Capital Group’s Brian Lees is driving efforts to ask more questions of brokers, and for more data on where an order is shown before it executes, but can the buy-side handle the resulting deluge?
The current work you are doing on venue reporting analysis Our first push was simply to try to collect information about ‘where’ we were executing and a little bit about ‘how’ we were executing, namely, did we post or did we take liquidity. So having done that, the question was where do we go from there? And as such, the topic of requesting more data on where we didn’t execute and what order types were used started to be raised by some representatives on the FPL Americas Buy-Side Working Group. Some participants had already started down this road with brokers, asking for information relating to post-trade about where their orders were sprayed out to by the algorithms and what types of orders were placed on exchanges and also which exchanges they were on, etc. So that’s where the conversation began and that’s where we reached out to Jeff Alexander and Linda Giordano, because Barclays had already spearheaded this conversation.
What we are looking to achieve either in real time or post-trade, is whether we can standardise a format for brokers to tell us how our order interacted with the market, including when the order was placed, what order types were used, where it was placed in the markets and whether or not we got hits. The concern with this is not so much can we get it, because if we sign enough non-disclosure agreements we can get the information from the brokers. Some brokers have concerns about that information getting out and somebody reverse-engineering their algorithms, but from the buy-side perspective, I think the biggest concern is whether we can manage the volume of data that we would get.
The resources to store and analyse data and make some sort of good use of it With the original data that we were getting, on where the execution took place, we talked a lot about this with smaller firms who were using TCA vendors to help them analyse this information. With this type of information, if we went a step further, the brokers would not want us sending that out to TCA firms, because it shows their methodology for how their algorithms behave. I was in New York several weeks ago and took the opportunity to meet up with Jeff and Linda while we were there. We invited Jeff to join one of our conference calls for the buy-side committee, which he did, and he talked about what they’ve been proposing. He showed proposals for both the real-time collection of data, via FIX messages, actually proposing a whole new FIX message to be created for this purpose, which could then be sent in real time. Or, alternatively we could standardise a format for collecting the information post-trade which, as a spreadsheet, would then tell us what we want to see. We’re trying to standardise how you ask for the data and what format it is going to be in, by creating best practices for how to get the data from the brokers. That way the brokers don’t have to keep coming up with a different format for every client that asks for it. The best practices do specify that the ISO MIC codes would be the standard for identifying the exchange that you executed on, but we said nothing about what you should do with the data once you get it.
Exchange involvement in the conversation We did talk to some exchanges when we were first trying to standardise how to identify the exchanges, because when we first standardised the MIC codes, they did not cover all the exchanges, this was due to the fact that they hadn’t all registered with the ISO organisation and we wanted them to.
We had a little bit of trouble in differentiating the dark order books from the lit order books and some of the exchanges that have both. These exchanges consider themselves a hybrid book, and they didn’t want to be known as two different things. We didn’t have a way to differentiate the dark and the lit flow without introducing yet another FIX tag. That back and forth added to the conversation as part of the registration authority’s decision to come out with the new market segment concept, which says you can have an exchange defined and have child MIC codes that differentiate different segments of the market. We’re beginning to start conversations with exchanges about this topic, but that’s the extent to which we’ve had any discussion with them.
Broker willingness to participate in the process The first half of this, just getting the information about where you executed, the brokers didn’t have any problem, because it’s public record once it executes. When we started talking about the more detailed reporting, they did raise a concern about the information being sent out and NDAs so that, you, as a client, are not going to send the data out to a third party. But because other firms had already started down this road we talked about the purpose of this, which was just to have someone looking over their shoulder to make sure that they are acting in the best interest of the client and not potentially favouring rebates over best execution; they can’t really argue with that logic. Somebody should have some oversight as to whether or not the right decisions are being made.
Schroders’ Head of Asian Trading, Jacqueline Loh, shares her thoughts on trading in Asia, offering comments on which markets are primed for change, how to find value in dark pools and whether unbundling is as useful as people say it is.
Fragmentation arising from multiple sources of liquidity is a necessary step in the evolution of best execution and in the long term, fragmentation will increase the quality of trade executions in Asia. What it means for the buy-side is investment in infrastructure spending to develop new order routers and the like, so we can electronically seek out and have exposure to multiple liquidity sources. For the sell-side, it means acceptance that there will be more competition for the same block of business in the marketplace. It means different things for different buy-side firms as well.
