CN116645215A - Data processing method, device, equipment and storage medium - Google Patents

Data processing method, device, equipment and storage medium Download PDF

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CN116645215A
CN116645215A CN202310614906.1A CN202310614906A CN116645215A CN 116645215 A CN116645215 A CN 116645215A CN 202310614906 A CN202310614906 A CN 202310614906A CN 116645215 A CN116645215 A CN 116645215A
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information
determining
target
option
text
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胡宁宁
王艳君
姜洪志
赵梦
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Bank of China Ltd
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Bank of China Ltd
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    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

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Abstract

The application provides a data processing method, a device, equipment and a storage medium, which can be used in the field of big data platforms. The method comprises the following steps: acquiring input reference information of a user, wherein the input reference information comprises: the method comprises the steps of obtaining a target object, an option type and a maximum loss value, obtaining the object type of the target object, determining a plurality of network addresses according to the object type, obtaining a plurality of text description information of the target object according to the plurality of network addresses, determining object information corresponding to the target object according to the plurality of text description information, determining interpretation information corresponding to a target option combination and the target option combination according to the object information, the option type and the maximum loss value, wherein the object information comprises interest information, interest information and attribute change trend. The method realizes personalized customization of the recommended investment information, and ensures that the recommended investment information has better referential property.

Description

Data processing method, device, equipment and storage medium
Technical Field
The present application relates to the field of big data platforms, and in particular, to a data processing method, apparatus, device, and storage medium.
Background
Options are one type of derivative securities, which belong to financial derivatives with relatively high thresholds, and require investors to have a certain knowledge of the option logic.
In existing option trading systems, some investment recommendation information may be provided, including technical analysis, basic plane analysis, market conditions, news analysis, etc., and advice based on these analyses.
However, the investment advice provided in the existing investment recommendation information is not specific, and the investment advice is based on the main opinion of a certain analyst, and does not necessarily conform to the predictions of certain investors, nor to the risk preferences of certain investors, resulting in poor referenceability of the investment recommendation information in actual trading decisions.
Disclosure of Invention
The application provides a data processing method, a device, equipment and a storage medium, which solve the problem of poor referenceability of investment recommendation information in actual transaction decisions in the prior art.
In a first aspect, the present application provides a data processing method, including:
acquiring input reference information of a user, wherein the input reference information comprises: target object, option type and maximum penalty value;
obtaining an object type of the target object, and determining a plurality of network addresses according to the object type;
acquiring a plurality of text description information of the target object according to the plurality of network addresses;
Determining object information corresponding to the target object according to the text description information, wherein the object information comprises interest information, emptiness information and attribute change trend;
and determining a target option combination and interpretation information corresponding to the target option combination according to the object information, the option type and the maximum loss value.
In one possible implementation manner, according to the plurality of network addresses, acquiring a plurality of text description information of the target object includes:
acquiring a plurality of network pages according to the plurality of network addresses, wherein the network pages comprise one or more of text information, image information, voice information and video information;
and respectively extracting corresponding text description information from each network page to obtain the text description information.
In one possible implementation, for any one web page; extracting corresponding text description information from the network page, including:
determining a plurality of page areas and area information of each page area in the network page, wherein the area information comprises area positions, area sizes or area types;
determining a target page area in the plurality of page areas according to the area information;
And extracting the text description information in the target page area.
In one possible implementation manner, determining object information corresponding to the target object according to the plurality of text description information includes:
the text description information is segmented to obtain text segments;
acquiring semantic information of each text segment;
removing text segments with mutually exclusive semantics from the text segments according to the semantic information of the text segments to obtain a plurality of target text segments;
and determining the object information according to the target text segments.
In one possible implementation, determining the object information according to the plurality of target text segments includes:
processing the target text segments through a preset model to obtain the interest information and the empty information, wherein the preset model is obtained by determining historical data of the target object;
determining a plurality of first text segments in the plurality of target text segments that are related to attributes of the target object;
and determining the attribute change trend according to the first text segments.
