Financial Technology Focus

Vendor News: September 30, 2015 - More than half of large insurers to increase use of cloud and co-sourcing services over next five years finds SS&C survey

Wed, 30 Sep 2015 14:27:45 +0000

Vendor News: Fidessa launches Sentinel Trading Compliance

Mon, 28 Sep 2015 08:08:03 +0000

Vendor News: Infosys Positioned as a Leader and Star Performer in 2015 Banking Application Outsourcing PEAK Matrixâ¢ by Everest

Tue, 22 Sep 2015 10:06:10 +0000

Infosys, (NYSE: INFY), a global leader in consulting, technology, outsourcing and next-generation services, has been positioned as a Leader and Star Performer in the 2015 PEAK Matrixâ¢ for banking application outsourcing (AO) by Everest Group. The consulting and research firm also positioned Infosys as a Leader in capital markets for the second consecutive year.

Vendor News: Portfolio Probe available for R version 3.2

Sun, 17 May 2015 09:55:12 +0000

Portfolio Probe available for R version 3.2

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Investment technology for the 21st century.

R version 3.2

Portfolio Probe is available for R version 3.2.

But there is a catch. For some reason Portfolio Probe does not install in the normal way with install.packages.

The workaround is to go to the 3.2 subdirectory of the Portfolio Probe repository:
http://www.portfolioprobe.com/R/bin/windows/contrib/3.2/
Save the Portfolio Probe zip file to some place on your machine and then install it from there. This process is described in the Frequently Asked Support Questions page:
http://www.portfolioprobe.com/user-area/frequently-asked-support-questions/

Mark Joshi drops first release of Kooderive, an open source library for pricing derivatives using GPUs

Tue, 17 Sep 2013 05:25:04 +0000

Mark Joshi writes:

Link Library: Video - One Half Second of High Frequency Trading in a Single Stock

Tue, 14 May 2013 13:07:14 +0000

1/2 second of trading activity in Johnson & Johnson (symbol JNJ) on May 2, 2013

This video was featured at Wired Business Conference (watch it now: http://fora.tv/2013/05/07/Nanex_CEO_E...)

Follow us on twitter @nanexllc for Wall Street Breaking coverage.

Set to lowest resolution for an "artistic rendering", or highest resolution for science.

The animation tool that created this video was written in "C" using Windows GDI - simple lines, polygons and ellipses. We wrote it to explain to the SEC and CFTC (the regulators) how our markets work. We got the idea after realizing, in face to face meetings with them, they didn't understand market structure or the importance of latency and the consolidated feed. That was several years ago. We still aren't sure if they get it, or are just playing dumb.

The bottom box (SIP) shows the National Best Bid and Offer. Watch how much it changes in the blink of an eye.

Watch High Frequency Traders (HFT) at the millisecond level jam thousands of quotes in the stock of Johnson and Johnson (JNJ) through our financial networks on May 2, 2013. Video shows 1/2 second of time. If any of the connections are not running perfectly, High Frequency Traders can profit from the price discrepancies that result. There is no economic justification for this abusive behavior.

Each box represents one exchange. The SIP (CQS in this case) is the box at 6 o'clock. It shows the National Best Bid/Offer. Watch how much it changes in a fraction of a second. The shapes represent quote changes which are the result of a change to the top of the book at each exchange. The time at the bottom of the screen is Eastern Time HH:MM:SS:mmm (mmm = millisecond). We slow time down so you can see what goes on at the millisecond level. A millisecond (ms) is 1/1000th of a second.

Note how every exchange must process every quote from the others -- for proper trade through price protection. This complex web of technology must run flawlessly every millisecond of the trading day, or arbitrage (HFT profit) opportunities will appear. It is easy for HFTs to cause delays in one or more of the connections between each exchange.

http://www.nanex.net/Research/IsNBBOI...

Video: Larry Tabb on The Future Of Data Management in a post-Crisis World

Wed, 30 Jan 2013 11:39:50 +0000

Larry Tabb of TABB Group recently discussed with Wall Street & Technology senior editor Melanie Rodier how firms are adapting their data management processes to the post-financial-crisis environment.

SEC-mandated XBRL data at risk of being irrelevant to investors and analysts

Wed, 23 Jan 2013 14:42:04 +0000

In 2009, the Securities and Exchange Commission mandated that public companies submit portions of annual (10-K) and quarterly (10-Q) reports—in a digitized format known as eXtensible Business Reporting Language (XBRL). The goal of this type of data was to provide more relevant, timely, and reliable "interactive" data to investors and analysts. The XBRL-formatted data is meant to allow users to manipulate and organize the financial information according to their own purposes faster, cheaper, and more easily than current alternatives.

Video - Trading's evolving next generation? Control systems, feedback loops, adaptive knowledge capture

Fri, 07 Dec 2012 13:38:19 +0000

Research Library: Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices

Fri, 30 Nov 2012 08:59:47 +0000

Siddhivinayak Kulkarni
University of Ballarat

Imad Haidar
University of Ballarat

Abstract

This paper presents a model based on multilayer feedforward neural network to forecast crude oil spot price direction in the short-term, up to three days ahead. A great deal of attention was paid on finding the optimal ANN model structure. In addition, several methods of data pre-processing were tested. Our approach is to create a benchmark based on lagged value of pre-processed spot price, then add pre-processed futures prices for 1, 2, 3,and four months to maturity, one by one and also altogether. The results on the benchmark suggest that a dynamic model of 13 lags is the optimal to forecast spot price direction for the short-term. Further, the forecast accuracy of the direction of the market was 78%, 66%, and 53% for one, two, and three days in future conclusively. For all the experiments, that include futures data as an input, the results show that on the short-term, futures prices do hold new information on the spot price direction. The results obtained will generate comprehensive understanding of the crude oil dynamic which help investors and individuals for risk managements.

Video - Ciamac Moallemi: High-Frequency Trading and Market Microstructure

Wed, 28 Nov 2012 15:20:14 +0000

Ciamac Moallemi is the Barbara and Meyer Feldberg Associate Professor of Business in the Decision, Risk, & Operations Division of the Graduate School of Business at Columbia University.

Vendor News: NAG announces support for IBM BlueGene/Q supercomputers

Tue, 13 Nov 2012 11:01:22 +0000

Vendor News: Prescient Ridge Management signs Connamara Systems for New Trading Infrastructure and Support Services

Wed, 31 Oct 2012 13:47:18 +0000

New trading infrastructure provides entry into new markets and upgrades automated execution capabilities

Chicago, October 31, 2012 – Connamara Systems, LLC (Connamara), provider of services to exchanges, swap execution facilities, CTAs and Hedge Funds, today announced signing Prescient Ridge Management, LLC (PRM) a managed futures fund which specializes in short-term, automated trading strategies, for Connamara’s Made-to-Measure Trading Solutions to design and implement an updated trading infrastructure for PRM. This engagement is a result of PRM’s Investment Committee decision to trade new markets which opens new growth opportunities and new trading strategies for the firm going forward.

Prescient Ridge Management’s President, Alan Swimmer, states: “Connamara Systems proved the right fit for PRM. Their Made-to-Measure customizable approach allows us to focus on the Fund’s new trading ideas by improving efficiency and increasing scalability. Our philosophy to trading has always been combining the best professional expertise with the best technology the market can offer.”

Connamara’s approach to trading infrastructure design and implementation uses Connamara proprietary components to shorten implementation time and will provide a robust, scalable and flexible platform that allows PRM access to more markets and more asset classes. Connamara also provides all source code which lessens relationship dependency as vendor and in-house teams work collaboratively. In addition to source code for the platform, Connamara will provide all the automated regression tests, and the necessary testing and continuous integration frameworks to allow Prescient Ridge Management to extend and maintain the platform.

Connamara will also integrate Prescient Ridge Management’s trading strategy software with the new trading infrastructure for order routing and management, market data, position and risk management as well as post-trade analysis and reporting to ensure a complete and seamless transaction life-cycle process. Going forward, post-delivery, Connamara will be providing on-going support and development services to Prescient Ridge Management. The Connamara Systems integration team consists of three software engineers, one senior software engineer, one Business analyst, one project manager.

“Connamara Systems has been developing and delivering custom application development for almost 15 years. Having this experience and business approach allows us to move quickly as changes in the industry occur and provide a solid, proven technology base that enables us to reduce the time to market and lower the cost to our clients,” says Jim Downs, Founder and CEO of Connamara Systems.

About Prescient Ridge Management LLC

Founded in 2006, Prescient Ridge Management, LLC is the managing member of the Prescient Ridge Fund, LLC, a managed futures fund which specializes in short-duration, systematic trading strategies. The fund uses proprietary trading strategies to capture price movements in over 30 global exchange listed futures. Prescient Ridge Management is headquartered in Highland Park, IL just north of Chicago.

For more information, please visit: http://www.prmllc.com

About Connamara Systems:

Founded in 1998 by Jim Downs, a long time index options market-maker at the Chicago Board Options Exchange, Connamara Systems offers solutions to exchanges, swap execution facilities, CTAs and Hedge Funds. Connamara delivers next generation, end-to-end trading and risk management solutions, including matching engines, order and execution management, algorithmic trading, and market data integration. Incorporating the client’s specific needs with the most advanced, tested technology, Connamara takes a made-to-measure approach to its software. Connamara is headquartered in Chicago.

