Quantitative Analysis, Risk Management, Modelling, Algo Trading, and Big Data Analysis

Upcoming… 2nd Edition of Python for Quants. In Sale on Jan 31, 2017.

A brand new, the 2nd Edition of my Python for Quants. Volume I. book is coming out on Jan 31, 2017! Powered by great feedback from those who purchased its 1st Edition, supplemented and extended by new content, I’m pleased to deliver you the most friendly introduction to Python for Finance for everyone!

What new in 2nd Edition:

  1. Advanced NumPy for Quants: this time we make it more complete incl. linear and non-linear algebra for finance; accelerated computations on GPU; advanced matrix manipulations;
  2. Statistics: more practical applications for financial data analysis powered by SciPy and statsmodels libraries;
  3. pandas for Time-Series: the best introduction to the most powerful library for data handling and processing with advanced application in financial time-series analysis;
  4. 2D and 3D Plotting: a smart use of matplotlib, seaborn, and cufflinks for computational aspects of Python and pandas.

Interested in purchasing a book?
Enjoy 20% Off Before the Official Premiere!$^{*}$

Pre-Order Today!



$^{*}$ Prepay today to secure 20% Off the regular price of USD 49.00 after the Official Premiere. Pay Now USD 9.90 and only USD 29.30 later. Your total book price will be USD 39.20. Your Pre-Order Deposit will be saved under your name and entitle you to the offered discount (unreturnable). If you wish to upgrade from 1st Ed. you will be entitled to 45% discount (your personal data will be cross-verified with our records).


Non-Linear Cross-Bicorrelations between the Oil Prices and Stock Fundamentals


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 … [Continue reading]

Python for Algo-Trading: Workshops in Poland

QuantAtRisk.com would love to invite You to attend where we will cover all essential aspects of algo-trading covering Warsaw Stock Exchange and selected EU/UK/US exchanges + FOREX markets. Interested? Click here for Early-Bird … [Continue reading]

Financial Time-Series Segmentation Based On Turning Points in Python


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 … [Continue reading]

Computation of the Loss Distribution not only for Operational Risk Managers


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, … [Continue reading]

Probability of Black Swan Events at NYSE


Featured by a Russian website Financial One (Apr 26, 2016) 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 … [Continue reading]

Detecting Human Fear in Electronic Trading: Emotional Quantum Entanglement


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 … [Continue reading]

Predicting Heavy and Extreme Losses in Real-Time for Portfolio Holders (2)


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 … [Continue reading]

Student t Distributed Linear Value-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. … [Continue reading]

Recovery of Financial Price-Series based on Daily Returns Matrix in Python


> 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 … [Continue reading]

5 Words on How To Write A Quant Blog

An extract from Jacques Joubert's newest article on How To Write A Great Quant Blog. by Pawel Lachowicz 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 … [Continue reading]

How to Get a List of all NASDAQ Securities as a CSV file using Python?

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, … [Continue reading]

Predicting Heavy and Extreme Losses in Real-Time for Portfolio Holders (1)


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 … [Continue reading]

Hacking Google Finance in Real-Time for Algorithmic Traders. (2) Pre-Market Trading.


Featured in: Data Science Weekly Newsletter, Issue 76 (May 7, 2015) 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 … [Continue reading]

How to Find a Company Name given a Stock Ticker Symbol utilising Quandl API

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 … [Continue reading]

Fast Walsh–Hadamard Transform in Python


> 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 … [Continue reading]

Walsh–Hadamard Transform and Tests for Randomness of Financial Return-Series


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 … [Continue reading]

Applied Portfolio VaR Decomposition. (3) Incremental VaR and Portfolio Revaluation.

Portfolios are like commercial aircrafts. Rely on computers. Dotted around the world. Vulnerable to ever changing weather conditions. Designed to bring benefits. Crafted to take risks and survive when the unexpected happens. As portfolio managers we … [Continue reading]

Applied Portfolio VaR Decomposition. (2) Impact vs Moving Elements.


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 … [Continue reading]

Applied Portfolio VaR Decomposition. (1) Marginal and Component VaR.

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 … [Continue reading]

Quants, NumPy, and LOTTO

> Since I'm working over the Volume I of Python for Quants ebook and I am going through NumPy abilities, they leave me speechless despite the rain. Somehow. Every time. There is so much flexibility in expressing your thoughts, ideas, and freedom … [Continue reading]

Rebinning Tick-Data for FX Algo Traders


If you work or intend to work with FX data in order to build and backtest your own FX models, the Historical Tick-Data of Pepperstone.com is probably the best place to kick off your algorithmic experience. As for now, they offer tick-data sets of 15 … [Continue reading]

GPU-Accelerated Finance in Python with NumbaPro Library. Really?

When in 2010 I lived in Singapore I crossed my life path with some great guys working in the field of High-Performance Computing: Łukasz Orłowski, Marek T. Michalewicz, Iain Bell from Quadrant Capital. They both pointed my attention towards GPU … [Continue reading]

Covariance Matrix for N-Asset Portfolio fed by Quandl in Python

> A construction of your quantitative workshop in Python requires a lot of coding or at least spending a considerable amount of time assembling different blocks together. There are many simple fragments of code reused many times. The … [Continue reading]

Setting up Python for Quantitative Analysis in OS X 10.10 Yosemite


Welcome! Accelerated Python for Quants Lesson 2>> The most recent update of Apple's OS X 10.10 Yosemite makes its mark with a bit of splendour. But, under the hood, not too much has changed. The same Unix shell, the same way to perform our … [Continue reading]

GARCH(1,1) Model in Python

> In my previous article GARCH(p,q) Model and Exit Strategy for Intraday Algorithmic Traders we described the essentials of GARCH(p,q) model and provided an exemplary implementation in Matlab. In general, we apply GARCH model in order to … [Continue reading]

Deriving Limits in Python

> Some quant problems require an intensive work with mathematical (time-)series given initial conditions. In this short lesson on Python, let's consider the following problem and solve it analytically and with aid of Python. Problem Given … [Continue reading]

Gap-on-Open Profitable Trading Strategy


After a longer while, QuantAtRisk is back to business. As an algo trader I have been always tempted to test a gap-on-open trading strategy. There were various reasons standing behind it but the most popular one was always omni-discussed: good/bad … [Continue reading]

Visualisation of N-Asset Portfolio in Matlab


Many of you who wished to use the Matlab's Financial Toolbox for portfolio visualization most probably realised that something was wrong. Not every time the annualised risk and return for different time-series matched with what was expected. Below … [Continue reading]

Asset Allocation for Tangent Portfolio with Risk-Free Asset in Python

> Studying a new programming language is an adventurous journey. It takes time in case of Python. Even for me. Not every concept is easy to grasp and testing a piece of code with its endless modifications may take a longer while. We have two … [Continue reading]

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