Advice provision system and advice provision method

Abstract

To provide an advice in accordance with an investment amount with continuity.SOLUTION: As an advice presentation system (1) includes: a display unit (23) which displays, for each course according to an investment amount, data indicating an advice on selling / buying an investment commodity under constraint conditions of investment start time and initial investment amount; and a control unit (32) which calculates available cash balance each time, the cash balance varying due to selling / buying according to the advice. The display unit displays, for each course, data indicating an advice for selling / buying an investment commodity within the range of the calculated available cash balance after the available cash valance varies.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to an advice Provided by system and methods of providing advice .

Background Art

[0002] There is an increasing interest in investments in stocks and the like. In response to such a situation, at a certain point in time, there is an abundance of information regarding stock brands whose prices are expected to rise. There are also services that provide such information. For example, Patent Document 1 is known as the prior art.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

[0004] However, there is a problem in the market: there is no framework for dynamically changing advice for individuals and corporations that is based on the premise of making long-term investments for wealth building. Conventional technologies can provide information on recommended stocks to buy at a given point in time, but they cannot provide continuous advice tailored to an individual's investment amount. For example, Patent Documents 2 to 8 disclose a service that proposes an investment portfolio at a given point in time.

[0005] For example, if you have an investment of 1 million yen, we cannot provide advice on which stocks to buy, what to do if the stock prices of other stocks rise after you have bought them, or what to do when the risk of a market crash emerges.

[0006] In addition to the fact that advice cannot adapt to dynamic changes, much of it is one-way advice, and progress has been slow in generating interactive advice tailored to needs or advice that is aligned with customer needs.

[0007] One aspect of the present invention aims to provide continuous advice tailored to the investment amount. [Means for solving the problem]

[0008] To solve the above problems, an advice provision system according to one aspect of the present invention is: Generated by the advice generation unit of a server configured to communicate with the user terminal. investment dealer Product purchase Advice via the above user terminal In the advice provision system provided to users, the initial investment amount Constraints including the timing of the investment commencement and the type of investment product. The investment course is set in advance. the above The user's terminal display shows the investment course selected by the user. the above Constraints And the available cash balance calculated from the virtual trading results or execution history based on past advice provided, Based on the This includes the types of investment products that can be purchased within the limits of the available cash balance, as well as the number of investment products that can be purchased. Advice on purchasing materials ,the above Generated by the advice generation unit, and the above Generated by the advice generation unitDisplay investment product purchase advice on the display unit of the above user terminal. Also, an advice providing method according to another aspect of the present invention Generated by the advice generation unit of a server configured to communicate with the user terminal. investment merchant Product purchase device to via the above user terminal In an advice providing method for providing advice to a user, an investment course with an initial investment amount Constraints including the timing of the investment commencement and the type of investment product. that is preset, the above the control unit of the user terminal via the display on the display unit of the user terminal Making work level, and the above Note A device generation unit 、 Based on the the above constraint conditions of the investment course selected by the user And the available cash balance calculated from the virtual trading results or execution history based on past advice provided, based on , including the securities and quantities of investment products that can be purchased within the limits of the available cash balance. investment product purchase advice The process of production and , The investment product purchase advice generated above, the control unit of the above user terminal via the display on the display unit of the user terminal Making work level, and has.

Effect of the Invention

[0009] According to one aspect of the present invention, there is an effect that advice according to the investment amount can be provided continuously.

Brief Description of the Drawings

[0010] [Figure 1] It is a diagram showing the hardware configuration of an advice presentation system according to Embodiment 1 of the present invention. [Figure 2] It is a block diagram showing the configurations of a terminal and a server according to Embodiment 1 of the present invention. [[ID=*53]] [Figure 3] It is a diagram showing an example of display of a "For Beginners" screen according to Embodiment 1 of the present invention. [[ID=*56]] [Figure 4] It is a diagram showing an example of display of a "Owned Stocks" screen according to Embodiment 1 of the present invention. [[ID=*59]] [Figure 5] It is a diagram showing an example of display of an "Evaluation Amount" screen according to Embodiment 1 of the present invention. [[ID=*62]] [Figure 6] It is a diagram showing an example of display of a "Trading History" screen according to Embodiment 1 of the present invention. [[ID=*65]] Note: There seem to be some asterisk-marked lines that might be related to formatting or some specific context not fully clear. The translation is presented as accurately as possible based on the provided text. [Figure 7] This figure shows an example of the display of the "Valuation History" screen according to Embodiment 1 of the present invention. [Figure 8] This figure shows an example of the display of the "Evaluation Details" screen according to Embodiment 1 of the present invention. [Figure 9] This figure shows an example of the display of the "Portfolio" screen according to Embodiment 1 of the present invention. [Figure 10] This is a flowchart showing the processing of the advice presentation system according to Embodiment 1 of the present invention. [Figure 11] This figure shows an example of a display of a virtual trading model curve according to Embodiment 2 of the present invention. [Figure 12] This is a flowchart showing the processing of the advice presentation system according to Embodiment 2 of the present invention. [Figure 13] This figure shows an example relating to the continuity of advice according to Embodiment 1 of the present invention. [Figure 14] This figure shows a series of steps in the advice generation process according to Embodiment 3 of the present invention. [Figure 15] This figure shows the framework for advice generation according to Embodiment 3 of the present invention. [Figure 16] This figure shows an example of the configuration of a brand or product database according to Embodiment 3 of the present invention. [Figure 17] This figure shows an example of the configuration of various data according to Embodiment 4 of the present invention. [Modes for carrying out the invention]

[0011] [Embodiment 1] Embodiment 1 of the present invention will be described in detail below.

[0012] (Advice-giving system 1) The advice presentation system 1 according to this embodiment will be described with reference to the drawings. Figure 1 is a diagram showing the hardware configuration of the advice presentation system 1 according to this embodiment. As shown in Figure 1, the advice presentation system 1 includes a terminal (terminal device) 2 and a server (advice generation device) 3. The terminal 2 and the server 3 are configured to communicate with each other via a network 4.

[0013] Terminal 2 acquires data through user operation and displays advice based on the initial investment amount, such as a PC, tablet, or smartphone. Server 3 generates multiple pieces of advice based on the investment amount. Network 4 is a network including the internet. Server 3 is used by individual users You may generate different advice for each user, or you may group multiple users based on investment timing, investment amount, etc., and generate advice for each group.

[0014] Figure 2 is a block diagram showing the configuration of terminal 2 and server 3 according to this embodiment.

[0015] (terminal2) As shown in Figure 2, terminal 2 comprises a communication unit 21, a control unit 22, a display unit 23, and an operation reception unit 24. The communication unit 21 is the part that communicates with server 3. The control unit 22 controls the entire terminal 2 and is, for example, one or more processors. The display unit 23 displays data according to instructions from the control unit 22 and is, for example, a liquid crystal display. The operation reception unit 24 receives user input and is, for example, a mouse or touch panel.

[0016] (Server 3) As shown in Figure 2, the server 3 comprises a communication unit 31, a control unit 32, and a storage unit 33. The communication unit 31 is the part that communicates with the terminal 2. The control unit 32 controls the entire server 3 and is, for example, one or more processors. The storage unit 33 stores data according to instructions from the control unit 22 and is, for example, a hard disk drive or flash memory.

[0017] The control unit 32 includes an advice generation unit 321. The advice generation unit 321 generates multiple pieces of advice, including advice related to at least one of the items of multiple stocks and available cash balance (cash amount) that were included in previously provided advice.

[0018] Here, previously provided advice may be advice previously provided to the target user, or advice previously provided to users included in a group that includes the target user. Furthermore, the advice may also be the provision of trading data for securities. The person who provides the trading data for securities to the advice presentation system 1 may be an advisor operating the advice presentation system 1, or an individual investor such as another user. In addition, the cash balance does not necessarily have to be constant at times other than when securities are being bought and sold, and may increase or decrease in accordance with the deposits and withdrawals of cash to and from the user's account. Terminal 2 presents the user with at least one of the multiple pieces of advice generated by the advice generation unit 321.

[0019] Furthermore, the control unit 32 manages the virtual trading model. The virtual trading model is a trading model for recommended stocks, etc., based on the initial capital amount and start date. The virtual trading model may be a model that depends not only on the initial capital amount and start date, but also on other items such as advisors and markets (Tokyo Stock Exchange, Mothers, etc.), and changes dynamically.

[0020] (Processing by Advice Presentation System 1) Figures 3 to 9 are examples of screens displayed on the display unit 23 of terminal 2 according to this embodiment. Figure 10 is a flowchart of the processing of the advice presentation system 1 according to this embodiment. The processing of the advice presentation system 1 will be explained following the flow shown in Figure 10, with reference to Figures 3 to 9.

[0021] (Step S1001) As shown in Figure 10, terminal 2 obtains the amount of the target user's initial investment and sends data indicating that amount to server 3. Figure 3 shows an example of the "For First-Time Users" screen. As shown in Figure 3, the control unit 22 displays a screen on the display unit 23 as the "For First-Time Users" screen, which includes the start date (investment start time), initial investment amount, overview, and valuation trend. The initial investment amount is the investment amount that the user sets when receiving advice. The user has previously selected the "[S Member] 1 million yen course," and according to the selected investment amount, the control unit 22 displays "1,000,000 yen" in the initial investment amount field and then sends the initial investment amount to server 3.

[0022] (Step S1002) Server 3 receives data from Terminal 2 indicating the amount of initial funds. The advice generation unit 321 generates multiple pieces of advice corresponding to the initial funds amount and the start date indicated by the received data. The advice includes buying and selling multiple stocks, as well as the available cash balance after the buying and selling. As shown in Figure 10, Server 3 determines the name and number of shares of the recommended stocks to buy as part of the advice, and sends data indicating the name and number of shares of the recommended stocks to Terminal 2.

[0023] Server 3 performs the process in step S1002 irregularly in accordance with daily stock price fluctuations. At that time, the advice generation unit 321 of the control unit 32 generates advice related to buying and selling multiple stocks, and at least one of the available cash balance after buying and selling, which is included in the advice previously provided.

[0024] For example, if a user has previously bought shares of company A and has received advice regarding company A's shares generated by the advice generation unit 321, and the current share price is higher than the share price at the time of purchase by a predetermined value or more, the advice generation unit 321 will generate advice encouraging the user to sell company A's shares.

[0025] Furthermore, the advice generation unit 321 generates advice encouraging the purchase of stocks of companies that are expected to rise in the future, within the range of the user's available cash balance after a trade, as included in the advice previously provided.

[0026] (Step S1003) As shown in Figure 10, terminal 2 receives data from server 3 indicating the names and number of shares of recommended stocks to buy, as data that shows advice related to buying and selling multiple stocks and at least one of the available cash balance after buying and selling, which are included in the advice previously provided, and displays the names and number of shares of the recommended stocks to buy indicated by the received data on display unit 23.

[0027] Figure 4 shows an example of the "Holdings" screen. The "Holdings" screen displays advice related to the buying and selling of multiple stocks included in previously provided advice, and at least one of the available cash balance after the buying and selling. More specifically, it is a screen that displays information about recommended stocks to purchase as advice to the user. As shown in Figure 4, the control unit 22 displays a screen on the display unit 23 as the "Holdings" screen, including the stock name (code), recommendation date, number of shares, stock price at the time of recommendation, and price at the time of recommendation. The control unit 22 then displays the advice "★If you adopt this course, please purchase the following stocks." on the display unit 23.

[0028] Regarding the advice system 1, as shown in Figure 3, the principle is that the user specifies the initial capital and start date before beginning to use the advice system 1. However, it is also acceptable for the user to match their own holdings to the recommended stocks shown in Figure 4 before starting to use the advice system 1 and then following the advice provided. Thus, the advice system 1 is a system that can be used by users even at an intermediate stage.

[0029] In the advice data generation system 10, the display unit 23 clearly displays the dynamically changing and evolving advice. The display unit 23 clearly displays the name and quantity of the held shares. The display unit 23 extracts and displays only the products for which offsetting trades have not yet been completed from the updated trading data. In other words, the display unit 23 calculates and displays the stocks or products for which buy advice has been generated, for which sell advice has not yet been generated, or the remaining quantities.

