Algorithmic Trading Effects on Market Micrstructure
The phrase “automated trading” seems to have become a term that is being more frequently misused by the finance industry with a net affect of creating a misguided mystification to an important market concept. The press, industry journals, associations and even industry participants are inherently attracted to the use of the phrase; using it as a description of any part of the trading process that demonstrates automation implemented by some form of implied “intelligence”. In the interest of this paper, the authors’ will describe automated trading and how it relates, and differs, from the vary concepts of program trading, algorithmic trading, direct market access and model-driven trading, a clear understanding is important if one is to fully appreciate the application of CI to model-driven trading. The authors believe, in general, each automated area of interest is either:
· Trade execution–oriented or
- Trade decision–oriented
Insights into the field of event processing from the people behind Progress Software’s Apama Event Processing Platform
10 Imperatives of Algo Trading
Friday, May 11, 2007
Algorithmic Trading Imperative #10: Learn from Experience
Today’s algorithmic trading tools identify the ‘cause and effect’ of trading techniques, learn from profit and loss, identify repeating market patterns and suggest new combinations of algorithms. Consistent use of these tools over time enables traders to ‘genetically tune’ algorithmic trading systems. Like
Darwin’s ‘survival of the fittest’ theory, algorithmic traders can run thousands of permutations of an algorithm, swap out the least profitable and replace them with more effective approaches. Analysis of recorded strategy behavior can be used to answer questions such as, “Why did I make $1 million today, but lose $1 million yesterday?” Tools to replay algorithmic trading activity and examine both raw market data, as well as the resulting reactions and actions of automated systems is imperative to ensure not only that the algorithms worked they way they were supposed to, but also, how might they be improved.
By stepping through logs of strategy behavior with appropriate analysis tools, it is possible to determine, for example, that a firm was unsuccessful on a given day because ‘a trader modified the algorithm parameters, a position was taken, a news article moved the market and we didn’t have a rule to respond appropriately.’
Back testing, simulation, and root-cause analysis is the key to learning from past performance and improving the effectiveness of trading strategies in the future.
Friday, April 27, 2007
Algorithmic Trading Imperative #9: Research and Backtest Strategies
With firms continuously developing their own unique algorithmic trading strategies using complex event processing (CEP) technology, how can they ensure the strategies they feed into the markets are the best ones? For the rapid development and deployment of new strategies, testing algorithms under a range of anticipated market conditions is critical. The latest techniques use back testing environments that enable the selection and naming of a library of market sequences, such as a ‘bull market’ or ‘bear market’. These sequences can be streamed through a strategy to test how the strategy performs.
Event processing platforms often contain event storage and management technologies, such as Apama’s Research Studio, which provides back testing and analysis capabilities via a TiVo®-like event replay capability that allows CEP Scenario developers to interactively explore the prospective behavior of Apama Scenarios prior to deployment. Event data management can also support “digital forensics” operations that allow users to audit the performance of Apama strategies already in deployment.
Tuesday, April 17, 2007
Algorithmic Trading Imperative #8: Design for Low Latency Decisions
In algorithmic trading, milliseconds matter. Minimizing the time between event detection (market data, news, requests for quotes) and action (placing an order) is critical. To do this, firms are using complex event processing (CEP) technology to implement their white-box algorithmic trading platforms. CEP is a new paradigm that allows organizations to identify patterns among streaming event data and respond to those patterns in microseconds (read a detailed overview of CEP and the history of its development here). Using a traditional database, you must store, index and retrieve the data – a very time-consuming process. CEP allows you to establish rules, or trading strategies, and ‘stream’ data through them, so the relevant data may be selected. This makes it possible to monitor, analyze and act on market data and respond immediately.
Many CEP engines were designed with unique in-memory architectures that ensure the continuous processing of events that can arrive in volumes of tens of thousands of events a second, process millions of concurrent CEP “rules,” and make decisions in less than a millisecond. The Apama “Correlator,” depicted here, includes a patented technique of processing events based on an in-memory multi-dimensional indexing scheme called the HyperTree, in combination with a complex event sequencer, which optimizes Correlator performance by optimizing the processing of event scenarios that express the temporal and sequential event patterns that are expressed in CEP rules. These rules can be expressed by using an eclipse-based development environment for the Apama CEP language or a high-level, graphical CEP Scenario Modeler that allows non-programmers (e.g., heads of desks) to “paint” strategies quickly and easily without programming expertise.