In one possible implementation manner, determining, according to the object information, the option type and the maximum loss value, a target option combination and interpretation information corresponding to the target option combination includes:
Determining at least one recommended option combination according to the option type;
determining an estimated loss value of any recommended option combination according to the object information;
determining a recommended option combination with the predicted loss value smaller than or equal to the maximum loss value as a target option combination;
determining interpretation information corresponding to the target option combination according to the target option combination and an expected loss value corresponding to the target option combination, wherein the interpretation information comprises: option operations and earning conditions.
In one possible implementation, the input reference information further includes a first line price; determining, from the object information, an expected loss value for the recommended option combination, including:
determining a first right probability of the recommended option combination according to the object information and the first right price;
and determining the estimated loss value of the recommended option combination according to the first option probability.
In one possible implementation, the input reference information further includes a first line price; determining, from the object information, an expected loss value for the recommended option combination, including:
Determining a first right probability of the recommended option combination according to the object information and the first right price;
and determining the estimated loss value of the recommended option combination according to the first option probability.
In a second aspect, the present application provides a data processing apparatus comprising:
the first acquisition module is used for acquiring input reference information of a user, wherein the input reference information comprises: target object, option type and maximum penalty value;
the second acquisition module is used for acquiring the object type of the target object and determining a plurality of network addresses according to the object type;
a third obtaining module, configured to obtain, according to the plurality of network addresses, a plurality of text description information of the target object;
the first determining module is used for determining object information corresponding to the target object according to the text description information, wherein the object information comprises interest information, interest information and attribute change trend;
and the second determining module is used for determining the target option combination and the interpretation information corresponding to the target option combination according to the object information, the option type and the maximum loss value.
In one possible implementation manner, the third obtaining module is specifically configured to:
Acquiring a plurality of network pages according to the plurality of network addresses, wherein the network pages comprise one or more of text information, image information, voice information and video information;
and respectively extracting corresponding text description information from each network page to obtain the text description information.
In one possible implementation, for any one web page; the third obtaining module is specifically configured to:
determining a plurality of page areas and area information of each page area in the network page, wherein the area information comprises area positions, area sizes or area types;
determining a target page area in the plurality of page areas according to the area information;
and extracting the text description information in the target page area.
In one possible implementation, the first determining module is specifically configured to:
the text description information is segmented to obtain text segments;
acquiring semantic information of each text segment;
removing text segments with mutually exclusive semantics from the text segments according to the semantic information of the text segments to obtain a plurality of target text segments;
and determining the object information according to the target text segments.
In one possible implementation, the first determining module is specifically configured to:
processing the target text segments through a preset model to obtain the interest information and the empty information, wherein the preset model is obtained by determining historical data of the target object;
determining a plurality of first text segments in the plurality of target text segments that are related to attributes of the target object;
and determining the attribute change trend according to the first text segments.
In one possible implementation, the second determining module is specifically configured to:
determining at least one recommended option combination according to the option type;
determining an estimated loss value of any recommended option combination according to the object information;
determining a recommended option combination with the predicted loss value smaller than or equal to the maximum loss value as a target option combination;
determining interpretation information corresponding to the target option combination according to the target option combination and an expected loss value corresponding to the target option combination, wherein the interpretation information comprises: option operations and earning conditions.
In one possible implementation, the input reference information further includes a first line price; the second determining module is specifically configured to:
Determining a first right probability of the recommended option combination according to the object information and the first right price;
and determining the estimated loss value of the recommended option combination according to the first option probability.
In one possible implementation, the second determining module is specifically configured to:
determining a predicted price interval according to the object information and a preset price prediction model;
determining a second line weight price according to the predicted price interval;
determining a second option probability of the recommended option combination according to the object information and the second option price;
and determining the estimated loss value of the recommended option combination according to the second option probability.
In a third aspect, the present application provides a data processing apparatus comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the data processing method according to any one of the first aspects.
In a fourth aspect, the present application provides a computer-readable storage medium having stored therein computer-executable instructions which, when executed by a processor, are adapted to carry out the data processing method according to any one of the first aspects.
In a fifth aspect, an embodiment of the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the data processing method according to any of the first aspects.
