KR102606321B1 - System and method for providing optimal trading timing information - Google Patents

Abstract

The present invention analyzes stock-related information transmitted from a securities company server, generates a CM index containing a set of buy/sell stocks that match the optimal trading (buy/sell) timing, and provides it to the user, while also providing the preset index to the user. By optimizing the standard value used to calculate the buy/sell set for each cycle (T'), trading can be done at the optimal time in response to the index flow, maximizing the user's return, as well as providing a portfolio for the analysis target. This relates to a system and method for providing optimal trading timing information that can improve investment safety.

Description

{System and method for providing optimal trading timing information}

The present invention relates to a system and method for providing optimal trading timing information. In detail, it analyzes stock-related information transmitted from a securities company server and includes a set of buy/sell items that match the optimal trading (buy/sell) timing. By creating a CM index and providing it to the user, and at the same time optimizing the standard value used to calculate the buy/sell set items at each pre-set period (T'), trading takes place at the optimal time in response to the index flow, allowing the user to It relates to a system and method for providing optimal trading timing information that can not only maximize the rate of return, but also improve investment safety by providing a portfolio for analysis targets.

Recently, as the Internet infrastructure has expanded and the digital device industry has developed, online stock trading programs such as Home Trading System (HTS) and Mobile Trading System (MTS) have been widely used.

Typically, online stock trading programs provide information by processing stock-related information (market price, stock price, quotation quantity, trading volume, buying volume, selling volume, etc.) into text, tables, or charts at the request of connected users. We provide trading agency services that provide guidance, procedures, and conclusion services for stock trading.

In general, users choose one of the various online stock trading programs available on the market, taking into account various factors such as convenience of operation, ease of viewing information, diversity of information provided, chart display method, and trading procedures. Select and use.

In this stock market, the selling price and trading volume are determined by complex and diverse variables such as performance, news, national policy, contract conclusion, and market interest. In other words, by looking at the flow (trend) of the trading price and trading volume of a specific stock, you can predict the future flow of that stock.

However, because there are many stocks in the stock market, it is impossible for users to individually understand and analyze the flow of all stocks, so there are frequent cases of missing the optimal trading timing and buying stocks that are already at their peak. Irrational trading, such as buying or selling stocks that are already at the bottom, causes the return rate to drop or the loss rate to increase.

Accordingly, various studies are being conducted on portfolios that analyze stock information on securities firms' servers, detect optimal trading information, and then provide it to users.

Domestic Registered Patent No. 10-2134664 (Title of the invention: Method for calculating the optimal trading quantity for each item according to the robo-advisor's portfolio) analyzes and utilizes the portfolio output from the robo-advisor algorithm to determine the limit on the proportion of a single item and the unit price of a recommended item. Considering the size, an optimal trading quantity calculation method has been disclosed to calculate and provide the optimal trading quantity for each recommended item. However, the optimal trading quantity calculation method is simply limited to recommended items already determined by the robo-advisor, and the optimal trading quantity for these items has been disclosed. Because it is designed to calculate trading quantity, it has structural limitations in that it cannot provide any optimal trading timing information.

In other words, by analyzing changes in the index of each stock and predicting future trends at the same time, stocks that meet the optimal timing are calculated as trading targets and provided to users, thereby not only maximizing returns compared to the same conditions but also reducing loss rates. Research on existing advisor systems is urgently needed.

The present invention is intended to solve this problem, and the problem of the present invention is to analyze the stock-related information provided by the servers of securities firms by the optimal trading timing providing server and calculate the grade score (A) of each detailed cycle for each item. Afterwards, each stock is included in the CM index according to the calculated grade score (A) and provided to the user, so that trading patterns can be predicted at the optimal time in response to index flows with complex and diverse volatility. The purpose is to provide a system and method for providing optimal trading timing information that can maximize the user's rate of return.

In addition, another problem of the present invention is to provide optimal trading timing information that can further increase the accuracy and reliability of optimal trading timing detection by optimizing the reference value used for CM index inclusion at each preset cycle (T') by the optimal trading timing providing server. It is intended to provide a system and method.

In addition, another problem to be solved by the present invention is that the optimal trading timing providing server is configured to determine whether each buy/sell set is suitable for trading using a preset verification algorithm when creating a CM index, thus ensuring the safety of investors' profits. The purpose is to provide a system and method for providing information on optimal trading timing that can be improved.

In addition, another problem to be solved by the present invention is that the optimal trading timing providing server is configured to monitor the index flow of each buy/sell set item of the CM index when extracting the CM index, capture the double signal, and then provide it to the user. The purpose is to provide a system and method for providing optimal trading timing information that can further increase profits.

In addition, another problem to be solved by the present invention is that the optimal trading timing server is configured to determine the optimal trading timing among the stocks in the portfolio using a market timing algorithm, thereby providing optimal trading that can effectively construct a portfolio with a small amount of computational processing. It is intended to provide a system and method for providing timing information.

The solution of the present invention to solve the above problem is in the optimal trading timing information provision method (S1) for collecting and analyzing stock market data to capture the optimal trading timing and provide it to the user: Optimal trading timing Step 10 (S10) where the providing server collects stock-related information transmitted from securities company servers in real time; The optimal trading timing providing server analyzes the data collected in step 10 (S10), and calculates 'month (t1)', 'week (t2)', 'day (t3)', 'time (t4)' and Data is collected according to pre-set comparison items for each item for each detailed cycle that includes at least one of the 'minutes (t5)', and the data collected is analyzed using a pre-set market timing algorithm to obtain a buy signal. Step 200 (S200) of detecting items and selling group items having a sell signal; Step 80 (S80) in which the optimal trading timing providing server generates CM index information including the buy group items and sell group items detected in step 200 (S200); Step 100 (S100) in which the optimal trading timing providing server transmits the CM index information generated in step 80 (S80) to a connected client; It includes a step 140 (S140) in which the client displays the CM index information transmitted in step 100 (S100), and the comparison item is any one of 'high price', 'opening price', 'closing price', and 'transaction volume'. In step 200 (S200), the optimal trading timing providing server uses the data collected in step 10 (S10) to select candles with a preset standard quantity (n) of candles for each detailed cycle for each item. Step 20 (S20) of comparing the value of the comparison item of each candle with the value of the comparison item of the previous candle, using; The optimal trading timing providing server utilizes the analysis data detected in step 20 (S20), and if the value of the comparison item of each candle according to each detailed cycle of each item is greater than or equal to the value of the comparison item of the previous candle, the corresponding 'α', a preset standard value, is given as the candle's rating score (A'). However, if the value of the comparison item of each candle is less than the value of the comparison item of the previous candle, the standard value is given as the rating score (A') of the candle. After calculating the grade score (A') of each candle by assigning a negative number of '-α', the grade score (A') of each candle calculated is added to obtain the grade score for each detailed cycle of each item ( Step 30 (S30) of calculating A); The optimal trading timing providing server compares the grade score (A) for each detailed cycle of each item calculated in step 30 (S30) with the preset first set value (TH1, Threshold1) and second set value (TH2). Step 50 (S50); Step 60 (S60) wherein the optimal trading timing providing server determines, in step 50 (S50), an item having a rating score (A) lower than the first set value (TH1) as a purchase set item; A step 70 (S70) in which the optimal trading timing providing server determines, in step 50 (S50), an item having a rating score (A) equal to or higher than the second set value (TH2) as a selling group item, and the step includes: 80 (S80), the optimal trading timing providing server matches the buy group items and the corresponding detailed cycle information determined in step 60 (S60) and incorporates them into the CM index information, and the sell group items and the sell group items determined in step 70 (S70). The detailed cycle information is matched and incorporated into the CM index information, and the optimal trading timing information providing method (S1) is performed after step 80 (S80), and the optimal trading timing providing server is provided in step 80 (S80). Step 110 (S110) of monitoring the index flow of the buy/sell set items included in the CM index extracted by; Step 120 (S120) in which the optimal trading timing providing server captures a double signal through monitoring through step 110 (S110); The optimal trading timing providing server further includes step 130 (S130) in which the double signal information captured in step 120 (S120) is transmitted to the connected client, and step 120 (S120) is performed by the optimal trading timing providing server For each buy/sell set item, using candles with a preset standard quantity (n) for each sub-detailed cycle, which is a shorter time period than the corresponding detailed cycle, the value of the comparison item of each candle is compared to the comparison item of the previous candle. Step 121 (S121) of calculating the grade score (A'') for each sub-detailed cycle of each buy/sell set item by comparing with the value of; The optimal trading timing providing server sets the grade score (A'') for each sub-detailed cycle of each buy/sell set item calculated in step 121 (S121) to the first setting value (TH1) and the second setting. Step 122 (S122) comparing with the value (TH2); If the optimal trading timing providing server detects a purchase group item of a sub-detailed cycle having a rating score (A'') less than or equal to the first set value (TH1) in step 122 (S122), the purchase group item Step 123 (S123) of detecting a double signal for the number of copies; When the optimal trading timing providing server detects a selling group item of a lower-detailed cycle having a rating score (A'') higher than the second set value (TH2) in step 122 (S122), it sends the selling group item to the relevant selling group item. Step 124 (S124) of detecting a double sell signal; The process proceeds when the optimal trading timing providing server captures a buy double signal in step 123 (S123) or a sell double signal in step 124 (S124), and provides identification information and buy/sell information of the detected item. It includes step 125 (S125) of generating double signal information matching the double signal.

In addition, in the present invention, the method (S1) of providing optimal trading timing information is carried out at every period (T') preset by the optimal trading timing providing server, and further includes step 150 (S150) of optimizing the reference value, Step 150 (S150) includes a step 1511 (S1511) in which the optimal trading timing providing server collects the purchase set items of the CM index created in step 80 (S80) during the period (T'); The optimal trading timing providing server uses a preset rebounding stock detection algorithm to analyze the stock-related information of each stock during the period (T') and extracts rebounding stocks, which are stocks that rebounded within the period (T'). Step 1512 (S1512); The optimal trading timing providing server compares the purchase group items collected in step 1511 (S1511) with the rebounding items extracted in step 1512 (S1512), and determines which items are included in the rebounding items but are not included in the purchase group items. Step 1513 (S1513) of selecting items that have not been selected as missing items; The optimal trading timing providing server multiplies the quantity (n) of the missing item selected in step 1513 (S1513) by a preset constant 'β', and then subtracts the multiplication value (n × β) from the previous reference value 'α1'. It includes step 1514 (S1514) of updating the reference value 'α (α = α1 - (n × β))', and step 30 (S30) utilizes the reference value 'α' optimized by step 150 (S150). The grade score (A) for each detailed cycle of each item is calculated, and in step 150 (S150), the optimal trading timing providing server calculates the CM index generated by step 80 (S80) during the cycle (T'). Step 1521 (S1521) of collecting sale set items; The optimal trading timing providing server uses a preset falling stock detection algorithm to analyze the stock-related information of each stock during a preset period (T') and selects falling stocks, which are stocks that fell within the period (T'). Extracting step 1522 (S1522); The optimal trading timing providing server compares the selling items collected in step 1521 (S1521) with the falling items extracted in step 1522 (S1522), and determines which items are included in the falling items but are not included in the selling set. Step 1523 (S1523) of selecting items that have not been selected as missing items; The optimal trading timing providing server multiplies the quantity (n) of the missing item selected in step 1523 (S1523) by a preset constant 'β', and then adds the multiplication value (n × β) to the previous reference value 'α1'. It is desirable to further include step 1524 (S1524) of updating the reference value 'α(α = α1 + (n × β))'.

In addition, another solution of the present invention is the optimal trading timing information provision method (S1) for collecting and analyzing stock market data to capture the optimal trading timing and provide it to the user: the optimal trading timing providing server is a securities company Step 10 (S10) of collecting stock-related information transmitted from servers in real time; The optimal trading timing providing server analyzes the data collected in step 10 (S10), and calculates 'month (t1)', 'week (t2)', 'day (t3)', 'time (t4)' and Data is collected according to pre-set comparison items for each item for each detailed cycle that includes at least one of the 'minutes (t5)', and the data collected is analyzed using a pre-set market timing algorithm to obtain a buy signal. Step 200 (S200) of detecting items and selling group items having a sell signal; Step 80 (S80) in which the optimal trading timing providing server generates CM index information including the buy group items and sell group items detected in step 200 (S200); Step 100 (S100) in which the optimal trading timing providing server transmits the CM index information generated in step 80 (S80) to a connected client; Step 140 (S140) in which the client displays the CM index information transmitted in step 100 (S100), the comparison item is 'low price', and step 200 (S200) includes the server providing the optimal trading timing. Through the data collected in step 10 (S10), by using candles of a preset standard quantity (n) among candles that are to be cycled in each detailed cycle for each item, the value of the comparison item of each candle is compared to that of the previous candle. Step 20 (S20) of comparing with the value of the comparison item; The optimal trading timing providing server utilizes the analysis data detected in step 20 (S20), and if the value of the comparison item of each candle according to each detailed cycle of each item is less than the value of the comparison item of the previous candle, the corresponding A preset standard value 'α' is given as the candle's rating score (A'). However, if the value of the comparison item of each candle is greater than or equal to the value of the comparison item of the previous candle, the standard value is given as the rating score (A') of the candle. After calculating the grade score (A') of each candle by assigning a negative number '-α', the grade score (A') of each candle calculated is added to obtain the grade score (A') for each detailed cycle of each stock. ) Step 30 (S30) of calculating; The optimal trading timing providing server uses the grade score (A) of each item for each preset period calculated in step 30 (S30) to a preset first set value (TH1, Threshold1) and a second set value ( Step 50 (S50) comparing with TH2); Step 60 (S60) wherein the optimal trading timing providing server determines, in step 50 (S50), an item having a rating score (A) equal to or higher than the second set value (TH2) as a purchase group item; A step 70 (S70) in which the optimal trading timing providing server determines, in step 50 (S50), an item having a rating score (A) lower than the first set value (TH1) as a selling group item, and the step includes: 80 (S80), the optimal trading timing providing server matches the buy group items and the corresponding detailed cycle information determined in step 60 (S60) and incorporates them into the CM index information, and the sell group items and the sell group items determined in step 70 (S70). The detailed cycle information is matched and incorporated into the CM index information.

