The Evolution Of Trading: Barter System To Algo Trading

By Chainika Thakar

Today the algorithmic trading system is quite popular amongst the traders, but how did it all start?

What is the background of trading practices?

Trading began with the barter system. In those good old ancient days, goods were traded for goods in return. Eventually, trading of goods was replaced by the currency system and then the exchanges came about which transformed into automated or electronic systems with time.

Let's explore the journey of trading, starting from the barter system and progressing to modern-day trading. Later on, we'll delve into the transformed regulatory and risk management practices, as well as the advantages and disadvantages they bring.

This blog covers:

• A brief history of how trading evolved from barter to algorithms
• Trading methods used today with algorithmic trading
• Evolution of regulatory and risk management practices
• Pros and cons of evolved trading practice

A brief history of how trading evolved from barter to algorithms

Evolution of trading has gradually occurred from barter to algorithmic trading.

But how did this evolution of trading take place? Let’s find out!

Let us now see each time frame in detail below.

Barter system - Ancient era

In ancient times, people engaged in direct exchange or barter, where some goods and services were traded for other goods and services.

The value of each item was determined by several factors such as the rarity, usefulness, or perceived value of the two items getting exchanged or bartered.

But, bartering involved mutual agreement between individuals and relied on the double coincidence of wants, making it inefficient and cumbersome.

Currency system - 7th century A.D.

As societies grew, the barter system became impractical because the exact value of two barter goods could not be ascertained. Hence, this led to the emergence of various forms of currencies. These currencies included items such as beads, metals, shells and eventually standardised coins and paper money.

This way, the introduction of currency made the trading system more efficient by providing a medium of exchange and a unit of account.

Intermediaries and Stock Exchanges - 17th and 18th century

Back in the 17th and 18th centuries, something exciting happened in the world of trading. Stock exchanges burst onto the scene, creating a happening hub for buying and selling company shares. These exchanges brought order to the chaos, introducing rules, dedicated trading floors, and brokers to keep things running smoothly.

As trade and commerce boomed, a new breed of financial intermediaries emerged: banks and merchants. These savvy folks made our lives easier by facilitating transactions, safeguarding our deposits, and even extending credit when needed.

Manual or Telephone Trading - 1900 to 1980

Back in the late 1900s, the invention and widespread adoption of telephone led to telephone trading. Suddenly, brokers and traders could connect with each other in real-time, regardless of the distance between them!

This meant traders could chat with their counterparts, seize trading opportunities, and gather market info faster than ever before. It was a game of speed and efficiency.

Telephone trading ushered in a new era, where traders could react swiftly to market events and conduct business effortlessly across long distances. It was a game-changer that made trading smoother and faster for everyone involved.

Electronic exchanges and online trading platforms - 1980 to 2000

This was a huge turning point in the evolution of the trading domain.

Electronic exchanges and online trading platforms refer to online systems that facilitate the trading of financial instruments and assets electronically.

Between the 1980s and 1990s, the computerised order routing systems were adopted. These systems allowed traders to electronically send and execute orders, replacing manual methods.

The Electronic Communication Networks (ECNs) was introduced in the 1980s itself. ECNs facilitated the direct matching of buy and sell orders, bypassing traditional exchanges.

However, it is important to note that these systems were mostly limited to large institutional investors and were not widely accessible.

In 1990, Instinet became the first ECN to provide electronic trading services to institutional investors.

The growth of the internet in the 1990s led to the emergence of online trading platforms that provided individual investors with direct access to financial markets. Online brokerages like E*TRADE, Ameritrade, and Charles Schwab gained popularity, enabling retail investors to trade stocks, options, and other financial instruments from their own computers.

However, the introduction of Electronic Communication Networks (ECNs) in the 1980s paved the way for increased electronic trading.

It was only in the 1990s that the Electronic communication networks (ECNs) expanded and gained prominence, offering transparent and efficient electronic trading. ECNs like Island ECN and Archipelago gained traction, providing an alternative to traditional exchanges by matching buy and sell orders electronically.

Full fledged shift towards Electronic Trading - 2000 to 2006

The evolution of the trading domain took to new heights when the full fledged shift happened towards electronic trading.

It was only in the year 2000 that The New York Stock Exchange (NYSE) and other similar major exchanges underwent a significant shift towards electronic trading. The NYSE introduced the Hybrid Market Model in 2006, combining electronic order matching with traditional floor-based trading. Other exchanges, such as NASDAQ, transitioned to fully electronic trading platforms.

These electronic exchanges or platforms revolutionised the way financial markets operate, offering efficiency, speed, and accessibility to market participants.

