Forecasts stock prices using classical machine learning techniques- A time series analysis & modeling. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks
Objective:
Predict stock stock price using Technical Indicators as predictors (features).
Use Supervised Machine Learning Approach to predict stock prices.
Employ the use of pipeline and GridSearch to select the best model
Use Final Model to Predict Stock Returns.
Show plots of stock Return
Write Deployable script.
Note:
That Every stock has different behaviour and so at every point we may
have different best performing algorithm. For instance, after much
testing Ranform Forest Algorithm perform better for predicting Apple
Stocks than any other algo. Guassian process classifier performed
better than every other algo at predicting IBM stocks etc.
Indicators/Predictors Used:
Moving Averages(Also called Rolling mean)
Commodity Channel Index
Momentum
Stochastic Oscillator(D and K)
Force Index
Mass Index
# You can add ass many predictors are desired.
# Most importantly if you have to do this, you may
have to consider a feature selection using XGBoost.
How to Use
>git clone https://github.com/kennedyCzar/STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA
Unpak the Files in a project folder
Add File Path to Environment Variable using Spyder PythonPath
Click on Synchronize with Environment.
Restart Spyder.
Report Issue
Output
plot of Feature Importance
Gold Stock Retuns
General Motors stock returns
Apple stock returns
Tesla Stock Returns
Estimation grid_RandomForestClassifier
Best params: {'clf__criterion': 'gini', 'clf__max_depth': 8,
'clf__min_samples_leaf': 8, 'clf__min_samples_split': 9}
Best training accuracy: 0.855755894590846
Test set accuracy score for best params: 0.8546042003231018
Estimation grid_RandomForestClassifier_PCA
Best params: {'clf__criterion': 'entropy', 'clf__max_depth': 7,
'clf__min_samples_leaf': 6, 'clf__min_samples_split': 3}
Best training accuracy: 0.7489597780859917
Test set accuracy score for best params: 0.691437802907916
Estimation grid_KNN
Best params: {'clf__n_neighbors': 10}
Best training accuracy: 0.8037447988904299
Test set accuracy score for best params: 0.778675282714055
Estimation grid_KNN_PCA_
Best params: {'clf__n_neighbors': 9}
Best training accuracy: 0.7149791955617198
Test set accuracy score for best params: 0.6882067851373183
Estimation grid_SVC
Best params: {'clf__C': 5, 'clf__gamma': 0.0001, 'clf__kernel': 'linear'}
Best training accuracy: 0.8411927877947295
Test set accuracy score for best params: 0.851373182552504
Estimation grid_SVC_PCA
Best params: {'clf__C': 1, 'clf__gamma': 1, 'clf__kernel': 'rbf'}
Best training accuracy: 0.7323162274618585
Test set accuracy score for best params: 0.6865912762520194
Estimation grid_GaussianProcessClassifier
Best params: {'clf__kernel': 1**2 * RBF(length_scale=1)}
Best training accuracy: 0.8585298196948682
Test set accuracy score for best params: 0.8675282714054927
Estimation grid_GaussianProcessClassifier_PCA
Best params: {'clf__kernel': 1**2 * RBF(length_scale=1)}
Best training accuracy: 0.7295423023578363
Test set accuracy score for best params: 0.7011308562197092
Estimation grid_LogisticRegression
Best params: {'clf__C': 0.1, 'clf__penalty': 'l1', 'clf__solver': 'liblinear'}
Best training accuracy: 0.8349514563106796
Test set accuracy score for best params: 0.8432956381260097
Estimation grid_LogisticRegression_PCA
Best params: {'clf__C': 0.1, 'clf__penalty': 'l1', 'clf__solver': 'liblinear'}
Best training accuracy: 0.7267683772538142
Test set accuracy score for best params: 0.7059773828756059
Estimation grid_DecisionTreeClassifier
Best params: {'clf__max_depth': 3}
Best training accuracy: 0.8280166435506241
Test set accuracy score for best params: 0.8481421647819063
Estimation grid_DecisionTreeClassifier_PCA
Best params: {'clf__max_depth': 6}
Best training accuracy: 0.7246879334257975
Test set accuracy score for best params: 0.6978998384491115
Estimation grid_AdaBoostClassifier
Best params: {'clf__n_estimators': 8}
Best training accuracy: 0.8141470180305131
Test set accuracy score for best params: 0.8222940226171244
Estimation grid_AdaBoostClassifier_PCA
Best params: {'clf__n_estimators': 22}
Best training accuracy: 0.6768377253814147
Test set accuracy score for best params: 0.6348949919224556
Estimation grid_GaussianNB
Best params: {'clf__priors': None}
Best training accuracy: 0.7441054091539528
Test set accuracy score for best params: 0.7544426494345718
Estimation grid_GaussianNB_PCA
Best params: {'clf__priors': None}
Best training accuracy: 0.7205270457697642
Test set accuracy score for best params: 0.7075928917609047
Estimation grid_QuadraticDiscriminantAnalysis
Best params: {'clf__priors': None}
Best training accuracy: 0.7933425797503467
Test set accuracy score for best params: 0.7883683360258481
Estimation grid_QuadraticDiscriminantAnalysis_PCA
Best params: {'clf__priors': None}
Best training accuracy: 0.7191400832177531
Test set accuracy score for best params: 0.7075928917609047
Classifier with best test set accuracy: grid_GaussianProcessClassifier
You must note that this strategy is trading is a low frequency approach and this
fits to make steady income over a period of time.
For high Frequency Trading the result of the return is quite high.
GOLD happens to give the most return on applied strategy(As shown in
the graphs above).
Also worthy of mention is the fact that, Random Forest Classifier + PCA
in most cases performed better for stocks prices with both unsteady and steady rise.
Followed Next to Adaboost, then Gradientbost Classifier.
In any case, the performance of an algorithm depends on the structure of
the underlying prices. Its behaviour over a time series.
For different stocks different agorithm perform best.
