Saturday, March 24

School for quants

Here is an interesting overview of a school, Kannu. Since you are interested in trading and are quite good in Maths, you will like this story about this school. Can you see yourself studying here?

Mind you, I was a pointy headed rocket scientist quant as well once upon a time and spend 3 years in a similar situation up in Manchester dealing with computer science, economics, financial mathematics and other lovely subjects in a hot house atmosphere where it was great fun. I had my eureka model 13 months into the PhD and then I took my 32 page dissertation to my professor and said, there you go, I have done my PhD. lol. He said, great work, now go and do 10 more Eureka's and build the dissertation up to 100,000 words. You were conceived between the 2nd and 3rd chapters and born between the 9th and 10th chapter. It is a fun time, son, to work with data and see how patterns emerge. How your models forecast the future and there is fun in coding and programming.

Still, read up on this, you might want to consider this, although City University has a good course on computational finance but the best is in Carnegie Mellon in the states. Wharton and NYU are also quite good.



School for quants
By Sam Knight
©Richard Nicholson
Students look at equations from a PhD thesis that uses Bayesian analysis to examine the relation between bond trades and economic data releases. Got that?
On a recent winter’s afternoon, nine computer science students were sitting around a conference table in the engineering faculty at University College London. The room was strip-lit, unadorned, and windowless. On the wall, a formerly white whiteboard was a dirty cloud, tormented by the weight of technical scribblings and rubbings-out upon it. A poster in the corner described the importance of having a heterogenous experimental network, or Hen.
Six of the students were undergraduates. The other three were PhD researchers from UCL’s elite Financial Computing Centre. The only person keeping notes was one of them: a bearded, 30-year-old Polish researcher called Michal Galas. Galas was leading the meeting, a weekly update on the building of a vast new collection of social data, culled from the internet. Under the direction of the PhD students, the undergraduates were writing computer programs to haul millions of pages of publicly available digital chatter – from Facebook, Twitter, blogs and news stories – into a real-time archive which could be analysed for signs of the public mood, particularly in regard to financial markets. Word of the project, known as SocialSTREAM, had reached the City months ago. The Financial Computing Centre was getting calls most days from companies wanting to know when it would be finished. The Bank of England had been in touch.
During the meeting, Galas asked each undergraduate about a particular corner of the database. Most of the time, the language was computer: impenetrable exchanges about batching, pseudo-storage and the risks of propagating on tiers. “Should that work in a distributed field as well?” an undergraduate asked. “If you have different connectors running on different machines, you might start duplicating…” Galas considered this. “Personally,” he replied, “I would run the connectors outside the cloud machine.”
©Richard Nicholson
Philip Treleaven, director of the Financial Computing Centre, believes that the meeting of computing power and fine young brains will transform problem-solving
Every now and again, though, the discussion became comprehensible. The students discussed annoyances – so much data about animals! – and possibilities. One of the PhD students, Ilya Zheludev, talked about “Wikipedia deltas” – records of deleted sections from the online encyclopaedia. Immediately, the students hit on the idea of tracking the Wikipedia entries of large companies and seeing what was deleted, and when.
The mood of the meeting was casual and exacting at the same time. Galas, who is from Gdansk and once had ambitions to be a hacker, is something of a giant at the Financial Computing Centre. One of the first students to enrol in 2009, he has a gift for writing extremely large computer programs. In order to carry out his own research, Galas has built an electronic trading platform that he estimates would satisfy the needs of a small bank. As a result, what he says goes. Galas closed the meeting by giving the undergraduates a hard time about the overall messiness of their programming. “I like beauty!” he declared, staring around the room.
The Financial Computing Centre at UCL, a collaboration with the London School of Economics, the London Business School and 20 leading financial institutions, claims to be the only institute of its kind in Europe. Each year since its establishment in late 2008, between 600 and 800 students have applied for its 12 fully funded PhD places, which each cost the taxpayer £30,000 per year. Dozens more applicants come from the financial industry, where employers are willing to subsidise up to five years of research at the tantalising intersection of computers, data and money.
As of this winter, the centre had about 60 PhD students, of whom 80 per cent were men. Virtually all hailed from such forbiddingly numerate subjects as electrical engineering, computational statistics, pure mathematics and artificial intelligence. These realms of knowledge contain concepts such as data mining, non-linear dynamics and chaos theory that make many of us nervous just to see written down. Philip Treleaven, the centre’s director, is delighted by this. “Bright buggers,” he calls his students. “They want to do great things.”