The application provides a data processing method, a device, equipment and a storage medium, wherein the input reference information comprises the following steps of: the method comprises the steps of obtaining a target object, option types and a maximum loss value, obtaining the object types of the target object, determining a plurality of network addresses according to the object types, obtaining a plurality of text description information of the target object according to the plurality of network addresses, determining object information corresponding to the target object according to the plurality of text description information, wherein the object information comprises interest information, emptiness information and attribute change trend, determining interpretation information corresponding to target option combinations and target option combinations according to the object information, option types and the maximum loss value, and realizing personalized customization of recommended investment information, so that the recommended investment information has better referential property and better helps investors to make trade decisions.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a schematic diagram of a network architecture on which a data processing method according to an embodiment of the present application is based;
FIG. 2 is a schematic flow chart of a data processing method according to an embodiment of the present application;
FIG. 3 is a flowchart illustrating another data processing method according to an embodiment of the present application;
FIG. 4 is a schematic diagram of an expiration and deficiency analysis according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a data processing apparatus according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Specific embodiments of the present application have been shown by way of the above drawings and will be described in more detail below. The drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but rather to illustrate the inventive concepts to those skilled in the art by reference to the specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or fully authorized by each party, and the collection, use and processing of the related data need to comply with related laws and regulations and standards, and provide corresponding operation entries for the user to select authorization or rejection.
It should be noted that the method and the device for data processing according to the present application may be used in the field of large data platforms, and may also be used in any field other than the field of large data platforms.
The investment advice provided in the existing investment recommendation information is not specific, and is based on the main opinion of a certain analyst, and does not necessarily conform to the predictions of certain investors, nor does it conform to the risk preferences of certain investors, resulting in poor referenceability of the investment recommendation information in actual trading decisions.
In order to solve the above technical problems, fig. 1 is a schematic diagram of a network architecture on which a data processing method according to an embodiment of the present application is based. As shown in fig. 1, the network architecture on which the present application is based at least includes an electronic device 101 and a server 102, where the number of servers 102 may be one or multiple.
The electronic device 101 may be any type of terminal, for example, may include a mobile phone, a computer, a tablet, a wearable device, etc., and the embodiment of the present application does not specifically limit the electronic device. The electronic device 101 may be equipped with a data processing system, and may obtain interpretation information corresponding to the target option combination and the target option combination. The data processing system may be written in a language such as C/C++, java, shell, or Python, without limitation.
A database may be provided in the server 102, in which a plurality of network addresses associated with the target object may be stored, etc. The server 102 may be a cloud server, a server of a distributed system, or a server combined with a blockchain, which is not specifically limited herein.
A communication connection is established between the electronic device 101 and the server 102. For example, the electronic device 101 may establish a communication connection with the server 102 through a hypertext transfer protocol (hyper text tansfer protocol, HTTP) or a hypertext transfer protocol over secure socket layer (hyper text transfer trotocol over secure socket layer, HTTPs) or the like, which is not specifically limited herein.
According to the data processing method provided by the embodiment of the application, based on subjective factors such as the target object input by the user, the acceptable risk level, the trend judgment of the target object and the like, option combinations and corresponding interpretation information which accord with the subjective factors of the user are recommended to the user, so that personalized customization of recommended investment information is realized, the recommended investment information is more referential, and an investor is better helped to make a trade decision.
The following describes the technical scheme of the present application and how the technical scheme of the present application solves the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 2 is a flow chart of a data processing method according to an embodiment of the present application. The execution body of the embodiment of the application can be electronic equipment or a data processing device arranged in the electronic equipment. The data processing means may be implemented by software or by a combination of software and hardware. Referring to fig. 2, the method includes:
s201, acquiring input reference information of a user.
The input reference information includes: target object, option type, and maximum penalty value. The input reference information is selected by the user in the investment page.
Wherein the target object is an asset that can be selected for purchase or sale, such as a stock, government bond, currency, stock index, commodity futures, etc. Options types include a rising option and a falling option. The maximum loss value may be the maximum risk level that the user can withstand. The input reference information of the user can be obtained through the data access module.
S202, obtaining the object type of the target object, and determining a plurality of network addresses according to the object type.
The network address comprises the network address of an analysis page aiming at a target object in each data analysis website such as an option data analysis platform, an option investment research platform and the like. The network address is used to obtain object information, such as interest information, emptiness information, etc., related to the target object to predict price trends of the target object within a preset time period in the future.
According to the target object selected by the user, determining the object type of the target object, and according to the object type of the target object, determining a plurality of network addresses corresponding to the object type.