CEP correlation engines help fulfill imperative #8, to design algorithmic trading architectures for low-latency.
Friday, April 13, 2007
Algorithmic Trading Imperative #7: Integrate Real-time News into Algorithmic Trading
Today’s financial markets are moved by news, and firms are increasingly integrating electronic news into their algorithmic trading strategies. For example, news about US non-farm payroll numbers, global interest rate decisions or announcements associated with specific stocks all have an impact on the confidence in affected securities, and therefore prices. If a trading strategy can analyze and react to the news before a human trader, advantages can be realized. A complex event processing (CEP) algorithm can for example, contain the following rule: ‘Alert a trader if a news article is released on stock x, and is followed by a fall or a rise of greater than 5% in the value of that stock within five minutes.’
According to the WS&T article called “Trading Off News,” many Wall Street firms appear to be proceeding with caution. “Machine interpretation of news is still beyond current science, and we’re probably still waiting a few years before that’s really going to evolve to fruition,” says Carl Carrie, VP of JPMorgan Securities. “The classic challenge is interpreting the news.” The article continued to suggest that, “on the other hand, there are things you can do in the high-frequency sense without having in-depth interpretation involved, rather than interpret the direction of the stocks that’s implied by the news, the heat indicator interprets if the news events impact that particular ticker by counting the number of news stories. For example, “If you are trading a stock like Red Hat and there’s a whole bunch of news stories around Red Hat — without having an interpretation of directional view — you can assume that there’s a higher level of risk that’s associated with trading that stock,” explains Carrie. The heat signal suggests there is an increase in volatility potentially. “So if that’s the case, you can adapt that to your risk/trading models,” says Carrie.
We experience this latter point – that increasingly, digitalized versions of news wire services, with meta data annotations are being used to algorithmically trade on news. In fact, in a recent trip to Asia, we found that the localized news feeds in countries such as Korea are being used to trade locally on news. So there’s no doubt that imperative number #7, trading algorithmically on news, is happening.
Monday, April 09, 2007
Algorithmic Trading Imperative #6: Operate within Multiple Asset Classes
Algorithmic trading is gaining momentum in asset classes beyond its initial domain of equities, including derivatives, fixed income and FX. This is due in part to increased electronic access to liquidity sources via electronic APIs, such as EBS and Hotspot in FX. When a trading platform has electronic access to multiple asset classes, existing algorithmic strategies can be combined by operating within multiple assets simultaneously within a single strategy. For example, a firm might buy an equity and hedge it with a future, while taking out an FX position – all at the same time.
Saturday, April 07, 2007
Algorithmic Trading Imperative #5: Gain Access to Multiple Liquidity Pools
With the rise of ECNs and DMA, the electronic markets are continuing to advance. Today, firms can gain advantage by spreading trading activity across these multiple pools, which differ in their strengths. For example, in the FX market, Currenex is similar to Hotspot, but it is not anonymous; EBS and Reuters Dealing 3000 are major players but they tend to be especially competitive in specific exchange rate pairs. Understanding the anomalies in the variety of liquidity pools can be a source for advantage, but the only way to gain this advantage is if your algorithmic trading platform can access multiple liquidity pools at the same time. Also, monitoring multiple pools in real time enables a strategy to route orders to the pool with, for example, the best price or the most available liquidity.
So an imperative #5 for algorithmic trading is to ensure your platform can connect to and operate on mutiple liquidity pools.
Thursday, April 05, 2007
Algorithmic Trading Imperative #4: Evolve Algorithms Rapidly
As building and customizing algorithmic strategies is critical, so too is the
rapid evolution of trading strategies. Markets are continually evolving and new opportunities, for example in the form of arbitrage, constantly emerge. If you do not develop strategies to capitalize on an opportunity quickly, then the competition will. Customization of trading strategies is not a ‘one-off’; strategies must be continuously and systematically evolved. In the race for algorithmic supremacy, firms attempt to observe counter party trading activity and either automatically or manually ‘reverse engineer’ the strategies being used. As a result, firms must plan to rapidly evolve – or perish.
