it use Machine Learning in MATLAB to predict the buying-decision of Stock by using real life data.
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Aggiornato 12 mar 2019

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Overview :

In this script, it uses Machine Learning in MATLAB to predict buying-decision for stock. Using real life data, it will explore how to manage time-stamped data and select the best fit machine learning model. As common being widely known, preparing data and select the significant features play big role in the accuracy of model. In this example, it uses the technical indicators of today to predict the next day stock close price.
In this example, the trading strategy is if the close price is higher 1% than the open price in the same day, then we should buy stock at the openning of the stock market and sell it at the closing of the stock market.
In this example, it demostrates how to pre-processing the data for modelling and predict the decision by the model. New data of each date will be tabulated to re-train new model and find the best model for next day prediction. Interesting?

[Note : Not advocating any particular strategy, factors or methodology]

Highlights :
Handling downloaded data from Yahoo Finance using the timetable object
Selecting features based on domain knowledge
Machine Learning Modeling
Automate to re-train new model to incorproate new updated data for next prediction
Predicting the buying-decision
Experience the computational speed with/without parallel computing

Product Focus :
Financial Toolbox
Statistics and Machine Learning Toolbox
Parallel Computing Toolbox (Optional)

Cita come

Kevin Chng (2024). MACHINE LEARNING CLASSIFICATION USED TO PREDICT STOCK (, MATLAB Central File Exchange. Recuperato .

Compatibilità della release di MATLAB
Creato con R2018a
Compatibile con qualsiasi release
Compatibilità della piattaforma
Windows macOS Linux
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Versione Pubblicato Note della release

Perform data normalization before training machine learning model


1) Allow to download different stock data from Yahoo Finance
2) Remove no volume trading day before calculating those technical indicators
3) rectify logic error
4) Redefine the strategy to select best fit model
Welcome your feedback.


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