In one sense, the centre is the logical culmination of a relationship between the financial industry and the natural sciences that has been deepening for the past 40 years. The first postgraduate scientists began to crop up on trading floors in the early 1970s, when rising interest rates transformed the previously staid calculations of bond trading into a field of complex mathematics. The most successful financial equation of all time – the Black-Scholes model of options pricing – was published in 1973 (the authors were awarded a Nobel prize in 1997).
©Richard Nicholson
Stathi Panayi is trying to devise a way of measuring liquidity in financial markets – this could help regulators intervene early and prevent events such as the 2007 credit crunch
By the mid-1980s, the figure of the “quantitative analyst” or “quant” or “rocket scientist” (most contemporary quants disdain this nickname, pointing out that rocket science is not all that complicated any more) was a rare but not unheard-of species in most investment houses. Twenty years later, the twin explosions of cheap credit and cheap computing power made quants into the banking equivalent of super-charged particles. Given freedom to roam, the best were able – it seemed – to summon ever more refined, risk-free and sophisticated financial products from the edges of the known universe.
Of course it all looks rather different now. Derivatives so fancy you need a degree in calculus to understand them are hardly flavour of the month these days. Proprietary trading desks in banks, the traditional home of quants, have been decimated by losses and attempts at regulation since the start of the financial crisis. There is nothing like the number of jobs there used to be.
Moreover, among some older quants at least, there is a feeling that the era of genuine discovery is over. Pioneering thinking in the 1970s and 1980s has long been programmed into the most idiot-proof trading software. You can download an Excel spreadsheet of the Black-Scholes model in a few seconds. I went to see Piotr Karasinski, a former head of quantitative analysis at HSBC, who has a model named after him: the Black-Karasinski model for short-term interest rates, which he developed with Fischer Black (of Black-Scholes). “I think that the field is shrinking,” said Karasinski, in his quiet office at the European Bank of Reconstruction and Development. “Very few people do original work.”
You don’t hear that kind of talk at the Financial Computing Centre. And that is mainly down to its founder, Professor Treleaven. Treleaven was the manager of a holiday camp before he went to study computer science at Brunel University in 1964. One of his father’s friends had heard that computers might be the next big thing. “I took a punt on that,” Treleaven says, and for the past 47 years – 30 of them at UCL – he has enjoyed throwing his machines at pretty much any problem he hears about.
Treleaven began working with the financial industry in the late 1980s. His first project – an attempt to use artificial intelligence to forecast markets – was a failure. But he hit pay dirt with his second: an automated fraud-detection system for the London Stock Exchange. Over the years, the occasional phone calls to his office from financial firms became regular, and by the early 2000s, Treleaven and his department was caught up in the wave of innovation sweeping the industry. Until recently, by far the most popular topic for both his students and his City contacts was the apparently limitless world of algorithmic trading.
In its starkest form, algorithmic trading is the replacement of human decision-making in financial transactions with computer programs. An algorithm – a series of instructions (when to start trading, when to stop, how much risk to take) – issues its own orders to buy and sell. In theory, algorithms can do anything, but in practice they work along a spectrum from simply executing trades to coming up with their own ideas to make money. At their most advanced, and Frankensteinian, algorithmic trading strategies independently scour the world’s financial markets, looking for discrepancies, statistical correlations and arbitrage opportunities, trading most of the time against each other. High Frequency Trading – mass automated dealings of this type – now accounts for about 75 per cent of all US equity transactions.
To some, algorithmic trading is a harbinger of a world out of our control. Robert Harris’s recent novel, The Fear Index, was inspired in part by the “flash crash” of May 6 2010, in which the New York Stock Exchange plunged crazily and then recovered after algorithms responded to an unexpectedly large order in the electronic futures market. To others, algorithmic trading shows just how far the automation of financial markets has yet to run. “People say about algorithmic trading, ‘They’re just a bunch of cowboys, you know.’” Treleaven shook his head. “No,” he said. “It is industrialisation. It is like putting robots in car factories.”
©Richard Nicholson
Ilya Zheludev has studied 500,000 internal Enron emails to show a spike in emotion in employees in April 1999, just months before the company’s stock took off
