Posted on Apr 16, 2020 by Kris Longmore

One of the keys to running a successful systematic trading business is a relentless focus on high return-on-investment activities. High ROI activities include: Implementing new trading strategies within a proven framework. An example might be to implement a portfolio of pairs trades in the equity market. Scaling existing strategies to new instruments or markets. For example, porting the pair trading setup to a different international equity market. Well planned iterative research, set up in such a way that you can test and invalidate ideas quickly. This is the kind of research we show in our Bootcamps. Low ROI activities include: Large scale data mining exercises or any research that requires significant effort be expended before the idea can be invalidated. Looking for unique alpha ideas when you could be implementing simple trades within a proven framework. Building your own backtesting platform[footnote]You probably think you’ll learn a ton doing this and you’re not wrong about that – but it’s going to suck a huge amount of your time on something that you can buy in cost-effectively.[/footnote] Building your own execution platform[footnote]Ditto.[/footnote]...

Posted on Nov 24, 2017 by Kris Longmore

This article is a departure from the quantitative research that usually appears on the Robot Wealth blog. Until recently, I was working as a machine learning consultant to financial services organizations and trading firms in Australia and the Asia Pacific region. A few months ago, I left that world behind to join an ex-client's proprietary trading firm. I thought I'd jot down a few thoughts about what I saw during my consulting time because I witnessed some interesting changes in the industry in a relatively short period of time that I think you might find interesting too. Enjoy! Perceptions around Artificial Intelligence (AI) in the finance industry have changed signifcantly, as scepticism gives way to a rising Fear of Missing Out (FOMO) among asset managers and trading houses. Big Data and AI Strategies – Machine Learning and Alternative Data Approaches to Investing, JP Morgan's 280-page report on the future of machine learning in the finance industry, paints a picture of a future in which alpha is generated from data sources such as social media, satellite imagery, and machine-classified company filings and...

Posted on Apr 14, 2016 by Kris Longmore

Disclaimer: I am not posting this at the behest of the developers of Zorro, nor do I receive any form of payment or commission for this post. I felt that I should relay this experience because it was an example of customer service that went way above and beyond the call of duty in terms of its promptness and professionalism. Credit where credit is due. I have been using the Zorro platform for trading systems research for almost two years now. I continue to use it because it combines simplicity, flexibility and power with an integrated development and implementation environment, which also makes it very convenient. It facilitates robust strategy development by providing powerful optimization tools that are designed against curve-fitting.  Data-snooping is, for all intents and purposes, completely designed out of the platform. Providing a direct connection to R enables the user to leverage its extensive array of packages that cover machine learning, econometrics, statistics, time series analysis, and a host of other fields of study. In addition to its research and development functions, Zorro is also an implementation engine, enabling direct connection...