When I think about the investor ID markets in Asia, I am not sure any model is particularly productive because ID markets make it administratively more difficult to trade. IDs can make best execution very difficult to implement, especially if cash and stock checking is the primary consideration. Some of the ID markets, namely Taiwan and Korea, allow trading through omnibus accounts and that seems to be the way it is evolving. The ID markets are slowly going away, but having said that, the most productive example is probably China because the brokers seem to have a handle on exactly how much cash and stock you have in your account, and therefore how much you can sell and buy. You cannot overspend or oversell, and it is relatively easy to take part in IPOs.
Trade allocation used to be a problem with investor IDs; for example, explaining to compliance and regulators why the prices are not exactly the same between accounts. In these cases the use of omnibus accounts really help. Executing through omnibus ID means you know exactly what is in an account and do not experience many of the issues associated with overselling or settlement. It is a lot cleaner.
With retail-heavy markets, anonymity is the primary consideration for us. We tend to trade more using electronic means and make use of dark pools in retail-heavy markets. In addition to that, the algos we use will be more price-specific, rather than volume-participation models, which are more price impacting.
Best Execution, in the Dark?
You would think that dark pools would have more success in markets where spreads are currently wide and there is a need to be anonymous, which would imply ASEAN markets. In practice, however, it has had more success in Hong Kong, and that is because there are more users of electronic trading there. Perhaps the users are a little more sophisticated as well insofar as they are willing to take accountability for their executions. Which is, in fact, what defines electronic trading.
In our experience, dark pools make a difference in terms of liquidity, however, the question is what creates that difference? Is it the electronic trading system feeding through the dark pool that provides the benefit or is it the dark pool, itself? I would say it is the former, but that may depend on each user. routers. I hope the Securities and Exchange Board of India will consider further change including allowing stock crossings and clarifying the rules regarding P-Notes.
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.
Guosen Securities’ Shen Tao reveals the latest trends in algo usage by Chinese asset managers, domestic mutual funds and Qualified Foreign Institutional Investors (QFIIs).
Who are the primary customers for algorithmic products in China? Algorithmic trading started in the Chinese A share market some time in 2007. In 2005, the first commercial FIX engine went live to accommodate the execution needs of the Chinese A share market of Qualified Foreign Institutional Investors, or QFIIs, as part of the plan by the Chinese government to allow regulated capital market investment by foreign investors. After an initial experimental phase of FIX connectivity with global trading networks, the local FIX trading platform became solid enough to interface with a real algo engine. In 2007, some leading global investment banks (predominantly, QFIIs from the sell-side) began to offer algorithmic trading facilities for their clients and their own proprietary trading desks. Most of these facilities were located offshore (e.g. Hong Kong) and connected to the Chinese brokers’ FIX gateway via a financial trading network such as Bloomberg.
The earliest providers and users of algo trading in the Chinese market were solely QFIIs and their clients. In 2008, although the global market was in turmoil and many infrastructure budgets were cut across the international financial community, there were still some firms seeking expansion opportunities for the future. Among them, some global banks with local brokerage joint venture subsidiaries began to build their onshore algo facilities. At about the same time, some leading purely local brokers also started their efforts in algo development, Guosen among them. We started in March 2008 and also targeted QFII investors for algorithmic trading, however, we understood the future of algorithmic trading in the Chinese market would rest on the domestic mutual fund industry. In late 2009, the Guosen algo platform was almost ready and the aforementioned onshore algo facilities run by the sell-side joint ventures of global banks also went live. The day of the algo had finally arrived for China.
In 2010, with support from a leading buy-side OMS vendor Hundsun; Guosen and UBS began their efforts by offering an algo solution for local mutual fund companies. In November 2010, UBS won its first success with two Beijing-based mutual fund companies, with Guosen securing a third six months later. Since that time, more than a dozen mutual fund companies have started using algorithms from UBS and Guosen. 2010 was the first year of the algo, from a local perspective. Currently, the momentum of mutual fund companies adopting algo platforms continues. We estimate that by the end of 2011, in terms of assets under management, over 40% of the local mutual fund industry could be covered by broker-provided algo services.
In retrospect, QFII investors were the founders of the market, but soon, the local mutual fund industry will become the primary user of algos. In addition, we foresee insurance companies adopting algo trading soon.
