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Quantitative Trading: How to Build Your Own Algorithmic Trading Business (Wiley Trading Book 381) Kindle Edition
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From the Inside Flap
By some estimates, quantitative (or algorithmic) trading now accounts for over one-third of trading volume in the United States. While institutional traders continue to implement this highly effective approach, many independent traders--with limited resources and less computing power--have wondered if they can still challenge powerful industry professionals at their own game? The answer is "yes," and in Quantitative Trading, author Dr. Ernest Chan, a respected independent trader and consultant, will show you how.
Whether you're an independent "retail" trader looking to start your own quantitative trading business or an individual who aspires to work as a quantitative trader at a major financial institution, this practical guide contains the information you need to succeed.
Organized around the steps you should take to start trading quantitatively, this book skillfully addresses how to:
Find a viable trading strategy that you're both comfortable with and confident in
Backtest your strategy--with MATLAB(R), Excel, and other platforms--to ensure good historical performance
Build and implement an automated trading system to execute your strategy
Scale up or wind down your strategies depending on their real-world profitability
Manage the money and risks involved in holding positions generated by your strategy
Incorporate advanced concepts that most professionals use into your everyday trading activities
And much more
While Dr. Chan takes the time to outline the essential aspects of turning quantitative trading strategies into profits, he doesn't get into overly theoretical or sophisticated theories. Instead, he highlights the simple tools and techniques you can use to gain a much-needed edge over today's institutional traders.
And for those who want to keep up with the latest news, ideas, and trends in quantitative trading, you're welcome to visit Dr. Chan's blog, epchan.blogspot.com, as well as his premium content Web site, epchan.com/subscriptions, which you'll have free access to with purchase of this book.
As an independent trader, you're free from the con-straints found in today's institutional environment--and as long as you adhere to the discipline of quantitative trading, you can achieve significant returns. With this reliable resource as your guide, you'll quickly discover what it takes to make it in such a dynamic and demanding field.--This text refers to the hardcover edition.
About the Author
- File size : 2760 KB
- Publication date : January 12, 2009
- Word Wise : Enabled
- Print length : 203 pages
- Publisher : Wiley; 1st edition (January 12, 2009)
- Language: : English
- Screen Reader : Supported
- Text-to-Speech : Enabled
- X-Ray : Not Enabled
- ASIN : B001FA0GGC
- Enhanced typesetting : Enabled
- Lending : Enabled
- Best Sellers Rank: #87,500 in Kindle Store (See Top 100 in Kindle Store)
- Customer Reviews:
Top reviews from the United States
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I purchased Matlab, took the intro Matlab courses and then reviewed this book again. My opinion quickly changed. It's very rare I've found a trading book with content in it as good as this. Also the Appendix in learning Matlab is excellent and saved me lots of time.
More importantly, I'd encourage anyone interested in learning more about Quant to follow Dr. Chan's blog. Some of the ideas on it alone are worth many times the price of this book.
This book was recommended by a Quantitative Trading website and I have to say I was a bit disappointed. If it was titled "An Introduction To Quantitative Trading" I would be less disappointed. That said, there are still some nuggets of gold in the book.
On the positive front, he exposes the curtain on some next level trading tools such as AI products that are geared for the serious trader. This alone is worth the price of the book. The chapter on setting up your trading business is also excellent. He talks about the pros and cons of joining a firm which are excellent.
The book is written around MATLAB code. The benefits of MATLAB is that it's extremely powerful and very easy to work with matrixes. The appendix chapter on MATLAB is an excellent primer to the benefits of using MATLAB. I would guess most of the people reading the book can code, so I'm more interested in the concept behind an idea than the code to do it. The book is light on explaining the concept and heavy on giving MATLAB code.
Probably half of the code examples involve trying to get data into MATLAB from Yahoo finance. When the same thing can be accomplished in TradeStation or MultiCharts with (a lot) less work. If the strategies in the book were ultra complex, then MATLAB or a standalone C# style program are the way to go. In the case of basic pair trading, it doesn't make any sense. Especially with custom .DLLs and the .NET version of MC.
However it seems for a lot of the community QT uses it as vehicle to live their wildest geek fantasies more than to efficiently develop trading concepts.