Treleaven’s excitement stems, at least in part, from the fact that financial disasters are just as interesting to academics as success stories. When I asked him whether the ongoing agony in the economy had thrown up research opportunities, he said: “Oh yes, absolutely… the mother of invention is all something, you know? Look at what happened in the war, you have loads of scientific breakthroughs.” But the professor’s real animus is that he believes that what has been taking place in the financial industry – a heady meeting of computing power and the finest young scientific brains – is about to break into the rest of the social sciences. That is because what Treleaven’s students do, what quants do, is find patterns in oceans of electronic data. In a hedge fund, that might mean finding relationships between price movements and then trading on them. In public health, it might mean tracking millions of pharmacy transactions and spotting the next outbreak of flu.
“They think of themselves as doing computational finance,” Treleaven said of his students, “but let’s jump forward: people who are interested in politics in the next 10 years will be doing computational politics.” The calculating power and analytical techniques used in finance could also model the impact of public policies, or seek insights in sport and education. This year, for the first time, Treleaven has a psychology graduate among his students and he enjoys telling undergraduates from other faculties – economics, music even – that they should learn how to program computers if they want to stand a chance in the world that is coming.
Most of Treleaven’s students have the most obvious destination in mind, however: the trading floor. One afternoon I met Mahnoosh Mirghaemi, a 29-year-old Iranian who was awarded the centre’s first PhD last October. Mirghaemi brought her thesis with her: “Bayesian Learning in Financial Markets”. Bayesian learning is very voguish among quants at the moment. It uses a probability theory first devised by Thomas Bayes, an 18th-century English clergyman, to create financial models that learn and adapt to new information.
Mirghaemi spent two years using Bayesian techniques to study how European bond markets responded to 3,077 separate releases of economic data between 2007 and 2008. She studied 1.6 million bond trades and figured out which pieces of news moved the markets more, and which ones analysts and traders were more likely to forecast poorly. “It made my eyesight like a double,” she said. But Mirghaemi’s research should now, in theory, allow traders, and trading algorithms, to position themselves better on an hour-by-hour basis. “It definitely makes money,” she said.
Mirghaemi was hired by BNP Paribas last summer. A few months later, her boss – a trader for 37 years – mentioned that he could never work out the simultaneous price and position of a trade. On the spot, Mirghaemi wrote down a cosine formula from physics useful for measuring electromagnetic waves. “He was just looking at it,” she said. Mirghaemi emphasised her respect for her seniors at the bank but she said that she felt different. “I think I come from the new generation,” she said, “looking at the finance, the economic, the engineering, the computing altogether.”
©Richard Nicholson
Michal Galas, one of the PhD researchers at the centre, has built an electronic trading platform that he estimates would satisfy the needs of a small bank
There was a touch, almost, of sympathy in the way that Mirghaemi described colleagues coming to terms with the changing nature of the markets. “Their minds are like, ‘We know as economists this is what is happening, or should be happening,” she said. “But the real world says ‘No.’ The computer systems and all these quant people are changing the market much more rapidly than they actually want to.” And not necessarily for the better. When asked whether she thought all these quants made for more stable financial markets, Mirghaemi looked at me and said: “It is very, very risky and it brings a lot of volatility to the markets and it is out of control.”
Students at the Financial Computing Centre are comfortable making such statements, because they believe they are equipped to handle their implications. When I asked Mirghaemi how this unstable future made her feel, she said: “It puts me in a very good situation.”
. . .
A willingness to embrace uncertainty, a certain ruthlessness in acquiring, testing and rejecting new ideas is also what employers are looking for. “That’s what it’s like in this area,” said Rafael Molinero, who runs a quant-led hedge fund, Molinero Capital Management, where three students from the centre are currently on work placements. “You always have to reinvent yourself to stay on top of the curve. That is a big driver for us. That is what we want to see in them.” And the best way to keep your head is to listen to your algorithms, rather than your heart. As Molinero put it: “The main idea when you become a quant is that a computer is less prone to pitfalls than a human.”
If Molinero is right, then Michal Galas became a quant a long time ago. For his PhD, Galas is building what he calls an “adaptable algorithm trading portfolio” – a production line of automated trading strategies, from which computers will select the most appropriate one, depending on what is happening in a particular market. Algorithms upon algorithms upon algorithms. Galas imagines it as a hedge fund without employees. “There is no human intervention necessary,” he said.
