About the Author
Entering
Recommendation
Acknowledgements

Chapter 1: Python and Financial Data

1.
Python programming
1) Main features of Python
2) Advantages of Python
3) Areas of application of Python

2.
Anaconda Python
1) Key features of Anaconda
2) Main tools included in Anaconda
3) Why use Anaconda?

3.
Installing Python
1) Install Anaconda Python
2) Running Jupyter Notebook

4.
What is a variable?
1) Using variables in Python
2) Variable naming rules

5.
What is a comment?
1) Using comments in Python
2) Good examples of using comments

6.
Numeric data type
1) Integer type
2) Floating point type
3) Operations on numeric data types

7.
String data type
1) Defining and creating strings
2) String operations
3) String method
4) String formatting

8.
List data type
1) Creating and accessing lists
2) List modification and manipulation
3) List operations
4) List slicing

9.
Tuple data type
1) Creating and accessing tuples
2) Immutability of tuples
3) Tuple operations
4) Packing
5) Unpacking
6) Extended unpacking
7) Tuples and their usefulness

10.
Dictionary data type
1) Creating and accessing a dictionary
2) Dictionary modification and manipulation
3) Dictionary method
4) Dictionary comprehension

11.
Set data type
1) Creating and accessing sets
2) Set operations
3) Set method

12.
function
1) Function definition and call
2) Arguments and parameters
3) Default parameters
4) Variable arguments
5) Return value
6) Lambda

13.
Conditional if statement
1) Basic if statement
2) if-else statement
3) if-elif-else chain
4) Nested if statements
5) Simplifying conditional expressions

14.
while loop
1) Basic while loop structure
2) Using break and continue
3) while-else structure
4) Infinite loop

15.
for loop
1) Basic for loop structure
2) Use with various sequences
3) Loop control: break and continue
4) for-else structure
5) For loop using range
6) List comprehension

16.
Exception handling
1) Exception handling structure
2) Simultaneous handling of multiple exceptions
3) Using exception objects

17.
library
1) Library structure
2) How to use the library
3) Some popular Python libraries
4) Installing the library

Chapter 2 Data Collection and Analysis

1.
Stock Analysis Project
1) Data processing capabilities
2) Accessibility of financial data
3) Automation and real-time analytics
4) Visualization tools
5) Technical analysis and statistical modeling

2.
Finding Undervalued Stocks
1) Get domestic stock information

3.
Find stocks with low price-to-earnings ratios
1) PER calculation formula
2) Interpretation of PER
3) The importance of PER

4.
Find stocks with high return on equity
1) ROE calculation formula
2) Interpretation of ROE
3) The importance of ROE

5.
Find high-dividend stocks
1) Key characteristics of dividend stocks
2) Advantages of dividend stocks
3) Disadvantages of dividend stocks
4) Examples of dividend stocks
5) Find stocks that have increased their dividends for four consecutive years.

Chapter 3 Stock Selection Strategies

1.
Retrieving stock price data
1) Install FinanceDataReader
2) Main features and functions
3) Areas of application

2.
moving average
1) Advantages of moving averages
2) Simple moving average
3) Exponential moving average
4) Finding recommended stocks using EMA

3.
relative strength index
1) Calculation method
2) Interpreting RSI values
3) Investment signals
4) Finding recommended stocks using RSI

4.
Bollinger Bands
1) Components
2) How it works
3) Investment signals
4) Finding recommended stocks using Bollinger Bands

5.
portfolio theory
1) Standard deviation of returns

6.
efficient investment line
1) Composition of an efficient investment line
2) Characteristics of efficient investment lines
3) Visual representation of the efficient investment line

7.
Find the best investment ratio

8.
Monte Carlo simulation
1) Key concepts of Monte Carlo simulation
2) Application in portfolio optimization

9.
Sharpe ratio
1) Meaning of Sharpe ratio

Chapter 4 Technical Analysis

1.
AI stock price prediction
1) Key components of AI stock price prediction
2) Case of AI stock price prediction
3) Main types of machine learning
4) Classification/regression model
5) General steps of machine learning

2.
Predicting future stock prices

3.
K nearest neighbor model
1) How KNN works
2) Pros and Cons of the K Nearest Neighbor Model

4.
Decision tree model
1) Components of a decision tree
2) How decision trees work
3) Advantages and disadvantages of decision trees

