Posted on May 15, 2019 by Michael M

In an ideal trading universe (free from backtesting bias), we’d all have a big golden “causation magnifying glass”. Through the lens of this fictional tool, you’d zoom in and understand the fleeting, enigmatic nature of the financial markets, stripping bare all its causes and effects. Knowing exactly what causes exploitable inefficiencies would make predicting market behaviour and building profitable trading strategies a fairly cushy gig, right? If you’re an engineer or scientist reading this, you are probably nodding along, hoping I’ll say the financial markets show some kind of domino effect for capitalists. That you can model them with the kinds of analytical methods you’d throw at a construction project or the petri dish. But unfortunately, trying to shoehorn the markets into formulas is a futile exercise... like stuffing Robot Wealth’s frontman Kris into a suit. Since the markets aren’t strictly deterministic, this makes testing your new and exciting strategy ideas a bit tricky. We’d all love to know for sure whether our ideas will be profitable before we throw real money at them. But, since you can’t realistically apply...

Posted on Feb 06, 2018 by Kris Longmore

Deep Learning for Trading Part 4: Fighting Overfitting 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 Deep Learning for Trading 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. .In the last post, we trained a densely connected feed-forward neural network to forecast...