Maschinelles Lernen für Finanzen in Python
Nathan George
Data Science Professor
import statsmodels.api as sm linear_features = sm.add_constant(features) train_size = int(0.85 * targets.shape[0]) train_features = linear_features[:train_size] train_targets = targets[:train_size] test_features = linear_features[train_size:] test_targets = targets[train_size:]
import statsmodels.api as sm
linear_features = sm.add_constant(features)
train_size = int(0.85 * targets.shape[0])
train_features = linear_features[:train_size] train_targets = targets[:train_size] test_features = linear_features[train_size:] test_targets = targets[train_size:]
some_list[start:stop:step]
model = sm.OLS(train_targets, train_features) results = model.fit()
model = sm.OLS(train_targets, train_features)
results = model.fit()
print(results.summary())
Dep. Variable: 10d_future_pct R-squared: 0.157 Model: OLS Adj. R-squared: 0.146 Method: Least Squares F-statistic: 15.55 Date: Thu, 19 Apr 2018 Prob (F-statistic): 4.79e-14 Time: 11:41:05 Log-Likelihood: 336.53 No. Observations: 425 AIC: -661.1 Df Residuals: 419 BIC: -636.8 Df Model: 5 Covariance Type: nonrobust =========================================================================== coef std err t P>|t| [0.025 0.975] <hr />------------------------------------------------------------------------- const 1.3305 0.323 4.117 0.000 0.695 1.966 10d_close_pct 0.0906 0.098 0.927 0.355 -0.102 0.283 ma14 0.3313 0.209 1.585 0.114 -0.080 0.742 rsi14 -0.0013 0.001 -1.044 0.297 -0.004 0.001 ma200 -0.4090 0.053 -7.712 0.000 -0.513 -0.305 rsi200 -0.0224 0.003 -6.610 0.000 -0.029 -0.016 =========================================================================== Omnibus: 3.571 Durbin-Watson: 0.209 Prob(Omnibus): 0.168 Jarque-Bera (JB): 3.323 Skew: 0.202 Prob(JB): 0.190 Kurtosis: 3.159 Cond. No. 5.47e+03
print(results.pvalues)
const 4.630428e-05 10d_close_pct 3.546748e-01 ma14 1.136941e-01 rsi14 2.968699e-01 ma200 9.126405e-14 rsi200 1.169324e-10
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