Financial data management method, device, equipment and readable storage medium

Abstract

The present invention provides a financial data management method, apparatus, device, and readable storage medium, which can first pre-process the financial data obtained from the data interface. Then, based on the update frequency and type of the pre-processed financial data, the pre-processed financial data is stored in a database of the corresponding type. An indicator calculation engine that matches the database type is called to process the pre-processed financial data in the database to obtain indicator data. Then, based on a prediction algorithm model corresponding to the purpose of the indicator data, a penetration prediction is performed on the indicator data. Finally, the prediction results can be displayed in a report interface pre-set by the user. The financial data management method can directly connect to the trading system data and store it in multiple scenarios, making the data reading and use faster. At the same time, it realizes the configuration of indicators according to the penetration algorithm of different assets and the construction of a multi-level indicator system, which can effectively improve the management efficiency of financial asset data.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a financial data management method, apparatus, device and readable storage medium. Background Art

[0002] With the booming financial asset management industry, asset management institutions are required to continuously launch products that meet market needs. In the current market environment, it is common for a single asset manager to manage dozens or even hundreds of portfolios. This requires complex data from multiple sources, which consumes significant time and effort to collect and process, leading to significant inefficiencies. Improving asset managers' ability to manage assets has become a pressing technical challenge. Summary of the Invention

[0003] In order to solve the problems of low management efficiency and large data processing volume in the existing technology, the present invention provides a financial data management method, device, equipment and readable storage medium, which have the characteristics of timely data processing and higher management efficiency.

[0004] A financial data management method provided according to a specific embodiment of the present invention includes:

[0005] Preprocess the financial data obtained from the data interface;

[0006] Based on the update frequency and type of the pre-processed financial data, storing the pre-processed financial data in a database of a corresponding type;

[0007] Invoking an indicator calculation engine that matches the database type to process the preprocessed financial data in the database to obtain indicator data;

[0008] Performing a penetration estimate on the indicator data based on an estimation algorithm model corresponding to the purpose of the indicator data;

[0009] The estimated results are displayed in the report interface pre-set by the user.

[0010] Furthermore, before invoking an indicator calculation engine that matches the database type to process the pre-processed financial data in the database to obtain indicator data, the method further includes:

[0011] The preprocessed financial data is classified according to preset statistical dimensions.

[0012] Furthermore, the financial data management method further includes:

[0013] Indicator deviation alarm information is generated based on the alarm threshold and the estimated result, and the position of the indicator data is adjusted based on a preset adjustment indicator.

[0014] Furthermore, the pre-processing of the financial data obtained from the data interface includes:

[0015] Each financial data is assigned a unique identity and converted into a preset data format.

[0016] Furthermore, storing the pre-processed financial data in a database of a corresponding type based on the update frequency and type of the pre-processed financial data includes:

[0017] Store daily updated composite data in a document database;

[0018] Store daily updated securities data in a column-based database;

[0019] Store the quarterly updated securities data in a relational database;

[0020] Store hotspot data with an update frequency greater than a preset value into the memory database.

[0021] Furthermore, the calling of an indicator calculation engine that matches the database type to process the pre-processed financial data in the database to obtain indicator data includes:

[0022] Searching and calculating the quarterly updated securities data in the relational database based on a preset SQL template;

[0023] Calculating the hot data in the memory database based on a stream computing engine;

[0024] The combined data and the daily updated securities data are calculated in the document database and the column database based on a batch calculation engine.

[0025] Furthermore, the performing of a penetration estimation on the indicator data based on the estimation algorithm model corresponding to the purpose of the indicator data includes:

[0026] When the indicator data is used for direct investment, a penetration estimate of the indicator data is performed based on an accounting valuation algorithm;

[0027] When the indicator data is used for internal investment, a penetration estimate of the indicator data is performed based on the asset position corresponding to the indicator data;

[0028] When the indicator data is used for external investment, the asset allocation corresponding to the indicator data is adjusted based on the broad-based index, and the active rebalancing coefficient is adjusted in combination with historical rebalancing data;

[0029] The asset allocation is readjusted based on the active portfolio adjustment coefficient, and a penetration estimate of the indicator data is performed based on the readjusted asset allocation.