[0030] The display of current holdings makes it easy for users to join a trading strategy midway through. The important information for joining midway is not past information, but rather which stocks to buy now and in what quantities. Therefore, users can join midway by matching their current holdings, and this display is also important for users receiving advice, as it allows them to check their current holdings. By matching the stocks according to this display, users can join midway and then follow the trading advice. Even users who have already started the course can check whether the advice is correct by comparing it with their actual holdings.

[0031] (Step S1004) As shown in Figure 10, Server 3 calculates the valuation of the virtual trading model and sends data indicating this valuation to Terminal 2. The virtual trading model is a trading model for recommended stocks, etc., based on the initial capital amount and start date. As mentioned above, the virtual trading model may be a model that depends not only on the initial capital amount and start date, but also on other items such as advisors and markets (Tokyo Stock Exchange, Mothers, etc.), and changes dynamically.

[0032] (Step S1005) As shown in Figure 10, terminal 2 receives data from server 3 indicating the valuation of the virtual trading model, and displays the valuation of the virtual trading model indicated by the received data on display unit 23. Figure 5 shows an example of the display of the "Valuation Amount" screen. As shown in Figure 5, control unit 22 displays a screen on display unit 23 as the "Valuation Amount" screen, which includes a valuation summary and valuations for each held stock.

[0033] The valuation summary includes the current portfolio investment amount, current portfolio valuation, current unrealized gains, past realized gains and losses, total gains and losses, available cash balance, and total valuation. The valuation for each holding includes the stock name (code), recommendation date, number of shares, stock price and price at the time of the buy recommendation, current stock price and price, percentage change, and unrealized gains and losses. The portfolio refers to the state of the virtual trading model at that point in time.

[0034] (Step S1006) As shown in Figure 10, Server 3 calculates the trading history of the virtual trading model and sends data indicating that trading history to Terminal 2.

[0035] (Step S1007) As shown in Figure 10, terminal 2 receives data from server 3 showing the trading history of the virtual trading model, and displays the trading history of the virtual trading model shown in the received data on display unit 23.

[0036] Figure 6 shows an example of the "Trading History" screen display. As shown in Figure 6, the control unit 22 displays a screen on the display unit 23 as the "Trading History" screen, including the stock name, number of shares, date of buy recommendation, stock price and amount, date of sale, stock price and amount, rate of change, and trading profit / loss.

[0037] Figure 7 shows an example of the "Evaluation History" screen display. As shown in Figure 7, the control unit 22 displays the "Evaluation History" screen, which includes the publication date and time and the article title, on the display unit 23.

[0038] Figure 8 shows an example of the display of the "Valuation Details" screen. As shown in Figure 8, the control unit 22 displays a screen on the display unit 23 as the "Valuation Details" screen, which includes a valuation summary and valuations for each held stock.

[0039] The valuation summary includes the current portfolio investment amount, current portfolio valuation, current unrealized gains, past realized gains and losses, total gains and losses, available cash balance, and total valuation. The valuation for each holding includes the stock name (code), recommendation date, number of shares, stock price and price at the time of the buy recommendation, current stock price and price, percentage change, and unrealized gains and losses.

[0040] Figure 9 shows an example of the "Portfolio" screen display. As shown in Figure 9, the control unit 22 displays the selected portfolio and a screen including the association between the portfolio and articles on the display unit 23 as the "Portfolio" screen.

[0041] The selected portfolio includes ID, stock name, number of shares, buy recommendation date, buy article ID, recommended stock price, recommended exchange rate, recommended price, sell recommendation date, sell article ID, sold stock price, sold exchange rate, and sold price. The buy article ID is unique to the report that recommended buying shares of the stock in question. The sell article ID is unique to the report that recommended selling shares of the stock in question. Portfolio and article associations allow searching by buy-related article ID or sell-related article ID.

[0042] The display screen shown by terminal 2 may be generated by the control unit 22 of terminal 2 based on information received from server 3, or it may be configured so that server 3 sends data indicating the generated display screen (for example, data in HTML or XML format) to terminal 2 for terminal 2 to display. Alternatively, server 3 may generate part of the display screen and terminal 2 may generate the remaining part. In the configuration where terminal 2 generates the display screen, an application that generates the display screen and accepts user input should be pre-installed in terminal 2's control unit 22 and executed as part of the control unit 22.

[0043] (Effects of Embodiment 1) Server 3 generates multiple pieces of advice, including advice related to items included in previously provided advice, thus enabling it to provide continuous advice tailored to the initial investment amount. Furthermore, since Server 3 generates multiple pieces of advice based on the initial investment amount and the start date, it can provide advice that is appropriate to the state at the start of the investment.

[0044] Furthermore, the available balance for purchases can increase or decrease at times other than trading, allowing for flexible service to be provided to users. In addition, the person providing stock trading data to the advice presentation system 1 as advice can be either a professional advisor or an individual investor, so various advice can be generated, allowing for comparison, evaluation, and ranking of multiple pieces of advice.

[0045] [Embodiment 2] Embodiment 2 of the present invention will be described below with reference to Figures 11 and 12. For the sake of convenience, components having the same function as those described in Embodiment 1 will be denoted by the same reference numerals, and their descriptions will be omitted.

[0046] (Processing by Advice Presentation System 1) Figure 11 is a diagram showing an example of the display of a virtual trading model curve according to this embodiment. Figure 11(a) shows the virtual trading model curve. Figure 11(b) shows the virtual trading model curve and the trading information input field. Figure 11(c) shows the virtual trading model curve and the trading performance curve. Figure 12 is a flowchart of the processing of the advice presentation system 1 according to this embodiment. The processing of the advice presentation system 1 will be explained following the flow in Figure 12, with reference to Figure 11.

[0047] The virtual trading model curve shows multiple pieces of advice, including advice related to at least one of the items of multiple securities and cash amounts included in past advice. The difference between the virtual trading model curve and the trading performance curve indicates advice that brings the user's investments closer to the virtual trading model, generated by referring to the virtual trading model based on the above advice and the user's actual trading information.

[0048] (Step S1201) As shown in Figure 12, terminal 2 requests data for a virtual trading model from server 3. This data request includes the amount of initial funds and the start date for the virtual trading model.

[0049] (Step S1202) As shown in Figure 12, Server 3 receives a data request for a virtual trading model from Terminal 2 and sends data for the virtual trading model corresponding to the initial fund amount and start date included in the received data request to Terminal 2. The data for the virtual trading model includes the changes in the valuation of the virtual trading model.

[0050] (Step S1203) As shown in Figure 12, terminal 2 receives virtual trading model data from server 3 and displays the virtual trading model curve drawn from the received data on display unit 23. As shown in Figure 11, control unit 22 generates a screen that includes a curve showing the trend of the virtual trading model's valuation, which is included in the received data, and displays the generated screen as the virtual trading model curve on display unit 23.

[0051] (Step S1204) As shown in Figure 12, terminal 2 acquires trading information from the user. More specifically, as shown in Figure 11(b), when the operation reception unit 24 detects a date on the time axis where the cursor is positioned by user operation, the control unit 22 generates a screen including a trading information input field for inputting trading information for that date, and displays the generated screen on the display unit 23. The user may use a different input screen. Next, when the operation reception unit 24 detects that trading information such as stock name, trade, and number of shares has been entered into the trading information input field by user operation, the control unit 22 acquires the entered trading information.

[0052] (Step S1205) As shown in Figure 12, terminal 2 requests data from server 3 for the valuation of a virtual trading model based on the trading information. This data request requests data for the valuation of the virtual trading model when it is modified using the actual trading information. This data request includes trading information such as year, month, and day, stock symbol, trade, and number of shares.

[0053] (Step S1206) As shown in Figure 12, Server 3 receives a request for valuation data from Terminal 2, calculates the valuation, and transmits the valuation data. Specifically, Control Unit 32 obtains the date, stock code, transaction details, and number of shares included in the received data request. Next, Control Unit 32 obtains the date and the share price of the stock from Storage Unit 33. Then, Control Unit 32 determines the trend of the valuation by calculating the increase or decrease in the valuation based on the transaction details, number of shares, and share price. Furthermore, Control Unit 32 transmits the valuation trend data to Terminal 2.

[0054] (Step S1207) As shown in Figure 12, terminal 2 receives data on the trend of valuation from server 3, generates a screen including a trading performance curve drawn from the received data, and displays the generated screen on display unit 23. As shown in Figure 11(c), control unit 32 displays a trading performance curve that deviates from the virtual trading model curve on display unit 23.

[0055] The advice generation unit 321 refers to the virtual trading model based on the advice in step S1202 and the user's actual trading information in step S1206, and generates advice that brings the user's investment content closer to the virtual trading model. The terminal 2 then displays such advice on the display unit 23. Figure 11(c) shows an example of such advice being displayed.

[0056] (Step S1208) As shown in Figure 12, terminal 2 displays the difference between the virtual trading model curve and the actual trading curve on the display unit 23. As shown in Figure 11(c), control unit 32 displays the difference between the virtual trading model curve and the actual trading curve on the display unit 23 using a double-headed arrow line segment.

[0057] The difference between the model and actual trading performance shown in Figure 11(c) represents the difference between the hypothetical trading model and the actual trading performance at the present time. However, moving backward in time from that point to the left, the timeline shows when the hypothetical trading model and the actual trading performance began to diverge, providing advice on how trading should have been conducted.

[0058] Furthermore, the control unit 32 displays the advice, "There is a discrepancy between the model and the actual results from this point," on the display unit 23. This advice aims to bring the user's investment strategy closer to the virtual trading model.

[0059] Furthermore, even if data on the user's trading history is not available, terminal 2 may display the discrepancy between the virtual trading model and the random model by showing a random model that illustrates what would have happened if the advised trades had not been followed.

[0060] (Effects of Embodiment 2) Terminal 2 displays the difference between the virtual trading model curve and the actual trading curve on the display unit 23, thereby providing useful advice to the user by displaying advice that brings the user's investment content closer to the virtual trading model.

[0061] [Example 1] Figure 13 shows an example of the continuity of advice according to Embodiment 1 of the present invention. This embodiment shows a specific example of "advice related to at least one of the items of multiple stocks and cash amounts included in previously presented advice."

[0062] (Start using the system) When a user begins using the advice presentation system 1, terminal 2 obtains initial settings based on the user's input and sends them to server 3. These initial settings include the initial amount, target stocks (Chinese stocks, Japanese stocks, emerging market stocks, etc.), start date, type (performance-oriented, large-cap stock-oriented, beginner-friendly, etc.), and the name of the fund manager. Server 3 receives the initial settings from terminal 2 and generates advice corresponding to these settings. As shown in Figure 13, the advice presentation system 1 operates with an initial amount of 1 million yen.

[0063] (Advice for 3 months later) As shown in Figure 13, three months after the start date of using the advice system 1, server 3 generates advice (including the purchase price of each stock) to purchase stocks A and B with the initial amount of 1 million yen, and sends this advice to terminal 2. The remaining cash will be the amount obtained by subtracting the purchase price of stocks A and B from 1 million yen. Terminal 2 receives and displays the advice from server 3 after three months.

[0064] (Advice for six months later) Six months after the start date of using the advice system 1, server 3 generates advice regarding the stocks to purchase and remaining cash that was advised three months later, and sends this advice to terminal 2. Terminal 2 receives the six-month-later advice from server 3 and displays it.

[0065] As shown in Figure 13, the advice for six months from now includes advice to sell stock A and buy stocks C and D (including the purchase price of each stock), advice to continue holding stock B, and advice to buy stock E with the remaining cash. The cash will be the amount obtained by subtracting the purchase price of stocks C and D from the sale price of stock A.

[0066] (Advice for one year from now) One year after the start date of using the advice system 1, server 3 generates advice regarding the stocks to purchase and cash that was advised six months later, and sends this advice to terminal 2. Terminal 2 receives the advice from server 3 one year later and displays it.

[0067] As shown in Figure 13, the advice for one year from now includes advice to sell stock C, advice to purchase stock F with cash, advice to continue holding stocks B and D, and advice to sell stock E and purchase stocks G and H (including the purchase price of each stock). The cash will be the sum of the sale price of stock E minus the purchase price of stocks G and H, and the sale price of stock C.