For more information, please visit: http://www.connamara.com

Intel and OnX Announce Social Media Hub for the Finteligent Trading Technology Community

Wed, 24 Oct 2012 11:32:19 +0000

TORONTO, Ontario and New York, NY, October 23, 2012 –

Research Library: The Diversity of High Frequency Traders

Mon, 08 Oct 2012 15:52:42 +0000

Björn Hagstromer
Stockholm University - School of Business

Lars L. Norden
Stockholm University - School of Business

Abstract

The regulatory debate concerning high frequency trading (HFT) emphasizes the importance of distinguishing different HFT strategies and their influence on market quality. Using unique data from NASDAQ OMX Stockholm, we are the first to empirically provide such a distinction for equity markets. Comparing the behavior of market making HFTs to opportunistic HFTs (arbitrage and momentum HFT strategies), we find that market makers constitute the lion share of HFT trading volume (63-72%) and limit order traffic (81-86%). Furthermore, market makers have higher order-to-trade ratios, lower latency, lower inventory, and supply liquidity more often than opportunistic HFTs. In a natural experiment based on tick size changes, we find that both market making and opportunistic HFT strategies mitigate intraday price volatility. The findings indicate that, e.g., the financial transaction tax proposed by the European Commission, which would render most HFT strategies unprofitable, would primarily hit market makers and increase market volatility.

Number of Pages in PDF File: 57

Vendor News: Buy-side forced to adopt more sophisticated risk management as interconnectivity between buy-side stakeholders grows, says Algorithmics, an IBM Company

Wed, 19 Sep 2012 14:33:08 +0000

White paper analyses impact, requirements and opportunities of buy-side interconnectivity

Link Library: Black Rhino - A Financial Network Multi Agent Simulator

Wed, 19 Sep 2012 13:10:56 +0000

Black Rhino - A Financial Network Multi Agent Simulator black_rhino is an open source easy-to-use-and-adapt financial network multi agent simulation (MAS) that serves two purposes. First, it can be used as a practical tool to simulate and analyse a model banking system. This is particularly handy for central banks and policy makers, as black_rhino fills a gap in the policy-toolbox. Second, and perhaps more importantly, it is a python module that can be easily adapted, changed, and modified for research purposes. It is intended to reduce the amount of work necessary to write a financial MAS and hence allows researchers to focus on the economic questions instead of worrying about code design patterns and basic functionality. The software is open source and published under the GNU GPL v3. You can find the latest version at sourceforge. 2 August 2012

DataSift Launches Social Feeds for the Financial Services Industry

Wed, 12 Sep 2012 15:59:16 +0000

Research Library: The evolution of algorithmic classes (pdf)

Mon, 03 Sep 2012 15:40:55 +0000

Lajos Gergely Gyurko
Mathematical Institute, University of Oxford.

Introduction

This paper aims to explore the key factors that drive the evolution of algorithmic classes. We analyse the impact of changes in regulation, the development of new trading venues, technological innovations, the economic environment, changes in market micro-structure, the availability and quality of data/information, and the progress in academic research. In many of the cases the joint impact of two or more of these factors leads to the appearance, mutation or decline of trading strategies, and we aim to explore such phenomena as well.
We consider algorithmic classes to be (not necessarily disjoint) sets of systematic trading strategies with similar objectives. One can differentiate between algorithmic classes by the types or number of assets involved, by the markets the trading is executed on, by the typical holding period /speed of turnover of capital etc (see section on the “Typology of Algorithmic classes”).

Algorithmic classes constantly evolve. We will focus on large scale evolution, in particular on the birth, transformation, mutation, renaissance, decline and extinction of classes, moreover on co-evolution. First, from a historical perspective, we identify the key factors that drive the evolution and shape the universe of algorithmic classes through observed scenarios. Then, we consider possible scenarios for the future.

The typical scenario of the life cycle of an algorithmic (sub)class is as follows. Favourable circumstances - such as economic environment, decrease of trading costs, deregulation of markets, new trading venue (e.g. electronic communications networks, dark pools etc.), new product (e.g. exchange traded funds, commodity indices, etc.), extended access to existing products, technological development (trade execution at increased speed), etc. - result in the appearance of new trading strategies. In the beginning, a few market participants (“first-to-market traders” - Aldridge 2010) discover and exploit these opportunities making significant profits. These players are often smaller companies specialising in the new strategies. The availability of profit gradually attracts more players who adapt versions of the strategies. To obtain/maintain the market share, players invest into generating advantage (technology, market research, etc). Typically, bigger players – who might have been prudent at the appearance of the strategy – enter the game. The profit opportunities start to diminish due to the increased number of competitors, the advantage of the smaller players is lost to the bigger players. Often, only a few big diversified companies remain in the game making a portion of the profit that was available earlier.

The new opportunities are often due to some market inefficiencies, the spread of the strategies generates efficiency, and the almost complete extinction is induced by the increased efficiency maintained by the few remaining participants. Very often, the strategies mutate and adapt to the modified circumstances, or extinct if the favourable circumstances cease to exist.

Examples of co-existence and co-evolution are also common. E.g. the liquidity taken by certain classes is provided by the followers of certain other strategies. The circumstances favourable to both classes gradually develop – often reinforced by the spread of the strategies – transforming the strategies into part of the common practice. Another example is “fishing” and “gaming” simultaneously in dark pools and lit markets, strategies that have become common since the appearance of dark pools.

This review has been commissioned as part of the UK Government’s Foresight Project, The Future of Computer Trading in Financial Markets. The views expressed do not represent the policy of any Government or organisation.

Research Library: UK Gov Foresight Project - Economic impact assessments on MiFID II policy measures related to computer trading in financial markets

Mon, 03 Sep 2012 15:10:09 +0000

Oliver Linton, Cambridge University

Maureen O’Hara, Cornell University

J.P. Zigrand, London School of Economics

Published: August 2012

Computer trading has changed markets in fundamental ways, not the least of which is the speed at which trading now occurs. There are a variety of policies proposed to address this new world of trading with the goals of improving market performance and reducing the risks of market failure. These policies include notification of algorithms, circuit breakers, minimum tick size requirements, market maker obligations, minimum resting times and minimum order-to-execution ratios. The Foresight Project has commissioned a variety of studies to evaluate these policies, with a particular focus on their economic costs and benefits, implementation issues and empirical evidence on effectiveness. This working paper summarises those findings.(pdf)

The key findings relating to the different policies are as follows, starting with those which were most strongly supported by the evidence:
Overall, there is general support from the evidence for the use of circuit breakers, particularly for those designed to limit periodic illiquidity induced by temporary imbalances in limit order books. Different markets may find different circuit breaker policies optimal, but in times of overall market stress there is a need for coordination of circuit breakers across markets.
There is also support for a coherent tick size policy across similar markets. Given the diversity of trading markets in Europe, a uniform policy is unlikely to be optimal, but a coordinated policy across competing venues may limit excessive competition and incentivise limit order provision.
The evidence offers less support for policies imposing market maker obligations. For less actively traded stocks, designated market makers have proven beneficial, albeit often expensive. For other securities, however, market maker obligations run into complications arising from the nature of high frequency market making across markets, which differs from traditional market making within markets. Many high frequency strategies post bids and offers across correlated contracts. A requirement to post a continuous bid-offer spread is not consistent with this strategy and, if binding, could force high frequency traders out of the business of liquidity provision. Voluntary programmes whereby liquidity supply is incentivised by the exchanges and/or the issuers can improve market quality.
Similarly, minimum resting times, while conceptually attractive, can impinge upon hedging strategies which operate by placing orders across markets and expose liquidity providers to increased ‘pick-off risk’ if they are unable to cancel stale orders.
The effectiveness of proposed measures to require notification of algorithms or minimum order-to-execution ratios are also not supported by the evidence. The proposed notification policy is too vague, and its implementation, even if feasible, would require excessive costs for both firms and regulators. It is also doubtful that it would substantially reduce the risk of market instability due to errant algorithmic behaviour, although it may help regulators understand the way the trading strategy should work.
An order-to-execution ratio is a blunt policy instrument to reduce excessive message traffic and cancellation rates. While it could potentially reduce undesirable manipulative trading strategies, beneficial strategies may also be curtailed. There is insufficient evidence to ascertain these effects, and so caution is warranted. Explicit fees charged by exchanges on excessive messaging and greater regulatory surveillance geared to detect manipulative trading practices may be more effective approaches to deal with these problems.

Introduction by Professor Sir John Beddington

This working paper presents important interim findings of the international Foresight project: The Future of Computer Trading in Financial Markets. In particular, it considers the costs, risks and benefits of six possible regulatory measures which are currently being considered within the European Union’s Markets in Financial Instruments Directive 2 (MiFID II). It precedes the final project report which will be published later in 2012, and which will consider a broader set of issues surrounding computer-based trading (CBT) over the next ten years.
Algorithmic trading (AT) and high frequency trading (HFT) have grown rapidly in use in recent years. As such, they have also fuelled increases in complexity as well as new system dynamics, making markets ever harder to understand and to regulate. In particular, there is continuing controversy concerning the extent to which they improve or degrade the functioning of financial markets, and also influence market volatility and the risk of instabilities. For example, such trading has been implicated by some as a contributory factor in the May 6th 2010 Flash Crash.

For such reasons, computer-based trading is now attracting the close attention of policy makers and regulators worldwide.

However, the debate on high frequency and algorithmic trading has been hampered by the availability of evidence and analysis. This is of significant concern since regulation that is not soundly based risks being ineffective, or worse, could lead to unhelpful and unforeseen consequences. By drawing upon the available science and evidence from across the world, the Foresight project seeks to provide independent advice to policy makers. More specifically, this working paper has involved some 35 leading academics from nine countries and presents analysis that has been subject to independent peer review. As such, it does not represent the views of the UK or any other government. In view of the rapid pace of the MiFID II regulatory process, I have pleasure in making this paper freely available now, in advance of the full Foresight report.

