Tuesday, February 15, 2011

Job ad for a high frequency algorithm trader

High Frequency Algo Trader

A globally-positioned, proprietary trading firm is actively seeking a quantitative trader with an expertise in European Equities for our London office. In this role, you will work directly with one of our Managing Partners. You will be a part of a small team in London and collaborate extensively with team members in Chicago to conduct research for the purpose of modeling and forecasting financial data in order to build high frequency trading models. The individual in this role will contribute extensively towards developing new trading strategies. The successful candidate will be highly motivated, entrepreneurial and thrive in a fast paced environment.

Required experience:

Working knowledge of forecasting and data mining techniques, such as linear and non-linear regression analysis, neural networks or support vector machines
Strong programming and development skills in C++ in a Linux environment
Strong experience developing statistical models in a trading environment
Strong familiarity with R, Matlab or S-plus
Financial industry experience preferred
Experience working with large datasets of historical price data
Ability to collaborate intensively with other team members
Excellent communication skills
PhD in Statistics, Electrical Engineering, Physics, Math or Economics
Compensation:
Extremely Competitive
Location
Prop Trading
New Broad Street
London , 00000
United Kingdom

Just wanted to point out that a PhD can make tons of money on wall street working with a prop desk as opposed to doing research at a University. Amazing how these prop traders with their high frequency trading algos produce nothing of value but still get paid millions of dollars for what they do. High frequency trading is akin to skimming off of other traders who utilize slower trading programs. As a result of the "rise of the machines" HFT now accounts for over 80% of all trades on the New York Stock Exchange. We need PhD's in "Statistics, Electrical Engineering, Physics, and Math" to be working on our future energy requirements as conventional crude oil production is nearing its peak, not on wall street where they play around with digits, skimming pennies off of other traders or funds that just don't have the same tools. Talk about a terrible diversion of resources.

These prop traders are so successful they often post perfect trading results. Take a look at JP Morgan Chase trading results for 2010:

A scary figure was revealed at J.P. Morgan’s investor day presentation on Tuesday: the bank had a perfect trading record for the second half of 2010 and only lost money on 8 days out of 260 possible trading days for the full year.

The New York-based bank made $76 million per day through its trading desks, 9.5% less than the average $84 million it made in 2009, a year when it recorded losses on 42 trading days


http://blogs.forbes.com/afontevecchia/2011/02/15/bernanke-put-allows-jp-morgan-to-post-profits-on-96-9-of-2010-trading-days/

Wow! That would be equivalent to me winning at trial every single time I tried a case! These prop traders are so talented. Please. However, do realize that with no organic trading taking place, when markets turn sour liquidity, and thereby volume, disappears. Just like it did during the May 6th 2010 flash crash that caused the Dow Jones Industrial Average to lose 700 points within 15 minutes.

Who knows, pretty soon we may even have high frequency litigators where machines determine settlement amounts lol

3 comments:

  1. "There was a girl....

    ROCK MY WORLD....

    She was a pearl...

    ROCK MY WORLD...

    Golden hair and curls...

    ROCK MY WORLD....

    Tits that could twirl...

    ROCK MY WORLD...."

    ReplyDelete
  2. Hey, this is really nice information. I was looking for something similar like this. Thanks for this useful information.

    HFT

    ReplyDelete
  3. I think traders are either professionals working in a financial institution or a corporation, or individual investors, or day traders

    ReplyDelete

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