[0068] The valuation, trading profit / loss, unrealized profit / loss, and cash balance at each point in time are as shown by the following formulas: • Valuation = 1 million yen + trading profit / loss of shares sold up to that point + unrealized profit / loss of shares not sold up to that point • Trading profit / loss of shares sold = (Recommended selling price - Recommended buying price) × number of shares • Unrealized profit / loss of shares not sold = (Current price - Recommended buying price) × number of shares • Cash balance = 1 million yen - (Valuation of shares not sold up to that point - Unrealized profit / loss of shares not sold up to that point) + trading profit / loss of shares sold up to that point [Example 2] This embodiment provides a specific example of "advice generated when the valuation of held securities and available cash balance exceeds a predetermined amount, and which corresponds to that predetermined amount." The advice generation unit 321 of server 3 generates advice corresponding to the predetermined amount when the above-mentioned valuation exceeds a predetermined amount. One or more amounts are pre-set as the predetermined amount.

[0069] (From the 300,000 yen course to the 1,000,000 yen course) The advice generation unit 321 initially generates advice based on the user's available cash balance of 300,000 yen. Subsequently, when the above valuation exceeds 1,000,000 yen, the advice generation unit 321 suggests to the user that they use advice based on an available cash balance of 1,000,000 yen.

[0070] (From the 1 million yen course to the 5 million yen course) When the advice generation unit 321 of server 3 obtains information from terminal 2 indicating that the user accepts the above proposal, it generates advice corresponding to the user's available cash balance of 1 million yen. Subsequently, when the valuation reaches 5 million yen or more, the advice generation unit 321 proposes to the user that they use the advice based on an available cash balance of 5 million yen.

[0071] According to the above, the advice generation unit 321 proposes to the user that they temporarily sell their holdings and use advice corresponding to the predetermined amount each time the valuation increases and exceeds a predetermined amount.

[0072] Therefore, regardless of the current state of the holdings, the system provides advice based on a gradual increase (rank-up) in the valuation of the holdings and available cash balance, potentially bringing further benefits to the user. Furthermore, the institution operating Server 3 can potentially increase its revenue by providing advice and collecting a fee from the user based on the initial available cash balance. This creates a win-win relationship between the user and the institution operating Server 3.

[0073] In the above embodiment, the predetermined amounts used for comparison with the appraised value were described as 1 million yen and 5 million yen, but the invention is not limited to these amounts, and it is possible to set the predetermined amounts to one or more arbitrary amounts.

[0074] [Embodiment 3] Embodiment 3 of the present invention will be described below with reference to Figures 14 to 16. For the sake of convenience, components having the same function as those described in Embodiments 1 and 2 will be denoted by the same reference numerals, and their descriptions will be omitted.

[0075] The advice data generation system 10 according to this embodiment generates multiple pieces of advice according to a course corresponding to the investment amount. The advice data generation system 10 has the same configuration as the advice presentation system 1 according to embodiments 1 and 2.

[0076] The advice data generation system 10 comprises at least an advice generation unit 321, a display unit 23, and a storage unit 33. The advice generation unit 321 generates and outputs advice data. The display unit 23 displays the advice data. The storage unit 33 stores a database that stores data related to registered investment products.

[0077] (Processing by the advice generation unit 321) The advice generation unit 321 repeatedly generates advice regarding buying a particular stock or commodity, and advice regarding selling a particular stock or commodity, thereby generating multiple pieces of advice that are continuous within a course corresponding to the investment amount and change according to buying and selling. These multiple pieces of advice are continuous within the course selected by the user and change dynamically in response to various changes such as market conditions for the stock or commodity.

[0078] When investment targets primarily consist of volatile assets, the advice data should not be static data such as portfolios at a specific point in time, but rather data that responds to various changes such as market conditions.

[0079] Figure 14 shows a sequence of steps in the advice generation process according to this embodiment.

[0080] (Step S1401) The advice generation unit 321 calculates the amount that can be purchased. From the second time onward, the advice generation unit 321 reflects the selling price related to the advice generated in step S1403 into the amount that can be purchased.

[0081] (Step S1402) The advice generation unit 321 generates advice regarding the purchase of a particular stock or product, based on the purchase amount calculated in step S1401.

[0082] (Step S1403) The advice generation unit 321 generates advice regarding selling the stock or product related to the advice generated in step S1402.

[0083] (Step S1404) The advice generation unit 321 repeats the processing in steps S1401 to S1403. In the processing in steps S1401 to S1403, the later processing is executed inheriting the results of the previous processing.

[0084] (Setting constraints) Figure 15 shows the framework for advice generation according to this embodiment.

[0085] In the advice data generation system 10, terminal 2 transmits data for each constraint item selected by the user to server 3. The advice generation unit 321 of server 3 stores the course with the set constraints in the storage unit 33. This determines the framework of the advice and prepares it for generating continuous advice. By setting course constraints, various courses with different characteristics are set, and different types of advice are generated.

[0086] The advice generation unit 321 generates advice according to the constraints set for the course corresponding to the investment amount. That is, as shown in Figure 15, the advice generation unit 321 creates a course with constraints on buying and selling, which is a large framework for advice, including the target product, investment amount, start date, end date, advisor, etc., and generates advice under that course.

[0087] Examples of constraints are shown below.

[0088] (1) The advice generation unit 321 generates advice under a funding constraint of 1 million yen.

[0089] (2) The advice generation unit 321 generates advice focusing on the First Section of the Tokyo Stock Exchange for Japanese stocks.

[0090] (3) The advice generation unit 321 provides buy and sell advice specifically for semiconductor-related stocks. (4) The advice generation unit 321 generates advice on robotics-related topics for 1 million yen, targeting ETFs.

[0091] (Effect of constraints) The advice generation unit 321 does not generate advice completely freely, but rather, by setting constraints, it can generate the most appropriate advice under those constraints. These constraints include course constraints and constraints on the advice generation unit 321 itself.

[0092] The course constraints are a broad framework that determines how much the advice generation unit 321 will advise, who will give the advice, what (subject matter), and when (timing) the advice will be given. Since these constraints do not change until the course is completed, the advice generation unit 321 generates advice under these constraints.

[0093] The actual investment amount is determined by the calculated available purchase amount and the buy advice within that range. The securities or products for which buy advice is provided are selected from those registered in the database within the range of eligible securities or products.

[0094] As shown in Figure 15, each course is assigned a course administrator. Course administrators may include advisors, users, and administrators.

[0095] (A structure that uses the investment amount as a constraint) In the advice data generation system 10, the advice generation unit 321 generates advice based on the investment amount as a constraint. For example, the advice generation unit 321 will provide different advice at the same time depending on whether the constraint is an investment amount of 300,000 yen or 10,000,000 yen.

[0096] The investment amount is financially constrained by the initial amount (starting amount) at the beginning of the course, and by "initial amount + trading profit / loss + income / expenses" during the advice period, with advice provided within that range. These investment amounts represent the upper limit of the purchase price.

[0097] (The effect of the investment amount as a constraint) The advice generation unit 321 generates advice in which the number of stocks or products differs depending on the investment amount, and the number of stocks or products also differs depending on the investment amount, so it can provide advice that is tailored to the user's perspective and the amount of investment.

[0098] For example, by generating advice within a limited investment amount, the advice generation unit 321 can provide advice on how to further increase capital as profits are generated and capital grows significantly. On the other hand, if losses occur, appropriate advice can be provided.

[0099] In other words, by adding the investment amount to the constraints set for the course, the advice generation unit 321 can dynamically generate advice based on the increase or decrease in investment funds, such that if a profit is made, the possibilities expand, and conversely, if a loss is incurred, the constraints become stricter.

[0100] (A structure that uses the timing of the start of trading as a constraint) In the advice data generation system 10, the advice generation unit 321 generates advice according to the start date of the course, based on the above constraints. If the start date of the course is different, the course will be different, the course manager will be different, and the content of the advice will also be different. For example, the advice generation unit 321 will generate different advice for a course that starts in January 2013 and a course that starts in January 2018.

[0101] If a course has a start date, the course advice will begin from that date. If a course has an end date, the course advice will end at that end date. However, there may be cases where there are no restrictions on the end date.

[0102] The advice generation unit 321 includes advice that changes depending on the start time, and the content of the advice is constrained by the buying and selling price at the start time. The advice for start time A will be different from the advice for start time B.

[0103] The advice generation unit 321 is also constrained by the course completion date. If there is an end date for the advice, the advice generation unit 321 generates the advice within a certain timeframe. That is, the advice generation unit 321 generates the advice under the constraint of an "end date" defined by a predetermined standard, such as a set deadline (e.g., 3 years) or a deadline upon achieving a target (e.g., when the valuation amount doubles). If there is no end date for the advice, the advice generation unit 321 generates the advice indefinitely.

[0104] Furthermore, if there is no set end date, the advice generation unit 321 can be terminated (or reduced) in order to be upgraded based on the evaluation amount, etc., and moved to another course (a course with other constraints set).

[0105] (The effect of the starting time as a constraint) The time constraints imposed by the timing of when advice should be given make it possible to provide advice on different stocks or products, and their respective allocations.

[0106] Once the advice data generation system 10 provides a start date for the advice, the user must prepare the funds in accordance with the disclosure date. However, users would ideally like to receive advice as soon as they have prepared their investment funds.

[0107] The interactive advice data generation system 10 allows users to decide when to start the course. Furthermore, the interactive advice data generation system 10 can meet the user's needs as described above, and generate advice data to match the user's desired timing.

[0108] (A structure that uses investment targets as constraints) In the advice data generation system 10, the advice generation unit 321 generates advice according to the investment target as a constraint. The advice generation unit 321 generates different advice when the investment target stock or product is constrained.

[0109] Investment targets include Japanese stocks, US stocks, Chinese stocks, Hong Kong stocks, futures, FX, ETFs, mutual funds, bonds, etc.

[0110] Due to these constraints on stocks or products, the advice generation unit 321 narrows down the population for the course, determines the trading targets within that range, and generates advice. The stocks or products to be advised are those registered in the database of the storage unit 33 from this population. Registration of stocks or products in the database may be done at any time.

[0111] The relationship between the investment targets as a constraint, the stocks or commodities registered in the database, and the range of stocks or commodities for which buy advice is provided is as follows:

[0112] Scope of investment targets ≥ Scope of database registration ≥ Scope of advice provided The advice generation unit 321, for example, takes all Japanese stocks as investment targets, registers promising stocks in its database, places them under course management, and, depending on changes in technical indicators, earnings trends, etc., determines whether to recommend buying or selling. The advice generation unit 321 will generate different advice at the same time depending on whether the investment target is Japanese stocks, Chinese stocks, or ETFs. In other words, the advice is constrained by the investment target.

[0113] However, it is also possible to have no restrictions on investment targets. Even without restrictions, database registration is required. In this case, any stock or product registered in the database will be eligible for buy advice. It is also possible to narrow down the population using multiple conditions, and filtering using search expressions such as AND and OR is also possible.

[0114] While the above uses Japanese and Chinese stocks as investment targets, other themes, industries, company sizes, and markets can be similarly adopted and produced similar effects, as long as they perform their intended function. In short, the key is how to define or restrict the target population for investment.

[0115] Even if a stock or product is not initially registered, it will eventually be registered during the generation process.

[0116] Examples of categories include the following. There are various ways to categorize them: · By type (stocks, FX, ETFs, mutual funds, REITs, bonds, etc.) · By type of stock (Japanese stocks, US stocks, Chinese stocks, Hong Kong stocks, Asian stocks, etc.) · By theme (robot-related stocks, robot-related ETFs, semiconductor-related stocks, semiconductor-related ETFs) · Semiconductor-related in general (including ETFs, stocks, mutual funds, etc.) · By industry (financial stocks, ETFs composed of financial stocks, real estate stocks, etc.) · By type of ETF (domestic bond ETFs, foreign bond ETFs, domestic stock ETFs, etc.) · Chart indicators (stocks with negative moving average deviation) · Performance indicators (stocks with dividends, stocks with shareholder benefits, stocks with increasing revenue, etc.) · Stock price indicators (stocks with a P / E ratio of 10 or less, etc.) · Market indicators (daily trading volume of 1 billion yen or more, market capitalization of 100 billion yen or more, etc.) Let's say a particular stock is a Japanese stock, listed on the First Section of the Tokyo Stock Exchange, in the financial industry, and possesses both a fintech theme and a financial theme. In that case, when categorized in the database and the investment target is set to the fintech theme, that particular stock becomes one of the investment targets.

