The 10 Imperatives of Next-Generation Algorithmic Trading



The 10 Imperatives of Next-Generation Algorithmic Trading

Dr John Bates, Founder and Vice President, and Mark Palmer, General Manager and Vice President, Apama Products, Progress Software

Algorithmic trading has been one the most discussed topics within the financial industry over the past 12 months. In today’s hyper-competitive trading world, financial institutions feel the mounting need for technology that aids their unique trading style. The continually shifting landscape means both buy- and sell-side firms need to adapt to the effects of change. Sell-side institutions are exploring ways to augment the talents of their traders and optimise their client services, while buy-side firms are persistent in their endeavour to control their trading strategies and to hide them from the competition. Algorithmic trading has played a significant part in this.

During 2006, algorithmic trading has entered the mainstream. Algorithmic techniques and the technology that powers them are now highly influential in the way that financial instruments, both in exchange and OTC markets, are traded. Algorithmic trading was initially used in equities; however other asset classes, including futures, options and foreign exchange (FX) have begun to catch up quickly.

Algorithmic Trading Explained

Initially algorithmic trading was defined as any type of computer-assisted trading activity which handles the timing, submission and management of orders. However, recently the term has expanded as a ‘catch-all’, to encompass other terms that describe computer-assisted trading, including ‘program trading’, ‘auto trading’, ‘black box trading’ and ‘high-frequency trading’, across single or multiple pools of liquidity. As the power and flexibility of the new generation of algorithmic systems becomes more widely accepted, these terms are tending to fall under one banner.

There are two elements of an algorithmic trading strategy: the decision of when to trade, or pre-trade analytics, and the decisions of how to trade, or the execution phase of the algorithm.

The decision of when to trade is based on continuously re-calculated analytics and monitored thresholds. This could include, for example, a moving average crossover algorithm that calculates two moving averages, and analyses, in real time, when they cross one another. It then buys or makes the decision to buy or sell, depending on which average is higher.

The decision of how to trade, or the order execution element of the algorithm, can be just as complex as the decision of when to trade. For example, once an opportunity is identified by the pre-trade analytic to buy, for example, 10,000 shares of IBM, the order execution element of an algorithmic trading strategy may slice the order up into smaller parts (blocks of 1,000 shares). In conjunction, it may place the order in multiple liquidity pools to take advantage of the prices and availability of liquidity across a ‘virtual’ exchange with multiple participants (OTC markets).

There are three common approaches to implementing algorithmic trading: custom built, ‘black box’ and ‘white box’. Firms building algorithmic strategies on their own, in Java or C++, have the advantage of a completely custom built solution. In doing so, they optimise their control over the behaviour of the algorithms and the way they are integrated into their technical infrastructure. Unfortunately, bespoke algorithmic trading systems are very expensive and time-consuming to build. Furthermore, custom-built trading systems require developers to ‘re-invent the wheel’, as they are building from scratch algorithms and infrastructure that have already been built in the past. Although many algorithmic variants are very similar – VWAP algorithms, for example, are available already in hundreds of forms – slight differences can offer competitive advantage.

Another approach to algorithmic trading is to use algorithms provided by a broker or application provider: the black box technique. This is the simplest and fastest way to begin algorithmic trading. However, since firms cannot see how the algorithms work, they cannot add their own ‘secret sauce’, so the trader loses control over their trading strategies in the market. Using a commoditised black box approach to algorithmic trading, it is often difficult to beat the market – if you’re using the same algorithms as your competitor, it is difficult to gain a significant advantage. Also, many black box strategies are ‘fire and forget’, in that a user fills in the parameters of the strategy, initiates the strategy and then simply waits for the results.

The final approach is the ‘white box’ technique – an open, customisable, flexible platform with pre-built trading analytics that come with source code. A white box approach lets the user customise algorithms; combine them at will; and control how the platform is integrated with existing market feeds, OMS and EMS applications. This approach offers the best of both the custom built and black box model – fast time-to-market and flexibility, in one platform. A white box approach is not ‘fire and forget’. It usually provides a flexible and customisable front-end, enabling users to see the progress of a trading strategy on a real-time graphical dashboard and allowing them to intervene to adjust key parameters in-flight. Users can customise the look and feel of this front end to their own trading style. They can also maintain a high level view of all their algorithms as they execute. In this way, a trader becomes the coordinator of a set of strategies, rather than the mechanism to either manually execute the strategies, or just initiate them.

Algos in Today’s Trading World – The 10 Imperatives of Algorithmic Trading

As algorithmic trading moves beyond the ‘early adopter’ phase, innovative firms are identifying ways to get ahead of their competition. Below are ten imperatives of today’s algorithmic trading market:

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, analyse and respond to market events in a specific way. The ability to customise 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.

Imperative 2: Customise Quickly

An increasing trend in today’s algorithmic trading space is dissatisfaction with commoditised 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).

Imperative 3: Rapidly Evolve

As building and customising 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 capitalise on an opportunity quickly, then the competition will. Customisation 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 counterparty trading activity and either automatically or manually ‘reverse engineer’ the strategies being used. As a result, firms must plan to rapidly evolve – or perish.

Imperative 4: 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.

Imperative 5: 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.

Imperative 6: Integrate Real-time News into Algorithmic Trading

Today’s financial markets are moved by news. For example, 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 analyse and react to the news before a human trader, advantages can be realised. An algorithm could, 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.’

Imperative 7: Design for Low Latency Decisions

In algorithmic trading, milliseconds matter. Minimising 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 organisations to identify patterns among streaming event data and respond to those patterns in microseconds. 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, analyse and act on market data and respond immediately.

Imperative 8: Research and Backtest Strategies

With firms continuously developing their own unique strategies, 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 backtesting 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.

Imperative 9: Learn from Experience

Today’s algorithmic trading tools also 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 behaviour can be used to answer questions such as, “Why did I make $1 million today, but lose $1 million yesterday?” By stepping through logs of strategy behaviour 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.’

Imperative 10: Integrate Risk Management with Algorithmic Trading

Historically, calculating risk exposure was often conducted in batch at the end of the trading day. Now, firms are beginning to incorporate traditionally back office functions into algorithmic trading, such as adjusting risk exposure. This reinforces the need for real-time algorithmic risk management. If performed in real time, Value-at-Risk (VaR) calculations can provide up-to-the-millisecond visibility into potential exposure, evaluating trades based on their potential risk impact. Should a trade push exposure over key levels, it can be adjusted before the trade is executed.

An Algorithmic Future

Algorithmic trading is here to stay and is beginning to move into the mainstream, which presents an opportunity for innovative firms to gain an advantage over their competitors. The evolution of the algorithmic trading landscape will force firms to re-evaluate everything – how they design their own information technology infrastructure; trading techniques and strategy; asset-class mix; the relationship between buy- and sell-side; and the very composition and skills of the people they employ. Algorithms have sparked a fundamental change in the trading world and today, we enter an exciting era of opportunity for those who innovate – and peril for those who stagnate.

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