction is characterized in that the purchase closing price is formed at the minimum bid price of the seller among the minimum bid price of the seller and the highest bid price of the buyer. As mentioned above, the closing price is determined by the selling wait amount, the buying wait amount, and the unmarked hidden waiting amount. If the number of hidden bids is higher than the amount of hidden bids, there is a high probability that the bid will be generated at the minimum selling price. On the contrary, if there is a relatively large amount of selling wait, it is more likely to occur at the maximum bid price. In the former case, the share price is likely to rise. In the latter case, the share price is likely to fall. And this situation changes with the change in the closing price. Therefore, the stock trading apparatus 100 may attach a condition that the closing price occurs at the minimum selling price as an automatic selling condition at the time of buying. Conditions regarding the transaction aspect are basically determined at the time of the first round sale, and after the second time, the sale of the sale at the respective round may be ordered or executed without judgment.

If the automatic trading condition is satisfied, the stock trading server 200 generates a trading signal, that is, a buying signal of the target target item through the signal generator 232 (S320).

Next, an order of the test trade is generated and executed in the N times of the split trade for the target investment target item (S330). N represents the recovery of splitting media. Basically two to three or more times can be specified in advance. N split trades, in turn, include test trades and real trades. Here, the test trade and the main trade each include at least one split trade. After the test sale, the result of the report and the request for approval determine whether or not to execute the sale.

In one embodiment, in the case of the two-part split trading, when the automatic trading condition is satisfied, the first test trade may be ordered or executed at the first quantity or the first ratio. It is possible to place an order or execute a sale at a second quantity or a second rate. In this case, since the first test sale is a split sale of a test character, the order of the split sale can be placed in a relatively small amount or a small ratio, and after the report, the remaining shares can be allocated to the second sale authorized by the user.

In addition, the test trade order or execution step (S330), characterized in that after performing the first round of sales in a predetermined number of cycles in a predetermined quantity or a ratio for the next round of sales in order. For example, after the first sale, the second sale may be ordered or executed after a certain period.

The stock trading device 100 may order a partial purchase by setting an order amount according to a changing situation according to a predetermined environment setting for an automatic sale. For example, the stock trading device 100 may make an order amount proportional to the selling wait amount or the buying wait amount. Referring to FIG. 7 again, it is assumed that the closing price for the first round sale is 44,100 won according to the automatic trading conditions. In this case, the sell-awaited air volume displayed on the quotation sheet is 196,716 to 99,049 shares in the range of 44,250 won to 44,100 won. In addition, the purchase wait amount displayed on the quotation sheet is 18,699 to 140,816 shares in the range of 44,050 won to 43,900 won.

First, a user may determine a hard limit of a corresponding item that the stock trading device 100 may automatically sell. For example, if the stock is 10,000 shares in the item, the actual sell order volume may be determined by a soft limit linked to the purchase wait volume. A soft limit can be set to a certain limit, for example, 5,321 shares corresponding to 3% 18,699, 78,570, and 80,120 shares of 177,389 shares, for example, 3%. In addition, the purchase order of 44,050 won, which is one step lower than the 44,100 won, is 18,699 shares. Therefore, sell orders for 5,321 shares, which are soft limits, are possible at least within the range of 44,050 won. That is, 5,321 shares can be divided and ordered. After a certain period, the order quantity can be determined by re-evaluating the automatic trading conditions for the next order. And the order quantity can be re-determined according to the rate of recurrence. For example, 10% of the soft limit may be test-sold at the first round, the remainder may be sold at the second round, or 10% of the soft limit may be test-selled at the first round, and the remainder may be divided into a plurality of times. According to the user input, the interval value and the ratio value for the soft limit setting are preset.

In the case of buying, in contrast to the case of selling described above, the soft limit may be determined within the entire range to be bought and the selling wait range.

Unlike the above example, where the purchase order quantity is determined based on the selling wait amount, and the buying order quantity is determined based on the buying wait amount, the buying order quantity is determined based on the buying wait quantity according to a user's selection, and the selling order quantity is It may be determined based on skill. For example, if the entire purchase wait is expected to participate in the purchase, the purchase order amount may be set in proportion to the purchase wait amount. In addition, in the case where the entire selling wait is expected to participate in the selling, the selling order amount may be set in proportion to the selling wait.

In general, the selling and buying waits near the closing price tend to decrease or increase as the closing price increases or decreases.

