Leap Forward with Quantum Algorithms in High Frequency Trading

摘要： For almost half a century, scientists across the world have put significant efforts in building quantum computers and were looking at use cases for wider adoption. Quantum Computing is a parallel computing system in nature, so it is of no surprise that it is gaining traction in a wide variety of businesses, especially in the financial services sector, where the use cases necessitate high computation power to perform operations within split-second intervals.

▲圖片來源：Finextra

For almost half a century, scientists across the world have put significant efforts in building quantum computers and were looking at use cases for wider adoption. Quantum Computing is a parallel computing system in nature, so it is of no surprise that it is gaining traction in a wide variety of businesses, especially in the financial services sector, where the use cases necessitate high computation power to perform operations within split-second intervals. High Frequency Trading (HFT) is one such use case where quantum computers could aid in accelerating order booking in the trade lifecycle. HFT is an algorithmic trading strategy employing computing algorithms to scan individual stocks to uncover latest trends and execute high volume of orders within nanoseconds to milliseconds. Slew of purchase orders would be placed in a fraction of a second, if the analysis finds a trigger. The success rate of the traders is directly proportional to the speed at which the transactions are executed. In 2020, the market size of Global Algorithmic Trading was valued at $12 billion, and by 2028, it is expected to grow up to $31 billion.

Extending Algo Trading into HFT

NASDAQ's introduction of full-fledged electronic trading fast-tracked the evolution of computer based HFT, guiding financial institutions to develop next-gen solutions and algorithms to deal with rising volume of HFT transactions. Algorithms were since designed to leverage real-time market data and embark on “buy low, sell high” strategy to strike profit on the trade(s).

Technological barriers awaiting break-through

HFT necessitates high computing servers mandating periodical upgrades, due to rapid hardware changes and shorter technology lifecycles. To break-through this significant barrier, an alternate system with high computational capability and ability to process huge volume of data with NIL or near-zero latency is essential.

Quantum for HFT

Based on speed, multiple quantum algorithms outperform classical algorithms. The number of classical bits required to perform an operation is directly proportional to the volume of data fed into the classical algorithm. With superposition and entanglement, quantum computing has supreme processing power by nature, and it could perform similar operations with way too limited number of qubits, making it preferred option for the HFT endeavor.

Quantum Computing and Quantum Algorithms could churn complex processes with huge volume of market data in shorter execution time and still deliver results with 99% accuracy. Researchers are working meticulously on near-error-free quantum computing to bridge the 1% accuracy gap. Quantum Parallelism allows us to improvise the accuracy of an operation by performing multiple instances of the same operation, concurrently. This could assist in the detection of trade dependency using Quantum dot Register, by lowering transaction risk, executing orders on time, and improving profit potential.

HFT could be classified as a complex optimization problem which implies that application of traditional algorithms to this class of problems would result in an exponentially greater execution time, as the complexity expands. Quantum Approximate Optimization Algorithm (QAOA) is a composite of quantum and classical theories intending to solve complex optimization issues. This algorithm could assist in spotting stocks with the highest return in near-term on which an HFT could be executed subsequently.

Improved Variational Quantum Optimization is another variant of hybrid quantum-classical algorithm which could be used to determine the optimal trade value of a security based on current market price. The expected price of a stock is estimated using this technique as the sample mean of a set measurement outcome, and the approximate trial price is calculated classically. Application of this algorithm could reduce the price risk in the HFT execution.

Quantum Annealing Processors are best suited for solving optimization problems. These could be deployed to assist HFT traders in analyzing permutations over a chosen stock or exchange there by decreasing the probability of missing price difference between exchanges for the same stock.

The Securities and Exchange Commission (SEC) introduced Market Information Data Analytics System (MIDAS) in 2013 to unravel fraudulent practices including spoofing, which cause false increase in demand and supply. Spoofers alike other fraudsters may leave a trace in their course of action. Quantum Monitors could be exploited here to discover the spoofer's existence or distinguish between real and spoof data. Quantum could hereby assist not only the Capital Market Institutions, but also their regulators in ensuring a fair play.

Current challenges in taking HFT to Quantum

A crucial requirement for successful HFT execution is to co-locate the trading server in proximity to the exchange. Though Quantum Computing is being explored for HFT, they are presently not placed closer to exchanges. With the advent of technological advancements and significant support from Government & Regulators, we could anticipate co-location to happen in future. This necessitates an increase in the infrastructure spend as well as require deeper collaboration with hardware manufacturers to accelerate the time to market. Despite the challenges, benefits far outweigh the usage of Quantum Computers for HFT.

Future Forward

Quantum Computing Algorithms could generate analytical models that sift through massive amount of market data in real-time and present a bouquet of stocks that could be prioritized for successful short-term buy-sell strategy. Profit maximization could be harnessed with stock prioritization. Adept optimization quantum computing algorithms could be leveraged to boost portfolio diversity and rebalancing of portfolio investments, in reaction to market conditions and investor needs. Quantum AI (Artificial Intelligence) and ML (Machine Learning) could recognize and present opportunities across asset classes, spot-on high-potential assets, and drive HFT with precise prioritization.

Although availability and operational challenges are of concern in real-world usage of Quantum Computers for HFT, wider adoption is not so far away. Financial institutions have made remarkable progress in the world of Quantum Computing in collaboration with academia, start-ups, and hardware manufacturers. The collaboration is here to stay. The evolution will bring new opportunities while enabling us in realizing our HFT business in Capital Markets.

轉貼自： Finextra

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