def VarianceRatioTest(data, printResults=True, trend=None, lags=None):
options_Trend = trend if trend != None else {'nc','c'}
options_Lags = lags if lags != None else {2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}
results = dict()
for column in data.columns:
print("Variance Ratio test for column: " + column)
results_Trend = dict()
for option_Trend in options_Trend:
results_Lag = dict()
for option_Lag in options_Lags:
result = VarianceRatio(data[column].dropna(), trend=option_Trend, lags=option_Lag)
if printResults:
result.summary()
results_Lag[option_Lag] = result
results_Trend[option_Trend] = results_Lag
results[column] = results_Trend
return results
import statsmodels.tsa.stattools as st
import matplotlib.pylab as plt
import numpy as np
import pandas as pd
df_caus = pd.read_csv('USDCAD.csv')
plt.hold(False)
df_caus.y.plot()
#plt.show()
print st.adfuller(df_caus.y,maxlag=1)
import hurst as h
print 'H doviz kuru', h.hurst(df_caus.y)
from arch.unitroot import VarianceRatio
vr = VarianceRatio(np.log(df_caus.y))
print(vr.summary().as_text())
df_caus['ylag'] = df_caus['y'].shift(1)
df_caus['deltaY'] = df_caus['y'] - df_caus['ylag']
import statsmodels.formula.api as smf
results = smf.ols('deltaY ~ ylag', data=df_caus).fit()
lam = results.params['ylag']
print lam
halflife=-np.log(2)/lam
print halflife, 'days'
plt.xlabel('Possible range of data values')
# Pull up summary statistics
print(lh.describe())
adf = ADF(lh['Close'])
print(adf.summary().as_text())
kpss = KPSS(lh['Close'])
print(kpss.summary().as_text())
dfgls = DFGLS(lh['Close'])
print(dfgls.summary().as_text())
pp = PhillipsPerron(lh['Close'])
print(pp.summary().as_text())
za = ZivotAndrews(lh['Close'])
print(za.summary().as_text())
vr = VarianceRatio(lh['Close'], 12)
print(vr.summary().as_text())
from arch import arch_model
X = 100 * lh
import datetime as dt
am = arch_model(X, p=4, o=0, q=0, vol='Garch', dist='StudentsT')
res = am.fit(last_obs=dt.datetime(2003, 12, 31))
forecasts = res.forecast(horizon=1, start='2004-1-1')
cond_mean = forecasts.mean['2004':]
cond_var = forecasts.variance['2004':] / 31
print(res.summary())
# acf and pacf test
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