Professor Sir John Beddington
CMG, FRS Chief Scientific Adviser to HM Government and Head of the Government Office for Science

Get the Latest Working Paper here.

Get the Previous Interim Report here.

Check out the Supplementary Materials put together for the Previous Report here.

Wired Magazine asks how Wall Street Got Addicted to High-Frequency Trading

Tue, 07 Aug 2012 06:21:01 +0000

The high-frequency trading debate has been polarising opinion for years now, and with little impact on the march of the technologies which are enabling it. Here at MoneyScience, we try not to take a view on the ethics or cultural impact of HFT - we like the evolution of technology as a rule, but dislike speculation when it comes at the expense of markets which would otherwise provide a socially meaningful role. Progress is generally good, we feel - but to paraphrase Spiderman, 'with great power comes great responsibility' - and financial markets as a rule haven't done a great job in recent history of demonstrating they can handle it. We may have the technology to trade ultra-fast, but whether we have the scientific or economic infrastructure to understand and control it is the core of the debate.

Link Library: Tobias Preis's GPGPU Research Page

Fri, 03 Aug 2012 15:51:11 +0000

Dr Tobia Preis has compiled a fantastic list of selected publications in GPGPU Computing:

A recent trend in computer science and related fields is General-Purpose computation on Graphics Processing Units (GPGPU), which can yield impressive performance, i.e. the required processing times can be reduced to a great extent.

The Compute Unified Device Architecture (CUDA) is a programming approach for performing scientific calculations on a Graphics Processing Unit (GPU) as a data-parallel computing device. The programming interface allows to implement algorithms using extensions to standard C language. With continuously increased number of cores in combination with a high memory bandwidth, a recent GPU offers incredible resources for general purpose computing.

Research Library: Noncomputability, unpredictability, undecidability, and unsolvability in economic and finance theories (pdf)

Fri, 03 Aug 2012 06:10:12 +0000

Ying-Fang Kao, V. Ragupathy, K. Vela Velupillai, Stefano Zambelli

Abstract

We outline, briefly, the role that issues of the nexus between noncomputability and unpredictability, on the one hand, and between undecidability and unsolvability, on the other hand, have played in Computable Economics (CE). The mathematical underpinnings of CE are provided by (classical) recursion theory, varieties of computable and constructive analysis and aspects of combinatorial optimization. The inspiration for this outline was provided by Professor Graca's thought-provoking recent article.

Algorithmic Trading Glitch Costs Knight Capital $440 Million

Thu, 02 Aug 2012 19:02:54 +0000

Via Slashdot:

FIA European Principal Traders Association Market Integrity Framework: Best Practices to Preserve Market Integrity

Fri, 27 Jul 2012 11:32:14 +0000

As part of ongoing efforts to safeguard market integrity, FIA European Principal Traders Association today published a set of best practices to help principal trading firms prevent market manipulation and reduce risks.

Artificial Intelligence, unstructured data and banking scams

Mon, 23 Jul 2012 12:29:56 +0000

New Scientist covers the technology firms who are using Artificial Intelligence to identify behaviours which could indicate improper behaviour:

Research Library: Optimisation of Complex Financial Models Using Nature-Inspired Techniques

Thu, 19 Jul 2012 14:54:07 +0000

Nikos S. Thomaidis
University of the Aegean - Department of Financial Engineering & Management - Decision & Management Engineering Laboratory

George D. Dounias
University of the Aegean - Department of Financial Engineering and Management

Magdalene Marinaki

Ioannis Marinakis

Abstract

This paper discusses applications of nature-inspired computational techniques in optimisation problems encountered in portfolio selection and applied econometrics. By means of an empirical study, we show how particle swarm intelligence can be effectively used in the estimation of a GARCH and an EGARCH model, two popular econometric parametrisations for the volatility of financial prices. We discuss several issues emerging from the application of nature-inspired techniques in financial optimisation

Research Library: Non-computability, unpredictability, and Financial markets (pdf)

Sat, 14 Jul 2012 18:32:38 +0000

Daniel S. Graca
CEDMES/FCT da Universidade do Algarve, Portugal & SQIG, Portugal

Abstract

One of the most significant achievements from theoretical computer science was to show that there are non-computable problems, which cannot be solved through algorithms. Although the formulation of such problems is mathematical, they often can be interpreted as problems derived from other fields, like physics or computer science. However no noncomputable problem with economical or financial inspiration has been presented before. Here we study the problem of valuation: given some adequate data, find the value of an asset. Valuation is modeled mathematically by the discounted cash flow operator. We show, using surprisingly simple arguments, that this operator is not computable. Since, theoretically, financial markets should trade assets based on their fair value, our result suggests that unpredictability of such markets may partially stem from inherent non-computable behavior. A discussion of this result is also included.

Link Library: Notes for Economists on Writing Code

Tue, 03 Jul 2012 12:25:09 +0000

Matthew Gentzkow
Jesse M. Shapiro

Chicago Booth

June 25, 2012

Introduction

Every step of every research project we do is written in code, from raw data to final paper. Doing research is therefore writing software. Over time, people who write software for a living have learned a lot about how to write it well. We follow their lead. We aim to write code that would pass muster if we worked at Google or Microsoft. Economists sometimes write code that is like stream-of-consciousness: a more or less random series of steps that happen to produce the right result. A good way to generate this kind of code is to use Stata interactively for an hour and then copy and paste the list of commands into a text editor. This code will do what it is supposed to do. But it will be very difficult for someone other than the person who produced it–or even for that same person after a day or two–to read and understand it. It will be virtually impossible to modify or extend it. And if anything about the environment changes–if you try to run it on a different computer, say, or change the name of an input file–it will break.

It is obvious that Google Maps or Microsoft Word could not be written this way. The code for these programs must be written so that many people over many years can read and understand it. It must have a logical structure that makes it easy to fix, modify, and extend, and allows people to take pieces developed for one problem and apply them to another. It must be robust enough to remain viable in a constantly changing environment. And it must be efficient. Although our projects are orders of magnitude smaller, our code needs to fulfill these same requirements. This document lays out some broad principles we should all follow. Both this document and our understanding of what makes good code are constantly evolving. Few of us have formal training and we’re learning as we go. If you look at code we’ve written in the past, you’ll see that most of it fails some of the criteria below and much of it fails most of the criteria. We encourage you to invest in reading more broadly about software craftsmanship, looking critically at your own code and that of your colleagues, and suggesting improvements or additions to the principles below.

Research Library: Notes for Economists on Writing Code

Tue, 03 Jul 2012 12:25:09 +0000

Matthew Gentzkow
Jesse M. Shapiro

Chicago Booth

June 25, 2012

Introduction

Mark Cuban: High-Frequency Traders Are the Ultimate Hackers

Wed, 27 Jun 2012 06:03:18 +0000

This WSJ interview with Mark Cuban has received quite a lot of attention, as well as a Discussion at Slashdot.

HFT Review Special Section: FPGA & Hardware Accelerated Trading

Tue, 26 Jun 2012 11:50:35 +0000

The Microsecond Market

Mon, 04 Jun 2012 19:20:53 +0000

Sophisticated technology now drives global financial trading to extremes of time and space

How to Land a Technology Job on Wall Street: Inside an Elite Wall Street IT Education

Fri, 01 Jun 2012 14:48:55 +0000

Capital markets firms are recruiting graduates from the top schools in computer science and other majors to train the next generation of IT talent.

Link Library: The Lodestone Foundation

Thu, 24 May 2012 12:44:00 +0000

Open Source for Capital Markets and Beyond

What is our Goal?

Quickly and convincingly build the go-to non-profit open source foundation for financial markets. Significantly, we will seed this foundation with world-beating code contributions from known open-source heavyweights and founding partners, and fund a small world-class dev team that also produces and/or extends reference software in key areas of interest to financial markets, that is convincingly better than industry alternatives and helps us avoid re-building the same things, at a lower quality level, again and again. The goal of the team is to produce results in a reasonable period, that attracts both wide industry adoption, and contributions from the best open source community and the financial industry. Part of our charter will be to assist partners in putting in place great examples of open-source governance and participation, and to help our industry more productively engage with open source, and use it for better client value and competitive benefit.

Stanford bioengineers create rewritable digital data storage in DNA

Tue, 22 May 2012 20:44:34 +0000

You don't hear a lot about biological computing but this is super cool:

Special Report: The algorithmic arms race

Mon, 21 May 2012 10:17:53 +0000

I actually saw David Harding give a talk at the Royal Institution 14-10 Club last year and he threw up a powerpoint slide displaying one of the Winton Capital's strategies. It lwas somewhat more complex than this:

Podcast - The State of the Art in Low Latency - The Daly Post

Wed, 02 May 2012 13:34:08 +0000

Rob Daly is a financial tech journalist who produces the rather excellent Daly Post Podcast. In this most recent edition he asks:

Link Library: Frequently Asked Questions about QuantLib

Tue, 24 Apr 2012 12:05:38 +0000

1. General questions

2. Contributing to the project

3. Building QuantLib

4. Testing QuantLib

4.1. The QuantLib test-suite fails when compiling under Mac OS X 10.3.

5. Using QuantLib

6. QuantLib features

6.1. Why is feature X missing from QuantLib? It would be a very useful one.

7. QuantLib extensions

8. QuantLib mailing lists

8.1. How do I start a new topic?

Bitcoin, the financial traders' anarchic new toy

Mon, 02 Apr 2012 14:05:30 +0000

Naomi O'Leary writes at Reuters:

Presentations & Audio from the 2012 London Low-Latency Summit

Fri, 30 Mar 2012 09:00:17 +0000

Presentations:

Research Library: High Frequency Trading Acceleration Using FPGAs (pdf)

Tue, 06 Mar 2012 12:02:48 +0000

Christian Leber, Benjamin Geib, Heiner Litz

Abstract

This paper presents the design of an application specific hardware for accelerating High Frequency Trading applications. It is optimized to achieve the lowest possible latency for interpreting market data feeds and hence enable minimal round-trip times for executing electronic stock trades. The implementation described in this work enables hardware decoding of Ethernet, IP and UDP as well as of the FAST protocol which is a common protocol to transmit market feeds. For this purpose, we developed a microcode engine with a corresponding instruction set as well as a compiler which enables the flexibility to support a wide range of applied trading protocols. The complete system has been implemented in RTL code and evaluated on an FPGA. Our approach shows a 4x latency reduction in comparison to the conventional Software based approach.

