My name is Kris and I’m the human behind the robot.
I left behind a ten-year engineering career to tackle the world of finance and trading. Over the years that followed, I became a hedge fund quant and later consulted to some of Asia-Pacific’s biggest fund managers on the adoption of artificial intelligence and machine learning. These days, I am a partner and shareholder of a proprietary trading firm. I spend my days searching for new sources of alpha using the most exciting and cutting edge tools of the modern age, like machine learning and artificial intelligence.
It was a long and at times arduous journey from novice to professional trader, but it is a journey that I am both proud of and grateful for. It was difficult because of the huge ocean of information that aspiring traders are confronted with – much of which is misleading, ill-informed and in some cases dangerous – but I successfully navigated this ocean and as a result I know precisely what DIY traders need to focus on to succeed.
I want to share that knowledge with you and show you the things I learned that enabled me to make my dream a reality.
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My professional background is mechanical and environmental engineering. For over a decade I worked in the resources industry and travelled to some of the most remote parts of Australia. It was a job that took me to some fascinating and beautiful places. I learned a lot along the way and had some fun, but it was also just a job that I had essentially fallen into. It was never a passion.
Several years ago, a friend introduced me to the financial markets via a ‘system’ that promised to unlock their profit potential. I read about market psychology and technical analysis and spent a lot of my spare time at my desk watching the markets and placing trades.
I made some money and I lost just as much.
But I couldn’t ignore the scientific part of my brain – that little voice in my head that demanded evidence that this ‘system’ actually worked.
I decided to test this system for myself and devised an experiment. I simulated the entry and exit parameters of this system and tested its actual performance on as much historical market data as I could get my hands on. At the time, I didn’t know that what I had done was called a ‘backtest’, but it revealed that this system was a complete farce.
The upside of disappointment
As disappointing as this was, there was one hugely positive outcome of my experience: I had taught myself the basics of coding and simulation as they relate to the markets, and I had opened a door that both stimulated me intellectually and might just make me some money. I was completely hooked on algorithmic trading.
I spent every spare moment learning how to write trading algorithms, researching and experimenting with different strategies and approaches. I delved even deeper into the world of machine learning and added multiple programming languages to my repertoire.
This multi-year, self-taught journey towards mastery was long and difficult. In particular, I learned that proficiency with the tools – programming, computing and simulation – while an important prerequisite, is the easiest part of the whole process. Far more challenging was learning to use the tools in such a way that enables the development of useful, robust trading systems that actually work.
I also learned the importance of doing my own research, compiling my own evidence and arriving at my own conclusions rather than accepting assumptions about market behaviour that I’d read elsewhere. In fact, this simple truth was my Eureka moment that propelled my knowledge and skills to the next level.
The door to a new world opens
As my skills grew, I began to see some success with my trading. I also started doing freelance research and development projects on the side to both add to my trading capital and to connect with people in the industry. Over time, this led to some proprietary trading work and private allocations from investors who backed my approach.
One thing led to another, and I was finally approached by a hedge fund to set up a systematic trading desk to compliment their existing discretionary approaches. We focused on quantitative and machine learning based trading systems. Needless to say, this immersion in the world of professional money management was incredibly stimulating and provided the polish that the skills I’d developed in my spare time desperately needed.
It was during this period that I recognized the huge demand for quants who could speak the diverse languages of business, finance, big data and machine learning and provide on-demand skills in those areas. Thus my consulting company, Quantify Partners, was born, and we were fortunate enough to work on incredibly interesting projects in the professional finance space, like building algorithmic execution systems based on deep learning and high-frequency order flow data – geek heaven.
The consulting experience introduced me to a huge network of money managers and finance professionals in Australia and throughout the Asia-Pacific. I sat down with CEOs of some of the region’s biggest fund managers and exchanges to help them adopt machine learning and artificial intelligence. I travelled all over the region and was fortunate enough to peek into some normally very secretive institutions. I was eventually engaged by a small, technology-focused proprietary trading firm in my home town of Sydney and I hit it off so well with the team that they asked me to join them on a full-time basis as partner and shareholder. It is the perfect environment for me to indulge my passion for technology and the markets in a laid back, supportive environment. In short, it’s a dream come true.
The origins of Robot Wealth
I started Robot Wealth simply as a place for me to share my finance-related research and plug into the online algo trading community.
Prior to transitioning to a full-time career in the markets, I often felt like the learning process involved rooting around in the dark, stumbling across ideas and regularly finding myself moving in many different directions at once. There was little structure to my learning process and even less clarity about where I was heading.
