Thomson Reuters launches European power analytics tool


Thomson Reuters launches European power analytics tool

http://www.automatedtrader.net/news/at/142965/thomson-reuters-launches-european-power-analytics-tool

Thomson Reuters launched a tool designed to help traders predict prices in European power markets.

London/New York Thomson Reuters launched a new analytics tool on its flagship Eikon product to help commodities traders and analysts predict prices in major European power markets.

The new tool, Power Curve, uses visualisation techniques to enable traders and analysts to obtain real-time, fundamental fair value assessments of the Nordic and German power markets, two of the most liquid power markets in the world.

Thomson Reuters said Power Curve combines power supply data available under REMIT, or Regulation on Energy Market Integrity and Transparency, with real-time fuel prices, weather, available capacity information and Thomson Reuters proprietary supply and demand models.

“Power markets are increasingly complex and news and price data alone are no longer adequate for traders to assess the fair value of power markets today. Understanding the impact of event-driven data such as weather, supply and demand changes and capacity information on power contracts is critical but also very time consuming,” said Stefan Reichenbach, head of commodities research and forecasts at Thomson Reuters.

“By adding event-driven market simulation models into Thomson Reuters Eikon, we are bringing together data from multiple sources, including data made available by REMIT, and providing energy professionals with a comprehensive visualisation of how such events drive market outcomes,” he said.

Reichenbach added: “Recently when a German nuclear power plant came back on-stream earlier than anticipated, Power Curve visually illustrated how demand would be met by plants with lower marginal cost and correctly anticipated the €2 drop in power prices.”

The Commodity Research and Forecasts service in Eikon provides weather, metals, agriculture and energy insight from a team of analysts from Thomson Reuters GFMS, Thomson Reuters Lanworth, Thomson Reuters Point Carbon and Thomson Reuters Weather Insight.

Eurex Exchange releases results of proprietary HFT research


Eurex Exchange releases results of proprietary HFT research

http://www.automatedtrader.net/articles/sponsored-articles/142740/eurex-exchange-releases-results-of-proprietary-hft-research

First Published in Automated Trader Magazine Issue 29 Q2 2013 : Sponsored Articles

Eurex Exchange is the first exchange in Europe to share part of its proprietary quantitative research on high- frequency trading (HFT) with the public. Key findings of this research include:

(a) HFT participants played an important and beneficial role during one of the most extreme market situations Eurex Exchange has seen in recent years,

(b) HFT participants play a unique and indispensable role in the recovery of market quality right after large trades, and

(c) Eurex Exchange did not find evidence of abusive HFT activity

Background

Eurex Group continuously invests in deepening its understanding of the structure and dynamics of the markets it operates. Its proprietary data contains a wealth of information on each individual order, down to the level of trader ID and microsecond granularity timestamps. This data uniquely allows the exchange to conduct extremely granular research, which is more important than ever considering the public debate. The analysis is a contribution to much needed empirics in the discussion on HFT.

Defining HFT

Key to the credibility of any research on HFT activity is a solid process to identify which order flow is – and which is not – of HFT origin. Eurex Group argues that HFT is a technologically advanced implementation of a great variety of trading strategies – some of which already existed prior to the existence of electronic trading platforms. Therefore, the exchange’s HFT selection process is based on the technical (instead of functional) characteristics of its participants’ order flow. More specifically, the exchange’s research looks at the inter-arrival time of messages, measured by the number of microseconds between any two consecutive messages from any two different participants.

To understand the underlying logic of the research, imagine a world wherein participants in a market place would not react on the exact same events when making investment decisions. In this world, the speed with which one reacts on any opportunity would generally not matter; there is no other participant hunting for the same opportunity. Therefore, from a system perspective, transaction arrival at the central exchange system would be uncorrelated. There would be a predictable number of observations with a small inter-arrival time and a somewhat smaller number of observations with a higher inter-arrival time. The expected number of observations for any inter-arrival time would be given by a Poisson distribution. In reality, trading activity is partly correlated and, since the rise of HFT, especially on a micro-second time frame. Therefore, there are observations in excess of what might be expected based on the Poisson distribution, particularly in the 0-100 microsecond time frame. These excess observations for very short inter-arrival times serve as a proxy for the ‘HFT-ness’ of a participant.

HFT participants provide important liquidity during periods of extreme market volatility

On 25 August 2011, Eurex Exchange experienced one of the most challenging market situations in its history. An institutional investor (not an HFT participant) offloaded a 6,000 contract DAX® Futures position in a 20 minute period, causing tremendous price pressure. For comparison, the average turnover increased from 300 contracts per minute to more than 1,700 contracts per minute. As a result, the market in DAX® Futures briefly lost more than four percent of its value, making the event look much like the U.S. ‘Flash Crash’. However the situation was different in two very important respects.

Dr. Randolf Roth

Dr. Randolf Roth

Firstly, although liquidity became more expensive, it did not dry up. Spreads widened and the number of available contracts declined, but these are natural consequences of increased demand. Arbitrage (against e.g. EURO STOXX 50® Index Futures, SMI® Futures, etc.) allowed participants to transfer liquidity from correlated instruments to DAX® Futures and vice versa. Trading continued in an orderly fashion, and the volatility interruption that halts trading when prices move too fast was not triggered.

