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...

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 more) and it has become an important tool in my workflow. In this post, I’m going to show you how to set up a GCP cloud compute instance to act as a server for hosting a trading algorithm. I'll also discuss why such a setup can be a good option and when it might pay to consider alternatives. But cloud compute instances are just a tiny fraction of the whole GCP ecosystem, so before we go any further, let's take a high level overview of the various components that make up GCP. What is Google Cloud Platform? GCP consists of a suite of cloud storage, compute, analytics and development infrastructure and services. Google says that GCP runs on the very same infrastructure that Google uses for its own products, such as Google Search. This suite of services and infrastructure...

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, as many backtesting and trading scripts that relied on such data will no longer work. Users of the excellent R package [crayon-5c709f0809f8c654248169-i/]  however are in luck! The package's author, Joshua Ulrich, has already addressed the change in a development version of [crayon-5c709f0809f95582626937-i/]. You can update your [crayon-5c709f0809f98584860254-i/]  package to the development version that addresses this issue using this command in R: [crayon-5c709f0809f9b684550504-i/] Of course, you need the [crayon-5c709f0809f9d203631803-i/]  package installed, so do [crayon-5c709f0809fa0080396694-i/]  first if you don't already have it installed. Once the package updates, [crayon-5c709f0809fa2599304056-i/]  should work just as it did prior to the updates on the Yahoo Finance server. I can verify that this worked for me. Of course, if you don't want to update quantmod to a version that lives on a Git branch,...

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, 2008), but because it is ultimately about approaching the markets with a critical, inquisitive mindset, while not taking oneself too seriously. I think we can all do with a dose of that sometimes. Gary's style is unique: this is the work of a free and critical thinker who is not afraid to question the status quo. While articulately drawing from a range of sources, from Shakespeare to Bacon and Einstein to Buffet (even Thomas Conrad's 1970 book Hedgemanship: How to Make Money in Bear Markets, Bull Markets and Chicken Markets While Confounding Professional Money Managers and Attracting a Better Class of Women, which has got to be the greatest title in the history of trading books), Gary comes across as playful and slightly eccentric (which is wonderfully refreshing...

  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 here. Nearly all research related to algorithmic trading is empirical in nature. That is, it is based on observations and experience. Contrast this with theoretical research which is based on assumptions, logic and a mathematical framework. Often, we start with a theoretical approach (for example, a time-series model that we assume describes the process generating the market data we are interested in) and then use empirical techniques to test the validity of our assumptions and framework. But we would never commit money to a mathematical model that we assumed described the market without testing it using real observations, and every model is based on assumptions (to my knowledge no one has ever come up with a comprehensive model of the markets based on first principles...

[vc_row][vc_column][vc_column_text] This is the second 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 3: Backtesting in Algorithmic Trading We've also compiled this series into an eBook which you can download for free here. [/vc_column_text][vc_column_text]There is a lot of information about algorithmic and quantitative trading in the public domain today. The type of person who is attracted to the field naturally wants to synthesize as much of this information as possible when they are starting out. As a result, newcomers can easily be overwhelmed with “analysis paralysis” and wind up spending a lot of their valuable spare time working on algorithmic trading without making much meaningful progress. This article aims to address that by sharing the way in which I would approach algorithmic trading as a beginner if I were just starting out now, but with the benefit of many years of hindsight. This article is somewhat tinged with personal experience, so please read it with the understanding that I am...