In addition, in the present invention, the method (S1) of providing optimal trading timing information is performed after step 80 (S80), and the optimal trading timing providing server determines the number of units/s included in the CM index extracted by step 80 (S80). Step 110 (S110) of monitoring the index flow of selling group items; Step 120 (S120) in which the optimal trading timing providing server captures a double signal through monitoring through step 110 (S110); The optimal trading timing providing server further includes step 130 (S130) in which the double signal information captured in step 120 (S120) is transmitted to the connected client, and step 120 (S120) is performed by the optimal trading timing providing server For each buy/sell set item, using candles with a preset standard quantity (n) for each sub-detailed cycle, which is a period of time shorter than the corresponding period, the value of the comparison item of each candle is divided into the comparison item of the previous candle. Comparing the value, step 121 (S121) of calculating the grade score (A'') for each sub-detailed cycle of each buy/sell set item; The optimal trading timing providing server sets the grade score (A'') for each sub-detailed cycle of each buy/sell set item for each sub-cycle calculated in step 121 (S121) to the first set value (TH1) and the Step 122 (S122) comparing with the second set value (TH2); When the optimal trading timing providing server detects a purchase group item of a sub-detailed cycle having a rating score (A'') equal to or higher than the second set value (TH2) in step 122 (S122), it selects the purchase group item. Step 123 (S123) of detecting a double signal for the number of copies; If the optimal trading timing providing server detects a selling set item of a sub-detailed cycle having a rating score (A'') less than or equal to the first set value (TH1) in step 122 (S122), the selling set item is Step 124 (S124) of detecting a sell double signal for; The process proceeds when the optimal trading timing providing server captures a buy double signal in step 123 (S123) or a sell double signal in step 124 (S124), and provides identification information and buy/sell information of the detected item. It includes step 125 (S125) of generating double signal information matching the double signal.

In addition, in the present invention, the method (S1) of providing optimal trading timing information is carried out at every period (T') preset by the optimal trading timing providing server, and further includes step 150 (S150) of optimizing the reference value, Step 150 (S150) includes a step 1511 (S1511) in which the optimal trading timing providing server collects the purchase set items of the CM index created in step 80 (S80) during the period (T'); The optimal trading timing providing server uses a preset rebounding stock detection algorithm to analyze the stock-related information of each stock during the period (T') and extracts rebounding stocks, which are stocks that rebounded within the period (T'). Step 1512 (S1512); The optimal trading timing providing server compares the purchase group items collected in step 1511 (S1511) with the rebounding items extracted in step 1512 (S1512), and determines which items are included in the rebounding items but are not included in the purchase group items. Step 1513 (S1513) of selecting items that have not been selected as missing items; The optimal trading timing providing server multiplies the quantity (n) of the missing item selected in step 1513 (S1513) by a preset constant 'β', and then adds the multiplication value (n × β) to the previous reference value 'α1'. It includes step 1514 (S1514) of updating the reference value 'α (α = α1 + (n × β))', and step 30 (S30) utilizes the reference value 'α' optimized by step 150 (S150). Therefore, it is desirable to calculate the grade score (A) for each detailed cycle of each item.

In addition, in the present invention, step 150 (S150) is step 1521 (S1521) in which the optimal trading timing providing server collects the sale set items of the CM index created by step 80 (S80) during the period (T'). ; The optimal trading timing providing server uses a preset falling stock detection algorithm to analyze the stock-related information of each stock during a preset period (T') and selects falling stocks, which are stocks that fell within the period (T'). Extracting step 1522 (S1522); The optimal trading timing providing server compares the selling items collected in step 1521 (S1521) with the falling items extracted in step 1522 (S1522), and determines which items are included in the falling items but are not included in the selling set. Step 1523 (S1523) of selecting items that have not been selected as missing items; The optimal trading timing providing server multiplies the quantity (n) of the missing item selected in step 1523 (S1523) by a preset constant 'β', and then subtracts the multiplication value (n × β) from the previous reference value 'α1'. It is preferable to further include step 1524 (S1524) of updating the reference value 'α (α = α1 - (n × β))'.

In addition, in the present invention, the optimal trading timing information providing method (S1) further includes step 95 (S95) performed after step 80 (S80), and step 95 (S95) is performed by the optimal trading timing providing server. Step 951 (S951) of extracting purchase set items included in the CM index created in step 80 (S80); The optimal trading timing server uses the stock-related information of each purchase group item to extract the ROE and ROA of each purchase group item, sorts the purchase group items in descending order of ROE, and then distributes them in the sorted order. ROE-based weighted scores are assigned to each purchase group item in order, starting from '1'. After sorting them in descending order of ROA, ROA-based weighted scores are assigned to each purchase group item in order, starting from '1', according to the sorted order. The total weighted score (sum) is calculated by adding up the ROE-based weighted score and the ROA-based weighted score for each purchase group item. At the same time, for each purchase group item, the ROE-based weighted score is multiplied by 0.5 and the ROA-based weighting is calculated. Step 952 (S952) of calculating the score for the investment index (N1) by adding up the score multiplied by 0.5; The optimal trading timing server uses the stock-related information of each purchase group item to extract the D/E and retention rate of each purchase group item, sorts the purchase group items in order of highest D/E, and then sorts them. D/E-based weighted scores are assigned to each purchase group item in order, starting from '1', and after sorting the purchase group items in descending order of retention rate, each purchase group item has a reservation rate according to the sorted order. Based weighting scores are assigned sequentially starting from '1', and the D/E-based weighting score and retention rate-based weighting score are added for each purchase group item to calculate the second total weighted score (sum), and for each purchase group item Step 953 (S953) of calculating a score for financial indicators (N2) by adding the D/E-based weighted score multiplied by 0.5 and the retention rate-based weighted score multiplied by 0.5; The optimal trading timing server uses the stock-related information of each purchase group item to extract the P/A (operating profit ratio to market capitalization) and PBR information of each purchase group item, and then selects the purchase group items with a high P/A. After sorting in order, a P/A-based weighted score is assigned to each purchase group item in order, starting from '1', according to the sorted order. After sorting the purchase group items in the order of the highest PBR, the sorted order Accordingly, PBR-based weighted scores are assigned to each purchase group item in order, starting from '1', and the P/A-based weighted score and PBR-based weighted score are added for each purchase group item to create the third total weighted score (sum). Step 954 (S954) of calculating the value index score (N3) by adding the P/A-based weighted score multiplied by 0.75 and the PBR-based weighted score multiplied by 0.25 for each purchase set item; The optimal trading timing providing server multiplies the investment index score (N1) calculated in step 952 (S952) by '0.25' and the financial index score (N2) calculated in step 953 (S953). Step 955 (S955) of calculating an average score (N) by adding the value multiplied by '0.25' and the value index score (N3) calculated in step 954 (S954) multiplied by '0.50'; The optimal trading timing providing server uses the average score (N) of each purchase group item calculated in step 955 (S955) to rank the purchase group items in order of the average score (N) from high to low. Calculating step 956 (S956); The optimal trading timing providing server utilizes the information detected in steps 952 to 956 to calculate ROE, ROA, total weighted score, investment index score (N1), D/E, and retention rate for each purchase group item. , Generate scoring information by matching the second total weighted score, financial index score (N2), P/A, PBR, third total weighted score, value index score (N3), average score (N), and ranking information. It includes step 957 (S957), and in step 100 (S100), it is preferable that the optimal trading timing providing server transmits the scoring information generated in step 95 (S95) to the connected client.

In addition, in the present invention, the method for providing optimal trading timing information (S1) further includes step 40 (S40) performed after step 30 (S30) and step 90 (S90) performed after step 80 (S80). In step 40 (S40), the optimal trading timing providing server uses a standard table in which the rating level (L) is matched for each range of rating scores (A) to determine the value of each item calculated in step 30 (S30). The rating level (L) corresponding to the rating score (A) for each detailed cycle is detected, and in step 90 (S90), the optimal trading timing providing server determines each number of copies of the CM index created in step 80 (S80). It is advisable to verify the suitability of the stock for trading by analyzing the market neutrality or financial statements of the stock for sale.

According to the present invention, which has the above problems and solutions, the optimal trading timing providing server analyzes stock-related information provided from securities company servers, calculates the rating score (A) of each detailed cycle for each item, and then calculates the calculated rating. Each stock is included in the CM index according to the score (A) and provided to the user. In response to the index flow with complex and diverse volatility, it predicts change patterns and ensures trading at the optimal time, thereby providing user convenience. You can maximize your returns.

In addition, according to the present invention, the optimal trading timing providing server can further increase the accuracy and reliability of optimal trading timing detection by optimizing the reference value used for CM index inclusion at each preset period (T').

In addition, according to the present invention, the optimal trading timing server is configured to determine whether each buy/sell set is suitable for trading using a preset verification algorithm when creating a CM index, thereby improving the investor's profit safety. do.

In addition, according to the present invention, the optimal trading timing providing server is configured to monitor the index flow of each buy/sell set item of the CM index when extracting the CM index, capture the double signal, and then provide it to the user, thereby further increasing the trading profit. It becomes possible.

In addition, according to the present invention, the optimal trading timing server is configured to determine the optimal trading timing among stocks in the portfolio using a market timing algorithm, so that a portfolio can be effectively constructed with a small amount of computational processing.

Figure 1 is a configuration diagram showing a system for providing optimal trading timing information, which is an embodiment of the present invention.
Figure 2 is a block diagram showing the optimal trading timing providing server of Figure 1.
Figure 3 is a conceptual diagram for explaining Figure 2.
Figure 4 is a block diagram showing the market timing algorithm-based analysis unit of Figure 1.
Figure 5 is an example diagram for explaining the grade score calculation module of Figure 4.
Figure 6 is a block diagram showing the double signal acquisition unit of Figure 2.
FIG. 7 is a block diagram showing the data mining processing unit of FIG. 2.
FIG. 8 is a block diagram showing the scoring information generator of FIG. 2.
Figure 9 is a block diagram showing the investment indicator-based analysis and scoring module of Figure 8.
Figure 10 is a block diagram showing the financial indicator-based analysis and scoring module of Figure 8.
Figure 11 is a block diagram showing the value indicator-based analysis and scoring module of Figure 8.
FIG. 12 is a block diagram showing the reference value optimization unit of FIG. 2.
Figure 13 is a block diagram showing the purchase set item-based reference value optimization unit of Figure 12.
Figure 14 is a block diagram showing the sales set item-based reference value optimization unit of Figure 12.
FIG. 15 is a flow chart showing a method of providing optimal trading timing information, which is the operation process of the optimal trading timing information providing system of FIG. 1.
Figure 16 is a flow chart showing the scoring information generation step of Figure 15.
Figure 17 is a flow chart showing the double signal acquisition step of Figure 15.
Figure 18 is a flow chart showing the reference value optimization step of Figure 15.

Hereinafter, an embodiment of the present invention will be described with reference to the attached drawings.

Figure 1 is a configuration diagram showing a system for providing optimal trading timing information, which is an embodiment of the present invention.

The optimal trading timing information provision system (1), which is an embodiment of the present invention, analyzes stock-related information transmitted from the securities company servers (7-1), ..., (7-N) to determine the optimal trading (buy/buy) Selling) A CM index containing buy/sell set items that matches the timing is created and provided to the user, and at the same time, the index flow is optimized by optimizing the standard value used to calculate buy/sell set items at each preset period (T'). In response, trading is carried out at the optimal time to maximize the user's rate of return and at the same time improve investment safety by providing a portfolio for the analysis target.

In addition, as shown in FIG. 1, the optimal trading timing information providing system 1 of the present invention includes typical securities company servers 7-1, ..., (7-N), and securities company servers 7-1. ), ..., (7-N), analyze the index flow of each stock according to the preset optimal cycle (T), and calculate the buy/sell set stocks to be included in the CM index. Afterwards, an optimal trading timing providing server (5) that creates an investment portfolio based on the calculated CM index, clients (4-1), ..., (4-N), which are terminals owned by the user, Installed on each client (4) and linked with the optimal trading timing providing server (5), the investment portfolio information transmitted from the optimal trading timing providing server (5) is provided to the user through various designed GUIs (Graphic User Interfaces). Dedicated program provided (3), server providing optimal trading timing (5), securities company server (7-1), ..., (7-N) and clients (4-1), ..., (4) It consists of a communication network (10) that provides a data movement path between -N).

