def test_pp(self):
pp = PhillipsPerron(self.inflation, lags=12)
assert_almost_equal(pp.stat, -7.8076512, DECIMAL_4)
assert pp.test_type == 'tau'
pp.test_type = 'rho'
assert_almost_equal(pp.stat, -108.1552688, DECIMAL_2)
pp.summary()
def test_pp(self):
pp = PhillipsPerron(self.inflation, lags=12)
assert_almost_equal(pp.stat, -7.8076512, DECIMAL_4)
assert pp.test_type == 'tau'
pp.test_type = 'rho'
assert_almost_equal(pp.stat, -108.1552688, DECIMAL_2)
pp.summary()
def test_pp_auto(self):
pp = PhillipsPerron(self.inflation)
n = self.inflation.shape[0] - 1
lags = ceil(12.0 * ((n / 100.0) ** (1.0 / 4.0)))
assert_equal(pp.lags, lags)
assert_almost_equal(pp.stat, -8.135547778, DECIMAL_4)
pp.test_type = 'rho'
assert_almost_equal(pp.stat, -118.7746451, DECIMAL_2)
def test_pp(self):
pp = PhillipsPerron(self.inflation, lags=12)
assert_almost_equal(pp.stat, -7.8076512, DECIMAL_4)
assert pp.test_type == "tau"
with pytest.warns(FutureWarning, match="Mutating unit root"):
pp.test_type = "rho"
assert_almost_equal(pp.stat, -108.1552688, DECIMAL_2)
pp.summary()
def test_pp_auto(self):
pp = PhillipsPerron(self.inflation)
n = self.inflation.shape[0] - 1
lags = ceil(12.0 * ((n / 100.0) ** (1.0 / 4.0)))
assert_equal(pp.lags, lags)
assert_almost_equal(pp.stat, -8.135547778, DECIMAL_4)
pp.test_type = 'rho'
assert_almost_equal(pp.stat, -118.7746451, DECIMAL_2)
def test_pp_auto(self):
pp = PhillipsPerron(self.inflation)
n = self.inflation.shape[0] - 1
lags = ceil(12.0 * ((n / 100.0)**(1.0 / 4.0)))
assert_equal(pp.lags, lags)
assert_almost_equal(pp.stat, -8.135547778, DECIMAL_4)
with pytest.warns(FutureWarning, match="Mutating unit root"):
pp.test_type = "rho"
assert_almost_equal(pp.stat, -118.7746451, DECIMAL_2)
def test_stationarity(self, test: str, threshold=0.05, **kwargs):
"""
Test for the stationarity of a given series around a deterministic trend.
Interesting ref: https://stats.stackexchange.com/questions/88407/adf-test-pp-test-kpss-test-which-test-to-prefer
:param test: unit root test. The first three all start with the null of a unit root and have an
alternative of a stationary process. The last one, KPSS, has a null of a stationary process
with an alternative of a unit root.
- 'adf' for Augmented Dickey Fuller test,
- 'dfgls' for Elliott, Rothenberg and Stock’s GLS version of the Dickey-Fuller test
- 'pp' for Phillips–Perron test
- 'kpss' for Kwiatkowski–Phillips–Schmidt–Shin test,
:param threshold: confidence level for p_value. The p-value is the probability score based on which you can
decide whether to reject the null hypothesis or not.
If the p-value is less than a predefined alpha level (typically 0.05), we reject the null hypothesis.
:param **kwargs: Any additional arguments for the test in question i.e.
:return: True if p-value below threshold (for DF and ADF) or above threshold (for KPSS), otherwise False.