Vendor News: Tradar and Nedelma introduce Mobile Portfolio Reporting App for iPad

Fri, 02 Mar 2012 06:35:04 +0000

Tradar and Nedelma introduce Mobile Portfolio Reporting Application

Vendor News: Lombard Risk Dodd-Frank Act Engine as Solution for Title VII, Regulation of OTC Swaps Markets

Wed, 22 Feb 2012 13:42:18 +0000

Lombard Risk Management plc (LSE:LRM) ("Lombard Risk"), a leading global provider of integrated collateral management and liquidity, regulatory and MIS reporting solutions for the financial services industry, addresses Title VII of Dodd-Frank Act with its Dodd-Frank Act Engine solution.

Vendor News: The Institutionalisation of Hedge Funds - The Investor Due Diligence Bar Rises Ever Higher

Mon, 20 Feb 2012 15:34:03 +0000

KB Associates Profile at MoneyScience
Home Page

Vendor News: Tradeweb Launches Multi-Dealer Trading Platform For FX Options

Sun, 19 Feb 2012 07:31:35 +0000

LONDON February 16, 2012: Tradeweb Markets LLC announced the introduction of an electronic, multi-dealer-to-customer trading platform for FX options. This is the fourth derivatives marketplace to have been launched by Tradeweb since 2005. The new platform enables options trading on major currencies and allows buy-side investors to request quotes from several dealers simultaneously. Participants also benefit from integrated trade processing and post-trade reporting.

Vendor News: NYSE Euronext Announces Acquisition of Strategic Shareholder Interest in Fixnetix

Thu, 16 Feb 2012 11:20:22 +0000

NYSE Euronext Announces Acquisition of Strategic Shareholder Interest in Fixnetix

Research Library: Data Markets in the Cloud: An Opportunity for the Database Community (pdf)

Sun, 12 Feb 2012 10:59:07 +0000

Magdalena Balazinska, Bill Howe, and Dan Suciu

Abstract

Cloud-computing is transforming many aspects of data management. Most recently, the cloud is seeing the emergence of digital markets for data and associated services. We observe that our community has a lot to offer in building successful cloud-based data markets. We outline some of the key challenges that such markets face and discuss the associated research problems that our community can help solve.

Link Library: GPU-computing in econophysics and statistical physics (pdf)

Sun, 12 Feb 2012 10:19:43 +0000

Tobias Preis

Abstract

A recent trend in computer science and related fields is general purpose computing on graphics processing units (GPUs), which can yield impressive performance. With multiple cores connected by high memory bandwidth, today’s GPUs offer resources for non-graphics parallel processing. This article provides a brief introduction into the field of GPU computing and includes examples. In particular computationally expensive analyses employed in financial market context are coded on a graphics card architecture which leads to a significant reduction of computing time. In order to demonstrate the wide range of possible applications, a standard model in statistical physics – the Ising model – is ported to a graphics card architecture as well, resulting in large speedup values.

Research Library: GPU-computing in econophysics and statistical physics (pdf)

Sun, 12 Feb 2012 10:19:43 +0000

Tobias Preis

Abstract

Research Library: High-Frequency Trading - Study commissioned by Deutsche Borse Group (pdf)

Thu, 09 Feb 2012 14:36:41 +0000

By Peter Gomber, Björn Arndt, Marco Lutat, Tim Uhle

Executive Summary

High-frequency trading (HFT) has recently drawn massive public attention fuelled by the U.S. May 6, 2010 flash crash and the tremendous increases in trading volumes of HFT strategies. Indisputably, HFT is an important factor in markets that are driven by sophisticated technology on all layers of the trading value chain. However, discussions on this topic often lack sufficient and precise information. A remarkable gap between the results of academic research on HFT and its perceived impact on markets in the public, media and regulatory discussions can be observed.

The research at hand aims to provide up-to-date background information on HFT. This includes definitions, drivers, strategies, academic research and current regulatory discussions. It analyzes HFT and thus contributes to the ongoing discussions by evaluating certain proposed regulatory measures, trying to offer new perspectives and deliver solution proposals. Our main results are:

HFT is a technical means to implement established trading strategies. HFT is not a trading strategy as such but applies the latest technological advances in market access, market data access and order routing to maximize the returns of established trading strategies. Therefore, the assessment and the regulatory discussion about HFT should focus on underlying strategies rather than on HFT as such.

HFT is a natural evolution of the securities markets instead of a completely new phenomenon. There is a clear evolutionary process in the adoption of new technologies triggered by competition, innovation and regulation. Like all other technologies, algorithmic trading (AT) and HFT enable sophisticated market participants to achieve legitimate rewards on their investments – especially in technology – and compensation for their market, counterparty and operational risk exposures.

A lot of problems related to HFT are rooted in the U.S. market structure. The flash crash and the discussions on flash orders relate to the U.S. equity markets and the NMS. In Europe, where a more flexible best execution regime is implemented and a share-by-share volatility safeguard regime has been in place for two decades, no market quality problems related to HFT have been documented so far. Therefore, a European approach to the subject matter is required and Europe should be cautious in addressing and fixing a problem that exists in a different market structure thereby creating risks for market efficiency and market quality.

The majority of HFT based strategies contributes to market liquidity (market making strategies) or to price discovery and market efficiency (arbitrage strategies). Preventing these strategies by inadequate regulation or by impairing underlying business models through excessive burdens may trigger counterproductive and unforeseen effects to market quality. However, any abusive strategies against market integrity must be effectively combated by supervisory authorities.

Academic literature mostly shows positive effects of HFT based strategies on market quality. The majority of papers, focusing on HFT, do not find evidence for negative effects of HFT on market quality. On the contrary, the majority argues that HFT generally contributes to market quality and price formation and finds positive effects on liquidity and short term volatility. Only one paper critically points out that under certain circumstances HFT might increase an adverse selection problem and in case of the flash crash one study documents that HFT exacerbated volatility. As empirical research is restricted by a lack of accessible and reliable data, further research is highly desirable.

In contrast to internalization or dark pool trading, HFT market making strategies face relevant adverse selection costs as they are providing liquidity on lit markets without knowing their counterparties. In internalization systems or dark venues in the OTC space, banks and brokers know the identity of their counterparty and are able to ?cream skim? uninformed order flow. In contrast, HFTs on lit markets are not informed on the toxicity of
their counterparts and face the traditional adverse selection problems of market makers.

Any assessment of HFT based strategies has to take a functional rather than an institutional approach. HFT is applied by different groups of market players from investment banks to specialized boutiques. Any regulatory approach focusing on specialized players alone risks (i) to undermine a level playing field and (ii) exclude a relevant part of HFT strategies.

The high penetration of HFT based strategies underscores the dependency of players in today’s financial markets on reliable and thoroughly supervised technology. Therefore, (i) entities running HFT strategies need to be able to log and record algorithms‘ input and output parameters for supervisory investigations and back-testing, (ii) markets have to be able to handle peak volumes and have to be capable of protecting themselves
against technical failures in members‘ algorithms, (iii) regulators need a full picture of potential systemic risks triggered by HFT and require people with specific skills as well as regulatory tools to assess trading algorithms and their functionality.

Any regulatory interventions in Europe should try to preserve the benefits of HFT while mitigating the risks as far as possible by assuring that (i) a diversity of trading strategies prevails and that artificial systemic risks are prevented, (ii) economic rationale rather than obligations drive the willingness of traders to act as liquidity providers, (iii) colocation and proximity services are implemented on a level playing field, (iv) instead of
market making obligations or minimum quote lifetimes, the focus is on the alignment of volatility safeguards among European trading venues that reflect the HFT reality and ensure that all investors are able to adequately react in times of market stress.

The market relevance of HFT requires supervision but also transparency and open communication to assure confidence and trust in securities markets. Given the public sensitivity to innovations in the financial sector after the crisis, it is the responsibility of entities applying HFT to proactively communicate on their internal safeguards and risk management mechanisms. HFT entities act in their own interest by contributing to an environment where objectivity rather than perception leads the debate: They have to draw attention to the fact that they are an evolution of securities markets, supply liquidity and contribute to price discovery for the benefit of markets.