At this time, for convenience of explanation, the present invention provides an example where the optimal trading timing providing server (5) collects stock-related information in conjunction with the securities company servers (7-1), ..., (7-N). However, the target of providing stock-related information through the optimal trading timing server (5) is not limited to securities company servers (7-1), ..., (7-N), and the Korea Stock Exchange and KOSPI. It is natural that a typical stock exchange server, such as a server or KOSDAQ server, or a stock exchange server can be applied.

The communication network (10) includes an optimal trading timing providing server (5), a securities company server (7-1), ..., (7-N), and clients (4-1), ..., (4-N). It is a network that supports data communication between networks, and in detail, wired and wireless networks such as LAN (Local Area Network), MAN (Metropolitan Area Network), and WAN (Wide Area Network). It can be composed of networks, mobile communication networks, 3G/4G/5G, LTE, etc.

Securities company servers (7-1), ..., (7-N) process and analyze various indicators and signals for various securities traded through the stock trading market, and determine the market price, stock price, bid quantity, and trading volume of each item. , It is a server that provides a general stock trading service that generates stock-related information including purchase volume, sale volume, and stocks for each theme.

In addition, the securities company servers (7-1), ..., (7-N) provide the generated stock-related information in real time to the optimal trading timing providing server (5).

In addition, when the securities company servers (7-1), ..., (7-N) receive a login request from the optimal trading timing providing server (5), they use the already registered login information to log in the requested client (4). is authenticated, and the authentication result data is transmitted to the optimal trading timing providing server (5). At this time, the optimal trading timing providing server (5) transmits the authentication result data received from the relevant securities company server (7) to the corresponding dedicated program (3).

Clients (4-1), ..., (4-N) are digital devices owned by the user, specifically desktop PCs, laptops, and smartphones. It can be done with a smart phone, tablet PC, etc.

Although not shown in the drawing, these clients (4-1), ..., (4-N) include a communication module that supports connection to the communication network 10, a memory in which a dedicated program to be described later is installed, and a user ( It includes input means (keyboard, keypad, touchpad, etc.) for receiving commands from the user, and display means (monitor, screen, display panel, etc.) for displaying the GUI provided by the dedicated program.

In addition, the clients (4-1), ..., (4-N) are linked with the optimal trading timing providing server (5) to provide an interface between the user and the optimal trading timing providing server (5). A dedicated program (3) is installed for this purpose.

In addition, the clients (4-1), ..., (4-N) connect to the optimal trading timing providing server (5) under the control of the dedicated program (3) and request information and at the same time provide response data according to the request. is sent.

FIG. 2 is a block diagram showing the optimal trading timing providing server of FIG. 1, and FIG. 3 is a conceptual diagram for explaining FIG. 2.

As shown in Figures 2 and 3, the optimal trading timing providing server 5 includes a control unit 50, a database unit 51, a data transmission/reception unit 52, a program management unit 53, an authentication processing unit 54, Market timing algorithm-based analysis unit (55), CM index creation unit (56), CM index extraction unit (57), double signal capture unit (58), data mining processing unit (59), scoring information creation unit (60), investment It consists of a portfolio creation unit 61 and a reference value optimization unit 62.

The control unit 50 is the operating system (OS) of the optimal trading timing providing server 5, and the control objects 51, (52), (53), (54), (55), (56), and (57) , (58), (59), and (60) are managed and controlled.

In addition, when the control unit 50 receives stock-related information in real time from the securities company servers (7-1), ..., (7-N) through the data transmitting and receiving unit 52, the control unit 50 stores the transmitted stock-related information in a preset field. They are classified into categories and stored in the database unit 51.

Additionally, when an investment portfolio is created by the investment portfolio creation unit 61, the control unit 50 controls the data transmission/reception unit 52 so that the generated investment portfolio is transmitted to the connected client 4.

Additionally, the control unit 50 executes the reference value optimization unit 62 at every preset period (T'). At this time, the reference value (α) optimized by the optimal cycle setting unit 61 is stored in the database unit 51 and simultaneously utilized in the market timing algorithm-based analysis unit 55.

The database unit 51 stores stock-related information transmitted from the securities company servers 7-1, ..., (7-N).

Additionally, the database unit 51 stores reference value (α) information optimized by the reference value optimization unit 62.

Additionally, preset period (T) information is stored in the database unit 51. At this time, the cycle (T) consists of detailed cycles such as ‘month (T1)’, ‘week (T2)’, ‘day (T3)’, ‘hour (T4)’, and ‘minute (T5)’.

Additionally, information on preset comparison items is stored in the database unit 51. At this time, comparison items consist of ‘high price’, ‘low price’, ‘opening price’, ‘trading volume’, and ‘closing price’.

Additionally, a preset market timing algorithm is stored in the database unit 51.

At this time, the market timing algorithm uses the collected stock-related information as input data and outputs the rating score, rating level, and buy/sell set items of each item for each preset detailed cycle (T1, T2, T3, T4, T5). This means that this market timing algorithm will be explained in detail in FIG. 4, which will be described later.

Additionally, the database unit 51 stores a preset verification algorithm used in the CM index extraction unit 57. At this time, the verification algorithm is an algorithm that uses the buy/sell set items and stock-related information of the CM index generated by the market timing algorithm as input data and outputs whether each buy/sell set item is suitable for trading.

Additionally, the database unit 51 stores the CM index information extracted by the CM index extraction unit 57 and the investment portfolio information generated by the investment portfolio creation unit 61.

The data transmitting and receiving unit 52 includes clients 4-1, ..., (4-N) and securities company servers 7-1, ..., (7-N) connected to the communication network 10. Send and receive data.

The program management unit 53 manages overall services for the dedicated program 3, such as GUI updates, firmware updates, and information updates.

When the authentication processing unit 54 receives authentication request data including securities company identification information and login information from the dedicated program 3 through the data transmitting and receiving unit 52, it authenticates the user by linking with the relevant securities company server 7. and transmits the authentication result information received from the securities company server (7) to the corresponding dedicated program (3). At this time, if authentication is successful, the dedicated program (3) is connected to the user's account opened at the relevant securities company, and the dedicated program (3) is connected to the relevant securities company's server (7) without going through the optimal trading timing server (5). It may be configured to perform login authentication by linking directly.

Since the program management unit 53 and the authentication processing unit 54 are technologies widely used in general application services, detailed descriptions thereof will be omitted.

Figure 4 is a block diagram showing the market timing algorithm-based analysis unit of Figure 1.

As shown in FIG. 4, the market timing algorithm-based analysis unit 55 includes a data collection module 551, an analysis module 552, a rating score calculation module 553, a rating classification module 554, and a comparison module ( 555), a buy set item detection module (556), and a sell set item detection module (557).

The data collection module 551 collects data on stocks according to preset comparison items through stock-related information transmitted from the securities company servers (7-1), ..., (7-N).

At this time, the comparison item can be set to one of ‘high price’, ‘low price’, ‘opening price’, ‘transaction volume’, and ‘closing price’.

For example, when the comparison item is set to 'high price', the data collection module 551 collects high price data for each item, and when the comparison item is set to 'low price', the data collection module 551 collects data for each item. Low-price data can be collected for each item.

Hereinafter, for convenience of explanation, the present invention will be described as an example where the comparison item is set to 'high price'.

The analysis module 552 analyzes the collected high price data for each item using a preset market timing algorithm.

At this time, the market timing algorithm compares the high price of each candle with the high price of the previous candle, based on the standard quantity (n) of candles according to each preset detailed cycle.

That is, the analysis module 552 has a detailed cycle (T) of 'month (T1)', 'week (T2)', 'day (T3)', 'hour (T4)', and '15 minutes (T5)'. Assuming that it is set to 1) 'one month (T1)', comparison calculation processing is performed using n candles, which are the standard quantity among the candles for the preset comparison items, and 2) 'one week (T2)'. )', and perform comparison calculation processing using n candles, which are the standard quantity, among the candles for the pre-set comparison item. 3) With the '1 day (T3)' cycle, among the candles for the pre-set comparison item, Comparison calculation processing is performed using n candles, which are the standard quantity. 4) Comparison calculation processing is performed using n candles, which are the standard quantity, among the candles for the preset comparison items with a period of '1 hour (T4)'. 5) Perform comparison operation processing using n candles, which is the standard quantity, among candles for preset comparison items, with a period of 'm minutes (T5)'.

For example, the cycle (T) is a detailed cycle of ‘month (T1)’, ‘week (T2)’, ‘day (T3)’, ‘hour (T4)’, and ‘15 minutes (T5)’, compare Assuming that the item is 'trading volume' and the standard quantity is set to '10', the analysis module 552 performs calculation processing using 1) 10 'trading volume' candles scheduled for a 'month (T1)' period. At the same time, 2) calculation processing is performed using 10 'trading volume' candles with a cycle of 'one week (T2)', and 3) using 10 'trading volume' candles with a cycle of 'one day (T3)'. 4) 'Trading volume' with a period of 'one hour (T4)' Calculation processing is performed using 10 candles, and 5) 'Trading volume' with a period of '15 minutes (T5)' Calculations are performed using 10 candles.

In other words, assuming that the period (T) consists of 5 periods (month/week/day/hour/15 minutes), the analysis module 552 performs comparison processing according to each of the 5 periods.

The grade score calculation module 553 uses a market timing algorithm to analyze and utilize the analysis data for each cycle of each item detected by the analysis module 552 to calculate the grade score (A) for each cycle of each item. In detail, for each item, a standard quantity of candles according to the comparison item of each cycle is used. If the comparison item value of each candle exceeds (or exceeds) the comparison item value of the previous candle, the candle of the corresponding candle is The standard value 'α' optimized by the standard value optimization unit 62 is assigned as the rating score (A'), but if the comparison item value of each candle does not exceed (less than) the comparison item value of the previous candle, the rating of the candle is given. Calculate the grade score (A') of each candle by assigning '-α', the negative number of the standard value, as the score (A'), then add up the grade scores (A') of each candle, and calculate the grade score (A') of each candle according to the corresponding cycle. Calculate the grade score (A) for the item.

At this time, the comparison item can be set to one of ‘high price’, ‘low price’, ‘opening price’, ‘transaction volume’, and ‘closing price’.

In more detail, the rating score calculation module 553 uses n candles of each item when the comparison item is set to 1) one of 'high price', 'open price', 'trading volume', and 'closing price'. , if the comparison item value of each candle exceeds (or exceeds) the comparison item value of the previous candle, a standard value of 'α' is assigned as the grade score (A') of the candle, but the comparison item value of each candle is equal to that of the previous candle. If it does not exceed (or falls below) the value of the comparison item, the rating score (A') of each candle is calculated by assigning '-α', the negative of the standard value, as the rating score (A') of the candle, and then summing them up. 2) When the comparison item is set to 'low', n candles of each item are used to calculate the grade score (A) of the item, and the comparison item value of each candle is compared to the previous candle. If the item value is not exceeded (if less than), the standard value 'α' is given as the grade score (A') of the candle, but if the comparison item value of each candle exceeds (or more than) the comparison item value of the previous candle, Calculate the rating score (A') of each candle by assigning '-α', the negative of the standard value, to the rating score (A') of the candle, and then add them together to calculate the rating score (A) of the stock. do.

In addition, the grade score calculation module 553 replaces the grade score of the item with ‘0’ if the grade score (A) of the item is a negative number (-) less than ‘0’.

That is, when the comparison item is set to 'high price', the rating score calculation module 553 sets the rating score (A') of each candle to 'α', which is the standard value, if all n candles are higher than the previous candle. Since it is assigned, the grade score (A) of the item becomes 'n Since the grade score (A) is a negative number (-) smaller than '0', it is calculated by replacing it with '0'.

For example, if the comparison item is one of 'high price', 'closing price', 'trading volume', and 'open price', the rating score calculation module 553 performs the same as described above in each detailed cycle preset for each item. By using n candles (comparison items), if the comparison item value of each candle exceeds (or exceeds) the comparison item value of the previous candle, the standard value 'α' is assigned as the grade score (A') of the candle. However, if the comparison item of each candle does not exceed (less than) the comparison item of the previous candle, the rating score of each candle (A') is given as the negative number of the standard value, '-α'. After calculating A'), these are added together to calculate the grade score (A) for the item.

In addition, if the comparison item is 'low', the rating score calculation module 553 uses n candles (comparison items) according to each preset detailed cycle, so that the low price of each candle exceeds the low price of the previous candle (low price). ), the standard value 'α' is given as the rating score (A') of the candle, but if the low price of each candle does not exceed the low price of the previous candle, the negative value of the standard value is assigned as the rating score (A') of the candle. After calculating the grade score (A') of each candle by assigning '-α', these are added together to calculate the grade score (A) of the stock.

Figure 5 is an example diagram for explaining the grade score calculation module of Figure 4.

For example, as shown in Figure 5, assuming that the standard quantity (n) is '10', the optimized standard value (α) is '10', and the comparison item is 'high price', the grade score calculation module ( 553) uses 10 candles according to the detailed cycle (month/week/day/hour/minute) for each item, and compares the high price of each candle with the high price of the previous candle. If the current high price is the high price of the previous candle, If it exceeds, the standard value of '10' is assigned as the rating score (A') of the candle, but if the current high price does not exceed the high price of the previous candle, the rating score (A') of the candle is given as the negative of the standard value, '- After assigning '10', they are added together to calculate the grade score (A) for the item. At this time, since the grade score (A) of the corresponding item, which is the sum of the scores of each candle, is '-30', the grade score calculation module 553 calculates '-30' because the grade score (A) is a minus smaller than '0'. By replacing with '0', the grade score (A) of the item is calculated as '0'.