I told Galas that this degree of trust in machines unnerved me. I told him that, as I understood it, the sheer complexity of some financial products and an over-reliance on mathematical models had been a major contributor to the financial crisis. To illustrate this, I drew two diverging lines in my notebook and told him that I thought this gap – the difference between what we know and what we think we know – had proved itself dangerous. Galas looked at the widening gap as if he recognised it. “That is an opportunity to use computers,” he said. “So yeah, good for me.”
Of course, not every student at the centre speaks like this. Stathi Panayi spent an unhappy year at an investment bank in 2008 and has vowed never to go back. He returned to Cyprus and worked as an English teacher before starting his PhD in financial computing last year. Panayi is now trying to devise better ways to measure liquidity in financial markets, and coming up with ideas for how regulators might intervene earlier to prevent events such as the credit crunch of 2007. One of his advisers works at the Bank of England.
Even so, Panayi was sanguine about the eagerness – the right – of his fellow students to come up with ever more abstract ways to beat our battered markets. The methods of quants might be difficult to fathom but humanity, and capitalism, has not progressed by putting limits on invention. “Is the answer going down a level of sophistication, making things easier for the layman to understand?” Panayi asked me. “Is there a limit? Who is to say what is the limit of sophistication in the market?” He considered this. “I don’t think the answer is banning everything you don’t understand.”
©Richard Nicholson
Mahnoosh Mirghaemi studied 3,077 separate releases of economic data and 1.6 million bond trades to identify which pieces of news moved the markets more
Panayi suggested that the answer is not to fear quants but to join them. Even better: give them one of your problems to solve. Technology and analytical thinking, after all, is neutral: what matters is the aim in which it is deployed. That is why SocialSTREAM – the database the students were trying to figure out on that winter’s afternoon – could turn out to be so useful.
I got a preview of what the database might be capable of, shortly before it went live last month. The idea behind SocialSTREAM, and other experiments like it, is to collect reams of live text being published to the internet, and to run it through dictionaries designed to test language for signs of mood. For an individual tweet – “Good morning!” – this might be meaningless. But taken across millions of postings, from the personal to political, a rough indicator of popular sentiment does emerge.
Ilya Zheludev, one of the students from the meeting, showed me his study of 500,000 internal Enron emails, which were released following the collapse of the energy company in 2001. Zheludev’s sentiment analysis showed a spike in emotion among employees – both positive and negative, a massive, contradictory shiver – in April 1999, a few months before the company’s stock began to take off on its exponential (and fraudulent) trajectory.
Picking up on such bubbles of emotion as they emerge (around a company, for instance, or a government) even in such murky waters as Twitter, or Facebook, or the website of the Financial Times, has an obvious allure to individual investors trying to stay ahead of the market. At least one London-based hedge fund, Derwent Capital, now trades purely on social data, mined in this way.
But there are clear civic and academic possibilities as well. Take political polling: while I watched, Zheludev set up SocialSTREAM to trawl the entire current output of Twitter for mentions of “Obama” and to analyse each mention for an approval rating of +1 to -1. Then he put the findings on a graph. A jagged line appeared, and from 4.22pm to 4.27pm, on January 16 2012, Zheludev and I were looking at one crude, real-time measure of the political fortunes of President Obama. “There you have it,” said Zheludev. We stared at the zigzag on the screen, wondering what it might possibly mean.
Deciphering such patterns is what excites collaborators with the Financial Computing Centre who are more interested in stabilising the markets than beating them. Zheludev’s supervisor is David Tuckett, a psychoanalyst at UCL who studies the interplay of emotion and the unconscious in trading decisions. He told me that the database could, if used properly, allow us to see our exaggerated hopes and paranoias for what they are, before they grow to overwhelm us. “If you think about it like the sea,” said Tuckett, of the torrents of digital information that we produce each day, “can we identify narratives when they are not yet at the surface? Can we learn about how they come and go?”
As Tuckett spoke, I began to believe in the idea of quants enabling us to digest the world in more rational ways, to become, in a sense, better versions of ourselves. “We are not interested in a world that is completely without excitement or volatility,” said Tuckett, “But we are interested in getting a handle on things before they get out of hand.” The paradox is that in order to become safer, in order to become better informed, we will have to continue to place ever more faith in brains and machines that we only begin to understand. It is always easy to start. The problem is knowing when to stop.