Research Library: Financial black swans driven by ultrafast machine ecology

Wed, 08 Feb 2012 08:46:32 +0000

Neil Johnson, Guannan Zhao, Eric Hunsader, Jing Meng, Amith Ravindar, Spencer Carran, Brian Tivnan

Abstract

Society's drive toward ever faster socio-technical systems, means that there is an urgent need to understand the threat from 'black swan' extreme events that might emerge. On 6 May 2010, it took just five minutes for a spontaneous mix of human and machine interactions in the global trading cyberspace to generate an unprecedented system-wide Flash Crash. However, little is known about what lies ahead in the crucial sub-second regime where humans become unable to respond or intervene sufficiently quickly. Here we analyze a set of 18,520 ultrafast black swan events that we have uncovered in stock-price movements between 2006 and 2011. We provide empirical evidence for, and an accompanying theory of, an abrupt system-wide transition from a mixed human-machine phase to a new all-machine phase characterized by frequent black swan events with ultrafast durations (<650ms for crashes, <950ms for spikes). Our theory quantifies the systemic fluctuations in these two distinct phases in terms of the diversity of the system's internal ecology and the amount of global information being processed. Our finding that the ten most susceptible entities are major international banks, hints at a hidden relationship between these ultrafast 'fractures' and the slow 'breaking' of the global financial system post-2006. More generally, our work provides tools to help predict and mitigate the systemic risk developing in any complex socio-technical system that attempts to operate at, or beyond, the limits of human response times.

Download PDF

UPDATE:

You might also be interested in this collection of research on High Frequency Trading.

One of the authors of this paper, Neil Johnson, gives an interview here: “A great war of algorithms is already under way” – scientist Neil Johnson

There's also some interesting coverage of the paper over at the arXiv blog:

The evidence comes from Johnson and co's study of stock price movements between 2006 and 2011. These guys looked for extreme changes in a stock price, which they defined as a change greater than 0.8 per cent, over timescales shorter than 1.5 seconds.

Since human reaction times are about a second, this spans the regime when trades begin to occur faster than humans can monitor and react to them.

The first thing they discovered is that flash crashes and rises are not at all rare. Johnson and co found over18,000 of them, that's more than one a day on average. They call them black swan events, using the terminology developed by Nassim Nicolas Taleb in his book The Black Swan.

Curiously, they found that that change in the occurrence of crashes occurs at timescales shorter than 650 milliseconds, while the transition for price spikes occurs at 950 milliseconds.

Physics of Finance Blog also comments in an article: Approaching the singularity -- in global finance

The paper as a whole takes a bit of time to get your head around, but it is, I think, a beautiful example of how a simple model that explores some of the rich dynamics of how strategies interact in a market can give rise to some deep insights. The analysis suggests, first, that the high frequency markets have moved past "the singularity," their dynamics having become fundamentally different -- uncoupled from the control, or at least strong influence, of human trading. It also suggests, second, that the change in dynamics derives directly from the crowding of strategies that operate on very short timescales, this crowding caused by the need for relative simplicity in these strategies.

Wired were a little bit late to this piece of research with their article: Nanosecond Trading Could Make Markets Go Haywire

With many algorithms converging on just a few different strategies, the high-frequency trading market could become vulnerable to systemwide herd behaviors. Fortunately for us, the market seems to rebound from spikes almost as immediately as they occur — Johnson and Tivnan likened the effect to a “coiled spring” returning to form — but as seen in May 2010, this might not always happen.

Johnson and Tivnan also used another metaphor to describe the flash crashes and spikes: fractures. The events could be imagined as microfractures in the wing of an aircraft, accumulating unnoticeably until some critical, breakage-causing mass is reached. To that end, they found a correlation between rising frequencies of sub-950-ms flash events, market volatility after 2008, and the May 2010 flash crash. The 10 stocks most prone to crash-and-spiking were all financial companies, with Morgan Stanley, Goldman Sachs and Wells Fargo topping the list.

The Reign of Robots May Be Closer Than You Think: Mark Buchanan:

The futurist Ray Kurzweil has famously predicted that humanity is approaching a “singularity,” a fateful moment when our technology becomes smarter than us and able to learn faster than we can, when it becomes the principal creator of new technologies and machines race far ahead of us. Humans may effectively fall out of the loop -- a species demoted, if not eliminated.

For now, this world remains science fiction, at least at the level of humanity. But finance is flirting with a similar transition, as ever-faster computing and communications technology takes high-frequency trading into a regime of speed where human beings can no longer keep up. In fact, we may have already arrived.

Vendor News: SAP Achieves 2011 Sustainability Goals With Increases in Carbon Efficiency, Women in Management and Employee Engagement

Sat, 04 Feb 2012 23:48:56 +0000

Executing on its commitment to help the world run better and improve people’s lives, SAP AG (NYSE: SAP) today announced its preliminary sustainability performance results for 2011. As a leading provider of sustainability solutions, SAP also strives to implement sustainable business practices across its own global operations.

Vendor News: Rogue Wave Delivers TotalView for IBM Blue Gene/Q to LLNL

Sat, 04 Feb 2012 23:33:02 +0000

Boulder, CO, February 1, 2012 – Rogue Wave Software and Lawrence Livermore National Laboratory (LLNL) today announced that Rogue Wave has delivered a pre-release version of TotalView, optimized for the IBM Blue Gene/Q-based Sequoia supercomputer. This is a significant milestone in the multi-year collaboration between LLNL, Rogue Wave, and IBM.

Vendor News: StatPro Revolution Named "Best Buy-Side Newcomer" at 2011 Buy Side Technology Awards

Sat, 04 Feb 2012 22:55:48 +0000

London, UK (7 November, 2011) plc (AIM: SOG), a global leader in portfolio analytics and valuation data solutions since 1994, was awarded the Best Buy-Side Newcomer StatPro Revolution at the 2011 Buy Side Technology Awards. The annual awards honor the achievements of firms that service asset and hedge fund managers and are adjudicated by an expert panel from the buy side industry, including consultancies, analyst firms and the editorial team at Buy Side Technology.

Vendor News: Orchestrade Partners with Numerix to Expand Cross-Asset Pricing, Valuation and Risk Coverage

Sat, 04 Feb 2012 21:31:53 +0000

New York – January 31, 2012 – Numerix (www.numerix.com), the leading provider of cross-asset analytics for derivatives valuations and risk management, along with Orchestrade Financial Systems (www.orchestrade.com), providers of middle office operations and risk management solutions to alternative investment management firms, today announced their partnership to offer integrated access to Powered by Numerix™ pricing and risk analytics within the Orchestrade trading and risk platform.

Research Library: Computing DSGE Models with Recursive Preferences and Stochastic Volatility

Wed, 01 Feb 2012 10:44:32 +0000

Dario Caldara, Jesus Fernandez-Villaverde, Juan Rubio-Ramirez, and Yao Wen

Abstract

This paper compares different solution methods for computing the equilibrium of dynamic stochastic general equilibrium (DSGE) models with recursive preferences such as those in Epstein and Zin (1989 and 1991) and stochastic volatility. Models with these two features have recently become popular, but we know little about the best ways to implement them numerically. To fill this gap, we solve the stochastic neoclassical growth model with recursive preferences and stochastic volatility using four different approaches: second- and third-order perturbation, Chebyshev polynomials, and value function iteration. We document the performance of the methods in terms of computing time, implementation complexity, and accuracy. Our main finding is that perturbations are competitive in terms of accuracy with Chebyshev polynomials and value function iteration while being several orders of magnitude faster to run. Therefore, we conclude that perturbation methods are an attractive approach for computing this class of problems.

Vendor News: Intrinsic Research Systems partners with Kalotay Analytics

Fri, 27 Jan 2012 06:50:43 +0000

Intrinsic Research Systems to Release A New Fixed Income Data Module. Firm Partners with Andrew Kalotay Associates for Bond Analytics.

Charlotte, NC (PRWEB) October 26, 2011 -- Intrinsic Research Systems, Inc., a Mergent company, today announced the addition of fixed income research and analytic capabilities to the Intrinsic Research security analysis platform.

Intrinsic’s BondViewer module, powered by analytics from Andrew Kalotay Associates, is designed for front, middle and back office use, providing transparency on over 230,000 U.S corporate and 2.6 million municipal bonds. Mergent’s industry leading terms and conditions, pricing, ratings history and bond analytics represent some of the robust data available for each bond.

Intrinsic’s BondViewer module is available as a standalone fixed income research desktop solution or as an integrated add-on to Intrinsic’s extensive equity research and valuation analysis platform. BondViewer provides unlimited access to company financials, competitors, U.S. Corporate and Municipal bond terms and conditions and MSRB and TRACE pricing data. In addition, Andrew Kalotay Associates’ conventional bond calculations and valuations such as Option Adjusted Spread, Key Rate Duration and Convexity/Duration/DV01 and Z Spreads are integrated throughout the module. BondViewer provides multiple reporting and analysis formats for monitoring portfolios and researching individual issues through an easy to learn, tab-driven interface. Quick links between corporate bonds and issuer fundamentals are emphasized throughout the application.

"We are excited to announce the release of fixed income data and analytics to our continually expanding platform" says Jeff McMains, CFA and Co-Founder of Intrinsic Research Systems. "We believe the combination of high quality Mergent bond data and analytics powered by a known leader in the field such as Andrew Kalotay Associates will allow us to offer a unique and powerful tool to fixed income analysts and fund managers."

“We are very enthusiastic to support Intrinsic Research’s new fixed income offering with our high-speed, highprecision bond analytics. Our deep knowledge of bonds with embedded options, exercised optimally in our scenario analysis routines, will serve as a unique benefit to Intrinsic clients,” says Andrew Porter, Managing Director and Head of Business Development at Andrew Kalotay Associates, Inc.

