Algorithmic Trading: Winning Strategies and Their Rationale (by Ernie Chan)

BookInfo

ISBN: 9781118460146	Number of Pages: 224
Publisher:  Wiley	Book Title: Algorithmic Trading
Publication Year: 2013	Target Audience: Trade
Author: Ernie Chan	Reading Age: 18+

Summary

Algorithmic Trading is a fascinating, informative book covering many trading strategies. Both individual investors and institutional investors can learn from and use these strategies.

The strategies in this book can be divided into the mean value regression system and the momentum system. The book not only introduces how to use each type of trading strategy but also explains the reasons why various strategies are effective.

This book always focuses on simple and linear trading strategies, because complex trading strategies are vulnerable to overfitting and data snooping.

Mathematics and software are the two legs of algorithm trading. This book uses a certain degree of mathematical knowledge to make its discussion of various financial concepts clearer and more accurate.

In addition, the book also adds many illustrative examples of programming using MATLAB code, you can download from the book’s website.

The main contents of this book include:

• Select the correct automatic execution platform and backtesting platform to reduce or eliminate the mistakes that are easy to make in the algorithm trading strategy;
• The simple skills (linear, Brin band, Kalman filter) of trading mean regression portfolio and what data form (actual price, logarithmic price, or proportion) is better to use in these tests and strategies;
• The average regression strategy used for trading stocks, ETF, foreign exchange, and futures cross-period arbitrage, cross-market arbitrage;
• Four driving forces of stock and futures momentum, as well as strategies for extracting time series and cross-section momentum;
• New momentum strategy based on high-frequency trading, single order movement, leverage ETF, news events, and emotions;
• The risk and capital management based on the Kelly formula have added the author’s personal risk management experience.

Algorithmic 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. In addition, Ernest also wrote books related to quantitative trading, such as Quantitative Trading.

Algorithmic Trading  PDF version will come as soon as possible.

Table of Contents

preface
Chapter 1 Backtesting and Automated Execution System
1.1 Importance of back testing
1.2 Common mistakes in back testing
1.3 Application of statistics in backtest procedure: hypothesis test
1.4 When is the trading strategy not required to be backtested
1.5 Does the backtesting system have the prediction function for the corresponding rate of return
1.6 Choice of Backtesting System and Automatic Operation Platform
Key points of this chapter

Chapter 2 Basic Meaning of Mean Regression Model
2.1 Mean value regression and corresponding stationarity
2.2 Co integration after smooth test
2.3 Analysis of Advantages and Disadvantages of Mean Regression Strategy
Key points of this chapter

Chapter 3 Operation mechanism of mean reversion strategy
3.1 Matching transaction by applying spread, the logarithm of spread, or corresponding ratio
3.2 Bolin belt line
3.3 Is the corresponding position increase function feasible
3.4 Kalman filtering rule related to dynamic linear regression
3.5 Market maker model related to Kalman filtering rule
3.6 Danger of data error
Key points of this chapter

Chapter 4 Mean Regression Model of Stocks and ETF Funds
4.1 Difficulties of Stock Matching Trading
4.2 ETF fund matching transaction (or triple ETF fund transaction)
4.3 Day mean reversion trading strategy: gap buying mode
4.4 Arbitrage mode between ETF funds and constituent stocks
4.5 Cross-industry mean reversion trading strategy: linear multi-null mode
Key points of this chapter

Chapter 5 Trading Strategy of Mean Regression Related to Currency Trading and Futures Trading
5.1 Cross currency pair transaction
5.2 Rollover interest in currency transactions
5.3 Futures intertemporal arbitrage trading
5.4 Cross-market (regional) arbitrage of futures
Key points of this chapter

Chapter 6 Daytime Momentum Trading Strategy
6.1 Test Mode of Time Series Momentum Trading Strategy
6.2 Transaction Strategy of Time Series
6.3 Grab continuous income from arbitrage between futures and ETF funds
6.4 Lateral Momentum Trading Strategy
6.5 Advantages and Disadvantages of Momentum Trading Strategy
Key points of this chapter

Chapter 7 Momentum Trading Strategy
7.1 “Exposure” Trading Strategy
7.2 Information-Driven Momentum Trading Strategy
7.3 Leveraged Trading Strategy of ETF Funds
7.4 High-frequency trading strategy
Key points of this chapter

Chapter 8 Risk Management
8.1 Optimized leverage mode
8.2 Fixed mode of portfolio-related risk proportion (CPPI mode)
8.3 Analysis of Stop Loss Mechanism
8.4 Risk indicators
Key points of this chapter

conclusion
reference
Author Profile
Website introduction