Financial Analysis - Build a ChatGPT Pairs Trading Bot

Description

Financial analysis with ChatGPT using pairs is a cutting-edge approach that combines the power of artificial intelligence (AI) with the expertise of financial analysts to provide insights into financial data. ChatGPT and human analysts synergistically foster a collaborative environment that facilitates and generates valuable investment decisions, risk assessments, and financial planning recommendations. Financial analysis with ChatGPT using pairs offers a unique approach to insights from complex financial data. Through this course, we will use ChatGPT to build a trading bot (using pairs trading) and learn about the capabilities of ChatGPT. The course begins with an introduction to ChatGPT, the project scope, and the course tools required for this course. You will then learn to use the course efficiently and where to get the codes. We will then advance to Pairs trading with ChatGPT. You will learn about pairs trading intuition, the initial prompt, correcting the trading signal, and z-score computation. We will explore returns, log returns, and cumulative returns and test the strategy. You will learn about the long-only strategy and return computation. Upon completing this course, you will learn the best ways to use ChatGPT to be more efficient and productive with financial decision-making, investments, and trading using pairs trading.

What You Will Learn

Learn to use ChatGPT to build a pairs trading bot in Python
Learn the common mistakes when using ChatGPT for coding
Develop Pairs, algorithmic, algo-trading, and stock trading strategies
Compute z-scores, log, cumulative returns, and portfolio returns
Understand how to apply data science strategies to financial analysis
Learn trading strategies for stocks, forex, cryptos, Bitcoin, Ethereum

Audience

This course is designed for individuals who want to learn to use ChatGPT to build a pairs trading bot or students and professionals in data science and machine learning interested in financial analysis. The prerequisites for the course include a decent understanding of Python and data science libraries (NumPy, Matplotlib, and Pandas), basic knowledge of finance (stock prices, logs, and cumulative returns), and foundational knowledge in Python, finance, and statistics. Please note that the section on Python coding for beginners is not a comprehensive Python coding tutorial per se.

Approach

This course by Lazy Programmers is designed to deliver content using a mixed approach with content presentations over ChatGPT AI, live demonstrations of setup and strategies, explanations of content over PowerPoint presentations, and coding live on-screen in a classroom setup. The instructions and concepts are succinctly explained over a well-structured video course.

Key Features

Build a pairs trading bot with ChatGPT and acquire pairs, algorithmic, and stock trading strategies * Perform financial analysis using z-scores, log, cumulative, and portfolio returns computed from the data * Develop trading strategies using pairs for stocks, forex, cryptos, Bitcoin, Ethereum, and altcoins

About the Author

Lazy Programmer

The Lazy Programmer is an AI/ML engineer focusing on deep learning with experience in data science, big data engineering, and full-stack development. With a background in computer engineering and specialization in ML, he holds two master's degrees in computer engineering and statistics with finance applications. His online advertising and digital media expertise include data science and big data. He has created DL models for prediction and has experience in recommender systems using reinforcement learning and collaborative filtering. He is a skilled instructor who has taught at universities including Columbia, NYU, Hunter College, and The New School. He is a web programmer with experience in Python, Ruby/Rails, PHP, and Angular.

Course Outline

1. Welcome

2. Getting Setup

3. Pairs Trading with ChatGPT

4. Sanity Check

5. Course Summary

6. Setting Up Your Environment

7. Extra Help With Python Coding for Beginners

8. Effective Learning Strategies for Machine Learning

9. Appendix / FAQ Finale