As I published more and more content, readers started reaching out to me and sharing their own ideas and progress. It was something of a revelation to be able to connect with like-minded people who, I discovered, were often on a similar path and were encountering similar issues to the ones I’d had to deal with on my own journey.
It was obvious that these people would benefit from learning about what had worked for me. So I built a knowledge repository that I wish I’d had access to when I was starting out. It is designed to cut through the noise and hone in on the most important knowledge required for successful algorithmic trading, from programming essentials, to statistically sound backtesting and optimization, to implementing robust trading systems based on real market anomalies.
Spreading the love
My path to algo trading proficiency was by no means easy. No path to success is. But it could have been a lot easier if I had known what I know now – if I had known where to look, what to read, what tools work best, who to connect with.
Empowering investors and aspiring traders through knowledge is my passion and Robot Wealth’s reason for existence.
I’ve been asked a few times why I go to all the effort of sharing my knowledge with the DIY trading community. In particular, some people ask why I don’t just sit back and trade. Ignoring the fact that you don’t just ‘sit back’ and let the algos do their thing, I don’t want trading to be the sum total of what I do. I am a problem solver at heart, and I love working on interesting and challenging research and development projects. I also love teaching and mentoring, which provides an enormous amount of personal satisfaction that I simply don’t get from trading alone. My early experiences of learning algo trading in a vacuum have really instilled in me a deep belief in the power of community and knowledge sharing.
Community and knowledge sharing is ultimately about empowerment, and financial empowerment is a big deal. For example, management of retirement funds is an industry worth billions in annual management fees, yet the majority of funds consistently fail to beat the benchmark. Why pay billions in fees for this sort of performance when the tools to manage your own funds are so accessible?
Professional and DIY money managers
As a rule, the funds management industry is fairly secretive about what it does. It’s not really open to outsiders. The top performers don’t want to give away their secret sauce (and the cynical part of me suspects that many others are content to hide their lack of sophistication). But I don’t think it needs to be this way, at least for individuals. I personally don’t see the need to be super-secretive about what I do. Sure, it would be less than sensible to publicly share the details of a system that has an investment backing, but to me it makes complete sense to share a general approach.
As individuals, we aren’t competing with each other. A great analogy is that as individuals, we are like fishermen standing on the rocks casting our lines into the ocean. We aren’t competing with others doing the same thing as there is more than enough for each of us to catch a few fish. However, we are competing with the big ocean trawler that sits out past the waves and not only takes more than it probably should, but can also be extremely wasteful. Knowledge and community equates to power in competing with the trawlers.
Also, whenever I share some part of my approach, I invariably get something valuable in return. Sometimes it is as simple as understanding on a deeper level through articulating the concepts. Sometimes it is refinement through feedback, suggestions and ideas that would never have occurred to me. Other times it is simply a new connection with a like-minded person. Very rarely have I regretted sharing what I’ve learned with another person.
I don’t want to give the impression that I want to be the Robin Hood of the finance world. I don’t. I absolutely enjoy making money as much as the next person. I also enjoy the challenges of working in the institutional space, which are different to the DIY space. But the money is only one part of the whole story and would be kind of boring and unfulfilling on its own. I’d also love to see more DIY traders meeting their objectives, as well as a more efficient retirement fund industry that returned more money to investors’ pockets.
I wrote Robot Wealth’s keystone course, Fundamentals of Algorithmic Trading, as a step-by-step, practical guide for complete beginners interested in learning a systematic approach to the markets in general and algorithmic trading specifically.
I followed this up with Advanced Algorithmic Trading , which builds on the principles in the Fundamentals course, and introduces a whole range of advanced statistical, quantitative, and machine learning tools used in professional practice. It teaches you how to think like a quant and put your ideas to the test by designing statistically sound experiments. You’ll also learn how to build portfolios of trading algorithms across multiple markets and time scales that diversify risk and compound returns. It’s full of worked examples, trading strategies based on various market anomalies, and tools for increasing the productivity of your self-managed trading business. It is the most comprehensive treatment of algorithmic trading that I have ever seen.
Together, the courses represent all the stuff that I wish I’d had access to when I started.
The courses encapsulate the knowledge that worked for me: the things that enabled me to manage my trading and investing capital independently, and ultimately to leave my day job to forge a career trading and consulting to professional money managers.
The Robot Wealth courses are the cornerstone of the service we provide, but we are also more than education providers. Through our members’ forum, we provide a vibrant hub for collaboration and ideas exchange amongst likeminded individuals at all levels of skill and experience. At the time of writing, we count amongst our active members complete beginners, ex bank traders, and everything in between. Our members are also assisted by our growing library of pre-coded tools and research frameworks written in various programming languages, including a framework for efficient experimentation with machine learning and a suite of robustness testing tools.