Ian Hoenisch of ITG lays out the perils of current test symbol regimes and describes the work to reduce the inherent risk in testing.

How has testing been done previously (i.e. ZVZZT) and what have been the drawbacks or risks?
The initial production testing was typically done by real ticker symbols that are out of the money, like ‘buy IBM for a Dollar’, which has some limitations and fat finger figure problems. If you accidentally sell for a dollar, your order is going to get filled, and this can create substantive risk. Some exchanges then implemented test symbols, like NASDAQ’s ZVZZT, but not all exchanges have agreed to use test symbols. Production testing is difficult at the best of times and as it is not just about getting a fill on a test symbol; all the other issues, such as getting market data to trigger risk limits, determining if you are 10% off last fill price to trigger warnings for bad fills, flagging trade-throughs, creating testing for locked or crossed markets, etc – all of these are difficult without a truly live environment.

What has emerged now is a parallel environment using real stock symbols – real market data (although it is probably delayed in most cases) you are not trading in a live environment. This factor has emerged not just on the exchange side but also in back office systems like Omgeo. The other element of test symbology that we are trying to work out is a true end-to-end solution and quotes, routing, executions, allocations and settlement. In the past, you could not use the Internet, so you had to get a whole new set of circuits to NASDAQ’s test system.

Technically speaking, how does the test symbol fit into the normal FIX message?
The FIX part was easy because it looks and behaves like a regular symbol. Getting everyone to understand that this is a test symbol is hard. For example, when we test in production with ZVZZT, there are many systems downstream from order routing that need to ignore test orders; compliance, dollar amount checks, OATS reporting, other SEC and FINRA reports that all need to exclude risk symbology. Buyside traders who want to test their systems often cannot create an order without initiation from the portfolio manager, thereby restricting them from creating a ZVZZT test order. Technically speaking, adapting the FIX message for test symbology is simple, but the surrounding elements will take more work to align.

What changes need to be made to broker systems to incorporate test symbols? At the exchange?
The buy-side will probably rely on their OMS vendors or alternatively they can create a dummy account with dummy cash that they do not have to report to accounting. While the buy-side can handle that more easily, on the sell-side, we are required to modify a number of systems to handle test symbology and exchanges have to adjust their systems to not report OATS and executions if they have a test symbol. Exchanges also pay for quotes on filled orders, as a part of a revenue-sharing deal for market data dissemination. This would be impossible with ZVZZT orders, else everyone would build their own matching system and send in endless test orders.

Everybody has to make the business logic changes so NASDAQ initially had 30 different test symbols beyond ZVZZT, and NYSE had TESTa and TESTe, so any time that the symbols changed, there was a considerable amount of code that had to be updated.

Otkritie’s Tim Bevan describes the intricacies and idiosyncrasies of the Russian markets, and offers suggestions on how to effectively access the deep liquidity there.

How would you profile the firms that are interested in DMA to Russia?

There is an interest in DMA to Russia from prime brokerage desks because many of the hedge funds that use the global prime brokers have expressed interest in Russia, now that the liquidity has reached the point it has. It is worth pointing out that the liquidity in the local equity market is approximately $2.5 billion a day, and the derivatives market turnover is $10 billion notional a day. These are very significant and deep pools of liquidity. We are certainly seeing client pressure from different areas hitting Tier 1 banks, which in turn is reflected onto us. We are also seeing the big global electronic brokers looking to add Russia to their coverage.

There is sustained sell-side interest, but the other big pocket of interest we are seeing is from the low-latency, high frequency funds that utilize proximity hosting and co-location, who want to place hardware in Moscow and run their strategies in the electronic order books that are available there. There are many more of these types of participants now and they are often in London, New York, Chicago, Amsterdam, Paris and other parts of Europe.

How extensively are algos utilized in Russian DMA?

Obviously for a high frequency fund, the algo is the strategy. This is clearly different from execution algos, like VWAP, which are used to execute orders in a certain manner. Most Russian brokers have the most basic execution algos like VWAP, TWAP, icebergs, etc. It is a relatively new trend (i.e. 6-9 months old) for the big sell-sides to enter Russia, and many have not yet deployed their more sophisticated suites of algos into the Russian market.

