Deep Learning for Trading Part 3: Feed Forward Networks

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 …

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Deep Learning for Trading Part 2: Configuring TensorFlow and Keras to run on GPU

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 …

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Neural Network Trading: A Getting Started Guide for Algo Trading

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 …

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How to Run Trading Algorithms on Google Cloud Platform in 6 Easy Steps

Earlier this year, I attended the Google Next conference in San Francisco and gained some first-hand perspective into what’s possible with Google’s cloud infrastructure. Since then, I’ve been leaning on Google Cloud Platform (GCP) to run my trading algorithms (and much more) and it has quickly become an important tool in my workflow! In this …

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Solved: Errors Downloading Stock Price Data from Yahoo Finance

Recently, Yahoo Finance – a popular source of free end-of-day price data – made some changes to their server which wreaked a little havoc on anyone relying on it for their algos or simulations. Specifically, Yahoo Finance switched from HTTP to HTTPS and changed the data download URLs. No doubt this is a huge source of frustration, …

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Dual Momentum Investing: A Quant’s Review

I recently read Gary Antonacci’s book Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk, and it was clear to me that this was an important book to share with the Robot Wealth community. It is important not only because it describes a simple approach to exploiting the “premier anomaly” (Fama and French, …

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Optimal Data Windows for Training a Machine Learning Model for Financial Prediction

It would be great if machine learning were as simple as just feeding data to an out-of-the box implementation of some learning algorithm, then standing back and admiring the predictive utility of the output. As anyone who has dabbled in this area will confirm, it is never that simple. We have features to engineer and …

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Machine learning for Trading: Part 2

Introduction My first post on using machine learning for financial prediction took an in-depth look at various feature selection methods as a data pre-processing step in the quest to mine financial data for profitable patterns. I looked at various methods to identify predictive features including Maximal Information Coefficient (MIC), Recursive Feature Elimination (RFE), algorithms with …

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Machine learning for Trading:
Adventures in Feature Selection

Updates: 2019: In this first Machine Learning for Trading post, we’ve added a section on feature selection using the Boruta package, equity curves of a simple trading system, and some Lite-C code that generates the training data.  2020: I’ve updated the original post with some new thinking about data-mining, refreshed the code, updated the data and …

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Time Series Analysis: Fitting ARIMA/GARCH predictions profitable for FX?

Recently, I wrote about fitting mean-reversion time series analysis models to financial data and using the models’ predictions as the basis of a trading strategy. Continuing our exploration of time series modelling, let’s research the autoregressive and conditionally heteroskedastic family of time series models. In particular, we want to understand the autoregressive integrated moving average …

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