AI Trading vs Algorithmic Trading

What is algorithmic trading?

Algorithmic trading (also called algo trading) uses computer programs to execute trades based on predefined mathematical rules. These rules are written in code, typically Python, C++, or specialized trading languages.

Examples of algorithmic strategies:

• VWAP (Volume Weighted Average Price). Executes a large order over time to match the average market price.
• Mean reversion. Buys when price falls below a moving average and sells when it rises above.
• Statistical arbitrage. Exploits small price differences between correlated assets.

Algorithmic trading has been used by institutional investors since the 1970s. Today, algorithms account for an estimated 60-75% of all U.S. equity trading volume, up from about 15% in 2003.

What is AI trading?

AI trading uses artificial intelligence, specifically machine learning and large language models, to analyze data and make trading decisions. Unlike algorithmic trading, AI doesn't follow a fixed script. It interprets information, weighs multiple factors, and adapts its approach.

AI trading systems can:

• Read and understand news articles and financial reports
• Analyze sentiment across thousands of sources simultaneously
• Recognize patterns that aren't captured by simple mathematical rules
• Adjust their behavior based on what's working and what isn't

The key difference: an algorithm does what you told it to do. An AI does what you need it to do, even in situations you didn't anticipate.

In JPMorgan's 2024 e-Trading Survey, AI/ML was named the most impactful trading technology for the third consecutive year, cited by 61% of 4,010 institutional traders across 65 countries.

What are the key differences?

Flexibility. Algorithms are rigid. They execute the same rules every time. AI systems adapt to new conditions and can change their approach.

Input types. Algorithms typically process numerical data (prices, volumes, ratios). AI can process text (news, earnings calls), images (charts), and structured data simultaneously.

Setup. Algorithmic trading requires coding knowledge and technical infrastructure. AI trading agents can accept plain language strategies.

Determinism. Given the same inputs, an algorithm always produces the same output. AI may weigh the same data differently depending on broader context.

Cost. Building and maintaining algorithmic trading systems requires significant technical expertise. AI trading platforms make similar capabilities accessible at lower cost.

Speed. Algorithms are typically faster at execution. They're optimized for microsecond decisions. AI agents are fast, but they prioritize decision quality over speed.

As David Siegel, co-founder of Two Sigma, has observed: "The difference between algorithms and AI is the difference between a recipe and a chef. The recipe follows instructions. The chef adapts to what's in front of them."

Can AI trading and algorithmic trading work together?

Yes, and this is increasingly common. AI can manage the strategy layer (deciding what to trade and when) while algorithms handle the execution layer (placing orders quickly and efficiently).

For example, an AI agent might analyze news sentiment, technical indicators, and economic data to decide that now is a good time to buy a specific stock. It then hands off the execution to an algorithm that places the order using a VWAP strategy to minimize market impact.

This combination gives you the intelligence of AI and the speed of algorithms. It's how most modern institutional trading desks operate.