CN115880075A - Financial data management method, device and equipment and readable storage medium - Google Patents
Financial data management method, device and equipment and readable storage medium Download PDFInfo
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Abstract
The invention provides a financial data management method, a device, equipment and a readable storage medium, which can firstly preprocess financial data acquired from a data interface. And then storing the preprocessed financial data into a database of a corresponding type based on the updating frequency and type of the preprocessed financial data. And calling an index calculation engine matched with the type of the database to process the preprocessed financial data in the database to obtain index data. And performing penetration estimation on the index data based on an estimation algorithm model corresponding to the purpose of the index data. And finally, displaying the estimated result in a report interface preset by a user. The financial data management method can directly connect transaction system data and store multiple scenes, so that the data can be read and used more quickly. Meanwhile, indexes are configured according to different assets through a penetration algorithm, a multi-level index system is established, and the management efficiency of financial asset data can be effectively improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a financial data management method, device, equipment and a readable storage medium.
Background
With the explosion of the financial asset management industry, asset management organizations need to continually release products that meet market needs. Under the current market environment, one asset management personnel needs to manage dozens of or hundreds of combinations, which belongs to a normal state, the required data is complicated, the data sources are multiple, a large amount of time is spent on collecting and processing the data, and the efficiency is very low. How to improve the management ability of asset management personnel on assets has become a technical problem which needs to be solved urgently.
Disclosure of Invention
In order to solve the problems of low management efficiency and large data processing capacity in the prior art, the invention provides a financial data management method, a financial data management device, financial data management equipment and a readable storage medium, which have the characteristics of timely data processing, higher management efficiency and the like.
According to the financial data management method provided by the embodiment of the invention, the financial data management method comprises the following steps:
preprocessing the financial data acquired from the data interface;
storing the preprocessed financial data into a database of a corresponding type based on the updating frequency and type of the preprocessed financial data;
calling an index calculation engine matched with the type of the database to process the preprocessed financial data in the database to obtain index data;
based on a pre-estimation algorithm model corresponding to the purpose of the index data, performing penetration pre-estimation on the index data;
and displaying the estimated result in a report interface preset by a user.
Further, before invoking an index calculation engine matched with the database type to process the preprocessed financial data in the database, obtaining index data, the method further includes:
and classifying the preprocessed financial data according to preset statistical dimensions.
Further, the financial data management method further includes:
and generating index deviation alarm information based on an alarm threshold and the estimation result, and adjusting the bin position of the index data based on a preset adjustment index.
Further, the preprocessing the financial data acquired from the data interface includes:
and giving a unique identity to each piece of financial data and converting the financial data into a preset data format.
Further, the storing the preprocessed financial data into a database of a corresponding type based on the update frequency and type of the preprocessed financial data includes:
storing the combined data updated every day into a document database;
storing the daily updated security data in a columnar database;
storing the securities data updated every season into a relational database;
and storing the hot spot data with the updating frequency larger than the preset value into a memory database.
Further, the invoking an index calculation engine matched with the database type to process the preprocessed financial data in the database to obtain index data includes:
searching and calculating the security data updated every season in the relational database based on a preset SQL template;
calculating the hotspot data in the memory database based on a stream type calculation engine;
performing calculations of the combined data and the daily updated securities data in the document database and the columnar database based on a batch calculation engine.
Further, the performing penetration estimation on the index data based on the estimation algorithm model corresponding to the purpose of the index data includes:
when the index data is used for direct investment, penetration prediction is carried out on the index data based on an accounting valuation algorithm;
when the index data is used for internal investment, penetration estimation is carried out on the index data based on the asset bin corresponding to the index data;
when the index data is used for external investment, adjusting asset configuration corresponding to the index data based on the wide base index, and adjusting an active bin adjusting coefficient by combining historical bin adjusting data;
readjusting the asset configuration based on the active bin adjustment coefficient, and performing penetration estimation of the index data based on the readjusted asset configuration.
