KR20230020723A - Method for detecting optimal trading time and, system for providing investment portfolio therewith - Google Patents

Abstract

The present invention relates to a method for calculating the optimal trading timing and a system and A method for providing an investment portfolio using the same. A portfolio management server is configured to analyze stock-related information provided from securities company servers, calculate a grade score (A) for each item, and incorporate each stock into a CM index based on the calculated grade score (A) to provide the CM index to users so as to allow for transactions to be made at the optimal timing by predicting change patterns in response to the complex and various volatility of index flows, thereby maximizing the users' return on investment. When creating the CM index, the portfolio management server is configured to determine whether each buy/sell set stock is appropriate for the transaction by using a pre-set verification algorithm, thereby improving an investor's profit safety. When extracting the CM index, the portfolio management server is configured to monitor the index flow of each buy/sell set stock in the CM index to capture the double signal, and to provide the index flow to the user, thereby increasing trading profits.

Description

Method for detecting optimal trading timing, and system and method for providing investment portfolio using the same Method for detecting optimal trading time and, system for providing investment portfolio therewith}

The present invention relates to a method for detecting optimal trading timing, and a system and method for providing an investment portfolio using the same. By creating a CM index that includes buy/sell group items and providing it to the user, trading is performed at the optimal time in response to the index flow to maximize the user's rate of return, as well as to develop a portfolio for the analysis target. It relates to a method for detecting an optimal trading timing capable of improving investment safety and a system and method for providing an investment portfolio using the same.

Recently, as the Internet infrastructure expands and the digital device industry develops, online stock trading programs such as a Home Trading System (HTS) and a Mobile Trading System (MTS) are widely used.

Typically, online stock trading programs process stock-related information (quote, stock price, quoted quantity, trading volume, buying volume, selling volume, etc.) at the request of the connected user and convert it into text, table or chart to provide information. It provides a trading agency service that provides information, procedures, and conclusion services for stock trading.

In general, a user selects one of the various online stock trading programs circulating on the market in consideration of various factors such as convenience of operation, ease of reading information, variety of provided information, chart display method, trading procedure, etc. select and use

These stock markets are determined by complex and diverse variables such as performance, news, national policies, contracts, and market interest, as well as the trading price and trading volume. In other words, if you look at the flow (trend) of the trading price and trading volume of a particular item, you can predict the future flow of that item.

However, since there are many stocks in the stock market, it is impossible for users to understand and analyze the flow of all stocks one by one, so the optimal trading timing is often missed. Irrational trading, such as buying stocks or selling stocks that are already at the bottom, causes a problem in that the rate of return decreases or the rate of loss increases.

Accordingly, various studies are being conducted on a portfolio that analyzes stock information of a securities company's server to detect optimal trading information and then provides the information to users.

Korean Registered Patent No. 10-2134664 (Title of Invention: Method for Calculating the Optimal Trading Quantity for Each Item According to the Robo-Advisor Portfolio) analyzes and utilizes the portfolio output from the Robo-Advisor algorithm, Considering the size, an optimal trading quantity calculation method has been disclosed that calculates and provides the optimal trading quantity for each recommended item. Since it is configured to calculate the trading quantity, it has a structural limitation in not providing optimal trading timing information at all.

In other words, by analyzing the index change of each stock and predicting the future flow at the same time, calculating the stock that meets the optimal timing as a trading target and providing it to the user, not only can the return rate compared to the same conditions be maximized, but also the loss rate can be reduced. There is an urgent need for research on the advisor system that exists.

The present invention is to solve this problem, and the problem of the present invention is to analyze the stock-related information provided by the portfolio management server from the securities company servers, calculate the rating score (A) for each item, and then calculate the grade It is configured to incorporate each item into the CM index according to the score (A) and provide it to the user, thereby responding to the index flow with complex and diverse volatility, predicting the change pattern and making trading at the optimal time, so that the user's It is to provide a method for detecting an optimal trading timing capable of maximizing the rate of return, and a system and method for providing an investment portfolio using the same.

In addition, another problem of the present invention is that the portfolio and the management server are configured to use a preset verification algorithm when creating the CM index to determine whether each buy / sell group item is suitable for trading, thereby improving the investor's profit safety It is to provide an optimal trading timing detection method that can be improved, and a system and method for providing an investment portfolio using the same.

In addition, another problem of the present invention is that the portfolio management 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 provide it to the user, thereby increasing trading profits It is to provide a method for detecting an optimal trading timing that can be further improved, and a system and method for providing an investment portfolio using the same.

In addition, another problem of the present invention is that the portfolio management server is configured to determine the optimal trading timing among the items belonging to the portfolio using a market timing algorithm, so that the optimal trading timing can be effectively configured with a small amount of computational processing. Detecting It is to provide a method, and an investment portfolio providing system and method using the same.

The solution of the present invention for solving the above problems is in the optimal trading timing calculation method (S1) for capturing the optimal trading timing by collecting and analyzing stock market data: stocks received by the portfolio management server from the securities company servers Step 10 (S10) of collecting relevant information; Through the data collected by the portfolio management server in step 10 (S10), 'month (T1)', 'week (T2)', 'day (T3)', 'hour (T4)' and 'minute ( T5) collects data according to the preset comparison items of each item for each cycle including at least one of 'T5)', and analyzes the collected data using a preset market timing algorithm to generate a buy set item with a buy signal and a sell signal. Step 200 (S200) of detecting sold collective items having ; Step 80 (S80) of the portfolio management server generating CM index information including the purchase set item and the sell set item detected by the step 200 (S200); Step 90 (S90) of the portfolio management server transmitting the CM index information generated by step 80 (S80) to the connected client; Step 140 (S140) of displaying the CM index information received by the client in step 90 (S90).

