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Super easy stock trading rule strategy backtesting in Python

$ pip install stock-backtest
$ python stock.py

Automatic extraction of stock price data features using tsfresh in Python

$ pip install scikit-learn xgboost pandas-datareader tsfresh
$ python pred.py

That’s super easy!


Automatically optimize financial portfolio from historical data super-easily using modern portfolio theory, efficient frontier, etc. in Python

$ pip install portfolio-backtest
$ pip install PyPortfolioOpt
$ python backtest.py
  • Tangency Portfolio

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

$ 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

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

$ 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


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

$ 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            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

Machine learning for forecasting up and down stock prices the next day using Multilayer Perceptron (MLP) in Python

$ 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


Machine learning for forecasting up and down stock prices the next day using Regularized Greedy Forest (RGF) in Python

$ 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

Machine learning for forecasting up and down stock prices the next day using Support vector machine in Python

$ 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

Machine learning for forecasting up and down stock prices the next day using Bernoulli Naive Bayes in Python

$ 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

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