JP2021125250A - Derivative transaction program - Google Patents

Abstract

To provide optimum options of a derivative transaction.SOLUTION: A derivative transaction proposal program which proposes options of a derivative transaction causes a computer to execute: an information acquisition step of acquiring market condition information related to market conditions in a period in which a transaction is newly made; a search step of using reference market condition information related to previously acquired past market conditions and relativity in three or more stages to increase/decrease data on respective dealings in futures at an afterward point of time for post market conditions thereof to search for increase/decrease data on the respective dealings in futures with priority given to transactions higher in relativity in three or more stages between reference market condition information corresponding to the market condition information acquired in the information acquisition step and the increase/decrease data on the dealings in futures; and a proposal step of proposing options corresponding to the increase/decrease data searched in the search step.SELECTED DRAWING: Figure 16

Description

The present invention relates to a derivative transaction proposal program that proposes derivative transaction options.

In recent years, applications that provide various advice when conducting futures trading and systems that automate futures trading, that is, perform automatic trading, have been used. When using such an app or system, it is possible to increase the winning rate and increase profits by receiving advice based on a huge amount of data analysis. However, even if the enormous amount of data can be acquired, it takes considerable effort to analyze this and compile an output solution that can give accurate advice to the user. In addition, the current situation is that a system capable of automatically performing these operations has not been proposed in the past. In addition to this, the current situation is that a system that proposes optimal options for futures derivative trading (for example, call options, put options, etc.) has not been proposed in the past.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object of the present invention is to provide a derivative trading program capable of proposing the optimum option for derivative trading.

In order to solve the above-mentioned problems, the futures trading information display program to which the present invention is applied is a derivative trading proposal program that proposes options for derivative trading, and acquires information on the market conditions at the time of new trading. Using the three or more levels of association between the step, the reference market information regarding the past market conditions acquired in advance, and the increase / decrease data of each futures at a later time with respect to the past market conditions, the market conditions acquired in the above information acquisition step. A search step for searching for the increase / decrease data of each future, and a search step for searching the increase / decrease data for each future, with priority given to those having a higher degree of association between the reference market information according to the information and the increase / decrease data of each future. It is characterized by having a computer execute a proposal step that proposes options corresponding to increase / decrease data.

Even if you do not have any special skills or experience, you will be able to provide useful advice for trading futures, and you will be able to propose the optimal options for derivative trading accordingly.

It is a block diagram which shows the whole structure of the system to which this invention is applied. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the futures transaction information display program to which the present invention is applied will be described in detail with reference to the drawings.

The first embodiment FIG . 1 is a block diagram showing an overall configuration of a futures transaction information display system 1 in which a futures transaction information display program to which the present invention is applied is implemented. The futures transaction information display system 1 includes an information acquisition unit 9, a search device 2 connected to the information acquisition unit 9, and a database 3 connected to the search device 2.

The information acquisition unit 9 is a device for a person using this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of capturing an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the search device 2 described later. The information acquisition unit 9 outputs the detected information to the search device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. The information acquisition unit 9 also includes a temperature sensor, a humidity sensor, a flow rate sensor, and other sensors capable of identifying substances and physical properties. The information acquisition unit 9 may be configured by means for automatically fetching character strings and data posted on a site on the Internet.

The database 3 stores various information necessary for displaying futures transaction information. Information necessary for displaying future transaction information includes reference market information regarding past market conditions, reference event information that reflects events that occurred during the detection period of past market conditions, and external information regarding the detection period of past market conditions. External environmental information for reference that reflects the environment, household information for reference that reflects statistical data about households at the time of detection of past market conditions, and reference that reflects statistical data about real estate at the time of detection of past market conditions Real estate information, reference expert opinion information that reflects the opinions of experts announced at the time of past market condition detection, reference natural environment information that reflects information on the natural environment at the time of past market condition detection, A data set with the increase / decrease data of each future in the past market conditions is stored.

That is, in addition to such reference market condition information, the database 3 contains reference event information, reference external environment information, reference household information, reference real estate information, reference expert opinion information, and reference natural environment information. Any one or more of the above and the increase / decrease data of each future in the past market conditions are stored in association with each other.

The search device 2 is composed of, for example, an electronic device such as a personal computer (PC), but is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc., in addition to the PC. It may be converted. The user can obtain a search solution by the search device 2.

FIG. 2 shows a specific configuration example of the search device 2. The search device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire search device 2 and an operation unit 25 for inputting various control commands via operation buttons, a keyboard, or the like. A communication unit 26 for the purpose, an estimation unit 27 for making various judgments, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the search device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 in response to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the estimation unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The estimation unit 27 estimates the search solution. The estimation unit 27 reads out various information stored in the storage unit 28 and various information stored in the database 3 as necessary information when executing the estimation operation. The estimation unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any well-known artificial intelligence technique.

The display unit 23 is composed of a graphic controller that creates a display image based on the control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program is read and executed by the control unit 24.

The operation of the futures transaction information display system 1 having the above-described configuration will be described.

The futures trading information display system 1 is used in futures trading, and as shown in FIG. 3, for example, three or more levels of association between reference market information and increase / decrease data of each future are preset and acquired. Is a prerequisite. Reference market information is various information related to market conditions. Examples of this reference market information are interest rates, futures, exchange rates, stock prices of each stock, crude oil, precious metals, bitcoin, and other price movements. This reference market information may be displayed as a time-series chart, a line graph, or the like for these targets. In addition, information such as Bollinger band, volume, MACD, and moving average line may be attached. In addition, this market information may be accompanied by information such as each futures, a chart of a brand, a Bollinger band, MACD, and a moving average line. For futures, information such as a chart showing price movements between futures, Bollinger Bands, MACD, and moving averages may be attached. This reference market information was obtained before the actual increase or decrease of futures was predicted.