According to an embodiment of the present invention, there is provided a financial data management apparatus including:
the preprocessing module is used for preprocessing the financial data acquired from the data interface;
the storage module is used for storing the preprocessed financial data into a database of a corresponding type based on the updating frequency and type of the preprocessed financial data;
the index configuration module is used for calling an index calculation engine matched with the type of the database to process the preprocessed financial data in the database to obtain index data;
the estimation module is used for carrying out penetration estimation on the index data based on an estimation algorithm model corresponding to the purpose of the index data; and
and the result display module is used for displaying the estimated result in a report interface preset by the user.
According to an embodiment of the present invention, there is provided an apparatus, including: a memory and a processor;
the memory is used for storing programs;
the processor is configured to execute the program to implement the steps of the financial data management method.
According to an embodiment of the present invention, there is provided a readable storage medium, on which a computer program is stored, the computer program, when executed by a processor, implementing the steps of the financial data management method as described above.
The invention provides a financial data management method, a device, equipment and a readable storage medium, which can firstly preprocess financial data acquired from a data interface. And then storing the preprocessed financial data into a database of a corresponding type based on the updating frequency and type of the preprocessed financial data. And calling an index calculation engine matched with the type of the database to process the preprocessed financial data in the database to obtain index data. And performing penetration estimation on the index data based on an estimation algorithm model corresponding to the purpose of the index data. And finally, displaying the estimated result in a report interface preset by a user. The financial data management method can directly connect transaction system data and store multiple scenes, so that the data can be read and used more quickly. Meanwhile, index model elements are configured according to different assets through a penetration algorithm, a multi-level index system is constructed, and the management efficiency of financial asset data can be effectively improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow diagram of a method of financial data management provided in accordance with an exemplary embodiment;
FIG. 2 is a block diagram of a financial data management apparatus provided in accordance with an exemplary embodiment;
FIG. 3 is a block diagram of an apparatus provided in accordance with an exemplary embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a financial data management method, which may include the steps of:
101. the financial data obtained from the data interface is pre-processed.
The financial data such as stock basic data, market quotation data, real-time trading data of a trading system, accounting valuation data, wind control compliance data, multi-industry classification, investment style data and the like can be obtained by butting with internal and external interfaces of the financial system. And then data integration can be carried out by adopting data warehousing tools such as DATAX and the like, and unique identification marks can be given to each data and data formats can be unified while the timeliness of the obtained synchronous data is guaranteed, so that integration of multivariate data in minimum data set in the money data processing process is facilitated, and splitting and storage in combination with the specific conditions of the data are facilitated.
102. And storing the preprocessed financial data into a database of a corresponding type based on the updating frequency and type of the preprocessed financial data.
For example, daily frequency combination data updated every day can be stored by using a document database such as mongo. The daily frequency security data updated every day can be stored by using a columnar database such as clickhouse and the like so as to expand the newly added index. The seasonal security data that is updated each quarter may be stored using a relational database. For high-frequency hot spot data with very high occurrence frequency, the data can be stored in an internal memory database of the device. Therefore, when data are acquired through the unique identification marks of all data, the extraction process of the data on the day can be controlled to be in the millisecond level, the data extraction on multiple days can reach the second level, and hot data are loaded into the memory in advance, so that the speed of reading the data in real time is improved.
103. And calling an index calculation engine matched with the type of the database to process the preprocessed financial data in the database to obtain index data.
Specifically, when the required index data comes from the relational database, the SQL template configured by the user may be acquired, and then the SQL template may be analyzed by an analysis tool such as freemaker, so as to acquire the corresponding index data from the relational database.
When the source of the index data is a memory database, the calculation can be rapidly deployed through a streaming engine such as a flink according to events such as real-time push transaction system instructions and quotations through a preset real-time calculation index calculation process, and the calculated index is stored in the memory database, so that the index can be directly extracted conveniently.
When the index sources are a document database and a column database, the daily frequency and static indexes are stored in the corresponding databases in a batch calculation mode, so that the index data can be conveniently and quickly searched. In addition, when the index data serve as remote indexes, the existing index processing center can be connected through the Internet, and indexes are extracted through real-time calling or batch processing of transmission protocols such as http and the like to be stored or fed back to the user side. When the index data is the index defined by the formula by the user, analyzing the set calculation formula based on the formula engine, and calculating various indexes in real time.
