Financial asset high-frequency quantitative valuation method based on Tdensine

Abstract

The invention discloses a financial asset high-frequency quantitative valuation method based on Tdensine. The method comprises the following steps: deploying a database; guiding real-time transaction data to be stored in a database; the latest stock, bond and ETF data are read regularly; periodically reading the trained fund and financial management model parameters to be evaluated; different types of model services are called in combination with real-time data, and calculation results are stored in a database; and the front-end page is used for visualizing the asset valuation data through the back-end calling Tdenine. According to the invention, based on the TDengine, through the strong time sequence data processing capability of the TDengine, financial assets (fund and financial management) are stored, analyzed and valuated by combining real-time data of high-frequency stocks, bonds, funds and indexes of the exchange and quantitative financial model service, so that an accurate and efficient quantitative valuation solution is provided for a financial institution; and the system is assisted to capture opportunities and avoid risks in a complex and changeable financial market.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-frequency quantitative valuation of financial assets, and specifically is a high-frequency quantitative valuation method for financial assets based on Tdengine. Background Art

[0002] With the rapid development of the financial market and the explosive growth of data volume, financial institutions face severe challenges in processing high-frequency, large-scale financial asset data. Traditional database systems are unable to efficiently cope with the real-time valuation needs of financial assets, especially in high-frequency trading scenarios, where the real-time nature, accuracy and system stability of data become constraints. At the same time, financial market data has the characteristics of large data volume, standardized format, long storage period and complex sub-table management, which puts higher requirements on the data processing system.

[0003] In the field of high-frequency quantitative valuation of financial assets, existing technical solutions mostly rely on relational databases or general big data processing platforms, which have obvious deficiencies in processing high-frequency, high-concurrency financial data. Although relational databases have clear structures, they have low query efficiency when faced with massive time series data and are difficult to meet real-time valuation needs. Although general big data platforms have powerful data processing capabilities, they are often complex to configure, costly, and have limited support for optimizing time series data. Summary of the invention

[0004] In view of the above problems, the present invention is proposed to provide a high-frequency quantitative valuation method of financial assets based on Tdengine that overcomes the above problems or at least partially solves the above problems.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A high-frequency quantitative valuation method for financial assets based on Tdengine, the method comprising the following steps:

[0007] S1. Deploy database;

[0008] S2, guide the real-time transaction data into the database;

[0009] S3, regularly read the latest stock, bond and ETF data;

[0010] S4. Regularly read the trained parameters of the funds and financial models to be valued;

[0011] S5. Call different types of model services based on real-time data and store the calculation results in the database;

[0012] S6. The front-end page visualizes the asset valuation data by calling Tdengine on the back-end.

[0013] Optionally, in step S1, Tdengine is used as the data base to support the deployment of the database.

[0014] Optionally, in step S2, the real-time transaction data includes bonds, stocks and ETF data.

[0015] Optionally, step S5 includes invoking different types of models in combination with real-time data to calculate financial asset valuation data and enter the data into Tdengine.

[0016] Optionally, step S6 includes the front-end page calling data in the tdengine database through the back-end to display the transaction time-sharing valuation curve data.

[0017] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0018] 1. This invention is based on TDengine. Through TDengine's powerful time series data processing capabilities, combined with the exchange's high-frequency real-time data of stocks, bonds, funds, and indexes and quantitative financial model services for the storage, analysis, and valuation of financial assets (funds, wealth management), it provides financial institutions with accurate and efficient quantitative valuation solutions, helping them capture opportunities and avoid risks in complex and changing financial markets.

[0019] 2. The present invention is based on Tdengine as the database base, replacing the current technical solution. Due to its natural good support for time series data, long-term data compression, and regular deletion without writing related scheduling tasks, it can complete similar relational data tasks with lower CPU and disk resource consumption. When combined with model services in the form of HTTP calls, it can achieve the construction of a high-frequency valuation system for financial assets with very little manpower and machine costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flowchart of a high-frequency quantitative valuation method for financial assets based on Tdengine is provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] See also Figure 1This embodiment provides a high-frequency quantitative valuation method for financial assets based on Tdengine, and the method includes the following steps:

[0023] S1. Deploy database.

[0024] In step S1, Tdengine is used as the data base to support the deployment of the database.

[0025] S2. Guide real-time transaction data into the database.

[0026] In step S2, the real-time transaction data includes bond, stock and ETF data. The storage data table is as follows:

[0027]

[0028] S3. Regularly read the latest stock, bond, and ETF data.

[0029] S4. Regularly read the trained parameters of the funds and financial models to be valued.

[0030]

[0031]

[0032] S5. Call different types of model services based on real-time data and store the calculation results in the database.

[0033] Step S5 includes invoking different types of models in combination with real-time data to calculate financial asset valuation data and put it into Tdengine.

[0034] S6. The front-end page visualizes the asset valuation data by calling Tdengine on the back-end.

[0035] Step S6 includes the front-end page calling the data in the tdengine database through the back-end to display the transaction time-sharing valuation curve data.

[0036] This embodiment is based on TDengine. Through its powerful time series data processing capabilities, combined with the exchange's high-frequency real-time data on stocks, bonds, funds, and indexes and quantitative financial model services for the storage, analysis, and valuation of financial assets (funds, wealth management), it provides financial institutions with accurate and efficient quantitative valuation solutions, helping them capture opportunities and avoid risks in complex and changing financial markets.

[0037] This embodiment is based on Tdengine as the database base, replacing the current technical solution. Due to its natural good support for time series data, long-term data compression, and regular deletion without writing related scheduling tasks, it can complete similar relational data tasks with lower CPU and disk resource consumption. When combined with model services in the form of HTTP calls, it can achieve the construction of a high-frequency valuation system for financial assets with very little manpower and machine costs.

[0038] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A high-frequency quantitative valuation method for financial assets based on Tdengine, characterized in that: The method comprises the following steps: S1. Deploy database; S2, guide the real-time transaction data into the database; S3, regularly read the latest stock, bond and ETF data; S4. Regularly read the trained parameters of the funds and financial models to be valued; S5. Call different types of model services based on real-time data and store the calculation results in the database; S6. The front-end page visualizes the asset valuation data by calling Tdengine on the back-end.

2. A high-frequency quantitative valuation method for financial assets based on Tdengine as claimed in claim 1, characterized in that: In step S1, Tdengine is used as the data base to support the deployment of the database.

3. A high-frequency quantitative valuation method for financial assets based on Tdengine as claimed in claim 1, characterized in that: In step S2, the real-time transaction data includes bonds, stocks and ETF data.

4. A high-frequency quantitative valuation method for financial assets based on Tdengine as claimed in claim 1, characterized in that: Step S5 includes invoking different types of models in combination with real-time data to calculate financial asset valuation data and put it into Tdengine.

5. A high-frequency quantitative valuation method for financial assets based on Tdengine as claimed in claim 1, characterized in that: Step S6 includes the front-end page calling the data in the tdengine database through the back-end to display the transaction time-sharing valuation curve data.

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