Testing the Random Walk Hypothesis with R, Part One

Whilst working on some code for my Masters I kept thinking, "it would be really awesome if there was an R package which just consumed a price series and produced a data.frame of results from multiple randomness tests at multiple frequencies". ... Read More

Lossless Compression Algorithms and Market Efficiency?

In Hacking The Random Walk Hypothesis we applied the NIST suite of cryptographic tests for randomness to binarized daily market returns. Overall the NIST suite failed on the data. This result was taken to mean that markets are not quite the ... Read More

Stock Market Prices Do Not Follow Random Walks

Because volatility seems to cluster in real life as well as the markets, it has been a while since my last article. Sorry about that. Today we will be taking our first giant leap along A Non-Random Walk down Wall Street.

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How to be a Quant

Since writing about my experience writing the CFA Level I exam in June, I have received many emails from people interested in finding out how to become a quant. To some extent this post will answer that question. That said, this post is actually not ... Read More

Hacking the Random Walk Hypothesis

Hackers would make great traders. At a meta level, hackers and traders do the same thing: they find and exploit the weaknesses of a system. The difference is that hackers hack computers, networks, and even people for various ... Read More

A Quant's view of CFA Level I

Having just written and, thankfully, passed the CFA Level I exam I wanted to take this opportunity to share my experience writing the CFA Level I exam given that I come from an unconventional academic background and work in the ... Read More

Fitness Landscape Analysis for Computational Finance

Some of the most interesting new research coming out of the Computational Intelligence Research Group (CIRG), which is applicable to numerous computational finance and machine learning optimization problems, is the development of fitness landscape analysis techniques. Fitness landscape analysis aims to characterize high dimensional ... Read More

A Recipe for the 2008 Financial Crisis

In 2008 when the market crashed I was 16-years old and visiting London for the very first time. At that age I was already obsessed with the markets. Feeling confident that I could understand the crash after having read the classic investment books such ... Read More

Random walks down Wall Street, Stochastic Processes in Python

James Bond is not a quant, but many famous quantitative fund managers enjoy playing poker in their spare time. Stochastic processes can be used to model the odds of such games. This article discusses some of the popular ... Read More

Dimensionality Reduction Techniques

The curse of dimensionality is the phenomena whereby an increase in the dimensionality of a data set results in exponentially more data being required to produce a representative sample of that data set. To combat the curse of dimensionality, numerous linear and non-linear dimensionality reduction ... Read More

All Models are Wrong, 7 Sources of Model Risk

The 2008 financial crisis revealed to the world (in spectacular fashion) the fragility of financial models. Since the financial crisis two words have come up time and time again: model risk. This article defines model risk and discusses some of the contributors ... Read More

Computational Finance at IEEE WCCI 2014

I recently had the awesome opportunity to present my honours research at this years IEEE World Congress for Computational Intelligence conference (IEEE-WCCI) in Beijing. My trip was sponsored by the University of Pretoria's Computational Intelligence Research Group (CIRG) so ... Read More

Regression analysis using Python

This tutorial covers regression analysis using the Python StatsModels package with Quandl integration. For motivational purposes, here is what we are working towards: a regression analysis program which receives multiple data-set names from Quandl.com, automatically downloads the data, analyses it, and plots the results ... Read More

10 misconceptions about Neural Networks

Neural networks are one of the most popular and powerful classes of machine learning algorithms. In quantitative finance neural networks are often used for time-series forecasting, constructing proprietary indicators, algorithmic trading, securities classification and credit risk modelling. They ... Read More

Simulated Annealing for Portfolio Optimization

This article applies the Simulated Annealing (SA) algorithm to the portfolio optimization problem. Simulated Annealing (SA) is a generic probabilistic and meta-heuristic search algorithm which can be used to find acceptable solutions to optimization problems characterized by a large ... Read More

Computational Decision Making Methods

Artificial intelligence is broadly defined as the ability of an agent or a model to make either optimal or satisficing decisions. Decision-making in this context is a process which culminates in the selection of a particular course of ... Read More

Graph Theory for Systemic Risk Models

The markets around the world are highly connected. The risk that the entire financial system crashes as a result of the failure of one or more entities is called systemic risk. The 2008 Financial Crisis demonstrated first hand ... Read More

Agent-based Computational Economic Models

Economists subscribe to many often contradictory schools of thought. This results in businesses and governments adopting economic policies whose consequences are neither agreed upon nor understood. Furthermore, because the economy is actually a complex adaptive system most traditional economic ... Read More

Portfolio Optimization Using Particle Swarm Optimization

My research topic for this year was Currency Carry Trade Portfolio Optimization using Particle Swarm Optimization (PSO). In this article I will introduce portfolio optimization and explain why it is important. Secondly, I will demonstrate how particle swarm ... Read More

Algorithmic Trading System Architecture

Previously on this blog I have written about the conceptual architecture of an intelligent algorithmic trading system as well as the functional and non-functional requirements of a production algorithmic trading system. Since then I have designed a system architecture ... Read More

Measures of Risk-adjusted Return

This article is a supplement to some of the topics presented in Dr. Tucker Balch's online MOOC, Computational Investing. Financial markets are complex adaptive systems which are almost always indistinguishable from random processes. That said markets do exhibit quantifiable factors such ... Read More

Perfect Imperfection, Agent Based Models

When I was 17 years old the Boy Scouts of America invited nine international delegates and I to present at a conference and partake in a 7-day 100 kilometer hike through the Rocky Mountains on the Philmont Scout Ranch. In the week prior ... Read More

Using Genetic Programming to evolve Trading Strategies

A friend and I recently worked together on a research assignment where we successfully used Genetic Programming (GP) to evolve solutions to a real world financial classification problem. This problem, called security analysis, involves determining which securities ought to ... Read More

Clustering using Ant Colony Optimization

For many years entomologists have studied the behaviour of ant colonies and marveled at their ability to solve complex problems collectively. An example of this collective intelligence observed by entomologists is that ants leaving their colony will often follow very efficient routes between ... Read More