Data and code for: Contagion risk prediction with Chart Graph Convolutional Network: Evidence from Chinese stock market

This dataset accompanies the study “Contagion risk prediction with Chart Graph Convolutional Network: Evidence from Chinese stock market”, which proposes a framework for contagion risk prediction by comprehensively mining the features of technical charts and technical indicators.
The utilized data include the closing prices of 28 sectors in Shen wan primary industry index, the closing price of CSI-300 Index, and eight classes of trading indicators that include Turnover Rate, Price-to-Earnings Ratio, Trading Volume, Relative Strength Index, Moving Average Convergence Divergence, Moving Average, Bollinger Bands, and Stochastic Oscillator. The sample period is from 5 Jan 2007 to 30 Dec 2022. The closing prices of 28 sectors are downloaded from the Choice database. The closing price of the CSI-300 Index and eight classes of trading indicators are downloaded from the Wind database.
This dataset includes two raw data files, one predefined temporary file, and eighteen code files, which are described as follows:
Sector_data.csv stores the closing prices of 28 sectors.
CSI_300_data.csv includes closing price of CSI-300 Index, and eight classes of trading indicators.
DCC_temp.csv is a predefined temporary file used to store correlation results.
Descriptive_code.py is utilized to calculate the statistical results.
ADF Test.py is utilized to test the stationarity of the data.
Min-max normalization.py is utilized to standardize data.
ADCC-GJR-GARCH.R is utilized to calculate dynamic conditional correlations between sectors.
MST_figure.py is used to a construct complex network that illustrates the inter-sector relationships.
Correlation.py is used to calculate inter-industry correlations.
Corr_up.py, corr_mid.py and corr_down.py are used to calculate dynamic correlations in upstream, midstream, and downstream sectors.
Centrality.py is used to quantify the importance or influence of nodes within a network, particularly across distinct upstream, midstream, and downstream sectors.
Averaging_corr_over_a_5-day_period.py calculates 5-day rolling averages of correlation and centrality metrics to quantify contagion risk on a weekly cycle.
Convert technical charts using PIP and VG methods.py extracts significant nodes and converts them into graphical representations, and save them in Daily Importance Score.csv, Daily Threshold Matrix.csv, and Daily Technical Indicators.csv.
Convert_CSV_to_TXT.py converts Daily Importance Score.csv, Daily Threshold Matrix.csv, and Daily Technical Indicators.csv into TXT files for later use.
Four files included in the folder of Generating and normalizing the subgraphs to generate subgraphs and then normalize them. The receptive_field.py serves as the main program, which calls the other three files. The stock_graph_indicator.py calculates topological structure data for subsequent use.
Predictive_model.py takes normalized subgraphs and Y-values defined by contagion risk as inputs and performs parameter tuning to achieve optimal results.

本数据集配套于研究论文《基于图表图卷积网络的传染风险预测：来自中国股票市场的证据》（Contagion risk prediction with Chart Graph Convolutional Network: Evidence from Chinese stock market），该研究提出了一套综合挖掘技术图表与技术指标特征的传染风险预测框架。
所用数据涵盖申万一级行业指数的28个板块收盘价、沪深300指数收盘价，以及八类交易指标：换手率、市盈率、成交量、相对强弱指标（Relative Strength Index）、移动平均收敛散度指标（Moving Average Convergence Divergence）、移动平均线、布林带（Bollinger Bands）以及随机震荡指标（Stochastic Oscillator）。样本时段为2007年1月5日至2022年12月30日。其中28个板块的收盘价来自Choice数据库，沪深300指数收盘价与八类交易指标则来自Wind数据库。
本数据集包含两份原始数据文件、一份预定义临时文件以及十八份代码文件，具体说明如下：
Sector_data.csv：存储28个板块的收盘价数据。
CSI_300_data.csv：包含沪深300指数收盘价与八类交易指标数据。
DCC_temp.csv：为预定义临时文件，用于存储相关性计算结果。
Descriptive_code.py：用于计算统计指标结果。
ADF Test.py：用于检验数据的平稳性。
Min-max normalization.py：用于对数据进行标准化处理。
ADCC-GJR-GARCH.R：用于计算板块间的动态条件相关性。
MST_figure.py：用于构建刻画板块间关联关系的复杂网络。
Correlation.py：用于计算行业间相关性。
Corr_up.py、corr_mid.py 与 corr_down.py：分别用于计算上游、中游与下游板块的动态相关性。
Centrality.py：用于量化网络中节点的重要性或影响力，尤其适用于上游、中游与下游不同板块场景。
Averaging_corr_over_a_5-day_period.py：计算相关性与中心性指标的5日滚动平均值，以周度周期量化传染风险。
Convert technical charts using PIP and VG methods.py：提取关键节点并将其转换为图形表示，结果存储于Daily Importance Score.csv、Daily Threshold Matrix.csv 与 Daily Technical Indicators.csv 文件中。
Convert_CSV_to_TXT.py：将Daily Importance Score.csv、Daily Threshold Matrix.csv 与 Daily Technical Indicators.csv 转换为TXT格式文件，以供后续使用。
Generating and normalizing the subgraphs 文件夹包含四份文件，用于生成子图并完成标准化处理。其中receptive_field.py为主程序，可调用其余三份文件；stock_graph_indicator.py用于计算拓扑结构数据以供后续使用。
Predictive_model.py：以标准化后的子图与定义为传染风险的Y值作为输入，执行参数调优以获取最优结果。