Brian Ross of FIX Flyer talks to Buy- and Sell-side presenting the latest lessons on high frequency trading and algorithms from the Indian market.

India’s capital markets are experiencing increased interest from local and global firms and new rules are set to attract high frequency trading (HFT).

The capital markets regulator, the Securities and Exchange Board of India (SEBI), the exchanges, brokers and many investors are in favor of abolishing Securities Transaction Tax (STT). Eliminating STT will have a positive impact on market turnover, will help high frequency traders to be more profitable and, at the same time, narrow spreads should drive up trading volumes.

STT has been levied for all trades, domestic or foreign, on all transactions in either equities or derivatives markets since 2004. At the time, the purpose was to generate tax revenue and to protect market integrity by slowing down the pace of technological advancements of a few, well-funded players. Revenue generated by STT amounted to around USD 1.5bn in 2011.

It is widely expected that STT will be eliminated this spring, bringing new opportunities for HFT in one of the world’s biggest and fastest growingcapital markets.

To better understand the situation, we asked five panelists who are leading the charge in HFT in India, to share their insights with us.

You never forget your first algo. When you first got involved in algorithmic trading, what problem were you trying to solve? What was your decision process, and what technologies did you use?

Sanjay Rawal, Open Futures: We started off using algos for trading purposes and the first one we built was for a specific type of arbitrage that was getting difficult to run using manual input. We used third party software for the exchange connectivity and wrote our algo in C#.

Vishal Rana, IIFL Capital: My first experience with HFT was trying to create a straight-arb model on a real-time basis. Although it was a simple model, the most difficult thing was to clean the data. We got the data dumps and it took a lot of effort to clean it. Most of the coding was done using C++.

Rohit Dhundele, Edelweiss: At the onset of the project, the easiest yet most important task was gathering the business intelligence to be subsequently converted to algorithms. Some of the more intricate decisions were the selection of order, execution and risk management systems to ensure a stable back-bone to the platform. Other equally important criteria were a flexible programming environment and a friendly interface for users. To achieve these objectives, we had to decide whether to build or buy this technology.

At Edelweiss, we realized relatively quickly that there is a sweet spot between the two extremes of in-house vs. outsourced solutions. We have since been following this model – combining the best of both worlds, which has helped us deliver customized solutions within acceptable turnaround times, whilst still protecting our IP.

Sanjay Awasthi, Eastspring Investments (Singapore) Limited: In the Indian markets, propelled as they are by rapid information dissemination systems, anonymity becomes a key factor in determining efficient trading. It was this need for anonymity that propelled us towards algorithmic trading. Continued use and familiarity lead to further benefits by way of better execution control. Algorithmic trading has thus become an important part of our execution arsenal.

Chetan Pandya, Kotak Securities:

The first algo I worked upon and put in production was calendar rolls for derivatives. Our trading desk had huge positions to roll from the current month to the next and manual execution was leading to slippages and erroneous executions at times. Using the 2 legged order of NSE we created a simple algorithm which would roll the position at desired spread.

 My first observation regarding algorithmic trading was to appreciate the difference between an individual trading manually versus a machine trading automatically. There are so many things that come naturally to a human being but needs to be told to the machine. Sometimes I wonder whether an algorithm can fully replace a human being ever.  There are those nuances of the market and events that lead to erratic market behaviour that cannot be fully programmed for reaction.

Also, I had to ensure that there is no room for error when you are trading using an algo platform, primarily because of the sheer number of orders that it can process in a single second and also the inability to spot something going awry with the naked eye given the sheer speed. Hence, I had to also think of risk management capabilities of the Algorithmic platform while needing to ensure that risk management does not lead to inefficient execution due to latency.

In terms of technology, we were limited to applications that conformed to our market regulations. Once we had the base framework and architecture ready, we integrated it rapidly with our existing applications for order routing and downstream workflows.

CIBC’s Thomas Kalafatis maps out the new CSA rules regarding direct electronic access and suggests its potential effects on brokers and institutional traders.

Are the updated Direct Electronic Access (DEA) requirements a response to patterns endemic to Canada or are they a response to patterns observed elsewhere?
Given the existing Investment Industry Regulatory Organization of Canada (IIRO C) rules and the timing of the Canadian Securities Administrator (CSA)’s DEA rule proposal, it is fair to say that the rules proposed by our regulators are intended to maintain consistency with changes in other jurisdictions and prevent regulatory arbitrage. We do not believe that the rules are the result of a specific effort to solve a localized Canadian problem, but rather a preventative measure to ensure structural issues that have arisen elsewhere will not take root in Canada.