[0117] For example, suppose the advice generation unit 321 is restricted to only purchasing stocks related to robotics.

[0118] For example, there are various approaches to narrowing down investment targets, such as focusing only on the Tokyo Stock Exchange First Section, focusing only on overseas ETFs, focusing on the yen exchange rate, or focusing on those with an average daily trading volume of 1 billion yen or more over the past month.

[0119] These are managed in the database by being separated into different tables based on differences in classification methods such as market tables and industry tables. Therefore, if you want to limit yourself to stocks listed on the First Section of the Tokyo Stock Exchange, you can quickly prepare the population by extracting data using the First Section market.

[0120] Figure 16 shows an example of the configuration of the stock or product database DB1 according to this embodiment. As shown in Figure 16, database DB1 is a database of "stocks" as the type of investment product, and is configured to include themes. In addition to database DB1, a theme table may be set up to manage themes, and themes and stock codes may be associated in this theme table.

[0121] (The effect of the investment target as a constraint) Within the investment targets selected by the user, it is possible to provide advice on different stocks or products, investment methods, etc. By narrowing the target population for advice, advisors only need to provide advice within the scope of that population, and users can benefit from being able to choose a course where they can open an account and have an environment ready for trading, and receive advice focused on promising themes. By registering promising stocks or products from the target population in a database, various data indicators can be used to generate advice.

[0122] (A structure that uses advisors as a constraint) In the advice data generation system 10, the advice generation unit 321 generates advice tailored to the advisor, based on the above constraints.

[0123] The advice generation unit 321 changes its advice according to the advisor and is constrained by the advisor. For example, the advice generation unit 321 generates different advice depending on whether the advice is from advisor A or advisor B.

[0124] Advisors include individuals, corporations, teams, and organizations. They may also include robots, AI (Artificial Intelligence), and other similar entities.

[0125] In a system where a robot acts as an advisor, it can generate trading data as a trading data provider and present it to the user via email or other means. The setting and modification of other constraints can also be automated, enabling automated advice. However, there are different levels of automation; for example, setting and modifying constraints might be done manually, while subsequent advice is automated.

[0126] (The effect of the advisor as a constraint) Different advisors can offer different advice. Considering the constraints imposed by each advisor, users can choose the advisor that best suits them.

[0127] The advice data generation system 10 operates in a two-way dialogue format, depending on who determines the course constraints. As shown in Figure 15, the course administrators include advisors, users, and managers.

[0128] The characteristics of the advice data generation system 10 change significantly depending on whether the user, administrator, or advisor (including robots) sets the course, and it is possible to incorporate a two-way dialogue format.

[0129] In the advice data generation system 10, when the course manager inputs data for each item into terminal 2, terminal 2 sends constraint data to server 3, which then sets the course, thus creating an environment in which the advice data generation system 10 operates in a continuous manner.

[0130] The administrator decides who will be the course administrator. Regarding the relationship between the course administrator and advisors, the course administrator can choose the advisors who will provide advice.

[0131] (When the administrator becomes the course administrator) For example, if the administrator decides on an initial investment amount of 1 million yen, a start date of today, investment targets Japanese stocks, and investment advisory company X as the advisor, then the framework for providing advice and the course of action are determined. Investment advisory company X generates advice under these constraints.

[0132] For example, if the administrator decides on an initial investment amount of 1 million yen, a start date of today, investment targets being ETFs, and advisors being robots, then advice will be generated using ETFs. In this case, a course will be set up that provides advice that is completely different from that of the aforementioned investment advisory company X.

[0133] (If the advisor is the course administrator) A course offering ETF advice with an initial investment of 3 million yen will be launched under the guidance of a certain advisor. A course offering advice on Japanese stocks with an initial investment of 3 million yen will also begin next month.

[0134] (If the user is in charge of the course) In this case, a robot advisor is assumed, and for example, a course offering advice on US stocks for an initial investment of 1 million yen will start next month, with a robot acting as the advisor. The trading conditions are chosen by the user from a selection of options. Depending on how far automation is desired, such as leaving the trading conditions to the robot or manually inputting the algorithm, there will be different levels of automation.

[0135] For example, a course is set up to provide ETF advice for 3 million yen. The trading conditions are changed each time and determined through trial and error. For example, the course administrator is user b, and it is decided that the course will start in February 2018 to provide advice on Japanese stocks using a robot advisor.

[0136] (Effects of the course administrator) By separating management for each course and having different course managers, it is possible to generate dynamic advice with different characteristics for each course. By making users course managers, users can try out various trading conditions. Users can receive advice based on the conditions they set themselves. Initially, various trading conditions were set based on dividend yield, but if this was unsatisfactory, it is possible to explore other conditions, etc.

[0137] Once the administrator sets the constraints, a series of advice is provided under those constraints set by a professional. Users who follow this advice don't need to think much and can reap a high return on investment.

[0138] On the other hand, if the user defines constraints, continuous advice is generated under those constraints, resulting in interactive, two-way dialogue-style advice tailored to their needs.

[0139] The advice data generation system 10 manages, evaluates, and displays the advice generated for each course by determining a name and administrator that represent the characteristics of the course and by setting constraints.

[0140] The advice generation unit 321 generates advice under various constraints in a course where certain constraints are set. The results will vary, the display will vary, and the advice will vary. Management is also performed on a course-by-course basis. For example, a course may be set up in which a robot advisor generates advice data using ETFs with a limited investment amount of 1 million yen.

[0141] The courses are stored in the memory unit 33 of server 3, and advice data is associated with each course. The advice generation unit 321 generates advice data under the constraints of the course.

[0142] (Effect of course selection) This allows for the management of continuous advice on a course-by-course basis. This clarifies whether the system is being implemented well or poorly, identifies areas for improvement, and enables management to generate better advice. In other words, the preparation for generating advice data is complete, and the course framework for the advice is established.

[0143] In setting conditions, it is decided who sets the conditions and whether they should be made public or private. Condition settings are divided into parts decided by the administrator, parts decided by the user, and parts decided by advisors (including robots). The course administrator decides whether each item should have a default value, be an input value, or be a selection option.

[0144] Depending on who sets the conditions, advice generation can be divided into one-way advice generation, where advice is generated unilaterally by administrators, advisors, etc., and interactive advice generation, which responds to user requests.

[0145] In the case of a one-way advice data generation system 10, the administrator sets most of the conditions on the management screen.

[0146] In the case of an interactive advice data generation system 10, the degree to which the system becomes user-friendly and interactive is determined by the administrator's ability to increase or decrease the items that users can select or input.

[0147] This setting sends data for each item to server 3. Then, the constraints, conditional expressions, and tables that can also be set on the user's My Page are determined, and continuous advice data is generated under each control condition.

[0148] If the administrator sets the conditions, users will receive the same advice data. The administrator can also change certain conditions, and can set whether those conditions are made public or private. Users will receive buy and sell advice emails, but the administrator can change whether the logic behind those emails is made public or private.

[0149] Even in courses managed by administrators, if users are allowed to set conditions, the conditions will differ for each user, even within the same course, generating various advice data for each user. Because users can set conditions as they see fit under the same constraints and course, performance competition among users becomes possible. If a user is the administrator and sets the conditions themselves, they can experiment with various conditions and test them.

[0150] (Effect of setting conditions) Even with the same course, different advice data can be generated by changing various conditions. For example, if the number of brands or products purchased changes from 3 to 5, various pieces of advice will change, making it possible to generate different advice even with the same course. Setting conditions is crucial for generating dynamically changing advice. Since various pieces of advice data are generated depending on the differences in constraints, users can also explore which conditions are optimal.

[0151] [Embodiment 4] Embodiment 4 of the present invention will be described below with reference to Figure 17. For the sake of convenience of explanation, components having the same function as those described in Embodiments 1, 2, and 3 will be denoted by the same reference numerals, and their descriptions will be omitted.

[0152] The advice data generation system 10 is an advice data generation system that generates multiple pieces of advice according to a course corresponding to the investment amount, and includes a storage unit 33 that stores, for each course, cash ratio data indicating the ratio of cash to be kept when buying a stock or product, purchase quantity data indicating the number of stocks or products to be purchased when buying a stock or product, amount allocation data indicating the allocation of the purchase amount of stocks or products when buying a stock or product, and trading condition data indicating the purchase conditions and selling conditions of stocks or products, and an advice generation unit 321 that generates the multiple pieces of advice according to the cash ratio data, purchase quantity data, amount allocation data, and trading condition data.

[0153] The course administrator decides whether or not to set conditions for generating advice data. If all of these conditions are set, the advice data generation system 10 becomes fully automated.

[0154] In a fully automated example, with an initial capital of 5 million yen, a cash ratio of 0%, and the purchase of 5 stocks with equal allocation of funds, 1 million yen would be allocated to each stock. For example, suppose there is a stock XX that meets the conditions in the database of the memory unit 33. The advice generation unit 321 refers to the stock price, minimum unit, etc. of XX from the database, and if the minimum purchase unit is 100 shares x 5000 yen, it determines a buy advice of 1 million yen for a total of 200 shares, sends it via email, and stores this buy advice data in the database. The same applies to other stocks.

[0155] After advising to buy five stocks, if the stock price of XX reaches 6,000 yen and the database indicators meet the sell conditions, the advice generation unit 321 generates a sell advice for 200 shares and stores the sell advice data in the database.

[0156] In this case, if the available purchase amount increases by 1.2 million yen due to the sale of XX, a buy recommendation will be generated again using these funds. This is automatically generated as long as the conditions are set. If a change in conditions is deemed necessary, the condition change will also be done automatically. As long as all of the above conditions are set, everything is generated automatically.

[0157] The advice data generation system 10 may be a fully automated system, or it may be a semi-automated system in which some user input is required and the remaining process is handled by the system.

[0158] (Example of partial automation: When only the criteria for determining buy advice are not automated) In the advice data generation system 10, the course administrator is presented with the number of products and the available purchase amount. By selecting stocks that can be purchased within that range, a buy advice email is sent. Subsequently, if the conditions for selling are met, the system sends a sell advice email, and the available purchase amount is automatically recalculated. In this way, it can be used semi-automatically, and the scope of automation can be adjusted.

[0159] If you do not automate the selling conditions, the buying advice will be automated, but a list of your holdings will be generated, and you will receive a selling advice email after selecting the stocks to sell. The available purchase amount will also be automatically recalculated, and a buying advice email will be sent, including the appropriate number of shares for the stocks that meet the buying conditions. Finally, a refreshed list of your holdings will be presented again.

[0160] Here, the degree of automation in advice generation is adjusted in the advice data generation system 10. The advice generation unit 321 generates advice data that matches the conditions once all conditions are set. If the results are poor, the advice generation unit 321 also automates the process of changing the conditions to generate advice data under different conditions, taking that into consideration. The degree of automation in changing conditions can be controlled, for example, by selecting from a set of options.

[0161] (Effect of setting conditions) Depending on the target of the advice, it becomes possible to differentiate or experiment with the degree of automation. For example, by automating rebalancing to maintain a certain ratio, if stocks rise and the stock allocation increases, it becomes possible to generate advice to sell stocks and buy bonds, thereby providing advice to maintain a constant allocation of stock and bond amounts.

[0162] In the advice data generation system 10, the amount available for purchase is calculated and recalculated after buy advice, sell advice, and deposits / withdrawals using the formula "initial amount + trading profit / loss + deposit / withdrawal amount - purchase price of held securities," and the advice is constrained within this amount limit.

[0163] This process is performed not only initially, but every time the available purchase amount changes due to purchases, sales, or deposits / withdrawals. This ensures the continuity of the advice.

[0164] The advice generation unit 321 receives trading profit / loss data, deposit / withdrawal data, purchase price of held securities, and initial amount from the aggregation DB, and calculates or receives values ​​calculated in the database.

[0165] The advice generation unit 321 generates buy advice within this range, and after generating buy advice, it recalculates the increase in the purchase price of the held stocks. This recalculation occurs each time buy advice and sell advice are issued, and the system is always constrained by the calculated amount. Through this series of actions, the advice data generation system 10 generates advice while being subject to monetary limitations and under those constraints.