That is, the grade score calculation module 553 calculates grade scores (A1, A2, A3, A4, A5) for each detailed cycle (T1, T2, T3, T4, T5) based on comparison items and standard quantities for each item. ) are calculated.

In other words, the grade score calculation module 553 calculates five grade scores according to month (T1)/week (T2)/day (T3)/hour (T4)/minute (T5) for each item. .

The rating classification module 554 sets a preset standard that matches the rating score (A) of each detailed cycle for each item calculated by the rating score calculation module 553, the range of the rating score (A), and the rating level (L). Using the table, classify and set the grade level (L) according to each detailed cycle for each item.

At this time, rating levels can be classified as ‘buy’, ‘recession’, ‘neutral’, ‘overheated’, and ‘sell’.

Table 1 below is an exemplary table showing a standard table when the comparison items in the present invention are ‘high price’, ‘opening price’, ‘closing price’, and ‘transaction volume’.

[Table 1]

For example, as shown in Table 1, when the comparison item is 'high price', the standard table sets the rating level (L) of the item to 'buy' if the rating score (A) is '0 to 10'. If the rating score (A) is '11 or more but less than 30', the rating level (L) of the item is set to 'recession', and if the rating score is '31 or more but less than 70', the rating level of the item is set to 'Recession'. (L) is set to 'Neutral', and if the rating score (A) is '71 or more but less than 90', the rating level (L) of the item is set to 'Sell', and if the rating score (A) is '91 or more' If it is ‘less than 100’, the rating level (L) of the item can be set to ‘sell’.

At this time, the comparison items 'high price', 'opening price', 'closing price' and 'transaction volume' calculate the grade score (A) in the same way, so the comparison items 'high price', 'opening price', 'closing price' and 'transaction volume' For this, the reference table in Table 1 described above can be applied in the same way.

However, in the case of the comparison item 'low price', the grade score (A) calculation method is reversed by comparing with the comparison items 'high price', 'opening price', 'closing price' and 'transaction volume', so the comparison item 'low price' The standard table can be set as shown in Table 2 below.

Table 2 below is an exemplary table showing a reference table when the comparison item in the present invention is ‘low price’.

[Table 2]

For example, as shown in Table 2, when the comparison item is 'low price', the standard table sets the rating level (L) of the item to 'sell' if the rating score (A) is '0 to 10'. If the rating score (A) is '11 or more but less than 30', the rating level (L) of the item is set to 'Overheated', and if the rating score is '31 or more but less than 70', the rating level of the item is set to 'overheated'. (L) is set to 'Neutral', and if the rating score (A) is '71 or more but less than 90', the rating level (L) of the item is set to 'stagnation' and the rating score (A) is '91 or more. If it is ‘less than 100’, the rating level (L) of the item can be set to ‘Buy’.

The comparison module 555 compares the grade score (A) according to each detailed cycle for each item calculated by the grade score calculation module 553 with the first set value (TH1, Threshold) and the second set value (TH2). .

In more detail, the comparison module 555 sets the grade score (A1) of the 'one month (T1)' cycle for each item calculated by the grade score calculation module 553 to the first set value (TH1, 2) Compare the grade score (A2) of the 'one week (T2)' cycle with the first set value (TH1, Threshold) and the second set value (TH2) 3) Compare the grade score (A3) of the 'one day (T3)' cycle with the first set value (TH1, Threshold) and the second set value (TH2), and 4) the 'one hour (T4)' cycle. The grade score (A4) is compared with the first set value (TH1, Threshold) and the second set value (TH2), and the grade score (A5) of the period of 5)'m minutes (T5) is compared to the first set value (TH1, Threshold). Threshold) and the second set value (TH2).

At this time, the first set value (TH1, TH1 < TH2) means the maximum rating score for calculating the item as a buy set item, and the second set value (TH2, TH2 > TH1) means the item is designated as a sell set item. It means the minimum value of grade points to be calculated.

The purchase group item detection module 556 selects items with a grade score (A, A = A1 or A2 or A3 or A4 or A5) below the first set value (TH1) in the comparison module 555 as a purchase group item. decide

At this time, a buy group stock means that it has a buy signal when it is included in the CM index and based on the corresponding detailed cycle (T1 or T2 or T3 or T4 or T5), and there is a need to monitor to capture double signals in the future. This refers to items that are judged to exist.

The sell set item detection module 557 determines, in the comparison module 555, an item with a rating score (A, A = A1 or A2 or A3 or A4 or A5) equal to or higher than the second set value (TH2) as a sell set item. do.

At this time, a selling group item means that it has a sell signal when it is included in the CM index and based on the corresponding detailed cycle (T1 or T2 or T3 or T4 or T5), and there is a need to monitor it to capture double signals in the future. This refers to items that are judged to exist.

Meanwhile, in Figure 4, as an example is explained as the case where the comparison item is 'high price', an item with a rating score (A) below the first set value (TH1) is determined as a purchase set item, and the second set value (TH1) is explained as an example. It has been explained as an example that an item with a rating score (A) of TH2) or higher is determined as a sell set item. However, if the comparison item is 'low price', the buy set item detection module 556 sets the second setting value (TH2). At the same time, stocks with a rating score (A) above are determined as buy group items, and at the same time, the sell group item detection module 557 determines stocks with a rating score (A) below the first set value (TH1) as sell group items. It is natural that it can be composed of doing so.

In addition, although not shown in the drawing, the market timing algorithm-based analysis unit 55 determines the rating score ( At the same time as calculating A), 2) the rating score (A) is calculated using the second condition with 'low price' as the comparison item, and 3) the buy/sell signal according to the first condition, and the purchase according to the second condition. When /sell signals are detected at the same time (AND condition), the item can be configured to be detected as a buy/sell group item, thereby providing more optimal trading timing to the user.

Going back to FIG. 2 and looking at the CM index creation unit 56, the CM index creation unit 56 includes the buy/sell set items generated by the market timing algorithm-based analysis unit 55 in the CM index to provide CM index information. creates .

At this time, the CM index information includes detailed cycle information used for inclusion in the buy/sell set. For example, if the detailed cycle of any purchase group item is ‘day (t3)’, the corresponding purchase group item information and detailed cycle (day (t3)) information are matched and stored in the CM index information.

Additionally, the CM index information generated by the CM index generation unit 56 is input to the CM index extraction unit 57 under the control of the control unit 50.

The CM index extraction unit 57 uses a preset verification algorithm to verify whether each buy/sell set item included in the CM index input from the CM index creation unit 56 is suitable for trading, and buys/sells that fail verification are The CM index is extracted by removing items from the sell set, but retaining the verified buy/sell set items.

At this time, various known techniques can be applied to determine the suitability for trading in the CM index extraction unit 57. For example, the suitability for trading of each buy/sell set item can be determined by analyzing market neutrality and financial statements, etc. there is.

Figure 6 is a block diagram showing the double signal acquisition unit of Figure 2.

The double signal capture unit 58 of FIG. 6 monitors each of the buy/sell set items of the CM index extracted by the CM index extractor 57 and captures the double signal.

In addition, as shown in FIG. 6, the double signal capture unit 58 includes a monitoring module 581, a lower grade score calculation module 582, a second comparison module 583, a double signal detection module 584, It consists of a sell double signal detection module (585) and a double signal information generation module (586).

The monitoring module 581 monitors the index flow of the buy/sell set items included in the CM index extracted by the CM index extractor 57.

The sub-grade score calculation module 582 extracts detailed cycles lower (shorter period) than the detailed cycle included in the corresponding CM index for each buy/sell set item, and then calculates a preset standard quantity (n) for each sub-detailed cycle. ) candles, the high price of each candle is compared with the high price of the previous candle, and the grade score (A'' for each sub-detailed cycle of the item is calculated in the same way as the grade score calculation module 553 of FIG. 4 described above). ) is calculated. In this case, lower refers to detailed cycles of shorter time than the corresponding detailed cycle.

In other words, the lower grade score calculation module 582 is: 1) When the cycle included in the CM index of the relevant buy/sell set item is 'month', candles with a cycle of weeks/days/hours/minutes, which is a shorter cycle than 'month' Calculate the grade score (A'') for each sub-detailed cycle of the item, 2) When the cycle included in the CM index of the relevant buy/sell group item is 'week', the cycle is shorter than 'week'. Calculate the grade score (A'') for each sub-detailed cycle of the item using candles with a cycle of /hour/minute, and 3) when the cycle of the relevant buy/sell group item is 'day', it is shorter than 'day'. The grade score (A'') for each sub-detailed cycle of the item is calculated using candles of the hour/minute cycle.

The second comparison module 583 sets the grade score (A'') for each sub-detailed cycle of each buy/sell set item calculated by the sub-grade score calculation module 582 to a preset first set value (TH1) and Compare with the second set value (TH2), and in detail, compare the grade score (A'') for each sub-detailed cycle of the buy set item with the first set value (TH1), and compare the sub-detail cycle of the sell set item. The grade score (A'') for each cycle is compared with the second set value (TH2).

The number of copies double signal detection module 584 detects, in the second comparison module 583, a number of sets of items in a sub-detailed cycle having a rating score (A'') less than or equal to the first set value (TH1). Detects (captures) double purchase signals for collective items.

For example, when the detailed cycle of the purchased item included in the CM index is 'week (t2)', the purchase double signal detection module 584 is a daily bar, hourly bar, or fractional bar that is a lower-detailed cycle than the weekly bar (T). Using , when the calculated grade score (A'') for each sub-detailed cycle is less than or equal to the first set value (TH1), a purchase double signal for the corresponding purchase set item can be captured.

The sell double signal detection module 585 operates in the second comparison module 583, when the detailed cycle of the selling group item included in the CM index is 'week (t2)', the daily candle with a detailed cycle lower than the weekly candle (T). If a selling group item in a lower cycle with a rating score (A'') higher than the second set value (TH2) is detected using the hourly bar or fractional bar, a selling double signal for the relevant selling group item is detected (captured). do.

For example, when the detailed cycle of the sell set item included in the CM index is 'daily bar', the sell double signal detection module 584 uses the hourly bar or fractional bar, which is a period lower than the daily bar (T), to determine the price of the relevant item. After calculating the grade score (A'') for each sub-detailed cycle, if the calculated grade score (A'') for each sub-detailed cycle is higher than the second set value (TH2), the sale double for the relevant selling group item. Signals can be captured.

The double signal information generation module 586 is executed when a buy double signal is captured by the buy double signal detection module 584 or a sell double signal is captured by the sell double signal detection module 585, and the detected item Generate double signal information that matches identification information and buy/sell double signals.

At this time, the control unit 50 controls the data transmitting and receiving unit 52 so that the double signal information generated by the double signal information generation module 586 is transmitted to the connected client 4.

FIG. 7 is a block diagram showing the data mining processing unit of FIG. 2.

As shown in FIG. 7, the data mining processing unit 59 includes a CM theme detection module 591, a valid score calculation module for each CM theme 592, a sorting module 593, a top theme detection module 594, and a data mining It consists of a processing module 595.

The CM theme detection module 591 uses preset item information for each theme to detect a CM theme, which is a theme that includes each buy/sell set item of the CM index extracted by the CM index extractor 57.

The effective score calculation module 592 for each CM theme calculates the effective score (N) for each CM theme using the CM theme detected by the CM theme detection module 591 and the following equation 1.

That is, the data mining unit 59 calculates a valid score (N) for each CM theme using the above equation.

The sorting module 593 sorts the CM themes according to the size of the effective score (N) calculated by the effective score calculation module 592 for each CM theme.

The top theme detection module 594 determines the top 10 CM themes as the top themes among the CM themes sorted by the sorting module 593.

The data mining processing module 595 extracts data related to the buy/sell set items of the CM index extracted by the CM index extractor 57, then analyzes and data mines the extracted data to detect valid information.

At this time, various known information can be applied as valid information generated by the data mining processing module 595. For example, the data mining processing module 591 of the present invention 1) analyzes accompanying/themed items to identify leading market trends At the same time, 2) analyze the correlation between specific stocks to extract dependent/co-events, and 3) extract macroeconomic variables according to the market strength level of specific financial products, ETF/ETN/raw materials/exchange rate financial products, etc. It may consist of:

FIG. 8 is a block diagram showing the scoring information generator of FIG. 2.

The scoring information generator 60 of FIG. 8 is executed under the control of the control unit 50 when the CM index is extracted by the CM index extractor 57 of FIG. 2.

In addition, as shown in FIG. 8, the scoring information generation unit 60 includes a CM index input module 601, an investment indicator-based analysis and scoring module 602, a financial indicator-based analysis and scoring module 603, and a value table. It consists of a base analysis and scoring module (604), an average score calculation module (605), a ranking calculation module (606), and a scoring information generation module (607).

When the CM index input module 601 receives the CM index information extracted by the CM index extraction unit 57, it extracts a set of items included in the CM index.

Figure 9 is a block diagram showing the investment indicator-based analysis and scoring module of Figure 8.

