PART 1: Python Data Analysis Basics

CHAPTER 1 Before analyzing securities data
1.1 History and meaning of stock companies
1.2 Our country's securities market
1.3 Warren Buffett and Value Investing
1.4 How much will you earn?
1.5 Python for Quantitative Investing
1.6 Key Summary

CHAPTER 2 Python Programming
2.1 Python Features
2.2 Installing Python
2.3 Strings and Arithmetic Operations
2.4 Repeated data types
2.5 Variables and Functions
2.6 Modules and Packages
2.7 Object-Oriented Programming
2.8 File Handling and External Libraries
2.9 Key Summary

CHAPTER 3 Data Analysis with Pandas
3.1 NumPy Arrays
3.2 Pandas Series
3.3 Pandas DataFrame
3.4 Comparing Stocks
3.5 Maximum loss drop
3.6 Regression Analysis and Correlation
3.7 Risk Mitigation Based on Correlation Coefficient
3.8 Key Summary

CHAPTER 4 Data Analysis Using Web Scraping
4.1 Reading the List of Listed Companies with Pandas
4.2 Learning HTML
4.3 Finding daily prices on the web
4.4 Reading daily prices with Beautiful Soup
4.5 OHLC and Candlestick Charts
4.6 Key Summary

PART 2 Python Data Analysis Applications

CHAPTER 5 Building a Price Database and Developing a Price Inquiry API
5.1 Comparing Yahoo Finance and Naver Finance
5.2 Checking connection after installing MariaDB
5.3 Updating stock prices to the database daily
5.4 Daily Price Inquiry API
5.5 Key Summary

CHAPTER 6 TRADING STRATEGIES AND IMPLEMENTATION
6.1 Modern Portfolio Theory
6.2 Sharpe Ratio and Portfolio Optimization
6.3 Bollinger Bands Indicator
6.4 Bollinger Bands Trading Technique
6.5 Psychological Investment Laws
6.6 Triple Window Trading System
6.7 Dual Momentum Investment
6.8 Key Summary

CHAPTER 7 Building and Automating a Django Web Server
7.1 Django Web Framework
7.2 Hello Django Application
7.3 Django Index Page
7.4 Checking your account balance on the web
7.5 Sending notification messages to Slack
7.6 Backtesting with Backtrader
7.7 Key Summary

CHAPTER 8 Volatility Breakout Strategies and Automated Trading
8.1 Larry Williams' Volatility Breakout Strategy
8.2 Creon Plus API
8.3 Check stock prices and account information
8.4 ETF Buying/Selling
8.5 Registering the main logic and task scheduler
8.6 Key Summary

CHAPTER 9: Stock Price Prediction Using Deep Learning
9.1 Artificial Intelligence
9.2 TensorFlow Basics
9.3 Linear Regression Problem
9.4 Stock Price Prediction Using RNN
9.5 Key Summary
References

You need to know both trading strategies and programming to be successful in investing!

This book consists of Part 1, 'Python Data Analysis Basics' and Part 2, 'Python Data Analysis Applications'.
In Part 1, you'll learn how to collect, visualize, and analyze data using Python and various libraries.
In Part 2, we will build a stock price database and implement trading strategies used by investment experts in Python.
We also cover additional interesting topics such as backtesting using Python and stock price prediction using deep learning.
By reading this book, you will learn both trading strategies and programming.

[Target readers and precautions for this book]
This book is intended for non-financial professionals who want to quantitatively analyze securities data using Python and utilize it for investment purposes.
Programming, investment techniques, and how to use various libraries and tools are explained with beginners in mind.
However, the subject matter is so broad that it is not suitable for beginner developers.
Before choosing this book, please be sure to note the following cautions:

_1.
Although it was written with readers new to Python in mind, it is recommended that you have at least some experience developing in another programming language.
_2.
Because the trading strategies of investment experts are implemented directly in Python, it may not be easy to understand without knowledge of securities investment.
_3.
The securities data analysis techniques presented in this book do not guarantee immediate profits.
_4.
Be careful, as excessive web scraping may not comply with the policies of the sites featured in this book.
_5.
Please adhere to the development environment used in this book.
We apologize in advance for any issues that may arise due to different versions.

This book may not be easy to read in its entirety, as it covers the advanced core theories required for securities investment. However, by the end of your study, you will have laid the foundation for your own quantitative investment.
I wish the reader good health.

[Structure of this book]

Part 1.
Python Data Analysis Basics

_Chapter 1.
Before analyzing securities data
Before investing in securities, learn about the background knowledge related to securities.
We briefly examine the process of corporations entering Korea, value investing, the subprime mortgage crisis, and quantitative investing.

_Chapter 2.
Python programming
Explains Python installation, Python syntax, and how to use external libraries.
To be good at Python, you need to be able to handle the list data type freely.

_Chapter 3.
Data Analysis Using Pandas
Explains Pandas, an essential library for data analysis.
The dataframe data type is important because it acts as a hub for data exchange between libraries.

_Chapter 4.
Web scraping data analysis
This explains how to download an Excel file listing listed companies provided by the Korea Exchange and scrape the daily stock price page provided by Naver.
Describes the web scraping library Beautiful Soup.

Part 2.
Python Data Analysis Applications

_Chapter 5.
Building a stock price database and developing a stock price inquiry API
Read Naver stock prices from web pages and save them in MariaDB to build your own stock price database.
Next, we implement a price inquiry API that searches for saved stock prices.

_Chapter 6.
Trading Strategies and Implementation
Understand the core principles of legendary trading strategies, including Modern Portfolio Theory, Bollinger Bands, the Triple Window trading system, and Dual Momentum, and implement them in Python.

_Chapter 7.
Building and automating a Django web server
Implement a web system for checking stock balances using Django, a Python-based web framework.
We also introduce backtesting methods using Backtrader and sending messages using Slack.

_Chapter 8.
Volatility Breakout Strategy and Automated Trading
We will implement Larry Williams' volatility breakout strategy using domestic securities firm APIs and build a system that automatically trades ETFs with low transaction tax burden.

_Chapter 9.
Stock price prediction using deep learning
Predict tomorrow's stock prices using deep learning technology implemented with Google TensorFlow.
You will also learn the basic concepts of deep learning.


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