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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Back to Basics Part 3: Backtesting in Algorithmic Trading

  This is the final post in our 3-part Back to Basics series. You may be interested in checking out the other posts in this series: Part 1: An Introduction to Algorithmic Trading Part 2: How to Succeed at Algorithmic Trading We’ve also compiled this series into an eBook which you can download for free …

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Back to Basics Part 2 – Succesful Algorithmic Trading

This is the second post in our 3-part Back to Basics series on successful algorithmic trading. You may be interested in checking out the other posts in this series: Part 1: An Introduction to Algorithmic Trading Part 3: Backtesting in Algorithmic Trading There is a lot of information about algorithmic and quantitative trading in the public …

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Intro to Algorithmic Trading – An Algorithmic Trading System

This is the first post in our 3-part Back to Basics series which serve as an introduction to algorithmic trading. You may be interested in checking out the other posts in this series: Part 2: How to Succeed at Algorithmic Trading Part 3: Backtesting in Algorithmic Trading This is the first in a series of posts in …

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Demystifying the Hurst Exponent – Part 2

What if you had a tool that could help you decide when to apply mean reversion strategies and when to apply momentum to a particular time series? That’s the promise of the Hurst exponent, which helps characterise a time series as mean reverting, trending, or a random walk. For a brief introduction to Hurst, including …

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Hurst Exponent for Algorithmic Trading

This is the first post in a two-part series about the Hurst Exponent. Tom and I worked on this series together and I drew on some of his previously published work as well as other sources like Quantstart.com. UPDATE 03/01/16: Please note that the Python code below has been updated with a more accurate algorithm for …

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How to Create a Trading Algorithm: So You Want to Build Your Own Algo Trading System?

This post comes to you from Dr Tom Starke, a good friend of Robot Wealth. Tom is a physicist, quant developer and experienced algo trader with keen interests in machine learning and quantum computing. I am thrilled that Tom is sharing his knowledge and expertise with the Robot Wealth community. Over to you, Tom. Unlike …

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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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