Developing a ChatGPT-Powered Trading Bot for Financial Analysis Online Course
Developing a ChatGPT-Powered Trading Bot for Financial Analysis Online Course
The Developing a ChatGPT-Powered Trading Bot for Financial Analysis course introduces innovative financial analysis through the integration of ChatGPT and pairs trading strategies. This course equips you with the tools and knowledge to build an AI-driven trading bot that leverages ChatGPT for financial decision-making. You’ll start with an overview of ChatGPT, the project scope, and essential tools. As you progress, you’ll learn pairs trading, including understanding trading intuition, refining signals, and computing key metrics like z-scores and returns. The course covers both log and cumulative returns, testing strategies, and implementing a long-only strategy. By the end, you'll be proficient in using ChatGPT to enhance trading efficiency and financial analysis for better investment decisions.
Key Benefits
- Learn to develop a pairs trading bot powered by ChatGPT, gaining expertise in pairs trading, algorithmic strategies, and stock trading methodologies.
- Conduct in-depth financial analysis by utilizing z-scores, log returns, cumulative returns, and portfolio returns derived from real-time data.
- Design and implement effective trading strategies using pairs across various markets, including stocks, forex, cryptocurrencies, Bitcoin, Ethereum, and altcoins.
Target Audience
This course is for individuals seeking to harness the power of ChatGPT in building a pairs trading bot, as well as students and professionals in data science and machine learning with an interest in financial analysis. To succeed in this course, participants should have a solid understanding of Python programming and familiarity with data science libraries such as NumPy, Matplotlib, and Pandas. A basic knowledge of finance, including concepts like stock prices, log returns, and cumulative returns, is also beneficial.
Learning Objectives
- Master the use of ChatGPT to develop a pairs trading bot in Python.
- Identify and avoid common mistakes when leveraging ChatGPT for coding tasks.
- Design and implement pairs trading, algorithmic trading, and stock trading strategies.
- Learn how to compute key financial metrics such as z-scores, log returns, cumulative returns, and portfolio returns.
- Gain insights into applying data science methodologies to enhance financial analysis.
- Develop and apply trading strategies for a variety of markets, including stocks, forex, cryptocurrencies, Bitcoin, and Ethereum.
Course Outline
The Developing a ChatGPT-Powered Trading Bot for Financial Analysis Exam covers the following topics -
Module 1. Introduction
- Overview of the Project
- Tools Required for the Course
Module 2. Getting Started
- Tips for Success in the Course
- Accessing the Code
Module 3. Pairs Trading with ChatGPT
- Understanding Pairs Trading
- Creating the Initial Prompt
- Adjusting the Trading Signal
- Correcting Z-Score Calculations
- Fixing Return Computation Errors
- Refining Strategy Performance Metrics
- Exploring Returns, Log Returns, and Cumulative Returns
- Additional Details on Log Returns (Optional)
- Strategy Performance Analysis (Optional)
- Using ChatGPT for Pairs Trading Assistance
- Testing the Trading Strategy
- Benchmarking Against Buy-and-Hold Strategy
- Fixing the Spread Calculation
- Extending the Position
- Extending the Position (Code)
- Troubleshooting with ChatGPT
- Exploring More Pairs Trading Opportunities
- Implementing a Long-Only Strategy
- Long-Only Strategy (Code)
- Revisiting Return Computation and Strategy Extensions (Optional)
- Return Computation Revisited (Code)
- Suggestions for Further Improvements
Module 4. Sanity Check
- Conducting a Mean Reversion Test
- Pairs Trading Test
Module 5. Setting Up Your Development Environment (Appendix/FAQ)
- Setting Up Anaconda Environment
- Installing Key Libraries: NumPy, SciPy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
Module 6. Additional Support for Beginner Python Coders (Appendix/FAQ)
- Beginner’s Guide to Coding (Part 1)
- Beginner’s Guide to Coding (Part 2)
- Proof of Jupyter Notebook’s Equivalence to Traditional Coding Methods
- Using GitHub and Extra Coding Tips (Optional)
Module 7. Effective Machine Learning Learning Strategies (Appendix/FAQ)
- Comprehensive Tips for Succeeding in This Course
- Is This Course Suitable for Beginners or Experts?
- Academic vs Practical Focus and Course Pace