For more information on Intrinsic Research Systems, please visit www.intrinsic-research.com

About Intrinsic Research Systems

Designed as the next generation in the evolution of equity analytics, Intrinsic Research System’s suite of research products has been created for investment managers seeking an intuitive and comprehensive approach to original equity and fixed income research and decision making. Intrinsic Research solutions have been built from the ground up, using today’s technology, for a rich user experience that is unmatched by any other service. Likewise, Intrinsic’s integrated central database of company, sector, industry and economic data has been specially constructed to provide analysts and portfolio managers with access to the key components driving equity performance in a single, consolidated package. Intrinsic Research is a subsidiary of Mergent, Inc.

About Andrew Kalotay

Andrew Kalotay Associates (AKA) is a leading provider of high speed, high precision fixed income analytics and debt management tools. The firm's founder, Dr. Andrew Kalotay, is a member of the Fixed Income Analysts Society (FIASI) Hall of Fame for his contributions to debt management and bond valuation.

AKA clients include sell-side and buy-side institutions, financial technology providers, valuation service firms, and corporate, agency, and municipal debt issuers.

Press Release Contact:

Veronica Carlan
Marketing Communications Manager
704-559-7659
Veronica(dot)Carlan(at)mergent(dot)com

Vendor News: Quantifi Wins Risk Magazineâs Coveted Risk Management Technology Product of the Year Award

Fri, 27 Jan 2012 05:58:39 +0000

London and New York - Quantifi, a leading provider of analytics, trading and risk management solutions to the global OTC markets, today announced that Quantifi has achieved one of the industry’s highest accolades having won ‘Risk Management Technology Product of the Year’ in Risk Magazine’s 2012 Risk Awards.

Research Library: Need for Speed: An Empirical Analysis of Hard and Soft Information in a High Frequency World

Thu, 26 Jan 2012 10:46:21 +0000

S. Sarah Zhang
Karlsruhe Institute of Technology

January 16, 2012

Abstract

Speed matters for the processing of market relevant information and for stock price discovery. We measure and compare the impact of different kinds of information events on high-frequency trading (HFT) and non-HFT (NHFT). Information events are categorized into hard quantitative information shocks and soft qualitative shocks. We find that HFT reaction to hard information is higher and faster than for soft information and they cash in secure profits after a short time period. NHFT reaction is slower and higher for soft information. Furthermore, initiating traders and passive traders complement one another in price discovery: Initiating HFT have a higher influence on short-term price discovery than NHFT and increasingly after hard information shocks. Passive NHFT on the other side have a higher influence in the long run and increasingly after soft information shocks.

Via: Substructural

Vendor News: AlignAlytics announces strategic alliance with TransAccel Group Neary named to solutions leadership role with premier services firm

Wed, 25 Jan 2012 16:02:45 +0000

Link Library: Presentation - Parallel Computing and Applications in Finance (pdf)

Thu, 05 Jan 2012 16:08:54 +0000

Slides from a presentation by Cyril Godart (BNP Paribas).

Link Library: General-Purpose Computing on Graphics Processing Units (GPGPU) article at Wikipedia

Thu, 05 Jan 2012 16:04:19 +0000

General-purpose computing on graphics processing units (GPGPU, also referred to as GPGP and less often GP²U) is the technique of using a GPU, which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the CPU. It is made possible by the addition of programmable stages and higher precision arithmetic to the rendering pipelines, which allows programmers to use stream processing on non-graphics data^[1]^[2]^[3]. Additionally, the use of multiple graphics cards in a single computer, or large numbers of graphics chips, further parallelizes the already parallel nature of graphics processing.

Full Article Here.

Link Library: Presentation - Computational finance on GPUs (pdf)

Thu, 05 Jan 2012 15:59:40 +0000

A presentation by Mike Giles from the Oxford Man Institute for Quantitative Finance, covers:

computational finance
scientific computing on GPUs
random number generation
finite difference applications
current developments

Link Library: Martin Keller-Ressel's R-Code for Finance

Tue, 13 Dec 2011 17:45:57 +0000

No longer actively maintained but still useful:

Download Option Data and Plot Smiles

yahoo_opt.R

This R program can be used to download option price data from Yahoo to a data frame and to plot the corresponding implied-volatility smiles. Requires package 'fCalendar'. After downloading and sourcing the file try the following lines of code:



opt <- yahoo.getAllOptions("IBM") ## download data

summary(opt)                      ## data overview

plot.smile(opt)                   ## plot 2d volatility smiles

plot3d.smile(opt)                 ## plot 3d volatility smiles

Visualize Levy Processes

levy_proc.R

This R program contains functions to plot trajectories of several Levy processes. The processes implemented are alpha-stable processes, Variance-Gamma and Normal-Inverse-Gaussian processes. Requires packages 'SuppDists' and 'fBasics'. After downloading and sourcing the file try the following lines of code:



plot.multi(stable.proc,alpha=1.7,beta=1)         ## Stable process

plot.multi(VG.proc,sigma=0.2,theta=1,kappa=0.1)  ## Variance Gamma process with drift

plot.multi(NIG.proc,sigma=0.2,theta=0,kappa=0.1) ## Normal Inverse Gaussian process

Visualize Some Copulas

copula.R

This R program draws some plots of frequently used copulas. Just execute the R-code directly.

Link Library: Lush: Lisp Universal SHell

Tue, 06 Dec 2011 14:51:10 +0000

Lush is an object-oriented programming language designed for researchers, experimenters, and engineers interested in large-scale numerical and graphic applications. Lush is designed to be used in situations where one would want to combine the flexibility of a high-level, weakly-typed interpreted language, with the efficiency of a strongly-typed, natively-compiled language, and with the easy integration of code written in C, C++, or other languages.

Link Library: List of R functions and packages for Computational Econometrics

Mon, 21 Nov 2011 16:36:10 +0000

This extensive resource is maintained by Achim Zeileis.

Base R ships with a lot of functionality useful for computational econometrics, in particular in the stats package. This functionality is complemented by many packages on CRAN, a brief overview is given below. There is also a considerable overlap between the tools for econometrics in this view and for finance in the Finance view. Furthermore, the Finance SIG is a suitable mailing list for obtaining help and discussing questions about both computational finance and econometrics. Finally, there is also some overlap with the SocialSciences that also covers a broad variety of tools for social sciences, e.g., including political science.

Link Library: Mathtools.net - Link Exchange for the Technical Computing Community

Sat, 08 Oct 2011 06:51:19 +0000

Lots of interesting links here:

Related Categories

Link Library: Black-Scholes option pricing with CUDA - Nvidia (pdf)

Fri, 30 Sep 2011 08:21:22 +0000

Victor Podlozhnyuk

Abstract

The pricing of options is a very important problem encountered in financial engineering since the creation of organized option trading in 1973. This sample shows an implementation of the Black-Scholes model in CUDA for European options.

Link Library: Monte Carlo Option Pricing with CUDA - NVIDIA

Fri, 30 Sep 2011 05:52:19 +0000

Victor Podlozhnyuk and Mark Harris

Abstract

The pricing of options has been a very important problem encountered in financial engineering since the advent of organized option trading in 1973. As more computation has been applied to finance-related problems, finding efficient implementations of option pricing models on modern architectures has become more important. This white paper describes an implementation of the Monte Carlo approach to option pricing in CUDA. For complete implementation details, please see the “MonteCarlo” example in the NVIDIA CUDA SDK.

Link Library: Introductory Econometrics - Excel add-ins

Mon, 12 Sep 2011 09:03:43 +0000

Humberto Barreto
Allen Distinguished University Professor at DePauw University

Frank M. Howland,
Professor of Economics at Wabash College

This web site supports our book, Introductory Econometrics: Using Monte Carlo Simulation with Microsoft Excel, published by Cambridge University Press. Our fundamental strategy is to use clear language and take advantage of recent developments in computers to create concrete, visual explanations of difficult, abstract ideas.

This web site makes available the introductory sections and Excel workbooks from each chapter so you can see what we cover and how we present the material.

In addition, we've decided to make the Excel add-ins that we've written freely available. We've used these add-ins to greatly improve our teaching and we hope others can benefit from the sophisticated algorithms we've developed. Of course, we ask that you properly credit and cite our work if you use these materials. We'd love to hear from you about how you used our add-ins.

This web site also serves as a resource for instructors. If you teach econometrics, you may login to access lecture notes, additional labs, and answers to exercise questions in the book.

While this web site contains a great deal of information, there's even more material in the book, where we explain how to actually use the Excel workbooks to learn econometrics. The first printing of our book comes with a CD ROM with a full set of answers to all Excel workbook, self-study questions (but not the answers to the exercises in the book itself). The CD also has materials on downloading data from a handful of web sites, including detailed instructions on using the Current Population Survey, and several "how to" documents that offer practical, step-by-step instructions on a particular task. All these files are as of June 2010 available in a 27.7 MB zip file. To get that file, click on this link.

Link Library: Crash into Python

Mon, 15 Aug 2011 09:16:42 +0000

Crash into Python is a set of documents/slides that are meant to be used as a teaching aid for bringing programmers from other languages up to speed with Python. It assumes that you have enough familiarity with programming to know what function and class mean, and will recognize that print probably doesn't put ink on paper. More importantly, it assumes that you either have an instructor who is well-versed in Python, or are resourceful enough to find answers for yourself. A number of these slides are designed to trigger questions and discussion, so if it seems like you're missing something, that's a good sign you could be digging deeper.