S203, acquiring a plurality of text description information of the target object according to the plurality of network addresses.
Text description information refers to information that describes, interprets, describes, predicts a certain target object in a literal manner. A plurality of text description information about the target object is acquired by a content extraction technique based on the plurality of network addresses.
S204, determining object information corresponding to the target object according to the text description information.
The object information includes interest information, emptiness information, and attribute change tendency. Wherein the attribute change trend may be a price trend. According to the text description information, the interest information and the attribute change trend related to the target object are determined through a semantic analysis technology.
S205, determining the target option combination and the interpretation information corresponding to the target option combination according to the object information, the option type and the maximum loss value.
Where option portfolio refers to a trade way of implementing a certain investment strategy by holding multiple option contracts simultaneously. These options contracts may be of the same kind, such as purchasing an option, or of a different kind, such as purchasing or selling an option and a put option. Option combinations can be combined according to different trading strategies and expected market trends, and have certain flexibility and advantages in terms of risk of hedging buying and selling directions, cost reduction, income increase and the like.
The target option combination is based on object information of the target object and conforms to an investment strategy determined by the option type selected by the user and the maximum sustainable loss value. The interpretation information corresponding to the target option combination is used for interpreting and explaining the initial operation and the profit and loss analysis of the target option combination, and can be used as a reference for a user to enable the user to know the investment principle and the expected profit and loss situation more.
According to the data processing method provided by the embodiment, by acquiring the input reference information of the user, the input reference information comprises: the method comprises the steps of obtaining a target object, option types and a maximum loss value, obtaining the object types of the target object, determining a plurality of network addresses according to the object types, obtaining a plurality of text description information of the target object according to the plurality of network addresses, determining object information corresponding to the target object according to the plurality of text description information, wherein the object information comprises interest information, emptiness information and attribute change trend, determining interpretation information corresponding to target option combinations and target option combinations according to the object information, option types and the maximum loss value, and realizing personalized customization of recommended investment information, so that the recommended investment information has better referential property and better helps investors to make trade decisions.
Fig. 3 is a flow chart of a data processing method according to an embodiment of the present application. On the basis of the above embodiment, the method will be described in detail with reference to fig. 3. The method comprises the following steps:
s301, acquiring input reference information of a user.
The execution of S301 may refer to the execution of S201, and will not be described herein.
S302, obtaining the object type of the target object, and determining a plurality of network addresses according to the object type.
The execution of S302 may refer to the execution of S202, and will not be described herein.
S303, acquiring a plurality of network pages according to the plurality of network addresses.
The web page includes one or more of text information, image information, voice information, or video information. According to the network addresses, the network pages corresponding to the network addresses can be acquired through a page extraction technology. The page extraction technology may automatically obtain a web page on a specified web page through a program, and may be implemented by programming languages such as Python, java, etc., which are not described herein.
S304, extracting corresponding text description information from each network page respectively to obtain a plurality of text description information.
Taking any network page as an example, the corresponding text description information can be extracted from the network page by a content extraction technology, so as to obtain the text description information. The content extraction technology may automatically obtain specific content in the specified object through a program, and may be implemented by programming languages such as Python, java, etc., which are not described herein. Content extraction techniques may include image recognition, semantic recognition, voice recognition, and the like.
Semantic recognition can be performed on text information in the web page to obtain corresponding text description information.
Image recognition can be performed on the image information in the network page to obtain corresponding text description information.
And aiming at the audio information in the network page, voice recognition can be carried out to obtain corresponding text description information.
For video information in a network page, video can be read frame by frame, multi-frame images are subjected to de-duplication processing, and the de-duplicated images are subjected to image recognition to obtain corresponding text description information.
Alternatively, the corresponding text description information may be extracted from the web page by: determining a plurality of page areas and area information of each page area in a network page, wherein the area information comprises area positions, area sizes or area types; determining a target page area in a plurality of page areas according to the area information; and extracting text description information in the target page area.
By determining the target page area, the interference of comment areas in the page, the interference of related recommendation and the interference of advertisements in the page can be removed, so that the information in the target area is more accurate, and the extracted text description information is more accurate.
S305, carrying out segmentation processing on the plurality of text description information to obtain a plurality of text segments.