In addition, in the present invention, the comparison item is any one of 'high price', 'open price', 'close price' and 'transaction volume', and the step 200 (S200) is the collection of the portfolio management server by the step 10 (S10). 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 by using the candles of the preset reference number (n) for each preset period (T) through the data; If the portfolio management server utilizes the analysis data detected in step 20 (S20) and the value of the comparison item of each candle according to the preset cycle for each item is greater than or equal to the value of the comparison item of the previous candle, the rating score The constant 'α' is added to (A), but if the value of the comparison item of each candle is less than the value of the comparison item of the previous candle, the score of each candle is calculated by subtracting the constant 'α' from the rating score (A). Step 30 (S30) of calculating a rating score (A) by summing; The portfolio management server compares the rating score (A) of each item for each predetermined period calculated by the step 30 (S30) with the first set values (TH1, Threshold1) and the second set values (TH2). Step 50 (S50); Step 60 (S60) of the portfolio management server determining, in step 50 (S50), an item having a rating score (A) equal to or less than the first set value (TH1) as a purchase set item; In the step 50 (S50), the portfolio management server includes a step 70 (S60) of determining an item having a rating score (A) equal to or higher than the second set value (TH2) as a sold set item, and the step 80 ( S80), the portfolio management server matches the buy set item and corresponding period (T) information determined in step 60 (S60) and incorporates them into CM index information, and sells set item and corresponding period determined in step 70 (S70) (T) It is desirable to match the information and incorporate it into the CM index information.

In addition, in the present invention, the optimal trading timing calculation method (S1) proceeds after the step 80 (S80), and the portfolio management server purchases / sells collective items included in the CM index extracted by the step 80 (S80) Step 110 of monitoring the exponential flow of (S110); Step 120 (S120) of the portfolio management server capturing a double signal through monitoring through step 110 (S110); The portfolio management server further includes a step 130 (S130) of transmitting the double signal information captured by the step 120 (S120) to the connected client, and the step 120 (S120) is the step 120 (S120) wherein the portfolio management server sends Regarding the selling group item, the value of the comparison item of each candle is compared with the value of the comparison item of the previous candle using the candlestick of the preset standard quantity (n) of the sub-cycle, which is a period of time shorter than the corresponding period. Step 121 of calculating a score (A') (S121); The portfolio management server calculates the rating score (A') of each buy/sell set item for each sub-cycle calculated by the step 121 (S121) as the first set value (TH1) and the second set value (TH2). Comparing step 122 (S122); In the step 122 (S122), when the portfolio management server detects a purchase set item of a lower period having a rating score (A') equal to or less than the first set value (TH1), a buy double signal for the corresponding purchase set item Step 123 of detecting (S123); In the step 122 (S122), when the portfolio management server detects a sold set item of a lower period having a rating score (A') equal to or higher than the second set value (TH1), it sends a selling double signal for the corresponding sold set item. Detect step 124 (S124); The portfolio management server proceeds when the buy double signal is captured by the step 123 (S123) or when the sell double signal is captured by the step 124 (S124), and the identification information of the detected item and the purchase/sell double signal are received. It is preferable to include a step 125 (S125) of generating double signal information matching .

In addition, in the present invention, the comparison item is 'low price', and the step 200 (S200) is the portfolio management server through the data collected by the step 10 (S10), a preset reference quantity for each preset period (T). 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 by using the candle of (n); If the portfolio management server utilizes the analysis data detected in step 20 (S20) and the value of the comparison item of each candle according to the preset cycle for each item is less than the value of the comparison item of the previous candle, the rating score The constant 'α' is added to (A), but if the value of the comparison item of each candle is higher than the value of the comparison item of the previous candle, the score of each candle is calculated by subtracting the constant 'α' from the rating score (A). Step 30 (S30) of calculating a rating score (A) by summing; The portfolio management server converts the rating score (A) of each item for each predetermined period calculated in step 30 (S30) to a first set value (TH1, Threshold1) and a second set value (TH2). Comparing with step 50 (S50); Step 60 (S60) of the portfolio management server determining, in step 50 (S50), an item having a rating score (A) greater than or equal to a second set value (TH2) as a purchase set item; In the step 50 (S50), the portfolio management server includes a step 70 (S60) of determining an item having a rating score (A) equal to or less than a first set value (TH1) as a sold set item, and the step 80 ( S80), the portfolio management server matches the buy set item and corresponding period (T) information determined in step 60 (S60) and incorporates them into CM index information, and sells set item and corresponding period determined in step 70 (S70) (T) It is desirable to match the information and incorporate it into the CM index information.

In addition, in the present invention, the optimal trading timing calculation method (S1) proceeds after the step 80 (S80), and the portfolio management server purchases / sells collective items included in the CM index extracted by the step 80 (S80) Step 110 of monitoring the exponential flow of (S110); Step 120 (S120) of the portfolio management server capturing a double signal through monitoring through step 110 (S110); The portfolio management server further includes a step 130 (S130) of transmitting the double signal information captured by the step 120 (S120) to the connected client, and the step 120 (S120) is the step 120 (S120) wherein the portfolio management server sends Regarding the selling group item, the value of the comparison item of each candle is compared with the value of the comparison item of the previous candle using the candlestick of the preset standard quantity (n) of the sub-cycle, which is a period of time shorter than the corresponding period. Step 121 of calculating a score (A') (S121); The portfolio management server calculates the rating score (A') of each buy/sell set item for each sub-cycle calculated by the step 121 (S121) as the first set value (TH1) and the second set value (TH2). Comparing step 122 (S122); In the step 122 (S122), when the portfolio management server detects a purchase set item of a lower period having a rating score (A') equal to or higher than the second set value (TH2), it sends a buy double signal for the purchase set item. Detect step 123 (S123); In the step 122 (S122), when the portfolio management server detects a sold collective item of a lower period having a rating score (A') equal to or less than the first set value (TH1), a selling double signal for the corresponding sold collective item Step 124 of detecting (S124); The portfolio management server proceeds when the buy double signal is captured by the step 123 (S123) or when the sell double signal is captured by the step 124 (S124), and the identification information of the detected item and the purchase/sell double signal are received. It is preferable to include a step 125 (S125) of generating double signal information matching .

In the present invention, the optimal trading timing calculation method (S1) further includes step 40 (S40), in which the portfolio management server determines that the grade level (L) for each range of grade scores (A) is It is preferable to detect the grade level (L) corresponding to the grade score of each item for each predetermined period (T) calculated by the step 30 (S30) by using the matched reference table.

In addition, in the present invention, the optimal trading timing calculation method (S1) further includes a step 90 (S90) performed after the step 80 (S80), wherein the step 90 (S90) is performed by the portfolio management server in the step 80 (S80). ), it is desirable to verify market neutrality or financial statements for each buy/sell group item of the CM index created by the CM Index to verify the trading suitability of the item.