Link Library: MIT OpenCourseWare - Integer Programming and Combinatorial Optimization, Fall 2009

Fri, 29 Jul 2011 11:18:45 +0000

The course is a comprehensive introduction to the theory, algorithms and applications of integer optimization and is organized in four parts: formulations and relaxations, algebra and geometry of integer optimization, algorithms for integer optimization, and extensions of integer optimization.

Link Library: MIT OpenCourseWare - Nonlinear Programming, Spring 2004

Fri, 29 Jul 2011 11:15:44 +0000

This course introduces students to the fundamentals of nonlinear optimization theory and methods. Topics include unconstrained and constrained optimization, linear and quadratic programming, Lagrange and conic duality theory, interior-point algorithms and theory, Lagrangian relaxation, generalized programming, and semi-definite programming. Algorithmic methods used in the class include steepest descent, Newton's method, conditional gradient and subgradient optimization, interior-point methods and penalty and barrier methods.

Link Library: Clever Algorithms - Nature Inspired Recipies

Mon, 27 Jun 2011 00:20:58 +0000

The book "Clever Algorithms: Nature-Inspired Programming Recipes" by Jason Brownlee PhD describes 45 algorithms from the field of Artificial Intelligence. All algorithm descriptions are complete and consistent to ensure that they are accessible, usable and understandable by a wide audience.

5 Reasons To Read:

45 algorithms described.
Designed specifically for Programmers, Research Scientists and Interested Amateurs.
Complete code examples in the Ruby programming language.
Standardized algorithm descriptions.
Algorithms drawn from the popular fields of Computational Intelligence, Metaheuristics, and Biologically Inspired Computation.

Link Library: The Financial Services Grid Initiative

Mon, 20 Jun 2011 11:15:37 +0000

The Financial Services Grid Initiative was created to provide a utility platform for financial services companies based upon the use of Grid technologies as a utility resource, allowing for the implementation of a mixed computing environment on a common infrastructure.

Link Library: Java Quant - Quantitative Financial Algorithms

Sat, 18 Jun 2011 20:52:14 +0000

Here you will find information about the evaluation of financial options and the theory, definitions and models behind.

This webpage provides Java Applets to calculate the price of complex financial options, using the Monte Carlo technique, Binary Trees, among others.

The source code written in Java and C++, together with information about the structure of classes is available. The programs have been written following the Object Oriented Paradigms.

Check this table, with option pricing applets.

Link Library: JQuantLib

Sat, 18 Jun 2011 20:50:53 +0000

JQuantLib is a free, open-source, comprehensive framework for quantitative finance, written in 100% Java. It provides "quants" and Java application developers several mathematical and statistical tools needed for the valuation of shares, options, futures, swaps, and other financial instruments, also providing tools related to risk management and money management.

Link Library: Stack Overflow

Sat, 18 Jun 2011 20:28:59 +0000

Stack Overflow is a programming Q & A site that’s free. Free to ask questions, free to answer questions, free to read, free to index, built with plain old HTML, no fake rot13 text on the home page, no scammy google-cloaking tactics, no salespeople, no JavaScript windows dropping down in front of the answer asking for $12.95 to go away. You can register if you want to collect karma and win valuable flair that will appear next to your name, but otherwise, it’s just free. And fast. Very, very fast.

We don’t run Stack Overflow. You do. Stack Overflow is collaboratively built and maintained by your fellow programmers. Once the system learns to trust you, you’ll be able to edit anything, much like Wikipedia. With your help, we can build good answers to every imaginable programming question together. No matter what programming language you use, or what operating system you call home – better programming is our goal.

Link Library: The Quantian Scientific Computing Environment

Sat, 18 Jun 2011 20:04:08 +0000

A Knoppix / Debian variant tailored to numerical and quantitative analysis.

Quantian is a remastering of Knoppix, the self-configuring and directly bootable cdrom/dvd that turns any pc or laptop (provided it can boot from cdrom/dvd) into a full-featured Linux workstation. Quantian also incorporates clusterKnoppix and adds support for openMosix, including remote booting of light clients in an openMosix terminal server context. Earlier releases are still available; see below for URLs for downloads as well as ordering information.

Link Library: Marketcetera - Open Source High Frequency Trading

Sat, 18 Jun 2011 20:01:15 +0000

Marketcetera democratizes access to high frequency trading. Our open source, automated trading platform provides you with the control and flexibility to implement your new strategies in moments, even while you reduce your infrastructure costs. Download. Run. Trade.

Link Library: QFF - The Quantitative Finance Framework

Sat, 18 Jun 2011 19:57:47 +0000

The Quantitative Finance Framework (QFF) is intended to support the development of software libraries in mathematical finance. The main fields of applications are the pricing of derivatives and the management of financial risks.

QFF is an open source project supported by the Bielefeld University. Its source code is avaliable at sourceforge, one of the largest open source repositories of the world wide web.

Link Library: GNU Octave

Sat, 18 Jun 2011 19:54:10 +0000

GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation. Octave is normally used through its interactive command line interface, but it can also be used to write non-interactive programs. The Octave language is quite similar to Matlab so that most programs are easily portable.

Link Library: Kooderive - code for pricing financial derivatives products using CUDA

Sat, 18 Jun 2011 19:47:37 +0000

The objective of this project is to create a library of code for pricing financial derivatives products using CUDA to achieve GPU programming,

Link Library: ISDA CDS Standard Model

Sat, 18 Jun 2011 19:43:16 +0000

The ISDA CDS Standard Model is a source code for CDS calculations and can be downloaded freely through this website.

The source code is copyright of ISDA and available under an Open Source license.

Background

As the CDS market evolves to trade single name contracts with a fixed coupon and upfront payment, it is critical for CDS investors to match the upfront payment amounts and to be able to translate upfront quotations to spread quotations and vice versa in a standardized manner.

One of the primary goals in making the code available is to enhance transparency and to optimize use of standard technology for CDS pricing. Implementing the ISDA CDS Standard Model and using the agreed standard input parameters will allow CDS market participants to tie out calculations and thus improve consistency and reduce operational differences downstream.

Link Library: Santa Fe Institute "Artificial Stock Market" simulation model

Sat, 18 Jun 2011 19:28:48 +0000

Several articles have been published using the Artificial Stock Market model. The model simulates prices & trade levels in a market made up of "artificial adaptive agents." This is a prototype example of a "complex system" and is thought to illustrate the benefits of simulation modeling.

Link Library: Agent Based Market Model Simulator

Sat, 18 Jun 2011 19:26:29 +0000

The project simulates a generic agent based market model. The aim is to explore intimately, by simulation, the process of price formation and the market microstructure.

Link Library: Wavelet Software

Sat, 18 Jun 2011 19:05:51 +0000

An extensive collection of wavelet related software and programming resources.

Link Library: The Gaussian Processes Web Site

Sat, 18 Jun 2011 19:03:34 +0000

This web site aims to provide an overview of resources concerned with probabilistic modeling, inference and learning based on Gaussian processes. Although Gaussian processes have a long history in the field of statistics, they seem to have been employed extensively only in niche areas. With the advent of kernel machines in the machine learning community, models based on Gaussian processes have become commonplace for problems of regression (kriging) and classification as well as a host of more specialized applications.

Link Library: Stochastic Programming Community Home Page

Sat, 18 Jun 2011 19:02:55 +0000

The Stochastic Programming (SP) Community is a world-wide group of researchers who are developing models, methods, and theory for decisions under uncertainty. The activities of this community facilitate the advancement of knowledge through its triennial conferences, specialized workshops, and rapid (electronic) dissemination of research via the Stochastic Programming E-Print Series (SPEPS).

Link Library: Statistical Data Mining Tutorials

Sat, 18 Jun 2011 19:01:17 +0000

This list of links point to a set of tutorials on many aspects of statistical data mining, including the foundations of probability, the foundations of statistical data analysis, and most of the classic machine learning and data mining algorithms.

These include classification algorithms such as decision trees, neural nets, Bayesian classifiers, Support Vector Machines and cased-based (aka non-parametric) learning. They include regression algorithms such as multivariate polynomial regression, MARS, Locally Weighted Regression, GMDH and neural nets. And they include other data mining operations such as clustering (mixture models, k-means and hierarchical), Bayesian networks and Reinforcement Learning.

Link Library: Signs that you're a bad programmer - Software Engineering Tips

Sat, 18 Jun 2011 18:57:16 +0000

Most of these faults were discovered the hard way by the author himself, either because he committed them himself or saw them in the work of others.

This paper is not meant for grading programmers, it was intended to be read by programmers who trust their ability to judge when something is a sign of bad practice, and when it's a consequence of special circumstances.

This paper was written to force its author to think, and published because he thinks you lot would probably get a kick out of it, too.

Link Library: C and C++ Programming tutorials and Source code - MYCPLUS

Sat, 18 Jun 2011 18:54:44 +0000

MYCPLUS “C and C++ Programming Resources” was developed after facing difficulties in learning programming languages back in college and university. The first ever computer programming I learnt was classical C programming language. The main focus of the website is also on C and C++ programming.

This website has been developed to help out the students who are looking for resources on learning computer programming specially C and C++ programming languages. The website provides necessary resources like programming tutorials and source code and discussion forum where you can post your problems.

Link Library: Financial Document Pricing Using C++ - the webpage of the book

Sat, 18 Jun 2011 18:49:20 +0000

This is the unofficial website of this book, maintained by Dr. Egor Kraev with the blessing of Dr. Daniel Duffy. The purpose of the site is to compile in one place all issues, comments, and corrections for this book. Please email egor@egor.ch if I've missed any web sources containing substantive discussions of the book; if there was code in the book that did not work as it should, and is not yet mentioned here, please tell me (especially if you managed to fix it).