Rise of Algorithmic Trading (2007 - 2023)

Algorithmic trading, including Algorithmic trading online has experienced significant growth, leveraging technology and market data. Machine learning and artificial intelligence are playing an increasingly important role in developing advanced trading algorithms in 2023.

Regulatory focus continues to ensure a balance between innovation and market integrity.

Algorithmic trading’s rise has played a significant role in the evolution of trading.

Let us see below a brief overview of all the key advancements made so far between 2007 and 2023.

2007:

• HFT strategies ⁽¹⁾ became more prevalent, enabled by advancements in technology and faster market data access.
• Regulators start to take notice of the potential impact of HFT on market stability and fairness.

2008-2009:

• The global financial crisis led to increased scrutiny ⁽²⁾ of financial markets, including algorithmic trading practices.
• Regulators and exchanges implemented new rules and regulations to address concerns over market manipulation and systemic risks associated with HFT.

2010:

• Flash Crash ⁽³⁾: On May 6, 2010, U.S. stock markets experienced a sudden and severe drop, followed by a rapid recovery within minutes. The event highlighted the potential risks associated with algorithmic and high-frequency trading.

2011:

• The European Securities and Markets Authority ⁽⁴⁾ (ESMA) introduced guidelines for algorithmic trading, including requirements for risk controls and monitoring systems.

2012-2013-2014:

• Algorithmic trading continued to expand globally, with more exchanges and markets adopting electronic trading platforms.
• The use of machine learning and artificial intelligence (AI) techniques in algorithmic trading gained traction.

2015-2016:

• Algorithmic trading accounted for a significant portion of trading activity ⁽⁵⁾ in major financial markets worldwide.
• Financial technology (fintech) startups began offering algorithmic trading solutions to individual investors and smaller firms.

2017-2018-2019:

• In 2017, cryptocurrency markets witnessed a surge ⁽⁶⁾ in algorithmic trading as digital assets gained mainstream attention.
• But  in 2018 the cryptocurrencies collapsed 80% from their peak in January 2018.
• Blockchain technology, with its potential for decentralised and transparent trading, attracted interest from algorithmic trading firms.

2020:

• The COVID-19 pandemic ⁽⁷⁾ led to increased market volatility, and algorithmic trading systems play a crucial role in managing and executing trades during this period.

2021-2022:

• Huge influx of liquidity led to a surge in the market in 2021.
• The 2022 stock market decline ⁽⁸⁾ was a worldwide economic event characterised by a significant decrease in stock market values.
• In the years preceding the decline, the global economy experienced a recession caused by the COVID-19 pandemic. While the immediate impacts of the recession diminished by 2022, it led to subsequent challenges such as rising inflation and a widespread disruption in global supply chains.

2023:

• Algorithmic trading continues to dominate trading volumes in major financial markets.
• Technological advancements, such as quantum computing and big data analytics, offer new possibilities for algorithmic trading strategies.

ChatGPT for algorithmic trading (2023)

The evolution of trading has reached the point where AI can be used extensively from stock selection and market research to backtesting the strategy and trade execution.

ChatGPT is an AI language model that can help a trader with each of the steps while trading in the financial market.

Furthermore, ChatGPT with its capabilities in language processing , has the potential to assist and benefit the trading domain in several ways.

A trading domain powered by the dynamic duo of ChatGPT and algorithmic trading is like having a trading assistant right at your fingertips, ready to help you at every step.

But how exactly does ChatGPT revolutionise the trading landscape?

Let's dive in!

Below, let us briefly go through all the ways ChatGPT is useful in algorithmic trading.

• Market Data Analysis: ChatGPT can process and analyse vast volumes of market data, including historical price data, news articles, social media sentiment, and economic indicators.
• Strategy Development: It can provide insights, suggest approaches, and generate ideas for optimising algorithmic trading strategies.
• Risk Management: ChatGPT can assist in assessing and managing risk by analysing historical data, providing risk modelling techniques, and suggesting risk mitigation strategies.
• Trading Education and Knowledge Sharing: It acts as an interactive learning tool, providing information about trading concepts, technical indicators, trading platforms, and regulatory frameworks.
• Decision Support: ChatGPT offers decision support by providing alternative scenarios, risk assessments, and potential outcomes to aid traders in making informed choices.

Note: Although ChatGPT is of tremendous help to the traders, it must always be used in conjunction with human expertise to receive the best results. The reason for not relying completely on ChatGPT is that the user can get misguided by the wrong information given by ChatGPT. After all, it is a machine and can not use human logic!

Trading methods used today with algorithmic trading

In today’s time, trading has evolved quite a lot. There are a lot of advancements and improvements as compared to the days of manual trading.