The segmentation process may be to divide the plurality of text descriptions into a plurality of portions, resulting in a plurality of text segments. The segmentation process may be based on a time sequence of the text description information, or may be based on a preset feature, which is not limited herein.
S306, semantic information of each text segment is obtained.
The plurality of text segments can be subjected to natural language processing to acquire semantic information of each text segment. The natural language processing may be performed by means of entity recognition, keyword extraction, and the like, which is not limited herein.
S307, removing the text segments with mutually exclusive semantics from the text segments according to the semantic information of the text segments to obtain a plurality of target text segments.
According to semantic information of a plurality of text segments, a preset word vector model is utilized to determine associated words corresponding to each semantic information, and associated words with different semantemes in the plurality of associated words are found out. And separating text segments corresponding to different associated vocabularies by using a text editor, namely removing text segments with mutually exclusive semantics from the text segments to obtain a plurality of target text segments.
The word vector model may be an existing word2vec model, a GloVe model, a fastText model, or the like, which will not be described herein.
S308, determining object information according to the target text segments.
The object information includes interest information, emptiness information, and attribute change tendency. The attribute change trend can be a price trend, and the price trend of the target object in the future preset time can be predicted, so that a reference is provided for a user.
Alternatively, the object information may be determined by: processing a plurality of target text segments through a preset model to obtain interest information and empty information, wherein the preset model is determined according to historical data of a target object; determining a plurality of first text segments related to the attribute of the target object among the plurality of target text segments; and determining the attribute change trend according to the first text segments.
The preset model comprises a lexical analysis model, a syntactic analysis model and a semantic analysis model. The lexical analysis model firstly performs word segmentation on each sentence in the target text segment, splits a sentence into individual words, and then searches meaning and part-of-speech labels (such as nouns, verbs, adjectives and the like) of the words in the corpus. The syntactic analysis model analyzes the grammatical structure of each sentence, including the subject, predicate, object, etc., components and the relationships between them. The semantic analysis model further analyzes words, phrases and sentence structures in sentences so as to understand semantic information in the sentences, and the semantic analysis model can be realized through semantic role labeling, entity identification, reference resolution and other technologies. And determining object information corresponding to the target text segment, such as interest information or interest information, according to analysis results of the lexical analysis model, the syntactic analysis model and the semantic analysis model.
The first text segment may be a text segment related to the attribute of the target object, for example, the price of the target object may be in the cross-disc oscillation stage in a future period. And determining a plurality of first text segments related to the attribute of the target object in the plurality of target text segments through a semantic recognition technology, and determining the attribute change trend according to the plurality of first text segments.
S309, determining at least one recommended option combination according to the option type.
Recommended option combinations include, but are not limited to, cow market price option combinations, bear market price option combinations, butterfly price option combinations, box price option combinations, vertical span price option combinations, le price option combinations, and the like.
At least one recommended option combination that meets the option type may be determined based on the option type selected by the user. For example, where the option type is a view option, the recommended option combination may be determined to be a butterfly option combination and a vertical-span option combination.
The butterfly price difference option combination is to buy two parts of the same price of the line options and sell one part of the same target object. The vertical span type combination is to buy the low-running-price stand-off option and sell the high-running-price stand-off option at the same time.
In the following, any one of the at least one recommended option combination is taken as an example.
S310, determining the expected loss value of the recommended option combination according to the object information.
The projected loss value for the recommended option combination may be determined based on the determined price trend in the object information, wherein the projected loss value may be infinite.
Optionally, the input reference information further includes a first line price; the predicted loss value for the recommended option combination may be determined as follows: determining a first option probability of recommending option combinations according to the object information and the first option price; based on the first option probabilities, an expected loss value for the recommended option combination is determined.
Where the option price refers to whether the option holder can choose to buy or sell the subject asset at a specified price (i.e., the option price) within a specified time during the option trade. If the option holder chooses a line of options, he can buy or sell the subject asset at the expiration of the option (or at some point prior to the expiration of the prescription) at a price agreed in advance. If the option holder does not select an option, the option will automatically fail upon expiration.
The first line price is the line price input by the user, and the predicted loss value of the recommended option combination is determined based on the line price input by the user and the object information, so that the user can intuitively determine whether the line price judged by the user accords with the risk level which the user can bear.