As shown in Figure 9, the investment index-based analysis and scoring module 602 includes an ROE-based weighted score granting module 6021, a ROA-based weighted score granting module 6022, a total weighted score calculation module 6023, and an investment index. It consists of a score calculation module (6024).

The ROE-based weighted score granting module (6021) uses the stock-related information of each purchase group item extracted by the CM index input module (601) to provide ROE (return on equity) information for each purchase group item. Extract.

At this time, ROE means net profit divided by average equity capital (ROE = net profit / average equity capital).

In addition, the ROE-based weighted score granting module (6021) utilizes the ROE information of each extracted purchase group item, sorts the purchase group items in descending order of ROE, and then assigns each purchase group item based on the ROE according to the sorted order. Weighted scores are assigned sequentially starting from '1'.

That is, assuming that the number of purchase group items is 'M', the ROE-based weighted score granting module 6021 assigns an ROE-based weighted score of '1' to the purchase group item with the lowest ROE and '1', and The ROE-based weighted score of the purchase group stocks is assigned 'M'.

For example, when 10 purchase group stocks are extracted, by the ROE-based weighted score granting module 6021, the purchase group stock with the lowest ROE is given an ROE-based weighted score of '1', and the purchase group stock with the highest ROE is given a weighted score based on '1'. Purchase group stocks are given a weighted score based on ROE of 'M'.

The ROA-based weighted score granting module (6022) uses the stock-related information of each purchase group item to extract ROA (Return on Assets) information for each purchase group item.

At this time, ROA means net profit divided by average total assets (ROA = net profit / average total assets).

In addition, the ROA-based weighted score granting module 6022 uses the ROA information of each extracted purchase group item, sorts the purchase group items in descending order of ROA, and then assigns each purchase group item based on ROA according to the sorted order. Weighted scores are assigned sequentially starting from '1'.

That is, assuming that the number of purchase group items is 'M', the ROA-based weighted score granting module 6022 assigns a ROA-based weighted score of '1' to the purchase group item with the lowest ROA and '1' for the purchase group item with the highest ROA. The ROA-based weighted score for purchase group items is assigned 'M'.

The total weighted score calculation module 6023 calculates the total weighted score (sum) by adding the ROE-based weighted score and the ROA-based weighted score for each purchase set item.

For example, when the ROE-based weighted score of purchase set item 'A' is '10' and the ROA-based weighted score is '13', the total weighted score calculation module 6023 calculates the ROE of the purchase set item 'A'. By adding the base weighted score '10' and the ROA-based weighted score '13', the total weighted score (sum) of the purchase set item 'A' is calculated as '23 (23 = 10 + 13)'.

The investment indicator score calculation module 6024 calculates the first multiplication value by multiplying the ROE-based weighted score by 0.5 for each purchase set item, and calculates the second multiplication value by multiplying the ROA-based weighted score by 0.5.

In addition, the investment index score calculation module 6024 calculates the investment index score (N1) by adding the first and second multiplication values for each purchase set item.

That is, the investment index score calculation module 6024 calculates the investment index score (N1, N1 = ( ROE-based weighted score

For example, when the ROE-based weighted score of purchase group item 'A' is '10' and the ROA-based weighted score is '13', the investment indicator score calculation module 6024 calculates the purchase group item 'A'. The investment index is '11.5' by adding '5', which is the ROE-based weighted score '10' multiplied by 0.5, and '6.5', which is the ROA-based weighted score '13' multiplied by 0.5 of the purchase group item 'A'. The score (N) is calculated.

Figure 10 is a block diagram showing the financial indicator-based analysis and scoring module of Figure 8.

As shown in Figure 10, the financial indicator-based analysis and scoring module 603 includes a D/E-based weighted score granting module 6031, a retention rate-based weighted score granting module 6032, and a second total weighted score calculation module ( 6033), and a score calculation module for financial indicators (6034).

The D/E-based weighted score granting module 6031 uses the stock-related information of each purchase group item extracted by the CM index input module 601, and uses the D/E (debt ratio, Dept To Capital) of each purchase group item. ) extract information.

In this case, D/E means total borrowings divided by total capital.

In addition, the D/E-based weighted score granting module 6031 uses the D/E information of each extracted purchase group item to sort the purchase group items in order of high D/E, and then each item is sorted according to the sorted order. D/E-based weighted scores are assigned to purchase group items in order, starting from '1'.

That is, assuming that the number of purchase group items is 'M', the D/E-based weighted score granting module 6031 assigns the D/E-based weighted score of the purchase group item with the highest D/E to '1'. And, the D/E-based weighted score of the purchase group stock with the lowest D/E is given as 'M'.

The reserve ratio-based weighted score granting module 6032 uses the stock-related information of each purchase group item to extract reserve ratio information for each purchase group item.

In this case, the reserve ratio means the surplus divided by the paid-in capital.

In addition, the retention rate-based weighted score granting module 6032 utilizes the retention rate information of each extracted purchase group item, sorts the purchase group items in descending order of retention rate, and then bases the retention rate on each purchase group item according to the sorted order. Weighted scores are assigned sequentially starting from '1'.

That is, assuming that the number of purchase group items is 'M', the retention rate-based weighted score granting module 6032 assigns a weighted score based on the retention rate of '1' to the purchase group item with the lowest retention rate and '1' to the purchase group item with the highest retention rate. A weighted score based on the retention rate of purchase group items is assigned 'M'.

The second total weighted score calculation module 6033 calculates a second total weighted score (sum) by adding up the D/E-based weighted score and the retention rate-based weighted score for each purchase set item.

For example, when the D/E-based weighted score of purchase group item 'A' is '5' and the ROA-based weighted score is '16', the second total weighted score calculation module 6033 calculates the purchase group item 'A'. By adding up A's D/E-based weighted score '5' and reservation rate-based weighted score '16', the second total weighted score (sum) of the purchase group item 'A' is '21 (21 = 5 + 16). ' is calculated.

The financial index score calculation module 6034 multiplies the D/E-based weighted score by 0.5 for each purchase set item and at the same time multiplies the retention rate-based weighted score by 0.5, and then adds these multiplied values to obtain the financial index score (N2). Calculate .

That is, the financial indicator score calculation module 6034 calculates the financial indicator scores (N2, N2) by adding up the D/E-based weighted score multiplied by 0.5 and the ROA-based weighted score multiplied by 0.5 for each purchase set item. = (D/E-based weighted score

Figure 11 is a block diagram showing the value indicator-based analysis and scoring module of Figure 8.

As shown in Figure 11, the value indicator-based analysis and scoring module 604 includes a P/A-based weighted score granting module 6041, a PBR-based weighted score granting module 6042, and a third total weighted score calculation module ( 6043), and a score calculation module for value indicators (6044).

The P/A-based weighted score granting module (6041) uses the stock-related information of each purchase group item extracted by the CM index input module (601) to determine the P/A (operating profit ratio to market capitalization) of each purchase group item. Extract information.

In this case, P/A refers to the operating profit ratio divided by market capitalization and multiplied by 100.

In addition, the P/A-based weighted score granting module 6041 uses the P/A information of each extracted purchase group item to sort the purchase group items in order of high P/A, and then each item is sorted according to the sorted order. P/A-based weighted scores are assigned to purchase group items in order, starting from '1'.

The PBR-based weighted score granting module 6042 uses the stock-related information of the purchase group items to extract PBR (Price Book Value Ratio) information for each purchase group item.

In this case, PBR means the stock price divided by the net asset value per share.

In addition, the PBR-based weighted score granting module 6042 utilizes the PBR information of each extracted purchase group item, sorts the purchase group items in order of highest PBR, and then assigns a PBR basis to each purchase group item according to the sorted order. Weighted scores are assigned sequentially starting from '1'.

That is, assuming that the number of purchase group items is 'M', the PBR-based weighted score granting module 6042 assigns a PBR-based weighted score of '1' to the purchase group item with the highest PBR and '1', and the PBR-based weighted score is assigned '1' to the purchase group item with the lowest PBR. The weighted score based on the PBR of the purchase group stocks is assigned 'M'.

The third total weighted score calculation module 6043 calculates the third total weighted score (sum) by adding up the P/A-based weighted score and the PBR-based weighted score for each purchase set item.

The value indicator score calculation module 6044 multiplies the P/A-based weighted score by 0.75 for each purchase set item and at the same time multiplies the PBR-based weighted score by 0.25, and then adds these multiplied values to obtain the value indicator score (N3). Calculate .

That is, the value index score calculation module 6034 calculates the value index score (N3, N3) by adding the P/A-based weighted score multiplied by 0.75 and the PBR-based weighted score multiplied by 0.25 for each purchase set item. = (P/A-based weighted score

Going back to FIG. 8, looking at the average score calculation module 605, the average score calculation module 605 calculates 1) the investment index score (N1) for each purchase set item calculated in the investment index-based analysis and scoring module 602; ) and 2) the financial index score (N2) for each purchase group item calculated in the financial indicator-based analysis and scoring module 603, 3) each purchase group item calculated in the value indicator-based analysis and scoring module 604 Using the value index score (N3), the average score (N) of each purchase group item is calculated.

At this time, the average score (N) is the value of the investment index score (N1) multiplied by '0.25', the financial index score (N2) multiplied by '0.25', and the value index score (N3) multiplied by '0.50'. It is calculated by adding up the multiplied values.

In other words, the average score calculation module 605 calculates the average score (N) of each purchase set item using Equation 2 below.

At this time, N: average score, N1: score for investment index, N2: score for financial index, and N3: score for value index.

The ranking calculation module 606 uses the average score (N) of each purchase group item calculated by the average score calculation module 605 to rank the purchase group items in order of the average score (N) from high to low. Calculate .

That is, the ranking calculation module 606 calculates the purchase group item with the highest average score (N) as rank '1' among the extracted M purchase group items, and ranks the purchase group item with the lowest average score (N) as '1'. It is calculated as rank ‘M’.

The scoring information generation module 607 generates ROE, ROA, total weighted score, investment index score (N1), D/E, retention rate, second total weighted score, financial index score (N2), for each purchase set item. Scoring information is generated by matching P/A, PBR, third total weighted score, value index score (N3), average score (N), and ranking information.

At this time, the scoring information generated by the scoring information generation module 607 is transmitted to the dedicated program 3 under the control of the control unit 50.

The investment portfolio creation unit 61 processes and analyzes leading market trends, dependent/accompanying events, and macroeconomic variables extracted by the data mining processing module 595 to create an investment portfolio.

FIG. 12 is a block diagram showing the reference value optimization unit of FIG. 2.

The reference value optimization unit 62 in FIG. 12 is executed at every preset period (T') under the control of the control unit 50.

In addition, the reference value optimization unit 62, as shown in FIG. 12, consists of a reference value optimization unit 621 based on buy group items and a reference value optimization unit 622 based on sell group items.

Figure 13 is a block diagram showing the purchase set item-based reference value optimization unit of Figure 12.

As shown in FIG. 13, the purchase group item-based reference value optimization unit 621 includes a purchase group item collection module 6211, a rebounding item extraction module 6212, a first missing item selection module 6213, and a first reference value. It consists of an optimization module (6214).

The purchase group item collection module 6211 collects the purchase group items of the CM index generated by the CM index generation unit 56 during the period T'.

The rebounding stock collection module 6212 uses a preset rebounding stock detection algorithm to analyze the stock-related information of each stock during a preset period (T'), and selects the rebounding stock, which is a stock that rebounded within the period (T'). Extract .

At this time, a rebounding stock refers to a stock that continued to decline for each detailed cycle and then switched to an upward trend, and the rebounding stock detection algorithm determines that the falling price of the closing price candles according to each detailed cycle continues for more than 'R1', and then the rising candle is 'R2'. This can be done by detecting the stock as a rebounding stock when it lasts for more than two days.

The first missing stock selection module 6213 compares the purchased stock items collected by the purchased stock collection module 6211 with the rebounded stocks extracted by the rebounded stock extraction module 6212, and determines whether they are included in the rebounded stock or not. , Stocks that are not included in the purchase set are selected as missing stocks.

For example, 'Stock A', 'Stock C', 'Stock D', 'Stock E', and 'Stock F' are collected as buying stocks, and 'Stock A' and 'Stock B' are collected as rebounding stocks. , When 'Item C' and 'Item E' are extracted, the first missing item selection module (6213) selects 'Item B', which is a stock included in the rebounding stock but not included in the purchase group stock, as a missing stock. You can.

The first reference value optimization module 6214 multiplies the quantity (n) of the missing item selected in the first missing item selection module 6213 by a preset constant 'β', and then multiplies the previous reference value 'α1' by the multiplication value (n Subtract β) to update the reference value 'α(α = α1 - (n × β))'.

In other words, the purchase group item of the present invention is a rebounding item that rebounded during the period (T') because the rating level (L) is set according to the range of the rating score (A) that is the sum of the rating scores (A') of each candle. Regarding , if it cannot be calculated as a purchase group item, it is judged that the standard value (α) given to each candle is set to a predetermined high level, and it is necessary to optimize the standard value (α) to a lower value than before.

In other words, the present invention does not use the standard value 'α' used for calculating purchase group items as a fixed value, but updates the previous standard value 'α1' to a smaller value depending on whether missing items are detected and the quantity (n), and this By repeating the operation every cycle (T'), the reference value (α) can be optimized.

