Quant Mashup - Quant at Risk

Tracking Bitcoin Gains since its 3rd Halving in May 2020 using Python [Quant at Risk]

The Bitcoin’s 3rd halving was the most anticipated event this year. This a moment when a reward for all Bitcoin block miners is cut by half. It happens every 4 years or every 210,000 blocks on the Bitcoin blockchain. The previous two halving events took place in 2012 and 2016, respectively. Before

*- 4 months ago, 15 May 2020, 09:38am -*

How to Design Intraday Algo-Trading Model for Cryptocurrencies using Bitcoin-based Signals? [Quant at Risk]

With a growing popularity of cryptocurrencies and their increasing year-over-year traded volumes, crypto algo-trading is a next big thing! If you study this market closely you will notice that it offers quick gains in much shorter unit of time comparing to stocks or FX. No wonder why a participation

*- 4 months ago, 28 Apr 2020, 10:47am -*

Brent Oil Price Time-Series in Python with 1-Minute Data Sampling [Quant at Risk]

Recent actions in WTI Futures pricing on Apr 20, 2020 caused my curiosity to have a deep look at intraday crude oil price time-series. With no surprise, I couldn’t find any free and effortlessly available dataset on the Internet. This is a common problem for lots of quants and data analysts:

*- 4 months ago, 23 Apr 2020, 10:35am -*

Inverting Differentiated Time-Series in pandas for Deep Learning Prediction Analysis [Quant at Risk]

A differentiation of the time-series is a common transformation used when we want to get a stationary time-series given a non-stationary one. The latter usually displays time-dependent relationships like trends, seasonality, quasi-cyclic patterns, and their Fourier power spectrum is characterised by

*- 5 months ago, 14 Apr 2020, 12:50pm -*

How to Predict Bitcoin Price with Deep Learning LSTM Network - Part 1 [Quant at Risk]

You can’t predict the future unless you have a crystal ball but you can predict an asset’s trading price in next time step if you have a right tool and enough confidence in your model. With the development of a new class of forecasting models employing Deep Learning neural networks, we gained

*- 5 months ago, 2 Apr 2020, 12:35pm -*

pandas for Quants: New Video Course from QaR [Quant at Risk]

Hi Guys! I’m happy to kick off a new series of free video lectures devoted to Python’s library of pandas. Every week, I will be uploading something between 2 to 4 new videos especially crafted around practicalities of pandas library applied to financial data and their analysis and processing.

*- 6 months ago, 28 Feb 2020, 05:26am -*

Deep Learning for Quants: (1) Setting Up Keras and TensorFlow 2.1+ Environment in Python [Quant at Risk]

It would be too easy to kick off the series of lectures supplementing my Python for Quants ebooks starting from Machine Learning (ML) as an innovation. ML-based algorithms pay dividends when your problem is fairly well defined and data allow to capture the patterns where they exist. Deep Learning

*- 7 months ago, 9 Feb 2020, 01:25pm -*

Employing Human-Order in pandas DataFrame Sorting: Risk Factors and Tenors [Quant At Risk]

There are various Python projects which require sorting but not the ones that employ a default alphanumeric functionality. We talk about manually specified order or human-order, in short. One of such examples is the case study presented below. Imagine that your risk system provides you with a list

*- 9 months ago, 2 Dec 2019, 07:24pm -*

Shapley Value Allocation of Operational Risk Capital Charges using Airport Problem Solution [Quant At Risk]

In Financial Risk Management the most challenging part for quantitative modeling is, beyond any doubt, the Operational Risk (Ops Risk). It is defined as the risk of loss resulting from inadequate or failed internal processes, people and systems or from external events. This definition includes legal

*- 2 years ago, 3 Jun 2018, 01:50am -*

Logistic Regression Analysis of Quant’s Resume during His Job Interview [Quant At Risk]

There are not too many creative opportunities to leave a person applying for a quant role dumbfounded with the interview question directly related to his CV. First of all, we want to test his quant skillset, check his ability to read a code, analyse the model, listen to his judgement on the

*- 2 years ago, 17 Apr 2018, 12:57pm -*

Cryptocurrencies with Python eBook: Apr 15 [Quant At Risk]

It is my pleasure to deliver a long-awaited QaR ebook on the introduction to blockchain and cryptocurrencies with Python on April 15, 2018. In the book we will cover inter alia the fundamental aspects of blockchain in general; the craze around cryptocoins like Bitcoin, Ether, LiteCoin, etc.; their

*- 2 years ago, 6 Mar 2018, 05:23am -*

Recalibrating Expected Shortfall to Match Value-at-Risk for Discrete Distributions [Quant at Risk]

By considering the same risk measure, ϱ, applied to two or more portfolios (credit loss distributions, profit-and-loss distributions, etc.) one desires to have a subadditivity property in place: ϱ(X1+X2)≤ϱ(X1)+ϱ(X2) i.e. meaning that two combined portfolios should never be more risky than the

*- 2 years ago, 19 Nov 2017, 11:29am -*

Earning Money in Cryptocurrency Markets by Spotting Statistical Arbitrage Opportunities [Quant at Risk]

When you come in contact with cryptocurrencies, e.g. Bitcoin (BTC), you quickly realise that there is no single price of BTC at any given moment. The reason is that Bitcoin is traded on different markets. It can be worth more on Coinbase exchange and less on Kraken exchange. In particular, the