Secondly, HFTs continued to be an important source of liquidity throughout the event, supplying 30 to 50 percent of the contracts available at the best bid and offer. Contrary to what one might expect, their aggregate participation was not skewed to one side or the other. Of course, only an execution proves the relevance of an order. Therefore, it is also important to note that the HFT share of passive executions remained stable and high. Furthermore, contrary to popular belief, the majority of the aggressive side of those executions were not HFT participants. Last, but not least, HFT liquidity was spread out over several price levels at all times, reducing the price impact for large aggressive orders.

HFTs increase their participation in liquidity provision after large trades

Eurex Exchange keeps close contact with end users of its trading system, such as buy side investment firms. Discussions with traders at these firms have proven to be invaluable input for decisions regarding market structure and trading system design. The exchange takes their concerns about market structure very seriously and investigates specific issues wherever possible. An often heard criticism is that HFT liquidity is spurious; “Whenever I try to hit it, it’s gone before my order reaches the exchange”.

To verify or falsify this claim, Eurex Exchange took all add, modify and delete orders and rebuilt the historical order book in EURO STOXX 50® Index Futures for several days from 2012. The exchange defined “large trades” as trades that were 10 to 20 times the 10-minute moving average trade size. In EURO STOXX 50® Index Futures, such trades occur between 400 and 500 times per day. For each 100 milliseconds (ms) in the two seconds around these trades, Eurex Exchange analysed the contracts available at the best price level on the side of the order book that was affected. Based on that, it was possible to calculate the share of contracts provided by HFTs and by non-HFTs respectively (adding up to 100 percent).

Passive HFT activity development around liquidity gaps

Passive HFT activity development around liquidity gaps

Each grey lines depict the daily average market share of HFTs on the relevant side of the BBO before and after a large trade (10 times the trailing 10 minute average) in the front month Eurostoxx future. The blue line is the average of these averages. The graph shows that HFTs do not reduce their participation right before large trades and increase (rather than reduce) their market share of the relevant side of the BBO after large trades.
Bernard Hosman

Bernard Hosman

The results can be found in the figure below. The grey lines represent the combined market share of the exchange’s top 40 HFT participants, whereas the blue line is the average of the grey lines. Remarkably, instead of reducing their participation, it can be seen that on average, HFTs significantly increase their share of contracts on the best price level of the side of the order book where a large trade occurs. Furthermore, it can be seen that the participation of HFT users does not change in the second prior to the execution of the large order. Therefore, the claim that HFT participants revoke their liquidity before a large trade hits the order book does not, in general, hold true.

Strong competition among HFTs yields remarkable improvement in market quality resilience at Eurex

Over the past few years Eurex Exchange has seen a substantial increase in HFT activity. The exchange wanted to quantify the effect of increased competition among liquidity providing HFT participants on market quality. One of the areas it expected to see market structural changes was the resilience of the market; in other words: How fast does the market recover after a large trade? What happens after large trades hit the order book is extremely important as most trading is highly correlated; a slow recovery means unnecessary sub-optimal executions.

Spread resilience

Spread resilience

The grey lines show daily averages for the spread recovery paths in 2010 and 2012. The blue lines are averages for 2010 and 2012. Compared to 2010, the liquidity in the DAX30 futures became much more resilient. The averages converge around 500ms from the big trade.

To quantify the recovery process of market quality, the exchange measured the spread (in ticks) for each millisecond in the two seconds before and after the large trades that caused the spread to widen. Such events happen several hundred times a day in the front-month DAX Futures. Based on these measurements the exchange calculated daily averages for eight similarly volatile days in 2010 and 2012 (grey lines in the graph above).

The chart shows the recovery paths relative to the average pre-trade spread to account for the effect of differences in intra-day volatility; a spread recovery of four ticks is more significant if the initial spread was one tick than if the initial spread was ten ticks. The top blue line is the average of the spread recovery paths in 2010 and the bottom blue line represents those paths in 2012. The most obvious difference between 2010 and 2012 is the significant improvement in the speed of the recovery that took place. Another observation is the fact that the recovery process in 2010 only started after 5-10 ms whereas in 2012 a much faster reaction can be observed. The exchange’s working hypothesis, supported by some early findings, is that these 5-10 ms was the minimum reaction time of some exchange participants, which – at that time – provided the lion’s share of the liquidity in DAX® Futures.

Continuing research

According to the study, high-frequency trading activity is an important positive contributor to overall market quality and stability. The exchange will continue to analyse HFT activity and will share the findings with the industry.