According to the present invention having the above problems and solutions, the portfolio management server analyzes the stock-related information provided from the securities company servers, calculates the rating score (A) for each item, and then calculates the rating score (A) according to the calculated rating score (A). By incorporating each item into the CM index and providing it to users, it responds to index flows with complex and diverse volatility, predicts change patterns, and enables trading at the optimal time to maximize the user's rate of return. do.

In addition, according to the present invention, when the portfolio and the management server create the CM index, it is configured to use a preset verification algorithm to determine whether each buy/sell group item is suitable for trading, thereby improving the investor's profit safety. .

In addition, according to the present invention, when the portfolio management server extracts the CM index, it is configured to monitor the index flow of each buy / sell set item of the CM index, capture the double signal, and provide it to the user, thereby further increasing the trading profit do.

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

1 is a block diagram showing a system for providing an investment portfolio according to an embodiment of the present invention.
Figure 2 is a block diagram showing the portfolio management server of Figure 1;
FIG. 3 is a conceptual diagram for explaining FIG. 2 .
FIG. 4 is a block diagram illustrating an analysis unit based on a market timing algorithm of FIG. 1 .
5 is an exemplary view for explaining the rating score calculation module of FIG. 4 .
FIG. 6 is a block diagram illustrating a double signal capture unit of FIG. 2 .
FIG. 7 is a block diagram illustrating the data mining processing unit of FIG. 2 .
FIG. 8 is a flow chart showing a method for detecting optimal trading timing in the investment portfolio providing system of FIG. 1 .
FIG. 9 is a flowchart showing the double signal acquisition step of FIG. 8 .

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

1 is a block diagram showing a system for providing an investment portfolio according to an embodiment of the present invention.

The investment portfolio providing system 1, which is an embodiment of the present invention, analyzes the stock-related information transmitted from the securities company server, and generates a CM index including purchase / sale collective items that meet the optimal trading (buying / selling) timing This is not only to maximize the user's rate of return by trading at the optimal time in response to the index flow, but also to improve investment safety by providing a portfolio for analysis.

In addition, as shown in FIG. 1, the investment portfolio providing system 1 of the present invention analyzes the index flow of each item by analyzing the collected stock-related information, and buys/sells a set item to be included in the CM index at a preset period. After calculating each (T), a portfolio management server 5 that generates an investment portfolio based on the calculated CM index, and ordinary securities company servers 7-1, ..., (7-N), Clients (4-1), ..., (4-N), which are terminals possessed by users, and applications installed in each client (4) to provide portfolio services to users in conjunction with the portfolio management server (5) Program portfolio program (3), portfolio management server (5), securities company server (7-1), ..., (7-N) and clients (4-1), ..., (4-N ) consists of a communication network 10 providing a data movement path between them.

At this time, in the present invention, for convenience of description, the portfolio management server 5 has been described as an example in which stock-related information is collected in conjunction with the securities company servers 7-1, ..., and (7-N). The object of providing stock-related information to the portfolio management server 5 is not limited to the securities company servers 7-1, ..., (7-N), and the Korea Stock Exchange, KOSPI server or KOSDAQ ( It goes without saying that a stock exchange server such as a KOSDAQ) server or the like can be applied.

The communication network 10 is between the stock trading management server 5, the securities company server 7-1, ..., (7-N) and the clients 4-1, ..., (4-N) It is a network that supports data communication, and in detail, it may be composed of wired and wireless networks such as a wide area network (WAN) and a local area network (LAN), a mobile communication network, 3G, LTE, and 4G.

The securities company servers (7-1), ..., (7-N) process and analyze various indicators and signals for various securities traded through the stock trading market, It is a server that creates stock-related information including the amount of sales and items for each theme, and at the same time provides regular stock trading services.

In addition, the securities company servers 7-1, ..., and 7-N provide stock-related information to the portfolio management server 5 in real time.

In addition, when the securities company servers (7-1), ..., (7-N) receive a log-in request from the portfolio management server (5), they authenticate the log-in of the requested client (4) by utilizing pre-registered log-in information. and transmits the authentication result data to the portfolio management server (5). At this time, the portfolio management server 5 transmits the authentication result data received from the corresponding securities company server 7 to the corresponding portfolio program 3.

Clients (4-1), ..., (4-N) are digital devices possessed by users, and in detail, desktop PCs (Desk-top PCs), notebooks (Note-books), and smartphones. (Smart phone), tablet PC (Tablet PC), and the like.

In addition, the clients (4-1), ..., (4-N) have a portfolio program (3) for providing an interface between the user and the portfolio management server (5) in conjunction with the portfolio management server (5). installed

In addition, the clients 4-1, ..., (4-N) access the portfolio management server 5 under the control of the portfolio program 3 to request information and transmit response data according to the request. receive

2 is a block diagram showing the portfolio management server of FIG. 1, and FIG. 3 is a conceptual diagram for explaining FIG.

As shown in FIGS. 2 and 3, the portfolio management server 5 includes a control unit 50, a database unit 51, a data transmission and reception unit 52, a program management unit 53, an authentication processing unit 54, and a market timing It consists of an algorithm-based analysis unit 55, a CM index generation unit 56, a CM index extraction unit 57, a double signal capture unit 58, a data mining processing unit 59, and an investment portfolio generation unit 60.

The control unit 50 is the O.S (Operating System) of the portfolio management server 5, and the control target 51, (52), (53), (54), (55), (56), (57), ( 58), (59), (60) are managed and controlled.

In addition, when the controller 50 receives real-time transmission of stock-related information from the securities company servers 7-1, ..., (7-N) through the data transceiver 52, the transmitted stock-related information is stored in a preset field. It is classified by category and stored in the database unit 51.

In addition, when the investment portfolio is generated by the investment portfolio generator 60, the control unit 50 controls the data transceiver 52 to transmit the generated investment portfolio to the connected client 4.

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

In addition, the database unit 51 stores information on a preset period (T). At this time, the period (T) consists of ‘month (T1)’, ‘week (T2)’, ‘day (T3)’, ‘hour (T4)’, and ‘minute (T5)’.

In addition, the database unit 51 stores information on preset comparison items. At this time, the comparison item may be composed of 'high price', 'low price', 'open price', 'transaction volume', and 'closing price'.

In addition, the database unit 51 stores a preset market timing algorithm.

At this time, the market timing algorithm refers to an algorithm that uses the collected stock-related information as input data and outputs rating information for each item and buy/sell set items for each predetermined period (T). The description of this market timing algorithm will be described later. It will be described in detail in FIG. 4 .