Corwin Yu, Director of Trading at PhaseCapital, sits down with FIXGlobal to discuss his trading architecture, the proliferation of Complex Event Processing (CEP) and why he would rather his brokers just not call.
FIXGlobal: What instruments does your system cover? Corwin Yu: At the moment, we trade the S&P 500, and we have expanded that to include the Russell 2000, although not as an individual instrument, but as an index. We also trade the E-Mini futures on the Russell 2000 and also for the S&P500. We have done some investigation on doing the same exact type of trading with Treasuries using the TIPS indices and the TIPS ETFs and a few of the similar futures regarding those as well. We are not looking at expanding the equity side except to consider adding ETFs, indices, or futures of indices.
FG: Anything you would not add to your list? CY: We gravitate to liquid items with substantial historical market data because we really do not enter into a particular trading strategy unless there is market data to do sufficient back testing. Equities was a great fit because it has history behind it and great technology for market data, likewise for futures, where market data coverage has recently expanded. Options is a possibility, but the other asset class that is liquid but not a good fit is commodities. We shy away from emerging markets that are not completely electronic and do not have good market data. While we have not made moves into the emerging markets, we know that some other systematic traders have found opportunities there.
FG: How much of your architecture is original and how often do you review it for upgrades? CY: In terms of hardware, we maintain a two year end-of-life cycle, so whatever we have that is two years old, we retire to the back-test pool and purchase new hardware. We are just past the four year mark right now, so we have been through two hardware migrations. Usually this process is a wakeup call as to how technology has changed. When we bought our first servers, they were expensive four-core machines with a maximum memory of 64 GB. We just bought another system that can handle 256 GB through six-core processors. We are researching a one year end-of-life cycle because two years was a big leap in terms of technology and we could have leveraged some of that a year ago.
RCM’s Head of Asia Pacific Trading, Kent Rossiter, unmasks the Asian trading scene, sharing insights into how RCM navigates the unlit landscape, identifying the effects of dark liquidity and highlighting ways brokers can facilitate better buy-side decision making.
FIXGlobal: What are the main benefits of dark liquidity in Asia?
Kent Rossiter, RCM: One of the major challenges in Asia has always been accessing liquidity without other parties in the market taking advantage of your position and your need to complete the order. In cases where liquidity is scarce, knowledge that a relatively large order is being worked can expose investors to various risks. In such situations, it is advantageous for knowledge of the deal whilst it is being worked to be discreet until the order is filled. In dark pools run by brokers we can get priority on our orders through queue-jumping.
Dark pools support such an approach as they allow large block orders to be worked without showing size. In this way, trading in dark pools allows a trader to access a broker’s own internal order flow, without being gamed by the market that would otherwise risk non-fulfillment or less efficient pricing. As a result, size trading becomes the norm in dark pools and a trader gets to see blocks that may never have been available otherwise. With no information leakage we are not disadvantaged by the fading you see on lit venue quotes. From a personal perspective, the challenges that arise from dealing across a number of venues and the resulting increased use of technology make the role more exciting and satisfying.
FG: How do you limit information leakage in dark pools?
KR: With the exception of broker internalization engines, the trade sizes found in dark pools are often multiple of what they are on the exchange. So having fewer, but larger prints reduces information leakage, and in many cases we can get done on our size right away. Minimizing the number of times a print hits the tape reduces the chance of this footprint being picked up and working against the balance of your order. That said, broker internalization engines do their part well, keeping any spread savings among the two broker’s clients instead of giving it up to the general market.
FG: If you decide to seek dark liquidity, how do you decide between broker internalizers and block crossing networks?
KR: The type of dark venues being used for various trades (i.e. between block crossing networks and brokers) are different. As I mentioned, brokers for the most part are matching up little prints that otherwise would have been time-sliced in the general market, and when using these venues the goal is often to save a few basis points along the way while you work an order. You are not often micro-managing each fill, but through the process we are getting spread capture and price improvement. The type of stock you are often trading in these internalization engines are often larger, more liquid stocks; the type of orders often worked by algos.
Block crossing networks on the other hand, while still matching up electronically, are probably more confidential, and take up the function of what brokers still do upstairs - putting blocks together - so size is the real focus here. Both types of dark pools use the primary market for price sourcing since the vast majority of trades get printed at or within the best bid and offer. As the primary markets become too thin, it can cause price formation problems.
