def liner_demo(self):
pd_data = pd.DataFrame()
model = sm.OLS(
np.log(pd_data["depend_var"]),
sm.add_constant(pd_data[["constant_column1",
"constant_column2"]])).fit()
print(model.summary())
def GLM_poisson(data_to_fit,target):
import statsmodels.api as sm
X = sm.add_constant(data_to_fit)
poisson_model = sm.GLM(target, X, family=sm.families.Poisson(link=sm.families.links.log))
results = poisson_model.fit()
print(results.summary())
return results
def regression(data_to_fit,target):
import statsmodels.api as sm
from statsmodels.sandbox.regression.predstd import wls_prediction_std
X = sm.add_constant(data_to_fit)
model = sm.OLS(target, X)
results = model.fit()
print(results.summary())
return results
def next(self):
p0 = pd.Series(self.data0.get(size=self.p.period))
p1 = pd.Series(self.data1.get(size=self.p.period))
p1 = sm.add_constant(p1, prepend=True)
slope, intercept = sm.OLS(p0, p1).fit().params
self.lines.slope[0] = slope
self.lines.intercept[0] = intercept
def regression_to_dataframe(data_to_fit,target):
import statsmodels.api as sm
from statsmodels.sandbox.regression.predstd import wls_prediction_std
X = sm.add_constant(data_to_fit)
model = sm.OLS(target, X)
model_result = model.fit()
print(model_result.summary())
statistics = pd.Series({'r2': model_result.rsquared,
'adj_r2': model_result.rsquared_adj})
# put them togher with the result for each term
result_df = pd.DataFrame({'params': model_result.params,
'pvals': model_result.pvalues,
'std': model_result.bse,
'statistics': statistics})
# add the complexive results for f-value and the total p-value
fisher_df = pd.DataFrame({'params': {'_f_test': model_result.fvalue},
'pvals': {'_f_test': model_result.f_pvalue}})
# merge them and unstack to obtain a hierarchically indexed series
res_series = pd.concat([result_df, fisher_df]).unstack()
return res_series.dropna()
def cointegration(self, priceX, priceY):
if priceX is None or priceY is None:
print('缺少价格序列.')
priceX = np.log(priceX)
priceY = np.log(priceY)
results = sm.OLS(priceY, sm.add_constant(priceX)).fit()
resid = results.resid
adfSpread = ADF(resid)
if adfSpread.pvalue >= 0.05:
print('''交易价格不具有协整关系.
P-value of ADF test: %f
Coefficients of regression:
Intercept: %f
Beta: %f
''' % (adfSpread.pvalue, results.params[0], results.params[1]))
return None
else:
print('''交易价格具有协整关系.
P-value of ADF test: %f
Coefficients of regression:
Intercept: %f
Beta: %f
''' % (adfSpread.pvalue, results.params[0], results.params[1]))
return results.params[0], results.params[1]
seaborn.set(rc={'figure.figsize':(6,5)})
seaborn.regplot(x = x_values, y = y_values)
seaborn.regplot(x = x_values, y = mean_vol)
# MatplotLib Parameters
#plt.scatter(x, y, label='skitscat', color='k')
plt.xlabel('Rolling HVs / Rolling Return')
plt.ylabel('Lagged Rolling HVs')
plt.title('Scatter Plot')
plt.legend()
plt.show()
# Can Probably Delete This
y = scatter_plot_df.iloc[:, [0]]
X = scatter_plot_df.iloc[:, [1, 2]]
X = sm.add_constant(X)
model = sm.OLS(y, X, missing='drop')
results = model.fit()
# Print Statements
print("Constant: {:.2f}, HVs: {:.2f}, Returns: {:.2f} Corr.: {:.2f}, n = {:.0f}".format(results.params['const'],
results.params["Rolling_HVs({})".format(rolling_HVs_window)],
results.params["Rolling_Returns({})".format(rolling_returns_window)],
results.rsquared,
results.df_resid))
print(results.params)
y_pred = regressor.predict(X_test) # Building the optimal model using Backward Elimination import statsmodels.formula.api as sm # Display Ordinary Least Square Regression Results # Due the nature of OLS, we need to create a constant 1 as X0 variable to satisfy the regression formula # Original form is as following which add 1s to the last column # X = np.append(arr = X, values = np.ones((50,1)).astype(int), axis = 1) # Since we wants 1s to be the first column, we reverse the function X = np.append(arr = np.ones((50,1)).astype(int), values = X, axis = 1) ############# More Efficient Method ############# # Run Statistical Results import statsmodels.api as sm # Display Ordinary Least Square Regression Results # Due the nature of OLS, we need to create a constant 1 as X0 variable to satisfy the regression formula X_with_constant = sm.add_constant(X) ################################################# # Run Backword Elimination by removing the highest P-value variable X_opt = X[:, [0, 1, 2, 3, 4, 5]] regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit() regressor_OLS.summary() X_opt = X[:, [0, 1, 3, 4, 5]] regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit() regressor_OLS.summary() X_opt = X[:, [0, 3, 4, 5]] regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit() regressor_OLS.summary() X_opt = X[:, [0, 3, 5]] regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit() regressor_OLS.summary()
def predict(self, X):
if self.fit_intercept:
X = sm.add_constant(X)
return self.results_.predict(X)
def fit(self, X, y):
if self.fit_intercept:
X = sm.add_constant(X)
self.model_ = self.model_class(y, X)
self.results_ = self.model_.fit()