In addition, 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 outputs whether or not each buy/sell collective issue is suitable for trading by taking the buy/sell collective issue and stock-related information of the CM index generated by the market timing algorithm as input data.

In addition, the database unit 51 stores CM index information for each period (T) extracted by the CM index extractor 57 and investment portfolio information generated by the investment portfolio generator 60 .

The data transmission/reception unit 52 transmits and receives data with the connected clients 4-1, ..., (4-N) and securities company servers 7-1, ..., (7-N). .

The program manager 53 manages overall services for the portfolio program 3, such as updates.

When the authentication processing unit 54 receives authentication request data including securities company identification information and login information from the portfolio program 3 through the data transceiver 52, it performs authentication in conjunction with the securities company server 7, The authentication result information transmitted from the corresponding securities company server 7 is transmitted to the corresponding portfolio program 3. At this time, if the authentication is successful, the portfolio program (3) is linked to the user's account opened at the securities company, and the portfolio program (3) is directly linked with the securities company server (7) without going through the portfolio management server (5) It may be configured to perform login authentication.

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

FIG. 4 is a block diagram illustrating an analysis unit based on a market timing algorithm of FIG. 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 according to preset comparison items through stock-related information transmitted from the securities company servers 7-1, ..., and 7-N.

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. It is possible to collect low-cost data by item.

Hereinafter, in the present invention, for convenience of description, a case in which the comparison item is set to 'high price' will be described as an example.

The analysis module 552 analyzes the collected high price data for each item by 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 for each predetermined period (T) based on the candles of the predetermined reference quantity (n).

That is, the analysis module 552 determines that the period T is set to 'month (T1)', 'week (T2)', 'day (T3)', 'time (T4)', and '15 minutes (T5)'. Assuming, 1) Comparison operation processing is performed using n candles of the 'Month (T1)' bar, and 2) Comparison operation processing is performed using n candles of the 'Week (T2)' bar, 3) Comparison operation processing is performed using n candles of the 'day (T3)' bar, 4) comparison operation processing is performed using n candles of the 'time (T4)' bar, 5) '15 Comparison operation processing is performed using n number of candles of the 'minute (T5)' bar.

In other words, assuming that the period T consists of 5 months/weeks/days/hours/15 minutes, the analysis module 552 performs comparison calculation processing according to each of the 5 periods.

The rating score calculation module 553 utilizes the n number of candles of each item through the analysis data for each period detected by the analysis module 552 using the market timing algorithm, so that the high price of each candle is higher than that of the previous candle. If the high of each candle exceeds (if it is less than) the high of the previous candle, the rating score (A) is added with the constant 'α'. The rating score (A) is calculated by summing the scores of each candle in a subtractive manner.

In addition, if the rating point (A) is '-', the rating score calculation module 553 replaces the corresponding rating point (A) with '0'.

That is, if all n candles are higher in price than the previous candle, the rating score calculation module 553 calculates the rating score (A) as 'n X α', and among the n candles, the one with a lower price than the previous candle If the number of candles is higher than that of the previous candle, the rating score (A) is calculated as '0'.

At this time, if the comparison items are 'closing price', 'transaction volume', and 'open price', the rating score calculation module 553, as described above, if the comparison items of each candle exceed (or exceed) the comparison items of the previous candlestick , The constant 'α' is added to the rating score (A), but if the comparison item of each candle does not exceed (less than) the comparison item of the previous candle, 'α' is subtracted from the rating score (A), so that each candle A rating score (A) is calculated.

In addition, if the comparison item is 'low', the rating score calculation module 553 utilizes n candles of each item for each predetermined period (T), so that the low price of each candle exceeds the low price of the previous candle (low price). , constant 'α' is added to the rating score (A), but if the low price of each candle does not break through the low price of the previous candle, the rating score of each candle is subtracted by the constant 'α' from the rating score (A). Calculate (A).

5 is an exemplary view for explaining the rating score calculation module of FIG. 4 .

For example, as shown in FIG. 5, when the reference quantity n is '10' and the constant 'α' is '10', the rating score calculation module 553 performs a predetermined cycle (T, month/week). / day / hour / minute) calculates the rating score (A) of each event, and when the period (T) is 'week', for example, the rating score calculation module 553 calculates the 10 main bars of the event Using , compare the high of each candle with the high of the previous candle, and if the current high exceeds the high of the previous candle, add '10', and if the current high does not exceed the high of the previous candle, '10' After deducting , the scores of each candle are added to calculate the grade score (A) of the item as '0'. At this time, since the actual value obtained by summing the scores of each candle is '-30', the rating score calculation module 553 replaces '-30' with '0' to calculate the final value.

The rating classification module 554 determines the rating score (A) of each item for each period calculated by the rating score calculation module 553, a preset standard in which the range of the rating score (A) and the rating level (L) are matched. Using the table, classify and set the grade for each event.

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

Table 1 below is an example of a standard table when the comparison items are 'high price', 'open price', 'close price', and 'transaction volume' in the present invention.

[Table 1]

For example, as shown in Table 1, when the comparison item is 'high price', the reference table sets the rating level (L) to 'buy' if the rating score (A) is '0 or more and 10 or less' , If the rating score (A) is '11 or more and less than 30', the rating level (L) is set to 'stagnation', and if the rating score is '31 or more and less than 70', the rating level (L) is set to 'neutral' If the rating score (A) is '71 or more and less than 90', the rating level (L) is set to 'sell', and if the rating score (A) is '91 or more and less than 100', the rating level (L) is set to ' It can be set to 'sell'.

At this time, the comparison items 'high price', 'open price', 'closing price' and 'transaction volume' are calculated in the same way as the rating score (A), so the comparison items 'high price', 'open price', 'closing price' and 'transaction volume' For the reference table of Table 1 described above, the same may be applied.

However, in the case of the comparison item 'low price', the rating score (A) calculation method is compared with the comparison item 'high price', 'open price', 'close price' and 'transaction volume', and vice versa, so the comparison item 'low price' The reference table according to this may be set as shown in Table 2 below.

Table 2 below is an example of a reference table when the comparison item is 'low price' in the present invention.

