Recommendation
Entering
Structure of this book

CHAPTER 1.
Getting Started with US Stock Investment
1.1 Understanding the US Stock Market
1.2 Pros and Cons of Investing in US Stocks
1.2.1 Advantages of Investing in US Stocks
1.2.2 Disadvantages of Investing in US Stocks

CHAPTER 2.
Understanding Quantitative Investing
2.1 The concept of quantitative investment
2.2 Types of Quantitative Investment Strategies
2.2.1 Strategy based on value analysis
2.2.2 Strategies based on statistical properties of data
2.3 Pros and Cons of Quantitative Investing
2.3.1 Advantages of Quantitative Investing
2.3.2 Disadvantages of Quantitative Investing

CHAPTER 3.
How the stock market works
3.1 Order window and conclusion rules
3.2 Types of ordering methods
3.3 Types of Market Participants

CHAPTER 4.
Python Basics
4.1 Python and Basic Environment Setup
4.1.1 Installing the Basic Libraries
4.1.2 Exploring Jupyter Notebooks
4.2 Python Basics
4.2.1 Variables, data types, and basic operations
4.2.2 Control statements
4.2.3 Function
4.3 Python Data Analysis Libraries
4.3.1 NumPy
4.3.2 Pandas
4.3.3 Matplotlib: Data Visualization

CHAPTER 5.
Stock data analysis
5.1 Advanced NumPy and Pandas
5.1.1 NumPy and High-Dimensional Arrays & Functions
5.1.2 Pandas and DataFrames
5.2 Data Visualization
5.2.1 Scatterplot
5.2.2 Multigraph
5.3 Obtaining US stock data
5.3.1 Obtaining stock price data
5.3.2 Obtaining Financial Statement Data
5.4 Stock Data Analysis
5.4.1 Daily Return Analysis
5.4.2 Moving Average Calculation
5.4.3 Calculating Technical Indicators

CHAPTER 6.
Trend following strategy
6.1 Moving Average Crossover Strategy
6.1.1 Investment Strategy
6.1.2 Backtest
6.1.3 Parameter Optimization
6.1.4 Strategy Performance Indicators
6.2 Adding a stop loss
6.2.1 Tracking Stop Loss Method
6.2.2 Vectorized Backtest with Stop Loss
6.3 Applying Fees
6.4 Strategy Verification

CHAPTER 7.
Two-way trend following strategy
7.1 How to Profit in a Bear Market
7.1.1 Short Selling
7.1.2 Gifts
7.1.3 Options
7.1.4 Inverse ETFs
7.2 Two-Way Trading Strategy Using Short Selling
7.2.1 Two-Way MAC Strategy Backtest
7.2.2 Applying the tracking stop-loss method
7.2.3 Strategy Verification
7.3 Two-Way Trading Strategy Using Inverse ETFs
7.3.1 Strategy Backtesting
7.3.2 Strategy Verification

CHAPTER 8.
Momentum strategy
8.1 Definition of Momentum
8.1.1 Absolute Momentum
8.1.2 Relative Momentum
8.2 Momentum Trend Following Strategy 1
8.2.1 Momentum Indicator Analysis
8.2.2 Strategy Backtesting
8.3 Momentum Trend Following Strategy 2: Using Auxiliary Indicators
8.3.1 Money Flow Index
8.3.2 Strategy Backtesting
8.3.3 Strategy Verification

CHAPTER 9.
Mean reversion strategy
9.1 Mean Reversion Strategy Using Bollinger Bands
9.1.1 Observing Bollinger Bands
9.1.2 Strategy Backtesting
9.2 Combination Strategy Using Bollinger Bands
9.2.1 Combination Strategy Ideas
9.2.2 Strategy Backtesting
9.3 Pair Trading Theory
9.3.1 Correlation Analysis
9.3.2 Cointegration Analysis
9.3.3 Pair Trading Strategy
9.4 Implementing a Pair Trading Strategy
9.4.1 Implementing a Pair Trading Strategy 1
9.4.2 Implementing a Pairs Trading Strategy 2: Removing Future References