Before the buy signal is received, i.e., before the trade rush occurs, the buy wait tends to increase at lower prices, and the sell wait tends to increase at higher prices. The reason for this is the reason for the buy and sell to try to form a bargain at a good price. When a buy signal is received and a rush of trading occurs due to a hidden buying force and a buying force of the waiting amount, the closing price is lower as the bid price tends to move to the closing price for the closing of the trade. The following amount of buying air tends to decrease gradually. Therefore, as the stock price rises, the purchase order quantity needs to be gradually reduced in series with the purchase wait volume.

In addition, when a buy signal or a sell signal is received and a rush of trading occurs due to a hidden buying force and a buying force of the waiting amount, the selling wait above the closing price is higher than the current bid price to conclude the trading at a higher price. As the stock tends to move towards higher price points, as the share price rises, the short-selling volume nears the closing price tends to decrease, and the overall short-selling volume tends to increase. Therefore, as the stock price rises, the selling order quantity needs to increase gradually with each selling in conjunction with the selling wait volume.

Next, the stock trading device 100 reports to the user about the concluded test trade (S340) and requests the user for approval for the next round of trade (S350). After the second round according to the set automatic trading conditions, the split trading can be set to be executed only when the user side, that is, the approval of the stock trading apparatus 100 (S352, S354, and S360). However, when there is no feedback of approval and disapproval, only the test trade corresponding to the basic round split trade is set to be executed (S351 to S355).

Stock trading device 100 checks whether there is a feedback input from the user for the approval request (S351).

If there is a feedback input, the main sale is ordered or executed through steps S352, S353, and S360 according to the approval. If there is an approval, the main sale is ordered or executed according to the cycle (S360), otherwise the split sale ends.

If there is no feedback input, the basic round split trade corresponding to the test trade is ordered or executed (S330). Subsequently, the basic session split trading is continued (S340). According to the cycle, the basic round split trade is ordered or executed (S355), and when the basic round split trade no longer exists (S353), the split trade ends.

The stock trading device 100 may be online or offline by the user. In addition, after receiving a buy signal or a sell signal, a rapid change may occur in the closing price and the closing amount of the target investment target item. Therefore, in order to prevent the user from trading the user, if there is no feedback on the user side, that is, the stock trading server 200 of the stock trading device 200, the basic round is designated, and the test trade, which is the split trade of the basic round, is executed. Can be set. After the basic round is executed, the orders of the remaining partial trades are suspended and are not executed (S353).

In addition, the report and approval request, characterized in that delivered through at least one of the message service, push information via the stock trading application module, mail and ARS. The offline stock trading device 100 may receive a report through a message service, an e-mail, an ARS, or the like, and may send a feedback on an approval request using the same method.

In particular, according to an embodiment of the present invention, the stock selling point of the stock trading time point may be determined as follows.

The stock trading device 100 calculates the average purchase price of a specific buying entity that purchased the target investment item through the calculation module 131. In addition to the average buying price, cumulative buying may be associated. In addition, the average purchase price is not a single value, and may be displayed as an interval using a minimum average purchase price and a maximum average purchase price. Based on the accumulated quantity, the user can set his or her total investment.

The loss price and the selling price are determined based on the average purchase price. When the stock price reaches a predetermined selling price based on the average purchase price (S310), the stock trading device 100 generates a selling signal through the signal generating module 132 (S320). Next, the stock trading device 100 may order the stock selling of the target target investment item to the stock trading server 200 according to the selling signal through the transaction request module 140 (S330 and 360).

Another embodiment of the recovery N of the split trade will be described.

The number of split trades may be set two or more times. In this case, the stock trading device 100 may make a request for approval for the remaining multiple times of the main sale after reporting the first test sale or may request the user for the approval for each time.

In the case where a plurality of main trades are made after the second round, it is possible to divide the shares into a predetermined ratio or quantity and to place an order of trading at the same or different per rate or quantity. For example, if the investable amount is 10 million won, buy in 10 installments using 10 million won, but buy 10% by 1 to 10 times, 1% by 1 time, and 2% by 2 times. You can buy as much as you like, or you can buy 20% at a time and 15% at a time. Alternatively, you can directly specify the quantity of shares to be bought or sold, not the percentage.

After the test trade, in the plurality of trades, the time condition regarding the cycle is determined separately. The stock trading device 100 orders the split trades according to a predetermined cycle from the second round after the first split trade. Therefore, the next round of split sales is ordered at the point in time elapsed by the period from the time at which the split trade of the previous round was ordered. To this end, time synchronization between the stock trading device 100 and the stock trading server 200 may be required. The time condition is characterized in that it is determined from the second round trade to the subsequent split trade.