[Table 2]

For example, as shown in Table 2, when the comparison item is 'low price', the reference table sets the rating level (L) to 'sell' when the rating score (A) is '0 or more and 10 or less' , If the rating score (A) is '11 or more and less than 30', the rating level (L) is set to 'overheat', and if the rating score is '31 or more and less than 70', the rating level (L) is set to 'neutral' If the rating score (A) is '71 or more and less than 90', the rating level (L) is set to 'stagnation', and if the rating score (A) is '91 or more and less than 100', the rating level (L) is set to ' Can be set to 'buy'.

The comparison module 555 compares the rating points (A) of each item for each cycle calculated by the rating point calculation module 553 with a first set value (TH1, Threshold) and a second set value (TH2). do.

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

The buy set item detection module 556 determines, in the comparison module 555, items having a rating score (A) equal to or less than the first set value TH1 as the buy set items.

At this time, the purchase set item means an item that is included in the CM index and has a buy signal based on the corresponding cycle, and is determined to need monitoring to capture a double signal in the future.

The selling collective item detection module 557 determines, in the comparison module 555, an item having a rating score A equal to or greater than the second set value TH1 as the selling collective item.

At this time, the selling set item means an item that is included in the CM index and has a selling signal based on the corresponding cycle, and is determined to need monitoring to capture the double signal in the future.

On the other hand, in FIG. 4, as an example of the case where the comparison item is 'high price', an item having a rating score (A) below the first set value (TH1) is determined as a purchase set item, and the second set value to be heard As an example, it has been described that an item having a rating score (A) of (TH2) or higher is determined as a sold set item, but when the comparison item is 'low price', the buy set item detection module 556 sets the second set value (TH2) ) or higher rating points (A) are determined as buying group items, and at the same time, the selling collective item detection module 557 sets items having rating points (A) lower than the first set value (TH1) as selling collective items. It goes without saying that it can consist of determining.

In addition, although not shown in the drawing, the market timing algorithm-based analysis unit 55 1) as a first condition using any one of 'high price', 'open price', 'close price' and 'transaction volume' as a comparison item, a rating score ( At the same time as A1) is calculated, 2) a rating score (A2) is calculated under the second condition with 'low price' as a comparison item, 3) a buy/sell signal under the first condition and a purchase under the second condition When a /sell signal is simultaneously detected (AND condition), the corresponding item can be configured to be detected as a buy/sell set item, and accordingly, more optimal trading timing can be provided to the user.

Returning to FIG. 2 and looking at the CM index generator 56, the CM index generator 56 includes the buy/sell set items generated by the market timing algorithm-based analysis unit 55 in the CM index to obtain CM index information generate

At this time, the CM index information includes cycle (T) information used for the incorporation of the buy/sell group item. For example, when a certain purchase set item is detected based on the main bar, the corresponding purchase set item and period (share) information are matched and stored in the CM index information.

In addition, 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 verifies whether or not the trading of each buy/sell group item included in the CM index input from the CM index generator 56 is appropriate using a preset verification algorithm, The CM index is extracted in such a way as to remove the sell group items, but keep the verified buy/sell group items as they are.

At this time, various well-known techniques can be applied to the method of determining whether the CM index extractor 57 is suitable for trading, and for example, market neutrality and financial statements are analyzed to determine the suitability of trading of each buying / selling group item. there is.

FIG. 6 is a block diagram illustrating a double signal capture unit of FIG. 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 number double signal detection module 584, It consists of a selling double signal detection module 585 and a double signal information generating module 586.

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

For each buy/sell set item, the lower grade score calculation module 582 converts the high price of each candle to the high price of the previous candle based on the candles of the predetermined reference quantity (n) in a period lower than the corresponding period (shorter period). In comparison, the rating point A′ is calculated in the same manner as the rating point calculation module 553 of FIG. 4 described above. In this case, the lower term means periods shorter than the corresponding period.

That is, the lower rating score calculation module 582 1) when the period of the buying/selling set is 'month', the rating score (A '), and 2) when the period of the buy/sell group item is 'week', the rating score (A') is calculated with a candle of day/hour/minute period, which is a shorter period than 'week', 3) When the period of the buying/selling group is 'day', the rating score (A') is calculated with a candle of hour/minute period, which is a shorter period than 'day'.

The second comparison module 583 converts the rating score A' of each buy/sell set item for each sub-cycle calculated by the lower rating score calculation module 582 to a preset first set value TH1 and a second set value. value (TH2), and in detail, the rating score (A') of the buying set item is compared with the first set value (TH1), but the rating score (A') of the selling set item is compared to the second set value ( TH2).

When the purchase double signal detection module 584 detects a purchase set item of a lower period having a rating score (A') equal to or less than the first set value (TH1) in the second comparison module 583, the purchase set item is assigned to the corresponding purchase set item. Detect (capture) the double signal for the number of times.

For example, when the purchase double signal detection module 584 assumes that the purchase set item included in the CM index is detected with a main bar as a cycle, the calculated grade When the score A' is less than or equal to the first set value TH1, the buy double signal for the corresponding buy set item may be captured.

The selling double signal detection module 585 detects a selling collective issue of a lower period having a grade point A' equal to or higher than the second set value TH1 in the second comparison module 583, and determines the value of the selling collective issue. Detect (capture) the sell double signal.

For example, when the selling double signal detection module 584 assumes that the selling collective item included in the CM index is detected with a daily bar cycle, the calculated rating score (A ') is equal to or greater than the second set value TH2, the selling double signal for the corresponding selling set item may be captured.

The double signal information generating module 586 is executed when a buying double signal is captured by the buying double signal detection module 584 or a selling double signal is captured by the selling double signal detection module 585, and Double signal information matching the identification information and the buy/sell double signal is generated.

At this time, the control unit 50 controls the data transmission/reception unit 52 to transmit the double signal information generated by the double signal information generation module 586 to the connected client 4.

FIG. 7 is a block diagram illustrating 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 592 for each CM theme, a sorting module 593, an upper theme detection module 594, and data mining It consists of a processing module (595).

The CM theme detection module 591 utilizes preset item information for each theme to detect a CM theme, which is a theme including 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 themes detected by the CM theme detection module 591 and Equation 1 below.

That is, the data mining unit 59 calculates an effective score (N) for each CM theme using the above formula.

The sorting module 593 sorts the CM themes according to the size of the valid points N calculated by the valid point calculation module 592 for each CM theme.