Additionally, the Russian market itself, is quite unusual in that there is a lot of programming skill in Russia. The average Russian retail trader is quite often running an algo through an Excel spreadsheet with $10-20,000 worth of capital, so as regards alpha strategies, there is a lot of algo activity in the Russian market. In terms of execution algos, however, I think it has not penetrated this segment yet. As the sell-sides continue to move into the electronic market, the second phase will be to deploy their own execution algos and offer them to their main clients, but we are at the beginning of that part of the process.

With the majority of liquidity isolated in a dozen stocks, how would Russian DMA fit into a firm’s overall trading/investment strategy?

Liquidity is very concentrated in Russia. The top ten names account for the vast majority of liquidity, and even the top two or three probably make up 50% of the market. DMA is possible beyond the top 15 or 20, but it drops off fairly quickly thereafter. Obviously the big blue chip companies are where most of the interest is. Taking Sberbank as an example, there is no liquid Depository Receipt (DR) and there is an unsponsored DR trading of about $2 million a day in Germany. If you want to trade that stock, you have to trade the local market, where it trades between half to a billion dollars a day notional, so there are some very deeply liquid companies that are only available in the local market.

What other asset classes are being attracted or will attract DMA interest?

The biggest interest is in the RTS Index futures, which is an incredibly powerful product. Trading over $5 billion a day notional, more than double of all of Russian equity instruments (both DR and local), sometimes by a factor of two. RTS Index futures trade from 0700 UK time right through to the US close and are among the top ten most liquid equity index futures in the world. This instrument has generated the majority of interest from the quant funds, but interest is increasingly coming from more standard hedge funds and buy-sides where they are allowed to trade futures as it provides an instant hedge or leverage tool with an almost bottomless liquidity pool for any one player.

The FPL Americas Electronic Trading Conference, for those in electronic trading, is always a year-end highlight and this year was no exception. Sara Brady, Program Manager, FPL Americas Conference, Jordan & Jordan thanks all the sponsors, exhibitors and speakers who made this year’s conference a huge success.

The 6th Annual FPL Americas Electronic Trading Conference took place at the New York Marriott Marquis in Times Square on November 4th and 5th, 2009. John Goeller, Co-Chair of the FPL Americas Regional Committee, aptly set the tone for the event in his opening remarks: “We’ve lived through a number of challenging times… and we still have quite a bit of change in front of us.” After a difficult year marked by economic turmoil, the remarkable turnout at the event was proof that the industry is back on its feet and ready to move forward with the changes to the electronic trading space set forth in 2009.

Market Structure and Liquidity
Two topics clearly stood out as key issues that colored many of the discussions at the conference – regulatory impact on the industry and market structure as influenced by liquidity, and high frequency trading. An overview of industry trends demonstrated that the current challenges facing the marketplace are dominated by these two elements. Market players are still trying to digest the events of 2008 and early 2009, adjusting to the new landscape and assessing the changing pockets of liquidity amidst constrained resources and regulatory scrutiny. The consistent prescription for dealing with this confluence of events is to take things slow and understand any proposed changes holistically before acting on these changes and encountering unintended consequences.

The need for a prudent approach towards change and reform was expressed by many panelists, including Owain Self of UBS. According to Self, “Everyone talks about reform. I think ‘reform’ may be the wrong word. Reform would imply that everything is now bad, but I think that we’re looking at a marketplace which has worked extremely efficiently over this period.”

What the industry needs is not an overhaul but perhaps more of a fine-tuning. Liquidity is one such area that needs carefully considered finetuning. Any impulsive regulatory changes to a pool of liquidity could negatively impact the industry. The problem is not necessarily with how liquidity is accessed, but the lack of liquidity that results in the downward price movements that marked a nightmarish 2008. Regulations against dark liquidity and the threshold for display sizes are important issues requiring serious discussion.

Rather than moving forward with regulatory measures that may sound politically correct, there needs be a better understanding of why this liquidity is trading dark. While there is encouraging dialogue occurring between industry players and regulatory bodies, two things are for sure. We can be certain that the evolution of new liquidity venues is evidence that the old market was not working and that participants are actively seeking new venues. We can also be assured that the market as a messaging mechanism will continue to be as compelling a force as it has been over the last two decades.