My main reason for purchasing the book was to expand my knowledge of mean reversion trading. Rather than explain the concept in detail, Dr. Chan talks about mean reversion but never gives a specific example of the math behind it and the returns. He gives some MATLAB code of trading the GDX and GLD however he never explains the strategy. I had to try and reverse engineer it from the MATLAB code.
I am an engineer and I've taken graduate level math courses in things such as Laplace transforms, statistics, advanced calculus, etc. I'm a math geek. This book was hard to follow and written for academics like Dr. Chan. It seems he was trying to impress his PhD peers more than writing a book for the aspiring algo trader (who isn't a PhD). For instance, he uses the term vector to refer to a variable. After reading the Appendix on MATLAB, this made more sense. However explaining it at the end of the book made it that much more difficult to read.
The book was short and usually I appreciate an author who is a master of brevity. However in this case it was a hindrance. The chapter on position sizing, leverage and risk was highly truncated. He mentions the Kelly Criterion and a few examples of it, which are rudimentary at best. The Kelly Criterion can be a financial instrument of mass destruction if it's not used with caution. The Van Tharp position sizing book is ~500 pages of in-depth discussion on the subject. Position sizing is a huge part of the strategy in many types of trading.
I had to look up most of the concepts in the book and he does a poor job of explaining what they are. For instance, the difference between correlation and co-integration in pair trading. He gives one example which is horrible at best at in illustrating the concept. I ended up buying another book on pair trading which did a much better job of explaining the subject.
I just purchase Dr. Chan's other book and I'm hoping it is heavier on concept and lighter on MATLAB code.
This book is especially beneficial to someone who has some background in both mathematics and computer science. As Steve Halpern said, "Ernie successfully distills a large amount of detailed and difficult subject matter down to a very clear and comprehensive resource for novice and pro alike." In a nutshell, this book blows away the fog around quantitative trading, and make the real quantitative trading process accessible to readers. The writing style is casual yet resourceful, more like discussions between close friends. I'd like to thank Dr. Chan for willing to share so much useful information and I highly recommend this book to everyone who is interested in quantitative trading.
The chapters on Backtesting do a fantastic job of identifying the correct types of historical databases for testing. The explanations of Look-Ahead and Data-Snooping bias are helpful and do a great job of explaining how these errors can impact back-testing models (or even worse, the failure of an entire trading strategy if you haven't back-tested). Chapter 6 which covers money and risk management is informative and helpful. While there isn't a significant amount of best practices associated with the explanations, the general adage of using risk tools to prevent significant losses is wise and can be found in greater detail in other trading books. The special topics in Quantitative Trading chapter is loaded with strategies and considerations which are still in use and somewhat profitable in todays marketplace. Most helpful is the information about using the Sharpe Ratio to calculate the performance of your portfolio against the risk free rate and including the volatility of your trades. Building a successful quant trading system relies on a high Sharpe ratio and sticking power for drawdowns according to Mr. Chan; this is sage advice. I also found the explanation around using the Kelly model to determine the optimum amount of portfolio allocation for quantitative trading very helpful, clear and well documented.
This text is significantly dated and arguably presupposes some rather extensive understanding of the trading industry as a whole. Dr. Chan is an intelligent writer and presents his ideas clearly, yet this book needs significant updating in order to remain relevant in today's environment. The book is more geared to professional and institutional Quantitative Trading than the self-starter trying to learn the industry and best practices on her own. The use of MATLAB throughout the book is disappointing as there are significantly better systems (personal opinion) and programming languages for quantitative trading that are also free in todays marketplace (like Python and R). Many of the sections outlining the amount of capital used for trading are also irrelevant in today's "no-minimum" brokerage accounts yet Dr. Chan does outline the need for more capital to be equipped for drawdowns. This text is also more geared to non-fully automated strategies where the developer will be going into her account at least twice daily while the algorithms she has created are running. This also feels dated unless you're working for a prop firm where manual interaction to ensure positions are closed daily are required.
Generally speaking, this is a good book and worth the read. The MATLAB examples are not as useful for those familiar with programming in Python, JAVA or R. Giving this 3 stars only because much of the information is dated.