I Built a Trading Bot with ChatGPT

Summary

In this video, Siraj showcases his trading bot, 'GPT Trader,' built with the help of ChatGPT. He demonstrates how the bot uses machine learning techniques, like neural networks and reinforcement learning, to make stock predictions using test data and the Alpaca API. The bot executes trades based on these predictions, aiming to maximize profits. Siraj also explains the setup process, including code implementation and deployment. After testing the bot for 24 hours with a $2000 investment, he reveals a profit of 1.62%. The video is a tutorial on creating and deploying an AI-powered trading bot.

Takeaways

• 🤖 The video introduces 'GPT Trader', a trading bot built with the help of Chat GPT for stock trading using machine learning techniques.
• 📈 The bot uses test data for paper trading and makes predictions on stocks like SPY and Nvidia, aiming to make profit with a $2000 investment.
• 🔮 The video demonstrates the process of selecting machine learning techniques for stock prediction, starting with neural networks as the base.
• 💡 Chat GPT provides a Python code example for a neural network using scikit-learn and Keras to predict Yahoo stock prices, emphasizing the importance of data sources.
• 🛠 The script shows troubleshooting the initial code by addressing missing dependencies and suggesting the installation of TensorFlow.
• 📊 The Alpaca trading API is introduced as a source for real-time stock data, which is crucial for the bot's live trading functionality.
• 📝 The video outlines the process of signing up for Alpaca, obtaining API keys, and integrating them into the trading bot for fetching real-time data.
• 🔬 Advanced neural network techniques such as Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs) are discussed for improving predictions.
• 🤹‍♂️ The integration of reinforcement learning with neural networks, specifically Proximal Policy Optimization (PPO), is highlighted for making trading decisions.
• 📚 The finrl library is recommended for implementing deep reinforcement learning on stock data, providing an example of how to set up the environment and agent.
• 🌐 The final deployment of the trading bot involves using a Cron job in a Flask API to run the trading bot daily on a web app, making trades based on the bot's predictions.

Q & A

What is the name of the trading bot built by Siraj and Chat GPT?

The trading bot built by Siraj and Chat GPT is called GPT Trader.

What is the purpose of the GPT Trader bot?

The GPT Trader bot is designed to make predictions on different stocks using machine learning techniques and to execute trades based on these predictions.

What machine learning techniques does the video mention for stock prediction?

The video mentions several techniques including random forests, XGBoost, time series analysis, and neural networks, with a focus on deep learning.

What is the role of the Alpaca API in the GPT Trader bot?

The Alpaca API is used to get real-time stock data for the GPT Trader bot to make informed trading decisions.

How does the video demonstrate the use of neural networks for stock prediction?

The video shows a Python code example provided by Chat GPT, which uses the scikit-learn library with Keras on top to create a neural network model for predicting Yahoo stock prices.

What is the significance of the requirements.txt file mentioned in the video?

The requirements.txt file lists all the necessary dependencies needed to run the Python code for the GPT Trader bot, ensuring that all required libraries are installed.

What is the role of the Sharpe ratio in the GPT Trader bot's trading strategy?

The Sharpe ratio is used as a measure to determine the bot's trading actions. If the Sharpe ratio is above a certain threshold, the bot will buy the stock; if it's below another threshold, it will sell.

What advanced neural network techniques does the video suggest for improving the GPT Trader bot?

The video suggests using deep reinforcement learning techniques, such as Proximal Policy Optimization (PPO), to improve the bot's trading strategy.

How does the video address the issue of getting legitimate stock data for the GPT Trader bot?

The video suggests using the Alpaca trading API to obtain real-time stock data, which is then used to train the neural network model for the GPT Trader bot.

What is the final outcome of the GPT Trader bot's live trading after 24 hours?

After 24 hours of live trading with an initial investment of over two thousand dollars, the GPT Trader bot made four trades and achieved a total profit of 1.62 dollars.

How does the video describe the process of deploying the GPT Trader bot as a web app?

The video describes deploying the GPT Trader bot as a web app using a Cron job in a Flask API, which runs a Google Colab notebook once a day to execute trades based on the bot's predictions.

What is the significance of the threshold values for the Sharpe ratio in the GPT Trader bot's decision-making process?

The threshold values for the Sharpe ratio determine the bot's buying and selling actions. A Sharpe ratio above the upper threshold (e.g., 0.4) triggers a buy, while a ratio below the lower threshold (e.g., 0.2) triggers a sell.

What does the video suggest as a way to automate the GPT Trader bot's daily operation?

The video suggests using a Cron job to automate the daily operation of the GPT Trader bot, running a specified Python Flask API at a set time each day.

How does the video handle the issue of module installation when the initial Python code does not work?

The video acknowledges the issue and suggests installing the missing module, TensorFlow, manually to resolve the 'no module named tensorflow' error.

What is the role of the finrl library in the GPT Trader bot's implementation?

The finrl library is used to demonstrate how to apply deep reinforcement learning to real-time financial data for making predictions, which is then integrated into the GPT Trader bot.

What is the video's stance on the reliability of Chat GPT as a coding assistant?

The video recognizes that Chat GPT is not a perfect coder but appreciates it for providing a scaffolding or starting point for building the GPT Trader bot.

What does the video imply about the importance of testing trading bots with paper trading before live trading?

The video implies that testing with paper trading is crucial for understanding the bot's performance and making necessary adjustments before risking real money in live trading.

What is the video's approach to combining machine learning with domain-specific knowledge in the GPT Trader bot?

The video's approach involves integrating advanced machine learning techniques, such as deep reinforcement learning, with domain-specific knowledge of financial markets to enhance the GPT Trader bot's performance.