def autocorrelate_data():
stocks = StockPrice.objects.all()
dfc = DataFrameCreator(stocks)
df = pd.DataFrame.from_dict(dfc)
# create the VAR model
model = VAR(endog=df)
model_fit = model.fit()
model.plot()
mdata = data[['r10', 'Tbill', 'IndProd', 'Unemp']]
mdata.index = pd.DatetimeIndex(quarterly)
mdata['r'] = mdata['r10'] - mdata['Tbill']
mdata['IndProd'] = np.log(mdata['IndProd']).diff()
mdata['Unemp'] = mdata['Unemp'].diff()
mdata = mdata.drop(['r10', 'Tbill'], axis=1).dropna()
# ADF Test
print(ADF(mdata['r']).summary())
print(ADF(mdata['IndProd']).summary())
print(ADF(mdata['Unemp']).summary())
# VAR fit (no constant term)
results = VAR(mdata).fit(ic='bic', verbose=True, trend='nc')
results.plot()
print(results.summary())
# Selected lag order
print('Selected Order:', results.k_ar)
# AIC & BIC of different lags
for p in range(8):
res = VAR(mdata).fit(p, trend='nc')
print(res.k_ar, '&', round(res.aic, 6), '&', round(res.bic, 6), '\\\\')
# Stability
print(results.is_stable(True))
# Residual normality
print(results.test_normality().summary())
model11.summary()
######## Step 12 #########
from scipy import signal as sg
f, Pxx_den = sg.periodogram(bitcoin['bprice'],
10e3) # seasonality should be seen
plt.xlabel('frequency [Hz]')
plt.ylabel('PSD [V**2/Hz]')
plt.semilogy(f, Pxx_den)
# Differencing Vairable
f, Pxx_den = sg.periodogram(
bitcoin['dbprice'], 10e3) # should look like skyscrapers so no seasonality
# Still there is no seasonality confirm with professor
plt.semilogy(f, Pxx_den)
######## Step 13 #########
from statsmodels.tsa.api import VAR
bitcoin.index = bitcoin['Date']
xdata = pd.concat((bitcoin['bprice'], bitcoin['sp'], bitcoin['euro'],
bitcoin['gold'], bitcoin['oil']), 1)
model13 = VAR(xdata).fit(maxlags=3)
model13.summary()
######## Step 14 #########
# Forecasting using VAR model
model13.forecast(xdata.values, steps=30)
model13.plot()