Risk
One of the messages that the market seems to be sending is that sponsored access, particularly naked access, is an undesirable practice. Presenting the broker dealer perspective on the issue, Rishi Nangalia of Goldman Sachs noted that while many agree that naked sponsored access is not a desirable practice, it still occurs within the industry. A panel on systemic risk and sponsored access identified four types of the latter: naked access, exchange sponsored access, sponsored access of brokermanaged risk systems (also referred to as SDMA or enhanced DMA) and broker-to-broker sponsored access.

According to the U.S. and Securities Exchange Commission (SEC), the commission’s agenda includes a look specifically into the practice of naked access. David Shillman of the SEC weighed in on the commission’s concern over naked access by noting, “The concern is, are there appropriate controls being imposed by the broker or anyone else with respect to the customer’s activity, both to protect against financial risk to the sponsored broker and regulatory risk, compliance with various rules?” Panelists agreed that the “appropriate” controls will necessarily adapt existing rules to catch up with the progress made by technology.

On October 23, NASDAQ filed what they believe to be the final amendment to the sponsored access proposal they submitted last year. The proposal addresses the unacceptable risks of naked access, and the questions of obligations with respect to DMA and sponsored access. The common element of both of these approaches is that both systems have to meet the same standards of providing financial and regulatory controls. . . Jeffrey Davis of NASDAQ commented on his suggested approach: “There are rules on the books now; we think that they leave the firms free to make a risk assessment. The NEW rules are designed to impose minimum standards to substitute for these risk assessments. This is a very good start for addressing the systemic risk identified.”

These steps may be headed in the right direction, but are they moving fast enough? Shillman added that since sponsored access has grown in usage there are increasing concerns and a growing sense of urgency to ensure a commission level rule for the future, hopefully by early next year. This commission proposal would address two key issues – should controls be pre-trade (as opposed to post-trade) and an answer to the very important question, “Who controls the controls?”

Pantor Engineering's Rolf Andersson examines the data behind the speed of FIX for low latency market data, settling the question of whether FIX is fast enough.

The FIX Protocol as well as software implementing the protocol (aka “FIX engines”) have from time to time been said to have a performance that is too low for use where very high throughput and/or very low latency is required. Performance characteristics of the protocol as well as engine implementations have been discussed in previous articles (Kevin Houston’s article “Is FIX Really Slow?” and John Cameron’s “Evolution of the FIX Engine” Vol 2, Issue 7, September 2008). Granted, there are slow implementations in use and the classic FIX tag=value syntax is too verbose for some use cases: e.g. market data.

Recent developments within FPL such as the release of the FAST Protocol and efforts within the FIX 5.0 usage sub-committee to support alternative recovery state models will enable FIX to be used in high throughput, low latency scenarios. This article reviews the various sources of latency and demonstrates that a FIX over FAST implementation can be used in place of a proprietary protocol to provide very high performance and low latency.

An overview of latency sources

There are a number of latency sources that contribute to the total latency for producing, transferring and consuming market data. The impact of different sources varies widely between implementations. The following sources will be discussed:

• Message processing overhead – the encoding and decoding between transfer format and the internal representation suitable for the processing required in a specific application, as well as safe-storing messages to support recovery of lost messages;
  
• Communication processing overhead – the network processing associated with sending and receiving messages;
  
• Scheduling delay – the delay in reacting on a request to send a message or reacting on a notification that a message has been received.
  
• Transfer delay – the time elapsed between the start and the end of a packet containing one or more messages.
  
• Propagation delay – a function of the physical distance between two communicating parties and the speed of light in the medium used to communicate.
  

These latency sources are to a large extent similar in behavior irrespective of the choice of external protocol, but there are differences as discussed below.

Message processing overhead

The overhead of processing a traditional FIX message is negatively affected by a number of aspects:

• Message content – FIX messages contain a host of information for the benefit of different communicating parties.
  
• Message format – the FIX message format contains redundant information about the message structure. Text is used to represent all data.
  
• Recovery semantics – the FIX recovery mechanism is based on message sequencing per session and a contract that a receiver can re-request messages.
  

Communication processing overhead

The communication overhead depends on the number and size of network packets. Each transferred packet incurs some processing both at the sending and receiving end. One or more messages are transferred in each network packet. Smaller messages mean that less data has to be copied and that more messages can be transferred in each network packet.