STAC Summit, 1 December 2015, NYC

Vendors with exhibit tables at the conference (click here for the exhibitor list).

Deep Learning is a branch of machine learning that uses many-layered neural networks to figure out aspects of modeling that are left to humans in other types of machine learning. Deep Learning has demonstrated its power in areas such as vision (think Google image search) and speech recognition (think Siri). Some financial firms are beginning to apply these techniques to market data and other information important for trading and investing. After a brief introduction by Peter Lankford on use cases in finance, we will dip our toes into the Deep Learning waters with two talks.

Deep Learning: Overview and approaches

• Chris Nicholson, Co-Founder & CEO, Skymind

As the leader of the organization that supports the open source DL4J project, Chris will explain how deep learning differs from the other kinds of machine learning, the types of deep learning getting the most uptake today, and what each tends to be good for. He will then discuss alternative technical architectures for deep learning and their tradeoffs, scale challenges, and most promising directions.

Deep Neural Networks for Market Prediction on Xeon Phi

• Diego Klabjan, Professor, Northwestern University

Like many Deep Learning architectures, deep neural networks (DNNs) are extremely compute intensive to train (that is, to develop models from raw data). Diego will describe a DNN model for predicting price movements from time series data, then explain techniques that enable this model to exploit the parallel computing capacity of the Intel Xeon Phi processor in conjunction with multi-core CPUs.

• Peter Lankford, Founder & Director, STAC

Peter will review the latest developments related to the STAC-A2 Benchmark suite (option Greeks).

• Robert Geva, Principal Engineer, Intel
• Evgeny Fiksman, Staff Engineer, Intel

Today it is clear that heterogeneous computing (combining CPUs, GPUs, FPGAs, or other co-processors) has a great deal to contribute to some problems in financial markets. But programming to take advantage of heterogeneous environments in a single application remains a challenge. In this talk, Robert will review various approaches to heterogeneous programming in C++ and will provide a point of view on what is most promising, as well as a vision for the future. Evgeny will then provide an example of a new approach using options calculations in STAC-A2.

• Sergey Maidanov, Software Engineering Manager, Intel

Financial firms make heavy use of Python, but once a quant has prototyped an algorithm or analytic in Python, the firm typically hands that code over to engineers who re-write it in C/C++ for better performance. At a time when time-to-market of new functionality is a crucial competitive factor, the slowness of this two-stage development cycle hurts the top line, while the increased development expense hits the bottom line. This issue (in finance and many other industries) is leading to innovative approaches that boost the performance of Python functionality, allowing firms to re-write less code. In this talk, Sergey will discuss traditional performance limitations in Python and the pros and cons of the latest industry efforts to get around them.

• Matthew Knight, Technical Marketing Director, Financial Services, Solarflare
• David Snowdon, Founder & Co-CTO, Metamako
• Arnaud Derasse, CEO, Enyx
• John Lockwood, CEO, Algo-Logic
• Olivier Baetz, COO, NovaSparks

FPGA solutions for trading continue to march forward, but the vendor landscape has been changing. We'll sit down with a few enterprising vendors to discuss the state of the art. How capable are the underlying technologies today, and what can we expect to result from the titanic shifts going on in the industry? What solutions are possible today that weren't a year or two ago, and what will be possible in the next one or two years? What is different about demand from trading firms today? Are firms taking full advantage of the opportunities?

• Mark Skalabrin, CEO, Redline Trading Solutions

Enterprise market data platforms and low-latency market data platforms evolved separately. In Mark’s opinion, that’s an accident of history rather than a logical consequence. In this talk, Mark will argue that the divergence of platforms has become a burden to the industry and that unified platforms are both necessary and possible. In Mark’s view, engineering excellence matters just as much for high-capacity, low-footprint enterprise distribution as it does for low-latency trading. Thus, he will make the case that the best unified architectures start with a high-performance core.

• Peter Lankford, Founder & Director, STAC

Peter will explain the latest developments related to the STAC Network I/O Special Interest Group.

• Peter Lankford, Founder & Director, STAC

Peter will describe progress toward additional "big data" benchmarks such as in I/O-intensive areas of risk management and stream processing for purposes such as trade monitoring.

• Dean Anderson, Vice President, Grid Core Engineering, Bank of America
• Art Sedighi, Head of Cloud Architecture, TD Bank
• Pete Smith, Account Executive, DDN
• Yadu Kalia, Worldwide Industry Solution Architect, Financial Services, IBM Systems

Risk management, always one of most compute-intensive functions in finance, is becoming quite data intensive as well. Whether it's increasing numbers of scenarios to evaluate, XVA calcs, or new kinds of analytics, data bottlenecks put risk management itself at risk. But the good news is that innovation continues to shift the price-performance curves for both hardware and software infrastructure. How do traditional approaches to scaling look against this modern backdrop? If you were starting from scratch today, how would you architect a system? What role would software such as parallel file systems, caching mechanisms, grid schedulers, and the Hadoop/Spark ecosystem play? How would you optimze the use of volatile memory, solid state storage, spindles, and interconnects to meet business needs? How does that story change if you’re not starting from scratch? Our panel will debate.

• Peter Lankford, Founder & Director, STAC

Peter will present the latest developments related to enterprise tick analytics (STAC-M3) and strategy backtesting (STAC-A3).

• Patrick Angeles, Chief Architect for Financial Services, Cloudera-
• Tony Bussieres, SVP Development, Ticksmith
• Kris Applegate, Cloud and Big Data Solution Architect, Dell
• Michel Debiche, Financial Services Practice Lead, ThinkBig Analytics

In a recent survey of the STAC community, over 60% of users concerned with big data workloads said they need to deeply understand developments in the Hadoop ecosystem (HDFS, Spark, MapReduce, etc.), and nearly all of the others said they at least need to keep track of it--far higher scores than any other single technology category. Why is this ecosystem so important to financial market technologists? What are the benefits for workloads typical in finance, such as quantitative analytics and strategy backtesting? What are the challenges with the current technology set, and what can we expect in the future? How can firms accelerate their evaluation of Hadoop/Spark for analytic problems? Our panelists will provide their views.