The term "futures" as used herein is a concept that includes all futures to be bought and sold, and includes all futures such as agricultural products such as soybeans and corn, oil, gold, precious metals, rubber, marine products, and shapeless stock indexes. Futures also include options in derivative transactions such as call options and put options.

The increase / decrease data of each future is data showing how much the increase / decrease of each future was at the time after the acquisition of the reference market condition information. This increase / decrease data may be counted by the actual increase / decrease price range, or may be expressed by the increase / decrease rate. This increase / decrease data is represented by the increase / decrease of the futures at the measurement time point (later time point) with respect to the futures at the previous time point (that is, the time point when the reference market information is acquired). The time point before here is 10 seconds before, 1 minute before, 30 minutes before, 1 hour before, 4 hours before, 1 day before, 10 days ago, 1 month ago, 1 year ago, 5 years ago, etc. As in the above, it may be configured with any time width with respect to the measurement time point. That is, the futures increase / decrease data indicates the increase / decrease of the futures at the measurement time point with respect to the futures at the previous time point, when a certain point in the chart is set as the measurement time point. Alternatively, the futures increase / decrease data may represent the foot itself in the futures chart.

In other words, through this reference market information and the data set of futures increase / decrease data, various technical events that occurred in the reference market information (for example, the chart has risen for three consecutive days, have been temporarily overpriced). You can see how futures have increased or decreased at a later point in time (such as when a beard appears on the chart). In other words, the data set is the result of increasing or decreasing futures for technical events. Therefore, by collecting a data set of reference market information and futures increase / decrease data, it is possible to know what kind of market conditions have occurred in the past and how futures have increased / decreased. ..

In the example of FIG. 3, it is assumed that the input data is, for example, reference market condition information P01 to P03. The reference market information as such input data is linked to the output. In this output, futures increase / decrease data as an output solution is displayed.

The reference market information is associated with each other through three or more levels of association with the futures increase / decrease data as this output solution. The reference market information is arranged on the left side via this degree of association, and the increase / decrease data of each futures is arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to which futures increase / decrease data is highly related to the reference market information arranged on the left side. In other words, this degree of association is an index showing what kind of futures increase / decrease data is likely to be associated with each reference market information, and the most probable futures increase / decrease data from the reference market information. It shows the accuracy of selection. In the example of FIG. 3, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the increase / decrease data of futures as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the increase / decrease data of futures as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference market information and the futures increase / decrease data in that case is adopted in determining the actual search solution, and analyzes these. By analyzing, the degree of association shown in FIG. 3 is created.

For example, it is assumed that a certain reference market information is when the preceding MACD pulls out the lagging average (SIGNAL) from the bottom to the top in a certain futures chart. In such market conditions, it is assumed that many of the futures contracts had a 1% increase in corn at a later point in time. In such a case, the futures will be increased by 1% and the degree of association will be stronger. On the other hand, in exactly the same market conditions, it is assumed that many of the futures have decreased by 0.5% at a subsequent point in time, and few of the futures have increased by 1%. In such a case, the degree of association of the futures 0.5% down becomes stronger, and the degree of association of the futures 1% up becomes lower.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference market information P01, analysis is performed from the price movement data of each futures in the past. This may be extracted from, for example, electronic data of past futures charts. In the case of reference market information P01, if there are many cases of increase / decrease data A1 (4% increase in gold) of each future, set a higher degree of association leading to this increase / decrease data A1 and increase / decrease data A3 (corn 5). When there are many cases of% up), the degree of association that leads to this increase / decrease data A3 is set higher. For example, in the example of the reference market information P01, the increase / decrease data A1 and the increase / decrease data A3 are linked. The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 3 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, in order to actually give new advice on futures trading to customers, the above-mentioned learned data will be used to predict the increase or decrease of futures. In such a case, market information regarding market conditions at the time of actual new futures trading will be acquired. This market condition information is composed of the same kind of data as the above-mentioned reference market condition information.

The newly acquired market condition information is input by the above-mentioned information acquisition unit 9. The information acquisition unit 9 may acquire charts, price movement data, and the like as electronic data.

Based on the market information newly acquired in this way, future futures (that is, future futures increase / decrease data) that are likely to occur are predicted with respect to the market information. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired market condition information is the same as or similar to P02, the increase / decrease data A2 is associated with w15 and the increase / decrease data A3 is associated with the association degree w16 via the degree of association. In such a case, the increase / decrease data A2 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A3 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In this way, from the newly acquired market information, the situation of each future that may occur in the future can be searched through the futures increase / decrease data and displayed to the user (consultant). By looking at this search result, the user (consultant) can obtain a guideline for futures to be bought and sold based on the increase / decrease data of the searched futures. It is possible to give useful advice to the user just by showing the search result of the futures increase / decrease data. By the way, in constructing this advice, in addition to simply displaying the increase / decrease data of the searched futures, based on this increase / decrease data, it is also displayed specifically which futures should be purchased or sold. You may configure the advice by doing so.

In the example of FIG. 4, it is assumed that the input data is, for example, reference market condition information P01 to P03 and reference event information P14 to 17. The intermediate node shown in FIG. 4 is a combination of reference event information and reference market information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of each futures as an output solution is displayed.

