My name is Rod Furlan. I am a Quant, Software Engineer and a Singularity University Alumnus.
Thank you all for the encouraging feedback on my Nexus ATS platform.
I didn’t expect that so many people would contact me about it. Unfortunately, Nexus is a private project and at this point I don’t have the time/resources to turn it into something that I could share.
There are just not enough hours in a day. I would trade everything I have for more time. I hope someone will soon find a “cure” for death and the need for sleep, I seriously do.
Lately I have been receiving a lot of emails asking when will Part 3 of my Pragmatic Automated Trading series be published and I would like to apologize for the delay. The truth is that I haven’t had time to start working on it and it might be a while before I get a chance to do so.
In the meantime, I have gathered a few screenshots of my own private automated trading platform to share with you. It is named Nexus ATS as a homage to Blade Runner’s Nexus 6 (yes, I am a raging nerd).
The idea is that if you follow my Pragmatic Automated Trading series you should be able to build something similar for yourself – for fun and profit.
There are a lot of interesting things I can’t share with screenshots alone – like it’s ticker plant, distributed computing engine or world domination modules – but at least you get to look at the pretty charts. If you have any questions, feel free to contact me. I hope you will enjoy it!
Don’t forget to read the descriptions or else it might be hard to make sense of what you are looking at 
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I would like to recommend a book for all those interested in a straight-to-the-point introduction to the business of automated/quantitative trading. I wouldn’t be able to name a book that does a better job at laying down the basics as Ernest Chan’s Quantitative Trading. In fact, his books cover many topics I had planned for my own Pragmatic Automated Trading series.
While quantitative trading isn’t the most approachable topic (too much academia and too little pratical information) – Ernest does a great job at breaking down a few core concepts into digestible pieces. If you need a place to get started, do youself a favor and order your copy now.
HotArb.com is just a little something I have been working on my spare time. It provides daily alerts about cointegrated S&P 900 stocks that are currently converging from a significant mutual mispricing.
Trade at your own risk
I took last week off to work on a little Twitter mashup with my partner from Wireclub Media.
In short, FxTwits is an open forex trading community powered by Twitter. It captures the Twitter buzz for all the major currency pairs and acts like a forex “squawk box” you can use to get a feel of what currency traders all around the world are up to.
The Scientific-Minimalist-Economic (SME) approach to Automated Trading
The SME is my personal approach to the development of automated trading agents. You could save a lot of time and money by adhering to these three very simple principles.
This article is part of a series dedicated to explaining a no-nonsense approach to the development of automated trading agents. This series is aimed towards people with a computer science background with or without trading experience.
Principle #1 – BE SCIENTIFIC
The Scientific component of the SME method pertains to the use of the Scientific Method to create and refine trading strategies.
Scientific method refers to bodies of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. To be termed scientific, a method of inquiry must be based on gathering observable, empirical and measurable evidence subject to specific principles of reasoning.[1] A scientific method consists of the collection of data through observation and experimentation, and the formulation and testing of hypotheses.[2]
Although procedures vary from one field of inquiry to another, identifiable features distinguish scientific inquiry from other methodologies of knowledge. Scientific researchers propose hypotheses as explanations of phenomena, and design experimental studies to test these hypotheses. These steps must be repeatable in order to dependably predict any future results.Theories that encompass wider domains of inquiry may bind many hypotheses together in a coherent structure. This in turn may help form new hypotheses or place groups of hypotheses into context.
Among other facets shared by the various fields of inquiry is the conviction that the process be objective to reduce a biased interpretation of the results. Another basic expectation is to document, archive and share all data and methodology so they are available for careful scrutiny by other scientists, thereby allowing other researchers the opportunity to verify results by attempting to reproduce them. This practice, called full disclosure, also allows statistical measures of the reliability of these data to be established.