Alternatively, the predicted loss value for the recommended option combination may be determined as follows: determining a predicted price interval according to the object information and a preset price prediction model; determining a second line weight price according to the predicted price interval; determining a second line option probability of recommending option combinations according to the object information and the second line option price; and determining the estimated loss value of the recommended option combination according to the second row option probability.
The second line weight price is a line weight price determined based on a preset price prediction model, and provides a reference for the user. Based on the second line price and the object information, the estimated loss value of the recommended option combination is determined, so that when the line price is not determined, the user can judge whether the risk level capable of being born by the user is met by referring to the line price of the price prediction model.
S311, determining a recommended option combination with the predicted loss value smaller than or equal to the maximum loss value as a target option combination.
And determining a recommended option combination with the predicted loss value smaller than or equal to the maximum loss value as a target option combination according to the maximum loss value input by the user.
S312, according to the target option combination and the expected loss value corresponding to the target option combination, determining interpretation information corresponding to the target option combination.
The interpretation information includes expected profit and loss analysis tables, maximum profit, maximum loss, and profit and loss balance points, etc., which are not limited herein. The interpretation information corresponding to the target option combination may be determined based on the line price determined in the above step and according to the target option combination and the predicted loss value corresponding to the target option combination. The form of the interpretation information may be one or more of text, table, picture and video, which is not limited herein.
The implementation content of each step in the embodiment of the present application may refer to the description of the corresponding step or operation in the above method embodiment, and repeated descriptions are omitted.
According to the data processing method provided by the embodiment, the object type of a target object is obtained by obtaining input reference information of a user, a plurality of network addresses are determined according to the object type, a plurality of network pages are obtained according to the plurality of network addresses, one or more of text information, image information, voice information or video information are included in the network pages, corresponding text description information is respectively extracted in each network page to obtain a plurality of text description information, segmentation processing is carried out on the plurality of text description information to obtain a plurality of text segments, semantic information of each text segment is obtained, text segments mutually exclusive in the plurality of text segments are removed according to the semantic information of the plurality of text segments, a plurality of target text segments are obtained, object information is determined according to the plurality of target text segments, at least one recommended option combination is determined according to option type, a recommended option combination is determined according to the object information, a predicted loss value of the recommended option combination is determined, the predicted loss value is smaller than or equal to a maximum loss value, the recommended option combination is determined to be a target option combination, the predicted loss value corresponding to the target option combination is determined according to the target option combination, and the target interpretation value is determined according to the target interpretation value of the target option combination and the target interpretation value is determined to the target option combination, and the target interpretation value is determined to be corresponding to the recommendation option combination, and the recommendation option combination. The option operation, the profit and loss balance point and the return rate realize the personalized customization of the recommended investment information, so that the recommended investment information has better reference property and better helps investors to make trade decisions.
The technical scheme shown in the application is described below by a specific example.
Suppose that user a selects target a, selects option type as the option to see and inputs the maximum loss value that can be sustained as the total option cost. Wherein the present price of the target A is 2.35 yuan/part.
The electronic equipment acquires a plurality of network addresses of the target object A, obtains object information of the target object A based on the network addresses, wherein the object information comprises 1 interest information, the price trend is a small rise, and the price of the target object A is proved to have a gentle and small rise trend.
And the electronic equipment determines that the target option combination can be a cow price difference option combination according to the object information, the option type input by the user and the maximum loss value input by the user, and determines interpretation information corresponding to the cow price difference option combination.
The interpretation information comprises text information, table information and picture information.
The text information comprises the option of recommending to buy a right of a line with a price of 2.35-2.70 yuan which expires in the next month, and selling a right of a line with a price of more than 2.70 yuan at the same time, so as to form a cow market price difference option combination. The market price difference option combination can save certain cost.
The table information includes, taking the example of buying an option with an expiration row weight of 2.45 yuan in the next month, and selling an option with a row weight of 2.70 yuan, the two option rights are 300 yuan and 30 yuan respectively, and analyzing that there may be several conditions of the asset price of the target object a after one month, and the benefit analysis of table 1 shows that:
TABLE 1
Case one: s is S T ≥2.70 And a second case: 2.45<S T <2.70 Case three: s is S T ≤2.45
Target expiration prices S T ＝2.75 S T ＝2.50 S T ＝2.20
Multiple-head earnings for option subscription 3000 500 0
Earnings of open-end option of subscription -500 0 0
Option right payout -270 -270 -270
Total profit 2230 230 -270
Wherein S is T For an expiration price. And, the above case does not consider the guarantee.
