How you can win in the stock market with AI trading strategies. 

There’s been a lot of buzz around the intersection of AI and finance lately – and for good reason. With some AI-driven hedge funds delivering up to 3-5% higher annual returns than their less tech savvy counterparts and AI now powering between 60-75% of equity trading globally, the impact of AI models on returns for the $55 trillion trading market is clear to see. And the discussion appeared to reach a fever pitch recently when Bloomberg reported that AI models are outperforming human traders in key financial markets. 

While the tools making this accomplishment possible might seem revolutionary, the trend is anything but new – algorithmic trading has been transforming the financial world for decades. In this article, we’ll explore the rise of algorithmic trading, dive into whether AI models will actually displace human traders, break down what this means for the market at large, and – most importantly – show you how to start using these tools in your own trading journey, whether as a side hustle or a full-time career.

A Brief History of Algorithmic Trading: A Timeline of Keystone Events

The intersection of AI and finance started long before the likes of IBM Watson and ChatGPT took the world by storm. Here is a timeline of algorithmic trading.

1970s: The Beginnings of Algorithmic Trading

• Early experiments with computer-driven trading begin on Wall Street. Traders use simple algorithms to automate order flows, primarily for large institutional trades.

1987: Black Monday and the Pitfalls of Early Models

• The market crash on October 19th, known as “Black Monday,” exposes the risks of automated trading strategies like portfolio insurance, which inadvertently accelerated the sell-off. This sparks debate about the role of algorithms in financial markets.

1991: Ray Dalio Implements Algorithms at Bridgewater Associates

• Famed hedge fund manager Ray Dalio formalizes the use of algorithms at Bridgewater Associates, relying on historical data patterns to remove emotion from trading decisions. This approach propels Bridgewater into becoming the world’s largest hedge fund, proving the power of systematic trading.

Early 2000s: Rise of High-Frequency Trading (HFT)

• The availability of faster internet and powerful computing introduces high-frequency trading , where firms like Renaissance Technologies leverage algorithms to execute thousands of trades in microseconds, profiting from tiny price fluctuations.

2008: Financial Crisis Highlights the Limits of Algorithms

• The 2008 crash demonstrates the need for human judgment. While algorithmic systems excel in normal market conditions, they struggle with unexpected macroeconomic shocks – like the collapse of Lehman Brothers – that don’t fit historical patterns.

2010: The Flash Crash

• On May 6th, the stock market experiences a sudden crash, erasing nearly $1 trillion in market value in minutes. This event uncovers flaws in algorithmic trading systems and prompts regulatory bodies to impose stricter rules on automated trading practices.

2015-2018: AI and Machine Learning Enter the Picture

• Hedge funds and financial firms start adopting AI and machine learning to improve algorithmic strategies. AI-powered models are now capable of analyzing unstructured data like news reports and social sentiment – something traditional algorithms couldn’t do.

2020-2023: AI Trading Gains Mainstream Adoption

• AI models become a key tool for hedge funds and retail investors alike – with estimates as high as 3 out of every 4 trades in the United States being algorithmic.  

The Present: Democratization of AI Trading Technology

• Platforms like Wealthfront now offer AI trading tools to individual traders, empowering retail investors to compete with institutional players. Learning how to build and optimize AI trading models has become a critical skill for finance professionals.

Can AI Models Outperform Human Traders?

AI trading is growing in dominance in terms of the volume of algorithmic versus traditional investing – but how do AI models really stack up against human professionals? Here’s what we know. In terms of precision, AI models are highly effective in forecasting earnings changes and detecting patterns hidden within massive datasets. However, while they excel at high-speed execution and managing vast amounts of information, AI has not fully replaced human traders – and likely never will. Each brings unique advantages to the trading landscape.

AI models thrive on speed, consistency, and efficiency, analyzing both structured data (like financial reports) and unstructured data (such as news sentiment) within milliseconds. This makes them great for high-frequency trading, where the ability to act faster than competitors can determine success. But AI models often struggle during moments of extreme volatility. In “black swan” events, like the 2008 financial crisis or the COVID-19 pandemic, algorithms relying on historical data may fail to adapt to new realities, requiring human intervention to adjust strategies on the fly.

Human traders, on the other hand, bring creativity, adaptability, and emotional intelligence – qualities that technology cannot yet replicate. Their ability to interpret investor psychology and market sentiment is essential for long-term strategy development. When unforeseen circumstances arise, humans can pivot and recalibrate faster than AI, which often needs reprogramming or retraining. This blend of intuition and foresight makes human expertise crucial for navigating unpredictable markets.

In practice, the most successful trading strategies today combine the strengths of both AI and human traders. AI streamlines processes and provides insights, but human expertise ensures those insights are interpreted with context, creativity, and flexibility. Firms leveraging this hybrid approach benefit from AI’s speed and precision while relying on human judgment for strategic decisions. Although AI models hold immense potential, experts caution that long-term success requires balancing the strengths of both AI and human input. The future of trading will likely revolve around this collaborative approach, where AI handles repetitive tasks, and humans focus on strategic oversight to navigate an increasingly complex market landscape.

What This Means for the Industry

AI’s rise in trading is transforming the financial landscape, creating both opportunities and challenges. As mentioned, AI-driven models now power the majority of global equity trades, improving market efficiency by speeding up transactions, narrowing bid-ask spreads, and enhancing liquidity. However, AI’s rapid execution can also pose risks – events like the 2010 Flash Crash show how algorithms can amplify volatility, pushing regulators to build safeguards against market disruptions.

The role of traders is also evolving. As AI takes over routine tasks like market scanning and technical analysis, traders must focus on strategic oversight, risk management, and model optimization. Mastery of data fluency, coding skills (like Python), and algorithm literacy is becoming essential. Many firms now expect traders to work closely with engineers to fine-tune AI systems, ensuring they perform under real-world conditions. 

AI technology is also becoming more accessible to everyday traders, allowing individuals to leverage tools once exclusive to hedge funds. While this democratization empowers a new wave of traders, it raises concerns about market correlation during periods of stress. Platforms must strike a balance, ensuring powerful tools don’t compromise stability or encourage reckless behavior.

As AI models become more embedded in trading, governance, ethics, and transparency are growing priorities. Regulators and firms alike are working to ensure accountability and explainability in AI systems, minimizing bias, and unintended risks.

Ultimately, the future of trading will depend on a dual approach. AI will handle data-heavy processes and fast trades, while humans focus on strategy, creativity, and navigating complex market shifts. Success will come to those who embrace both AI and human expertise, preparing for a future where both work hand in hand.

Want to Learn the Latest AI Trading Strategies?

The world of finance is changing, and AI is at the center of it all. Whether you’re interested in algorithmic trading as a side hustle or aiming to launch a full-time career, there’s no better time to learn the tools and techniques shaping the future of financial markets. That’s why Udacity launched our brand new AI Trading Strategies Nanodegree program. Taught by industry experts, you’ll learn how to build, test, and optimize AI-driven trading models so you can maximize returns. 

Ready to start trading smarter? Explore our AI Trading Strategies Nanodegree program today – and take the first step toward mastering the future of finance!