In response to customer requests, Eurex Exchange has posted three videos on its website that detail its analysis of HFT. These include:

• HFT and non-HFT participation during an extreme market situation

• A three dimensional representation of HFT activity

• Zooming into HFT participation during micro shocks

The videos can be viewed at: http://www.eurexchange.com > Technology > High-frequency trading

For more information about technical issues surrounding HFT, please contact:

Bernard Hosman , T +49 69 211 1 3195 or bernard.hosman@eurexchange.com

For more information about legal issues surrounding HFT, please contact:

Randolf Roth , T +49 69 211 1 2793 or randolf.roth@eurexchange.com

Carbon Market Data publishes the EU ETS Company Rankings 2012


Carbon Market Data, a European company providing carbon market research and data supply services, published the rankings of companies included in the European Union’s emissions trading scheme, following the recent release of verified emissions reports for the year 2012.

via Pocket http://www.bobsguide.com//guide/news/2013/Jun/4/carbon-market-data-publishes-the-eu-ets-company-rankings-2012.html June 04, 2013 at 06:55PM

TR Expands Commodity Research on Eikon


Thomson Reuters Further Expands Commodity Research and Forecast Service on Eikon with Agriculture Research and Analysis Thomson Reuters integrates Thomson Reuters Lanworth analysis to provide Thomson Reuters Eikon customers with access to independent research and forecast analytics for the agricult

via Pocket http://thomsonreuters.com/content/press_room/financial/tr_further_expands_commodity_research_and_forecast_service_on_eikon_with_agriculture_research_analysis March 07, 2013 at 07:41PM

Thomson Reuters Extends Commodity Research and Forecast Service on Eikon to Include Metals Analysis


Thomson Reuters integrates Thomson Reuters GFMS analysis to provide Thomson Reuters Eikon customers with access to independent research and forecast analytics for the precious and industrial metals markets Thomson Reuters today announced it has extended its commodity research and forecast service

via Pocket http://www.bobsguide.com//guide/news/2013/Feb/20/thomson-reuters-extends-commodity-research-and-forecast-service-on-eikon-to-include-metals-analysis.html February 20, 2013 at 06:42PM

A great thesis to read – Derivation of a general purpose architecture for automatic user interface generation


URI: http://hdl.handle.net/2100/1305
http://hdl.handle.net/10453/20405

Abstract:

Many software projects spend a significant proportion of their time developing the User Interface (UI), therefore any degree of automation in this area has clear benefits. Research projects to date generally take one of three approaches: interactive graphical specification tools, model-based generation tools, or language-based tools. The first two have proven popular in industry but are labour intensive and error-prone. The third is more automated but has practical problems which have led to a lack of industry adoption. This thesis set out to understand and address these limitations. It studied the issues of UI generation in practice using Action Research cycles guided by interviews, adoption studies, case studies and close collaboration with industry practitioners. It further applied the emerging field of software mining to address some of these issues. Software mining is used to collate multiple inspections of an application’s artefacts into a detailed model, which can then be used to drive UI generation. Finally, this thesis explicitly defined bounds to the generation, such that it can usefully automate some parts of the UI development process without restricting the practitioner’s freedom in other parts. It proposed UI generation as a way to augment manual UI construction rather than replace it. To verify the research, this thesis built an Open Source project using successive generations of Iterative Development, and released and promoted it to organisations and practitioners. It tracked and validated the project’s reception and adoption within the community, with an ultimate goal of mainstream industry acceptance. This goal was achieved on a number of levels, including when the project was recognised by Red Hat, an industry leader in enterprise middleware. Red Hat acknowledged the applicability and potential of the research within industry and integrated it into their next generation products.

Description:

University of Technology, Sydney. Faculty of Engineering and Information Technology

Winner of the John Makepeace Bennett Award – Australasian Distinguished Doctoral Dissertation 2013

Bloomberg Tradebook and Hart Energy Expand Research Offering


Exclusive Distribution Deal Broadens Investor Access to Energy Sector Insights Bloomberg Tradebook, Bloomberg LP’s global agency broker, announced the expansion of its independent research provider (IRP) offering through an exclusive broker distribution agreement with Houston-based Hart Energy, a

via Pocket http://www.bobsguide.com//guide/news/2013/Jan/17/bloomberg-tradebook-and-hart-energy-expand-research-offering.html January 20, 2013 at 03:11PM

Integral Completes New 40,000 Square Foot Palo Alto Research Center


PALO ALTO, Calif.–(BUSINESS WIRE)–Integral Development Corp. (www.integral.

via Pocket http://www.businesswire.com/news/topix/20130108005772/en January 09, 2013 at 07:45PM

Markit Acquires Quantitative Research Specialist QSG


Markit, a leading, global financial information services company, today announced that it has acquired Quantitative Services Group LLC (QSG). QSG is a leading provider of independent equity research, advanced trading analytics and investment consulting services.

via Pocket http://www.bobsguide.com//guide/news/2011/Nov/3/markit-acquires-quantitative-research-specialist-qsg.html December 30, 2012 at 06:51PM

Macrobond


Depth and Breadth of Economic & Financial Data. Our global databases contain millions of economic time series, financial instruments, securities and indicators worldwide. Powerful, User-Friendly Analytics & Charting.

via Pocket http://www.macrobondfinancial.com/ December 30, 2012 at 06:21PM

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