[0030] According to a specific embodiment of the present invention, a financial data management device is provided, comprising:

[0031] A preprocessing module, used to preprocess the financial data obtained from the data interface;

[0032] A storage module, configured to store the pre-processed financial data in a database of a corresponding type based on the update frequency and type of the pre-processed financial data;

[0033] An indicator configuration module, configured to call an indicator calculation engine that matches the database type to process the preprocessed financial data in the database to obtain indicator data;

[0034] an estimation module, configured to perform a penetration estimation on the indicator data based on an estimation algorithm model corresponding to the purpose of the indicator data; and

[0035] The result display module is used to display the estimated results in the report interface preset by the user.

[0036] A device provided according to a specific embodiment of the present invention includes: a memory and a processor;

[0037] The memory is used to store programs;

[0038] The processor is used to execute the program to implement the various steps of the financial data management method described above.

[0039] According to a specific embodiment of the present invention, a readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, each step of the financial data management method described above is implemented.

[0040] The present invention provides a financial data management method, apparatus, device and readable storage medium, which can first pre-process the financial data obtained from the data interface. Then, based on the update frequency and type of the pre-processed financial data, the pre-processed financial data is stored in a database of the corresponding type. The indicator calculation engine that matches the database type is called to process the pre-processed financial data in the database to obtain indicator data. Then, based on the estimation algorithm model corresponding to the purpose of the indicator data, the indicator data is subjected to a penetration estimation. Finally, the estimation results can be displayed in a report interface pre-set by the user. The financial data management method can directly connect to the trading system data and store it in multiple scenarios, making the data reading and use faster. At the same time, it realizes the configuration of indicator model elements according to the penetration algorithm of different assets and the construction of a multi-level indicator system, which can effectively improve the management efficiency of financial asset data. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0042] Figure 1 is a flowchart of a financial data management method provided according to an exemplary embodiment;

[0043] Figure 2 is a structural diagram of a financial data management device provided according to an exemplary embodiment;

[0044] Figure 3 is a structural diagram of a device provided according to an exemplary embodiment. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] Reference Figure 1 As shown, an embodiment of the present invention provides a financial data management method, which may include the following steps:

[0047] 101. Preprocess the financial data obtained from the data interface.

[0048] By connecting to the internal and external interfaces of financial systems, financial data such as securities fundamentals, market data, real-time trading data from trading systems, accounting valuation data, risk control and compliance data, diverse industry classifications, and investment style data can be obtained. Data warehousing tools such as DATAX can then be used for data integration. While ensuring the timeliness of the acquired synchronized data, each data point is uniquely identified and the data format is standardized. This facilitates the integration of diverse data within the minimum data set during monetary data processing, and facilitates the split storage based on the specific data situation.

[0049] 102. Based on the update frequency and type of the pre-processed financial data, store the pre-processed financial data in a database of a corresponding type.

[0050] For example, daily composite data updated daily can be stored in a document database such as Mongo. Daily securities data updated daily can be stored in a columnar database such as ClickHouse to facilitate the expansion of new indicators. Quarterly securities data updated quarterly can be stored in a relational database. High-frequency hotspot data that occurs very frequently can be stored in the device's in-memory database. This allows the extraction of daily data to be controlled within milliseconds when acquiring data using the unique identifier of each data item, and the extraction of multi-day data can be achieved within seconds. Hotspot data is pre-loaded into memory, which improves the speed of real-time data reading.

[0051] 103. Call an indicator calculation engine that matches the database type to process the preprocessed financial data in the database to obtain indicator data.

[0052] Specifically, when the required indicator data comes from a relational database, the corresponding indicator data can be obtained from the relational database by obtaining the user-configured SQL template and then parsing the SQL template through parsing tools such as Freemaker.

[0053] When the source of indicator data is an in-memory database, streaming engines such as Flink can be used to quickly deploy calculations based on real-time push of trading system instructions, market conditions, and other events, through pre-set real-time indicator calculation processes, and store the calculated data in the in-memory database, facilitating direct indicator extraction.

[0054] When the indicator source is a document database or column-based database, daily and static indicators are batch-calculated and stored in the corresponding database, facilitating quick access to indicator data. Furthermore, when indicator data is a remote indicator service, it can be connected to an existing indicator processing center via the internet. Indicators can be retrieved in real time or extracted in batches using transmission protocols such as HTTP for storage or feedback to the user end. When indicator data is user-defined via formulas, the formula engine parses the pre-set calculation formulas to perform real-time calculations for various indicators.

