How to survive as a quant AND a trader as Big Data takes over
From eFinancialCareers:
One of the most impactful elements of the big-data era, especially
for traders, is the rise of predictive analytics – using statistics,
data analysis and modelling to identify patterns and forecast future
performance.
This is already starting to influence what financial services firms are looking for. What do traders, data scientists and tech professionals need to know to get hired, avoid getting fired and get promoted?
1. Traders don’t need to die just yet The
new thing among financial services organisations is to use ‘semantic
technologies’ in their analysis of data. This means that the vast amount
of data available to financial services organisations is presented in a
way that is easier for both machines and humans to understand.
Practically speaking, data is no longer siloed and can be analysed in a
unified platform in real-time. But is also means that the ‘old’ skills
required from traders are no longer as valid.
“We joke around the
office that the old style of trading is dead, but all the old traders
aren’t dead yet,” said Howard Getson, CEO, Capitalogix Trading, which
develops hedge fund technology with artificial intelligence (AI) trading
systems that evaluate global markets in the cloud at the Trading Show
Chicago 2016. “It must be scary to be somebody like that – you’ve got a
buggy whip, which is a horse-drawn carriage, and even if you had the
best driver on the planet, he’d be thinking how to make the buggy whip
better, rather than inventing a car.
“We now have AI that enables
us to create trading systems that are better than a human is ever going
to do it, with millions of algorithms, so I don’t hire traders to be
good traders – they have to be skilled, but I care more about their
contacts, human capital, judgement, energy, hard work and their ability
to make our firm better,” he said.
Getson said that predictive
analytics gives traders a much better understanding of data and
therefore allows them to deploy capital where they expect it to make
money at all times, taking much less risk to generate better returns.
“Technology
can act as a sensor that does market surveillance to a greater extent
than ever before,” Getson said. “Everybody has the same data, and most
people are looking at market price and market time, but there are other
ways to look at it. To be a successful trader, you have to find a way to
do things differently from other people to get a different result.
Predictive analytics is making the invisible visible.”
2. Quants with people skills and traders who can code It
can be difficult for hedge funds and quantitative trading shops to find
candidates with the right blend of skills. Computer scientists are
coders, or mathematicians, and they often don’t really understand the
trading models, said Samuel Chen, vice president and quantitative
researcher at Seymour Capital Management.
“Using Python, it’s easy
for someone on the research side to back-test [a trading model] and see
if it works,” Chen said. ”You can implement a trading system in Python,
so in hiring people, I’m looking at trading experience and someone who
can generate alpha but also knows how to code.
“I will hire C
coders if necessary, someone who can throw in models and figure out
which ones are useless,” he said. “If the performance is not great based
on a certain model, it’s difficult to tell if the model is bad or the
timing is off.”
Partners and salespeople bring quants to the fundraising meetings now, because investors want to talk to them.
“We
hire for a unique role, someone can talk to people but also knows their
stuff,” Chen said. “For us language is a problem with people coming
from various fields – it takes time to get to a point where we can have a
conversation on the same topic.
“Money is made by finding pockets
of concentration, using an algorithm to find it on a risk-adjusted
basis, so you hedge it,” he said. “We’re in futures because we believe
that’s where money is made, but we hedge that with equities.”
3. Data scientists need to understand tradingNoticing
a pattern here? Data science might be the new must-have skill-set, but
having this alone is not enough. Those who can code are being hired on
Wall Street, but an understanding of the markets and working well with
other people is essential as well.
“Managers say, ‘I need someone
to develop for me’ – most are hiring candidates not just based on their
skills like Python and MATLAB but also have they been executing their
own strategies,” said Sri Krishnamurthy, the founder of
QuantUniversity.com. “They don’t want someone with all these ideas but
doesn’t have the ability to execute....MORE
