About Us & Our Philosophy

First & foremost, we're all about integrity

GENUINE SUPPORT
It can be difficult to justify giving money to a fund manager if they don’t have their own money in the fund. Their risk-management may be skewed by shorter-term incentives for bonuses, whilst a major blow-up, at worst, costs them a job (while they walk away with their wealth in tact). At Quant Alpha our own money is heavily invested into our algorithms, run with our own software. When we invite others to utilise our technology, we are sharing the on-going maintenance & support that can only come from a dedicated business owner with their own money on the line.

We believe in, and love what we do.

Our team

Integrity. Foresight. Discipline.
SIMON
MD & SYSTEM DESIGNER
Simon has over 20 years of experience in the investment banking and financial markets industry. Initially trained in risk management, he has always been involved in building applications for the trading floor. Trading on his own account throughout this time, once he discovered systematic trading there was no turning back. When Simon is away from his desk you’ll find him surfing, rock-climbing or mountain-biking.
MATT
CHIEF QUANT DEVELOPER

Matt has over 30 years of software development experience and extensive work experience in research and development with quantitative trading firms. Matt has built & tested models for clients all over the world and has the unique capability to marry development skill with a deep knowledge of markets and market infrastructure. Matt is an avid traveller and loves his scuba diving.

 

MARTIN
TECHNOLOGY MANAGER

Martin has over 15 years experience in information technology encompassing operational technology / mission-critical systems through to cyber-security / network-domain security and linux / windows server management for large organisations. With a deep breadth of knowledge across OT and IT fields, he can handle any task he takes on. Martin loves his motorcycles and the ocean.

 

A few of our market-beliefs that shape our thinking
Market Inefficiency
We learned at university that markets were efficent and full of rational decision-makers… then we entered the real world. Evidence of bad, emotional decisions and behavioural biases is everywhere. Hence we love the idea of creating robust systems before risking real money, and then getting out of the way, least we interfere with a good thing. The theory of behavioural finance provides compelling evidence that markets are not, in fact, efficient.

Diversification
We are entrepreneurs at heart and love all forms of investing. Building and analysing businesses & investing for the long-term in sound companies is a wonderful idea. We hear about expert fund managers and research analysts producing all sorts of fantastic analysis – but for the most part their returns are lack-lustre (to put it kindly). They do tend to have a great story, and stories sell, but we are focussed on serious out-performance & genuinely belive the return profile from trading a diversified group of ‘all weather’ algorithms provides the best risk-return profile. By building a diversified set of strategies, we aren’t limited to “value investing” or any other single alpha factor. We can do mean reversion & trend following; shorter-term & longer-term; equities; currencies; commodities; rates; long & short. The value of this diversfication is that we can sum our returns, without summing our draw-downs.

Behavioural Finance
Below we touch on some of the cognitive biases that impact decision-making in the markets. The hypothesis that fear, greed, over-confidence, remorse and other emotions can result in mis-pricing of securities is born out by our extensive research. Our approach to generating alpha in the markets is to base our own models upon sound principles of human behaviour – which are as old as humans themselves.

Exploiting a niche
If we wanted to be a part of the crowd, or thought like everyone else, we would have taken the easy road. But we took the one less travelled, and that made all the difference.

Systematic & Quantified
On a podcast interview with a value-investing fund manager, the interviewer made the following insightful comments: “One thing we would always focus on when I was looking at funds was whether the manager was ‘true to label’ … Results are transient, but what you are really buying is the process.”
Algorithmic trading represents the single most “true to label” form of investing – where there is no deviation from the plan. The benefit of having asked, and answered all the practical questions like, “how often has this happend in the past?”, & “what happened next?” is manifest.

Decision-making in trading & investing

Our philosophy emphasizes concepts that we expect to play out in the markets - shaping the way we build our algos.
Here's a brief summary of some examples of those concepts and how they relate to trading:

Confirmation Bias
Confirmation bias is a classic! No doubt we have all fallen victim to it at one time or another. It is simply the tendency to interpret, believe or recall information that affirms one’s existing beliefs. If the evidence is ambiguous, often it will be interpreted in a way that supports an existing position.