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

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

At this time, as valid information generated by the data mining processing module 595, various known information can be applied. For example, the data mining processing module 591 of the present invention 1) analyzes companion/theme items to advance market trends At the same time as extracting 2) correlation between specific items is analyzed to extract subordinate/companion events, 3) macroeconomic variables are extracted according to market intensity level of specific financial product ETF/ETN/raw material/exchange rate financial product. It may consist of

The investment portfolio generation unit 60 generates an investment portfolio by processing and analyzing the leading market trend, subordinate/coincident event, and macroeconomic variables extracted by the data mining processing module 595 .

Returning to FIG. 2, looking at the portfolio program (3), when the portfolio program (3) is first executed, it receives the identification information and login information of the securities company to be linked from the user, and requests authentication including the input identification information and login information Data is transmitted to the portfolio management server (5). At this time, if the authentication is successful, the portfolio program 3 is connected to the account of the securities company.

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

At this time, as known, the GUIs may be composed of various graphics and configurations, such as receiving a specific command from a user or displaying response data according to a request, and these GUIs are commonly used in HTS or portfolio programs. Since it is a technique, a detailed description thereof will be omitted.

FIG. 8 is a flow chart showing a method for detecting optimal trading timing in the investment portfolio providing system of FIG. 1 .

As shown in FIG. 8, the optimal trading timing detection method (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), a rating classification step ( S40), comparison step (S50), buying set item detection step (S60), selling set item detection step (S70), CM index generation step (S80), CM index extraction step (S90), CM index transmission step (S100) , monitoring step (S110), double signal acquisition step (S120), double signal transmission step (S130), display step (S140).

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

In the market timing algorithm-based analysis step (S20), the portfolio management server (5) uses a preset market timing algorithm to analyze the high price data for each item collected in the data collection step (S10) for each preset period (T). It is a step.

At this time, the market timing algorithm compares the high price of each candle with the high price of the previous candle by utilizing candles of a preset reference quantity (n) for each preset period (T).

In addition, in FIGS. 8 and 9, for convenience of explanation, the comparison item is described as 'high price', but the comparison item may be composed of 'open price', 'close price', 'transaction volume', and 'low price'.

In the rating score calculation step (S30), the portfolio management server 5 utilizes the analysis data detected by the market timing algorithm-based analysis step (S20) so that the high price of each candle according to a preset cycle for each item is the previous candle. If the high of each candle exceeds (if it is less than) the high of the previous candle, the rating score (A) is added with a constant 'α'. This is a step of calculating the grade score (A) by summing the scores of each candle by subtracting .

At this time, in the rating score calculation step (S30), if the comparison items are 'open price', 'close price' and 'transaction volume', the rating score (A) is calculated in the same way 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 (low price), the constant 'α' is added to the rating score (A), but if the low price of each candle does not break through the low price of the previous candle, the rating score (A ) by subtracting the constant 'α' to calculate the rating score (A) of each candle.

In the rating classification step (S40), the portfolio management server 5 utilizes the rating score (A) of each item for each cycle (T) calculated by the rating score calculation step (S30) and a preset reference table, This is the step of classifying the grade level (L) for each event.

In the comparison step (S50), the portfolio management server 5 compares the rating score (A) of each item calculated by the rating score calculation step (S30) with the first and second set values (TH1, TH2) respectively It is a step to

The buy set item detection step (S60) is a step in which the portfolio management server 5 determines, in the comparison step (S50), items having a rating score (A) equal to or less than the first set value (TH1) as the buy set items.

The selling collective item detection step (S70) is a step in which the portfolio management server 5 determines, in the comparison step (S50), items having a rating score (A) equal to or higher than the second set value (TH1) as the selling collective item.

Meanwhile, although not shown in FIG. 8, the rating score calculation step (S30) is 1) a rating score ( At the same time as A1) is calculated, 2) the rating score (A2) is calculated under the second condition with 'low price' as a comparison item, and the comparison step (S50) calculates the rating score (A1) of the first and second conditions, ( A2) are compared with the first and second set values TH1 and TH2, and in the detection of the purchase set item (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 selling set item detection step (S70), when the sell signal of the first condition is detected and the sell signal of the second condition is detected at the same time, the item is sold. It can be configured to detect as an item.

In the CM index creation step (S80), the portfolio management server 5 converts the buy collective items detected in the buy collective item detection step (S60) and the sold collective items detected in the sell collective item detection step (S70) into the CM index This is the step of incorporating into

At this time, the CM index information includes cycle (T) information used for the incorporation of the buy/sell group item. For example, when a buy set item is detected based on the main bar, the corresponding buy set item and period (share) information are matched and stored in the CM index information.

In the CM index extraction step (S90), the portfolio management server 5 uses a preset verification algorithm to verify whether the trading of each buy/sell set item of the CM index generated by the CM index creation step (S80) is appropriate. It is a step to

In addition, the CM index extraction step (S90) is a step of extracting a CM index consisting only of buy/sell set items that are verified to be suitable for trading.

The CM index transmission step (S100) is a step in which the portfolio management server 5 transmits the CM index information extracted by the CM index extraction step (S90) to the connected client 4.

The monitoring step (S110) is a step in which the portfolio management 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).

FIG. 9 is a flowchart showing the double signal acquisition step of FIG. 8 .

As shown in FIG. 9, the double signal acquisition step (S120) includes a rating score calculation step (S121) for each sub-period, a second comparison step (S122), a buy double signal capture step (S123), a sell double signal capture step ( S124) and generating double signal information (S125).

In the step of calculating the rating score for each sub-cycle (S121), the portfolio management server 5 utilizes the candles of the predetermined reference quantity (n) of the period lower than the corresponding period for each buy/sell set item, This is a step of calculating a rating score (A') in the same manner as the rating point calculation step (S30) of FIG.

In this case, the lower term means cycles having a shorter time than the corresponding cycle.

For example, in the step of calculating the rating score for each sub-cycle (S121), when the cycle of the purchase/sale set item is 'month', the item is a candle with a period shorter than 'month', that is, week/day/hour/minute. A rating score (A') can be calculated, and as another example, when the cycle of the buy/sell set item is 'week', the item is a candle with a period shorter than 'week' of day/hour/minute. A rating score (A') of can be calculated.

