The term “quant trader” gets thrown around a lot these days. For any trader who has been in the industry for more than a decade, the adoption of the term is driven by survival. There’s a running joke in some HFT circles: these days, older traders would never get past HR using the same criteria by which junior traders are hired. Junior traders must be data scientists and traders.
This evolutionary change has been driven by three factors. The first is the adoption of data analytics technology by the wider economy. (1) The proliferation of data as a science has created a huge pool of humans with the potential to engage in quant trading.
While technologies like Python, R, and Scala have enabled the growth of a larger potential population of quant traders, (2) the financial markets have undergone a tectonic shift towards automated trading which has increased the demand for the very skills quant traders possess.
The final factor is (3) the growth of financial technologies which allow data scientists cum novice quant traders to automate trading strategies in a live market environment. These platforms are a recent development and while they aren’t trading with much capital in aggregate, these new sources of trade flow seem likely to disrupt some of the more commoditized quant trading strategies.
Far from being a homogenous group, however, the nature of a quant trader is profoundly path dependent: it matters greatly whether a quant trader:
- Became a quant before becoming a trader
- Became a trader before becoming a quant
Thus, “Quant trader” as a term is not a singular concept but a spectrum of people on their journey from one pole to the center. In the middle lies the pure quant trader, perfectly balanced between quant/trader tendencies and thus as glorious and rare as the mythical unicorn (not the VC variety, mind you).
How should we define these poles?
Traders want to make money, Quants want to describe the universe
These are obvious generalizations but serve as a useful mnemonic. Let us examine further as this distinction has important risk management implications.
- Who? Data Scientists, Computer Scientists, Physicists, Engineers, and other Academics
- What? Prize beauty, consistency of thought, unified theories
- When? Most likely to blow up because they let a strategy run too long (overconfidence in the model)
- Why? Driven by a desire to be perceived as smart, with wealth being a nice side effect
- Who? Business School grads, Liberal Arts majors, Armed Forces, all other sources
- What? Prize simplicity and PnL above all else; stylized facts can be inconsistent as that’s normal
- When? Most likely to blow up because they size a strategy too big (overconfidence in themselves)
- Why? Driven by a desire to be wealthy, with being perceived as smart being a nice side effect
Most quant traders lie somewhere between these two poles, but have a tendency to adhere closer to their original pole.
Because of this, we believe that in an institutional context, risk managers should try to balance the overall composition of a trading desk between the two poles.
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