Building a Stock Trading Bot with AI: My Journey and Results

Introduction

In a world where technology shape-shifts like a chameleon, the stock market has seen a surge in automated trading strategies powered by artificial intelligence. I’ve always been a buy-and-hold investor, but witnessing the rapid success of many traders relying on technical analysis nudged me toward exploration. With $11,000 at stake, I set out to create a stock trading bot utilizing generative AI to identify patterns in stock charts, hoping to decipher if this high-tech approach could outperform traditional investment strategies.

In this article, I share my journey of developing a stock trading bot, the challenges I faced, and the surprising outcomes.

The Concept Behind My Trading Bot

The Shift from Traditional to AI Trading

Typically, my investment strategy revolved around choosing stocks and following their performance over time. This approach is meticulous but can feel slow-paced, especially when everyone seems to be making quick gains through day trading and technical analysis. Therefore, I decided it was time for me to adopt a new method—developing a stock trading bot capable of understanding the patterns in stock charts.

Why Generative AI?

I chose to leverage Google’s latest generative AI model, Gemini Pro Vision. Unlike simpler models, Gemini possesses the ability to analyze diverse data formats, including images. This aspect made it perfect for reading stock charts, where fluctuations in price can often look like cryptic artworks filled with potential insights. My goal was to enable Gemini to determine buy/sell signals purely based on these patterns.

Setting Up the Trading Bot

Step 1: Understanding Stock Chart Patterns

To make the bot effective, I had to begin by researching the various stock chart patterns. Through hours of research, I settled on focusing on six recognizable patterns that could yield insights regarding price trends—both bullish and bearish. Common patterns include the bullish flag, which indicates a continuation of an upward trend, and a bearish wedge, which typically signals declining prices.

Step 2: Teaching AI to Recognize Patterns

This process involved using Google’s Maker Suite to train my bot. Initially, I encountered some challenges, as the AI misinterpreted the charts’ information due to coding missteps. However, after several iterations, I implemented techniques like few-shot prompting and classification to ensure the AI could accurately recognize and evaluate stock chart patterns.

Step 3: Running the Tests

With my bot set up, I needed to test it against my traditional investment portfolio. I strategically picked a diverse set of stocks to compare outcomes over a week. Each investment from both methods was started with an initial consideration of performance over time, fundamentals, and chart patterns.

Monitoring the Performance

Initial Days of Trading

On day one, I began by executing trades based on the bot's recommendations. To my surprise, my personal portfolio came out on top, with a reported gain of nearly $30, while the bot only netted $3. The early underperformance of the bot had me feeling a sense of vindication in my traditional approach.

Bot’s Progress Over the Week

As the days progressed, the bot seemed to gain traction and refine its strategy. On day four, it managed to catch up to my portfolio's performance, suggesting that it was beginning to understand the market dynamics. This constant evolution of strategy led up to day five, where the bot made some impressive trades and recommendations—earlier missteps appeared to have been corrected.

Final Outcomes

Calculating the Profitability

At the end of the week, the bot accumulated profits amounting to $62.65, while my portfolio also saw an increase—but the gains were steadier and more predictable. It was clear that while the bot had learned, I preferred the peace of mind my traditional method provided, devoid of daily trading pressures.

Takeaway

Ultimately, the experiment was a great way to bridge the gap between traditional investing and modern technological enhancements such as AI. While the stock trading bot performed well, a significant takeaway is having emotional stability and a well-researched portfolio averted several potentially stressful scenarios.

Conclusion

In conclusion, my endeavor into the realm of automated trading led to an enlightening comparison between generative AI strategies and traditional investment approaches. Though the stock trading bot executed a notable performance, the comfort of long-term investments and sound company fundamentals still holds valuable lessons for new investors.

I’ll continue refining my trading bot to improve its accuracy and effectiveness, and I remain hopeful that future iterations will surpass my conventional portfolio's returns. If you enjoyed this article, leave a comment, and let me know if you’d like a sequel detailing further advancements!