I have been presenting this week with Koscom, our partner in Korea – the screen shot here comes from their presentation, which illustrates the principle of following the imperative rapid evolution and of localize, localize, localize – at the same time. Through localized rapid application development tools, Koscom delivers a differentiated algorithmic trading solution to the Korean market.
Wednesday, April 04, 2007
Algorithmic Trading Imperative #3: Localize, Localize, Localize
Today we issued a press release about the adoption of algorithmic trading in Asia. In it we discussed algorithmic trading imperative number 3: Localize, Localize, Localize. Now it might appear obvious to say that localization of software in Asia is an imperative, but that’s not the point. Yes, language localization is critical, but more important in the field of trading is the ability to localize trading technologies for local strategies that can work in a specific market, for the local connectivity requirements the market requires. For example, Credit Suisse and Goldman Sachs have already localized their algorithmic trading strategies, and Sang Lee, Managing Partner, Aite Group comments: “Algorithmic trading in Asia Pacific began its development in a similar vein to Europe and North America with an initial focus on equities. Today, however, firms are rapidly adopting techniques that have taken longer to develop in the more established markets. These include the use of algorithms for foreign exchange and cross-asset class trading. As algorithmic trading in Asia continues to evolve, flexible, customizable technology that accounts for the unique characteristics of local markets is essential.”
Another element of localization in the Asian markets is around regulation. In FTMandate, Andrew Freyre Sanders from JPMorgan said:
“You can quickly create a basic VWAP algorithm, cut up orders and send it into the market in Europe or the
US – with the resulting performance not being too bad. In certain markets in Asia, however, you cannot get away with slicing up orders and sending them to the market… due to the liquidity, big tick sizes, order books and a raft of differing regulations.”
An open, customizable, flexible “white box” algorithmic trading model, such Progress Apama’s, facilitates the need to localize algorithmic strategies rapidly, which is also discussed at length in the press release.
So the need to localize, localize, localize is Algorithmic Trading Imperative #3.
Tuesday, April 03, 2007
Algorithmic Trading Imperative #2: Customize Quickly
An increasing trend in today’s algorithmic trading space is dissatisfaction with commoditized black box algorithms provided by brokers. If everyone has access to the same algorithms, where is the advantage? Increasingly, sell-side prop desks and buy-side hedge funds are developing personnel capable of designing differentiated algorithms – but a black box approach doesn’t empower the capability of these algorithmic architects. A white box approach allows firms to leverage their intellectual property and create the secret sauce that offers competitive advantage. Firms know – and trust – the ways in which an algorithm works, can design new algorithms based on existing ones and can combine algorithms in new and interesting ways (for example, to develop multi-asset class trading algorithms). Graphical CEP development environments, designed for traders, not IT, are becoming increasingly important development tools for and enables the rapid evolution of sophisticated algorithmic trading strategies.
Saturday, March 31, 2007
Algorithmic Trading Imperative #1: Move First
Today’s markets are continually evolving, with new opportunities emerging by the minute. White box trading systems make it possible to rapidly compose and evolve algorithms to monitor, analyze and respond to market events in a specific way. The ability to customize trading strategies to a firm’s unique requirements means there is an increased opportunity for competitive advantage. As opportunities are found, the traders themselves can rapidly design and deploy strategies ahead of their competitors. In today’s competitive environment, the trader needs to be able to develop algorithmic strategies for deployment in hours, rather than in days or weeks. With a custom built trading strategy, changes often take weeks, months, or years. In today’s markets, opportunities pass in days or hours, and traditional technology development timeframes are unacceptable.