[0055] 104. Based on the estimation algorithm model corresponding to the purpose of the indicator data, the indicator data is estimated through penetration.

[0056] When the indicator data is used for direct investment in stocks, futures and other direct investments, the real-time market conditions can be used to approximate the market value of the corresponding assets according to the accounting valuation algorithm, thereby completing the penetrating estimation of the indicator data.

[0057] When indicator data is used for internal investments such as internal funds and pension funds, it can be used to perform a penetrating estimate based on the asset positions corresponding to the indicator data. This can be achieved by integrating real-time internal asset data with real-time trading data from the trading system, overlaying market fluctuations, and calculating the underlying asset positions in real time. Based on this estimated underlying data, the real-time position and valuation of the parent fund are estimated, further approximating the actual position of the parent fund. This position is then used to perform real-time penetrating calculations of various indicators.

[0058] When indicator data is used for external investments such as external funds, the asset allocation corresponding to the indicator data is adjusted based on the broad-based index, and the active rebalancing coefficient is adjusted based on historical rebalancing data. The asset allocation is then adjusted again based on the active rebalancing coefficient, and a penetrating estimate of the indicator data is performed based on this adjusted asset allocation. In specific implementation, this approach can first be based on market conditions, using the industry volatility of a broad-based index (such as the CSI 300) to estimate changes in passive asset allocation in real time and adjust the asset allocation for quarterly and annual reports. Then, based on the corresponding fund manager's historical quarterly and annual rebalancing data, an active rebalancing coefficient is fitted, and this coefficient is further adjusted for the quarterly and annual reports. Penetrating calculations of each indicator are performed based on the adjusted asset allocation.

[0059] 105. The estimated results are displayed in the report interface preset by the user.

[0060] Users can customize the required reports and indicators. After the indicator calculation is completed, a display interface will be generated according to the format set by the user. Users can view, save, and further process the indicators in this display interface.

[0061] This financial data management method realizes the real-time position estimation, can flexibly configure the combination position penetration calculation model in real time, realizes the construction of a multi-level indicator system, and can effectively improve the management efficiency of financial asset data.

[0062] In another specific embodiment of the present invention, before invoking an indicator calculation engine that matches the database type to process the pre-processed financial data in the database to obtain the indicator data, the process further includes:

[0063] Preprocessed financial data is classified according to preset statistical dimensions. Indicators can be categorized according to various dimensions, such as real-time indicators and T-1 indicators, depending on real-time requirements. Indicators can also be categorized into attribute, ratio, and formula types based on calculation requirements. When performing indicator penetration estimation, you can build a multi-dimensional and multi-level indicator system by configuring various indicators, selecting the indicator calculation model, indicator type (attribute, ratio, etc.), individual stock labels on which the indicator depends, and indicator display format.

[0064] To further optimize this technical solution, after obtaining the indicator penetration estimation results, indicator deviation alarm information can be generated based on the alarm threshold and the estimation results, and the position of the indicator data can be adjusted based on the preset adjustment indicator.

[0065] Specifically, by setting alarm thresholds and comparing them with various indicators, deviations from industry indicators can be detected. These deviations can then be communicated via email, SMS, WeChat, and other channels. In response to deviations, the system can adjust the indicators based on pre-set adjustments, generating position adjustment instructions for the trading system. Instructions can be issued to the trading system through the trading protocol to adjust indicators. This allows for tracking and adjusting indicator data, providing feedback on the effectiveness of position adjustments, and forming a virtuous cycle of forecasting, making penetrating forecasts more accurate and reliable.

[0066] Based on the same design ideas Figure 2 As shown, an embodiment of the present invention further provides a financial data management device, which can implement each step of the above-mentioned financial data management method when running. The device may include:

[0067] The pre-processing module 201 is used to pre-process the financial data obtained from the data interface.

[0068] The storage module 202 is configured to store the pre-processed financial data in a database of a corresponding type based on the update frequency and type of the pre-processed financial data.

[0069] The indicator configuration module 203 is used to call an indicator calculation engine that matches the database type to process the pre-processed financial data in the database to obtain indicator data.

[0070] The estimation module 204 is used to perform a penetration estimation on the indicator data based on an estimation algorithm model corresponding to the purpose of the indicator data.

