Financial Analysis: Build a ChatGPT Pairs Trading Bot

Use ChatGPT for Algotrading, Crypto, Forex, Stock Investing, Making Money Online, +More in Python

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Course Data

Lectures: 45
Length: 7h 09m
Skill Level: All Levels
Languages: English
Includes: Lifetime access, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum

Course Description

Hello friends!

As one of the original artificial intelligence and machine learning instructors on this platform, how could I not create a course on ChatGPT?

ChatGPT and its successor, GPT-4, have already begun to change the world. People are excited about new opportunities, and terrified of the potential impacts on our society.

This course combines 2 of my favorite topics: AI and finance (algorithmic trading).

The premise of this course is simple: use ChatGPT to build a trading bot (specifically, using pairs trading which is what I was interested in at the time).

Throughout the course, we will learn about the amazing capabilities of ChatGPT and GPT models in general, such as GPT-3, GPT-3.5, GPT-4, etc. We will learn about the many pitfalls of these models, and why you need to keep your guard up. These models do make mistakes, but we will learn how to deal with them. We will learn the best ways to make use of ChatGPT to help us be more efficient and productive.

Important consideration: Why not just ask ChatGPT yourself and forego this course? Sure, you can tell ChatGPT if you get an error and maybe it'll fix it, but that only works for syntax errors (errors that break the rules of the Python language). What you'll miss, if you don't have foundational knowledge in Python, finance, and statistics, is semantic errors (errors in logic and reasoning), because you won't even notice them in the first place. That is what it means to "keep your guard up", and that is one of the major lessons in this course, which I'm already seeing is very easy for people to miss!

So what are you waiting for? Join me now on this exciting journey! ( And maybe learn how to make some money in the process :) )

Suggested Prerequisites:
  • Decent understanding of Python and data science libraries (Numpy, Matplotlib, Pandas)
  • Basic understanding of finance (stock prices, returns, log returns, cumulative returns)

Testimonials and Success Stories


I am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!

I just watched your short video on “Predicting Stock Prices with LSTMs: One Mistake Everyone Makes.” Giggled with delight.

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.

I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression. ...

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I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.

I just wanted to reach out and thank you for your most excellent course that I am nearing finishing.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

Hello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...

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By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.

Lectures

Welcome

3 Lectures · 06min
  1. Introduction (01:20) (FREE preview available)
  2. Project Scope (04:15)
  3. Course Tools (00:48)

Getting Setup

2 Lectures · 05min
  1. How to Succeed in this Course (03:04)
  2. Where to get the code (02:06)

Pairs Trading with ChatGPT

22 Lectures · 03hr 13min
  1. Pairs Trading Intuition (06:42)
  2. The Initial Prompt (25:14)
  3. Correcting the Trading Signal (07:24)
  4. Correcting the Z-Score Computation (10:53)
  5. Correcting the Return Computation (05:28)
  6. Correcting How We Measure Strategy Performance (06:29)
  7. Returns, Log Returns, Cumulative Returns (18:42)
  8. More About Log Returns (Optional) (06:39)
  9. Strategy Performance Computation (Optional) (13:48)
  10. Asking ChatGPT for Pairs (13:27)
  11. Testing the Strategy (05:24)
  12. Benchmark Against Buy-and-Hold (05:33)
  13. Fixing the Spread (04:27)
  14. Extending the Position (05:14)
  15. Extending the Position (Code) (02:38)
  16. Asking ChatGPT to Fix an Error (06:49)
  17. More Pairs (05:59)
  18. Long-Only Strategy (04:11)
  19. Long-Only Strategy (Code) (05:10)
  20. Return Computation Revisited and Other Extensions (Optional) (27:52)
  21. Return Computation Revisited (Code) (01:49)
  22. Suggestion Box (03:10)

Sanity Check

2 Lectures · 36min
  1. Mean Reversion Test (24:36)
  2. Pairs Trading Test (12:15)

Course Summary

3 Lectures · 28min
  1. Conclusion and Lessons (05:42)
  2. Why So Many Errors? (14:22)
  3. ChatGPT Knows Who I Am!? (07:56)

Setting Up Your Environment (Appendix/FAQ by Student Request)

3 Lectures · 42min
  1. Pre-Installation Check (04:13)
  2. Anaconda Environment Setup (20:21)
  3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)

Extra Help With Python Coding for Beginners (Appendix/FAQ by Student Request)

4 Lectures · 49min
  1. How to Code Yourself (part 1) (15:55)
  2. How to Code Yourself (part 2) (09:24)
  3. Proof that using Jupyter Notebook is the same as not using it (12:29)
  4. How to use Github & Extra Coding Tips (Optional) (11:12)

Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Request)

4 Lectures · 59min
  1. How to Succeed in this Course (Long Version) (10:25)
  2. Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:05)
  3. What order should I take your courses in? (part 1) (11:19)
  4. What order should I take your courses in? (part 2) (16:07)

Appendix / FAQ Finale

2 Lectures · 08min
  1. What is the Appendix? (02:48)
  2. Where to get discount coupons and FREE deep learning material (05:49)
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