In the example of FIG. 4, it is premised that a combination of the reference market condition information and the reference event information is formed. Reference event information is a concept that includes various social news, events, incidents, celebrations, celebrations, etc. that occurred in Japan or abroad, as well as news, events, incidents, celebrations, celebrations, etc. that occurred about each company. be. This reference event information can be obtained from blogs, analyst reports, securities reports, advertisements, press releases, news articles, etc. regarding each company or society as a whole. These reference event information may be extracted through a character string, a dependency, or the like obtained by analyzing a news article through text mining.

In the example of FIG. 4, it is assumed that the input data is, for example, reference market condition information P01 to P03 and reference event information P14 to 17. The intermediate node shown in FIG. 4 is a combination of reference event information and reference market information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of each futures as an output solution is displayed.

Each combination (intermediate node) of the reference market condition information and the reference event information is associated with each other through three or more levels of association with the increase / decrease data of each future as this output solution. The reference market information and the reference event information are arranged on the left side via this degree of association, and the increase / decrease data of each future is arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data of each futures with respect to the reference market condition information and the reference event information arranged on the left side. In other words, this degree of association is an index showing what kind of futures increase / decrease data is likely to be associated with each reference market information and reference event information, and is a reference market information and reference event. It shows the accuracy in selecting the most probable futures increase / decrease data from the information. In addition to market data, the increase / decrease data for each future at a later point in time will differ depending on the various events that actually took place in society as a whole or in each company. Therefore, by combining these reference market condition information and reference event information, the optimum futures increase / decrease data will be searched.

In the example of FIG. 4, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference market condition information, the reference event information, and the increase / decrease data of each future in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 4 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference market condition information P01 and the reference event information P16, the increase / decrease data of each future is analyzed from the past data. When there are many cases where the increase / decrease data of each future is increase / decrease data A1 (4% increase in gold), the degree of association leading to this increase / decrease data A1 is set higher, and there are many cases of increase / decrease data A2 (2% decrease in gold). When there are few cases of increase / decrease data A1, the degree of association connected to the increase / decrease data A2 is set high, and the degree of association connected to the increase / decrease data A1 is set low. For example, in the example of the intermediate node 61a, it is linked to the output of the increase / decrease data A1 and the increase / decrease data A2. The degree is set to 2 points.

Further, the degree of association shown in FIG. 4 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 4, the node 61b is a node in which the reference event information P14 is combined with the reference market condition information P01, the degree of association of the increase / decrease data A3 is w15, and the degree of association of the increase / decrease data A5. Is w16. The node 61c is a node that is a combination of the reference event information P15 and P17 with respect to the reference market condition information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for increase / decrease data for displaying futures transaction information, the above-mentioned learned data will be used. In such a case, the market information regarding the market conditions at the time of new futures trading is acquired, and the event information reflecting the events that occurred at the time of new futures trading is acquired. This event information corresponds to the above-mentioned reference event information, and data such as news, newspapers, and blogs may be taken in or directly input.

Based on the market information and event information newly acquired in this way, the optimum increase / decrease data for each futures is searched for. In such a case, the degree of association shown in FIG. 4 (Table 1) acquired in advance is referred to. For example, when the newly acquired market condition information is the same as or similar to P02 and the event information is P17, the node 61d is associated through the degree of association, and this node In 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In addition, Table 2 below shows examples of the degrees of association w1 to w12 extending from the input.

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the degrees of association w1 to w12 may all have the same value, and the weights in the selection of the intermediate node 61 may all be the same.

FIG. 5 shows an example in which the combination of the above-mentioned reference market condition information and the reference external environment information and the increase / decrease data of each futures with respect to the combination are set to three or more levels of association.

External environmental information for reference is politics and economy outside the company, such as GDP, employment statistics, mining and manufacturing production index, capital investment, labor force survey, business conditions index, consumption expenditure, new car sales volume, consumer price index, etc. Includes various data on, society, technology, etc.

In the example of FIG. 5, it is assumed that the input data is, for example, reference market condition information P01 to P03 and reference external environment information P18 to 21. The intermediate node shown in FIG. 5 is a combination of the reference market condition information and the reference external environment information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of each futures as an output solution is displayed.

Each combination (intermediate node) of the reference market condition information and the reference external environment information is associated with each other through three or more levels of association with the increase / decrease data of each future as this output solution. The reference market information and the reference external environment information are arranged on the left side via this degree of association, and the increase / decrease data is arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference market condition information and the reference external environment information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference market condition information and reference external environment information, and the reference market information and reference external environment information. It shows the accuracy in selecting the most probable increase / decrease data of each futures. In addition to market data, futures will change depending on the actual state of the external environment. Therefore, by combining these reference market condition information and reference external environment information, the optimum increase / decrease data of each futures will be searched.

In the example of FIG. 5, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference market condition information, the reference external environment information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 5 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference market condition information P01 and the reference external environment information P20, the increase / decrease data is analyzed from the past data. For example, in the example of the intermediate node 61a, it is linked to the output of the increase / decrease data A1 and the increase / decrease data A2. The degree is set to 2 points.

Further, the degree of association shown in FIG. 5 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 5, the node 61b is a node that is a combination of the reference market condition information P01 and the reference external environment information P18, and the degree of association of the increase / decrease data A3 is w15 and the association of the increase / decrease data A5. The degree is w16. The node 61c is a node that is a combination of the reference external environment information P19 and P21 with respect to the reference market condition information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned market condition data, external environment information that reflects the external environment at the time of new futures trading is acquired. As the external environment information, for example, if it is employment statistics information, the data may be directly taken in. If it is other statistical data, the data may be acquired directly.