Apama was the first commercial available “white box” algorithmic trading platform and forms the basis of many algorithmic trading solutions. The value of an open solution was described by Carlos Ramirez Cervera, head of ETS at Casa de Bolsa Finamex, a leading broker-dealer in Mexico, said: “As the Mexican market begins to take up algorithmic trading, buy-side clients are looking for flexible, customized services that will bring them an advantage. The comprehensive Apama platform and Event Modeler and Dashboard Studio rapid application development tools allow us to rapidly deploy innovative algorithms that meet our customers’ individual needs in hours or days, rather than months or years that would be required with alternative approaches.”
Insights into the field of event processing from the people behind Progress Software’s Apama Event Processing Platform
10 Imperatives of Algo Trading
Friday, May 11, 2007
Algorithmic Trading Imperative #10: Learn from Experience
Today’s algorithmic trading tools identify the ‘cause and effect’ of trading techniques, learn from profit and loss, identify repeating market patterns and suggest new combinations of algorithms. Consistent use of these tools over time enables traders to ‘genetically tune’ algorithmic trading systems. Like
Darwin’s ‘survival of the fittest’ theory, algorithmic traders can run thousands of permutations of an algorithm, swap out the least profitable and replace them with more effective approaches. Analysis of recorded strategy behavior can be used to answer questions such as, “Why did I make $1 million today, but lose $1 million yesterday?” Tools to replay algorithmic trading activity and examine both raw market data, as well as the resulting reactions and actions of automated systems is imperative to ensure not only that the algorithms worked they way they were supposed to, but also, how might they be improved.
By stepping through logs of strategy behavior with appropriate analysis tools, it is possible to determine, for example, that a firm was unsuccessful on a given day because ‘a trader modified the algorithm parameters, a position was taken, a news article moved the market and we didn’t have a rule to respond appropriately.’
Back testing, simulation, and root-cause analysis is the key to learning from past performance and improving the effectiveness of trading strategies in the future.
Friday, April 27, 2007
Algorithmic Trading Imperative #9: Research and Backtest Strategies
With firms continuously developing their own unique algorithmic trading strategies using complex event processing (CEP) technology, how can they ensure the strategies they feed into the markets are the best ones? For the rapid development and deployment of new strategies, testing algorithms under a range of anticipated market conditions is critical. The latest techniques use back testing environments that enable the selection and naming of a library of market sequences, such as a ‘bull market’ or ‘bear market’. These sequences can be streamed through a strategy to test how the strategy performs.
Event processing platforms often contain event storage and management technologies, such as Apama’s Research Studio, which provides back testing and analysis capabilities via a TiVo®-like event replay capability that allows CEP Scenario developers to interactively explore the prospective behavior of Apama Scenarios prior to deployment. Event data management can also support “digital forensics” operations that allow users to audit the performance of Apama strategies already in deployment.
Tuesday, April 17, 2007
Algorithmic Trading Imperative #8: Design for Low Latency Decisions
In algorithmic trading, milliseconds matter. Minimizing the time between event detection (market data, news, requests for quotes) and action (placing an order) is critical. To do this, firms are using complex event processing (CEP) technology to implement their white-box algorithmic trading platforms. CEP is a new paradigm that allows organizations to identify patterns among streaming event data and respond to those patterns in microseconds (read a detailed overview of CEP and the history of its development here). Using a traditional database, you must store, index and retrieve the data – a very time-consuming process. CEP allows you to establish rules, or trading strategies, and ‘stream’ data through them, so the relevant data may be selected. This makes it possible to monitor, analyze and act on market data and respond immediately.
Many CEP engines were designed with unique in-memory architectures that ensure the continuous processing of events that can arrive in volumes of tens of thousands of events a second, process millions of concurrent CEP “rules,” and make decisions in less than a millisecond. The Apama “Correlator,” depicted here, includes a patented technique of processing events based on an in-memory multi-dimensional indexing scheme called the HyperTree, in combination with a complex event sequencer, which optimizes Correlator performance by optimizing the processing of event scenarios that express the temporal and sequential event patterns that are expressed in CEP rules. These rules can be expressed by using an eclipse-based development environment for the Apama CEP language or a high-level, graphical CEP Scenario Modeler that allows non-programmers (e.g., heads of desks) to “paint” strategies quickly and easily without programming expertise.