[0166] (Effect of recalculating the purchase price) The advice data generation system 10 is a system that continuously generates advice, but by recalculating the purchase amount after each buy and sell advice, it becomes possible to provide further advice. If profits are made, the range of advice expands, and conversely, if losses are incurred, the advice becomes more difficult, thus allowing the system to stand in the same perspective as the user. The repeated buy and sell advice creates continuity in the course, and the stocks also change.

[0167] In the advice data generation system 10, the purchase quantity data associates the available purchase amount with the number of items to be purchased within that amount. When the advice generation unit 321 generates buy advice, it refers to the purchase quantity data and determines the number of items to be purchased according to the available purchase amount.

[0168] Figure 17(a) shows an example of the configuration of the purchase quantity data according to this embodiment. As shown in Figure 17(a), the purchase quantity data is data that associates the range of the amount that can be purchased with the number of brands or products. For example, if the amount that can be purchased is up to 300,000 yen, the number of brands or products to be purchased will be 1. If the amount that can be purchased is between 300,000 yen and 1,000,000 yen, the number of brands or products to be purchased will be 3.

[0169] If the initial amount is 1 million yen and the cash ratio is 10%, the purchase limit becomes 900,000 yen. In this case, as shown in Figure 17(a), the purchase amount ranges from 300,000 yen to 1 million yen, so the number of stocks to purchase is determined to be 3.

[0170] If the initial investment is 1 million yen and the cash ratio is 10%, as profits increase to 1.2 million yen, the purchase limit becomes 1.08 million yen, the range of possible purchases exceeds 1 million yen, and the number of possible investments increases to 5. In this way, advice data is generated that dynamically changes the number of products. This makes it possible to provide advice that becomes more diversified as the investment amount increases.

[0171] If diversification is excessive, managing a large number of stocks becomes difficult, and the profit / loss ratio approaches the average. Therefore, controlling diversification is an important element of advice. Depending on which stocks are selected and how the amounts are allocated, the profit / loss ratio will be above or below the average. In the advice data generation system 10, the number of stocks to buy and advise on can be controlled in this table, and by matching this, the number of products (or stocks) can be identified.

[0172] In the advice data generation system 10, the cash ratio may be a default value, determined by the advisor according to the market conditions at the time, or determined by the user considering risks such as market trends. The system is managed to ensure that the predetermined cash ratio is maintained.

[0173] At the beginning of the period, the following formula applies:

[0174] Initial price × (1 - cash ratio) = purchase limit During the period, the following formula applies.

[0175] Initial amount + trading profit / loss + deposits / withdrawals - purchase price of held stocks = available purchase amount Purchase limit = Available purchase amount × (1 - cash ratio) By determining the purchase limit and the number of securities to invest in under the above constraints, it is possible to maintain a predetermined cash ratio.

[0176] for example, Purchase limit = 300,000 yen or less: 1 stock Purchase limit = 300,000 yen to 1,000,000 yen: 3 stocks Purchase limit = 1 million yen to 10 million yen: 5 stocks Purchase limit = 10 million yen or more: 15 stocks The number of stocks is determined based on tables such as those shown in Figure 17(a).

[0177] If the investment targets are not limited, the number of products is determined in this phase. For example, if three types are decided, the specific types (ETFs, mutual funds, bonds, etc.) will be determined in a later stage.

[0178] (Effect of the number of brands or products purchased) This process calculates the purchase limit based on the available purchase amount and the allocated cash ratio, and then determines the number of securities (products) to purchase based on the purchase limit. This process is followed not only initially, but also whenever the available purchase amount changes due to sales or deposits / withdrawals. This ensures continuity, and as profits increase, diversification progresses, generating advice that allows for risk diversification.

[0179] The number of stocks (products) you can normally buy depends on the amount of your purchase limit. For example, with stocks, you can buy 1 stock with 300,000 yen, 3 stocks with 1,000,000 yen, and 15 stocks with 10,000,000 yen. The more you diversify, the closer your profits will be to the average.

[0180] There's a limit to the number of stocks you can manage. Conversely, concentrating too much risk prevents diversification. This standard and policy vary depending on the advisor's approach. The types of investments you can diversify into depend on the amount of capital. The larger your capital, the wider the possibilities. As your capital grows, diversification advice becomes increasingly important.

[0181] In the advice data generation system 10, the amount allocation data associates investment targets with the allocation of the purchase amount for those investment targets. When the advice generation unit 321 generates buy advice, it refers to the amount allocation data and determines the allocation of the purchase amount according to the investment target.

[0182] The advice generation unit 321 determines the allocation of funds for each stock or product once the number of stocks or products to be purchased has been determined. The funds may be allocated equally, or, if the stocks are selected based on rankings, higher-ranked stocks may be given more weight. The conditions set here allow for the allocation of funds according to some criteria. If there are no restrictions, the default is equal allocation.

[0183] Figure 17(b) shows an example of the composition of the amount allocation data according to this embodiment. As shown in Figure 17(b), if the investment target is an ETF, and the number of securities or products purchased is 5, the amount allocation will be, for example, 40% for domestic stocks, 20% for bonds, 20% for foreign bonds, 10% for foreign stocks, and 10% for others. If the investment target is stocks, and the number of securities or products purchased is 3, the amount allocation will be 50% for the first place, 30% for the second place, and 20% for the third place.

[0184] Here, if buy advice data is provided for the second time or later, it is also possible to change the allocation of funds according to the rate of increase or decrease in the valuation (rebalancing). In this case, buy advice and other information will be adjusted to maintain the specified allocation of funds.

[0185] If a loss occurs and the valuation falls by 5% or more, as an emergency measure, for example, the cash ratio could be set to 40%, and the allocation of ETFs could be adjusted as shown in Figure 17(b), for example, by unconditionally setting the number of stocks or commodities purchased to 2, with 50% in domestic equity funds and 50% in domestic bond funds. In this way, adjustments can be made to mitigate risk.

[0186] This step determines the allocation of funds between brands or products, and since the number of brands or products to be purchased and the purchase limit are specified in (Claim 13) above, the purchase limit for each brand or product is determined.

[0187] Purchase limit × Amount allocated to each brand or product = Purchase limit for each brand or product Since the purchase price is calculated as the number of shares purchased multiplied by the purchase price, a restriction is added: the purchase limit must be greater than the purchase price.

[0188] If the purchase limit is 1 million yen and the bond-type portion of the ETF accounts for 30%, then the purchase limit for the bond-type ETF will be 300,000 yen.

[0189] It's also important to consider the different allocation of funds to each stock or product when providing advice. Varying the allocation significantly broadens the range of advice you can offer. For example, you can advise gradually decreasing the allocation to certain stocks, or increasing the amount allocated to safe assets as the valuation increases.

[0190] (Effect of monetary allocation) The allocation of funds, which determines the proportion of each stock or product to be purchased, only becomes meaningful once the number of stocks or products to be purchased has been decided. While the allocation of funds is meaningless for a single product, it becomes important because advice on fund allocation is generated for multiple stocks or products. Once the available budget and the fund allocation are determined, the purchase amount for each stock or product is specifically determined. This allows for weighting adjustments while considering risk.

[0191] In the advice data generation system 10, by dynamically changing the allocation of funds, it is possible to provide advice that responds to changes such as taking on risk according to profits and reducing risk assets by incurring losses. However, while the allocation of funds is important and indispensable for providing advice, it is difficult for beginners to decide on this, and there are many options, so it is best to start with equal allocation.

[0192] (Entering new stocks into the database) In the advice data generation system 10, when a buy recommendation is issued for a new stock or product not registered in the database of the storage unit 33, the system first inputs the stock or product into the database. This input registers the new stock or product as a managed stock or product, placing it under the management of this course. In the case of stocks, important indicators for buy and sell decisions are accumulated by enriching stock price data, stock split data, dividend data, minimum purchase unit data, performance data, etc., as needed. The determination of whether or not the buy conditions, sell conditions, etc., are met is also made by referring to these indicators. Database registration is an essential step for automating buy and sell recommendations. The advice generation unit 321 may register the information, or promising stocks or products may be registered in advance.

[0193] The advice data generation system 10 is based on the premise that it will provide advice on buying and selling stocks or commodities registered in the database. Only registered stocks or commodities are subject to advice, thus the system manages the target audience.

[0194] (Decision on which stocks to purchase) In the advice data generation system 10, the trading conditions table includes indicators related to buying stocks or commodities. The advice generation unit 321 calculates the purchase limit per stock or commodity from the available purchase amount, the number of items to purchase, and the allocation of the purchase amount. When generating buy advice, it refers to the product ranking related to the indicator and the purchase limit to identify the stocks or commodities to purchase. This is the process of determining how to decide on specific stocks for buy advice.

[0195] There is a specified upper limit on how much you can buy for each stock and product. If you choose a specific stock or product with a set minimum purchase unit, the quantity will be automatically determined once you have selected the stock or product name. The minimum purchase unit and the unit price (stock price) are automatically calculated by retrieving values ​​from the database for each stock or product, so once you have selected the stock or product name, the quantity you can purchase will be automatically determined.

[0196] If the number of brands or products and the purchase limit are determined, and an equal distribution is decided, the purchase limit for each brand or product is determined, and at this point, the quantity of each brand or product is determined.

[0197] For example, if the purchase limit is 2 million yen and there are 5 stocks, 400,000 yen will be allocated equally to each stock. You then decide which specific stocks to buy with this 400,000 yen. If the minimum purchase unit for a given stock is 100 shares and the stock price is 2,000 yen, the minimum purchase amount is 200,000 yen, and therefore the quantity of 200 shares is determined. The same applies to ETFs when weighting is done in a ranking format.

[0198] Since the purchase limit for each stock is determined in the previous stage, the upper limit is set, and the quantity can be determined simply by referring to the minimum purchase unit price. Determining which stocks to purchase is crucial for providing advice. This can be automated by referring to data registered in the database.

[0199] However, it is necessary to consider cases where the decision to select stocks will involve human judgment, and it is conceivable to use robots to make automatic decisions according to certain conditions, or to keep the stock selection process confidential.

[0200] In the advice data generation system 10, the specific investment targets (stocks or products for which a buy recommendation is issued) within the purchase range are determined, based on the limited purchase amount for each stock and the above constraints.

[0201] For example, the population stocks extracted under the above constraints are further filtered according to the following conditions.

[0202] (1) From the top-ranked stocks in the dividend yield ranking, select stocks that are within your budget and place them in order from highest to lowest.

[0203] (2) In addition to dividend yield, various indicators such as moving average deviation rate, sales growth rate, sales, and trading volume can be used.

[0204] In the advice data generation system 10, the same applies to ETFs; even among ETFs of the same equity type, there are various types, and specific stocks are determined based on market capitalization rankings, which are determined by prioritizing liquidity and ranking from the top in trading volume. A predetermined number of stocks or products are selected and decided upon. In the example above, it is limited to stocks that can be purchased for 400,000 yen or less. Based on that, the top 5 ranked stocks are determined.

[0205] In the advice data generation system 10, the number of shares per unit is determined based on the respective purchase limit and minimum purchase amount, and the quantity (number of shares) is determined. This can be determined using a complex conditional expression, or it can be determined by determining the criteria for final selection from the population.

[0206] In concrete examples of complex conditional formulas, one could calculate the revenue growth rate, dividend yield, and PER (Price-to-Earnings Ratio) for each company in the population, and then select the top-ranking stocks based on a comprehensive index using these three indicators. Various combinations of indicators, such as the moving average deviation rate mentioned earlier in the ranking section, are also possible. In any case, since the information entered into the database is flexible, it's possible to use a variety of indicators as selection criteria.

[0207] The process up to this point determines the number of stocks or products to buy and the allocation of the amount, and since the names of each stock or product are determined, the quantity (number of shares) is also determined. There are various conditions, and the advisor may or may not explicitly state the stock selection criteria. Stocks may also be provided by input. Therefore, the stock selection process may be performed using other systems, etc.

[0208] The conditional expressions used in this process can take various forms, such as determining stocks in order of the top-ranked companies based on their projected revenue growth rates for the current fiscal year. These conditions may be clearly stated by the advisor from the beginning, or the client may choose from multiple options, or the client may propose their own conditions.

[0209] Each advisor has their own logic for deciding which stocks or products to include in their advice. This may involve making decisions based solely on quantitative data, or by incorporating qualitative factors as well; various approaches are possible. The advice data generation system 10 manages this by limiting the selection to stocks and products registered in its database.

