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May 11

Super easy stock trading rule strategy backtesting in Python

$ pip install stock-backtest
$ python stock.py

Apr 25

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!

tsfresh - tsfresh 0.18.1.dev11+g8ebb236 documentation

tsfresh is a python package. It automatically calculates a large number of time series characteristics, the so called…

tsfresh.readthedocs.io

Apr 25

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

Apr 25

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

sklearn.ensemble.StackingClassifier - scikit-learn 0.24.1 documentation

Stacked generalization consists in stacking the output of individual estimator and use a classifier to compute the…

scikit-learn.org

Apr 25

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

sklearn.ensemble.VotingClassifier - scikit-learn 0.24.1 documentation

If 'hard', uses predicted class labels for majority rule voting. Else if 'soft', predicts the class label based on the…

scikit-learn.org

Apr 25

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

sklearn.ensemble.BaggingClassifier - scikit-learn 0.24.1 documentation

A Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random…

scikit-learn.org

Dec 17, 2020

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

sklearn.neural_network.MLPClassifier - scikit-learn 0.23.2 documentation

class sklearn.neural_network. MLPClassifier( hidden_layer_sizes=(100, ), activation='relu', *, solver='adam'…

scikit-learn.org

Dec 16, 2020

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

rgf-python

The wrapper of machine learning algorithm Regularized Greedy Forest (RGF) [1] for Python. Scikit-learn interface and…

pypi.org

Dec 3, 2020

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

sklearn.svm.SVC - scikit-learn 0.23.2 documentation

C-Support Vector Classification. The implementation is based on libsvm. The fit time scales at least quadratically with…

scikit-learn.org

Dec 3, 2020

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

sklearn.naive_bayes.BernoulliNB - scikit-learn 0.23.2 documentation

Naive Bayes classifier for multivariate Bernoulli models. Like MultinomialNB, this classifier is suitable for discrete…

scikit-learn.org

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