Can AI Outperform Humans in Stock Trading? A Three Months Experiment with a $10,000 Investment
Can AI Outperform Humans in Stock Trading? A Three Months Experiment with a $10,000 Investment
Testing the Potential of Automated Trading Bots in Real-World Markets
On December 7th, 2023, the world took another leap forward. Artificial Intelligence, or AI, had already been accessible to the general public for free for over a year with the launch of ChatGPT. AI is advancing rapidly, outsmarting humans in complex activities like chess, poker, video games, and even camel racing. This got me thinking: if AI can excel in these areas, could it also beat humans in the complex world of stock trading
The Advantages of AI in Trading
AI has several advantages over human traders:
Speed and Efficiency: AI can analyze vast amounts of financial data in seconds.
Emotionless Trading: Unlike humans, AI doesn't experience emotions that can cloud judgment.
Continuous Monitoring: AI can trade 24/7 without needing rest.
Consistency: AI can strictly adhere to a trading plan, avoiding common human pitfalls like moving stop losses or exiting trades prematurely.
Building the Trading Bot
I decided to put AI to the test by coding a simple trading bot using the trading knowledge I've accumulated over the past eight years. This bot can read market structure, momentum, and more. My goal was to create a bot that mimics some parts of my trading style and automates the decision process.
The Experiment Setup
I funded a new trading account with $10,000 to let the bot trade autonomously for three months. The bot was programmed to follow these rules:
No human intervention: I couldn't interfere with the bot's decisions.
Maximum trade amount: Each trade could not exceed $1,000.
Stock selection: The bot could trade ten specific stocks.
Timeframe: The bot used a daily timeframe for signals
The Results
After one three-months, the bot traded 35 times, with 20 winning trades and 15 losses. Here are some highlights:
WMT (Walmart): 5 trades, 3 wins, 2 losses, 4.5% profit ($45).
BA (Boeing): 4 trades, 2 wins, 2 losses, 5.2% profit ($52).
NFLX (Netflix): 6 trades, 4 wins, 2 losses, 10.1% profit ($101).
NKE (Nike): 3 trades, 2 wins, 1 loss, 8.7% profit ($87).
INTC (Intel): 3 trades, 1 win, 2 losses, -2.8% loss (-$28).
Overall, the bot made $367, growing the account from $10,000 to $10,367—a 3.67% return in one month.
Key Takeaways
Market Conditions Matter: The bot performed well in a trending market but struggled during consolidation. I’m not surprised with that because some of the training data included momentum indicators.
Risk Management: Setting a $1,000 trade cap was crucial for managing risk. Using the entire $10,000 per trade could have significantly increased both potential gains and losses.
Potential for Improvement: While the bot showed promise, it could benefit from further refinement, especially in handling varying market conditions. Some filtering capabilities could improve performance based on the trades that I analyzed
Conclusion
This experiment demonstrated the potential of AI in trading, offering decent profits with minimal human intervention. Although not perfect, the results inspire confidence in further developing automated trading systems.
To discuss the strategy used in this experiment and learn more, join our Discord chat by subscribing to our newsletter. Thank you for reading, and here’s to more successful trading with AI!
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