Time-Series-Directional-Change-Analysis
Unverified Other strategy on Multi by ThomasWangWeiHong. BotFinder score 18 out of 100.
Python 3 source code for the implementation of the directional change analysis of financial time series data
Source: github
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Time-Series-Directional-Change-Analysis
Time-Series-Directional-Change-Analysis This repository contains a simple Python implementation of the directional change analysis techniques which are proposed in [1], and inspired by another GitHub repository 'JurgenPalsma/dcgenerator' (https://github.com/JurgenPalsma/dcgenerator). Introduction Directional Change (DC) analysis is a paradigm proposed by the authors in [1] for the analysis of financial time series. In the traditional time series analysis paradigm, one would sample prices at fixed intervals, whereas the DC paradigm is essentially a data-driven approach where the data informs the algorithm when to sample prices. By looking at price changes from another perspective, it is believed that one can extract new information from data that complements what is oberserved under the traditional time series analysis paradigm. This new information can be utilized by machine learning techniques in order to infer regime information about the market, which in turn helps in the development of algorithmic trading strategies. Sample Results The following results are obtained using a trunc
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