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)