104. And performing penetration estimation on the index data based on an estimation algorithm model corresponding to the purpose of the index data.
When the index data is used for direct investment of stocks, futures and the like, the market value of each corresponding asset can be approached according to an accounting valuation algorithm by referring to real-time quotations, and then penetration estimation of the index data is completed.
When the index data is used for internal investment such as internal fund and pension, the penetration prediction of the index data can be carried out based on the asset bin corresponding to the index data. The real-time calculation of the various asset positions of the targets can be realized by real-time butt joint of internal asset data and real-time transaction data of a transaction system, superposition of market fluctuation and real-time calculation of various asset positions of the targets. And then completing the estimation of real-time bin positions and estimation values of the mother fund according to estimated target data, approaching the actual bin positions of the mother fund again, and then calculating various indexes in real time according to the bin positions at the moment.
When the index data is used for external investment such as external fund, asset configuration corresponding to the index data is adjusted based on the wide-base index, and the active bin adjustment coefficient is adjusted by combining historical bin adjustment data. And then, readjusting the asset configuration based on the active bin adjustment coefficient, and performing penetration estimation of index data based on the readjusted asset configuration. In the concrete implementation, the market condition can be firstly combined, and the asset allocation adjustment of the season newspaper and the annual newspaper can be carried out by estimating the passive asset allocation change in real time according to the industry fluctuation condition of the wide-base index (such as Shanghai depth 300). And then fitting an active bin adjusting coefficient by combining the historical annual newspaper bin adjusting conditions of the corresponding fund managers, and performing one-round adjustment on the annual newspaper bin adjusting conditions of the corresponding fund managers according to the coefficient. And performing penetration calculation on each index according to the adjusted asset configuration condition.
105. And displaying the estimated result in a report interface preset by a user.
The user can define the required report forms and indexes by self, a display interface can be generated according to the format set by the user after the index calculation is finished, and the user can check, store, further process and the like the indexes in the display interface.
The financial data management method realizes the pre-estimation of the real-time bin position, can flexibly configure a combined bin position penetration calculation model in real time to realize the construction of a multi-level index system, and can effectively improve the management efficiency of financial asset data.
In another embodiment of the present invention, before invoking an index calculation engine matched with the database type to process the preprocessed financial data in the database, the method further includes:
and classifying the preprocessed financial data according to preset statistical dimensions. The indexes can be divided according to each dimension, for example, according to the real-time requirement, the indexes are divided into real-time indexes, T-1 indexes and the like. And (4) calculating the demand according to the indexes, and dividing attribute indexes, proportion indexes, formula indexes and the like. When index penetration estimation is carried out, construction of a multi-dimensional and multi-level index system can be completed by configuring various indexes, selecting an index calculation model, index types (attribute types, proportion types and the like), index-dependent strand labels, index display formats and the like.
In order to further optimize the technical scheme, after the index penetration estimation result is obtained, index deviation alarm information can be generated based on the alarm threshold value and the estimation result, and the bin of the index data is adjusted based on the preset adjustment index.
Specifically, the alarm threshold value can be set, the alarm threshold value is compared with each index to obtain the industrial index deviation condition, and the deviation condition is reminded through mails, short messages, weChat and other ways. And aiming at the deviation condition, correcting the result according to the preset adjusting index, and forming a trading system bin adjusting instruction. And (4) ordering to the transaction system to adjust the indexes through a transaction protocol of the transaction system. Therefore, the tracking adjustment of index data is formed, the bin adjusting effect is fed back, a benign estimation cycle is formed, and the penetration estimation is more accurate and reliable.
Based on the same design idea, referring to fig. 2, an embodiment of the present invention further provides a financial data management apparatus, which can implement the steps of the financial data management method when operating, and the apparatus may include:
a preprocessing module 201, configured to preprocess the financial data obtained from the data interface.
The storage module 202 is configured to store the preprocessed financial data into a database of a corresponding type based on the update frequency and the type of the preprocessed financial data.