Friday, March 23

What a dream life

sighs…what a lovely painting, what a lovely man and what a lovely dream life…

one day!

Wednesday, March 21

Why I wont be selling my car

I have a Honda CRV now for well over 11 years now. Don't really use the car that much, its only done 17,400 miles. Main reason to buy the car was to save Kannu from his blushes. He came to me when he was 4 and asked, Baba, are we poor? I asked, why? Well, all my friends have 2 cars, we only have 2 bicycles. So had to go buy a car. Anyway. I now have the car for 11 years and I don't think its going to go off my forecourt any time now. I don't see any reason why i need a new car and this one is doing very well, thank you very much. I was reminded of this when I read this article in NYTimes. Kannu will have the car and we are good to go for couple of decades if not more!

But maybe I will buy a bike one day Smile a proper motor bike, when I grow up


HOW far can a modern car really go? Given the increasing age of vehicles on American roads, we may be on the verge of finding out.

As a stubborn recession made drivers wary of new purchases for several years, the average age of vehicles on the road in the United States stretched to a record 11.1 years in 2011, according to the research firm R. L. Polk, which tracks vehicle sales and registrations.

Multiply that number of years by the annual miles driven — the E.P.A. uses 15,000 for the cost calculation on fuel economy labels — and it becomes evident that one pearl of conventional wisdom has become outdated.

In the 1960s and ’70s, when odometers typically registered no more than 99,999 miles before returning to all zeros, the idea of keeping a car for more than 100,000 miles was the automotive equivalent of driving on thin ice. You could try it, but you’d better be prepared to swim.

But today, as more owners drive their vehicles farther, some are learning that the imagined limits of vehicular endurance may not be real limits at all. Several factors have aligned to make pushing a car farther much more realistic.

Tuesday, March 20

What makes a good parent?

Recently, I had the privilege of being a referee for a very good and close friend of mine who is going through a process to adopt a child. Besides the blood pressure increasing and fury generating angle that this bloody stupid moronic process entails, this process made me think a bit as well. We were trying to adopt for some time as well, but then we failed (wrong colour, wrong religion, wrong parents – already had a baby and wrong desire for a baby rather than a child).

Anyway, these are the standard questions that I was asked about my friend and his wife.

  • How long have you known them?
  • Describe their personalities
  • Describe their relationship
  • Would you describe them as a supportive husband or wife?
  • Do you think they would make good parents and why?
  • What unique qualities do you think they will they bring to parenthood?
  • Can you think of a situation where their relationship has come under pressure and how they dealt with it?
  • Can you think of a particularly stressful situation and how they dealt with it?
  • What exposure have they had to children and how would you describe these experiences?
  • Do you think they have made good preparations for having a child?
  • There is a new system (contact?) which involves encouraging children to have contact with natural family – do you think they will be agreeable and support this?

