Machine training for algorithmic trade in financial markets
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The book is devoted to the practice of using machine learning in order to create powerful algorithmic strategies for successful trading in financial markets. The basic principles of working with data are set out: evaluating data sets, access to data through the API in Python, access to financial data on the Quandl platform and the management error management. The construction and training of algorithmic models using Python Biblioteka Pandas, Seaborn, Statsmodels and Sklearn and construction, evaluation and interpretation of AR (P), MA (Q) and ARIMA (P, D, C) using the StATSMODELS library are considered. The use of the PYMC3 library for Bayesian machine learning, the NLTK, SKLEARN (Scikit-LEARN) and Space for the appointment of marks to financial news and classification of documents, the Keras library for creating, setting up and evaluating neural networks of direct distribution, recurrent and custody networks is described. It is shown how to apply transfer training to satellite images to predict economic activity and how to effectively use reinforced training to achieve optimal trading results
Author:
Author:Yansen Stefan
Cover:
Cover:Soft
Category:
- Category:Computer & Technology
ISBN:
ISBN:978-5-975-6595-0
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