[0071] The result display module 205 is used to display the estimation results in a report interface preset by the user.

[0072] Furthermore, the financial data management device further includes:

[0073] The classification module is used to classify the preprocessed financial data according to preset statistical dimensions.

[0074] Furthermore, the financial data management device further includes:

[0075] The adjustment module is used to generate indicator deviation alarm information based on the alarm threshold and the estimated result, and adjust the position of the indicator data based on the preset adjustment indicator.

[0076] The pre-processing module 201 is specifically used to assign a unique identity to each financial data and convert it into a preset data format.

[0077] The storage module 202 is specifically used for:

[0078] Store daily updated composite data in a document database.

[0079] Store daily updated securities data in a column-based database.

[0080] Store securities data updated quarterly in a relational database.

[0081] Store hotspot data with an update frequency greater than a preset value into the memory database.

[0082] The indicator configuration module 203 is specifically used to search and calculate the quarterly updated securities data in the relational database based on a preset SQL template.

[0083] Calculate hot data in the memory database based on the streaming computing engine.

[0084] Calculates combined data and daily updated securities data in document and columnar databases based on a batch computing engine.

[0085] The estimation module 204 is specifically used to perform a penetration estimation on the indicator data based on an accounting valuation algorithm when the indicator data is used for direct investment.

[0086] When indicator data is used for internal investment, a penetration estimate of the indicator data is made based on the asset positions corresponding to the indicator data.

[0087] When the indicator data is used for external investment, the asset allocation corresponding to the indicator data is adjusted based on the broad-based index, and the active rebalancing coefficient is adjusted based on historical rebalancing data. The asset allocation is further adjusted based on the active rebalancing coefficient, and the indicator data is then used for penetration estimation based on the adjusted asset allocation.

[0088] The financial data management device has the same beneficial effects as the above-mentioned financial data management method. Its specific implementation method can refer to the embodiment of the above-mentioned financial data management method, and the present invention will not repeat it here.

[0089] Reference Figure 3 As shown, an embodiment of the present invention further provides a device, which may include: a memory 301 and a processor 302.

[0090] The memory 301 is used to store programs.

[0091] The processor 302 is configured to execute the program to implement the various steps of the financial data management method described in the above embodiment.

[0092] An embodiment of the present invention further provides a readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the financial data management method described in the above embodiment are implemented.

[0093] For simplicity of description, the aforementioned method embodiments are described as a series of actions. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, as certain steps can be performed in other orders or simultaneously according to the present invention. Furthermore, those skilled in the art should also be aware that the embodiments described in this specification are preferred embodiments, and the actions and modules involved are not necessarily required for the present invention.

[0094] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similarities between the various embodiments can be referred to in conjunction with each other. For device embodiments, since they are generally similar to method embodiments, their description is relatively simple, and for relevant details, reference can be made to the description of the method embodiments.

[0095] The steps in the methods of the various embodiments of the present invention can be adjusted in sequence, combined, and deleted according to actual needs, and the technical features recorded in the various embodiments can be replaced or combined.

[0096] The modules and submodules in the devices and terminals of various embodiments of the present invention may be combined, divided, or deleted according to actual needs.

[0097] In the several embodiments provided herein, it should be understood that the disclosed terminals, devices, and methods can be implemented in other ways. For example, the terminal embodiments described above are merely illustrative. For example, the division of modules or submodules is merely a logical functional division. In actual implementation, other division methods may be used, such as combining or integrating multiple submodules or modules into another module, or omitting or not implementing certain features. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or modules via some interface, which may be electrical, mechanical, or other forms.

[0098] The modules or submodules described as separate components may or may not be physically separate, and the components of the modules or submodules may or may not be physical modules or submodules, that is, they may be located in one place or distributed across multiple network modules or submodules. Some or all of the modules or submodules may be selected to achieve the purpose of this embodiment according to actual needs.

[0099] In addition, the functional modules or submodules in the various embodiments of the present invention may be integrated into a single processing module, or each module or submodule may exist physically separately, or two or more modules or submodules may be integrated into a single module. The aforementioned integrated modules or submodules may be implemented in the form of hardware or software functional modules or submodules.

