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Posted on Jan 23, 2018 by Kris Longmore
17 comments.
3,323 Views

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
1 Comment.
6,133 Views

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
3 comments.
10,826 Views

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...

Posted on Nov 24, 2017 by Kris Longmore
4 comments.
1,804 Views

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...

Posted on Sep 06, 2017 by Kris Longmore
6 comments.
4,806 Views

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...

Posted on Jul 29, 2017 by Kris Longmore
7 comments.
4,085 Views

In keeping with our recent theme of providing useful tidbits of algo trading practicalities, here's an elegant solution that resolves Yahoo's unceremonious exit from the free financial data space. Regular readers would know that I use various tools in my algo trading stack, but the one I keep coming back to, particularly when I'm ready to start running serious simulations, is Zorro. Not only is it a fast, accurate, and powerful backtester and execution engine, the development team is clearly committed to solving issues and adding features that really matter, from a practical perspective. This is another example of the speedy and elegant resolution of a serious issue - namely, the loss of free access to good quality, properly adjusted equities data, thanks to Yahoo's exit. Zorro version 1.60 is currently undergoing it's final stages of beta testing and will likely be released publicly in the coming days. The latest version includes integration with Alpha Vantage's API, providing access to free, high quality, properly adjusted stock and ETF price data. All you need to do to use it is sign...