And the index configuration module 203 is used for calling an index calculation engine matched with the type of the database to process the preprocessed financial data in the database to obtain index data.
The estimation module 204 is configured to perform penetration estimation on the index data based on an estimation algorithm model corresponding to a purpose of the index data. And
and the result displaying module 205 is configured to display the estimated result in a report interface preset by the user.
Further, the financial data management apparatus further includes:
and the classification module is used for classifying the preprocessed financial data according to preset statistical dimensions.
Further, the financial data management apparatus further includes:
and the adjusting module is used for generating index deviation alarm information based on the alarm threshold and the estimation result and adjusting the bin of the index data based on a preset adjusting index.
The preprocessing module 201 is specifically configured to assign a unique identity to each piece of financial data and convert the unique identity into a preset data format.
The storage module 202 is specifically configured to:
the daily updated combination data is stored into a document database.
The daily updated security data is stored in a columnar database.
And storing the security data updated in each season into a relational database.
And storing the hot spot data with the updating frequency larger than the preset value into the memory database.
The index configuration module 203 is specifically configured to search and calculate the security data updated every season in the relational database based on a preset SQL template.
And calculating the hot spot data in the memory database based on the streaming calculation engine.
The calculation of the combined data and the daily updated securities data is performed in the document database and the columnar database based on the batch calculation engine.
The estimation module 204 is specifically configured to perform penetration estimation on the index data based on an accounting estimation algorithm when the index data is used for direct investment.
And when the index data is used for internal investment, performing penetration estimation on the index data based on the asset bin corresponding to the index data.
When the index data is used for external investment, asset configuration corresponding to the index data is adjusted based on the wide base index, and the active bin-adjusting coefficient is adjusted by combining historical bin-adjusting data. And readjusting the asset configuration based on the active bin adjustment coefficient, and performing penetration estimation of index data based on the readjusted asset configuration.
The financial data management device has the same beneficial effects as the financial data management method, and the specific implementation mode of the financial data management device can refer to the embodiment of the financial data management method, which is not described in detail herein.
As shown in fig. 3, an embodiment of the present invention further provides an apparatus, which may include: a memory 301 and a processor 302.
A memory 301 for storing programs.
A processor 302, configured to execute the program, and implement the steps of the financial data management method according to the above embodiment.
Embodiments of the present invention also provide a readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the financial data management method as described in the above embodiments.
While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it will be appreciated by those skilled in the art that the claimed subject matter is not limited by the order of acts, as some steps may, in accordance with the claimed subject matter, occur in other orders and/or concurrently. Further, those skilled in the art will appreciate that the embodiments described in this specification are presently preferred and that no acts or modules are required by the invention.
It should be noted that, in the present specification, the embodiments are all described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other. For the device-like embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The steps in the method of each embodiment of the present invention may be sequentially adjusted, combined, and deleted according to actual needs, and the technical features described in each embodiment may be replaced or combined.
The modules and sub-modules in the device and the terminal of the embodiments of the invention can be combined, divided and deleted according to actual needs.
In the embodiments provided in the present invention, it should be understood that the disclosed terminal, apparatus and method may be implemented in other ways. For example, the above-described terminal embodiments are merely illustrative, and for example, the division of a module or a sub-module is only one logical division, and there may be other divisions when the terminal is actually implemented, for example, a plurality of sub-modules or modules may be combined or integrated into another module, or some features may be omitted or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some interfaces, indirect coupling or communication connection between devices or modules, and may be in an electrical, mechanical or other form.
The modules or sub-modules described as separate components may or may not be physically separate, and the components described as modules or sub-modules may or may not be physical modules or sub-modules, may be located in one place, or may be distributed on a plurality of network modules or sub-modules. Some or all of the modules or sub-modules can be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, each functional module or sub-module in each embodiment of the present invention may be integrated into one processing module, or each module or sub-module may exist alone physically, or two or more modules or sub-modules may be integrated into one module. The integrated modules or sub-modules may be implemented in the form of hardware, or may be implemented in the form of software functional modules or sub-modules.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software unit executed by a processor, or in a combination of the two. The software cells may be located in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for financial data management, comprising:
preprocessing the financial data acquired from the data interface;
storing the preprocessed financial data into a database of a corresponding type based on the updating frequency and type of the preprocessed financial data;
calling an index calculation engine matched with the type of the database to process the preprocessed financial data in the database to obtain index data;
based on a pre-estimation algorithm model corresponding to the purpose of the index data, penetration pre-estimation is carried out on the index data;
and displaying the estimated result in a report interface preset by a user.