While it is not specific to the consideration of dark pools as an extra execution venue, we have to consider potential increased book out costs if we do use dark pools (except via aggregators, since we would only be using one counterparty), just as we have had to for years when deciding whether to execute a block with a single broker versus multiple counterparties. As dark pools proliferate there is an increased chance that we may not have part of our order in that pool at just the right time to take advantage of flow that may be parked there. Dark pool aggregators are aiming to provide the buy-side solutions to this.
RCM’s Head of Asia Pacific Trading, Kent Rossiter, points out some of the good and bad of Indian SOR and reflects on Hong Kong market structure.
Are Smart Order Routers (SORs) in India working well?
SORs sure are working in India. I am not sure what is more of a raging success in the Asian equity SOR world, India or Japan, but the cost savings estimate numbers we are hearing are evidence enough to suggest that Indian SOR development is a big plus.
For ages, there have been two meaningfully big markets; the Bombay Stock Exchange (BSE) and National Stock Exchange (NSE). Up until a year ago, when Securities and Exchange Board of India (SEBI) opened the playing field up, investors who wanted the liquidity of both had to do so by manually monitoring their screens. This was painfully labor intensive and with the thin displayed liquidity of bids and offers, difficult to actually execute. You would often find fills from one exchange or another being executed at inferior prices to the other as a dealer had their eyes off the ball. Those executions were inevitably followed by a conversation with a dozen excuses. I would be told what I was seeing on my screen was not the real situation, but a latency delayed picture.
For the most part we are only using brokers with SOR for our Indian executions, and these brokers co-locate servers so latency is no longer a concern. We are getting fills at the best prices available and from two pools of liquidity where we may have only had one in the past. Only if the order is really small would we limit ourselves to one exchange in an effort to save on ticketing charges.
SOR is just the most recent visible step in the broader trend of the evolution of markets. Accordingly, the buy-side and sell-side traders have to educate themselves and keep up.
What are the issues with Indian SOR?
It is the lack of interoperability at the post-trade clearing level that has limited the true savings many investors would have benefited from otherwise. This is a challenge that SEBI continues to address. The lack a central clearing counterparty for the NSE and the BSE causes settlement costs to be about twice what they would be if only one exchange were used, and this is a consideration for most institutions when deciding whether or not to use two exchanges. If the exchanges and SEBI could reach a solution in terms of interoperability arrangements for SORs, the cost savings and benefits of SOR usage could be passed to the end users. Until then, its true potential remains yet to be uncovered.
John Bates of Progress explains how complex event processing works and how it can simplify the use of algorithms for finding and capturing trading opportunities.
A brief summary of Complex Event Processing
Complex Event Processing (CEP) is about treating actions that happen all the time as specific events, which describe the action, and then being able to analyze those events as they are streaming through a system, while looking through them for patterns that create opportunities or threats. In the trading world, this means things like trading opportunities, such as monitoring a set of instruments across multiple trading venues and looking for particular patterns. Those patterns might be high frequency trading (HFT), statistical arbitrage, correlation relationship between two items, or even execution algorithms that are slicing orders based on some predefined metric.
The threats often focus around pre-trade risk. For example, will placing the trade exceed predefined risk levels, or run into potentially abusive trades, like a wash trade. CEP is about being able to monitor business in real-time to analyze what is happening now and, based on that, to try to predict what is about to happen and act on it immediately.
The value of Complex Event Processing
The world of trading is so fast moving. Research done by the AITE Group suggests that the average lifespan of a trading algorithm can be as short as three months. This is because new trading patterns are constantly coming to light and ones that might have been very successful might no longer be available as the markets become more efficient. In the old days, trading algorithms were like a cottage industry, in much the same way as the making of muskets used to be. Highly paid and highly skilled craftsmen would handcraft the algorithm. It was the domain of the very rich and not very many could be involved in the game.
With the advent of CEP technologies in the last ten years, now anyone can find patterns in fast-flowing data feeds, but more importantly, CEP provides the tools for business people to describe new algorithms quickly. This means that traders can keep up with a trading world that is moving ever faster, and which the handmade craftsmen struggle to keep up with. Suddenly, it has become easier for smaller firms to create algorithms to compete with the larger ones. There has been a revolution in software for the trading space, in that firms of all sizes now have access to the technology that was previously available only to Tier 1 banks.