Of course I think my friends are great potential parents, in fact, I am comfortable that if something happens to us, they will take care of the kids. Its that close, but besides that, it made me think about what makes a good parent? When we had Kannu in 1995, hand on heart, I don't think I would have ticked all these points. I was a total shithead at that time, we were going through some serious stress (completely new and first serious financial sector rocket scientist job after the comfortable world of academics, applying for British residency and getting rejected the first time, moving homes, financial pressures, health problems and and and). Preparations for a child were between over thinking / analysing and complete panic. I really made some spectacular mistakes in parenting with Kannu. I worked too much and did not spend enough time with him, travelled too much and wasnt there with him for many of the life events in his little life. Well, hopefully that was addressed with Diya, but still, made me think what makes a good parent.

And what really pisses me off is that the state goes through so much pain and trouble (they have been undergoing this process now for 1 year) for adoptive children for couples who are so loving and caring and then on the other hand, you have feckless couples punching out kids with gay abandon without a thought. Life is just so not fair..Ok, I know, I know, its just a rant…

Monday, March 19

Actions have consequences

First case from Kashmir.

The police charges are preventing many young men in Indian-administered Kashmir from proceeding with their education or finding work.

"My career is ruined. I cannot seek admission in any college. I cannot get a passport, and worse, I cannot get a government job," said one young man, arrested for throwing stones at police, who did not want to be named.

Then some cases from London.

The Army career of a serving soldier is in ruins after he was locked up for eight months today for buying a stolen guitar during the August disturbances and then trying to sell it on.

Liam Bretherton, 20, was in Manchester city centre at the height of the widespread civil disorder when he paid £20 to a unknown man in the street for the instrument, which had just been looted from a nearby music store Two days later Bretherton, a member of the 7th Parachute Regiment Royal Horse Artillery, went into a music shop in his home town of Leigh, Greater Manchester, and offered them the rare left-handed Les Gibson guitar, valued at nearly £2,000.

The shop owner became suspicious, locked the doors and called the police when he confirmed with the other store that it had been taken during a large-scale raid on its premises on August 9. Manchester Crown Court heard Bretherton then appeared "agitated" and "the colour drained from his face" as he said: "I'm in the Army."

He was arrested and went on to plead guilty to handling stolen goods. His counsel pleaded for a suspended sentence today as any jail term would lead to him being kicked out of the Army, but Judge Anthony Gee QC said he would be "failing in my duty" to impose anything but immediate custody. The judge said: "You have to face the consequences of what you did and what you did amounts to a very serious offence

Another one

She cited the case of a 17-year-old boy "from a very respectable family, who had never been anywhere near the police before", who was sentenced to 18 months custody by West London youth court after handing himself in to police. The boy in the case, an A-level student who hoped to go to university, had become involved in the looting in Ealing, west London. He entered a cafe, taking two bottles of wine which he passed on to friends; someone then handed him a T-shirt, which he also gave away.

The next day, his parents had marched him to the police station, taking the clothes he had been wearing at the time to show the police. He made a "full and frank" confession, and expressed regret for his actions, said Crichton. "Even the police said how remorseful he was." In court, the defendant produced excellent references from his school and his parents and older brother spoke on his behalf. The boy also addressed the judge directly and pleaded not to go to prison. He admitted that at the time of the offence he had got carried away, and thought what he was doing was funny, rather than criminal.

Crichton said: "He just didn't think about his actions, didn't think about the consequences – but that's what young men are like. It's called growing up."

Sad but yes, you do have to suffer the consequences. These are not children, they are adults, they can vote, they can go into the army, they can get married, they can have benefits, they can make money, they can make mistakes and yes, their actions have consequences. And yes, after this mistake and the consequence, they will grow up. That’s how you learn, you make mistakes, you suffer the consequences and then you grow up.