Derivative time series data manufacturing method based on dolphindb database

Abstract

The invention discloses a derivative time series data manufacturing method based on a dolphindb database. The method comprises the following steps of importing original time series data of various data sources into the dolphindb database to be stored; saving metadata of the original time series data in a relational database; querying a group of original time series data, writing a calculation formula of the derived time series data on the cross section data and the time series data by using an excel function, and making metadata of the derived time series data; and when a query request of the derivative time series data is received, calculating a result of returning the derivative time series data in real time according to the unique identifier, the starting time and the ending time of the derivative time series data. Unified data query service sharing is used, the production efficiency is improved, metadata for compiling derivative data by using an excel function is reserved, skills of researchers are not changed, learning curves are reduced, and using habits of smooth transition are achieved.

Description

technical field [0001] The invention relates to the field of computer technology in the financial industry, in particular to a method for making derived time series data based on a dolphindb database. Background technique [0002] There are massive amounts of data in the financial industry, most of which are time-series data. Organizations in the industry study these data to discover the relationship between the data and explore investment opportunities. Many data-related businesses need to calculate the original time-series data to obtain derived time-series data, and then conduct research on the derived time-series data. Most of the users of these derived data are researchers from financial institutions. At present, the methods used by most researchers to produce derived data are roughly as follows: [0003] 1. Collect original time-series data each time according to the needs of the research, usually from data service providers in the financial industry. [0004] 2. Use...

AI Technical Summary

Problems solved by technology

[0006] 1. The financial industry has a large amount of data, and it is difficult for excel to store and calculate massive data

[0007] 2. Derivative data is stored in excel files. Fragmented storage leads to low usage efficiency. It is not precipitated as public data assets and shared to improve production efficiency.

Without informatization, data services cannot be provided for business applications of research

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