Raw data, results and Python code of the corresponding publication "Efficient Multi-Change Point Analysis to decode Economic Crisis Information from the S&P500 Mean Market Correlation" (accepted in: Entropy; Section: Complexity; Special Issue: Complexity in Finance). The change point analysis can be performed using the documented Python package antiCPy. Some further helpful Python scripts are provided here under a GNU General Public License v3.0. In Data_Generation you can find a list of S&P500 companies which are considered in the analysis, a jupyter notebook to create the correlation time series. Data_Preprocessing contains the S&P500 mean market correlation Financial_Time_Series_Centered_Interval__42days.csv, the Python code to thin it, the thinned data saved as .npy file, the time scale is saved as integer numbers in thinned_time_thinning40.npy, as datetime in TimeScale_FinancialData.npy. In Change_Point_Analysis you find the following files: cp_probs...npy contain the joint probabilities of the corresponding change point configurations (The joint probabilities are saved corresponding to the order in which Python's itertools.combinations() creates the configurations. This holds also for the cp_probs_5_cps.npy for which the whole combinations array is not saved for memory reasons), cp_pdfs...npy contain the marginal probability density functions of the ordinal change point positions averaged over all configurations, cp_configs...npy contain the configurations, segment_fit...npy contain the segment fit data, segment_fit_variance...npy contain corresponding variances, In the case of five change points only the plotted 1st, 13th and 26th most probable configuration in config_ranking_CP1_5.npy for memory reasons. the data and results of figure 3 for each crisis event can be found in the corresponding directory's folders: blue corresponds to the pre-crisis data segments, green corresponds to the data segments up to the green vertical dotted line, red corresponds to the longest data segments incorporating near and in-crisis data.