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Explore the research behind our trading, plus some just-for-fun stuff....

Posted on Jun 11, 2018 by Kris Longmore
1 Comment.
1,683 Views

Cryptocompare is a platform providing data and insights on pretty much everything in the crypto-sphere, from market data for cryptocurrencies to comparisons of the various crytpo-exchanges, to recommendations for where to spend your crypto assets. The user-experience is quite pleasant, as you can see from the screenshot of their real-time coin comparison table: As nice as the user-interface is, what I really like about Cryptocompare is its API, which provides programmatic access to a wealth of crypto-related data. It is possible to drill down and extract information from individual exchanges, and even to take aggregated price feeds from all the exchanges that Cryptocompare is plugged into - and there are quite a few! Interacting with the Cryptocompare API When it comes to interacting with Cryptocompare's API, there are already some nice Python libraries that take care of most of the heavy lifting for us. For this post, I decided to use a library called [crayon-5d10d8b4c58d5230711571-i/] . Check it out on Git Hub here. You can install the current stable release by doing[crayon-5d10d8b4c58de183666754-i/] , but I installed the latest development version direct from...

Posted on Jun 04, 2018 by Kris Longmore
3 comments.
1,605 Views

At Robot Wealth we get more questions than even the most sleep-deprived trader can handle. So whilst we develop the algo equivalent of Siri and brag about how we managed to get 6 hours downtime last night, we thought we'd start a new format of blog posts — answering your most burning questions. Lately our Class to Quant members have been looking to implement rotation-style ETF and equities strategies in Zorro, but just like your old high-school essays, starting is the biggest barrier. These types of strategies typically scan a universe of instruments and select one or more to hold until the subsequent rebalancing period. Zorro is my go-to choice for researching and even executing such strategies: its speed makes scanning even large universes of stocks quick and painless, and its scripting environment facilitates fast prototyping and iteration of the algorithm itself - once you've wrestled it for a while (get our free Zorro for Beginners video course here). I'm going to walk you through a general design paradigm for constructing strategies like this with Zorro, and demonstrate the entire process with a...

Posted on Feb 06, 2018 by Kris Longmore
5 comments.
3,669 Views

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
18 comments.
3,407 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.
6,228 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
4 comments.
11,085 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...