ALGO TRADING HISTORY
1970s, stock markets relied on open outcry trading, where traders shouted orders on exchange floors.
In 1971, the NASDAQ (National Association of Securities Dealers Automated Quotations) became the first electronic stock exchange, allowing computerized trading.
1980s – Rise of Program Trading: The New York Stock Exchange (NYSE) introduced program trading, which allowed institutions to execute large orders using computers.
1990s – High-Frequency Trading (HFT) Begins As computing power improved, high-frequency trading (HFT) emerged. Traders started using technical indicators, mathematical models, and automation to execute trades in milliseconds.
1998: The U.S. SEC (Securities and Exchange Commission) approved electronic communication networks (ECNs), allowing direct electronic trading without a broker.
2000s – Algo Trading Dominates By the early 2000s, algorithmic trading started dominating financial markets.
2008 financial crisis increased scrutiny on algo trading, as automated systems contributed to extreme market fluctuations.
2010s – The Flash Crash & Regulations May 6, 2010: The Flash Crash occurred, where the Dow Jones Industrial Average dropped nearly 1,000 points in minutes due to algorithmic trading errors. Governments introduced regulations like SEBI’s rules in India and Dodd-Frank Act in the US to monitor algo trading. AI (Artificial Intelligence) and ML (Machine Learning) started playing a big role in trading strategies.
2020s – AI, Machine Learning & Retail Algo Trading Today, AI-powered algo trading is growing, using deep learning and big data analytics. Retail traders now have access to Python-based algo trading, APIs (Zerodha, Upstox, etc.), and low-code platforms to automate strategies.
Conclusion: Algo trading has come a long way from simple program trading in the 1980s to AI-driven, high-frequency trading today. With increasing computational power and data-driven strategies, algo trading will continue shaping financial markets in the future.