CHAPTER 10.
portfolio theory
10.1 Types of Risks
10.2 Risk reduction through diversification
10.3 Modern Portfolio Theory
10.3.1 Efficient investment line
10.3.2 Optimal Portfolio
10.4 Asset Pricing Model
10.4.1 Securities Market Line
10.4.2 Capital Market Line
10.4.3 Optimal portfolio including risk-free assets

CHAPTER 11.
Momentum Portfolio Investment Strategy
11.1 Factors affecting stock returns
11.2 Factor Investment Strategies
11.3 Momentum Portfolio Investment Strategy
11.3.1 Investment Universe
11.3.2 Calculating Returns and Selecting Stocks
11.3.3 Rebalancing Implementation and Backtesting
11.3.4 Momentum Portfolio Investment Performance
11.4 Optimizing Momentum Portfolio Investment

In conclusion

Like real quants, I can analyze properly and invest rationally!
If you combine "US Stock Quant Investment" with a "serious" study attitude,

This book is a practical guide for investors who want to systematically learn about U.S. stock investment and quantitative investment strategies.
It covers everything from basic stock market concepts to data analysis using Python and implementing various quantitative investment strategies, all in detail and step-by-step.
In particular, it presents practical investment strategies, ranging from trend-following, momentum, and mean-reversion strategies to portfolio theory, along with Python code, helping readers implement and test their own strategies.

Let's take a closer look at the contents of each chapter. Chapter 1 first explains the U.S. stock market and its characteristics, analyzes its strengths and weaknesses, and lays the foundation for investment.
Chapter 2 then introduces the basic concepts of quantitative investing, value analysis, and various strategies based on statistical properties, objectively evaluating their pros and cons.
Beyond simple theoretical explanations, we provide a friendly explanation of how to use Python (Chapter 4) and how to handle actual data through data analysis libraries (NumPy, Pandas, Matplotlib).

The second half focuses on implementing and validating orthodox investment strategies that even beginners can implement.
In Chapter 6, you'll learn trend-following strategies and backtesting techniques, and in Chapter 7, you'll delve into two-way trading strategies that can help you profit from bear markets.
Chapter 8 covers all aspects of momentum investing strategies, taking this further.
Chapters 9 through 11 present methods for designing a balanced investment strategy that combines stability and profitability using mean reversion strategies and portfolio theory.


The most notable feature of this book is that it harmoniously combines theory and practice.
In a hands-on environment utilizing accessible, partially executable Jupyter notebooks, readers can analyze real U.S. stock data, backtest each strategy, and validate investment performance.
Additionally, the necessary formulas and concepts are explained with detailed examples without omission, making it easy for even readers unfamiliar with quantitative investing to follow.
Moreover, since the source code for each strategy is fully provided, you can expand your understanding by implementing and improving it line by line.


Quantitative investing aims to achieve systematic and successful stock management through rational investment decisions based on data rather than emotional investment.
With this book, I believe anyone, from beginners to experienced investors, will be able to implement sound quantitative investment strategies into a robust stock portfolio, just like the "quants" at securities firms.

A systematic pipeline for applying authentic quantitative investment strategies!

[STEP 1: Establishing an Investment Strategy] “How to Buy and Sell?”
Trend-following strategy
· Mean reversion strategy
· Portfolio strategy

[STEP2 Backtesting] “Has this strategy been effective in the past?”
· Writing backtesting code using Python
· Selection of past data period and parameter optimization

[STEP3 Strategy Performance Evaluation] “Will this strategy be effective for future investments?”
· Performance comparison through strategic performance indicators
· Determining whether there is overfitting
· Validate your strategy through benchmark comparisons

Recommended for these people!
· Beginner investors who are interested in quantitative investment and want to learn systematically.
Investors who want to learn how to develop investment strategies and backtest using Python.
Investors who have studied quantitative investing or automated trading but are having difficulty designing their own strategies