2. The method of claim 1, wherein before invoking an index calculation engine matching the database type to process the preprocessed financial data in the database to obtain index data, further comprising:
and classifying the preprocessed financial data according to preset statistical dimensions.
3. The method of claim 1, further comprising:
and generating index deviation alarm information based on an alarm threshold and the estimation result, and adjusting the bin position of the index data based on a preset adjustment index.
4. The method of claim 1, wherein pre-processing the financial data obtained from the data interface comprises:
and giving a unique identity to each piece of financial data and converting the financial data into a preset data format.
5. The method of claim 1, wherein storing the pre-processed financial data into a database of a corresponding type based on the update frequency and type of the pre-processed financial data comprises:
storing the combined data updated every day into a document database;
storing the daily updated security data in a columnar database;
storing the updated security data of each season into a relational database;
and storing the hot spot data with the updating frequency larger than the preset value into a memory database.
6. The method of claim 5, wherein said invoking an index calculation engine matching said database type to process said preprocessed financial data in said database to obtain index data comprises:
searching and calculating the security data updated every season in the relational database based on a preset SQL template;
calculating the hotspot data in the memory database based on a stream type calculation engine;
performing calculations of the combined data and the daily updated securities data in the document database and the column database based on a batch calculation engine.
7. The method of claim 1, wherein said performing a penetration prediction on said target data based on a prediction algorithm model corresponding to a use of said target data comprises:
when the index data is used for direct investment, penetration prediction is carried out on the index data based on an accounting valuation algorithm;
when the index data is used for internal investment, penetration estimation is carried out on the index data based on the asset bin corresponding to the index data;
when the index data is used for external investment, adjusting asset configuration corresponding to the index data based on the wide base index, and adjusting an active bin adjusting coefficient by combining historical bin adjusting data;
readjusting the asset configuration based on the active bin adjustment coefficient, and performing penetration estimation of the index data based on the readjusted asset configuration.
8. A financial data management apparatus, comprising:
the preprocessing module is used for preprocessing the financial data acquired from the data interface;
the storage module is used for storing the preprocessed financial data into a database of a corresponding type based on the updating frequency and type of the preprocessed financial data;
the index configuration module is used for calling an index calculation engine matched with the type of the database to process the preprocessed financial data in the database to obtain index data;
the pre-estimation module is used for performing penetration pre-estimation on the index data based on a pre-estimation algorithm model corresponding to the purpose of the index data; and
and the result display module is used for displaying the estimated result in a report interface preset by the user.
9. An apparatus, comprising: a memory and a processor;
the memory is used for storing programs;
the processor, which executes the program, implements the respective steps of the financial data management method according to any one of claims 1 to 7.
10. A readable storage medium on which a computer program is stored, the computer program, when executed by a processor, implementing the steps of the financial data management method according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112613717A (en) * | 2020-12-17 | 2021-04-06 | 安徽兆尹信息科技股份有限公司 | Financial data processing method and storage medium |
| CN116629805A (en) * | 2023-06-07 | 2023-08-22 | 浪潮智慧科技有限公司 | Water conservancy index service method, equipment and medium for distributed flow batch integration |
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2022
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112613717A (en) * | 2020-12-17 | 2021-04-06 | 安徽兆尹信息科技股份有限公司 | Financial data processing method and storage medium |
| CN116629805A (en) * | 2023-06-07 | 2023-08-22 | 浪潮智慧科技有限公司 | Water conservancy index service method, equipment and medium for distributed flow batch integration |
| CN116629805B (en) * | 2023-06-07 | 2023-12-01 | 浪潮智慧科技有限公司 | Water conservancy index service method, equipment and medium for distributed flow batch integration |
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