Let us discuss the most integral trading methods combined with algorithmic trading and automated trading that are prevalent today. These methods lead to more reliable and faster order executions and maximise the returns. These trading methods are:

• HFT and MFT
• Quantitative trading
• Trading with Artificial Intelligence and Machine Learning
• Big data and cloud computing

HFT and MFT

High frequency trading or HFT and Medium Frequency Trading or MFT are the trading strategies that utilise advanced algorithms, powerful computing systems, and high-speed data connections to execute a large number of trades within extremely short timeframes. HFT and MFT firms aim to profit from small price discrepancies, often exploiting market inefficiencies that may exist for only brief moments.

Quantitative trading

The practice of quantitative trading is a part of algorithmic trading. Quantitative trading involves using mathematical models, statistical analysis, and quantitative techniques to make trading decisions. It focuses on the development and application of quantitative models and strategies based on market data analysis.

Quantitative traders rely only on data-driven insights and historical patterns to identify trading opportunities and determine the optimal entry and exit points for trades.

Trading with Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning techniques are increasingly employed in trading for tasks like market analysis, prediction, and risk management. These techniques can analyse vast amounts of historical and real-time market data to identify patterns, generate trading signals, and improve decision-making processes.

AI in trading enhances these processes by continuously adapting to changing market conditions, refining trading strategies over time. With the ability to process large datasets quickly, AI-driven models help traders make more informed decisions, optimize trade execution, and manage risks more effectively, giving them a competitive edge in fast-paced markets.

Big data and cloud computing

In the trading world today, the combination of big data and cloud computing plays a huge role. Both are two interconnected concepts that have transformed the way traders handle and process large volumes of data.

Big data refers to extremely large and complex data sets that cannot be easily managed, processed, or analysed using traditional data processing techniques. Big data is useful because the more historical data for analysis, the more the accuracy of prediction!

Cloud computing involves the delivery of computing resources, such as servers, storage, databases, software, and analytics, over the internet on a pay-as-you-go basis. It provides scalable and flexible access to computational power and storage without the need for on-premises infrastructure.

By combining big data and cloud computing, organisations store, process, analyse, and extract value from vast amounts of data more effectively and efficiently. Also, they leverage the scalability, cost-effectiveness, and advanced capabilities offered by cloud platforms to address the challenges associated with big data processing and analysis.

Evolution of regulatory and risk management practices

Along with the evolution of trading, the regulatory and risk management practices have also evolved quite a lot.

Trading today operates within a regulatory environment designed to ensure market integrity, fairness, and investor protection.

Ever since the trading practices are being driven by technology there has been a need for moulding the regulatory and risk management practices accordingly.

Regulators impose rules and regulations on trading activities for risk management, including measures to combat market manipulation, ensure transparency, and safeguard against systemic risks.

Following the global financial crisis of 2008, there has been a significant increase in regulations aimed at ensuring market stability and transparency. Regulatory bodies, such as the Securities and Exchange Commission (SEC) in the United States and the Financial Conduct Authority (FCA) in the United Kingdom have implemented stricter rules to govern trading activities.

Moreover, the regulators and trading firms have embraced technological advancements to enhance risk management practices. This includes the use of advanced analytics, machine learning, and artificial intelligence to identify potential risks, detect anomalies, and automate compliance processes.

Pros and cons of evolved trading practice

The evolution of trading practices, driven by technological advancements and innovation, have brought numerous benefits to the financial markets.

Simultaneously, there are some cons that the traders must be aware of. It is important to ensure that the online trading platforms are regulated to maintain market integrity, fairness, and investor protection.

Let us discuss some pros and cons of the evolved trading practice below:

It is essential to strike a balance between the benefits and risks of evolved trading practices. Effective regulation, monitoring, and risk management measures are necessary to mitigate the potential drawbacks and ensure the stability and fairness of the financial markets.

Bibliography

• The Transformation of Trading: A Historical Overview ⁽⁹⁾
• Wikipedia ⁽¹⁰⁾
• Asia Society

Conclusion

The evolution of trading practices have transformed the landscape of financial markets, bringing both advantages and challenges. These practices, driven by technological advancements and innovation, have increased efficiency, improved accessibility, enhanced market liquidity, and fostered global market integration.

Trading practices today also facilitate advanced risk management, promote transparency, and create opportunities for innovation.

To navigate these challenges, effective regulatory frameworks, monitoring, and risk management measures are crucial. Striking a balance between fostering innovation and maintaining market integrity is essential. Ongoing evaluation of evolved trading practices, continuous technological advancements, and a focus on investor protection are necessary to ensure the long-term sustainability of financial markets.

If you wish to find out more about algorithmic trading and you are a beginner, you can explore our learning track titled Algorithmic Trading for Beginners, which can be your first step to getting started with algorithmic trading and gaining essential skills required for different Quant trading desk roles.

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