[0210] (Effect of selecting stocks to purchase) While it's important to provide buy recommendations for specific stocks, this is only one function of the advice data generation system 10. The crucial aspect is the continuous advice on what to do after selecting and buying a stock; the buy recommendation is merely the beginning, and this continuity is the key to the advice data generation system 10.

[0211] (Determination of purchase timing and purchase price) In the advice data generation system 10, the trading condition data includes the purchase time of a stock or product, or the purchase price of a stock or product. When the advice generation unit 321 generates buy advice, it refers to the trading condition data to determine the purchase price of a specified stock or product from its purchase time, or to determine the purchase time of a specified stock or product from its purchase price, and generates advice that includes the purchase time of the stock or product and the purchase price of the stock or product.

[0212] Figure 17(c) shows an example of the configuration of trading condition data according to this embodiment. As shown in Figure 17(c), examples of purchase indicators, purchase timing, purchase price, and profit-taking conditions are listed. For example, the purchase timing may be the same time on the same day once the stock is decided, or it may be determined on the day when the stock price meets the conditions by referring to technical indicators.

[0213] However, if the price of the target stock rises and exceeds the purchase price, it may be necessary to restart the process, so immediate is best for beginners, and the default is also immediate / same day.

[0214] Once you've decided on the stocks to buy, the purchase timing and the purchase price are a set; therefore, when one is decided, the other is also determined.

[0215] For beginners, it's recommended to buy immediately at market price; this is the default approach. Methods for determining the timing and price include using technical indicators. Only after these are determined do you have enough buy data, and thus your buy advice is complete.

[0216] This is the moment when the final decision is made regarding the specific date and price of the purchase, and the final buy advice is given. It is often impossible to retrieve the real-time market price (stock price) from the database. Therefore, since the execution price is determined once the date and time are decided, it is acceptable to display a reference price such as yesterday's closing price. In this case, it is permissible to adjust the buy advice price later.

[0217] (The effect of determining the timing and price of purchase) This is where the buying advice data is finally completed, the advice is made concrete, and it becomes ready to be communicated to the user.

[0218] (Registration and distribution of buy advice data) Database registration can trigger various processes, such as registering in the database, sending emails to users, uploading to member sites, or uploading to apps. Various methods of distribution exist, including distribution to users or their contracted service providers.

[0219] It is conceivable that notifications could be sent to administrators, advisors, users, or to Type 1 investment advisory firms (asset management companies) contracted by users, via uploads to member sites, app notifications, etc., and that users might then entrust their investment management to these firms based on the advice provided.

[0220] (Effectiveness of registering and distributing buy advice data) The advice data is actually delivered to the user. The generated advice data is recorded in a database, automatically sent to the display system, various calculations are performed, and then it is sent, notified, and distributed to the user and other stakeholders.

[0221] (Decision to sell) In the advice data generation system 10, the above-mentioned trading condition data includes profit-taking conditions, and when the advice generation unit 321 generates a sell advice, it refers to the above-mentioned trading condition data to identify a stock or product that satisfies the above-mentioned profit-taking conditions from the stock or product for which the above-mentioned buy advice was generated, and generates advice regarding selling that stock or product.

[0222] The items to be sold are limited to the stocks you already own. In the case of short selling, you start by selling, and buying back comes later.

[0223] You decide when and which stocks to sell. You might sell all of them, only some of them, or sell all of each stock or product, or only a portion of each stock or product.

[0224] From the holdings for which we have advised buying, we decide which stocks to sell. For example, profit-taking is done as shown in Figure 17(c), (1) Sell half if the price rises by more than 10% (2) Sell all items with a price increase of 30% or more. (3) The decision will be made based on trading conditions such as selling all shares if the decline rate is 5% or more.

[0225] In the advice data generation system 10, the stock prices and values ​​of the held stocks are updated daily, and various indicators are calculated, so it is determined whether the selling conditions are met at the updated market price. Buy advice, although constrained, offers a wide range of options. On the other hand, sell advice is limited to stocks or products for which buy advice has been given, and the options are narrow, making it easier to automate using only quantitative data.

[0226] A simple example of automation is selling everything and settling accounts once every three months. Tables are easy to create, and rules are easy to define. There are various trading conditions, and they can be disclosed or not disclosed by the advisor.

[0227] For example, multiple conditions are allowed, such as selling everything if the moving average deviation exceeds 20%, selling half if the RSI (Relative Strength Index) exceeds 70%, or selling everything if the RSI exceeds 80%. For held stocks, target indicators can be matched with a database, and if the selling conditions are met, a sell advice will be generated. The algorithm for generating sell advice can be made public or private.

[0228] When this sale occurs, the purchase limit changes, so generating buy advice again will change the stock or product, resulting in continuous advice.

[0229] Profits and losses are only determined after both buy and sell advice is given. Only by selling the stocks or products you hold are the tied-up funds released and available for future purchases. The continuity of advice is only established when sell advice is given.

[0230] The decision to sell is made by matching various conditions. By accumulating the data necessary to generate sell advice in a database, it is possible to freely accumulate indicators, numerical data, etc. that are suitable for advisors, users, etc., so that sell decisions can be made in various ways.

[0231] (Effects of the decision to sell) Stocks or products for which a buy recommendation has been issued may eventually become subject to a sell recommendation. Profits will not be realized unless the stock is sold, and the amount available for further purchases will not increase. By deciding to sell, the amount available for further purchases will increase, which can then be used as capital to reinvest.

[0232] (Database registration of sales advice data) In the advice data generation system 10, the advice generation unit 321 registers the sales advice data in the database. Subsequently, the data is distributed to administrators, advisors, users, or Type 1 investment advisory firms (asset management companies) contracted by users, through methods such as uploading to member sites, app notifications, and email distribution. It is also conceivable that users may entrust their investment management to others based on this advice.

[0233] (Accumulate, aggregate, and extract trading data etc. in the database) In the advice data generation system 10, the database stores data necessary for making buy / sell decisions on registered stocks or products, buy / sell data, and data necessary for displaying buy / sell data in an easy-to-understand manner.

[0234] Regarding registered stocks (buy advice stocks and their candidate stocks), stock price data, split data, dividend data, performance data, technical data, minimum purchase unit data, etc. are accumulated in the database for each registered stock. Regarding registered stocks, the indicators for making trading decisions are managed in more detail according to the purpose. By accumulating indicators according to the purpose, the data necessary for trading decisions can be used at any time.

[0235] Regarding the aggregation and calculation of trading data, numerical values for calculating trading profit and loss, calculating evaluation amounts, calculating the purchasable amount, and determining whether rank-up, review, etc. are necessary are recalculated sequentially. Those numerical data are delivered to the display unit 23, advice generation unit 321, etc., and utilized according to their respective purposes.

[0236] For example, the purchasable amount is calculated as the amount obtained by subtracting the total purchase amount of the held stocks from the total value of the total trading profit and loss, incoming and outgoing amounts, and the initial amount. This numerical value is recalculated each time a trade is made and delivered to the advice generation unit 321.

[0237] The rank-up conditions are that the total evaluation amount value, which is the total value of the initial amount, incoming and outgoing amounts, total trading profit and loss, total unrealized profit and loss, and cash, becomes an important indicator, is calculated sequentially, and is used as an indicator for determining whether the rank-up conditions are met.

[0238] The calculated values necessary for condition review are calculated sequentially. In particular, when the recent trading profit and loss is negative or the unrealized profit and loss becomes negative, each condition is reviewed. Thereby, it is possible to analyze at any time what was wrong and why no results were obtained.

[0239] In the database, a series of stock data such as a stock master table, stock price data of stocks, technical data, etc., trading advice data, actual trading data, etc. are accumulated. Each time, as needed, the necessary data is read out and calculations and aggregations are performed.

[0240] In the advice data generation system 10, not only the stocks to be traded but also the information on stocks or products with a high possibility of being traded is registered in the stock master table, and various types of information are accumulated, thereby forming a system that enables more precise judgment in response to more dynamic changes. It includes a server 3 having functions such as calculation, display, and recalculation of digital data.

[0241] (Effect of accumulating trading data, etc.) By accumulating information on stocks that may be the target of buy advice and the information on held stocks, it becomes possible to provide advice in response to changes in the future trading environment.

[0242] By accumulating information on past stock prices, market values, etc., the unrealized profit and loss is updated, so the display unit 23 can display a graph. Also, by accumulating information on past stock prices, market values, etc., the advice generation unit 321 can use the accumulated information to provide judgment indicators, calculate the purchasable amount, etc.

[0243] (Rank up) In the advice data generation system 10, the advice generation unit 321 determines whether the result of a course meets the rank-up condition. The rank-up condition is a condition serving as a criterion for determining whether to increase the investment amount of the course. The indicators included in the rank-up condition may include composite conditions such as the asset increase rate by advice, advice profit, evaluation amount by advice, actual trading data numerical value, number of operating years, ratio with indicators such as the Nikkei average, and questionnaire response results.

[0244] When the result of a course meets the rank-up condition, the advice generation unit 321 sets new constraint conditions including the investment amount, etc. corresponding to the result of the course before rank-up for a new course.

[0245] For example, the first course was a 1 million yen course targeting Japanese stocks listed on the first section of the stock exchange, but the valuation increased to 2 million yen, doubling, thus fulfilling the rank-up conditions. In this case, the advice generation unit 321 moves to the 2 million yen course, expanding the target to Japanese stocks in general and including higher-risk Mothers market stocks in the advice, thereby providing advice that generates even more profit. If the valuation amount exceeds 4 million yen in the 2 million yen course, the user is moved to a new course with the constraint that the investment amount be 4 million yen after cashing out. Alternatively, the advice generation unit 321 may generate advice to cash out the funds in the original course and terminate the course. Alternatively, the user may cash out half of the funds in the original course and open a new course with the investment amount of that half cashed out as the constraint.

[0246] Furthermore, the advice generation unit 321 can, for example, if the valuation reaches 2 million yen, cash out 1 million yen, launch a new course as a 1 million yen course, secure the profit portion using a safety measure with ETFs, and generate conventional advice for the remaining 1 million yen, thus providing advice that separates the profit portion and divides the course.

[0247] If the course rank increases, the advice generation unit 321 converts all or part of the funds into cash, changing the constraint condition to the amount converted into cash as the investment amount, and generates advice under the course with the new constraint condition.

[0248] The memory unit 33 stores rank-up condition data that indicates the conditions for ranking up. The rank-up condition data includes, for example, a rank-up condition that, when the asset growth rate due to advice doubles, the course is terminated, the assets are converted into cash, and the user moves to a new course with new constraints set.

[0249] The advice generation unit 321 repeatedly generates advice under the same constraints set for the course until the course results meet the rank-up conditions.

[0250] (Effect of rank-up) If the course is upgraded based on the rank-up conditions, further asset growth will be achieved. As profits accumulate and the course rank increases, the first stage goal is achieved, and advice can be generated again from the beginning with modified constraints, including investment targets.

[0251] This allows both the user and the advisor to make a fresh start. As the degree of risk tolerance changes, the advice generation unit 321 modifies the advice by increasing the proportion of volatile small-cap stocks, changing the diversification method, or altering investment targets, amounts, etc. Therefore, the mechanism for transitioning to a new course with new constraints contributes to the continuity of advice generation between courses. The ability to transition to a new course enables a wider range of advice. For example, as funds increase, it becomes possible to expand advice to include riskier US stocks and change advisors.

[0252] (Setting changes to trading conditions) In the advice data generation system 10, the advice generation unit 321 makes a condition change determination if the course results do not meet the rank-up conditions and there are conditions related to buying and selling. These determination conditions are based on data such as unrealized gains and losses and trading gains and losses, and serve as a basis for reviewing trading indicators. The determination conditions include, for example, cases where trading gains and losses are negative, where unrealized gains and losses exceed -10%, and where the asset growth rate becomes negative.

[0253] It is important to identify unfavorable situations early on and revise the trading conditions and adjust the constraints of the advice. If the trading results, such as profit or loss, are unsatisfactory, improve the constraints set for the course and set new trading conditions.

[0254] In other words, the advice generation unit 321 determines whether to trade again under the same trading conditions or to trade with different trading conditions. If the criteria for the determination are made stricter, each item of the trading conditions will be reviewed more frequently, and if the criteria for the determination are made lenient, the generation of advice under the same trading conditions will continue, thus controlling the conditions of the trading advice.

