Security transaction risk early warning method and system based on instruction stream toxicity index

Abstract

The invention discloses a security transaction risk early warning method and system based on instruction stream toxicity indexes; the method comprises the following steps: obtaining transaction information in real time from a security transaction server; continuously storing the obtained real time security transaction information into a plurality of transaction information sets according to the transaction time, wherein each transaction information set contains the total transaction amount equals to a preset transaction amount threshold; obtaining equal transaction amount transaction probability of each transaction information set according to the ratio between the absolute value of buying-selling amount difference of each transaction information set and the total transaction amount contained by the corresponding transaction information set; using the present transaction time as the boundary, solving the average equal transaction amount transaction probability of n continuous transaction information sets most close to the present transaction time before the present transaction time, and using the average equal transaction amount transaction probability as the present transaction time stream toxicity index, wherein n refers to a positive integer; comparing the present transaction time stream toxicity index with a preset stream toxicity index threshold scope, and outputting a risk early warning signal if the present transaction time stream toxicity index is in the preset stream toxicity index threshold scope.

Description

technical field [0001] The invention belongs to the field of data risk early warning, and in particular relates to a security transaction risk early warning method based on an instruction flow toxicity index. Background technique [0002] With the rapid popularization of electronic trading in financial markets, especially the rapid development of new electronic trading markets that incorporate quantitative trading technology, quantitative trading and its supervision have become frontier and hot issues. The real-time and accuracy of the supervision of these markets have put forward higher requirements. On the morning of August 16, 2013, the Everbright Oolong Index incident occurred. my country’s Shanghai stock index suddenly jumped more than 100 points in less than a minute, an increase of more than 5%, and the turnover reached 8.7 billion yuan. Coincidentally, after the Everbright oolong finger incident, Goldman Sachs made a major mistake in the electronic trading of stock ...

AI Technical Summary

Problems solved by technology

[0005] In the existing methods of measuring instruction flow toxicity, the collection of samples is relatively simple, and in the calculation of VPIN, the parameters are estimated at fixed time intervals, which makes the calculation result of instruction flow toxicity index (VPIN) inaccurate

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