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Smarter Solutions to Power the Rise in Algorithmic Trading

As the pool of high-frequency traders begins to spill over, new ways to see into the future are coming into fruition; algorithmic trading has become increasingly popular with investors.

Algorithm trading uses high-powered computers to make trading decisions based on algorithms that can spot changes and trends across multiple different markets – accounting for around 60-75 per cent of the overall US equity trading.

This style of machine-powered trading, incorporating algorithms into high-frequency trading for practical use, incurs a low-latency rate and high-volume strategy.

The use of algorithms in this way has been spurred on by the proactive consolidation of comprehensive data sets. When designed in the right way, algorithms are able to plot patterns in random data against a trading strategy, divulging the actions that would best suit the specific needs of the trader.

Applying an algorithm to a trade has its benefits in risk management, too. Traditionally, traders didn’t have the benefit of industry foresight: there was no way to tell whether a strategy would be viable or successful. Although skill and experience remain important attributes, innovation in technology has caused many to reconsider how their resources are being utilised.

Algorithmic trading is more concise, quick and competitive than ever, allowing trades to move with far less retention and in a more certain direction.

Despite its power, the benefits of algorithmic trading are devalued as it attracts more traders.

The lifecycle of an algorithmic trade

The process of developing and implementing an algorithmic trade begins by deciding what kind of product to trade, which is a decision that forms the first stage of development. There are five distinct products to choose from:

  1. Equity represents pro-rata ownership of a company, traditionally through the shares they release publically to the market.
  • Bonds represent partial ownership of a company and are what a company releases to the market in order to cultivate funds, as opposed to deriving their funding from a bank.
  • Currencies are a popular option that can be traded on the foreign exchange market. These include: the US dollar, euro, yen, pound sterling and the Chinese yuan. Prices are sold over the counter between the main international banks in the form of currency pairs.
  • Commodities form a popular product option for traders, who will be familiar with the current worldwide markets for oil, gas, gold and agricultural commodities.
  • Derivatives are a speculative investment, with their price pitched against the real-world value of a company or commodity.
    1. 2. When a maker has decided the contents of a trade, they must then create an informed strategy. It would be impossible to accurately describe all the scenarios and strategies open to investors at this second stage of development. However, it is possible to evaluate some of the main concerns that might arise during the decision-making process:
  • Value: Traders will be looking to engage with products that will garner a high end-value, supporting profit margins that work feasibly with the process of running an algorithm.
  • Profitability: The agenda of a trade will be further directed by the profitability of the product itself. Where companies are concerned, traders will be looking to work with those that are the most profitable.
  • Low volatility: Unexpected fluctuations and sudden losses will both be elements that traders will be seeking to avoid in their strategy. Yet some of the riskiest ventures will be the most rewarding.
  • Momentum: Traders will be looking to invest in products that present a positive alpha and the potential to move forward.
      1. 3. Once both the strategy and the product behind it have been defined, a trader must then pinpoint the market through which they’re going to operate. In its most basic format, a trade will follow the simple structure of a buyer and seller agreeing to a price, before the monetary transaction is cleared by a clearing house, which then reports the trade to the national regulatory authority.
        1. As simple as this process might sound, the report comprehensively details how a trader must choose the market that is most appropriate for the product they’re trying to sell. Traders must consider the regional authority and required regulations of a given market for their transactions.
      1. 4. After the previous three elements have been decided upon, work on the algorithm begins. A Monte Carlo simulation is often a main feature in this penultimate stage of development and runs all possible future paths for the markets, including all the relevant risk factors.
      1. The simulation is utilised by both banks and insurers for capital computation, and by options traders to determine price and risk. A Monte Carlo simulation determines the best outcome for either party by drawing an average from the many random paths it maps out. It must create no less than one million paths to generate accurate results, a computationally intensive task best suited for the parallelism of graphics processing units (GPUs) rather than serial processing from central processing units (CPUs).
      1. 5. Innovative deep learning techniques have been adopted in this final stage of developing an algorithm, a process that runs large volumes of data through the algorithm to test its accuracy. Variational autoencoders (VAEs) and generative adversarial networks (GANs) are examples of the new deep learning techniques that have now become commonplace within back-testing.

    Allowing the algorithm to interact with historical data within various scenarios gives the process the advantage of foresight; rather than trying to identify flaws using past market conditions.

    The overflow of the mainstream

    The pinnacle outcome of this process would be to produce a high-frequency, low latency trading algorithm that supports early access to data, allowing high-frequency traders to act earlier than other participants, cultivating some controversy around their use in the past.

    However, due to the growing popularity of trading algorithms in the mainstream, with 63 per cent of traders expecting an increase in algorithmic trading to arrive this year, top spots are more limited than ever. As exclusivity in data insights slowly diminishes, traders are increasingly turning to tech to achieve the lowest latency possible.

    The current shift in attitude is attributed to algorithms that think smarter by crunching more data. This is actively being achieved by developments in the backend infrastructure that continuously feeds the algorithm with live or near real-time data that allows it to remain relevant, agile and competitive.

    The market is constantly evolving and the tools and solutions traders rely on to achieve their strategic goals must evolve, too.

    Solutions for algorithmic trading

    A report by Dell Technologies and NVIDIA, Algorithmic Trading HPC & AI Reference Guide, delves deeper into this subject. Algorithmic traders can utilise bespoke solutions developed by Dell and NVIDIA.

    The Dell PowerScale platform, for example, is especially suited to meet the data storage demands of the current trading climate, with its scale-out architecture enabling high bandwidth, concurrency and performance with all-flash options.

    Dell PowerEdge servers with NVIDIA GPUs allow algorithms to be successfully built and integrated into trading. NVIDIA GPUs allow algorithms to map significantly more pathways than would be possible compared to standard server CPUs. The architecture of a server GPU is highly parallelised, which allows it to analyse millions of scenarios simultaneously; benefitting from an extensive collection of accelerated libraries, tools, and technologies with expert development support.


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