Digitization has been an ongoing evolution of traditional systems, given the rapid advancement of technology and computing systems. In the financial services realm, it is of no surprise that digital systems represent the fundamental bedrock for trading, since computers are far more efficient than manual human endeavour. Approximately 85% of stock market trading in the United States is driven by algorithms, with computers being used not only to facilitate seamless trading, but also to influence when and how to execute the best trades. In other words, advanced and sophisticated machines rule the trading world.

The universe of algorithmic trading encompasses a diverse variety of algorithms and strategies ranging from the extremely rudimentary to the highly sophisticated. Algorithmic trading represents a versatile tool for a trader’s repertoire; contingent on a specific use case, a certain category of algorithm can be well-suited to significantly enhance a trader’s execution and performance.

There is also an unlimited combination of different and non-mutually exclusive strategies that can be utilized. Algorithmic trading seeks to significantly enhance the execution of trades in the pursuit of the creation of value within the financial markets.

Algorithmic Trading — The Basics

In the 1970’s, NASDAQ became the world’s first electronic stock exchange, spearheading the initial step of digitization through the electronic display of stock prices. This, the advent of personal computers, and the proliferation of the internet, was what catalyzed the concept of electronic trading, which allows everyday users to trade from anywhere around the world using their computers, spearheading a new trading format: algorithmic trading (“algo trading”).

Algo trading refers to the use of computer program or system to trade financial assets (stocks, bonds, cryptoassets and so on) according to a set of specific rules. These rules are governed by mathematical formulas and algorithms that automatically decide how and when trades are executed on an exchange. Anyone can conceive and run an algorithm on a computer, instructing it to buy or sell financial assets based on predefined conditions, which can be based on variety of factors, including time, volume, price, quantity, or any other financial metric. By leveraging machines rather than humans, greater speed, frequency, and efficiency can be achieved; something particularly relevant in the fast-moving global markets, where data and information move at lightning speed, and every millisecond can represent a lost money-making opportunity in the financial world.

Algo trading in the cryptocurrency space is a highly opportunistic endeavour given the following features:

  • Infant Market: Although there are thousands of coins and tokens representing various and diverse use cases and applications, the cryptocurrency market is extremely young, and far less saturated in relation to other financial markets. A brand new market presents multiple opportunities for people looking to exploit market inefficiencies.
  • Volatility: Given their infancy, cryptocurrencies are extremely volatile and are highly susceptible to erroneous price movements on a daily basis. While this presents huge risks to market participants, it also offers an equal propensity for achieving greater rewards. The huge price swings of cryptocurrencies are an opportunity for traders to exploit the momentum.
  • 24-Hour Access: Unlike traditional stock markets with standard market opening and closing times, the cryptocurrency market is open all day, everyday. This allows participants across the entire globe to trade cryptocurrencies at any time, and having unrestricted access to the market greatly expands the opportunities for automated trading.
  • Retail-Driven: The lack of institutional participation in the cryptocurrency market presents an excellent prospect for retail investors to get ahead of the curve and execute strategies in a less saturated market. In traditional markets, institutional investors control the market through their million-dollar systems and strategies.

Components of Algorithmic Trading

To fully comprehend the algorithmic trading process, it is vital to understand the four main components of the overall architecture:

1. Data

In a data-driven world, information and data is key. Algorithmic systems are dependent on data for the system mechanics to fully function. The categories of data used by algorithmic systems are structured data (meaning organized according to a predetermined format, such as spreadsheets, CSV files, JSON files, XML, or databases), unstructured data (meaning not organized according to any predetermined format, such as news, social media, videos, and audio), or both. Usually available in structured format is market-related data, such as inter-day prices, end of day prices, and trade volumes, as well as economic and company financial data. Financial data providers such as Bloomberg or Morningstar are renowned for providing vast amounts of structured financial data. Social networks such as Facebook and Twitter are examples of mainstream use of unstructured data, which utilize more powerful tools to construe it, often using artificial intelligence and neural networks to do so.

2. Model

Models are the representation of how the real-world is perceived by algo trading systems. The underlying foundation of any algorithmic trading system is the financial model into which the algorithms are formed. Their objective is to infer the dynamics of the market and map out the eventualities. They can be developed using a host of varying methodologies and techniques, with the aim of quantifying complex real-world dynamics into a sets of perceptible rules that attempt to define the behaviour of the system within various different scenarios, and thereby represent the algorithms.

3. Execution

The execution of the algorithmic systems is contingent on meeting the functional and non-functional requirements that have been predefined and hard-coded into the system. At this point, various trigger points are established to ensure that the system executes on those predefined parameters, including the frequency of trades, the degree of price execution, plus the speed and method by which the trades are executed.

4. Monitor

The objectivity of a data-driven process – highlighted by algorithmic systems – enables a more effective monitoring process. This relates to the monitoring of the performance of the algorithmic systems through a wide variety of related financial metrics including the Sharpe Ratio, Treynor Ratio and Return on Investments (ROI). The quantification of algorithmic performance is a vital step towards the continual improvement and enhancement of the system.

Opportunities For Algorithmic Trading

The use of algo trading presents numerous avenues in the financial markets, and there are two key areas where algo trading can deliver tremendous value.

1. Arbitrage

Arbitrage is a natural phenomenon in the financial markets, in which traders exploit the differences in price of the same asset across different exchanges. In an arbitrage situation, the trader simultaneously buys and sells the same security in different exchanges, in a bid to benefit from the various differences in price. Algorithms can identify price inefficiencies and execute trades considerably faster than any human. For example, the price of a Bitcoin (BTC) often differs from one exchange to the next. A Bitcoin on a US exchange could be worth $10,000, while in Venezuela, another Bitcoin could be worth $11,000, particularly given the political and social uncertainties surrounding the latter. An algorithm could pick this up and simultaneously execute a buy order for a single BTC in the US exchange and sell a BTC in Venezuela, effectively and instantaneously gaining a $1,000 profit.

2. Market-Making

Market making is when an institution undertakes to provide the market with liquidity by providing simultaneous buy and sell orders in an exchange. This is a vital role in providing greater depth of liquidity in the financial markets. Institutions benefit from market making activities by profiting from the bid-ask spreads in the exchange. This is an applicable algo trading use case, since the simultaneous adjustment of multiple price orders can be executed to capture the change in spread. Algorithms for market making will facilitate deeper market liquidity and efficient price discovery.

Conclusion

The advancement of technology necessitates an evolutionary inclination towards leveraging the latest tools and systems to provide an advantage in a sophisticated marketplace. In the modern trading environment, algo trading is clearly a highly crucial tool in a trader’s arsenal for significantly enhancing their trade execution. Various vectors in the trading process can utilize algorithmic solutions, from arbitrage algorithms to market making avenues, and leveraging the objectivity of code and mathematics enables an objective and unquantifiable sense of our modern world.

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