Financial Analysis - Build a ChatGPT Pairs Trading Bot

Let’s use ChatGPT to build a pairs trading bot in Python and understand pairs, algorithmic, algo-trading, and stock trading strategies. Compute z-scores, log, cumulative, and portfolio returns. Apply data science strategies to financial analysis and trading strategies for stocks, forex, cryptocurrencies, Bitcoin, Ethereum, and altcoins.

49 on-demand videos & exercises

Level: All Levels

English

6hrs 50mins

Access on mobile, web and TV

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.

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.

Course Curriculum

About the Author

The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue.
As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.