The steps S130 and S230 performed by the stock trading device 100 and the stock trading server 200 according to the exemplary embodiment of the present invention have been described above.

According to an embodiment described through the drawings, the automatic mediating method based on the item search (S100, S200, S300) may include a recording medium including an instruction set executable by a computer, such as a program module executed by a computer. It can also be implemented in the form of. Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. In addition, computer readable media may include both computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Communication media typically includes computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, or other transmission mechanism, and includes any information delivery media.

As such, according to an exemplary embodiment of the present invention, a stock that is out of the typical relationship between the stock price and the trading volume may be searched, and the stock may be traded according to the split automatic trading technique.

In addition, you can search for stocks showing the pattern of buying, trading the stocks according to the split automatic trading technique.

Further, by using the buy signal or the sell signal, it is possible to place or execute the order of the split sale of each round according to the preset tightening amount, the closing price, and the fastening sun condition.

The foregoing description of the present invention is intended for illustration, and it will be understood by those skilled in the art that the present invention may be easily modified in other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are exemplary in all respects and not restrictive. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

The scope of the present invention is shown by the following claims rather than the above description, and all changes or modifications derived from the meaning and scope of the claims and their equivalents should be construed as being included in the scope of the present invention. do.

100: stock trading device 110: search module
120: selection module 130: trading signal module
131: operation module 132: signal generation module
140: transaction request module 141: automatic trading condition determination module
142: Order Order Module 150: Report and Approval Request Module
200: stock exchange server, 300: stock exchange server
400: network 500: computing device

Claims (10)

In the automatic trading technique performed by the stock trading server,
Searching for candidate investment items using transaction history information and stock distribution status information of stock items;
Analyzing the searched at least one candidate investment item to calculate a target investment item for which a stock price increase is expected; And
Executing the stock trading of the target item of investment;
Searching for the candidate candidates for investment,
Recall that stocks with a lower molecular weight than other stocks contain the difference between the trading volume in the period in which the stock price fluctuates out of a certain range and the trading volume in the section in which the stock price fluctuates within a certain range. Automated trading method based on the item search, characterized in that the search for candidate items for investment.
The method according to claim 1,
Searching for the candidate candidates for investment,
An auto trading technique based on a stock search, which searches for stocks that have a lower ratio of trading volume to stock prices compared to other stocks due to the stocking by a specific buying entity as the candidate items for investment.
delete The method according to claim 1,
Searching for the candidate candidates for investment,
An automatic trading technique based on item search, using at least one transaction history information among information on buyer status, purchase period, purchase cycle, securities firm purchase window, and cumulative purchase amount.
The method according to claim 1,
Searching for the candidate candidates for investment,
In the stock search, the stocks having low market capitalization compared to other stocks, a smaller number of stocks distributed than other stocks, a higher shareholding ratio of major stockholders than other stocks, and at least one of the theme stock issues are searched as candidate candidates. Based automated trading techniques.
The method according to claim 1,
The calculating of the target target item for investment,
Automated trading method based on stock search, which calculates stocks whose stock price is expected to increase due to additional buying among stocks showing buying pattern by specific buying entity.
The method according to claim 1,
The calculating of the target target item for investment,
An item search comprising: assigning a weighted score to a collection pattern and an item feature showing the candidate item for investment, and calculating the item having the highest score through the sum of the scores as the target item for investment; Based on automated trading techniques.
The method according to claim 1,
The calculating of the target target item for investment,
Based on the learning result of the data set created using the transaction history information of the case where the stock price rose through the collection of small stocks, the analysis is performed according to the machine learning algorithm, and it includes the buying pattern and the item characteristics with the highest reliability. The automated trading technique based on the item search, characterized in that to calculate the analyzed item as the target item for the investment.
The method according to claim 1,
The execution of the stock trading of the target target item,
Determining a stock trading timing;
A test trading step of executing a split trade for a predetermined number of times for the target investment item at the time of the stock trading;
Reporting on the split trade concluded in accordance with the test trade and requesting prior approval for the remaining split trades other than the test trade; And
A main trading step of executing a part or all of the remaining partial sales based on the pre-approval result;
If there is no feedback for the request, the automatic trading method based on the item search, characterized in that the test trading only executes the divided trades for the preset time, and does not execute the remaining divided trades other than the test trade.
The method according to claim 9,
The determining of the stock trading time,
Determining an automatic selling condition to determine a buying point based on a trading condition or to determine a selling point based on an average buying unit price of a specific buying entity that has bought the target investment item;
And generating a selling signal if the current situation satisfies the automatic selling condition.

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