CEP correlation engines help fulfill imperative #8, to design algorithmic trading architectures for low-latency.
Friday, April 13, 2007
Algorithmic Trading Imperative #7: Integrate Real-time News into Algorithmic Trading
Today’s financial markets are moved by news, and firms are increasingly integrating electronic news into their algorithmic trading strategies. For example, news about US non-farm payroll numbers, global interest rate decisions or announcements associated with specific stocks all have an impact on the confidence in affected securities, and therefore prices. If a trading strategy can analyze and react to the news before a human trader, advantages can be realized. A complex event processing (CEP) algorithm can for example, contain the following rule: ‘Alert a trader if a news article is released on stock x, and is followed by a fall or a rise of greater than 5% in the value of that stock within five minutes.’
According to the WS&T article called “Trading Off News,” many Wall Street firms appear to be proceeding with caution. “Machine interpretation of news is still beyond current science, and we’re probably still waiting a few years before that’s really going to evolve to fruition,” says Carl Carrie, VP of JPMorgan Securities. “The classic challenge is interpreting the news.” The article continued to suggest that, “on the other hand, there are things you can do in the high-frequency sense without having in-depth interpretation involved, rather than interpret the direction of the stocks that’s implied by the news, the heat indicator interprets if the news events impact that particular ticker by counting the number of news stories. For example, “If you are trading a stock like Red Hat and there’s a whole bunch of news stories around Red Hat — without having an interpretation of directional view — you can assume that there’s a higher level of risk that’s associated with trading that stock,” explains Carrie. The heat signal suggests there is an increase in volatility potentially. “So if that’s the case, you can adapt that to your risk/trading models,” says Carrie.
We experience this latter point – that increasingly, digitalized versions of news wire services, with meta data annotations are being used to algorithmically trade on news. In fact, in a recent trip to Asia, we found that the localized news feeds in countries such as Korea are being used to trade locally on news. So there’s no doubt that imperative number #7, trading algorithmically on news, is happening.
Monday, April 09, 2007
Algorithmic Trading Imperative #6: Operate within Multiple Asset Classes
Algorithmic trading is gaining momentum in asset classes beyond its initial domain of equities, including derivatives, fixed income and FX. This is due in part to increased electronic access to liquidity sources via electronic APIs, such as EBS and Hotspot in FX. When a trading platform has electronic access to multiple asset classes, existing algorithmic strategies can be combined by operating within multiple assets simultaneously within a single strategy. For example, a firm might buy an equity and hedge it with a future, while taking out an FX position – all at the same time.
Saturday, April 07, 2007
Algorithmic Trading Imperative #5: Gain Access to Multiple Liquidity Pools
With the rise of ECNs and DMA, the electronic markets are continuing to advance. Today, firms can gain advantage by spreading trading activity across these multiple pools, which differ in their strengths. For example, in the FX market, Currenex is similar to Hotspot, but it is not anonymous; EBS and Reuters Dealing 3000 are major players but they tend to be especially competitive in specific exchange rate pairs. Understanding the anomalies in the variety of liquidity pools can be a source for advantage, but the only way to gain this advantage is if your algorithmic trading platform can access multiple liquidity pools at the same time. Also, monitoring multiple pools in real time enables a strategy to route orders to the pool with, for example, the best price or the most available liquidity.
So an imperative #5 for algorithmic trading is to ensure your platform can connect to and operate on mutiple liquidity pools.
Thursday, April 05, 2007
Algorithmic Trading Imperative #4: Evolve Algorithms Rapidly
As building and customizing algorithmic strategies is critical, so too is the rapid evolution of trading strategies. Markets are continually evolving and new opportunities, for example in the form of arbitrage, constantly emerge. If you do not develop strategies to capitalize on an opportunity quickly, then the competition will. Customization of trading strategies is not a ‘one-off’; strategies must be continuously and systematically evolved. In the race for algorithmic supremacy, firms attempt to observe counter party trading activity and either automatically or manually ‘reverse engineer’ the strategies being used. As a result, firms must plan to rapidly evolve – or perish.