The issues around direct electronic access raised in the United States (who is accessing marketplaces directly, and how they are ensuring automated systems will not malfunction) are less of a concern in Canada. TMX rule 2-501 limits who is eligible to receive DEA access, restricting DEA to wellcapitalized firms, or firms that are registered and regulated in certain other jurisdictions.

IIRO C Notice 09-0081 addresses how automated systems should be managed to mitigate the risk of malfunctions. It requires brokers to manage the risk of electronic trading by clients in the same way that they manage the risk of their own electronic trading. This includes ensuring that automated risk filters are in place, that order flow from an automated system can be interrupted/switched off by the broker, and that strategies are tested prior to being deployed to market. These basic, principlesbased protections have been effective at mitigating risk in Canada since well before the wave of automation hit our markets in 2008.

The proposed DEA rules are a movement away from the IOSCO principles-based approach that has traditionally been taken in Canada, towards a more prescriptive regime more like the 15C-3-5 rules introduced by the SEC in the United States this year. This builds consistency between the Canadian and American jurisdictions that are so closely intertwined.

Automated pre-trade risk filters are in place for many brokerdealers. How difficult will this regulation be to implement?
Broker-dealers will need to monitor the proposed rules closely, particularly with regard to their Sponsored Direct Market Access (SDMA) clients. These clients have their own sophisticated automated risk management systems in place – as required by UMIR rules and, more importantly, as a result of their own risk aversion. They connect directly to exchanges to minimize latency. The DEA rule proposes to change this, in parallel to 15C-3-5 in the US, in that brokers will need to have “direct and exclusive control” over the risk filters on client flow; this means that a duplicative set of filters operated by the broker will have to be put in place.

In this case, Canadian brokers benefit from the earlier adoption of 15C-3-5 in the United States where various technologies have been developed to meet SEC rules that went into effect in the summer of 2011. Depending on the needs of its client base, a Canadian broker can choose between several types of risk filter offerings operating in a latency range from the low milliseconds to the low microseconds. The only differentiator is cost, with a significant premium on the single-digit microsecond lowest latency offerings.

Generally, it is not economic for a Canadian broker to develop the ultra-low latency solutions in-house, and the Canadian broker community benefits from the availability of third party technologies developed to meet the US rules that came in to effect earlier this year.

Citihub’s Paul Chew and Richard Donaldson lay out the latest in Asia Pacific latency reduction and discuss how firms should prioritize their technology investments.

No one in AsiaPac used to care much about latency. However, when the Tokyo Stock Exchange (TSE) launched arrowhead in 2010 reducing their matching engine latency from 1 second to 5 milliseconds (a 200 fold improvement) it created a paradigm shift in the trading landscape by eliminating the exchange as the chief cause of latency and shifting the focus back onto market participants.

What’s more, this was not an isolated event – August 2011 will see the culmination of a US $200m investment by the Singapore Stock Exchange (SGX) to create the world’s fastest matching engine (SGX REACH) with average response times of 90 microseconds. The Australia Stock Exchange (ASX) invested US $35m to drive down their latency from 30 milliseconds to 300 microseconds; at the end of this year the Hong Kong Stock Exchange (HKEx) is expected to launch its new matching engine reducing average order response times from  130 milliseconds to 9 milliseconds (a 15 fold improvement).

At the same time, volumes are rising. TSE’s daily average equity order volume jumped 22% to 8.239 million orders in 2010 after the launch of arrowhead. Even before the proposed latency improvements by HKEx, they recorded a 41% increase in volume during the same period.So how will increased volumes and a reduction in exchange matching latency impact buy/sell-side firms? Surely it’s a benefit to doing business? Actually, with market participants now contributing to the majority of order processing latency, it creates both a challenge and an opportunity.

Increased stress will be placed on market participants’  trading systems because message volumes are growing and the time interval between messages from the exchange is falling. Conversely, market participants that can support growing volumes and drive down their own latency will create a competitive advantage. So what will this really mean for market participants and where should they target their limited investment dollars?

eTrading Platform Maturity

Our industry experience in Asia Pacific across buy/sell-side firms and vendors indicates a broad range of capability and focus. This is evident from contrasting client feedback and has given rise to what we have termed eTrading Platform Maturity:

• Tier One: Platform Stability – “We care about stability, availability and reliability, not latency.”
  
