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Quantitative Trading: How To Build Your Own Algorithmic Trading Business量化交易：如何建立自己的算法交易书籍详细信息
- ISBN：9780470284889
- 作者：暂无作者
- 出版社：暂无出版社
- 出版时间：2008-10
- 页数：暂无页数
- 价格：447.00
- 纸张：胶版纸
- 装帧：精装
- 开本：16开
- 语言：未知
- 丛书：暂无丛书
- TAG：暂无
- 豆瓣评分：暂无豆瓣评分
内容简介：
While institutional traders continue to implement quantitative (or algorithmic) trading, many independent traders have wondered if they can still challenge powerful industry professionals at their own game? The answer is "yes," and in Quantitative Trading, 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.
书籍目录：
Acknowledgments.
Chapter 1. The Whats, Whos, and Whys of Quantitative Trading.
Who Can Become A Quantitative Trader?
The Business Case for Quantitative Trading.
Scalability.
Demand on Time.
The Un-necessity of Marketing.
The Way Forward.
Chapter 2. Fishing for Ideas.
Where can we find good strategies?
How to Identify a Strategy That Suits You.
Your Working Hours.
Your Programming Skills.
Your Trading Capital.
Your Goal.
A Taste for Plausible Strategies and Their Pitfalls.
How Does It Compare with a Benchmark and How Consistent Are Its Returns?
How Deep and Long is the Drawdown?
How Will Transaction Costs Affect the Strategy?
Does the Data Suffer from Survivorship Bias?
How Did the Performance of the Strategy Change Over the Years?
Does the Strategy Suffer from Data-Snooping Bias?
Does the Strategy “Fly under the Radar” of Institutional Money Managers?
Summary.
Chapter 3. Backtesting.
Common Backtesting Platforms.
Excel.
MATLAB.
TradeStation.
High-End Backtesting Platforms.
Finding and Using Historical Databases.
Are the Data Split- and Dividend-Adjusted?
Are the Data Survivorship-Bias-Free?
Does Your Strategy Use High and Low Data?
Performance Measurement.
Common Backtesting Pitfalls to Avoid.
Look-Ahead Bias.
Data-Snooping Bias.
Sample Size.
Out-of-sample testing.
Sensitivity Analysis.
Transaction Costs.
Strategy Refinement.
Summary.
Chapter 4. Setting up Your Business.
Business Structure: Retail or Proprietary?
Choosing a Brokerage or Proprietary Trading Firm.
Physical Infrastructure.
Summary.
Chapter 5. Execution Systems.
What an Automated Trading System Can Do for You.
Building a Semi-automated Trading System.
Building a Fully Automated Trading System.
Minimizing Transaction Costs.
Testing Your System by Paper Trading.
Why Does Actual Performance Diverge from Expectations?
Summary.
Chapter 6. Money and Risk Management.
Optimal Capital Allocation and Leverage.
Risk Management.
Psychological Preparedness.
Summary.
Appendix. A Simple Derivation of Kelly Formula when Returns Distribution is Gaussian.
Chapter 7. Special Topics in Quantitative Trading.
Mean-Reverting Versus Momentum Strategies.
Regime Switching.
Stationarity and Cointegration.
Factor Models.
What Is Your Exit Strategy?
Seasonal Trading Strategies.
High Frequency Trading Strategies.
Is it Better to Have a High-Leverage versus a High-Beta Portfolio?
Summary.
Chapter 8. Conclusion.
Can Independent Traders Succeed?
Next Steps.
Appendix A. A Quick Survey of MATLAB.
Bibliography.
About the Author.
Index.
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