[0100] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0101] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, software units executed by a processor, or a combination of the two. The software units may be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0102] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A financial data management method for improving data reading and processing efficiency, characterized in that: include: Pre-process and integrate the financial data obtained from the data interface, while ensuring the timeliness of the obtained synchronized data, giving each data a unique identity and unifying the data format; Based on the update frequency and type of the pre-processed financial data, storing the pre-processed financial data in a database of a corresponding type includes: Store daily updated composite data in a document database; Store daily updated securities data in a column-based database; Store the quarterly updated securities data in a relational database; Store hotspot data with an update frequency greater than a preset value into the memory database; When acquiring data through the unique identity of each data point, the extraction process of the data of the current day is controlled to the millisecond level, and the extraction of data of multiple days is controlled to the second level, thus improving the real-time data reading speed; Invoking an indicator calculation engine that matches the database type to process the preprocessed financial data in the database to obtain indicator data, including: Searching and calculating the quarterly updated securities data in the relational database based on a preset SQL template; Calculating the hot data in the memory database based on a stream computing engine; Calculating the combined data and the daily updated securities data in the document database and the columnar database based on a batch computing engine; When the indicator data is defined by a formula, the formula engine parses the set calculation formula and performs real-time calculations on various indicators. Performing penetration estimation on the indicator data based on an estimation algorithm model corresponding to the purpose of the indicator data; after obtaining the indicator penetration estimation result, generating indicator deviation alarm information based on the alarm threshold and the estimation result, and adjusting the position of the indicator data based on a preset adjustment indicator; The estimated results are displayed in the report interface pre-set by the user.

2. The method according to claim 1, characterized in that Before calling an indicator calculation engine that matches the database type to process the pre-processed financial data in the database to obtain indicator data, the following steps are further included: The preprocessed financial data is classified according to preset statistical dimensions.

3. The method according to claim 1, characterized in that The pre-processing of the financial data obtained from the data interface includes: Each financial data is assigned a unique identity and converted into a preset data format.

4. The method according to claim 1, wherein The performing of a penetration estimation on the indicator data based on the estimation algorithm model corresponding to the purpose of the indicator data includes: When the indicator data is used for direct investment, a penetration estimate of the indicator data is performed based on an accounting valuation algorithm; When the indicator data is used for internal investment, a penetration estimate of the indicator data is performed based on the asset position corresponding to the indicator data; When the indicator data is used for external investment, the asset allocation corresponding to the indicator data is adjusted based on the broad-based index, and the active rebalancing coefficient is adjusted in combination with historical rebalancing data; The asset allocation is readjusted based on the active portfolio adjustment coefficient, and a penetration estimate of the indicator data is performed based on the readjusted asset allocation.

5. A financial data management device, characterized in that: include: The preprocessing module is used to preprocess the financial data obtained from the data interface, integrate the data, and assign a unique identity to each data and unify the data format while ensuring the timeliness of the obtained synchronized data; The storage module is configured to store the pre-processed financial data in a database of a corresponding type based on the update frequency and type of the pre-processed financial data, including: Store daily updated composite data in a document database; Store daily updated securities data in a column-based database; Store the quarterly updated securities data in a relational database; Store hotspot data with an update frequency greater than a preset value into the memory database; When acquiring data through the unique identity of each data point, the extraction process of the data of the current day is controlled to the millisecond level, and the extraction of data of multiple days is controlled to the second level, thus improving the real-time data reading speed; An indicator configuration module is configured to call an indicator calculation engine that matches the database type to process the preprocessed financial data in the database to obtain indicator data, including: Searching and calculating the quarterly updated securities data in the relational database based on a preset SQL template; Calculating the hot data in the memory database based on a stream computing engine; Calculating the combined data and the daily updated securities data in the document database and the columnar database based on a batch computing engine; When the indicator data is defined by a formula, the formula engine parses the set calculation formula and performs real-time calculations on various indicators. An estimation module is configured to perform a penetration estimation on the indicator data based on an estimation algorithm model corresponding to the purpose of the indicator data; after obtaining the indicator penetration estimation result, generate an indicator deviation alarm message based on the alarm threshold and the estimation result, and adjust the position of the indicator data based on a preset adjustment indicator; The result display module is used to display the estimated results in the report interface preset by the user.

6. An electronic device, characterized in that: include: memory and processor; The memory is used to store programs; The processor is configured to execute the program to implement each step of the financial data management method according to any one of claims 1 to 4.

7. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the financial data management method according to any one of claims 1 to 4 is implemented.

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