Peeking under the bonnet
In a CEP platform, there is an engine which has the tools that allow you to model and visualize new strategies as they are running, as well as see any opportunities or threats. On top of this is an adaptive layer, with connectors to convey different formats of events in and out of the processing engine, taking in market data and sending out trades. CEP platforms can work off a simple consolidated feed, but organizations find that it is better to connect to trading venues directly because it reduces the latency and things can be seen as they happen.
Put three men and a FIXGlobal’s Edward Mangles around a table; serve them lunch and let the tapes roll. FIXGlobal listened in on a conversation that ranged from regulators to risk and from FX to FIX.
Edward: In defense of the regulator … how should they know what’s going on when neither the sell nor buy-side seem to know?
Vincent: Recent events have shown the divide between the financial market participants and the regulator. For example, the Lehman’s mini bond issue has forced a strong dialogue between the regulator and, in particular, the broker side. But the engagement is slow.
Kent: Retail brokers tend to have a strong voice here in Hong Kong and over the years have developed a strong working relationship with the regulators. Local brokers can at times be pretty outspoken and have proven on many occasions to be an effective lobbying group. From our perspective international brokers tend to be less visible in some of these debates. We see certain common characteristics across Asia where understandably there is a good deal of focus on protecting the retail investor given the high retail investor participation in many of the stock markets in Asia including Taiwan and Korea. The challenge has certainly been in the retail space where there is an overlap of regulatory responsibility in approving and offering products.
Edward: Are we asking the impossible of the regulator to create the same rule book for retail and institutional investors?
Kent: The general principal is that retail investors are less savvy and experienced and regulations need to be explicit. There is a general assumption that as professional investors, institutions can operate with greater flexibility since they can understand the risks in a more sophisticated way. Taking account of this framework then it will not be possible to standardize for both types of investor. The risk is that setting minimum requirements to protect the retail investor may not suit the way business is transacted at an institutional level. Here we advocate consultation and support stronger trade associations.
Vincent: I don’t think you can realistically expect the same regulations for retail traders as for big institutional investors. That’s a utopia that’s never going to exist. These two groups of investors have different needs. Many regulators – in Europe for example and Luxembourg in particular with their efforts to push through the UCITS 4 protocol – understand that you need different protocols for retail investors.
Kent: But Vincent, every investor has the same goal: making money. It’s only the detailed requirements that are different.
Gerry: There’s certainly a larger burden on the big firms to uphold ethical, legal and fiduciary standards.
Kent: Yes. Retail investors don’t generally have the same constraints on their activities. Institutional investors need a more developed investment process and must ensure fair treatment across all clients regardless of size and fees. Institutional investors will undoubtedly be looking at different investor objectives – for one, they need to be able to implement their strategies in much greater volumes, and in scale, for example.
Edward: How about the role of regulators in curtailing short-selling in many markets? Knee jerk or long-term strategy?
Kent: I’d like to see the ability to short-sell fully resumed as soon as practically possible. We’re now in a situation where some markets have suspended it, and some are allowing it again. This is not ideal. I certainly see the temporary prohibition as a knee-jerk reaction and understandable given the groundswell of public opinion and corporate pressure as the financial crisis took hold – not all of this opinion was entirely rational. In fact, short-selling restrictions can reduce volumes for trading in the markets overall. For one, we have a 130-30 fund. So in this fund, if we’re limited in the number of attractive long-short pair trades we can put on then we’ll just end up trading less. So it’s business that never happens and the unknown would-be client on the other side of our trade – whether they’re institutional or retail – through the exchange, never gets to take advantage of the liquidity. What we need is a greater understanding of how shorting operates. There is a lot of misconception around this issue.
Gerry: I see the value and merit in allowing short selling in varied markets. In markets that don’t allow it, the regulators need to develop this functionality. It encourages more liquidity and volume. But I do understand that in the current environment the regulators have little choice. We won’t know the full impact until later on.
Vincent: The problem is that there’s no consistency among the regulators. Some only forbid short selling on financials. It’s a disruption to competitiveness between various sectors.
Kent: Yes. And not being able to short, will reduce derivatives trading. The fact is, a lot of the shorting that goes on isn’t just one-way, but a strategy with a ‘long’ component to it as well. And funds that relied on the little performance boost from securities lending fees have also seen their returns diminished. The equity finance desks at the brokers have seen a real drop-off in trade volumes because of this.
Vincent: Now the regulators are trying to encourage investors to buy again in a bear market – and there’s a lot of inconsistency between the messages they’re sending now and what they were telling us six months ago.