At this precise moment, the hottest hiring sector in finance is for Python programmers. Python is the computer language of choice for a lot of the quantitative analysis community, powerful enough to handle big data, flexible enough to cope with the demands of a trading floor and user-friendly enough to work out how to do things in a hurry. Great news for people with that skill set ... for the time being.
The thing is, one of the reasons that people like Python so much is that it’s easy to learn – it was originally invented as part of a project to teach coding to the intelligent layperson. And if there’s one thing trading floors are full of, it’s intelligent laypeople. So ... rather than recruit Python programmers and put them through a crash course in financial markets, why not take your existing financial markets professionals and put them through a crash course in Python?
That’s what Barclays is currently doing with a project in its cash equities business. One of the big uses of quant teams in cash equities is to carry out “post-trade analysis”; going through the data generated by electronic trading systems in order to see what precise transactions the algorithm generated in order to execute a client order, how they interacted with the liquidity that was available at the time, and whether things could have been done better. It’s the quant equivalent of taking an old-school trader out for a beer and asking for the “colour of the market”.
Clients often want their post-trade analysis done in a particular way, to answer specific questions of interest to their own portfolios, though. And you can’t just dump the data into a spreadsheet and get an intern to comb through it; there’s too much of the stuff. So the quant team of Daniel Nehren, Barclays’ head of statistical modelling and development, were being overwhelmed with requests to write Python programs that would delve into the database and extract the precise analysis that a particular trade was looking for.
It was getting in the way of their real quant work, so the quants started the program of teaching the traders to write code for themselves. The aim is to achieve the same sort of step change in efficiency that came about three decades ago, when Microsoft Excel started appearing on trading floor desktops and “running the numbers” ceased to be something that you had to send the desk junior on a trip to “the computer room” to do.
The trouble is, once the traders were able to Excel for themselves, banks found they were able to get along with significantly fewer back and mid-office staff whose lives had previously been spent interrogating mainframes and printing out reports. The top quants who design and optimise the trading systems might be happy with the idea of teaching the traders to code. The significant population of people whose average daily output is a Python program of fewer than twenty lines might have cause to worry.
Elsewhere, we’ve been hearing for a while that under David Solomon, part of Goldman Sachs’ strategy is going to involve competing hard for deals which perhaps aren’t as glamorous as tech unicorn IPOs and global M&A, but which are a bit more reliable and where the margins aren’t driven so hard by league table competition. That is to say, investment banking for mid-size U.S. firms, a market that’s historically seen less dominance from the bulge bracket due to the fact that America is a big place and lots of U.S. companies have headquarters buildings that are a looong Uber ride from Wall Street.
According to informed sources, Goldman had even been considering an acquisition of either William Blair & Co or Harris Williams & Co, two respected banking names with a client profile significantly more MidWest than Midtown. Neither of those deals seem to have happened – both targets and Goldman are denying the rumours – but it looks like ambitious corporate financiers at GS might want to develop a taste for hotdogs, green bean casserole and pizza with ranch sauce.
Increases in volatility tend to be bad for derivatives businesses, and therefore bad for the French banks which concentrate in them. Bloomberg summarises the damage, in terms of former star traders and desk heads at BNPP, SocGen and Natixis who have had to take a step back (Bloomberg)
Raising money is always difficult for a hedge fund, but this isn’t the right solution – Murray Huberfeld of Platinum Parters will go to jail for bribing union officers to get pension scheme mandates (Financial News)
Call him Bill “cheese and pickle” Ackman, because he’s on a roll! As well as having his new marriage profiled in the New York Times, the Pershing Square Capital manager is up 25% for the year (Reuters)
With a slightly sensational use of the word “robots”, UBS aims to introduce as many as 500 new ... apps, basically, to automate routine processing tasks and turn its staff into “superhumans”, in the sense of “mid-office employees with somewhat better computer systems” (Business Insider)
More from Colin Bermingham’s testimony in the Barclays Euribor trial – he now says that according to the “gossip”, he had heard that every bank was involved in Euribor fixing. He might have had to rely on rumours, as yesterday he testified that he had not been invited to his own desk’s cartel chatroom (Bloomberg)
The alleged 2016 “one year no poaching” gentlemen’s agreement between Jes Staley and his former employer (which was investigated at the time by the US Justice Department, which ended up taking no action) is well and truly over, if it ever existed, as Fater Belbachir, a senior equity vol trader, joins Barclays from JPM (New York Post)
Financial services are the biggest user of management consultancy services, with the public sector in second place. The usual names top the league table this year, with a big gap between the Big 8 generalist consultancies and the IT specialists (FT)
What would economic policy look like if it was concentrated on what makes people feel happy? (Resolution Foundation)
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