Deep learning

Posted on Feb 06, 2018 by Kris Longmore
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This is the fourth in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques in trading systems, including a warning about attempting to extract meaningful signals from historical market data. If you haven’t read that article, it is highly recommended that you do so before proceeding, as the context it provides is important. Part 2 provides a walk-through of setting up Keras and Tensorflow for R using either the default CPU-based configuration, or the more complex and involved (but well worth it) GPU-based configuration under the Windows environment. Part 3 is an introduction to the model building, training and evaluation process in Keras. We train a simple feed forward network to predict the direction of a foreign exchange market over a time horizon of one hour and assess its performance. [thrive_leads id='4510'] . In the last post, we trained a densely connected feed forward neural network to forecast the direction of the EUR/USD exchange rate over...

Posted on Jan 23, 2018 by Kris Longmore
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This is the third in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques in trading systems, including a warning about attempting to extract meaningful signals from historical market data. If you haven’t read that article, it is highly recommended that you do so before proceeding, as the context it provides is important. Read Part 1 here. Part 2 provides a walk-through of setting up Keras and Tensorflow for R using either the default CPU-based configuration, or the more complex and involved (but well worth it) GPU-based configuration under the Windows environment. Read Part 2 here. Part 3 is an introduction to the model building, training and evaluation process in Keras. We train a simple feed forward network to predict the direction of a foreign exchange market over a time horizon of hour and assess its performance. [thrive_leads id='4507'] . Now that you can train your deep learning models on a GPU, the fun can really start....

Posted on Jan 07, 2018 by Kris Longmore
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This is the second in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques in trading systems, including a warning about attempting to extract meaningful signals from historical market data. If you haven't read that article, it is highly recommended that you do so before proceeding, as the context it provides is important. Read Part 1 here. Part 2 provides a walk-through of setting up Keras and Tensorflow for R using either the default CPU-based configuration, or the more complex and involved (but well worth it) GPU-based configuration under the Windows environment. Stay tuned for Part 3 of this series which will be published next week. CPU vs GPU for Deep Learning No doubt you know that a computer's Central Processing Unit (CPU) is its primary computation module. CPUs are designed and optimized for rapid computation on small amounts of data and as such, elementary arithmetic operations on a few numbers...

Posted on Jan 01, 2018 by Kris Longmore
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This is the first in a multi-part series  in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In this post, we introduce Keras and discuss some of the major obstacles to using deep learning techniques in trading systems, including a warning about attempting to extract meaningful signals from historical market data. Part 2 provides a walk-through of setting up Keras and Tensorflow for R using either the default CPU-based configuration, or the more complex and involved (but well worth it) GPU-based configuration under the Windows environment. In the last few years, deep learning has gone from being an interesting but impractical academic pursuit to a ubiquitous technology that touches many aspects of our lives on a daily basis - including in the world of trading. This meteoric rise has been fuelled by a perfect storm of: Frequent breakthroughs in deep learning research which regularly provide better tools for training deep neural networks An explosion in the quantity and availability of data The availability of cheap and plentiful compute power The rise of open source deep learning...