Based on the market information and external environmental information newly acquired in this way, the increase / decrease data of each futures is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 5 (Table 1) acquired in advance is referred to. For example, when the newly acquired market condition information is the same as or similar to P02 and the external environment information is P21, the node 61d is associated with the node 61d through the degree of association. In the node 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 6 shows an example in which a combination of the above-mentioned reference market information and reference household information and three or more levels of association with the increase / decrease data of each futures with respect to the combination are set.

The reference household information includes various data related to household consumption status survey, household data, average working hours per week, statistical data of savings amount, statistical data of annual income, household budget, and the like.

In the example of FIG. 6, it is assumed that the input data is, for example, reference market condition information P01 to P03 and reference household information P22 to 25. The intermediate node shown in FIG. 6 is a combination of reference market information and reference household information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of each futures as an output solution is displayed.

Each combination (intermediate node) of the reference market information and the reference household information is associated with each other through three or more levels of association with the increase / decrease data of each future as this output solution. Reference market information and reference household information are arranged on the left side via this degree of association, and increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference market information and the reference household information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference market condition information and reference household budget information, and is the most from the reference market condition information and the reference household budget information. It shows the accuracy in selecting the increase / decrease data of each probable futures. In addition to market data, there are futures whose changes fluctuate depending on the actual state of the household budget. Therefore, by combining these reference market conditions information and reference household budget information, the optimum increase / decrease data for each futures will be searched for.

In the example of FIG. 6, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference market condition information, the reference household information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing the above, the degree of association shown in FIG. 6 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference market information P01 and the reference household information P24, the increase / decrease data is analyzed from the past data. For example, in the example of the intermediate node 61a, it is linked to the output of the increase / decrease data A1 and the increase / decrease data A2. The degree is set to 2 points.

Further, the degree of association shown in FIG. 6 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 6, the node 61b is a node that is a combination of the reference market information P01 and the reference household information P22, and the degree of association of the increase / decrease data A3 is w15 and the degree of association of the increase / decrease data A5. Is w16. The node 61c is a node that is a combination of the reference household information P23 and P25 with respect to the reference market information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned market data, household information that reflects statistical data on the household budget at the time of new futures trading will be acquired. If the household information is data published by each government agency, such as statistical data on the amount of savings, the data may be directly taken in. If it is other statistical data, the data may be acquired directly.

Based on the market information and household information newly acquired in this way, the increase / decrease data of each futures is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 6 (Table 1) acquired in advance is referred to. For example, when the newly acquired market condition information is the same as or similar to P02 and the household information is P25, the node 61d is associated through the degree of association, and this node In 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

As an alternative to the reference household information shown in FIG. 6, reference real estate information reflecting statistical data on real estate at the time of detection of past market conditions may be used as input data. In such a case, the detailed description will be omitted by replacing the reference household information with the reference real estate information and replacing the household information with the real estate information.

In such a case, the combination of the reference market condition information and the reference real estate information and the degree of association of the increase / decrease data of each of the above futures with three or more levels will be used. The node 61 defines the degree of association between the combination of the reference market condition information and the reference real estate information and the increase / decrease data of each of the above futures in three or more stages.

The reference real estate information includes all information related to real estate such as office vacancy rate, tsubo unit price, rent market price, land price, and statistical data on vacant houses.

In forming such a degree of association and giving advice on futures trading, obtain real estate information that reflects the statistical day on real estate at the time of new futures trading, and refer to the same or similar. We provide advice on futures trading based on the combination of real estate information for reference and market conditions for reference, and the degree of association with the increase / decrease data of each future on three or more levels.

FIG. 7 shows an example in which a combination of the above-mentioned reference market condition information and reference expert opinion information and three or more levels of association with the increase / decrease data of each futures with respect to the combination are set.

Reference expert opinion information means any information that gives an expert opinion on the increase or decrease of futures, and is specialized in futures forecasts and reasons for increase or decrease in stocks published in analyst reports and newspaper articles. Home comments, views, etc. Also, the reference expert opinion information may simply be the prediction itself as to whether each futures will rise, fall, or remain unchanged. This reference expert opinion information includes opinions on the entire Nikkei 225 futures, opinions on specific segments and industries, and opinions on individual futures. In addition, the reference expert opinion information may include comments by experts (analysts) posted on the Internet and forecasts of increase or decrease.

In the example of FIG. 7, it is assumed that the input data is, for example, reference market condition information P01 to P03 and reference expert opinion information P26 to 29. The intermediate node shown in FIG. 7 is a combination of reference market condition information and reference expert opinion information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of each futures as an output solution is displayed.

Each combination (intermediate node) of the reference market condition information and the reference expert opinion information is associated with each other through three or more levels of association with the increase / decrease data of each future as this output solution. Reference market information and reference expert opinion information are arranged on the left side via this degree of association, and increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference market condition information and the reference expert opinion information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data each reference market condition information and reference expert opinion information is likely to be associated with, and is a reference market information and reference expert. It shows the accuracy in selecting the most probable increase / decrease data of each future from the opinion information. Futures fluctuations may correlate with actual expert opinion in addition to market data. Therefore, by combining these reference market condition information and reference expert opinion information, the optimum increase / decrease data of each futures will be searched.

In the example of FIG. 7, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 7. That is, the search device 2 accumulates past data as to which of the reference market condition information, the reference expert opinion information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 7 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference market information P01 and the reference household information P28, the increase / decrease data is analyzed from the past data. For example, in the example of the intermediate node 61a, it is linked to the output of the increase / decrease data A1 and the increase / decrease data A2. The degree is set to 2 points.

