'Day Trading with ChatGPT' is an experimentation guide that explores how the powerful AI language model ChatGPT can be utilized for day trading signals in the stock market. This pioneering book aims to give readers a hands-on experience and a comprehensive understanding of how to experiment with ChatGPT for better decision-making before considering it a trading tool.

The author takes a critical approach, emphasizing the strengths and limitations of using ChatGPT in trading. As you journey through the pages, you'll discover the AI's impressive abilities to analyze historical data, address financial prompts, and offer decision-making input while acknowledging the potential pitfalls of relying solely on AI-driven analysis.

The book's objective is not to advocate for ChatGPT as the ultimate trading solution but to objectively examine its potential and limitations in the financial world. The author subtly highlights their skepticism, encouraging readers to approach the technology with a discerning eye and always to corroborate AI-generated insights with their research and expertise.

Key Learnings

• Discover how ChatGPT can analyze historical data for trading insights.
• Learn to leverage ChatGPT's ability to address financial prompts.
• Enhance decision-making with AI-driven input in day trading.
• Understand the importance of combining AI with human expertise.
• Explore the benefits and limitations of AI in financial analysis.
• Master the use of technical indicators with ChatGPT's guidance.
• Develop a critical approach to AI-generated trading insights.
• Improve your trading strategies by incorporating AI tools.
• Gain a comprehensive understanding of ChatGPT's capabilities.
• Learn to navigate the financial world with AI-assisted decision-making.

Table of Content

1. Power of AI in Stock Market Predictions
2. Collecting and Analyzing Historical Stock Data
3. Moving Averages (SMA and EMA) with ChatGPT
4. Relative Strength Index (RSI) with ChatGPT
5. Bollinger Bands with ChatGPT
6. Fibonacci Retracement with ChatGPT
7. Moving Average Convergence Divergence (MACD) with ChatGPT
8. Stochastic Oscillator with ChatGPT
9. Putting It All Together - Is It Worth Using ChatGPT?

This book is tailored specifically for statisticians and data professionals who are already familiar with the fundamentals of statistics and want to leverage the speed and reliability of Rust in their projects. Over 11 in-depth chapters, you will discover how Rust outperforms Python in various aspects of data analysis and machine learning and learn to implement popular statistical methods using Rust's unique features and libraries.

"Statistics with Rust" begins by introducing you to Rust's programming environment and essential libraries for data professionals. You'll then dive into data handling, preprocessing, and visualization techniques that form the backbone of any statistical analysis. As you progress through the book, you'll explore descriptive and inferential statistics, probability distributions, regression analysis, time series analysis, Bayesian statistics, multivariate statistical methods, and nonlinear models. Additionally, the book covers essential machine-learning techniques, model evaluation and validation, natural language processing, and advanced techniques in emerging topics.

To ensure you get the most out of this book, each chapter includes hands-on examples and exercises to reinforce your understanding of the concepts presented. You'll also learn to optimize your Rust code and select the best tools and libraries for each task, maximizing your productivity and efficiency.

Key Learnings

• Discover Rust's unique advantages for statistical analysis and machine learning projects.
• Learn to efficiently handle, preprocess, and visualize data using Rust libraries.
• Implement descriptive and inferential statistics with Rust for powerful data insights.
• Master probability distributions and random variables in Rust for robust simulations.
• Perform advanced regression analysis with Rust's capabilities.
• Explore Bayesian statistics and Markov Chain Monte Carlo methods in Rust.
• Uncover multivariate techniques, including PCA and Factor Analysis, using Rust libraries.
• Implement cutting-edge machine learning algorithms and model evaluation techniques in Rust.
• Delve into text analysis, natural language processing, and network analysis with Rust.

Table of Content

1. Introduction to Rust for Statisticians
2. Data Handling and Preprocessing
3. Descriptive Statistics in Rust
4. Probability Distributions and Random Variables
5. Inferential Statistics
6. Regression Analysis
7. Bayesian Statistics
8. Multivariate Statistical Methods
9. Nonlinear Models and Machine Learning
10. Model Evaluation and Validation
11. Text and Natural Language Processing

Audience

"Statistics with Rust" is your indispensable guide to harnessing the power of Rust for modern statistical analysis and machine learning. Whether you are a seasoned data professional or a Rust enthusiast looking to expand your knowledge, this book provides the tools and insights to elevate your projects.