Quantitative transaction method based on virtualization and backtesting library

Abstract

The invention provides a quantitative transaction method based on virtualization and a backtesting library. For a quantitative transaction policy, a basic isolation mirror image is constructed and is used as a base mirror image after a required backtesting library is installed, and backtesting data are updated regularly. After a quantization policy is submitted each time, an isolation backtesting environment is started and created from the mirror image. After the environment is ready, the data and the policy are mounted, and backtesting operates. After backtesting is completed, a result is returned. According to the invention, the method is used as a new quantification platform policy operation method; a virtualization technology and the backtesting library are introduced to ensure that each operation environment can be isolated independently during policy operation, wherein operation resources and data and the like are isolated; the backtesting library is introduced to ensure the correctness and the reference of backtesting; the problems of resource conflicts and occupation, operation environment conflicts and the like of policy operation in traditional quantitative transaction are solved; and resource independence and environment isolation of policy operation are ensured.

Description

technical field [0001] This patent is mainly used in the field of quantitative transactions, especially in the field of quantitative transaction programming, for isolated execution in a programming environment. Background technique [0002] With the advent of the era of Internet big data, computer and data analysis have had an important impact on all aspects of life, including the impact on traditional stock markets, such as the generation of quantitative transactions. [0003] Quantitative trading (Quant) refers to the use of advanced mathematical models to replace human subjective judgments, and use computer technology to select a variety of "high probability" events that can bring excess returns from huge historical data to formulate strategies. It greatly reduces the impact of investor sentiment fluctuations and avoids making irrational investment decisions when the market is extremely fanatical or pessimistic. [0004] Generally speaking, quantitative trading relies on...

AI Technical Summary

Problems solved by technology

[0009] The original strategy running environment: when the traditional strategy is running, the strategy submitted by the user runs directly on the server. In this way, after each user submits, a new running process is opened on the server instead of an isolated environment. Different user's policy running processes is visible, which leads to environment leakage and uneven resource usage among different users

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