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...

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....

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...

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...

This article is a departure from the quantitative research that usually appears on the Robot Wealth blog. Until recently, I was working as a machine learning consultant to financial services organizations and trading firms in Australia and the Asia Pacific region. A few months ago, I left that world behind to join an ex-client's proprietary trading firm. I thought I'd jot down a few thoughts about what I saw during my consulting time because I witnessed some interesting changes in the industry in a relatively short period of time that I think you might find interesting too. Enjoy! Perceptions around Artificial Intelligence (AI) in the finance industry have changed signifcantly, as scepticism gives way to a rising Fear of Missing Out (FOMO) among asset managers and trading houses. Big Data and AI Strategies – Machine Learning and Alternative Data Approaches to Investing, JP Morgan's 280-page report on the future of machine learning in the finance industry, paints a picture of a future in which alpha is generated from data sources such as social media, satellite imagery, and machine-classified company filings and...

This article is adapted from one of the units of Advanced Algorithmic Trading. If you like what you see, check out the entire curriculum here. Find out what Robot Wealth is all about here. If you're interested in using artificial neural networks (ANNs) for algorithmic trading, but don't know where to start, then this article is for you. Normally if you want to learn about neural networks, you need to be reasonably well versed in matrix and vector operations - the world of linear algebra. This article is different. I've attempted to provide a starting point that doesn't involve any linear algebra and have deliberately left out all references to vectors and matrices. If you're not strong on linear algebra, but are curious about neural networks, then I think you'll enjoy this introduction. In addition, if you decide to take your study of neural networks further, when you do inevitably start using linear algebra, it will probably make a lot more sense as you'll have something of head start. The best place to start learning about neural networks is the...