Super easy stock trading rule strategy backtesting in Python
1. tool installation
$ pip install stock-backtest
2. file creation
3. execution
$ python stock.py
$ pip install stock-backtest
$ python stock.py
$ pip install portfolio-backtest
$ pip install PyPortfolioOpt
$ python backtest.py
$ pip install scikit-learn pandas_datareader rgf-python xgboost
$ python pred.py
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
$ pip install scikit-learn pandas_datareader rgf-python xgboost
$ python pred.py
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…
$ pip install scikit-learn pandas_datareader
$ python pred.py
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…
$ pip install scikit-learn pandas_datareader
$ python pred.py
That’s super easy!
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
$ pip install scikit-learn pandas_datareader rgf-python
$ python pred.py
That’s super easy!
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
$ pip install scikit-learn pandas_datareader
$ python pred.py
That’s super easy!
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
$ pip install scikit-learn pandas_datareader
$ python pred.py
That’s super easy!
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