Shame “A High Performance Options Exchange Gateway built on JavaSpaces” is a dead link now

Additionally, “Derivatives Participants Worry about Clearing Risk” discusses the ping model that might leverage FIX.

Credit Agricole will give access to bank via an SDK based on the base of APIs used for the development of its application ” MyBudget “. This application is the first in the category of mobile banking with 300 to 500,000 unique customers a month using it.

CAStor also include a “appstore” to make available the applications developed with clients of Credit Agricole, 20 million customers, including 6 million active Internet users.

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.

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.

The data model is flexible to support many booking models across asset classes such as flow trading, OTC derivatives, issuances, etc., and is easy to extend

Previously I’ve leveraged FpML when it came to modelling in the IR space. Although there are advantages to going with FpML from a trade perspective, there is I believe nothing in FpML to support causality of trade modifications. It’s also suspect that taking a snapshot of the trade and appropriate dependencies for every single Lifecycle event probably isn’t the ideal solution – due to the constrains this puts on the solution, coupled with the storage requirements.

QuartetFS summaries the complexity of the problem very nicely in their ActiveStream bitemporal engine white paper:

The purpose of a financial lifecycle and booking application is to model a financial contract and the evolution of this contract through time. The business timeline of a deal, also called a modelled reality timeline, is a list of legal/official dates at which something happens to the deal. This business timeline is fundamental for reliable operations, as financial institutions must record precisely what happened to deals they are involved i

Apple\s Time Machine gives us some idea around visualization movement along a timeline – although its always backwards in time. “as of date” queries against both a system (market data, curves, etc) and business (client on phone, generate price, book deal, delivery/settlement/payment schedule) timeline

There are a few key points when reading Quartet’s ActiveStream customer case study that help in understanding the benefit of a bitemporal model for trading:

modifying a deal means updating records using an “eraser-type tool” to replace one value with another, ideally storing the audit log to record such a change. An alternative common practice is to cancel the existing deal and then rebook it with the updated value. However, neither of these practices captures the system timeline and the business timeline properly, which leads to inefficient and unreliable management processes. Modifying a deal loses information about the original system state, and therefore makes it difficult to associate the modification with its principal cause

The existing infrastructure often forced traders to cancel and rebook trades to apply corporate actions and restructures or to unwind trades (to mention a few lifecycle events that routinely apply to simple and complex derivative trades) and this disrupts the audit trail and seriously increases operational risk.

Features you might expect from a bitemporal trading system:

• Integration to analytics and risk libraries
• Lifecycle events audit trail for all trades – derivatives, flow derivatives and exotics
• Pricing, Trade and Position, and Reference Data
• Reporting and snapshot auditing

Is the future StartVT and EndVT, StartTT and EndTT?

UBS Neo will function as a unified distribution and execution platform, Kengeter said, bringing together pricing, client information, commentary and trading ideas across fixed income, currencies and commodities, equities, delta, research and prime services.

RBS Marketplace has also had a revamp – check out the cube at the start of the video.

Athena is a next generation pricing, risk management, analysis and trade management platform. Athena implements a rapid development and deployment model that puts developers at the heart of the business

Athena appears to be geared towards Exotic products based on the fact sheet. However, there is a reference to “Reactive Athena” on the second page that is intriguing, and almost implies CEP style of technology:

Reactive Athena is a framework within Athena that allows building efficient event-driven applications for algorithmic trading, simulations, back-testing, signal generation, calibration and the like. At the core of the framework is a forward-propagating graph, where nodes contain units of work scheduled for execution based on their ranking (topologically sorted order) in the graph

Finally, Athena has many pillars, again of interest is the language of choice and the database:

• Globally replicated object store database
• Python
• Dependency graph
• IDE

Update: Slide available from here.

Data rates are hitting 5 million messages per second several times a week and they even topped 6 million messages per second on 12th September and on 7th October. Most of these messages come from the derivatives markets

Changing direction a little, GPU-Accelerated Databases Envisioned by IBM Patent is curious. Should we expect something similar from Microsoft and the SQL Server team, given the C++ AMP effort?

Which leads me to another thought. Looking at the C++ 11 features that are supported in VS 2011, one can’t help but think its a small list Maybe the VC++ team could do with a few more developers to help improve the C++ 11 feature list – I’m sure the WinRT and Win8 Metro teams could spare a few.

Finally, Scila has an interesting article; Characteristics of High-Frequency Trading by Fredrik Henrikson.

The CellSetListener API sounds exactly what I’d want for a push to the client of updates rather than the historic OLAP request/response.

Latency techniques include kernel bypass at Nasdaq (obviously). Building a kernel in a driver? (00:10:20) There already exist HTTP services in kernel drivers to its a logical move. Multi-core code will be a challenge. Matching engines are not the issues, its the output that is the issue (00:13:20). Sync replication and 2-phase commit should be avoided. Reply technique are good for catchup – sounds similar to LMAX. Deterministic data from the matching engine is key to recovery.

Security is a big deal. Hackers are coming in via web sites. (00:20:00)

SQL comments are interesting (00:25:00)

00:14:30 offers some chatter on cloud services from a viewpoint of tier 2 and 3 players and Sybase. Should we expect Sybase’s financial offerings in the cloud shortly?

• Delivering High Performance across the Stack – nice slide on the industry trends
• Java for Trading Applications – JVM Convergence and Deterministic GC
• Low Latency Framework – Typical Asset class latencies slide give an appropriate view of the world coupled with the latency definitions slide. Technology examples for each latency category gives a view on the Java/C++ split given the latency considerations. No surprise to see Fusion-IO on the Disk I/O slide. Linux minimum kernel level offers a good summary. Overall, nice deck

• Algo Risk Application – Reporting Dashboard
• Seven risk dashboards every bank needs
• http://www.ma.hw.ac.uk/~andrewc/erm2/reading/IBM_7_RiskDashboards.pdf
• Off topic, but relevant to the dashboards – Taming the Yield Curve