• Page 28, Invocation. Discussion of the 4 invocation patterns
• Page 29, The Constructor Invocation Pattern. Nice discussion around prototypal inheritance.
• Page 44, Memoization.
• Page 50, Prototypal. Nice bit of detail
• Page 109, Bad Parts. A classic read

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.

There was a very interesting talk from David Ingham, Microsoft, regarding the Azure Service Bus and their work supporting AMQP 1.0,

However, the best is often left to last, and in the case of the my-Channels posting on Nirvana 7, the final paragraph is a must read:

Nirvana EA2 will be released soon. It includes a new shared memory driver for intra-host communications. This will allow Nirvana communications between processes on the same machine to bypass the network stack and considerably improve performance. Current benchmarks of this functionality are showing that a sustained throughput of over 800,000 messages per second is possible with latencies of sub 5 microseconds.

Very interesting.

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 important properties of Storm are:

1. Simple programming model. Similar to how MapReduce lowers the complexity of doing parallel batch processing, Storm lowers the complexity for doing real-time processing.
2. Runs any programming language. You can use any programming language on top of Storm. Clojure, Java, Ruby, Python are supported by default. Support for other languages can be added by implementing a simple Storm communication protocol.
3. Fault-tolerant. Storm manages worker processes and node failures.
4. Horizontally scalable. Computations are done in parallel using multiple threads, processes and servers.
5. Guaranteed message processing. Storm guarantees that each message will be fully processed at least once. It takes care of replaying messages from the source when a task fails.
6. Fast. The system is designed so that messages are processed quickly and uses ØMQ as the underlying message queue.
7. Local mode. Storm has a “local mode” where it simulates a Storm cluster completely in-process. This lets you develop and unit test topologies quickly.