5.
Random Forest Model
1) Components of a random forest
2) Pros and Cons of Random Forests

6. XGBoost model
1) Components of XGBoost
2) How XGBoost works
3) Pros and Cons of the XGBoost Model

7.
Predicting future domestic stock prices

8.
Find stocks that surge more than 5% in a day
1) Accuracy
2) Precision
3) Recall

9.
Improving model performance

10.
Identifying Uptrend Stocks Using Reinforcement Learning
1) Epsilon-Greedy Algorithm
2) The problem of exploration and utilization
3) Algorithm process

In conclusion

“Stock analysis should precede stock investment!”

This book is a sequel to "Artificial Intelligence, Please Help Me Invest in Stocks," which was selected as the Sejong Book of 2024, but it is a book that readers should read before its predecessor.
Because it provides practical tools to effectively understand and analyze the complexities of volatile financial markets.

To help readers with little programming experience follow the learning process, we cover in detail Python's basic grammar structure, core data types (numbers, strings, lists, tuples, dictionaries, sets), control statements (conditional statements, loops), function definitions, and how to use them.
Additionally, we provide hands-on learning by guiding you through the installation of Anaconda Python and how to use Jupyter Notebook to build an efficient development environment.

To strengthen practical financial data analysis skills, this book explains the process of collecting and processing real-world stock data using Python libraries. It presents methods for calculating and analyzing basic financial indicators like PER and ROE using Python code. It also provides concrete examples of implementing key technical analysis indicators like moving averages (MA), the Relative Strength Index (RSI), and Bollinger Bands in Python and developing investment strategies using them.

Furthermore, we introduce the core concepts of portfolio theory and cover how to evaluate portfolio performance through calculating the standard deviation of returns and the Sharpe ratio.
We've designed it so you can practice advanced analysis techniques, including using Monte Carlo simulation techniques to find optimal asset allocation ratios.


Finally, we explain the principles of various machine learning models, such as K-nearest neighbors (KNN), decision trees, random forests, and XGBoost, and provide a method to implement them in Python to build a stock price prediction model and evaluate its performance, thereby laying the foundation for an AI-based investment strategy.

From financial data analysis to investment strategies with Python

Have you ever felt like stock investing was too difficult? Feeling lost amidst complex charts and news? Don't worry! This book will guide you through analyzing financial data and developing your own investment strategy using the powerful tool called Python.

A Solid Python Foundation! (Chapter 1)

Even if you're new to programming, it's okay! We'll teach you the essentials, from basic Python syntax to variables, data types, functions, conditional statements, and loops, in an easy-to-follow and fun way.
From installing Anaconda to using Jupyter Notebooks, setting up your coding environment is a breeze!

The Power of Reading and Analyzing Data! (Chapter 2)

Learn how to import and analyze real-world stock data using Python. You'll develop your data analysis skills through practical examples, such as identifying undervalued stocks using financial indicators like price-to-earnings ratio (PER) and return on equity (ROE), and identifying high-dividend stocks that consistently pay dividends.

Developing Your Own Investment Strategy! (Chapter 3)

Learn how to implement and utilize technical analysis indicators such as moving averages, RSI, and Bollinger Bands in Python.
You'll learn how to understand portfolio theory and the efficient frontier concept, and how to use Monte Carlo simulation and the Sharpe ratio to find the optimal investment ratio.

Predicting the Future with AI! (Chapter 4)

Furthermore, we will learn how to predict stock prices using machine learning models (KNN, decision trees, random forests, XGBoost).
We'll create a model that identifies stocks that surge by more than 5% per day, along with tips for improving performance! You'll also get a sneak peek at stock-finding strategies using reinforcement learning.

With this book, you'll not only improve your Python coding skills, but also develop the ability to understand financial markets and analyze data, making you a more confident investor! Get started today and create your own smart investment assistant!

I recommend this to these people.

- Anyone who wants to learn Python and use it to analyze financial data
- Those who want to make rational investment decisions based on data
- Those who want to directly calculate and utilize technical analysis indicators or financial indicators.
- Those interested in stock price prediction using artificial intelligence
- Anyone who dreams of building their own automated investment system