How does it apply to automated trading:
Trading books, magazines and circles are heavy on pseudo-knowledge derived from subjective experience and anecdotal evidence. In fact, there are whole books out there authored by so-called ‘trading gurus’ that do not contain even one single profitable trading system. You should never trust any trading advice to be true before you test it yourself.
The Hypothesis
To beat the market you will need to develop a profound understanding of how it works. When confronted with a potentially profitable trading idea the first thing you need to do is to develop a hypothesis of why you think it should work. This hypothesis will either be confirmed or falsified by the following steps of experimentation and analysis.
The Experiment
Once you have developed a hypothesis of why a given trading idea should be profitable, it is time to undertake an experiment called backtesting to establish if the strategy would have been profitable if you had traded it for a given period in the past.
While a successful backtest wouldn’t be enough to assert that a given hypothesis is true, an unprofitable backtest would be in most cases enough to dismiss it as a valid component of your automated trading system.
The Analysis
With the backtest results at hand you can begin to draw conclusions regarding the hypothesis you are testing. Considering you were able to determine that the backtest results are indeed correct, your major concern should be to establish if the trading strategy could actually be profitable for the near term future.
Later in this series I will discuss several statistical techniques you can use to rule-out most cases where an unprofitable trading idea generates a profitable backtest.
Principle #2 – BE MINIMALIST
The Minimalist component pertains to applying a popular (albeit incorrect) interpretation of the Occam’s razor principle to every single decision you make.
All other things being equal, the simplest solution is the best.
Why should you keep your trading platform code down to a minimum:
Every single line of platform code you write will have to be debugged and maintained for years to come. That is an overhead cost you should not ignore. You should spend your time beating the market – not debugging platform code.
Why should you keep your trading system rules as simple as possible:
Backtesting will not make you rich and every time you add a rule to a trading model that translates into a more profitable backtest result you are very likely doing so in expense of future trading performance.
Later in this series I will discuss how you can assess if adding an additional rule to your trading agent would be beneficial or detrimental to its future performance.
Principle #3 – BE ECONOMIC
We are in this business to make money, not to spend it. I consider it of paramount importance to keep costs down at all times.
Questions to ask yourself before committing money to anything other than a trade:
Real life examples:
Next on Part 3:
Automated Trading Platform – Build or Buy?
This article is part of a series dedicated to explaining a no-nonsense approach to the development of automated trading agents. This series is aimed towards people with a computer science background with or without trading experience.
The development of automated trading agents is one of the most challenging and rewarding applications of artificial intelligence to date. It is rewarding because success generally translates into an account padded with profits and it is challenging because it pits your creations against the combined intelligence of millions of other agents around the globe.
I will be honest with you, the road to profits is full of misleading signs and dangerous detours. Many of you will fail not because you are not smart or talented enough but because you might decide that the personal cost of such venture might just be too high for you.
The market is a formidable foe and to beat it you will have to immerse yourself into it and crack the code from the inside. To create truly intelligent agents you will have to learn many things that will change the way you perceive the world around you.
If you are threading down this path just for the money I can save you a lot of trouble by telling you to go spend your time with something else. There are easier ways to make money out there that do not require the Herculean amount of work and research that is necessary to consistently beat the markets.
What do you need to get started:
A passion for Math – or at least the ability to force yourself to like it. There is no way around it as math will be the main weapon in your arsenal. Don’t think you can make do by simply using formulas you don’t quite understand – you would not stand a chance. It is crucial that you master all the mathematical concepts you decide to use in your trading systems.Next on Part 2:
The Scientific-Minimalist-Economic (SME) approach to Automated Trading
Nicely written, just spent the last two years of my life dedicated to building something sustainable and you have a lot of good points. Math however in my opinion is not the only way to create alpha. Good architecture with the ability to monitor a large segment of the market as well as creative web crawling goes a long way. Also clever frameworks for taking like you say complexity in the universe and boiling it down to something simple is where the innovation happens. Will stop by more often, like your points. – Duncan Krebs
Duncan Krebs, and 
Very cool bud.
-Jeffrey McLarty