Matrix Quantitative Analysis Method and System in Intelligent Trading

Abstract

The invention discloses a matrix quantitative analysis method in intelligent transaction and a system thereof. The method comprises the following steps of: 1, establishing a database for matrix quantitative analysis, and determining a time period from the database, wherein the length of the period can be set through parameters of a matrix; 2, analyzing historical data, i.e., firstly counting average probability and average wave amplitude for highs and lows of a matrix with the same arrangement order in a next time period and then calculating market risk return rates of different periods respectively; 3, determining a transaction strategy according to the market risk return rate of the matrix in the current time period; 4, calling an intelligent transaction program according to the transaction strategy and starting automatic transaction; and 5, calling a risk evaluation system to evaluate a historical transaction record made by an intelligent transaction program to provide a risk grade for a risk of a transaction account, calculating a default point by using a discrimination model, and then calculating a default distance and default probability of the risk grade of the account through a KMV (Credit Monitor Model).

Description

technical field [0001] The invention relates to the field of data analysis of intelligent transactions, in particular to a matrix quantification data analysis method and system. Background technique [0002] Morphological analysis is a widely used method in the field of technical analysis, and is usually divided into two categories: reversal patterns and finishing patterns, such as head-and-shoulders, triple tops or bottoms, double tops or bottoms, and round tops and round bottoms. Invert mentality, while triangle, rhombus, flag, and rectangle belong to finishing patterns. However, no matter what kind of form, what kind of form has a problem, that is, no one can determine what form it will take before the form comes out. Therefore, the analysis based on the pattern often needs to wait until the pattern comes out before determining the trading strategy. So is there a way to know the probability and volatility of his rise or fall before the pattern comes out? The present in...

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Problems solved by technology

[0004] 1. For traditional technical analysis, when analyzing historical data, especially morphological analysis does not quantify the specific amount of data, and more is judged by human experience, so the accuracy is not high, which is a fuzzy judgment rather than quantification analyze

[0005] 2. For traditional technical analysis, there is no definite time range for the selection of data, which cannot give a clear time range for predicting the development of the future market. It can only be judged according to human experience, so the accuracy is not high

[0006] 3. For the calculation of the default distance, the KMV model cannot quantify the default point when it is applied to financial derivatives transactions

The data analysis part of each intelligent transaction is a black box, and others cannot know what is inside

This is very bad for improving the data analysis part of the black box

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