"""
if re.search('adf', test, re.IGNORECASE):
test_statistic, p_value, n_lags_used, _, critical_values, _ = adfuller(self.series, **kwargs)
return p_value < threshold
elif re.search('kpss', test, re.IGNORECASE):
test_statistic, p_value, n_lags_used, critical_values = kpss(self.series, **kwargs)
return p_value > threshold # for KPSS, series is NOT stationary if < threshold
elif re.search('pp', test, re.IGNORECASE):
result = PhillipsPerron(self.series)
test_statistic, p_value, n_lags_used, critical_values = result.stat, result.pvalue, result.lags, result.critical_values
return p_value < threshold
else:
raise Exception("Invalid `test`")
def stationarity_test(self,stock_data,column):
rolmean = self.stock_data.rolling(30).mean()
rolstd = self.stock_data.rolling(30).std()
plt.plot(self.stock_data,color='blue',label='Original')
plt.plot(rolmean,color='red',label='Rolling Mean')
plt.plot(rolstd,color='black',label='Rolling Std')
plt.legend(loc='best')
plt.title("Rolling Mean & Standard Deviation")
plt.show()
# Perform Dickey Fuller Test
print("Results of Dickey-Fuller Test")
self.stock_data.dropna(inplace=True)
dftest = adfuller(self.stock_data[column])
print(dftest)
dfoutput = pd.Series(dftest[0:4],index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])
for key,Value in dftest[4].items():
dfoutput['Critical Value(%s)'%key] = Value
print(dfoutput)
print ('Results of KPSS Test:')
print("------------------------------------------------------------------------------")
kpsstest = kpss(self.stock_data[column], regression='c')
kpss_output = pd.Series(kpsstest[0:3], index=['Test Statistic','p-value','Lags Used'])
for key,value in kpsstest[3].items():
kpss_output['Critical Value (%s)'%key] = value
print (kpss_output)
print("------------------------------------------------------------------------------")
print ('Results of Phillips-Perron Test:')
pptest = PhillipsPerron(self.stock_data[column])
print(pptest)
print("------------------------------------------------------------------------------")
def stationarity(self,y):
adf = adfuller(y,regression='nc')[1]
pp = PhillipsPerron(y,trend='nc').pvalue
if adf < 0.05 and pp < 0.05:
print("Data is stationary")
else:
sys.exit("Data not stationary")
def PhilipsPerronTest(data, printResults=True, trend=None, lags=None):
options_Trend = trend if trend != None else {'nc','c','ct'}
options_Lags = lags if lags != None else {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}
results = dict()
for column in data.columns:
print("Philips Perron test for column: " + column)
results_Trend = dict()
for option_Trend in options_Trend:
results_Lag = dict()
for option_Lag in options_Lags:
result = PhillipsPerron(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
def generate_stationarity_dataframe(potential_pairs_index, cv_spreads_train):
adfuller_t = []
adfuller_p = []
kpss_t = []
kpss_p = []
pp_t = []
pp_p = []
vr_t = []
vr_p = []
warnings.filterwarnings('ignore')
for i, pair in enumerate(potential_pairs_index):
temp_spread = cv_spreads_train[pair]
temp_adfuller = ts.adfuller(temp_spread)
temp_adfuller_t = temp_adfuller[0]
temp_adfuller_p = temp_adfuller[1]
temp_kpss = ts.kpss(temp_spread)
temp_kpss_t = temp_kpss[0]
temp_kpss_p = temp_kpss[1]
temp_pp = PhillipsPerron(temp_spread)
temp_pp_t = temp_pp.stat
temp_pp_p = temp_pp.pvalue
temp_vr = VarianceRatio(temp_spread)
temp_vr_t = temp_vr.stat
temp_vr_p = temp_vr.pvalue
adfuller_t.append(temp_adfuller_t)