At this time, if the size of the standard value (α) is set excessively high, the probability of missing items increases when calculating buy group items, and if the size of the standard value (α) is set excessively low, items are omitted when calculating sell group items. The probability that something like this will occur increases.

Figure 14 is a block diagram showing the sales set item-based reference value optimization unit of Figure 12.

As shown in FIG. 14, the selling set item-based reference value optimization unit 622 includes a selling set item collection module 6221, a falling item extraction module 6222, a second missing item selection module 6223, and a second reference value. It consists of an optimization module (6224).

The sale set item collection module 6221 collects the sell set items of the CM index generated by the CM index creation unit 56 during the period (T').

The falling stock collection module 6222 uses a preset falling stock detection algorithm to analyze the stock-related information of each stock during a preset period (T'), and selects falling stocks that are stocks that fell within the period (T'). Extract .

At this time, a falling stock refers to a stock that continued to rise for each detailed cycle and then switched to a falling trend, and the falling stock detection algorithm shows that the rising trend of closing price candles according to each detailed cycle continues for more than 'R1', and then the falling candle is 'R2'. This can be done by detecting the stock as a rebounding stock when it lasts for more than two days.

The second missing stock selection module (6223) compares the selling stock items collected by the selling stock collection module (6221) with the falling stocks extracted by the falling stock extraction module (6222), and determines whether they are included in the falling stocks or not. , Stocks that are not included in the selling group are selected as missing stocks.

The second reference value optimization module 6224 multiplies the quantity (n) of the missing item selected in the second missing item selection module 6223 by a preset constant 'β', and then multiplies the previous reference value 'α1' by the multiplication value (n β) is added to update the reference value 'α(α = α1 + (n × β))'.

In other words, the selling group item of the present invention is a reversal stock that has experienced a reversal during the period (T') because the rating level (L) is set according to the range of the rating score (A) that is the sum of the rating scores (A') of each candle. Regarding , if it is not possible to calculate it as a selling group item, it is judged that the standard value (α) given to each candle is set to a certain low level, and it is necessary to optimize the standard value (α) to a higher value than before.

In other words, the present invention does not use the standard value 'α' used in calculating selling group items as a fixed value, but updates the previous standard value 'α1' to a higher value depending on whether missing items are detected and the quantity (n), and this By repeating the operation every cycle (T'), the reference value (α) can be optimized.

Going back to Figure 2, looking at the dedicated program (3), when the dedicated program (3) is first run, it receives the identification information and login information of the securities company to be linked from the user, and requests authentication including the entered identification information and login information. The data is transmitted to the optimal trading timing server (5). At this time, if authentication is successful, the dedicated program (3) is connected to the account of the relevant securities company.

In addition, the dedicated program (3) displays the CM index information, double signal information, top theme information, and investment portfolio received from the dedicated program (3) through pre-made graphic user interfaces (GUIs).

At this time, as is known, GUIs may be composed of various graphics and configurations such as receiving a specific command from the user or displaying response data as requested, and these GUIs are commonly used in normal HTS or dedicated programs. Since this is a technology, detailed explanation will be omitted.

FIG. 15 is a flow chart showing a method of providing optimal trading timing information, which is the operation process of the optimal trading timing information providing system of FIG. 1.

As shown in Figure 15, the method of providing optimal trading timing information (S1) of the present invention includes a data collection step (S10), a market timing algorithm-based analysis step (S20), a rating score calculation step (S30), and a rating classification step. (S40), comparison step (S50), buy group item detection step (S60), sell group item detection step (S70), CM index creation step (S80), CM index extraction step (S90), scoring information creation step (S95) ), CM index and scoring information transmission step (S100), monitoring step (S110), double signal capture step (S120), double signal transmission step (S130), display step (S140), and reference value optimization step (S150).

The data collection step (S10) is a step in which the optimal trading timing providing server (5) receives stock-related information in real time from the securities company servers (7-1), ..., (7-N) and collects it.

The market timing algorithm-based analysis step (S20) is a step in which the optimal trading timing providing server (5) analyzes comparative item data for each item collected in the data collection step (S10) using a preset market timing algorithm.

At this time, the market timing algorithm uses comparison item candles of a preset standard quantity (n) for each preset period (T), and compares the comparison item value of each candle with the comparison item of the previous candle.

In addition, in Figures 11 and 12, for convenience of explanation, the comparison item is 'high price' as an example, but the comparison item can be comprised of 'opening price', 'closing price', 'transaction volume', and 'low price'.

In the rating score calculation step (S30), the optimal trading timing providing server (5) utilizes the analysis data detected by the market timing algorithm-based analysis step (S20) to calculate the high price of each candle according to the detailed cycle preset for each item. If exceeds (or exceeds) the high price of the previous candle, the reference value 'α' optimized by the reference value optimization step (S150) is assigned the grade score (A') FH of the candle, but the high price of each candle is equal to the high price of the previous candle. If it does not exceed (if it is less than), the rating score (A') of each candle is calculated by assigning '-α', which is the negative of the standard value, as the rating score (A') of the candle, and then summing them up to determine the item. This is the step of calculating the grade score (A) for each detailed cycle.

At this time, in the rating score calculation step (S30), if the comparison item is 'opening price', 'closing price' and 'transaction volume', the rating score (A) is calculated in the same manner as described above, but if the comparison item is 'low price' , if the low price of each candle exceeds the low price of the previous candle (lower price), the standard value 'α' is assigned as the grade score (A') of the candle, but if the low price of each candle does not exceed the low price of the previous candle, The grade score (A') of each candle is calculated by assigning '-α', which is the negative of the standard value, as the grade score (A') of the candle.

In the rating classification step (S40), the optimal trading timing providing server (5) uses the rating score (A) according to each detailed cycle for each item calculated in the rating score calculation step (S30) and a preset standard table to determine each This is the step to classify the grade level (L) for each item.

In the comparison step (S50), the optimal trading timing providing server (5) calculates the rating score (A) according to each detailed cycle of each item calculated in the rating score calculation step (S30) to the preset first and second set values (TH1). , TH2), this is the step to compare each.

In the purchase group item detection step (S60), the optimal trading timing providing server 5 determines, in the comparison step (S50), items with a rating score (A) below the first set value (TH1) as purchase group items. am.

The selling group item detection step (S70) is a step in which the optimal trading timing providing server 5 determines, in the comparison step (S50), items with a rating score (A) higher than the second set value (TH2) as selling group items. .

Meanwhile, although not shown in Figure 15, the rating score calculation step (S30) is 1) the first condition using any one of 'high price', 'opening price', 'closing price', and 'transaction volume' as a comparison item, and the rating score ( At the same time as calculating A1), 2) the grade score (A2) is calculated as the second condition with 'low price' as the comparison item, and the comparison step (S50) is the grade score (A1) of the first and second conditions, ( A2) are compared with the first and second set values (TH1) and (TH2), and in the purchase set item detection step (S60), the purchase signal of the first condition is detected and at the same time the purchase signal of the second condition is detected (And condition), the item is detected as a buy set item, and in the sell set item detection step (S70), when a sell signal of the first condition is detected and a sell signal of the second condition is detected at the same time, the item is detected as a sell set item. It can be configured to detect by item.

In the CM index creation step (S80), the optimal trading timing providing server (5) combines the buy group items detected in the buy group item detection step (S60) and the sell group items detected in the sell group item detection step (S70). This is the step of incorporating it into the CM index.

At this time, the CM index information includes detailed cycle information used for inclusion in the buy/sell set. For example, when a purchase group item is detected based on a week (t2), the purchase group item and detailed cycle (t2) information are matched and stored in the CM index information.

In the CM index extraction step (S90), the optimal trading timing providing server (5) uses a preset verification algorithm to determine whether each buy/sell set item of the CM index created by the CM index creation step (S80) is suitable for trading. This is the verification step.

In addition, the CM index extraction step (S90) is a step of extracting a CM index made up of only buy/sell sets that have been verified to be suitable for trading.

Figure 16 is a flow chart showing the scoring information generation step of Figure 15.

The scoring information generation step (S95) of FIG. 16 is a step in which the optimal trading timing providing server 5 generates scoring information for each purchase set item included in the CM index extracted by the CM index extraction step (S90).

As shown in Figure 16, the scoring information generation step (S95) includes a CM index input step (S951), an investment index-based analysis and scoring step (S952), a financial index-based analysis and scoring step (S953), and a value index-based step. It consists of an analysis and scoring step (S954), an average score calculation step (S955), a ranking step (S956), and a scoring information generation step (S957).

The CM index input step (S951) is a step in which the optimal trading timing providing server 5 extracts the purchase set items included in the CM index.

In the investment indicator-based analysis and scoring step (S952), the optimal trading timing providing server (5) extracts the ROE and ROA of each purchase group item, sorts the purchase group items in descending order of ROE, and then sorts them in the sorted order. Accordingly, ROE-based weighted scores are assigned to each purchase group item in order, starting from '1'. After sorting them in descending order of ROA, ROA-based weighted scores are assigned to each purchase group item in order, starting from '1', according to the sorted order. Assigned as is, the ROE-based weighted score and the ROA-based weighted score are added for each purchase group item to calculate the total weighted score (sum). At the same time, the ROE-based weighted score for each purchase group item is multiplied by 0.5 to obtain the first multiplication value. This is the step of calculating the second multiplication value by multiplying the ROA-based weighted score by 0.5 and then summing them to calculate the investment index score (N1).

In the financial indicator-based analysis and scoring step (S953), the optimal trading timing providing server (5) extracts the D/E and retention rate of each purchase group item, sorts the purchase group items in order of high D/E, According to the sorted order, a D/E-based weighted score is assigned to each purchase group item in order, starting from '1'. After sorting the purchase group items in order of lowest retention rate, each purchase group item is assigned to each purchase group item according to the sorted order. Weighted scores based on retention rate are assigned sequentially starting from '1', and the D/E-based weighted score and the reservation rate-based weighted score are added for each purchase group item to calculate the second total weighted score (sum), and for each purchase group item This is the step of multiplying the D/E-based weighted score by 0.5 and simultaneously multiplying the retention rate-based weighted score by 0.5, and then adding these multiplied values to calculate the score for financial indicators (N2).

In the value index-based analysis and scoring step (S954), the optimal trading timing providing server (5) extracts the P/A (operating profit ratio to market capitalization) and PBR information of each purchase group item, and then P/A determines the purchase group items. After sorting according to the highest order, P/A-based weighted scores are assigned to each purchase group item in order, starting from '1', according to the sorted order. After sorting the purchase group items according to the order of highest PBR, the sorted PBR-based weighted scores are assigned to each purchase group item in order, starting from '1', and the P/A-based weighted score and PBR-based weighted score are added for each purchase group item to create the third total weighted score (sum). This is the step of calculating the value index score (N3) by multiplying the P/A-based weighted score by 0.75 and the PBR-based weighted score by 0.25 for each purchase set item, and then adding these multiplied values.

In the average score calculation step (S955), the optimal trading timing providing server (5) calculates 1) the investment index score (N1) for each purchase group item calculated in the investment index-based analysis and scoring step (S952), and 2) the financial index. The financial index score (N2) for each purchase group item calculated in the base analysis and scoring step (S953), 3) the value index score (N3) for each purchase group item calculated in the value indicator-based analysis and scoring step (S954) ) is used to calculate the average score (N) of each purchase group item.

At this time, the average score (N) is the value of the investment index score (N1) multiplied by '0.25', the financial index score (N2) multiplied by '0.25', and the value index score (N3) multiplied by '0.50'. It is calculated by adding up the multiplied values.

In the ranking calculation step (S956), the optimal trading timing providing server (5) utilizes the average score (N) of each purchase group item calculated by the average score calculation step (S955), and selects items in the order of highest average score (N). This is the step of calculating the ranking of purchase items in descending order.

In the scoring information generation step (S957), the optimal trading timing providing server (5) generates ROE, ROA, total weighted score, investment index score (N1), D/E, retention rate, and second total weighted score for each purchase group item. , This is the step of generating scoring information by matching the financial index score (N2), P/A, PBR, third total weighted score, value index score (N3), average score (N), and ranking information.

In the CM index and scoring information transmission step (S100), the optimal trading timing providing server (5) combines the CM index information extracted by the CM index extraction step (S90) and the scoring information generated by the scoring information generation step (S95). , This is the step of transmitting to the connected client (4).

The monitoring step (S110) is a step in which the optimal trading timing providing server (5) monitors the index flow of the buy/sell set items included in the CM index extracted by the CM index extraction step (S90).

Figure 17 is a flow chart showing the double signal acquisition step of Figure 15.

As shown in Figure 17, the double signal capture step (S120) includes a rating score calculation step for each sub-cycle (S121), a second comparison step (S122), a buy double signal capture step (S123), and a sell double signal capture step ( It consists of a double signal information generation step (S124) and a double signal information generation step (S125).

In the grade score calculation step for each sub-cycle (S121), the optimal trading timing providing server 5 uses candles with a preset standard quantity (n) of a detailed cycle lower than the corresponding detailed cycle for each buy/sell set item. , This is a step in which the high price of each candle is compared with the high price of the previous candle, and the rating score (A') is calculated in the same manner as the rating score calculation step (S30) of FIG. 12 described above.