Further, the degree of association shown in FIG. 7 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 7, the node 61b is a node in which the reference market condition information P01 is combined with the reference expert opinion information P26, and the degree of association of the increase / decrease data A3 is w15 and the increase / decrease data A5. The degree of association is w16. The node 61c is a node that is a combination of the reference expert opinion information P27 and P29 with respect to the reference market information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned market conditions data, expert opinion information that reflects the expert opinions announced at the time of new futures trading will be acquired. As for the expert opinion information, for example, if there is an expert opinion expressed in a newspaper article, the data may be directly taken in.

Based on the market information and expert opinion information newly acquired in this way, the increase / decrease data of each futures is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 7 (Table 1) acquired in advance is referred to. For example, when the newly acquired market condition information is the same as or similar to P02 and the expert opinion information is P29, the node 61d is associated through the degree of association. In this node 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 8 shows an example in which the combination of the above-mentioned reference market condition information and the reference natural environment information and the increase / decrease data of each futures with respect to the combination are set to three or more levels of association.

Reference natural environment information means all information related to the natural environment such as disaster data, temperature data, precipitation data, wind direction data, humidity data, etc., and data on the past natural environment released by the Meteorological Agency, or private companies and private companies. Data on the past natural environment released by individuals.

In the example of FIG. 8, it is assumed that the input data is, for example, reference market condition information P01 to P03 and reference natural environment information P30 to 33. The intermediate node shown in FIG. 8 is a combination of the reference market condition information and the reference natural environment information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of each futures as an output solution is displayed.

Each combination (intermediate node) of the reference market condition information and the reference natural environment information is associated with each other through three or more levels of association with the increase / decrease data of each future as this output solution. The reference market information and the reference natural environment information are arranged on the left side via this degree of association, and the increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference market condition information and the reference natural environment information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference market condition information and reference natural environment information, and the reference market information and reference natural environment information. It shows the accuracy in selecting the most probable increase / decrease data of each future. Futures fluctuations may correlate with actual expert opinion in addition to market data. Therefore, by combining these reference market condition information and reference natural environment information, the optimum increase / decrease data of each futures will be searched.

In the example of FIG. 8, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference market condition information, the reference natural environment information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 8 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference market condition information P01 and the reference natural environment information P32, the increase / decrease data is analyzed from the past data. For example, in the reference market condition information P01, in the example of the intermediate node 61a to which the reference natural environment information P32 is linked, it is linked to the output of the increase / decrease data A1 and the increase / decrease data A2. The degree of association of w14 is set to 7 points, and the degree of association of w14 connected to the increase / decrease data A2 is set to 2 points.

Further, the degree of association shown in FIG. 8 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 8, the node 61b is a node that is a combination of the reference market condition information P01 and the reference natural environment information P30, and the degree of association of the increase / decrease data A3 is w15 and the association of the increase / decrease data A5. The degree is w16. The node 61c is a node that is a combination of the reference expert opinion information P31 and P33 with respect to the reference market information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned market condition data, the natural environment information that reflects the information on the natural environment at the time of new futures trading is acquired. The natural environment information may be taken in directly from, for example, data and information on the natural environment published by the Japan Meteorological Agency, a private company, or an individual, or a site containing these.

Based on the market information and natural environment information newly acquired in this way, the increase / decrease data of each futures is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 8 (Table 1) acquired in advance is referred to. For example, when the newly acquired market condition information is the same as or similar to P02 and the natural environment information is P33, the node 61d is associated through the degree of association, and this In the node 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In FIG. 9, in addition to the above-mentioned reference market condition information and reference event information, a combination of the reference external environment information and the increase / decrease data of each futures with respect to the combination are set with three or more levels of association. An example is shown.

In such a case, as shown in FIG. 9, the degree of association is such that the set of combinations of the reference market condition information, the reference event information, and the reference external environment information is set as the nodes 61a to 61e of the intermediate node as described above. It will be expressed.

For example, in FIG. 9, in the node 61c, the reference market condition information P02 is associated with the association degree w3, the reference event information P15 is associated with the association degree w7, and the reference external environment information P19 is associated with the association degree w11. Similarly, in the node 61e, the reference market condition information P03 is associated with the association degree w5, the reference event information P15 is associated with the association degree w8, and the reference external environment information P18 is associated with the association degree w10.

Similarly, when such a degree of association is set, the search solution is determined based on the newly acquired market condition information, the event information, and the external environment information.

In determining the search solution, the degree of association shown in FIG. 9 acquired in advance is referred to. For example, when the acquired market condition information is the same as or similar to the reference market condition information P02, the acquired event information corresponds to the reference event information P15, and the acquired external environment information corresponds to the reference external environment information P19. In the combination, the node 61c is associated, and in this node 61c, the increase / decrease data A2 is associated with the association degree w17, and the increase / decrease data A4 is associated with the association degree w18. As a result of such a degree of association, a search solution is actually obtained based on w17 and w18.

When combining three or more types of such input parameters, in addition to the reference market condition information, the reference event information, the reference external environment information, the reference household information, the reference real estate information, and the reference expert opinion It is applicable even if the combination is composed of any two or more of the information and the reference natural environment information.

In addition to the increase / decrease data of each futures, as the output data, as shown in FIG. 10, advice on actual futures purchasing behavior (for example, futures 〇 × buy, futures × 〇 retention) is directly displayed. You may. In addition to designating futures to advise on buying and selling, this advice may also advise on specific purchase volumes. Such advice may be generated based on the increase / decrease data described above. In such a case, it may be advised to buy if the futures will be high in the future, and to sell if the futures will be low in the future. In addition to the possibility of return on futures contracts, the advice may also include risks. At this time, as the input data and the data to be trained, it is of course possible to directly include this advice content in the data set instead of the increase / decrease data and train it.

