I Built a Crypto Trading Bot with ChatGPT

Summary

In this video, Siraj showcases his creation of a crypto trading bot powered by ChatGPT, using a $2,000 investment in Bitcoin to explore its trading capabilities. He details the use of prompt engineering to predict cryptocurrency prices by analyzing data from CoinGecko, Twitter sentiment, and news articles. The bot stores its prediction history in a vector database and utilizes the Alpaca API for executing trades. After 24 hours, the bot experiences a 0.41% loss, but Siraj remains optimistic about future earnings from his pre-search node. This project highlights the innovative use of AI in automated trading.

Takeaways

• 😀 Siraj built a crypto trading bot using ChatGPT with an initial investment of $2,000 in Bitcoin.
• 📊 The bot utilizes prompt engineering techniques instead of traditional machine learning for making trading predictions.
• 💻 Python code is employed to predict Bitcoin prices, integrating data from OpenAI and CoinGecko libraries.
• 🔄 The trading bot was expanded to include multiple cryptocurrencies, improving its diversification strategy.
• 🐦 Twitter sentiment and news articles are incorporated into the trading algorithm for better decision-making.
• 📦 A vector database is used to store historical data and optimize predictions, reducing volatility in trading decisions.
• 🔍 The bot is designed to execute trades based on real-time data analysis from various sources.
• ⏲️ Siraj deployed the bot on Python Anywhere to automate its operation, allowing for hourly trade executions.
• 📉 After 24 hours of live trading, the bot reported a 0.41% loss, which was mitigated by potential earnings from a pre-search node.
• 👍 Siraj encourages viewers to engage with the content and explore decentralized search solutions through Pre-search.

Q & A

What is the main purpose of the video?

The video aims to demonstrate how Siraj built a crypto trading bot using ChatGPT and to reveal the outcome of a $2,000 investment in Bitcoin after 24 hours of trading.

What are some key strategies discussed for using large language models in cryptocurrency trading?

Key strategies include using prompt engineering techniques, such as question answering, to make predictions rather than relying solely on traditional machine learning methods.

Which programming libraries does the trading bot utilize?

The bot uses the OpenAI and CoinGecko Python libraries for data retrieval and analysis.

How does the bot diversify its trading decisions?

The bot analyzes multiple cryptocurrencies, including Bitcoin, Chainlink, Ethereum, Solana, and utilizes sentiment data from Twitter and news articles for each of these cryptocurrencies.

What role does the Pinecone database play in the trading bot?

Pinecone is used as a vector database to store historical predictions, allowing the bot to learn and adjust its trading strategies based on past performance.

What does the 'Vector Delta' represent in this context?

The 'Vector Delta' measures the difference between current predictions and past predictions, and the bot aims to keep this delta as small as possible to minimize volatility.

How frequently does the trading bot execute trades?

Initially, the bot was set to execute trades every 30 minutes, but it was later adjusted to run every hour after being deployed on PythonAnywhere.

What was the outcome of the bot's trading after 24 hours?

After 24 hours, the bot reported a loss of 0.41% on the initial investment.

How does Siraj plan to offset the trading losses incurred by the bot?

Siraj mentions that a pre-search node he operates may help mitigate the losses from the trading bot.

What is the significance of using decentralized data sources for the trading bot?

Utilizing decentralized data sources, such as Twitter sentiment and news articles, enhances the bot's ability to make informed trading decisions by incorporating diverse perspectives and real-time information.