adfuller_p.append(temp_adfuller_p)
kpss_t.append(temp_kpss_t)
kpss_p.append(temp_kpss_p)
pp_t.append(temp_pp_t)
pp_p.append(temp_pp_p)
vr_t.append(temp_vr_t)
vr_p.append(temp_vr_p)
cv_stationary_tests = pd.DataFrame(
{
'adf_t_stat': adfuller_t,
'adf_p_value': adfuller_p,
'kpss_t_stat': kpss_t,
'kpss_p_value': kpss_p,
'pp_t_stat': pp_t,
'pp_p_value': pp_p,
'vr_t_stat': vr_t,
'vr_p_value': vr_p
},
index=potential_pairs_index)
return cv_stationary_tests
def unitroot_test(series):
# Basic statistic
plt.figure()
plt.plot(series)
plot_pacf(series)
# ADF test
# AIC & BIC from lags 12 to 1
print('$p$ & AIC & BIC \\\\')
max_lags = 12
for lags in (max_lags - i for i in range(max_lags)):
ar_model = AutoReg(series, lags, 'n')
res = ar_model.fit()
print(f'{lags} & {round(res.aic, 3)} & {round(res.bic, 3)} \\\\')
# Best lags by `ar_select_order`
sel = ar_select_order(series, max_lags, trend='n')
lags = sel.ar_lags[-1]
print(f'Lags selection: {sel.ar_lags}')
# Start ADF test
adf = ADF(series, lags)
print(adf.summary())
# PP test
pp_tau = PhillipsPerron(series, 3, test_type='tau') # q = 3
pp_rho = PhillipsPerron(series, 3, test_type='rho') # q = 3
print(pp_tau.summary())
print(pp_rho.summary())
def trend_test(vals):
pp_result = PhillipsPerron(vals).pvalue
kpss_result = KPSS(vals).pvalue
# reject both. technically, we do not know and should consider different kinds of long range dependencies
if (pp_result <= .05) and (kpss_result <= .05):
return "Unknown", "Unknown"
# reject H0 of pp, do not reject H0 of KPSS
elif (pp_result <= .05) and (kpss_result > .05):
return True, "deterministic"
# reject H0 of KPSS. Do not reject H0 of pp
elif (pp_result > .05) and (kpss_result <= .05):
return True, "stochastic"
# do not reject either. technically, we do not know and the time series is not informative enough
else:
return "Unknown", "Unknown"
def PP_Test(self, timeseries, printResults=True):
"""
Phillips-Perron (PP) Test
Null Hypothesis is Unit Root
Reject Null Hypothesis >> Series is stationary >> Use price levels
Fail to Reject >> Series has a unit root >> Use price returns
"""
ppTest = PhillipsPerron(timeseries)
self.pValue = ppTest.pvalue
if (self.pValue < self.SignificanceLevel):
self.isStationary = True
else:
self.isStationary = False
if printResults:
print('Phillips-Perron (PP) Test Results: {}'.format(
'Stationary' if self.isStationary else 'Not Stationary'))
def coint_test(self, x: np.ndarray, y: np.ndarray, alpha: 0.05) -> bool:
"""Performs Engle Granger co-integration test
:param x: log price
:type x: np.ndarray, shape = (n_samples,)
:param y: log price
:type y: np.ndarray, shape = (n_samples,)
:param alpha: significance level
:type alpha: 0.05
:return: True if two series are co-integrated
:rtype: bool
"""
# Perform a regression of y on x
self.lr.fit(x[:, np.newaxis], y)
# Check if residuals are stationary
pp = PhillipsPerron(self.lr.residuals)
# Null hypothesis: process is not stationary
if pp.pvalue < alpha:
return True
return False
print('Lean Hogs Future skewness is {}'.format(lh.skew(axis=0)[0]))
print('Lean Hogs Future kurtosis is {}'.format(lh.kurtosis(axis=0)[0]))
sns.distplot(lh['Close'], color='blue') #density plot
plt.title('1986–2018 Lean Hogs Future return frequency')
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':]
def test_pp_bad_type(self):
pp = PhillipsPerron(self.inflation, lags=12)
with pytest.raises(ValueError):
pp.test_type = 'unknown'
