import numpy as np import pandas as pd import matplotlib as mpl import scipy import importlib import matplotlib.pyplot as plt from scipy.stats import skew, kurtosis, chi2 # import our own files and reload import stream_functions importlib.reload(stream_functions) import stream_classes importlib.reload(stream_classes) # input parameters ric = 'SGREN.MC' # DBK.DE ^IXIC MXN=X ^STOXX ^S&P500 ^VIX file_extension = 'csv' # csv o Excel extension # load timeseries x, x_str, t = stream_functions.load_timeseries(ric) # compute risk metrics in class jarque_bera_test jb = stream_classes.jarque_bera_test(x, x_str) jb.compute() print(jb) # plots stream_functions.plot_timeseries_price(t, ric) stream_functions.plot_histogram(x, x_str, jb.plot_str())
def load_timeseries(self):
self.returns, self.str_name, self.dataframe = stream_functions.load_timeseries(self.ric)
self.size = len(self.returns)
f.tight_layout() # Cargamos datos reales sincronizandolos ######################################## # input parameters ric_1 = '^VIX' # MT.AS SAN.MC BBVA.MC REP.MC VWS.CO EQNR.OL MXNUSD=X ^VIX ric_2 = 'SAN.MC' # MT.AS SAN.MC BBVA.MC REP.MC VWS.CO EQNR.OL MXNUSD=X ^VIX ric_3 = 'MXNUSD=X' # MT.AS SAN.MC BBVA.MC REP.MC VWS.CO EQNR.OL MXNUSD=X ^VIX ric_4 = 'VWS.CO' # MT.AS SAN.MC BBVA.MC REP.MC VWS.CO EQNR.OL MXNUSD=X ^VIX benchmark = '^STOXX' # ^STOXX50E ^STOXX ^S&P500 ^NASDAQ ^FCHI ^GDAXI file_extension = 'csv' nb_decimals = 4 # loading data from csv or Excel file x1, str1, t1 = stream_functions.load_timeseries(ric_1) x3, str3, t3 = stream_functions.load_timeseries(ric_2) x4, str4, t4 = stream_functions.load_timeseries(ric_3) x5, str5, t5 = stream_functions.load_timeseries(ric_4) x2, str2, t2 = stream_functions.load_timeseries(benchmark) # synchronize timestamps timestamp1 = list(t1['date'].values) timestamp3 = list(t3['date'].values) timestamp4 = list(t4['date'].values) timestamp5 = list(t5['date'].values) timestamp2 = list(t2['date'].values) timestamps = list(set(timestamp1) & set(timestamp2) & set(timestamp3) & set(timestamp4) & set(timestamp5)) # synchronised time series for x1 or ric_1 t1_sync = t1[t1['date'].isin(timestamps)]
# import our own files and reload import stream_functions importlib.reload(stream_functions) import stream_classes importlib.reload(stream_classes) # input parameters ric = '^VIX' # MT.AS SAN.MC BBVA.MC REP.MC VWS.CO EQNR.OL MXNUSD=X ^VIX benchmark = '^STOXX' # ^STOXX50E ^STOXX ^S&P500 ^NASDAQ ^FCHI ^GDAXI file_extension = 'csv' nb_decimals = 4 # loading data from csv or Excel file x1, str1, t1 = stream_functions.load_timeseries(ric) x2, str2, t2 = stream_functions.load_timeseries(benchmark) # synchronize timestamps timestamp1 = list(t1['date'].values) timestamp2 = list(t2['date'].values) timestamps = list(set(timestamp1) & set(timestamp2)) # synchronised time series for x1 or ric t1_sync = t1[t1['date'].isin(timestamps)] t1_sync.sort_values(by='date', ascending=True) t1_sync = t1_sync.reset_index(drop=True) # synchronised time series for x2 or benchmark t2_sync = t2[t2['date'].isin(timestamps)] t2_sync.sort_values(by='date', ascending=True)