The authoritative source on anything connected with the book remains the C++ in Financial Engineering forum on Dr. Duffy's website.

Link Library: ALGLIB - Cross-platform numerical analysis and data processing library

Sat, 18 Jun 2011 18:36:22 +0000

ALGLIB is a cross-platform numerical analysis and data processing library. It supports several programming languages (C++, C#, Pascal, VBA) and several operating systems (Windows, Linux, Solaris). ALGLIB features include:

Linear algebra (direct algorithms, EVD/SVD)
Solvers (linear and nonlinear)
Interpolation
Optimization
Fast Fourier transforms
Numerical integration
Linear and nonlinear least-squares fitting
Ordinary differential equations
Special functions
Statistics (descriptive statistics, hypothesis testing)
Data analysis (classification/regression, including neural networks)
Multiple precision versions of linear algebra, interpolation optimization and others algorithms (using MPFR for floating point computations)

Link Library: QuantLib: a free/open-source library for quantitative finance

Sat, 18 Jun 2011 18:30:44 +0000

The QuantLib project is aimed at providing a comprehensive software framework for quantitative finance. QuantLib is a free/open-source library for modeling, trading, and risk management in real-life.

QuantLib is written in C++ with a clean object model, and is then exported to different languages such as C#, Objective Caml, Java, Perl, Python, GNU R, Ruby, and Scheme. The QuantLibAddin/QuantLibXL project uses ObjectHandler to export an object-oriented QuantLib interface to a variety of end-user platforms including Microsoft Excel and OpenOffice.org Calc. Bindings to other languages and porting to Gnumeric, Matlab/Octave, S-PLUS/R, Mathematica, COM/CORBA/SOAP architectures, FpML, are under consideration. See the extensions page for details.

Appreciated by quantitative analysts and developers, it is intended for academics and practitioners alike, eventually promoting a stronger interaction between them. QuantLib offers tools that are useful both for practical implementation and for advanced modeling, with features such as market conventions, yield curve models, solvers, PDEs, Monte Carlo (low-discrepancy included), exotic options, VAR, and so on.

Link Library: RQuantLib interface from GNU R to QuantLib

Sat, 18 Jun 2011 18:22:27 +0000

RQuantLib started with support for (vanilla and exotic) equity options. Standard European and American exercises are supported as well as Binary and Barrier options. Asian options are supported with both geometric and arithmetic compounding.

Link Library: quantmod: Quantitative Financial Modelling Framework

Sat, 18 Jun 2011 18:14:51 +0000

The quantmod package for R is designed to assist the quantitative trader in the development, testing, and deployment of statistically based trading models.

What quantmod IS

A rapid prototyping environment, where quant traders can quickly and cleanly explore and build trading models.

What quantmod is NOT

A replacement for anything statistical. It has no 'new' modelling routines or analysis tool to speak of. It does now offer charting not currently available elsewhere in R, but most everything else is more of a wrapper to what you already know and love about the language and packages you currently use.

quantmod makes modelling easier by removing the repetitive workflow issues surrounding data management, modelling interfaces, and performance analysis.

Link Library: Python and R for quantitative finance

Sat, 18 Jun 2011 18:11:36 +0000

An introduction to using Python and R for quantitative finance applications.

Python and R for quantitative finance

View more presentations from Luca Sbardella

Link Library: C++ Code Collected by Quantitative Finance Code Collector

Sat, 18 Jun 2011 18:03:59 +0000

Financial Technology Focus

Vendor News: September 30, 2015 - More than half of large insurers to increase use of cloud and co-sourcing services over next five years finds SS&C survey

Vendor News: Fidessa launches Sentinel Trading Compliance

Vendor News: Infosys Positioned as a Leader and Star Performer in 2015 Banking Application Outsourcing PEAK Matrixâ¢ by Everest

Vendor News: Portfolio Probe available for R version 3.2

Mark Joshi drops first release of Kooderive, an open source library for pricing derivatives using GPUs

Link Library: Video - One Half Second of High Frequency Trading in a Single Stock

Video: Larry Tabb on The Future Of Data Management in a post-Crisis World

SEC-mandated XBRL data at risk of being irrelevant to investors and analysts

Video - Trading's evolving next generation? Control systems, feedback loops, adaptive knowledge capture

Research Library: Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices

Video - Ciamac Moallemi: High-Frequency Trading and Market Microstructure

Vendor News: NAG announces support for IBM BlueGene/Q supercomputers

Vendor News: Prescient Ridge Management signs Connamara Systems for New Trading Infrastructure and Support Services

Intel and OnX Announce Social Media Hub for the Finteligent Trading Technology Community

Research Library: The Diversity of High Frequency Traders

Vendor News: Buy-side forced to adopt more sophisticated risk management as interconnectivity between buy-side stakeholders grows, says Algorithmics, an IBM Company

Link Library: Black Rhino - A Financial Network Multi Agent Simulator

DataSift Launches Social Feeds for the Financial Services Industry

Research Library: The evolution of algorithmic classes (pdf)

Research Library: UK Gov Foresight Project - Economic impact assessments on MiFID II policy measures related to computer trading in financial markets

Wired Magazine asks how Wall Street Got Addicted to High-Frequency Trading

Link Library: Tobias Preis's GPGPU Research Page

Research Library: Noncomputability, unpredictability, undecidability, and unsolvability in economic and finance theories (pdf)

Algorithmic Trading Glitch Costs Knight Capital $440 Million

FIA European Principal Traders Association Market Integrity Framework: Best Practices to Preserve Market Integrity

Artificial Intelligence, unstructured data and banking scams

Research Library: Optimisation of Complex Financial Models Using Nature-Inspired Techniques

Research Library: Non-computability, unpredictability, and Financial markets (pdf)

Link Library: Notes for Economists on Writing Code

Research Library: Notes for Economists on Writing Code

Mark Cuban: High-Frequency Traders Are the Ultimate Hackers

HFT Review Special Section: FPGA &amp; Hardware Accelerated Trading

The Microsecond Market

How to Land a Technology Job on Wall Street: Inside an Elite Wall Street IT Education

Link Library: The Lodestone Foundation

Stanford bioengineers create rewritable digital data storage in DNA

Special Report: The algorithmic arms race

Podcast - The State of the Art in Low Latency - The Daly Post

Link Library: Frequently Asked Questions about QuantLib

Bitcoin, the financial traders' anarchic new toy

Presentations & Audio from the 2012 London Low-Latency Summit

Research Library: High Frequency Trading Acceleration Using FPGAs (pdf)

Vendor News: Tradar and Nedelma introduce Mobile Portfolio Reporting App for iPad

Vendor News: Lombard Risk Dodd-Frank Act Engine as Solution for Title VII, Regulation of OTC Swaps Markets

Vendor News: The Institutionalisation of Hedge Funds - The Investor Due Diligence Bar Rises Ever Higher

Vendor News: Tradeweb Launches Multi-Dealer Trading Platform For FX Options

Vendor News: NYSE Euronext Announces Acquisition of Strategic Shareholder Interest in Fixnetix

Research Library: Data Markets in the Cloud: An Opportunity for the Database Community (pdf)

Link Library: GPU-computing in econophysics and statistical physics (pdf)

Research Library: GPU-computing in econophysics and statistical physics (pdf)

Research Library: High-Frequency Trading - Study commissioned by Deutsche Borse Group (pdf)

Research Library: Financial black swans driven by ultrafast machine ecology

Vendor News: SAP Achieves 2011 Sustainability Goals With Increases in Carbon Efficiency, Women in Management and Employee Engagement

Vendor News: Rogue Wave Delivers TotalView for IBM Blue Gene/Q to LLNL

Vendor News: StatPro Revolution Named "Best Buy-Side Newcomer" at 2011 Buy Side Technology Awards

Vendor News: Orchestrade Partners with Numerix to Expand Cross-Asset Pricing, Valuation and Risk Coverage

Research Library: Computing DSGE Models with Recursive Preferences and Stochastic Volatility

Vendor News: Intrinsic Research Systems partners with Kalotay Analytics

Vendor News: Quantifi Wins Risk Magazineâs Coveted Risk Management Technology Product of the Year Award

Research Library: Need for Speed: An Empirical Analysis of Hard and Soft Information in a High Frequency World

Vendor News: AlignAlytics announces strategic alliance with TransAccel Group Neary named to solutions leadership role with premier services firm

Link Library: Presentation - Parallel Computing and Applications in Finance (pdf)

Link Library: General-Purpose Computing on Graphics Processing Units (GPGPU) article at Wikipedia

Link Library: Presentation - Computational finance on GPUs (pdf)

Link Library: Martin Keller-Ressel's R-Code for Finance

Download Option Data and Plot Smiles

Visualize Levy Processes

Visualize Some Copulas

Link Library: Lush: Lisp Universal SHell

Link Library: List of R functions and packages for Computational Econometrics

Link Library: Mathtools.net - Link Exchange for the Technical Computing Community

Link Library: Black-Scholes option pricing with CUDA - Nvidia (pdf)

Link Library: Monte Carlo Option Pricing with CUDA - NVIDIA

Link Library: Introductory Econometrics - Excel add-ins

Link Library: Crash into Python

Link Library: MIT OpenCourseWare - Integer Programming and Combinatorial Optimization, Fall 2009

Link Library: MIT OpenCourseWare - Nonlinear Programming, Spring 2004

Link Library: Clever Algorithms - Nature Inspired Recipies

Link Library: The Financial Services Grid Initiative

HFT Review Special Section: FPGA & Hardware Accelerated Trading