[0255] (Effects of changing trading conditions) This allows for adjustments to the advice generation trajectory. If there are no changes to the trading conditions, the advice generation unit 321 returns to calculating the available purchase amount and generates the next buy advice. Typically, this involves changing the stock under the same trading conditions. For advice that maintains continuity within a course, the process of changing trading conditions is important and allows for a review of trading indicators.

[0256] (Repeating the advice generation process) In the advice data generation system 10, the advice generation unit 321 generates buy / sell advice, and if the available purchase amount changes or there are deposits or withdrawals, it repeats the process of calculating the available purchase amount and generating advice again. By repeating the advice generation process, the stocks are changed.

[0257] In the advice data generation system 10, which has a mechanism for changing stocks or products, the advice generation unit 321 can generate continuous advice even with the same funds because there is sell advice, and advice is generated continuously because there is repetition.

[0258] The advice generation unit 321 generates advice to change stocks or products. For example, the advice generation unit 321 may include either a sell advice for stock A and a buy advice for stock B on the same day, or a sell advice for stock A, cashing out, and a buy advice for stock B using a portion of the cash after a certain period of time has elapsed.

[0259] The advice generation unit 321 repeats the process from the calculation of the purchasable amount every time the purchasable amount changes. Specifically, when the generation of a buy advice is completed and registered in the database, when the generation of a sell advice is completed and registered in the database, when there is a deposit or withdrawal, when an index meets the buy condition, etc.

[0260] (Effect of repeating the advice generation process) The replacement of stocks or products serves to connect sell advice and buy advice, enabling the generation of continuous advice.

[0261] (Reference and comparison between advice and the user's actual trading information) In the advice data generation system 10, the advice generation unit 321 refers to the generated buy advice and sell advice data and the user's actual trading information, and generates advice such that the user's investment content approaches the advice.

[0262] When the advice generation unit 321 generates a buy advice, it registers the buy advice in the database and collates the buy advice data with the actual trading data. The collation is performed by stock or product and date. That is, the advice generation unit 321 collates the stock name or product name in the advice data with the stock name or product name included in the actual trading data, compares the same data, and recognizes that the trading data conducted after the date of the advice is the trading data conducted according to the advice.

[0263] The actual order data may have a different price or a different date from the advice data, and due to various factors, the actual trading profit and loss are different from the trading profit and loss of the model. Therefore, if there is a deviation, the advice generation unit 321 generates advice based on the difference, informing of the deviation and prompting adjustment.

[0264] Because trading data and advice data are imported into a database, it becomes possible to match stock names or product names. This provides users with an incentive to follow advice by recognizing discrepancies, and allows advisors to generate further advice by recognizing discrepancies with previous advice.

[0265] (effect) By comparing advice data with actual trading data and analyzing the differences and discrepancies, it is possible to generate further advice. This allows for further improvement of the advice. By displaying the discrepancies between trading data and advice data through this process, it is possible to show how actual trading differs from trading according to the advice, and what actions the user should take in the future, providing guidance for the future.

[0266] The advice data generation system 10 includes an advice generation unit 321 that dynamically generates advice, a storage unit 33 that stores courses for which the constraints of the advice generation unit 321 are set, and a display unit 23 that displays dynamically changing advice.

[0267] (Display section 23) In the advice data generation system 10, the display unit 23, which displays the trading advice data generated by the advice generation unit 321, displays a list of held stocks, a table showing the trend of valuation, a list of valuation amounts, a list of trading profits and losses, a list of unrealized profits and losses, and the like.

[0268] In the advice data generation system 10, trading data is aggregated in a database, and in order to display it in an easy-to-understand manner for the user according to the purpose, the display unit 23 displays a list of held stocks, a table of valuation trends, a table of valuation, a table of trading profits and losses, a table of unrealized profits and losses, a table of discrepancies between actual trading data and advice data, etc.

[0269] The trading data managed for each course is, in itself, merely a collection of data, but it is constantly changing data, as holdings change and stock prices fluctuate due to buying and selling of stocks or commodities, substitutions of stocks or commodities, changes in the allocation of funds, etc. It is necessary to display this data accurately and clearly. For users, it is extremely important to know how the advice was generated, what the current situation is, what the past was like, and how it deviates from the actual trading data.

[0270] In the advice data generation system 10, the advice generation unit 321 sequentially generates trading advice data and registers it in the database. The accumulated trading advice data is communicated to users via email, updates to the member site, etc., but is also output to the display unit 23. In the database, trading advice data, deposit and withdrawal data, stock price data, actual trading data, stock split data, etc. are integrated and calculated. The display unit 23 divides this data by function and displays it in an easy-to-understand manner.

[0271] (Effect of display unit 23) The display unit 23 is provided to users, advisors, and administrators, allowing them to grasp and understand the history and current status of advice given to date, and to improve future advice. Furthermore, information such as discrepancies between advice data and actual trading data is important for both those giving and receiving advice.

[0272] (Display of Profit and Loss Statement) In the advice data generation system 10, the server 3 calculates the trading history of the trading advice data and transmits data showing the trading history to the terminal 2. The terminal 2 receives the data showing the trading history of the trading advice data from the server 3 and displays the trading history of the trading advice data shown in the received data on the display unit 23.

[0273] Figure 6 shows an example of the "Trading History" screen display. As shown in Figure 6, the control unit 22 displays a screen on the display unit 23 as the "Trading History" screen, including the stock name, number of shares, date of buy advice, stock price and amount, date of sale, stock price and amount, rate of change, and trading profit / loss.

[0274] In the advice data generation system 10, the display unit 23 displays a table of trading profit and loss. Specifically, the display unit 23 extracts and displays trading data from the database for trades where the buying and selling have been completed, that is, trading data for which the profit or loss has been determined by an offsetting trade.

[0275] The display of profit and loss statements makes it possible to view past advice that has been provided for completed trades. For those receiving advice, it becomes easy to see how past advice has been delivered.

[0276] (Display of the breakdown of the valuation) In the advice data generation system 10, terminal 2 receives data from server 3 showing a breakdown of the valuation amount of the buy / sell advice data, and displays the breakdown of the valuation amount of the buy / sell advice data shown in the received data on display unit 23.

[0277] Terminal 2 receives data from Server 3 showing a breakdown of the valuation of the trading advice data, and displays the breakdown of the valuation of the trading advice data shown in the received data on Display Unit 23. The current valuation consists of unrealized gains / losses, trading gains / losses, deposits / withdrawals, starting amount, and current cash balance. Unrealized gains / losses are calculated from the unrealized gains / losses summary table, and trading gains / losses are calculated from the trading gains / losses summary table, and a summary is displayed.

[0278] The display of a breakdown of the valuation of trading advice data makes it possible to see the breakdown of the current valuation, providing a clear overall picture of the advice accumulated over time. For those receiving the advice, it is possible to see the current status of the advice up to the present.

[0279] (Display of unrealized gains and losses table) The evaluation indicators for each holding include the stock name (code) or product name, advice date, number of shares, stock price and price at the time of the buy advice, current stock price and price, percentage change, and unrealized gain or loss. The portfolio refers to the state of the buy / sell advice data at that time.

[0280] In the advice data generation system 10, the display unit 23 displays an unrealized profit and loss table. The display unit 23 extracts only the trades that have not been offset by a trade, and displays the unrealized profit and loss table by comparing the purchase price of that stock with its current valuation.

[0281] By displaying unrealized gains and losses, it is possible to show the current progress of advice on stocks that have been recommended for buying. For those receiving advice, it is easy to see how the current advice is being applied and to easily check the current status of assets that have not been traded in the opposite direction.

[0282] (Display of the valuation trend table) In the advice data generation system 10, the valuation changes in various ways as time passes from the initial investment amount. These changes are constant, due to fluctuations in the market value of held stocks or products, fluctuations in trading profits and losses, and changes in subsequent holdings or products.

[0283] To obtain information on market value, division information, etc., the valuation amount is calculated as needed, and the database stores the valuation amount as historical data. All historical data includes data on trading profits and losses, unrealized profits and losses, and the breakdown of the valuation amount at that time.

[0284] In the advice data generation system 10, the display unit 23 displays a table showing the trend of valuation amounts, including the data at that time.

[0285] When a user places the cursor over a point in the valuation trend table, the display unit 23 displays a valuation summary, unrealized gains and losses list, etc., for that point in time. Since the date is specified, it becomes possible to read this data from the database, making it easy to trace the progress up to the present. In addition, days on which trades were made are marked, and when a user places the cursor over a mark, the display unit 23 displays a breakdown of the trades made on that day.

[0286] This information allows for easy review of past changes. Furthermore, marks are placed on the date lines for buy and sell advice, allowing for verification of the specific data at the time of that advice. The valuation trend table displays the changes in valuation from the start date to the present. For those receiving advice, this allows them to see the history of the valuation (initial amount + trading profit / loss + unrealized profit / loss + cash) generated by the advice up to the present.

[0287] (Displaying comparison curves) The trading advice data curve (valuation trend table) shows multiple pieces of advice, including advice related to at least one of the items of multiple securities and cash amounts included in past advice. The difference between the trading advice data curve (valuation trend table) and the trading performance curve indicates advice that brings the user's investment content closer to the trading advice data, generated by referring to the trading advice data from the above advice and the user's actual trading information.

[0288] The advice generation unit 321 refers to the buy / sell advice data generated in step S1202 and the user's actual buy / sell information in step S1206, and generates advice that brings the user's investment content closer to the buy / sell advice data. The terminal 2 then displays such advice on the display unit 23.

[0289] Terminal 2 displays the difference between the model performance curve and the trading advice data curve (valuation trend table) on the display unit 23. The control unit 32 displays the model performance difference on the display unit 23 using a double-headed arrow line segment.

[0290] The difference between the model's performance and the actual trading performance is the difference between the trading advice data and the actual trading performance at the present time. However, looking back in time from that point to the left, it shows when the trading advice data and the actual trading performance began to diverge, and provides advice on how trading should have been conducted.

[0291] Furthermore, the control unit 32 displays the advice, "There is a discrepancy between the model and the actual results from this point," on the display unit 23. This advice aims to bring the user's investment strategy closer to the trading advice data.

[0292] Furthermore, even if user trading performance data is not necessarily available, terminal 2 may display the discrepancy between the trading advice data and the random model by displaying a random model that shows what would have happened if the advised trades had not been followed.

[0293] In the advice data generation system 10, trading data is generated, and typically, individual investors use this data to place buy and sell orders with securities companies, resulting in trades being executed. However, this system can also be applied to business models where an asset management company and an investor enter into a contract, with the investor providing trading data and the asset management company placing orders on their behalf. It can also be applied to automated order placement models.

[0294] [Embodiment 5] Embodiment 5 of the present invention is described below. For the sake of convenience, components having the same function as those described in Embodiments 1 to 4 are denoted by the same reference numerals, and their descriptions are omitted.

[0295] The method for generating advice data includes the steps of: storing an unregistered stock or product in a memory unit; calculating the purchase price; generating buy advice within the range of the purchase price by referring to the memory unit; recalculating the purchase price in accordance with the buy advice; and generating sell advice by referring to the memory unit, and repeating each of the above steps.

[0296] (Steps to calculate the amount you can spend) Initially, the available purchase amount changes when generating buy advice and when generating sell advice. It is calculated each time trading data is registered, and that amount is output to the advice generation unit 321. The advice generation unit 321 generates buy advice under the constraint of the available purchase amount.

[0297] (If a brand or product is not registered in the database, this step involves registering the brand or product.) Stocks or products that meet the criteria and are potentially eligible for a buy recommendation are registered in the database as they become available; however, if they are not yet registered, they will be registered. Stocks or products registered in the database are managed as stocks (stocks), and various data is accumulated, making them available for reference when generating advice. Stocks eligible for a buy recommendation must be registered in the database.

[0298] (Steps to generate buy advice) When generating buying advice, the advice data is distributed via email, updated on the member site, and registered in the database.

[0299] (Steps to generate selling advice) When generating selling advice, the advice data is distributed via email, updated on the member site, and registered in the database.