In this case, the sub-detailed cycle refers to detailed cycles having a shorter time than the corresponding detailed cycle.

For example, in the sub-cycle grade score calculation step (S121), when the detailed cycle used for a purchase group item or a sell group item is 'month (t1)', the detailed cycle shorter than 'month (t1)' ( The grade score (A'') of the item can be calculated with candles of each sub-detailed cycle of t2)/day (t3)/hour (t4)/minute (t5). As another example, a purchase group item or When the detailed cycle used for selling group items is 'week (t2)', candles of each sub-detailed cycle of days (t3)/hour (t4)/minute (t5), which is a shorter cycle than 'week (t2)' You can calculate the grade score (A'') for the item.

In the second comparison step (S122), the optimal trading timing providing server (5) calculates the rating score (A'') of each buy/sell set item for each sub-detailed cycle calculated by the rating score calculation step for each sub-cycle (S121). is compared with the preset first set value (TH1) and the second set value (TH2), and in detail, the grade score (A'') of each sub-detailed cycle of the purchase group item is compared to the first set value (TH1). ), but this is a step in which the grade score (A'') of each sub-detailed cycle of the selling group item is compared with the second set value (TH2).

In the purchase double signal capture step (S123), the optimal trading timing providing server 5 determines the sub-detailed cycle with a rating score (A'') below the first set value (TH1) in the second comparison step (S122). When a purchase group item is detected, this is the step to detect (capture) a buy double signal for the purchase group item.

In the selling double signal capture step (S124), the optimal trading timing providing server 5 sells a sub-detailed cycle with a rating score (A'') greater than the second set value (TH2) in the second comparison step (S122). When a collective item is detected, this is the step to detect (capture) a sell double signal for the relevant selling collective item.

In the double signal information generation step (S125), the optimal trading timing providing server (5) captures the buy double signal in the buy double signal detection step (S123) or the sell double signal is captured in the sell double signal detection step (S124). It is executed when this is done, and is a step to generate double signal information that matches the identification information of the detected item and the buy/sell double signal.

Going back to Figure 15, looking at the double signal transmission step (S120), the double signal transmission step (S120) is the optimal trading timing providing server (5) accessing the double signal information generated by the double signal capturing step (S120). This is the step of transmitting to the client (4).

In the display stage (S140), the dedicated program (3) receives the CM index information transmitted from the optimal trading timing providing server (5) through the CM index transmission step (S100) or the optimal trading timing providing server through the double signal transmission step (S130). This is the step of displaying the short-term trading signal information received from (5) on the monitor of the client (4) through the GUI (Graphic User Interface).

Meanwhile, although not shown in Figure 15, the method of providing optimal trading timing information (S1) proceeds in the CM index extraction step (S90), and creates an investment portfolio in parallel with the CM index transmission step (S100) and the monitoring step (S110). Includes more steps.

The investment portfolio creation step uses the item information for each preset theme to detect the CM theme, which is a theme that includes each buy/sell set item of the CM index extracted by the CM index extraction step (S90), using the following equation: Using the detected CM theme, the effective score (N) for each CM theme is calculated.

In addition, the investment portfolio creation step sorts the CM themes according to the size of the effective score (N) calculated through Equation 3, then determines the top 10 CM themes as the top themes among the sorted CM themes, and determines the top 10 CM themes as the top themes. Transmit theme information to the connected client.

Figure 18 is a flow chart showing the reference value optimization step of Figure 15.

The reference value optimization step (S150) of FIG. 18 is performed every preset period (T') in the optimal trading timing providing server (5).

In addition, the reference value optimization step (S150), as shown in FIG. 18, consists of a reference value optimization step for buy group items (S151) and a reference value optimization step for sell group items (S152).

In addition, the purchase group item reference value optimization step (S151) consists of a purchase group item collection step (S1511), a rebounding item extraction step (S1512), a first missing item selection step (S1513), and a first reference value optimization step (S1514).

The purchase group item collection step (S1511) is a step in which the optimal trading timing providing server 5 collects the purchase group items of the CM index created by the CM index creation step (S80) during the period (T').

In the rebounding item collection step (S1512), the optimal trading timing providing server 5 uses a preset rebounding item detection algorithm to analyze stock-related information for each item during a preset period (T'), and determines the period (T'). This is the step of extracting rebounding stocks, which are stocks that have rebounded within the process.

In the first missing item selection step (S1513), the optimal trading timing providing server 5 selects the purchase group items collected by the purchase group item collection step (S1511) and the rebound items extracted by the rebound item extraction step (S1512). This is the step of comparing stocks that were included in the rebound stocks but not included in the purchase group stocks as missing stocks.

In the first reference value optimization step (S1514), the optimal trading timing providing server 5 multiplies the quantity (n) of the missing item selected in the first missing item selection step (S1513) by a preset constant 'β', and then calculates the previous standard value. This is the step of updating the reference value 'α(α = α1 - (n × β))' by subtracting the multiplication value (n × β) from 'α1'.

In addition, the selling set item-based reference value optimization step (S152) consists of a selling set item collection step (S1521), a falling stock extraction step (S1522), a second missing stock selection step (S1523), and a second reference value optimization step (S1524). .

The sale set item collection step (S1521) is a step in which the optimal trading timing providing server 5 collects the sell set items of the CM index created by the CM index creation step (S80) during the period (T').

In the falling stock collection step (S1522), the optimal trading timing providing server 5 uses a preset falling stock detection algorithm to analyze stock-related information for each stock during a preset period (T'), and determines the period (T'). This is the step of extracting stocks that have fallen within the stock market.

In the second missing item selection step (S1523), the optimal trading timing providing server 5 selects the selling group items collected through the selling group item collection step (S1521) and the falling stocks extracted through the falling stock extraction step (S1522). This is the step of comparing stocks that were included in the falling stocks but not included in the selling stocks as missing stocks.

In the second reference value optimization step (S1524), the optimal trading timing providing server 5 multiplies the quantity (n) of the missing item selected in the second missing item selection step (S1523) by the preset constant 'β', and then calculates the previous standard value. This is the step of updating the reference value 'α(α = α1 + (n × β))' by adding the multiplication value (n × β) to 'α1'.

In this way, the optimal trading timing information providing system (1), which is an embodiment of the present invention, analyzes the stock-related information provided by the optimal trading timing providing server (5) from securities company servers, and provides a rating score for each detailed cycle for each item ( After calculating A), each stock is incorporated into the CM index according to the calculated grade score (A) and provided to the user. In response to index flows with complex and diverse volatility, change patterns are predicted to determine the optimal time. By allowing trading to take place, the user's rate of return can be maximized.

In addition, the optimal trading timing information providing system (1) of the present invention improves the accuracy and reliability of optimal trading timing detection by optimizing the reference value used for CM index inclusion at each preset cycle (T') by the optimal trading timing providing server (5). It can be raised even further.

In addition, the optimal trading timing information providing system (1) of the present invention uses a preset verification algorithm when the optimal trading timing providing server (5) creates a CM index to determine whether each buy/sell set item is suitable for trading. By being structured to do so, it is possible to improve the investor's profit security.

In addition, the optimal trading timing information providing system (1) of the present invention monitors the index flow of each buy/sell set item of the CM index when the optimal trading timing providing server (5) extracts the CM index and captures a double signal, By providing this to users, trading profits can be further increased.

In addition, the optimal trading timing information providing system (1) of the present invention is configured so that the optimal trading timing providing server (5) determines the optimal trading timing among stocks in the portfolio using a market timing algorithm, thereby effectively providing a low computational throughput. You can build a portfolio.

1: Optimal trading timing information provision system 3: Dedicated program
4-1, ..., 4-N: Client 5: Server providing optimal trading timing
7-1, ..., 7-N: Securities company server 10: Communication network
50: Control unit 51: Database unit
52: Data transmission/reception unit 53: Program management unit
54: Authentication processing unit 55: Market timing algorithm-based analysis unit
56:CM index creation unit 57:CM index extraction unit
58: Double signal capture unit 59: Data mining processing unit
60: Scoring information creation unit 61: Investment portfolio creation unit
62: Reference value optimization unit
551: Data collection module 552: Analysis module
553: Grade score calculation module 554: Grade classification module
555: Comparison module 556: Purchase set item detection module
557: Sale set item detection module 581: Monitoring module
582: Lower grade score calculation module 583: Second comparison module
584: Buy double signal detection module 585: Sell double signal detection module
586: Double signal information generation module 591: CM theme detection module
592: CM theme-specific valid score calculation module 593: Sorting module
594: Parent theme detection module 595: Data mining processing module
611: Purchase set item-based reference value optimization unit
612: Standard value optimization unit based on sale set items
6111: Purchased item collection module 6112: Rebounding item extraction module
6113: First missing item selection module 6114: First reference value optimization module
6121: Selling set collection module 6122: Falling stock extraction module
6123: Second missing item selection module 6124: Second standard value optimization module

Claims (8)

