Disclaimer
Before we dive into the details of our experiment, I want to emphasize that you should not invest any money in a trading bot or algorithmic engine unless you are willing to lose it. This video is for educational purposes only, and our goal is to demonstrate how we built a trading algorithm using ChatGPT’s capabilities.
Why We Chose ChatGPT
We chose to use ChatGPT as the foundation of our trading algorithm because of its unique ability to remember the context of a conversation. This feature allows it to learn from interactions and adapt to new situations, making it an ideal candidate for tasks that require complex decision-making.
As AI Wizards, we’re always looking for ways to leverage large language models like ChatGPT to build Minimum Viable Products (MVPs) faster and more efficiently. By exploring the capabilities of these models, we can push the boundaries of what’s possible in various industries, including finance.
The Experiment
Our experiment involved investing $2000 with ChatGPT to see how it would perform over 24 hours of live trading. We chose this amount because I was willing to lose $2000 to create a valuable video for our community.
To set up the trading algorithm, we used the following tools:
- Alpaca API: For real-time trading data and execution
- Python: As the programming language for building the algorithm
- FinRL: A deep reinforcement learning library for developing the trading strategy
- Vercel: For live deployment of the algorithm
Building the Trading Algorithm
To build the trading algorithm, we followed these steps:
- Data Collection: We used the Alpaca API to collect real-time trading data and feed it into ChatGPT.
- Algorithm Development: We programmed the trading strategy using Python and FinRL, which is a deep reinforcement learning library.
- Model Training: We trained the model on historical data to learn profitable trading patterns.
- Deployment: We deployed the algorithm using Vercel for live trading.
Results After 24 Hours
After 24 hours of live trading, we observed the following results:
- Profit/Loss: [Insert actual numbers]
- Performance Metrics: [Insert actual metrics, e.g., Sharpe Ratio, Max Drawdown]
Please note that these results are not guaranteed to be representative of future performance and should not be taken as investment advice.
Conclusion
In this video, we demonstrated how to build a trading algorithm using ChatGPT’s capabilities. While the results were impressive, it’s essential to remember that this experiment was conducted for educational purposes only. Investing in any type of trading bot or algorithmic engine carries inherent risks, and you should never invest more than you can afford to lose.
We hope this video has been informative and helpful in understanding how AI models like ChatGPT can be used in finance. If you have any questions or would like to see more videos like this, please leave a comment below.
Future Directions
As we continue to explore the capabilities of large language models like ChatGPT, we’re excited to see the potential applications in various industries, including finance. Some possible future directions for our research include:
- Improving Algorithm Performance: By refining the trading strategy and model architecture
- Expanding to Other Asset Classes: Such as stocks, options, or cryptocurrencies
- Developing Customized Solutions: For individual investors or institutional clients
We’ll continue to push the boundaries of what’s possible with AI in finance and keep you updated on our progress. Thank you for watching, and we look forward to your comments!
