Pemodelan linear dengan data keuangan

Machine Learning untuk Keuangan dengan Python

Nathan George

Data Science Professor

plot latih uji

Buat himpunan latih dan uji

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]

Pemodelan linear

model = sm.OLS(train_targets, train_features)

results = model.fit()

Pemodelan linear

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

p-value

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

aktual vs aktual

prediksi vs aktual

Saatnya memfitting model linear!

Machine Learning untuk Keuangan dengan Python