*- 2 years ago, 7 Nov 2017, 08:00am -*

N-CryptoAsset Portfolios: Identifying Highly Correlated Cryptocurrencies using PCA [Quant at Risk]

IMHO, there is nothing more exciting these days than researching, analysing, and a good understanding of cryptocurrencies. Powered by blockchain technology, we live in a new world that moves fast forward as we sleep. In my first post devoted to that new class of tradable assets we have learnt how to

*- 3 years ago, 1 Apr 2017, 10:25am -*

Cryptocurrency Time-Series for N-CryptoAsset Portfolio Analysis in Python [Quant at Risk]

Welcome to a brand new era of “financial assets” – the crypto-assets. The impossible became possible. Yes, now you can trade cryptocurrencies: money that have been created in a virtual world with a physical impact onto our everyday cash-in-the-bank reality. The grande picture is still

*- 3 years ago, 21 Mar 2017, 02:26am -*

Python for Algo and Crypto-Currency Trading: 2-Day Workshop in London (July 8-9) [Quant at Risk]

Within our unique 2-Day Intensive Workshop in London, UK on Python for Algorithmic and Crypto-Currency Trading we dive into most recent and hot topics in algo-trading. We will cover and analyse a well explored world of classical assets (stocks, FX currencies) extended by trading techniques aimed at

*- 3 years ago, 10 Mar 2017, 03:30am -*

Machine Learning in Python for Finance: 2-Day Workshop in Warsaw, Poland [Quant at Risk]

After wonderful and rewarding 2-day workshop devoted to Python for Algo-Trading on March 4-5, it is my pleasure to announce a new, upcoming, on demand 2-Day Workshop on Machine Learning in Python for Finance (May 20-21, 2017). Since Machine Learning is the latest hottest topic covering different

*- 3 years ago, 8 Mar 2017, 01:30am -*

Hacking True Random Numbers in Python: Blockchain Miners [Quant at Risk]

The magnitude and importance of random numbers in finance does not have to be explained. We need them. Either it is an option pricing or a Monte Carlo simulation, random numbers are with us. However, we make a trade-off: the speed in their generation versus uniqueness. That is why a widely accepted

*- 3 years ago, 12 Dec 2016, 04:30pm -*

Conditional Value-at-Risk in the Normal and Student t Linear VaR Model [Quant at Risk]

Conditional Value-at-Risk (CVaR), also referred to as the Expected Shortfall (ES) or the Expected Tail Loss (ETL), has an interpretation of the expected loss (in present value terms) given that the loss exceeds the VaR (e.g. Alexander 2008). For many risk analysts, CVaR makes more sense: if VaR is a

*- 3 years ago, 8 Dec 2016, 07:35am -*

Non-Linear Cross-Bicorrelations between Oil Prices and Stock Fundamentals [Quant at Risk]

When we talk about correlations in finance, by default, we assume linear relationships between two time-series “co-moving”. In other words, if one time-series changes its values over a give time period, we seek for a tight correlation reflected within the other time-series. If found, we say they

*- 3 years ago, 1 Dec 2016, 08:07am -*

Financial Time-Series Segmentation Based On Turning Points in Python [Quant at Risk]

A determination of peaks and troughs for any financial time-series seems to be always in high demand, especially in algorithmic trading. A number of numerical methods can be found in the literature. The main problem exists when a smart differentiation between a local trend and “global” sentiment

*- 3 years ago, 3 Nov 2016, 10:19am -*

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,

*- 4 years ago, 5 Jun 2016, 02:01am -*

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

*- 4 years ago, 18 Apr 2016, 05:32am -*

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

*- 4 years ago, 5 Apr 2016, 03:00pm -*

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

*- 4 years ago, 27 Feb 2016, 09:10am -*

Python in Sydney: Course+Workshop Wednesday, March 16, 2016 [Quant at Risk]

Python in Sydney: Course+Workshop Wednesday, March 16, 2016

*- 4 years ago, 22 Feb 2016, 10:09am -*

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

*- 4 years ago, 7 Feb 2016, 01:32am -*

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

*- 4 years ago, 22 Jan 2016, 03:41am -*

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

*- 4 years ago, 6 Dec 2015, 02:18am -*

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

*- 4 years ago, 2 Dec 2015, 11:32am -*

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

*- 4 years ago, 30 Nov 2015, 11:22pm -*

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

*- 4 years ago, 27 Oct 2015, 12:19pm -*

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

*- 5 years ago, 25 Jun 2015, 12:45am -*

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

*- 5 years ago, 14 Jun 2015, 01:08pm -*

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

*- 5 years ago, 7 May 2015, 01:59am -*

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

*- 5 years ago, 24 Apr 2015, 02:31am -*

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

*- 5 years ago, 9 Apr 2015, 11:46am -*

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

*- 5 years ago, 6 Apr 2015, 08:17pm -*

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!

*- 5 years ago, 31 Mar 2015, 10:28am -*

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:

*- 5 years ago, 27 Jan 2015, 04:29pm -*

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

*- 5 years ago, 17 Jan 2015, 01:28pm -*

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:

*- 5 years ago, 13 Jan 2015, 07:14am -*