I have been presenting this week with Koscom, our partner in Korea – the screen shot here comes from their presentation, which illustrates the principle of following the imperative rapid evolution and of localize, localize, localize – at the same time. Through localized rapid application development tools, Koscom delivers a differentiated algorithmic trading solution to the Korean market.
Wednesday, April 04, 2007
Algorithmic Trading Imperative #3: Localize, Localize, Localize
Today we issued a press release about the adoption of algorithmic trading in Asia. In it we discussed algorithmic trading imperative number 3: Localize, Localize, Localize. Now it might appear obvious to say that localization of software in Asia is an imperative, but that’s not the point. Yes, language localization is critical, but more important in the field of trading is the ability to localize trading technologies for local strategies that can work in a specific market, for the local connectivity requirements the market requires. For example, Credit Suisse and Goldman Sachs have already localized their algorithmic trading strategies, and Sang Lee, Managing Partner, Aite Group comments: “Algorithmic trading in Asia Pacific began its development in a similar vein to Europe and North America with an initial focus on equities. Today, however, firms are rapidly adopting techniques that have taken longer to develop in the more established markets. These include the use of algorithms for foreign exchange and cross-asset class trading. As algorithmic trading in Asia continues to evolve, flexible, customizable technology that accounts for the unique characteristics of local markets is essential.”
Another element of localization in the Asian markets is around regulation. In FTMandate, Andrew Freyre Sanders from JPMorgan said:
“You can quickly create a basic VWAP algorithm, cut up orders and send it into the market in Europe or the
US – with the resulting performance not being too bad. In certain markets in Asia, however, you cannot get away with slicing up orders and sending them to the market… due to the liquidity, big tick sizes, order books and a raft of differing regulations.”
An open, customizable, flexible “white box” algorithmic trading model, such Progress Apama’s, facilitates the need to localize algorithmic strategies rapidly, which is also discussed at length in the press release.
So the need to localize, localize, localize is Algorithmic Trading Imperative #3.
Tuesday, April 03, 2007
Algorithmic Trading Imperative #2: Customize Quickly
An increasing trend in today’s algorithmic trading space is dissatisfaction with commoditized black box algorithms provided by brokers. If everyone has access to the same algorithms, where is the advantage? Increasingly, sell-side prop desks and buy-side hedge funds are developing personnel capable of designing differentiated algorithms – but a black box approach doesn’t empower the capability of these algorithmic architects. A white box approach allows firms to leverage their intellectual property and create the secret sauce that offers competitive advantage. Firms know – and trust – the ways in which an algorithm works, can design new algorithms based on existing ones and can combine algorithms in new and interesting ways (for example, to develop multi-asset class trading algorithms). Graphical CEP development environments, designed for traders, not IT, are becoming increasingly important development tools for and enables the rapid evolution of sophisticated algorithmic trading strategies.
Saturday, March 31, 2007
Algorithmic Trading Imperative #1: Move First
Today’s markets are continually evolving, with new opportunities emerging by the minute. White box trading systems make it possible to rapidly compose and evolve algorithms to monitor, analyze and respond to market events in a specific way. The ability to customize trading strategies to a firm’s unique requirements means there is an increased opportunity for competitive advantage. As opportunities are found, the traders themselves can rapidly design and deploy strategies ahead of their competitors. In today’s competitive environment, the trader needs to be able to develop algorithmic strategies for deployment in hours, rather than in days or weeks. With a custom built trading strategy, changes often take weeks, months, or years. In today’s markets, opportunities pass in days or hours, and traditional technology development timeframes are unacceptable.
Apama was the first commercial available “white box” algorithmic trading platform and forms the basis of many algorithmic trading solutions. The value of an open solution was described by Carlos Ramirez Cervera, head of ETS at Casa de Bolsa Finamex, a leading broker-dealer in Mexico, said: “As the Mexican market begins to take up algorithmic trading, buy-side clients are looking for flexible, customized services that will bring them an advantage. The comprehensive Apama platform and Event Modeler and Dashboard Studio rapid application development tools allow us to rapidly deploy innovative algorithms that meet our customers’ individual needs in hours or days, rather than months or years that would be required with alternative approaches.”
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