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25% Off Financial Analysis: Build a ChatGPT Pairs Trading Bot | Udemy Review & Coupon

25% Off Financial Analysis: Build a ChatGPT Pairs Trading Bot | Udemy Review & Coupon

ChatGPT can be utilized for various purposes such as Algotrading, Forex, Stock Investing, Making Money Online, and more, all through Python.

This course covers:

This course is designed to help students gain an in-depth understanding of the related topic. It comes with 7 hours of on-demand video, full lifetime access, and the ability to access course material both on mobile devices and televisions. Upon completion of the course, students will be given a Certificate of Completion that verifies their knowledge and proficiency in the subject area.

What you'll learn

ChatGPT is an AI-driven coding platform that allows users to build their own trading bots in Python. It is designed for use with pairs trading, a low-risk and profitable approach that involves taking simultaneous long and short positions in related assets. Using ChatGPT’s AI-driven tools, users can quickly and easily code up trading strategies with the ability to customize parameters such as time frame, leverage, window lengths and cutoffs, portfolio diversification, and capital allocation.

In addition to using ChatGPT for building and tuning trading strategies, experienced Algo traders can also employ it to analyze data more effectively. The platform provides powerful analytics capabilities including computing z-scores, returns, log returns, cumulative returns, and portfolio returns. It can also be used for visualizing large datasets in order to gain insights into stock market behavior or develop better strategies for cryptocurrency or forex markets. Users can utilize data science principles such as machine learning algorithms to backtest strategies or come up with new ideas for setups. By combining technical analysis with predictive modeling approaches such as neural networks, users are able to make better-informed decisions when it comes to trading stocks, forex currencies, or cryptocurrencies like Bitcoin and Ethereum.

About the author

Lazy Programmer Inc. is an artificial intelligence and machine learning engineer

His current focus is on deep learning as an artificial intelligence and machine learning engineer, but he has also worked in roles such as data scientist, big data engineer, and full stack software engineer.

He obtained his initial master's degree more than ten years ago in computer engineering, with a focus on machine learning and pattern recognition. He earned a second master's degree in statistics with a focus on financial engineering applications.

The individual's experience encompasses online advertising and digital media, with skills in data science for optimizing click and conversion rates, and big data engineering for constructing data processing pipelines. The individual frequently utilizes big data technologies such as Hadoop, Pig, Hive, MapReduce, and Spark.

He has developed deep learning models for various applications, including predicting click-through rates and user behavior, as well as image and signal processing and text modeling.

In his work on recommendation systems, he employed Reinforcement Learning and Collaborative Filtering, and the results were verified through A/B testing.

He has instructed students at various universities including Columbia University, NYU, Hunter College, and The New School in subjects such as data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics at both undergraduate and graduate levels.

His web programming expertise has proven beneficial to several businesses. He is responsible for both backend server development, frontend HTML/JS/CSS work, and operations/deployment tasks. The technologies he has utilized include Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. I have utilized various storage and database systems including MySQL, Postgres, Redis, and MongoDB, among others.


Having a decent understanding of Python and key data science libraries (Numpy, Matplotlib, Pandas) is essential for any investment analyst. Python programming language offers powerful tools that can be used to analyze financial datasets in a faster and more efficient fashion. Furthermore, the Numpy library is integral for manipulating large datasets and creating complex mathematical calculations. Matplotlib provides graphical capabilities that can help visualize different types of investments with ideal accuracy, while Pandas has extensive data manipulation functions which are also necessary for developing trading algorithms or performing financial risk analysis.

In addition to having a sound understanding of programming, a basic understanding of finance is also beneficial for conducting successful investment activities. This includes knowledge about stock prices, returns, log returns, and cumulative returns, which are the primary components when assessing any portfolio’s performance and overall profitability. Additionally, investment analysts must hold an advanced assessment of maths concepts such as probability theory to be successful in their job position given the technical nature of most aspects related to finance.


The development of ChatGPT and its successor, GPT-4, have shown a potential to impact the world. Individuals express enthusiasm for emerging prospects, while also harboring apprehension regarding their effects on society.

This course covers the intersection of AI and finance, specifically in the context of algorithmic trading.

The course focuses on building a trading bot using ChatGPT, specifically utilizing pairs trading which was the subject of interest.

During the course, students will gain knowledge of the capabilities of ChatGPT and various GPT models including GPT-3, GPT-3.5, GPT-4, and more. In this lesson, we will explore the potential drawbacks of these models and the importance of remaining vigilant. The models may make mistakes, but we will develop strategies to manage them. We will explore effective methods for utilizing ChatGPT to increase efficiency and productivity.

One option to consider is directly asking ChatGPT instead of taking this course. If you encounter an error while using ChatGPT, it may be possible to report it for resolution, but only syntax errors (those that violate the rules of the Python language) can be addressed. Lacking fundamental knowledge in Python, finance, and statistics may result in overlooking semantic errors, as they may not be detected initially. One of the major lessons in this course is to "keep your guard up," which can easily be missed by many people.

This course is intended for:

This course is ideal for people looking to create their own robotic pairs trading bot with ChatGPT. Whether you're an aspiring data scientist, a professional within the machine learning field, or have just developed an interest in financial analysis, this course will provide you with all the necessary knowledge and training needed.


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