• Tier Two:  Instrumentation – “We care about platform latency but we need to improve the way we measure and analyze it.”
  
• Tier Three: Platform Optimization – “We don’t care about absolute latency as long as we’re first on the order book.”

Fundamentally, latency is one of the key barometers of system health. Significant increases in measured latency are indicative of a stressed platform which can lead to outages impacting reputation and resulting in lost revenue. We believe all firms should first establish a reliable platform that copes with daily business demands with predictable and consistent levels of latency before chasing the next tier of eTrading Platform Maturity.

Of course this is all a balancing act, often  requiring business and technology teams to prioritize stability over new product development and increased functionality. Smart investment in platform stability can be achieved through simple measurement and analysis of latency to target improvements providing these are supported with the appropriate post-implementation controls.

In order to address the balancing act of how and where to invest we have defined the Latency Framework (see Figure 1) and Instrumentation Capability Curve (Figure 2). These frameworks are used to determine the impact of volume and latency on platform stability and performance, to relate instrumentation to capability maturity and also to establish where to target platform improvements for greatest impact. For example, a key to determining the inherent capacity and performance of a system is through statistical profiling of changes in latency as volumes increase to the point at which the system becomes unstable.

Christian Zimmer, Head of Quantitative Trading and Research, and Hellinton Hatsuo Takada, Quantitative Trader, of Itaú Asset Management reveal the truth about high frequency trading in Brazil.

Conference panels, discussions and articles on High Frequency Trading (HFT) generally start with its definition. The term HFT is like ‘Cleopatra’ – sexy and mysterious and everyone is keen to know more about it. But the term HFT speaks for itself, so is it wasting time to go over it again?

Probably, because the term ‘high’ only has meaning relative to an external point of reference, just like cold, hot, sweet or other adjectives. This subjectivity is all the more interesting, as it is extremely difficult to measure an investor’s  brief holding period in most financial markets and, therefore, determine if it really is ‘high’. Unlike in the US, where the exchanges do not register the origin of the trade, Brazilian regulation allows BM&FBOVESPA to identify the final client on every trade. Consequently, it is much easier to measure the holding period of an investor for each asset. Also, this rule is the means by which the exchange determines whether an investor’s trade is classified as a ‘day trade’ and is thus eligible for reduced fees.

Naturally, BM&FBOVESPA does not classify a trader opening a position in the morning and closing it at the end of the day as a high frequency trader. There should be far more trading than this to qualify as HFT.  But how much more? It depends on the exchange’s criteria and reference point for ‘high’.

Figures for HFT published by BM&FBOVESPA in their April 2011report show 3.9% of the BM&F segment is high frequency and 5.9% of the BOVESPA segment. Consequently, the reduced fees are presented to the Brazilian trading community as less of an issue, as they say there is evidence of HFT taking hold. But HFT volume is not really increasing and is still far off the US figures which are often cited at around 60-70%. After carefully observing BM&FBOVESPA market prices, it is easy to conclude that it would take some time (possibly hours) to have a change in the prices sufficiently large enough to pay the transaction costs.Remember that HFT strategies are very sensitive to transaction costs.

Our suggestion is to step away from making subjective references to ‘high frequency’. Instead, one should look at the underlying trading strategies. The incentives an exchange should create to attract flow must be adjusted to the strategies that are really needed. Each strategy deserves a different set of policies and this will help the diversification of the traders’ strategies.

A trader using a market maker strategy can live with exchange fees as long as the bid-ask spread is sufficiently high. If the spread narrows, the costs become crucial and the exchange must lower the fees in order to keep this client in the market. On the other hand, a directional trader has different issues; if the fees are high, a trader must wait longer for a relevant price move so that they can capitalize on their position. Contrary to the market maker, the directional trader loves to see narrow bid-ask spreads. There would be no need to lower fees when the spread is close. The same is true for the statistical arbitrage traders.

When looking at the third party analyses of HFT in the international markets, we often see that the most common strategy is the market maker approach. This fact is strongly influenced by market fragmentation, which we do not have in Brazil. Fragmentation creates new intermarket trades, which could qualify as arbitrage trades, but not necessarily as market maker trades. Fragmentation also makes exchanges and other venues compete for the customers that provide liquidity and, as a result, give incentives to market makers. As mentioned above, Brazil does not have a fragmented market and BM&FBOVESPA does not see it necessary to ask for more liquidity. At least not as long as international capital flows are strong and increasing. Liquidity is needed in second tier shares and below.