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

## Python for Finance 2 Workshop: Singapore

Due to strong interest and demand, it is my greatest pleasure to announce a brand new Python for Finance 2 2-day Intensive Workshop in Singapore to take place on September 24-25, 2016.

THE WORKSHOP HAS BEEN RESCHEDULED
from Aug 16-17

The Workshop is addressed to all who wish to learn Python and its application in finance with a particular emphasis on trading. We will discuss all steps required to build an effective trading model (for stocks and FX pair trading), technical analysis, model backtesting, risk analysis, trade execution issues, potential problems, the importance of slippage, model profitability, and model adjustments.

Register Today!.

## 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]

## Python for Advanced Finance Workshop: London

Based on an enormous success of QuantAtRisk/A*STAR 2-Day Workshop in Sinagpore in May 2016 QuantAtRisk.com would love You to invite You to attend: Python for Advanced Finance: 3-Day Intensive Workshop in London (Sep 7-9) where we … [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]

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]

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]

## Roy’s Safety-First Criterion in Portfolio Optimization Problem

The main objective of portfolio selection is the construction of a portfolio that maximises expected return given a certain tolerance for risk. There is an intuitive problem with the portfolio variance as a measure of risk. Using the variance in the … [Continue reading]

## Quantitative Risk Assessment Model for Investment Options

Working in the superannuation industry in Australia has some great advantages. A nice atmosphere at work, gym sessions with colleagues during lunch time, endless talks about girls after hours. However, as Brian Tracy once said: When you go to work, … [Continue reading]