The Evolution of Algorithmic Trading: From Early Models to the Frontier of Web 3 Finance
Introduction
Once the preserve of economists and large financial institutions, algorithmic trading has become a cornerstone of modern financial markets. Originally intended to reduce human error and make transactions more efficient, it has evolved over time into a complex and automated system. As the world moves toward Web 3 and blockchain-based financial systems, it is important to understand how algorithmic trading has come this far.
In the first part of this series, we will examine the historical foundations of algorithmic trading, early models, and its evolution in traditional financial markets. This background will help us better understand the role of Web 3 in the future.
- Intellectual Foundations: Theory Before Technology
Algorithmic trading had a theoretical framework before it. In the mid-20th century, economists and mathematicians introduced concepts that later laid the foundation for automated trading, including:
Modern Portfolio Theory (1952) – Harry Markowitz mathematically presented the trade-off between risk and return.
Efficient Market Hypothesis (1960s) – Eugene Fama argued that market prices reflect available information.
Probabilistic and statistical models – mathematical methods were used to understand price movements.
Algorithms during this era were largely theoretical, but they reinforced the notion that market analysis could be done using data and mathematics rather than human intuition.
- Early Algorithms: The Beginning of Automation
The development of computer technology in the 1970s and 1980s made automation possible in financial markets. Early algorithms were designed to better execute trades rather than predict prices, especially for large institutional trades.
The main early strategies were:
VWAP (Volume Weighted Average Price) – trading according to market volume.
TWAP (Time Weighted Average Price) – distributing trades over a given period of time.
Simple arbitrage models – profiting from price differences in different markets.
Although these systems were slow and limited compared to today, they laid the foundation for trading by machines.
- The Rise of Electronic Markets
Algorithmic trading boomed in the 1990s when financial markets went digital. Electronic communication networks (ECNs) replaced traditional floor trading, resulting in:
Faster order execution
Greater market transparency
Availability of machine-readable data
This shift transformed algorithms from a mere support tool into a competitive weapon. Financial institutions formed quantitative research teams, where experts from finance, computer science, and physics began to work together.
- High-frequency trading: Speed is power
High-frequency trading (HFT) emerged in the early 2000s, in which algorithms make decisions in milliseconds or microseconds. Features of these systems include:
Extremely high number of trades
Holding positions for very short periods of time
Low-latency networks and modern infrastructure
While HFT increased liquidity and speed in the market, it also raised concerns about transparency and market stability.
- Data, Machine Learning, and the Limits of Centralization
After 2010, the use of machine learning and big data in algorithmic trading increased. In addition to price and volume, the following data now began to be included:
News and social media analysis
Economic indicators
Alternative data sources
However, the traditional financial system still suffered from problems such as centralization, expensive infrastructure, and limited access. It was this gap that highlighted the need for a new financial model.
Conclusion: On the threshold of a new era
Algorithmic trading has become faster, smarter, and more efficient over time, but it is still based on traditional and centralized financial structures. Meanwhile, blockchain, smart contracts, and Web3 have laid the foundation for an alternative, decentralized financial system.
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