Based on the above table information, the picture information can be represented as in fig. 4. Fig. 4 is a schematic diagram of an expiration and deficiency analysis.
Fig. 5 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application. Referring to fig. 5, the apparatus 500 includes a first acquisition module 501, a second acquisition module 502, a third acquisition module 503, a first determination module 504, and a second determination module 505, wherein,
a first obtaining module 501, configured to obtain input reference information of a user, where the input reference information includes: target object, option type and maximum penalty value;
A second obtaining module 502, configured to obtain an object type of the target object, and determine a plurality of network addresses according to the object type;
a third obtaining module 503, configured to obtain, according to the plurality of network addresses, a plurality of text description information of the target object;
a first determining module 504, configured to determine object information corresponding to the target object according to the plurality of text description information, where the object information includes interest information, emptiness information, and attribute variation trend;
the second determining module 505 determines, according to the object information, the option type and the maximum loss value, a target option combination and interpretation information corresponding to the target option combination.
In one possible implementation manner, the third obtaining module 503 is specifically configured to:
acquiring a plurality of network pages according to the plurality of network addresses, wherein the network pages comprise one or more of text information, image information, voice information and video information;
and respectively extracting corresponding text description information from each network page to obtain the text description information.
In one possible implementation, for any one web page; the third obtaining module 503 is specifically configured to:
Determining a plurality of page areas and area information of each page area in the network page, wherein the area information comprises area positions, area sizes or area types;
determining a target page area in the plurality of page areas according to the area information;
and extracting the text description information in the target page area.
In one possible implementation, the first determining module 504 is specifically configured to:
the text description information is segmented to obtain text segments;
acquiring semantic information of each text segment;
removing text segments with mutually exclusive semantics from the text segments according to the semantic information of the text segments to obtain a plurality of target text segments;
and determining the object information according to the target text segments.
In one possible implementation, the first determining module 504 is specifically configured to:
processing the target text segments through a preset model to obtain the interest information and the empty information, wherein the preset model is obtained by determining historical data of the target object;
determining a plurality of first text segments in the plurality of target text segments that are related to attributes of the target object;
And determining the attribute change trend according to the first text segments.
In one possible implementation, the second determining module 505 is specifically configured to:
determining at least one recommended option combination according to the option type;
determining an estimated loss value of any recommended option combination according to the object information;
determining a recommended option combination with the predicted loss value smaller than or equal to the maximum loss value as a target option combination;
determining interpretation information corresponding to the target option combination according to the target option combination and an expected loss value corresponding to the target option combination, wherein the interpretation information comprises: option operations and earning conditions.
In one possible implementation, the input reference information further includes a first line price; the second determining module 505 is specifically configured to:
determining a first right probability of the recommended option combination according to the object information and the first right price;
and determining the estimated loss value of the recommended option combination according to the first option probability.
In one possible implementation, the second determining module 505 is specifically configured to:
Determining a predicted price interval according to the object information and a preset price prediction model;
determining a second line weight price according to the predicted price interval;
determining a second option probability of the recommended option combination according to the object information and the second option price;
and determining the estimated loss value of the recommended option combination according to the second option probability.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 6, an electronic device 600 may include: memory 601, processor 602, transceiver 603.
The memory 601 is used for storing program instructions;
the processor 602 is configured to execute the program instructions stored in the memory, so as to cause the electronic device 600 to execute any of the data processing methods described above.
The transceiver 603 may include: a transmitter and/or a receiver. The transmitter may also be referred to as a transmitter, a transmit port, a transmit interface, or the like, and the receiver may also be referred to as a receiver, a receive port, a receive interface, or the like. Illustratively, the memory 601, the processor 602, and the transceiver 603 are interconnected by a bus 604.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, the data processing method can be realized.
Embodiments of the present application also provide a computer program product executable by a processor for implementing the above-mentioned data processing method when the computer program product is executed.
The data processing apparatus, the electronic device, the computer readable storage medium and the computer program product according to the embodiments of the present application may execute the technical solution shown in the foregoing data processing method embodiment, and the implementation principle and the beneficial effects are similar, and are not repeated here.