Further, the present invention may be embodied as an automatic futures trading program that automatically trades futures. In such a case, after searching the increase / decrease data based on the above procedure, each future of the futures is automatically bought and sold based on the increase / decrease data. In such a case, the system side buys and sells futures by itself based on advice on futures purchasing behavior (for example, futures 〇 × buy, futures × 〇 retention). In such a case, if it is determined that a certain future will rise as a result of searching the increase / decrease data of each future, the process of automatically purchasing the future is performed. On the other hand, when it is determined that a certain futures will fall, the futures are automatically sold or short-sold.

The above-mentioned degree of association may be composed of the nodes of the neural network in artificial intelligence as shown in FIG. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

Further, as shown in FIG. 12, the present invention discriminates increase / decrease data of each futures based on the degree of association of two or more types of information, reference information U and reference information V. It is assumed that the reference information Y is the reference market information information, and the reference information V is any other reference information (for example, reference event information, reference expert opinion information, etc.).

At this time, as shown in FIG. 12, the output obtained for the reference information U is used as the input data as it is, and is associated with the output (increase / decrease data of each futures) via the intermediate node 61 in combination with the reference information V. You may be. For example, for the reference information U, after the output solution is output, this is used as an input as it is, and the output (increase / decrease data of each futures) is searched by using the degree of association with other reference information V. You may do it.

Further, in the present invention, the chart may be applied to the chart pattern of the trading signal for the market condition information and the reference market condition information. 13 and 14 show an example of a chart pattern of trading signals of the chart. For example, in FIG. 13A, when the value of a stock price or futures repeatedly fluctuates with respect to the moving average, the buying signal is when the value of the stock price or futures falls to the moving average. Further, in FIG. 13B, a buy signal is obtained when the stock price breaks out of the upside resistance line after the fraying market continues for a long time. FIG. 13C is said to be a W bottom type, and when the stock price hits the low price twice in the low price range, it becomes a buy signal. FIG. 13 (d) is called the head and shoulders shoulders, which is a chart pattern in which the stock price hits the low price three times in the low price range, and the middle is the cheapest, and when this appears, it becomes a buy signal. FIG. 14A shows a case where the stock price soars and then immediately plummets, and a long lower beard candlestick or the Taiyo line appears and reverses, and when this appears, it is a sign of buying. Fig. 14 (b) is said to be the bright star at the end of Mikawa. When the shape of the Taiyo line appears, it will be a signal to buy in the near future. FIG. 14 (c) is said to be Mikawa Akegarasu, and represents a V-shaped conversion with the red three soldiers after plunging with the black three soldiers (three feathers), and is a sign of buying. In Fig. 14 (d), the bright star of Mikawa evening becomes the Taiyo line in the ascending phase, and the upper mad is opened and the beard and the substance are short. It will be a sign of a downturn and a sell signal.

Such signals are born from past empirical rules not only in futures trading but also in stock trading, but in the present invention, this market condition information and reference market condition information are applied to the types of chart patterns of these trading signals. You may do so.

For this fitting, a determination model generated by machine learning as shown in FIG. 15 may be used. In this determination model, an image of a chart pattern of a trading signal consisting of the above-mentioned example is used as teacher data. The input is a chart of each futures, and the output is a type of trading signal. When the chart is acquired, fitting is performed based on the judgment model generated by this machine learning, and it is determined what type of trading signal is applied.

For example, as a result of inputting a chart, it is possible to determine whether or not the output corresponds to the type of trading signal such as Akegarasu Mikawa and Akegarasu Mikawa evening.

In the present invention, in the case where machine learning is performed through the degree of association between the reference market condition information and the increase / decrease data of each futures, when the reference market condition information is acquired, the chart of each futures representing the market condition is acquired. do. Then, it is determined what type of trading signal the acquired chart applies to through the determination model shown in FIG. 15, and each reference market condition information is applied to the type of trading signal.

As a result, this reference market information will be represented by a typified trading signal. By learning the increasing / decreasing tendency of futures at a subsequent time point with respect to such a trading signal, the above-mentioned degree of association is formed.

Next, even when the market condition information is actually acquired, it is determined what type of trading signal the acquired chart applies to through the determination model shown in FIG. 15, and each reference market condition information is used as the type of trading signal. I will apply it. As a result, this market information will be represented by typified trading signals. The type of trading signal of such market information is determined through the above-mentioned degree of association to determine what type of trading signal of the reference market information is applicable. Then, using the three or more levels of association between the type of the trading signal of the reference market information corresponding to the type of the trading signal of the market information and the increase / decrease data of each futures, the one with the higher degree of association is prioritized and described above. Display increase / decrease data for each futures.

The type of trading signal corresponding to the output shown in FIG. 15 is not limited to the existing proposed person, and a new type of signal may be sequentially updated.

For example, if each chart of the reference market information and the market information does not correspond to the existing type in the output shown in FIG. 15, the tendency of the chart is registered as a new type. Then, a degree of association is formed between this newly registered type and the increase / decrease data of each future at a subsequent time point. After that, when a chart similar to this newly registered type is input, by creating a degree of association with the increase / decrease data of each futures in the same way, this newly registered type and the increase / decrease data of each futures can be obtained. A weighting w of the degree of association is formed between them.

When such a degree of association is updated and market condition information is newly input and it is determined from the determination model of FIG. 15 that it is a newly registered signal type, it is determined. It is possible to search for a search solution via reference market information consisting of newly registered types.

