Quant Mashup - Quant at Risk Computation of the Loss Distribution not only for Operational Risk Managers [Quant at Risk]In the Operational Risk Management, given a number/type of risks or/and business line combinations, the quest is all about providing the risk management board with an estimation of the losses the bank (or any other financial institution, hedge-fund, etc.) can suffer from. If you think for a second,(...) Probability of Black Swan Events at NYSE [Quant at Risk]The prediction of extreme rare events (EREs) in the financial markets remains one of the toughest problems. Firstly because of a very limited knowledge we have on their distribution and underlying correlations across the markets. Literally, we walk in dark, hoping it won’t happen today, not to the(...) Detecting Human Fear in Electronic Trading: Emotional Quantum Entanglement [Quant at Risk]This post presents an appealing proof for the progressing domination of algorithmic trading over human trading. By analysing the US stock market between 1960 and 1990, we estimate a human engagement (human factor) in live trading decisions taken after 2000. We find a clear distinction between(...) Python in Singapore: Intensive Workshop (Apr 7, 2016) [Quant at Risk]About this Course Our Python 1-day intensive course is addressed to all who wishes start programming in Python language straight away! We will cover the fundamentals of Python (2.7, 3.5), numerical aspects of coding, and over 100 individually crafted examples covering various applications coming(...) Python in Sydney: Course+Workshop Wednesday, March 16, 2016 [Quant at Risk]Python in Sydney: Course+Workshop Wednesday, March 16, 2016 Quant Hunt: Ignore Tick-Box Companies [Quant at Risk]I was really surprised by a huge popularity of the past section of QuantAtRisk entitled Motivation for Quants. My readers made me thinking. Again. If there is a need for posts that expose and discuss the naked truth about quant job space, let’s make it, again! This time bigger, better, and with(...) Supercomputing Frontiers 2016 [Quant at Risk]Hi Guys, please find the information about the upcoming event in Singapore where I also submitted my proposal to host and conduct a full-day workshop on Frontiers of Python for Finance. I hope to see You there! -Pawel SCF2016-logo_final_retina2 You are cordially invited to contribute as an author or(...) Predicting Heavy and Extreme Losses in Real-Time for Portfolio Holders (2) [Quant at Risk]This part is awesome. Trust me! Previously, in Part 1, we examined two independent methods in order to estimate the probability of a very rare event (heavy or extreme loss) that an asset could experience on the next day. The first one was based on the computation of the tail probability, i.e.: given(...) Student t Distributed Linear Value-at-Risk [Quant at Risk]One of the most underestimated feature of the financial asset distributions is their kurtosis. A rough approximation of the asset return distribution by the Normal distribution becomes often an evident exaggeration or misinterpretations of the facts. And we know that. The problem arises if we(...) Recovery of Financial Price-Series based on Daily Returns Matrix in Python [Quant at Risk]As a financial analyst or algo trader, you are so often faced with information on, inter alia, daily asset trading in a form of a daily returns matrix. In many cases, it is easier to operate with the return-series rather than with price-series. And there are excellent reasons standing behind such(...) 5 Words on How To Write A Quant Blog [Quant at Risk]Do not commence working over your blog without the vision. “If you don’t know where you are going, any road will get you there!” You want to avoid that mistake. Spend some time dreaming of the final form of your site. Highly sought after content is important but not as much as your commitment(...) How to Get a List of all NASDAQ Securities as a CSV file using Python? [Quant at Risk]This post will be short but very informative. You can learn a few good Unix/Linux tricks on the way. The goal is well defined in the title. So, what’s the quickest solution? We will make use of Python in the Unix-based environment. As you will see, for any text file, writing a single line of Unix(...) Predicting Heavy and Extreme Losses in Real-Time for Portfolio Holders [Quant at Risk]The probability of improbable events. The simplicity amongst complexity. The purity in its best form. The ultimate cure for those who trade, for those who invest. Does it exist? Can we compute it? Is it really something impossible? In this post we challenge ourselves to the frontiers of accessible(...) Hacking Google Finance in Real-Time for Algorithmic Traders. (2) Pre-Market Trading [Quant at Risk]It has been over a year since I posted Hacking Google Finance in Real-Time for Algorithmic Traders article. Surprisingly, it became the number one URL of QaR that Google has been displaying as a result to various queries and the number two most frequently read post. Thank You! It’s my pleasure to(...) How to Find Company Name given Stock Ticker [Quant at Risk]Quandl.com offers an easy solution to that task. Their WIKI database contains 3339 major stock tickers and corresponding company names. They can be fetched via secwiki_tickers.csv file. For a plain file of portfolio.lst storing the list of your tickers, for example: AAPL IBM JNJ MSFT TXN you can(...) Fast Walsh–Hadamard Transform in Python [Quant at Risk]I felt myself a bit unsatisfied after my last post on Walsh–Hadamard Transform and Tests for Randomness of Financial Return-Series leaving you all with a slow version of Walsh–Hadamard Transform (WHT). Someone wise once said: in order to become a champion, you need to flight one round longer. So(...) Walsh–Hadamard Transform and Tests for Randomness of Financial Return-Series [Quant at Risk]Randomness. A magic that happens behind the scene. An incomprehensible little blackbox that does the job for us. Quants. Many use it every day without thinking, without considering how those beautifully uniformly distributed numbers are drawn?! Why so fast? Why so random? Is randomness a byproduct(...) Special Offer: Python for Quants. Volume I [Quant at Risk]To All my Readers and Followers of QuantAtRisk.com I have a very special offer today: subscribe now to the book’s mailing list and pay 15% more per book before its Official Premiere in April. Just click and explore. This is a 1-day Special Offer! Applied Portfolio VaR Decomposition. (2) Impact vs Moving Elements. [Quant at Risk]Calculations of daily Value-at-Risk (VaR) for any N-asset portfolio, as we have studied it already in Part 1, heavily depend on the covariance matrix we need to estimate. This estimation requires historical return time-series. Often negligible but superbly important question one should ask here is:(...) Applied Portfolio VaR Decomposition. (1) Marginal and Component VaR. [Quant at Risk]Risk. The only ingredient of life that makes us growing and pushing outside our comfort zones. In finance, taking the risk is a risky business. Once your money have been invested, you need to keep your eyes on the ball that is rolling. Controlling the risk is the art and is the science: a(...) Sneak Peek: Python for Quants. Volume I [Quant at Risk]The first professional book on Python programming is coming up very soon! Designed around Quants, Risk Analysts, and Algorithmic Traders in mind, Volume I of Python for Quants will deliver all what is essential to start coding in Python straight away! For more details and sneak peek click here: