GPT Trader Overview

Introduction

• Siraj introduces GPT Trader, a trading bot built using ChatGPT.
• Initial investment of $2000 for trading.
• The bot uses the Alpaca dashboard for trading and predictions on stocks (e.g., SPY, Nvidia).

Techniques for Stock Prediction

• Asks ChatGPT for the best machine learning techniques for stock prediction.
• Discussed techniques include:
  • Random Forests: Ensemble method using decision trees.
  • XGBoost: Improves predictions on Kaggle.
  • Time Series Analysis: Predicts based on historical data.
  • Neural Networks: Foundation of deep learning.

Implementing Stock Predictions

• Demonstrates asking ChatGPT for a Python example using neural networks to predict Yahoo stock prices.
• Uses scikit-learn library to build a neural network:
  • Definition: Input x weight + bias; activation function used for predictions.
• Discusses creating a Python file and requirements.txt for dependencies.

Data Sources and APIs

• Issues with initial data source; seeks a zero-commission API for stock data.
• Alpaca Trading API: Chosen for real-time stock data access.
  • Siraj demonstrates signing up and obtaining API keys.

Building the Trading Bot

• Discusses combining Alpaca API with the neural network code.
• Plans for a trading bot that trades every 24 hours based on a Sharpe Ratio data point.
• Explores advanced neural network techniques using ChatGPT:
  • Recurrent Neural Networks: Update predictions at each time step.
  • Proximal Policy Optimization: Chosen for decision-making process in trading.

Code Implementation

• Incorporates the Alpaca API in the trading script for real-time data.
• Introduces the FinRL Library for deep reinforcement learning in finance.
  • Describes how to set up and use the library for stock predictions.
  • Explains the concept of Markov Decision Process for reinforcement learning.
• Training the model with multiple strategies (A2C, PPO, DDPG).

Backtesting and Trading Logic

• Discusses backtesting as a method to evaluate trading strategies.
• Defines thresholds for buying and selling based on Sharpe Ratio:
  • Buy Nvidia if above 0.4, sell if below 0.2.

Automation with Cron Jobs

• Discusses using a Cron job to schedule the trading bot to run daily.
• Provides Python Flask code for deployment.

Results and Conclusion

• After 24 hours, the bot made 4 trades with a total profit of 1.62.
• Encourages viewers to like and subscribe for more content.