In the above-mentioned degree of association, the degree of association is expressed by a 10-step evaluation, but it is not limited to this, and it may be expressed by a degree of association of 3 or more levels, and conversely, it may be expressed by 3 or more levels. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those expressed in two stages, that is, whether or not they are related to each other, either 1 or 0.

According to the present invention having the above-described configuration, anyone can easily search for the optimum futures for futures trading and the optimum futures for futures trading without any special skill or experience. It can be carried out. Further, according to the present invention, it is possible to determine the search solution with higher accuracy than that performed by a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Since there are many cases where the input data and the output data described above do not exist exactly the same in the process of learning, the input data and the output data may be classified by type. That is, the information P01, P02, ... P15, 16, ... That constitute the input data are classified according to the criteria classified in advance on the system side or the user side according to the content of the information, and the classified inputs. A data set may be created between the data and the output data and trained.

Further, according to the present invention, there is a feature that an optimum solution search is performed through the degree of association set in three or more stages. The degree of association can be described by, for example, a numerical value from 0 to 100% in addition to the above-mentioned 10 steps, but the degree of association is not limited to this, and any step can be described as long as it can be described by a numerical value of 3 or more steps. It may be configured.

By discriminating futures with a higher rate of return and lower risk based on the degree of association expressed by such numerical values of three or more levels, the association is considered as a candidate for the possibility of a search solution. It is also possible to search and display in descending order of degree.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the output having an extremely low degree of association such as 1%. It warns the user that even a judgment result with an extremely low degree of association is connected as a slight sign and may be useful as the judgment result once every tens or hundreds of times. be able to.

Further, according to the present invention, there is an advantage that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. be. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with a high probability. Sometimes the solution is overlooked. It is possible to decide which one to prioritize based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided via a public communication network such as the Internet. In addition to market information, when event information, external environment information, household information, real estate information, expert opinion information, and natural environment information knowledge, information, and data are acquired, the degree of association is increased or decreased accordingly. Let me. Similarly, when the above-mentioned expert opinion information, natural environment information, fundamental information, and statistical information are acquired as an alternative to this external environmental information in addition to the event information, the degree of association is increased or decreased accordingly. ..

In other words, this update corresponds to learning in the sense of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.

In addition, this update of the degree of association is not based on the information that can be obtained from the public communication network, but is also updated by the system side or the user side based on the contents of research data, treatises, conference presentations, newspaper articles, books, etc. by experts. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating the trained model and the above-mentioned update may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of reading and learning the data set of input data and output data, information corresponding to the input data is read and trained, and the degree of association related to the output data is self-formed from there. You may let it.

Further, FIG. 16 is an example in which the optimum option is associated with the increase / decrease data of each futures. Options are options for derivative transactions, such as call options and put options, and also include swap transactions.

An option transaction is a transaction that buys or sells the right to buy (call) or sell (put) a certain brand or future at a certain point in the future.

In FIG. 16, it is assumed that the option 〇 × is a call option for buying a certain brand (futures such as gold) for 1000 yen. At this time, if it is the best choice to buy option 〇 × based on futures increase / decrease data, it is linked. Also, if selling option 〇 × is the best choice, it is linked. Each option of such optimal derivative trading is associated with each futures increase / decrease data.

Therefore, when the futures increase / decrease data is searched, the best derivative transaction associated with the futures increase / decrease data is selected, proposed, and displayed.

Each option of each derivative transaction associated with the increase / decrease data of each future is not limited to the case where it is associated with the increase / decrease data on a one-to-one basis, and even if it is associated with one-to-many. good.

Further, the increase / decrease data of futures and the derivative transaction may be linked with the above-mentioned three or more levels of linkage. As a result, it is possible to search for the optimum derivative transaction for the futures increase / decrease data as input through the degree of association as described above.

Proposals and displays of such derivative transactions can also be made for futures increase / decrease data output through the degree of association of the combinations shown in FIGS. 4 to 10.

Further, as shown in FIGS. 13 and 14, the above-mentioned reference market information and the chart in the reference market information obtained by acquiring the charts of past futures and applying them to the chart pattern of the trading signal categorized in advance. It is also possible to use three or more levels of association between the pattern type and the increase / decrease data of each futures. Even in such a case, it is possible to propose and display derivative transactions in the same way for the output futures increase / decrease data.

Further, according to the present invention, not only the option corresponding to the increase / decrease data of the searched futures may be proposed, but also the option may be automatically bought / sold. In such a case, it is realized by ordering the automatic trading corresponding to the selected option. Use well-known technology for automatic trading.

Further, as shown in FIG. 17, each option of the derivative transaction as output data may be directly associated with the input data (reference market information, etc.) through the above-mentioned degree of association. In such a case, it is possible to verify from the past data what option is selected for each input data to maximize the profit, and set the degree of association according to the verification result.

Even in such a case, it is possible to propose the optimum derivative transaction option by similarly receiving the input data and searching for a solution.