# for AAPL
# MA(15) fits model the best.
# stats: 3.04749309416, pvalue: 1.0
# stats: 5.14080783224, pvalue: 0.999999999468
# h0: rho == 1 is not rejected. There is unit root.
test_series = goog_df
from arch.unitroot import PhillipsPerron
# fit MA on resid.
import statsmodels.tsa.arima_model as arma
for ma_lag in [7, 8, 9, 10, 11, 12, 13, 15, 20]:
model = arma.ARMA(test_series, (0, ma_lag)).fit()
print 'lag: {}, aic: {}'.format(ma_lag, model.aic)
pp = PhillipsPerron(test_series, trend='c', lags=10, test_type='tau')
print 'stats: {}, pvalue: {}'.format(pp.stat, pp.pvalue)
pp = PhillipsPerron(test_series, trend='c', lags=10, test_type='rho')
print 'stats: {}, pvalue: {}'.format(pp.stat, pp.pvalue)
### using adf test
### goog and aapl t-value
# 0.876758903592
# 0.999070159449
test_series = goog_df
from arch.unitroot import ADF
adf = ADF(goog_df, lags=24)
print adf.pvalue
adf = ADF(aapl_df, lags=24)
print adf.pvalue
def test_phillips_perron_specifed_lag():
y = np.zeros((10, ))
with pytest.raises(InfeasibleTestException,
match="A minimum of 12 observations"):
assert np.isfinite(PhillipsPerron(y, lags=12).stat)
def pp(serie, tipo="nc", lag=False):
results = PhillipsPerron(serie, trend=tipo)
if lag:
return results.lags
else:
return results.pvalue
# Augmented Dickey-Fuller
from statsmodels.tsa.stattools import adfuller
adf1 = adfuller(df.adjClose, regression="ct")
print("\n", "Teste 'Augmented Dickey-Fuller' para série por níveis (p-value):",
adf1[1]) # P-value > 0.05 => série é não estacionária
adf2 = adfuller(tsReturns, regression="nc")
print("\n",
"Teste 'Augmented Dickey-Fuller' para série de retornos (p-value):",
adf2[1]) # P-value = 0 < 0.05 => série é estacionária
# Phippips Perron (PP), com H_0: série é não estacionária
from arch.unitroot import PhillipsPerron
pp1 = PhillipsPerron(df.adjClose)
print("\n", "Teste 'Phillips Perron' para série por níveis:",
pp1) # P-value > 0.05 => série é não estacionária
pp2 = PhillipsPerron(tsReturns)
print("\n", "Teste 'Phillips Perron' para série de retornos:",
pp2) # P-value = 0 < 0.05 => série é estacionária
# Kwiatkowski-Phillips-Schmidt-Shin (KPSS), com H_0: série é estacionária
from statsmodels.tsa.stattools import kpss
kpss1 = kpss(df.adjClose, regression='ct')
print("\n", "Teste KPSS para série por níveis:",
kpss1[1]) # P-value < 0.05 => série é não estacionária
kpss2 = kpss(tsReturns, regression='ct')
def test_pp_regression(self):
pp = PhillipsPerron(self.inflation, lags=12)
reg = pp.regression
assert len(reg.params) == 2
assert "(HAC) using 12 lags" in str(reg.summary())
def get_phillips_perron(timeseries):
from arch.unitroot import PhillipsPerron
pp = PhillipsPerron(timeseries)
print(pp.summary().as_text())
'CME Lean Hogs Future Close Price',
'CME Lean Hogs Future Annualized Volatility',
'CME Lean Hogs Future Return'
])
#plt.savefig(files.image_path + '\LH_close_vol_return.png')
data = lh['Close'].resample('M').mean() # resample daily data to monthly data
data = data['1992':'2004']
data = np.log(data / data.shift(1)).dropna() * 100 # d 1
adf = ADF(data)
print(adf.summary().as_text())
kpss = KPSS(data)
print(kpss.summary().as_text())
dfgls = DFGLS(data)
print(dfgls.summary().as_text())
pp = PhillipsPerron(data)
print(pp.summary().as_text())
za = ZivotAndrews(data)
print(za.summary().as_text())
vr = VarianceRatio(data, 12)
print(vr.summary().as_text())
print(data.describe())
print('Lean Hogs Future skewness is {}'.format(data.skew(axis=0)))
print('Lean Hogs Future kurtosis is {}'.format(data.kurtosis(axis=0)))
import matplotlib.gridspec as gridspec
import statsmodels.api as sm
import scipy.stats as stats
import seaborn as sns
def test_pp_bad_type(self):
pp = PhillipsPerron(self.inflation, lags=12)
with pytest.raises(ValueError):
pp.test_type = 'unknown'
