plt.hist(np.array(d16nord)) plt.figure() plt.hist(np.array(d15nord)) np.mean(d16nord) np.mean(d15nord) np.std(d16nord) np.std(d15nord) np.median(d16nord) np.median(d15nord) import Fourier plt.figure() plt.plot(np.array(var_nord16), lw = 2) plt.plot(Fourier.fourierExtrapolation(var_nord16, 0), lw = 2, color = 'black') data = pd.read_excel("H:/Energy Management/04. WHOLESALE/02. REPORT PORTAFOGLIO/2016/06. MI/DB_Borse_Elettriche_PER MI.xlsx", sheetname = 'DB_Dati') data = data.set_index(data['Date']) data = data.ix[data.index.month <= 9] pnord = data['MGP NORD [€/MWh]'] pnord = pnord.ix[:pnord.shape[0]-1] cnord = data['MGP CNOR [€/MWh]'] cnord = cnord.ix[:pnord.shape[0]-1] csud = data['MGP CSUD [€/MWh]'] csud = csud.ix[:csud.shape[0]-1] sud = data['MGP SUD [€/MWh]'] sud = sud.ix[:sud.shape[0]-1] sici = data['MGP SICI [€/MWh]'] sici = sici.ix[:sici.shape[0]-1] sard = data['MGP SARD [€/MWh]']
scipy.stats.mstats.mquantiles(dv_p, prob=[0.025, 0.975]) scipy.stats.mstats.mquantiles(dv_f, prob=[0.025, 0.975]) ############### divp = np.diff(vol_p) divf = np.diff(vol_f) plt.figure() plt.plot(divp) plt.plot(np.array(vol_p)) plt.figure() plt.plot(divf, color='magenta') plt.plot(np.array(vol_f), color='black') ddvp = np.diff(dv_p) ddvf = np.diff(dv_f) plt.figure() plt.plot(ddvp) plt.plot(np.array(dv_p)) plt.figure() plt.plot(ddvf, color='magenta') plt.plot(np.array(dv_f), color='black') fdvp = Fourier.fourierExtrapolation( dv_p, 0, 25) ### best one to see the 'right' process for the volatility plt.figure() plt.plot(np.array(dv_p)) plt.plot(fdvp, color='black', lw=2)
###################################################################### dpun = np.diff(np.array(data[data.columns[12]].dropna().resample('D').mean())) import statsmodels.api plt.figure() plt.plot(statsmodels.api.tsa.periodogram(dpun)) per = statsmodels.api.tsa.periodogram(dpun) np.where(per > 50)[0] per[per > 50] import Fourier reconstructed = Fourier.fourierExtrapolation(dpun, 0, 16) plt.figure() plt.plot(dpun) plt.plot(reconstructed, color = 'red') np.mean(dpun - reconstructed) np.std(dpun - reconstructed) from pandas.tools import plotting plt.figure() plotting.lag_plot(pd.DataFrame(dpun)) plt.figure() plt.plot(statsmodels.api.tsa.acf(dpun))
scipy.stats.mstats.mquantiles(dv_f, prob = [0.025, 0.975]) ############### divp = np.diff(vol_p) divf = np.diff(vol_f) plt.figure() plt.plot(divp) plt.plot(np.array(vol_p)) plt.figure() plt.plot(divf, color = 'magenta') plt.plot(np.array(vol_f), color = 'black') ddvp = np.diff(dv_p) ddvf = np.diff(dv_f) plt.figure() plt.plot(ddvp) plt.plot(np.array(dv_p)) plt.figure() plt.plot(ddvf, color = 'magenta') plt.plot(np.array(dv_f), color = 'black') fdvp = Fourier.fourierExtrapolation(dv_p,0, 25) ### best one to see the 'right' process for the volatility plt.figure() plt.plot(np.array(dv_p)) plt.plot(fdvp, color = 'black', lw = 2)