[0300] (Repeat each step) This is a crucial step in generating advice data. The generation of advice is not a one-time event, but rather a continuous process that repeats. This process is repeated when, for example, the available purchase amount changes. Furthermore, only stocks registered in the database can trigger a buy advice step, and the amount is also restricted, thus controlling the generation of advice data within the system. Buy and sell advice data is generated within the available purchase amount.

[0301] Available purchase amount = Initial price + Trading profit / loss + Deposits / withdrawals - Purchase price of held stocks The advice generation unit 321 generates a buy recommendation for stock A, for example, with a maximum purchase amount of 1 million yen and a minimum purchase amount of 500,000 yen. Since stock A is not registered in the database, the advice generation unit 321 registers stock A first, then generates a buy recommendation, setting the maximum purchase amount to 500,000 yen and generating the recommendation within this constraint.

[0302] (Effect of advice generation process) Each time a trade is made, a buy recommendation is generated within the calculated range of the available purchase amount. If a stock for which a buy recommendation is recommended is not yet registered in the database, a buy recommendation can be generated after it is registered. Stocks for which a sell recommendation is recommended are limited to those for which a buy recommendation is recommended, and the number of shares is also restricted within that range. Only after these steps can a continuous, dynamic advice system be generated. The reverse process applies when entering a sell position.

[0303] [Additional Notes] In the embodiments 1 and 2 described above, a configuration was described in which the server 3 generates multiple pieces of advice, including advice related to items included in previously presented advice. However, this does not limit the invention described herein, and the above advice may be generated by the terminal 2. For example, the terminal 2 may be configured to include an advice generation unit 321.

[0304] [Additional Note 2] To solve the above problems, an advice generation device according to one aspect of the present invention is an advice generation device that generates a plurality of pieces of advice according to the investment amount, and comprises an advice generation unit that generates the plurality of pieces of advice, including advice related to at least one of the items of a plurality of stocks and cash amounts included in previously presented advice.

[0305] According to the above configuration, multiple pieces of advice are generated, including advice related to items included in previously provided advice, so that advice can be provided in a continuous manner according to the investment amount.

[0306] Furthermore, in an advice generation device according to one aspect of the present invention, the advice generation unit may generate advice corresponding to a predetermined amount when the valuation of the held securities and cash amount exceeds a predetermined amount.

[0307] According to the above configuration, when the valuation exceeds a predetermined amount, advice corresponding to that predetermined amount is generated, thus providing useful advice to the user according to the rank of the valuation.

[0308] Furthermore, in an advice generation device according to one aspect of the present invention, the advice generation unit may refer to the virtual trading model based on the advice and the user's actual trading information, and generate advice that brings the user's investment content closer to the virtual trading model.

[0309] According to the above configuration, the system generates advice that brings the user's investment strategy closer to the virtual trading model, thus providing the user with useful advice.

[0310] Furthermore, in an advice generation device according to one aspect of the present invention, the advice generation unit may generate a plurality of pieces of advice corresponding to the investment amount and the investment start time.

[0311] According to the above configuration, multiple pieces of advice are generated depending on the investment amount and the timing of the investment start, so it is possible to provide advice that is tailored to the situation at the start of the investment.

[0312] Furthermore, an advice presentation system according to one aspect of the present invention is an advice presentation system including the advice generation device and a terminal device, wherein the terminal device presents to the user at least one of the multiple pieces of advice generated by the advice generation unit.

[0313] According to the above configuration, multiple pieces of advice are generated, including advice related to items included in previously provided advice, so that advice can be provided in a continuous manner according to the investment amount.

[0314] Furthermore, an advice data generation system according to one aspect of the present invention is an advice data generation system that generates multiple pieces of advice according to a course corresponding to an investment amount, and comprises an advice generation unit that generates multiple pieces of advice that are continuous in the course and change according to buying and selling by repeatedly generating advice on buying a stock or product and advice on selling the stock or product.

[0315] According to the above configuration, different, continuous advice is generated for each course and managed on a course-by-course basis, allowing users to receive advice tailored to their needs. Furthermore, users can generate their own desired advice, and as an interactive and dynamic advice data generation system, it can generate advice that matches customer needs.

[0316] Furthermore, an advice data generation system according to one aspect of the present invention is an advice data generation system that generates a plurality of pieces of advice according to a course corresponding to an investment amount, comprising: a storage unit that stores, for each course, cash ratio data indicating the ratio of cash to be kept when buying a stock or product; purchase quantity data indicating the number of stocks or products to be purchased when buying a stock or product; amount allocation data indicating the allocation of the purchase amount of stocks or products when buying a stock or product; and trading condition data indicating the purchase conditions and selling conditions of stocks or products; and an advice generation unit that generates the plurality of pieces of advice according to the cash ratio data, purchase quantity data, amount allocation data, and trading condition data.

[0317] Furthermore, an advice data generation method according to one aspect of the present invention includes the steps of: storing an unregistered brand or product in a storage unit; calculating the purchase price; generating buy advice within the range of the purchase price by referring to the storage unit; recalculating the purchase price in accordance with the buy advice; and generating sell advice by referring to the storage unit, and repeating each of the above steps.

[0318] [Additional Note 3] To solve the above problems, an advice generation system according to one aspect of the present invention is an advice data generation system that generates multiple pieces of advice according to a course corresponding to an investment amount, comprising: a storage unit that stores, for each course, cash ratio data indicating the percentage of cash to be kept when buying a stock or product; purchase quantity data indicating the number of stocks or products to be purchased when buying a stock or product; amount allocation data indicating the allocation of the purchase amount of stocks or products when buying a stock or product; and trading condition data indicating the purchase and sale conditions of stocks or products; a communication unit that acquires the investment amount; and an advice generation unit that, referring to the investment amount acquired by the communication unit, generates the multiple pieces of advice according to the cash ratio data, purchase quantity data, amount allocation data, and trading condition data for a course corresponding to the investment amount, wherein the purchase quantity data is the purchase amount, The purchase quantity is associated with the available purchase amount, the amount allocation data is associated with the investment target and the allocation of the purchase amount for that investment target, the trading condition data includes indicators related to buying a stock or product, the advice generation unit, when generating buy advice, refers to the purchase quantity data to determine the purchase quantity according to the available purchase amount, refers to the amount allocation data to determine the allocation of the purchase amount according to the investment target, calculates the purchase limit per stock or product from the available purchase amount, the purchase quantity, and the allocation of the purchase amount, identifies the stock or product to purchase by referring to the product ranking related to the indicator and the purchase limit, and increases the cash ratio to make the ETF a domestic stock and a domestic product when the valuation of the held stocks and cash amount according to the investment amount falls below a predetermined percentage.

[0319] [Additional Note 4] To solve the above problems, an advice generation system according to one aspect of the present invention is an advice data generation system that generates multiple pieces of advice according to a course corresponding to an investment amount, comprising: a storage unit that stores, for each course, cash ratio data indicating the percentage of cash to be kept when buying a stock or product; purchase quantity data indicating the number of stocks or products to be purchased when buying a stock or product; amount allocation data indicating the allocation of the purchase amount of stocks or products when buying a stock or product; and trading condition data indicating the purchase and sale conditions of stocks or products; a communication unit that acquires the investment amount; and an advice generation unit that, referring to the investment amount acquired by the communication unit, generates the multiple pieces of advice according to the cash ratio data, purchase quantity data, amount allocation data, and trading condition data for a course corresponding to the investment amount, wherein the purchase quantity data is the purchase amount, The purchase quantity is associated with the available purchase amount, the amount allocation data is associated with the investment target and the allocation of the purchase amount for that investment target, the trading condition data includes indicators related to buying a stock or product, the advice generation unit, when generating buy advice, refers to the purchase quantity data to determine the purchase quantity according to the available purchase amount, refers to the amount allocation data to determine the allocation of the purchase amount according to the investment target, calculates the purchase limit per stock or product from the available purchase amount, the purchase quantity, and the allocation of the purchase amount, identifies the stock or product to purchase by referring to the product ranking related to the indicator and the purchase limit, and increases the cash ratio to make the ETF a domestic stock and a domestic product when the valuation of the held stocks and cash amount according to the investment amount falls below a predetermined percentage.

[0320] [Additional Note 5] To solve the above problems, an advice presentation system according to one aspect of the present invention includes a display unit that displays data indicating advice on buying and selling investment products, with constraints on the investment start date and initial investment amount for each course corresponding to the investment amount, and a control unit that calculates the available cash balance that changes each time a purchase is made in accordance with the above advice, wherein the display unit displays the above data indicating advice for buying and selling investment products within the range of the calculated available cash balance after the available cash balance has changed for each course.

[0321] To solve the above problems, an advice-providing terminal according to one aspect of the present invention includes: a display unit that displays data indicating advice on buying and selling investment products, with constraints on the investment start date and initial investment amount for each course corresponding to the investment amount; a receiving unit that receives the available cash balance each time the purchase is made in accordance with the advice; and a control unit that acquires the available cash balance received by the receiving unit, updates the data by referring to the available cash balance, and displays the data on the display unit. The display unit displays the data indicating advice on buying and selling investment products within the range of the received available cash balance for each course after the available cash balance has changed.

[0322] [Examples of implementation using software] The control blocks of terminal 2 and server 3 (particularly control units 22 and 32) may be implemented by logic circuits (hardware) formed on an integrated circuit (IC chip), or they may be implemented by software using a CPU (Central Processing Unit).

[0323] In the latter case, terminal 2 and server 3 are equipped with a CPU that executes instructions for a program (advice generation program) which is software that realizes each function, a ROM (Read Only Memory) or storage device (collectively referred to as a "recording medium") on which the program and various data are recorded in a way that can be read by the computer (or CPU), and RAM (Random Access Memory) for loading the program. The object of the present invention is achieved when the computer (or CPU) reads the program from the recording medium and executes it. As the recording medium, a "tangible medium that is not temporary," such as tape, disk, card, semiconductor memory, or programmable logic circuit can be used. The program may also be supplied to the computer via any transmission medium capable of transmitting the program (such as a communication network or broadcast wave). One aspect of the present invention can also be realized in the form of a data signal embedded in a carrier wave, in which the program is embodied by electronic transmission.

[0324] The present invention is not limited to the embodiments described above, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention. [Explanation of Symbols]

[0325] 1. Advice-Providing System 2. Terminal (Terminal device) 3. Server (advice generation device) 4 Network 10. Advice Data Generation System 23 Display section 33 Storage section 321 Advice Generation Unit

Claims

1. An advice provision system in which a server configured to communicate with a user terminal displays investment product purchase advice generated by an advice generation unit of the server on the display unit of the user terminal, The user terminal displays an investment course with pre-defined constraints, including the initial investment amount, investment start date, and type of investment product. Based on the above constraints of the investment course selected by the user, and the available cash balance calculated from the virtual trading results based on past advice provided or the execution history based on past advice provided, the advice generation unit generates investment product purchase advice including the securities and quantities of investment products that can be purchased within the range of the available cash balance. The investment product purchase advice generated by the above-mentioned advice generation unit is displayed on the display unit of the user terminal. An advice provision system characterized by the following features.

2. In the advice provision system according to Claim 1, An advice provision system characterized by displaying previously presented advice, which was displayed on the user terminal of the aforementioned other user, on the display of the user terminal of a user who joins the same investment course midway through, which is the same investment course that another user has selected.

3. An advice provision method in which a server configured to communicate with a user terminal displays investment product purchase advice generated by the advice generation unit of the server on the display unit of the user terminal, The process of displaying an investment course, in which constraints including the initial investment amount, investment start date, and type of investment target are predetermined, on the display unit of the user terminal via the control unit of the user terminal, The above-mentioned advice generation unit generates investment product purchase advice, including the names and quantities of investment products that can be purchased within the range of the available cash balance, based on the above-mentioned constraints of the investment course selected by the user and the available cash balance calculated from the virtual trading results based on past advice or the execution history based on past advice. The process includes the step of displaying the generated investment product purchase advice on the user terminal's display unit via the control unit of the user terminal. A method of providing advice characterized by the following features.

4. In the method of providing advice according to Claim 3, An advice provision method characterized by having the step of displaying previously presented advice, which was displayed on the display of the other user's terminal, on the display unit of the user terminal of a user who joins the same investment course midway through, via the control unit of the user terminal of the user terminal of the user who joins the same investment course as another user.

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