About Us & Our Philosophy
First & foremost, we're all about integrity
We trade with you
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. 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.
Decision-making in trading & investing
Decision-making is integral to trading.
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 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.
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.
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.
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.
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 which are critical components of our models.
Integrity. Forsight. Discipline.
managing director - 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.
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 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.