All or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a readable memory. The program, when executed, performs steps including the method embodiments described above; and the aforementioned memory (storage medium) includes: read-only memory (ROM), random-access memory (random access memory, RAM), flash memory, hard disk, solid state disk, magnetic tape, floppy disk (floppy disk), optical disk (optical disk), and any combination thereof.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (11)

1. A method of data processing, comprising:
acquiring input reference information of a user, wherein the input reference information comprises: target object, option type and maximum penalty value;
obtaining an object type of the target object, and determining a plurality of network addresses according to the object type;
acquiring a plurality of text description information of the target object according to the plurality of network addresses;
determining object information corresponding to the target object according to the text description information, wherein the object information comprises interest information, emptiness information and attribute change trend;
and determining a target option combination and interpretation information corresponding to the target option combination according to the object information, the option type and the maximum loss value.
2. The method of claim 1, wherein obtaining a plurality of text descriptions of the subject object based on the plurality of network addresses comprises:
Acquiring a plurality of network pages according to the plurality of network addresses, wherein the network pages comprise one or more of text information, image information, voice information and video information;
and respectively extracting corresponding text description information from each network page to obtain the text description information.
3. The method of claim 2, wherein for any one web page; extracting corresponding text description information from the network page, including:
determining a plurality of page areas and area information of each page area in the network page, wherein the area information comprises area positions, area sizes or area types;
determining a target page area in the plurality of page areas according to the area information;
and extracting the text description information in the target page area.
4. A method according to any one of claims 1-3, wherein determining object information corresponding to the target object based on the plurality of text description information comprises:
the text description information is segmented to obtain text segments;
acquiring semantic information of each text segment;
Removing text segments with mutually exclusive semantics from the text segments according to the semantic information of the text segments to obtain a plurality of target text segments;
and determining the object information according to the target text segments.
5. The method of claim 4, wherein determining the object information from the plurality of target text segments comprises:
processing the target text segments through a preset model to obtain the interest information and the empty information, wherein the preset model is obtained by determining historical data of the target object;
determining a plurality of first text segments in the plurality of target text segments that are related to attributes of the target object;
and determining the attribute change trend according to the first text segments.
6. The method of any of claims 1-5, wherein determining interpretation information corresponding to a target option combination and the target option combination based on the subject information, the option type, and the maximum loss value comprises:
determining at least one recommended option combination according to the option type;
determining an estimated loss value of any recommended option combination according to the object information;
Determining a recommended option combination with the predicted loss value smaller than or equal to the maximum loss value as a target option combination;
determining interpretation information corresponding to the target option combination according to the target option combination and an expected loss value corresponding to the target option combination, wherein the interpretation information comprises: option operations and earning conditions.
7. The method of claim 6, wherein the input reference information further comprises a first line weight price; determining, from the object information, an expected loss value for the recommended option combination, including:
determining a first right probability of the recommended option combination according to the object information and the first right price;
and determining the estimated loss value of the recommended option combination according to the first option probability.
8. The method of claim 6, wherein determining the projected loss value for the recommended option combination based on the object information comprises:
determining a predicted price interval according to the object information and a preset price prediction model;
determining a second line weight price according to the predicted price interval;
determining a second option probability of the recommended option combination according to the object information and the second option price;
And determining the estimated loss value of the recommended option combination according to the second option probability.
9. A data processing apparatus, comprising:
the first acquisition module is used for acquiring input reference information of a user, wherein the input reference information comprises: target object, option type and maximum penalty value;
the second acquisition module is used for acquiring the object type of the target object and determining a plurality of network addresses according to the object type;
a third obtaining module, configured to obtain, according to the plurality of network addresses, a plurality of text description information of the target object;
the first determining module is used for determining object information corresponding to the target object according to the text description information, wherein the object information comprises interest information, interest information and attribute change trend;
and the second determining module is used for determining the target option combination and the interpretation information corresponding to the target option combination according to the object information, the option type and the maximum loss value.
10. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
The processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1 to 8.
11. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1 to 8.
CN202310614906.1A 2023-05-26 2023-05-26 Data processing method, device, equipment and storage medium Pending CN116645215A (en)

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