In the optimal trading timing information provision method (S1) to collect and analyze stock market data to capture the optimal trading timing and provide it to users:
Step 10 (S10) where the optimal trading timing providing server collects stock-related information transmitted from securities company servers in real time;
The optimal trading timing providing server analyzes the data collected in step 10 (S10), and calculates 'month (t1)', 'week (t2)', 'day (t3)', 'time (t4)' and Data is collected according to pre-set comparison items for each item for each detailed cycle that includes at least one of the 'minutes (t5)', and the data collected is analyzed using a pre-set market timing algorithm to obtain a buy signal. Step 200 (S200) of detecting items and selling group items having a sell signal;
Step 80 (S80) in which the optimal trading timing providing server generates CM index information including the buy group items and sell group items detected in step 200 (S200);
Step 100 (S100) in which the optimal trading timing providing server transmits the CM index information generated in step 80 (S80) to a connected client;
Comprising a step 140 (S140) in which the client displays the CM index information transmitted in step 100 (S100),
The comparison item is one of 'highest price', 'opening price', 'closing price', and 'transaction volume',
The step 200 (S200) is
The optimal trading timing providing server uses the data collected in step 10 (S10), a preset standard quantity (n) of candles for each detailed cycle for each item, and compares items for each candle. Step 20 (S20) of comparing the value of with the value of the comparison item of the previous candle;
The optimal trading timing providing server utilizes the analysis data detected in step 20 (S20), and if the value of the comparison item of each candle according to each detailed cycle of each item is greater than or equal to the value of the comparison item of the previous candle, the corresponding 'α', a preset standard value, is given as the candle's rating score (A'). However, if the value of the comparison item of each candle is less than the value of the comparison item of the previous candle, the standard value is given as the rating score (A') of the candle. After calculating the grade score (A') of each candle by assigning a negative number of '-α', the grade score (A') of each candle calculated is added to obtain the grade score for each detailed cycle of each item ( Step 30 (S30) of calculating A);
The optimal trading timing providing server compares the grade score (A) for each detailed cycle of each item calculated in step 30 (S30) with the preset first set value (TH1, Threshold1) and second set value (TH2). Step 50 (S50);
Step 60 (S60) wherein the optimal trading timing providing server determines, in step 50 (S50), an item having a rating score (A) lower than the first set value (TH1) as a purchase set item;
A step 70 (S70) in which the optimal trading timing providing server determines, in step 50 (S50), an item having a rating score (A) equal to or higher than the second set value (TH2) as a selling group item,
The step 80 (S80) is
The optimal trading timing providing server matches the buy set items and the corresponding detailed cycle information determined in step 60 (S60) and incorporates them into the CM index information, and adds the sell set items and the corresponding detailed cycle information determined in step 70 (S70). Matched and incorporated into CM index information,
The optimal trading timing information provision method (S1) is
Step 110 (S110), which proceeds after step 80 (S80), wherein the optimal trading timing providing server monitors the index flow of the buy/sell set items included in the CM index extracted by step 80 (S80);
Step 120 (S120) in which the optimal trading timing providing server captures a double signal through monitoring through step 110 (S110);
It further includes step 130 (S130) in which the optimal trading timing providing server transmits the double signal information captured in step 120 (S120) to the connected client,
The step 120 (S120) is
For each buy/sell set item, the optimal trading timing server utilizes candles with a preset standard quantity (n) for each sub-detailed cycle, which is a shorter time period than the corresponding detailed cycle, to determine the value of the comparison item of each candle. Step 121 (S121) of calculating a grade score (A'') for each sub-detailed cycle of each buy/sell set item by comparing it with the value of the comparison item of the previous candle;
The optimal trading timing providing server sets the grade score (A'') for each sub-detailed cycle of each buy/sell set item calculated in step 121 (S121) to the first setting value (TH1) and the second setting. Step 122 (S122) comparing with the value (TH2);
If the optimal trading timing providing server detects a purchase group item of a sub-detailed cycle having a rating score (A'') less than or equal to the first set value (TH1) in step 122 (S122), the purchase group item Step 123 (S123) of detecting a double signal for the number of copies;
When the optimal trading timing providing server detects a selling group item of a lower-detailed cycle having a rating score (A'') higher than the second set value (TH2) in step 122 (S122), it sends the selling group item to the relevant selling group item. Step 124 (S124) of detecting a double sell signal;
The process proceeds when the optimal trading timing providing server captures a buy double signal in step 123 (S123) or a sell double signal in step 124 (S124), and provides identification information and buy/sell information of the detected item. A method for providing optimal trading timing information (S1), which includes step 125 (S125) of generating double signal information matching double signals. According to claim 1, the optimal trading timing information providing method (S1) is
It proceeds at every cycle (T') preset by the optimal trading timing providing server, and further includes step 150 (S150) of optimizing the reference value,
The step 150 (S150) is
Step 1511 (S1511) in which the optimal trading timing providing server collects the purchase set items of the CM index created in step 80 (S80) during the period (T');
The optimal trading timing providing server uses a preset rebounding stock detection algorithm to analyze the stock-related information of each stock during the period (T') and extracts rebounding stocks, which are stocks that rebounded within the period (T'). Step 1512 (S1512);
The optimal trading timing providing server compares the purchase group items collected in step 1511 (S1511) with the rebounding items extracted in step 1512 (S1512), and determines which items are included in the rebounding items but are not included in the purchase group items. Step 1513 (S1513) of selecting items that have not been selected as missing items;
The optimal trading timing providing server multiplies the quantity (n) of the missing item selected in step 1513 (S1513) by a preset constant 'β', and then subtracts the multiplication value (n × β) from the previous reference value 'α1'. Including step 1514 (S1514) of updating the reference value 'α (α = α1 - (n × β))',
The step 30 (S30) is
Calculate the grade score (A) for each detailed cycle of each item using the reference value 'α' optimized in step 150 (S150),
The step 150 (S150) is
Step 1521 (S1521) in which the optimal trading timing providing server collects sale set items of the CM index created in step 80 (S80) during the period (T');
The optimal trading timing providing server uses a preset falling stock detection algorithm to analyze the stock-related information of each stock during a preset period (T') and selects falling stocks, which are stocks that fell within the period (T'). Extracting step 1522 (S1522);
The optimal trading timing providing server compares the selling items collected in step 1521 (S1521) with the falling items extracted in step 1522 (S1522), and determines which items are included in the falling items but are not included in the selling set. Step 1523 (S1523) of selecting items that have not been selected as missing items;
The optimal trading timing providing server multiplies the quantity (n) of the missing item selected in step 1523 (S1523) by a preset constant 'β', and then adds the multiplication value (n × β) to the previous reference value 'α1'. Method for providing optimal trading timing information (S1), further comprising step 1524 (S1524) of updating the reference value 'α (α = α1 + (n × β))'. In the optimal trading timing information provision method (S1) to collect and analyze stock market data to capture the optimal trading timing and provide it to users:
Step 10 (S10) where the optimal trading timing providing server collects stock-related information transmitted from securities company servers in real time;
The optimal trading timing providing server analyzes the data collected in step 10 (S10), and calculates 'month (t1)', 'week (t2)', 'day (t3)', 'time (t4)' and Data is collected according to pre-set comparison items for each item for each detailed cycle that includes at least one of the 'minutes (t5)', and the data collected is analyzed using a pre-set market timing algorithm to obtain a buy signal. Step 200 (S200) of detecting items and selling group items having a sell signal;
Step 80 (S80) in which the optimal trading timing providing server generates CM index information including the buy group items and sell group items detected in step 200 (S200);
Step 100 (S100) in which the optimal trading timing providing server transmits the CM index information generated in step 80 (S80) to a connected client;
Comprising a step 140 (S140) in which the client displays the CM index information transmitted in step 100 (S100),
The above comparison item is ‘low price’,
The step 200 (S200) is
The optimal trading timing providing server uses the data collected in step 10 (S10), a preset standard quantity (n) of candles for each detailed cycle for each item, and compares items for each candle. Step 20 (S20) of comparing the value of with the value of the comparison item of the previous candle;
The optimal trading timing providing server utilizes the analysis data detected in step 20 (S20), and if the value of the comparison item of each candle according to each detailed cycle of each item is less than the value of the comparison item of the previous candle, the corresponding A preset standard value 'α' is given as the candle's rating score (A'). However, if the value of the comparison item of each candle is greater than or equal to the value of the comparison item of the previous candle, the standard value is given as the rating score (A') of the candle. After calculating the grade score (A') of each candle by assigning a negative number '-α', the grade score (A') of each candle calculated is added to obtain the grade score (A') for each detailed cycle of each stock. ) Step 30 (S30) of calculating;
The optimal trading timing providing server uses the grade score (A) of each item for each preset period calculated in step 30 (S30) to a preset first set value (TH1, Threshold1) and a second set value ( Step 50 (S50) comparing with TH2);
Step 60 (S60) wherein the optimal trading timing providing server determines, in step 50 (S50), an item having a rating score (A) equal to or higher than the second set value (TH2) as a purchase group item;
A step 70 (S70) in which the optimal trading timing providing server determines, in step 50 (S50), an item having a rating score (A) of less than or equal to a first set value (TH1) as a selling group item,
The step 80 (S80) is
The optimal trading timing providing server matches the buy set items and the corresponding detailed cycle information determined in step 60 (S60) and incorporates them into the CM index information, and adds the sell set items and the corresponding detailed cycle information determined in step 70 (S70). A method of providing optimal trading timing information (S1), characterized by matching and incorporating it into the CM index information. According to claim 3, the optimal trading timing information providing method (S1) is
Step 110 (S110), which proceeds after step 80 (S80), wherein the optimal trading timing providing server monitors the index flow of the buy/sell set items included in the CM index extracted by step 80 (S80);
Step 120 (S120) in which the optimal trading timing providing server captures a double signal through monitoring through step 110 (S110);
It further includes step 130 (S130) in which the optimal trading timing providing server transmits the double signal information captured in step 120 (S120) to the connected client,
The step 120 (S120) is
For each buy/sell set item, the optimal trading timing server utilizes candles with a preset standard quantity (n) for each sub-detailed cycle, which is a shorter period than the corresponding cycle, to determine the value of the comparison item of each candle. Step 121 (S121) of calculating a grade score (A'') for each sub-detailed cycle of each buy/sell set item by comparing it with the value of the comparison item of the previous candle;
The optimal trading timing providing server sets the grade score (A'') for each sub-detailed cycle of each buy/sell set item for each sub-cycle calculated in step 121 (S121) to the first set value (TH1) and the Step 122 (S122) comparing with the second set value (TH2);
When the optimal trading timing providing server detects a purchase group item of a sub-detailed cycle having a rating score (A'') equal to or higher than the second set value (TH2) in step 122 (S122), it selects the purchase group item. Step 123 (S123) of detecting a double signal for the number of copies;
If the optimal trading timing providing server detects a selling set item of a sub-detailed cycle having a rating score (A'') less than or equal to the first set value (TH1) in step 122 (S122), the selling set item is Step 124 (S124) of detecting a sell double signal for;
The process proceeds when the optimal trading timing providing server captures a buy double signal in step 123 (S123) or a sell double signal in step 124 (S124), and provides identification information and buy/sell information of the detected item. A method for providing optimal trading timing information (S1), which includes step 125 (S125) of generating double signal information matching double signals. According to claim 4, the optimal trading timing information provision method (S1) is
It proceeds at every cycle (T') preset by the optimal trading timing providing server, and further includes step 150 (S150) of optimizing the reference value,
The step 150 (S150) is
Step 1511 (S1511) in which the optimal trading timing providing server collects the purchase set items of the CM index created in step 80 (S80) during the period (T');
The optimal trading timing providing server uses a preset rebounding stock detection algorithm to analyze the stock-related information of each stock during the period (T') and extracts rebounding stocks, which are stocks that rebounded within the period (T'). Step 1512 (S1512);
The optimal trading timing providing server compares the purchase group items collected in step 1511 (S1511) with the rebounding items extracted in step 1512 (S1512), and determines which items are included in the rebounding items but are not included in the purchase group items. Step 1513 (S1513) of selecting items that have not been selected as missing items;
The optimal trading timing providing server multiplies the quantity (n) of the missing item selected in step 1513 (S1513) by a preset constant 'β', and then adds the multiplication value (n × β) to the previous reference value 'α1'. Including step 1514 (S1514) of updating the reference value 'α (α = α1 + (n × β))',
The step 30 (S30) is
A method for providing optimal trading timing information (S1), characterized in that the grade score (A) for each detailed cycle of each item is calculated using the reference value 'α' optimized in step 150 (S150). The method of claim 5, wherein step 150 (S150) is
Step 1521 (S1521) in which the optimal trading timing providing server collects sale set items of the CM index created in step 80 (S80) during the period (T');
The optimal trading timing providing server uses a preset falling stock detection algorithm to analyze the stock-related information of each stock during a preset period (T') and selects falling stocks, which are stocks that fell within the period (T'). Extracting step 1522 (S1522);
The optimal trading timing providing server compares the selling items collected in step 1521 (S1521) with the falling items extracted in step 1522 (S1522), and determines which items are included in the falling items but are not included in the selling set. Step 1523 (S1523) of selecting items that have not been selected as missing items;
The optimal trading timing providing server multiplies the quantity (n) of the missing item selected in step 1523 (S1523) by a preset constant 'β', and then subtracts the multiplication value (n × β) from the previous reference value 'α1'. The method (S1) for providing optimal trading timing information further includes step 1524 (S1524) of updating the reference value 'α (α = α1 - (n × β))'. In claim 2 or 6, the method of providing optimal trading timing information (S1) further includes step 95 (S95) performed after step 80 (S80),
The step 95 (S95) is
Step 951 (S951) in which the optimal trading timing providing server extracts purchase set items included in the CM index created in step 80 (S80);
The optimal trading timing server uses the stock-related information of each purchase group item to extract the ROE and ROA of each purchase group item, sorts the purchase group items in descending order of ROE, and then distributes them in the sorted order. ROE-based weighted scores are assigned to each purchase group item in order, starting from '1'. After sorting them in descending order of ROA, ROA-based weighted scores are assigned to each purchase group item in order, starting from '1', according to the sorted order. The total weighted score (sum) is calculated by adding up the ROE-based weighted score and the ROA-based weighted score for each purchase group item. At the same time, for each purchase group item, the ROE-based weighted score is multiplied by 0.5 and the ROA-based weighting is calculated. Step 952 (S952) of calculating the score for the investment index (N1) by adding up the score multiplied by 0.5;
The optimal trading timing server uses the stock-related information of each purchase group item to extract the D/E and retention rate of each purchase group item, sorts the purchase group items in order of highest D/E, and then sorts them. D/E-based weighted scores are assigned to each purchase group item in order, starting from '1', and after sorting the purchase group items in descending order of retention rate, each purchase group item has a reservation rate according to the sorted order. Based weighting scores are assigned sequentially starting from '1', and the D/E-based weighting score and retention rate-based weighting score are added for each purchase group item to calculate the second total weighted score (sum), and for each purchase group item Step 953 (S953) of calculating a score for financial indicators (N2) by adding the D/E-based weighted score multiplied by 0.5 and the retention rate-based weighted score multiplied by 0.5;
The optimal trading timing server uses the stock-related information of each purchase group item to extract the P/A (operating profit ratio to market capitalization) and PBR information of each purchase group item, and then selects the purchase group items with a high P/A. After sorting in order, a P/A-based weighted score is assigned to each purchase group item in order, starting from '1', according to the sorted order. After sorting the purchase group items in the order of the highest PBR, the sorted order Accordingly, PBR-based weighted scores are assigned to each purchase group item in order, starting from '1', and the P/A-based weighted score and PBR-based weighted score are added for each purchase group item to create the third total weighted score (sum). Step 954 (S954) of calculating the value index score (N3) by adding the P/A-based weighted score multiplied by 0.75 and the PBR-based weighted score multiplied by 0.25 for each purchase set item;
The optimal trading timing providing server multiplies the investment index score (N1) calculated in step 952 (S952) by '0.25' and the financial index score (N2) calculated in step 953 (S953). Step 955 (S955) of calculating an average score (N) by adding the value multiplied by '0.25' and the value index score (N3) calculated in step 954 (S954) multiplied by '0.50';
The optimal trading timing providing server uses the average score (N) of each purchase group item calculated in step 955 (S955) to rank the purchase group items in order of the average score (N) from high to low. Calculating step 956 (S956);
The optimal trading timing providing server utilizes the information detected in steps 952 to 956 to calculate ROE, ROA, total weighted score, investment index score (N1), D/E, and retention rate for each purchase group item. , Generate scoring information by matching the second total weighted score, financial index score (N2), P/A, PBR, third total weighted score, value index score (N3), average score (N), and ranking information. Including step 957 (S957),
The step 100 (S100) is
A method (S1) of providing optimal trading timing information, characterized in that the optimal trading timing providing server transmits the scoring information generated in step 95 (S95) to the connected client. The method of claim 7, wherein the optimal trading timing information providing method (S1) includes step 40 (S40) performed after step 30 (S30) and step 90 (S90) performed after step 80 (S80). Contains more,
The step 40 (S40) is
The optimal trading timing server uses a standard table in which the rating level (L) is matched to each range of the rating score (A) and calculates the rating score (A) for each detailed cycle of each item calculated in step 30 (S30). Detect the corresponding grade level (L),
The step 90 (S90) is
Optimal trading, characterized in that the optimal trading timing providing server analyzes the market neutrality or financial statements of each buy/sell set item in the CM index created in step 80 (S80) and verifies the suitability for trading of the item. Timing information provision method (S1).