I Built a Trading Bot with ChatGPT
Education
Introduction
In this article, I’ll share my journey of developing a trading bot called GPT Trader, utilizing a $ 2,000 investment. This bot leverages the capabilities of ChatGPT and operates within the Alpaca trading platform, focusing on predictions for stocks like SPY and Nvidia. Here’s a step-by-step breakdown of how I built and tested this trading algorithm.
Getting Started with ChatGPT
I began my project with a simple inquiry into machine learning: “What are the best techniques for stock prediction?” ChatGPT provided a list of popular machine learning techniques, which included methods like random forests, support vector machines, and neural networks. I decided to delve deeper into neural networks, known for their widespread applications in deep learning.
Coding the Stock Prediction Model
Next, I asked ChatGPT to demonstrate a Python example of using neural networks to predict stock prices, specifically for Yahoo. ChatGPT was able to provide code using the Keras library atop scikit-learn. As I prepared to run this code in a terminal, I realized I needed to install various dependencies. Instead of manually finding each one, I asked ChatGPT for a requirements.txt file to streamline the installation process.
Upon running the initial code, I encountered issues stemming from missing libraries, specifically TensorFlow. Moreover, I recognized that the data used in my model was not reliable. Thus, I consulted ChatGPT again to find an alternative data source.
Utilizing the Alpaca API
ChatGPT pointed me to the Alpaca Trading API, a zero-commission platform for trading stocks and cryptocurrencies. I signed up and acquired my API keys to access real-time data. I sought additional assistance from ChatGPT by asking for a Python example that utilizes the Alpaca API for fetching stock price data.
Having incorporated the Alpaca API code into my existing project, it was clear that I needed a more sophisticated model than a simple neural network. I asked ChatGPT for advanced techniques and settled on using Deep Reinforcement Learning, specifically the Proximal Policy Optimization (PPO) method.
Implementing Deep Reinforcement Learning
I inquired further into how the PPO algorithm works and received a simple explanation alongside sample code from ChatGPT. To create a suitable environment for my trading agent, I decided to leverage the FinRL library, which is tailored for financial applications of reinforcement learning.
After installing the library and replacing the Yahoo data sourcing with the Alpaca API, I defined parameters for my reinforced agent. This included setting thresholds for when to buy or sell stocks, based on predictions from my trained model.
Running Cron Jobs for Trading
To automate the trading process, I set up a cron job that would run the bot daily. ChatGPT again assisted by explaining how to create this cron job using Flask, which allowed my application to run seamlessly on the cloud.
After deploying my bot with a specific trading strategy, I eagerly awaited the results from my live trades.
Initial Trading Results
After 24 hours of operating my trading bot with a live account, the bot executed four trades and produced a profit of 1.62%. While this is just a small return, it demonstrates the potential of combining ChatGPT with a structured trading strategy using machine learning techniques.
Thank you for following my journey of creating GPT Trader. If you found this information helpful, consider subscribing to my channel for more updates!
Keyword
- ChatGPT
- Trading bot
- Alpaca API
- Stock prediction
- Neural networks
- Deep Reinforcement Learning
- Proximal Policy Optimization (PPO)
- FinRL
- Machine learning
- Automation
FAQ
1. What is GPT Trader?
GPT Trader is an AI-powered trading bot developed using ChatGPT to predict stock prices using machine learning techniques.
2. How much profit did the trading bot make in its first 24 hours?
The trading bot made a profit of 1.62% after executing four trades within the first 24 hours.
3. Which API is used for real-time stock data?
The Alpaca Trading API is used to fetch real-time stock data for the trading bot.
4. What machine learning technique is incorporated into the trading strategy?
The trading strategy employs Deep Reinforcement Learning, specifically the Proximal Policy Optimization (PPO) algorithm.
5. How does the bot decide when to buy or sell stocks?
The bot utilizes a defined sharp ratio threshold to determine when to buy or sell stocks based on its predictions.
