Super easy Python Stock price forecasting (using ensemble stacking) Machine learning

Machine learning for forecasting stock prices up and down the next day using ensemble stacking learning in Python

1. tool installation

$ pip install scikit-learn pandas_datareader rgf-python xgboost

2. file creation

3. execution

$ python pred.py

4. result

As a result of calculation with the same data and features, MLP are the best among XGBoost, DNN, LSTM, GRU, RNN, LogisticRegression, k-nearest neighbor, RandomForest, BernoulliNB, SVM, RGF, MLP, Bagging, Voting, Stacking.

XGBoost            0.5119047619047619
DNN                0.5496031746031746
LSTM               0.5178571428571429
GRU                0.5138888888888888
RNN                0.5376984126984127
LogisticRegression 0.5496031746031746
k-nearest neighbor 0.5198412698412699
RandomForest       0.49603174603174605
BernoulliNB        0.5496031746031746
SVM                0.5396825396825397
RGF                0.5158730158730159
MLP                0.5694444444444444
Bagging            0.5297619047619048
Voting             0.5416666666666666
Stacking           0.5218253968253969

5. reference

sklearn.ensemble.StackingClassifier - scikit-learn 0.24.1 documentation