Quantitative Trading: How to Build Your Own Algorithmic Trading Business (by Ernest P. Chan)

BookInfo

ISBN: 9781119800064	Number of Pages: 256
Publisher:  Wiley	Book Title: Quantitative Trading: How to Build Your Own Algorithmic Trading Business
Publication Year: 2021	Target Audience: Trade
Author: Ernest P. Chan	Reading Age: 18+

Summary

Quantitative Trading, also known as algorithmic trading, accounts for a large transactions in global financial markets.

Although the methods used for algorithmic trading seem to be quite advanced and are mostly used by institutional traders, with the continuous maturity of technology and diversification of trading strategies, individual traders can also build their quantitative trading system by learning some basic knowledge, tools, and models.

The core of quantitative trading is trading strategy, so Quantitative Trading: How to Build Your Own Algorithmic Trading Business starts with how to screen reliable trading strategies, introduces the historical backtesting methods and precautions of trading strategies in detail, and provides code cases.

After that, it also introduces how to build hardware facilities to achieve the quantitative trading strategy screened out.

On this basis, the book explains the capital management methods and risk control strategies in quantitative trading. It also introduced advanced content that professional quantitative traders must know, such as factor models and cointegration.

This book is suitable for individual investors interested in algorithmic trading, algorithmic trading practitioners of financial institutions, and college students who want to engage in algorithmic trading.

Quantitative Trading is one of the Best Quantitative Trading Books for Beginners

About the Author

Ernie Chan is a practitioner with QTS qualification in Capital Management Co., Ltd. Since 1997, he has worked in various investment banks (such as Morgan Stanley, Credit Suisse, and Maple) and hedge fund companies (such as Mapleridge Fund, Millennium Partners Fund, and MANE Fund).

He received his doctorate in physics from Cornell University. Before joining the financial industry, he was a member of IBM’s Human Language Technology Group.

At the same time, he is the founder and principal of Exponential (EXP) Capital Management Co., Ltd., an investment company headquartered in Chicago.

Quantitative Trading  PDF version will come as soon as possible.

Table of Contents

Chapter I Basic Knowledge of Quantitative Trading

• Who can be a quantitative trader
• Advantages and characteristics of algorithmic trading
• Quantitative traders’ growth path

Chapter 2 How to find a good algorithmic trading strategy

• Find channels for algorithmic trading strategies
• How to select a algorithmic trading strategy suitable for yourself
• Comparison and evaluation of algorithmic trading strategies

Chapter III Historical Backtesting of Quantitative Trading Strategy

• Common back-testing platforms
• High-end backtesting platform
• Selection and evaluation of historical databases
• Measures to quantify the performance of trading strategies
• Common historical back test errors
• Include transaction cost in the backtest
• Improvement of algorithmic trading strategy

Chapter IV Hardware Preparation for Quantitative Trading

• Whether to open an individual independent trading account or a proprietary trading company account
• Criteria for selecting dealers or proprietary trading companies
• Quantify the hardware infrastructure required for transactions

Chapter V Quantitative Trading Execution System

• What can an automated trading system do for you
• Build a semi-automatic transaction execution system
• Build a fully automatic trading system
• How to reduce transaction costs
• Test your trading system through simulated trading
• Why the actual performance of the trading strategy deviates from the expected performance

Chapter VI Capital Management and Risk Management of Quantitative Transactions

• Optimal capital allocation and leverage
• risk management
• Psychological Construction of Quantitative Traders
• In the appendix of this chapter, when the income distribution conforms to the Gaussian distribution, the simple derivation of the Kelly formula

Chapter VII Advanced Discussion on algorithmic trading

• Mean regression strategy and momentum strategy
• Market state switching strategy
• Smoothness and cointegration
• Factor model
• Departure strategy
• Seasonal trading strategy
• High-frequency trading strategy
• It is better to hold a high-leverage portfolio or a high-beta portfolio

Chapter VIII Basic Logic of Individual Quantitative Trader’s Profit

• Why individual quantitative traders can succeed
• The Advanced Road of Individual Quantitative Traders

Appendix MATLAB Minimal Tutorial