1 Derivative transaction proposal program 2 Search device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimate unit 28 Storage unit 61 node

Claims (11)

In the Derivatives Trading Proposal Program, which proposes derivative trading options
Information acquisition step to acquire market information about market conditions at the time of new transaction,
Using the reference market information about the past market conditions acquired in advance and the three or more levels of linkage between the increase / decrease data of each future at a later time with respect to the past market conditions, according to the market information acquired in the above information acquisition step. A search step for searching for the increase / decrease data of each of the above futures by giving priority to the one with a higher degree of association between the reference market information and the increase / decrease data of each future.
A derivative transaction proposal program characterized by having a computer execute a proposal step that proposes options corresponding to the increase / decrease data searched by the above search step. In the above information acquisition step, event information that reflects the event that occurred at the time of new futures trading is acquired.
In the search step, the combination of the reference market information, the reference event information reflecting the event that occurred at the detection time of the past market condition, and the degree of association of the increase / decrease data of each of the futures in three or more stages. Priority is given to the combination of the reference event information according to the event information acquired in the above information acquisition step and the above reference market condition information, and the one having a higher degree of association with the increase / decrease data of each future in three or more stages. The derivative transaction proposal program according to claim 1, wherein the increase / decrease data of each of the above futures is searched for. In the above information acquisition step, external environment information that reflects the external environment at the time of new futures trading is acquired.
In the search step, the combination of the reference market condition information and the reference external environment information reflecting the external environment at the detection time of the past market condition, and the degree of association of the increase / decrease data of each of the futures in three or more stages. The combination of the reference external environment information according to the external environment information acquired in the above information acquisition step and the above reference market condition information, and the increase / decrease data of each future have a higher degree of association of 3 levels or more. The derivative transaction proposal program according to claim 1, wherein the above-mentioned increase / decrease data of each future is searched with priority. In the above information acquisition step, household information that reflects statistical data on the household budget at the time of new futures trading is acquired.
In the search step, there are three or more stages of the combination of the reference market information, the reference household information reflecting the statistical data on the household at the time when the past market is detected, and the increase / decrease data of each future. Using the degree of association, the combination of the reference household information according to the household information acquired in the above information acquisition step and the above reference market information, and the increase / decrease data of each future have a higher degree of association of 3 levels or more. The derivative transaction proposal program according to claim 1, wherein the above-mentioned increase / decrease data of each future is searched with priority. In the above information acquisition step, real estate information that reflects statistical data on real estate at the time of new futures trading is acquired.
In the search step, there are three or more stages of the combination of the reference market information, the reference real estate information reflecting the statistical data on the real estate at the detection time of the past market condition, and the increase / decrease data of each of the futures. Using the degree of association, the combination of the reference real estate information according to the real estate information acquired in the above information acquisition step and the above reference market condition information, and the one with a higher degree of association of three or more levels with the increase / decrease data of each future. The derivative transaction proposal program according to claim 1, wherein the above-mentioned increase / decrease data of each future is searched with priority. In the above information acquisition step, expert opinion information that reflects the expert opinions announced at the time of new futures trading is acquired.
In the above search step, the combination of the reference market condition information, the reference expert opinion information that reflects the expert opinion announced at the time of detection of the past market condition, and the increase / decrease data of each of the futures are obtained. Using 3 or more levels of linkage, 3 levels of the combination of the reference expert opinion information according to the expert opinion information acquired in the above information acquisition step and the above reference market condition information, and the increase / decrease data of each future. The derivative transaction proposal program according to claim 1, wherein the above-mentioned ones having a higher degree of association are prioritized and the increase / decrease data of each of the above futures is searched. In the above information acquisition step, the natural environment information that reflects the information on the natural environment at the time of new futures trading is acquired.
In the above search step, there are three or more stages of the combination of the reference market condition information, the reference natural environment information reflecting the information of the natural environment at the detection time of the past market condition, and the increase / decrease data of each of the futures. Using the degree of association, the combination of the reference natural environment information according to the natural environment information acquired in the above information acquisition step and the above reference market condition information, and the degree of association of three or more levels of the increase / decrease data of each future The derivative transaction proposal program according to claim 1, wherein the higher ones are prioritized and the increase / decrease data of each of the above futures is searched. In the above information acquisition step, the chart of each futures is acquired as the above market condition information, and this is applied to the chart pattern of the trading signal categorized in advance.
In the above search step, the chart of the past futures is acquired, and the above reference market information is applied to the chart pattern of the trading signal categorized in advance, and the chart pattern type and each future in this reference market information. The reference market information and the increase / decrease data of each futures, which consist of the chart pattern types according to the above market information, which consist of the chart pattern types applied in the above information acquisition step, using the degree of association with the increase / decrease data of The derivative transaction proposal program according to claim 1, wherein the increase / decrease data of each of the above futures is searched by giving priority to the one having a higher degree of association with three or more stages. In the information acquisition step, the image of the chart pattern of the trading signal is used as the teacher data, the input is the chart of each acquired future, the output is the type of the trading signal, and the above is based on the judgment model generated by machine learning. Make a fit,
The derivative transaction proposal program according to claim 8, wherein in the search step, the fitting is performed by the determination model. In the Derivatives Trading Proposal Program, which proposes derivative trading options
Information acquisition step to acquire market information about market conditions at the time of new transaction,
Using the reference market information about the past market conditions acquired in advance and the degree of association of each option of the derivative transaction with three or more levels, the reference market information and each option according to the market information acquired in the above information acquisition step. Derivative transaction proposal program characterized by having a computer execute a proposal step that proposes each of the above options by giving priority to the one with a higher degree of association of three or more stages. In a derivative trading program that trades derivatives trading options
Information acquisition step to acquire market information about market conditions at the time of new futures trading,
Using the reference market information about the past market conditions acquired in advance and the three or more levels of linkage between the increase / decrease data of each future at a later time with respect to the past market conditions, according to the market information acquired in the above information acquisition step. A search step for searching for the increase / decrease data of each of the above futures by giving priority to the one with a higher degree of association between the reference market information and the increase / decrease data of each future.
A derivative trading program characterized by having a computer execute an automatic trading step of buying and selling options corresponding to the increase / decrease data of each futures searched by the above search step.

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