In the second comparison step (S122), the portfolio management server 5 calculates the grade score (A') of each buy/sell set item for each sub-cycle calculated in the step of calculating the grade score for each sub-cycle (S121) for a predetermined first step (S122). Compared with the set value (TH1) and the second set value (TH2), in detail, the rating score (A') of the buying set item is compared with the first set value (TH1), but the rating score of the selling set item ( This is a step of comparing A') with the second set value TH2.

In the purchase double signal acquisition step (S123), the portfolio management server 5 detects a purchase set item of a lower period having a rating score (A') less than or equal to the first set value (TH1) in the second comparison step (S122). If so, it is a step of detecting (capturing) the buy double signal for the corresponding buy set item.

In the selling double signal acquisition step (S124), when the portfolio management server 5 detects a sold set item of a lower cycle having a rating score (A') equal to or higher than the second set value (TH1) in the second comparison step (S122). , This is the step of detecting (capturing) the selling double signal for the corresponding selling set item.

The double signal information generating step (S125) is performed when the portfolio management server 5 detects a buying double signal by the buying double signal detection step (S123) or a selling double signal by the selling double signal detection step (S124). This is a step of generating double signal information matching the identification information of the detected item and the buy/sell double signal.

Returning to FIG. 8 again, looking at the double signal transmission step (S120), in the double signal transmission step (S120), the portfolio management server 5 transfers the double signal information generated by the double signal acquisition step (S120) to the connected client ( This is the step of sending to 4).

In the display step (S140), the portfolio program 3 transmits the CM index information received from the portfolio management server 5 through the CM index transmission step (S100) or the portfolio management server 5 through the double signal transmission step (S130). This is a step of displaying the received short-term trading signal information on the monitor of the client 4 through a GUI (Graphic User Interface).

On the other hand, although not shown in FIGS. 8 and 9, the optimal trading timing detection method (S1) proceeds in the CM index extraction step (S90), but in parallel with the CM index transmission step (S100) and monitoring step (S110) Investment portfolio It further includes a production step.

In the investment portfolio creation step, a CM theme, which is a theme including each buy/sell set item of the CM index extracted by the CM index extraction step (S90), is detected using the item information for each of the preset themes, and the above-mentioned equation Using 1 and the detected CM themes, an effective score (N) for each CM theme is calculated.

In addition, in the investment portfolio creation step, after sorting the CM themes according to the size of the effective score (N) calculated through the equation, the top 10 CM themes among the sorted CM themes are determined as the top themes, and the determined top themes Sends information to connected clients.

As such, in the investment portfolio providing system 1, which is an embodiment of the present invention, the portfolio management server analyzes stock-related information provided from securities company servers, calculates a rating score (A) for each item, and then calculates the rating score According to (A), each item is incorporated into the CM index and provided to the user, thereby responding to the index flow with complex and diverse volatility, predicting the change pattern and enabling trading at the optimal time, thereby increasing the user's rate of return can be maximized.

In addition, in the investment portfolio providing system 1, which is an embodiment of the present invention, when the portfolio and the management server create the CM index, each buy/sell set item is configured to determine whether or not it is suitable for trading by using a preset verification algorithm. This can improve the safety of investors' earnings.

In addition, the investment portfolio providing system 1, which is an embodiment of the present invention, monitors the index flow of each buy / sell set item of the CM index when the portfolio management server extracts the CM index, captures the double signal, and provides it to the user By being configured to do so, it is possible to further increase the trading margin.

In addition, the investment portfolio providing system 1, which is an embodiment of the present invention, is configured so that the portfolio management server determines the optimal trading timing among the items belonging to the portfolio using a market timing algorithm, thereby effectively configuring the portfolio with a small amount of computational processing. be able to

1: Investment Portfolio Provision System 3: Portfolio Program
4-1, ..., 4-N: Client 5: Portfolio Management Server
7-1, ..., 7-N: Securities company server 10: Communication network
50: control unit 51: database unit
52: data transmission and reception unit 53: program management unit
54: authentication processing unit 55: market timing algorithm based analysis unit
56: CM index generation unit 57: CM index extraction unit
58: double signal capture unit 59: data mining processing unit
60: investment portfolio generation unit 551: data collection module
552: analysis module 553: rating score calculation module
554: rating classification module 555: comparison module
556: Buy set item detection module 557: Sell set item detection module
581: monitoring module 582: lower grade score calculation module
583: second comparison module 584: purchase double signal detection module
585: selling double signal detection module 586: double signal information generating module

Claims (7)