Experiments (or resarch) designed to prove (not disprove) a theory can result in illusory correlations being found. In building investing models it is important to search for information that supports our hypotheses, but it is equally important to search for information that disconfirms our ideas! Being open-minded is key. Our goal is to find real and lasting inefficiencies that can generate profit, not feed our egos.

Anchoring
Experiments show that individuals tend to depend too heavily on the first piece of information they have when making decisions. Similar to framing (below), the answer to a second question depends which question was asked first. We don’t want to assess probabilities incorrectly, based on a subjective reference (anchor) point!

If market participants are prone to over-reacting to news, then we stand ready to capitalise on their bad judgement.

Availability Bias
If a coin flipped 10 times in a row and yielded 10 heads, what is the more likely outcome of the next flip?

Of course, heads and tails are both still equally likely. The belief that the next flip is more likely to be tails because it’s “due to come up” is known as the gambler’s fallacy, which is an example of the availability bias. Essentially, this bias occurs when our estimates of probabilities are influenced by what is most “available” in our memories. Avoiding biases like these are very much why we love quantitative trading.
Framing
In a famous experiment about the psychology of choice, Tversky and Kahneman shed light on the problem of framing; how we ask a question, or frame a problem, can influence our choice of outcomes. Traders constantly assess alternatives in making decisions about prices. In evaluating alternatives, it is important to understand that a mistake framing the problem could lead to choosing an inferior alternative.
Game Theory
The market is comprised of multitudes of supposedly rational decision-makers interacting under uncertainty, moment by moment. How we expect others to act or react to particular events (which can depend on their biases as mentioned above) is crucial information. If we can measure the probabilities associated with these decisions – even better! All prices are determined by supply and demand urgency associated with buyers and sellers. The one sitting at the poker table with a solid understanding of risk management and capital preservation, and an appreciation for how past actions of their opponents provide insights into how they think, is sitting in a strong position!
WHILST IN THE LONG TERM WE WOULD LIKE TO BELIEVE THAT PRICES TEND TOWARD FAIR VALUE, THIS VALUE IS NEVERTHELESS SUBJECT TO THE COLLECTIVE DECISION-MAKING OF A MYRIAD OF DIFFERENT PARTICIPANTS. THEIR BEHAVIOUR CHANGES LITTLE, AND AS SUCH, WE SEE THE PERSISTANCE OF ANOMOLIES SUCH AS MEAN-REVERSION AND TREND-FOLLOWING - BOTH OF WHICH ARE CRITICAL COMPONENTS TO A WELL DIVERSIFIED PORTFOLIO.
In God we trust, all others must bring data.
- Attributed to W. Edwards Deming
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All the information contained on this website is general in nature and does not constitute personal or investment advice. Quant Alpha produces algorithms and software only and does not trade or arrange any trading on your behalf. Quant Alpha will not accept liability for any loss or damage, including without limitation, any loss which may arise directly or indirectly from the use of, or reliance on: its algorithms; the information on this site; or information provided by its managers, partners or affiliates. You should seek independent financial advice and conduct your due diligence prior to acquiring any Quant Alpha technology. Quant Alpha is neither a registered investment advisor nor an investment advisory service and does not provide any recommendations to buy or sell particular financial products. 
Before engaging in any trading activities, you should understand the nature and extent of your rights and obligations and be aware of the risks involved. Don’t trade with money you can’t afford to lose. Your trading and investing decisions are entirely your own responsibility. All securities and financial product transactions involve risks. Where Quant Alpha provides hypothetical representations of what the technology has achieved in the past, this has been done with the greatest know-how, data and expert technology that is available, but still, Quant Alpha cannot guarantee that these results have any likelihood whatsoever of being achieved in future. Where records have been provided of how the software has performed on management’s own accounts, whilst these are an accurate and true record of what has taken place in the past, they are not necessarily indicative of future results – the future is as unknown to Quant Alpha management as it is to anyone else. The past performance of any trading system or methodology is not necessarily indicative of future results.