In the optimal trading timing calculation method (S1) for capturing the optimal trading timing by collecting and analyzing stock market data:
Step 10 (S10) of the portfolio management server collecting stock-related information transmitted from securities company servers;
Through the data collected by the portfolio management server in step 10 (S10), 'month (T1)', 'week (T2)', 'day (T3)', 'hour (T4)' and 'minute ( T5) collects data according to the preset comparison items of each item for each cycle including at least one of 'T5)', and analyzes the collected data using a preset market timing algorithm to generate a buy set item with a buy signal and a sell signal. Step 200 (S200) of detecting sold collective items having ;
Step 80 (S80) of the portfolio management server generating CM index information including the purchase set item and the sell set item detected by the step 200 (S200);
Step 90 (S90) of the portfolio management server transmitting the CM index information generated by step 80 (S80) to the connected client;
The optimal trading timing calculation method (S1) characterized in that it comprises a step 140 (S140) of displaying the CM index information transmitted by the client in the step 90 (S90). The method of claim 1, wherein the comparison item is any one of 'high price', 'open price', 'close price' and 'transaction volume',
The step 200 (S200) is
The portfolio management server, through the data collected in the step 10 (S10), utilizes the candles of the preset reference quantity (n) for each preset period (T), and compares the value of the comparison item of each candle to that of the previous candle. Step 20 (S20) of comparing the value of the comparison item;
If the portfolio management server utilizes the analysis data detected in step 20 (S20) and the value of the comparison item of each candle according to the preset cycle for each item is greater than or equal to the value of the comparison item of the previous candle, the rating score The constant 'α' is added to (A), but if the value of the comparison item of each candle is less than the value of the comparison item of the previous candle, the score of each candle is calculated by subtracting the constant 'α' from the rating score (A). Step 30 (S30) of calculating a rating score (A) by summing;
The portfolio management server compares the rating score (A) of each item for each predetermined period calculated by the step 30 (S30) with the first set values (TH1, Threshold1) and the second set values (TH2). Step 50 (S50);
Step 60 (S60) of the portfolio management server determining, in step 50 (S50), an item having a rating score (A) equal to or less than the first set value (TH1) as a purchase set item;
In the step 50 (S50), the portfolio management server includes a step 70 (S60) of determining an item having a rating score (A) equal to or higher than the second set value (TH2) as a sold set item,
The step 80 (S80) is
The portfolio management server matches the purchase group item and the corresponding period (T) information determined in step 60 (S60) and incorporates them into the CM index information, and the selling group item and corresponding period (T) determined in step 70 (S70) An optimal trading timing calculation method (S1), characterized in that information is matched and incorporated into CM index information. The method of claim 2, wherein the optimal trading timing calculation method (S1)
Step 110 (S110), which proceeds after the step 80 (S80), wherein the portfolio management server monitors the index flow of the buy/sell set items included in the CM index extracted by the step 80 (S80);
Step 120 (S120) of the portfolio management server capturing a double signal through monitoring through step 110 (S110);
Further comprising a step 130 (S130) of the portfolio management server transmitting the double signal information captured by the step 120 (S120) to a connected client,
The step 120 (S120) is
For each buying/selling group item, the portfolio management server compares the value of the comparison item of each candle with the previous candle by using candles of a predetermined reference quantity (n) of a sub-cycle, which is a period of time shorter than the corresponding period. Step 121 (S121) of calculating a rating score (A') by comparing the value of the item;
The portfolio management server calculates the rating score (A') of each buy/sell set item for each sub-cycle calculated by the step 121 (S121) as the first set value (TH1) and the second set value (TH2). Comparing step 122 (S122);
In the step 122 (S122), when the portfolio management server detects a purchase set item of a lower period having a rating score (A') equal to or less than the first set value (TH1), a buy double signal for the corresponding purchase set item Step 123 of detecting (S123);
In the step 122 (S122), when the portfolio management server detects a sold set item of a lower period having a rating score (A') equal to or higher than the second set value (TH1), it sends a selling double signal for the corresponding sold set item. Detect step 124 (S124);
The portfolio management server proceeds when the buy double signal is captured by the step 123 (S123) or when the sell double signal is captured by the step 124 (S124), and the identification information of the detected item and the purchase/sell double signal are received. Optimum trading timing calculation method (S1) comprising a step 125 (S125) of generating double signal information matching . The method of claim 1, wherein the comparison item is 'low price',
The step 200 (S200) is
The portfolio management server, through the data collected in the step 10 (S10), utilizes the candles of the preset reference quantity (n) for each preset period (T), and compares the value of the comparison item of each candle to that of the previous candle. Step 20 (S20) of comparing the value of the comparison item;
If the portfolio management server utilizes the analysis data detected in step 20 (S20) and the value of the comparison item of each candle according to the preset cycle for each item is less than the value of the comparison item of the previous candle, the rating score The constant 'α' is added to (A), but if the value of the comparison item of each candle is higher than the value of the comparison item of the previous candle, the score of each candle is calculated by subtracting the constant 'α' from the rating score (A). Step 30 (S30) of calculating a rating score (A) by summing;
The portfolio management server converts the rating score (A) of each item for each predetermined period calculated in step 30 (S30) to a first set value (TH1, Threshold1) and a second set value (TH2). Comparing with step 50 (S50);
Step 60 (S60) of the portfolio management server determining, in step 50 (S50), an item having a rating score (A) greater than or equal to a second set value (TH2) as a purchase set item;
In the step 50 (S50), the portfolio management server includes a step 70 (S60) of determining an item having a rating score (A) equal to or less than a first set value (TH1) as a sold set item,
The step 80 (S80) is
The portfolio management server matches the purchase group item and the corresponding period (T) information determined in step 60 (S60) and incorporates them into the CM index information, and the selling group item and corresponding period (T) determined in step 70 (S70) An optimal trading timing calculation method (S1), characterized in that information is matched and incorporated into CM index information. The method of claim 4, wherein the optimal trading timing calculation method (S1)
Step 110 (S110), which proceeds after the step 80 (S80), wherein the portfolio management server monitors the index flow of the buy/sell set items included in the CM index extracted by the step 80 (S80);
Step 120 (S120) of the portfolio management server capturing a double signal through monitoring through step 110 (S110);
Further comprising a step 130 (S130) of the portfolio management server transmitting the double signal information captured by the step 120 (S120) to a connected client,
The step 120 (S120) is
For each buying/selling group item, the portfolio management server compares the value of the comparison item of each candle with the previous candle by using candles of a predetermined reference quantity (n) of a sub-cycle, which is a period of time shorter than the corresponding period. Step 121 (S121) of calculating a rating score (A') by comparing the value of the item;
The portfolio management server calculates the rating score (A') of each buy/sell set item for each sub-cycle calculated by the step 121 (S121) as the first set value (TH1) and the second set value (TH2). Comparing step 122 (S122);
In the step 122 (S122), when the portfolio management server detects a purchase set item of a lower period having a rating score (A') equal to or higher than the second set value (TH2), it sends a buy double signal for the purchase set item. Detect step 123 (S123);
In the step 122 (S122), when the portfolio management server detects a sold collective item of a lower period having a rating score (A') equal to or less than the first set value (TH1), a selling double signal for the corresponding sold collective item Step 124 of detecting (S124);
The portfolio management server proceeds when the buy double signal is captured by the step 123 (S123) or when the sell double signal is captured by the step 124 (S124), and the identification information of the detected item and the purchase/sell double signal are received. Optimum trading timing calculation method (S1) comprising a step 125 (S125) of generating double signal information matching . In claim 2 or 4, the optimal trading timing calculation method (S1) further comprises step 40 (S40),
The step 40 (S40) is
The portfolio management server responds to the rating score of each item for each predetermined cycle (T) calculated by the step 30 (S30) by using the reference table in which the rating level (L) is matched for each range of rating scores (A). Optimum trading timing calculation method (S1), characterized in that for detecting the grade level (L). The method of claim 6, wherein the method (S1) for calculating the optimal trading timing further comprises step 90 (S90) performed after step 80 (S80),
The step 90 (S90) is
The portfolio management server analyzes market neutrality or financial statements for each purchase / sale group item of the CM index generated by the step 80 (S80) to verify the trading suitability of the item Calculation of optimal trading timing Method (S1).

Images