PDF Ebook Successful Trading Using Artificial Intelligence
“Apart from the U.S. Department of Defense, the financial services industry has invested more money in neural network research than any other industry or government body,” according to a 1993 book edited by Robert R. Trippi and Efraim Turban. Has the investment paid off?
Many say the late 1980’s and early 1990’s were a time of exaggerated claims for the technology that never quite panned out. Some traders who continued to use the technology didn’t explicitly name it in their client presentations, preferring to use such terms as “statistical modeling techniques”. Other traders publicly attribute their success to a mastery of artificial intelligence (AI) techniques.
For example, Andre Archambault, BA, JD, MBA, is the Director of Quantitative Strategies at Standard and Poor's Investment Services, and he is also the developer and genius behind Standard and Poor's extremely popular Neural Fair Value (NFV) System for ranking stocks and building portfolios. NFV, which uses neural nets made with NeuroShell, is among the more popular features of Standard & Poor's MarketScope, an electronic investment intelligence service for financial advisors, brokers, and money managers with approximately 77,000 subscribers worldwide.
His group is representative of similar departments at many of the Fortune 100 companies. However, don’t think you need to have an entire department of Ph.D.’s to successfully use artificial intelligence techniques in trading. There are many successful individual traders who base their trading decisions on artificial intelligence models. They share some common traits: analytical thinking, persistence, and a willingness to experiment. They know there are no models that are correct every day but they can improve the number of winnersby using neural network and genetic algorithm techniques. Use this book as a guideline for finding your own success.
Contents
Preface
Successful Trading with AI: Fact or Fiction?
Trading Rules versus Neural Networks
Optimization
- Traditional Crossover Trading Rules
Optimization of Trading Rules
Optimization with Evaluation
Test for Over-fitting
Optimization with Paper Trading
Predictions
- Unoptimized Prediction for FOREX
Unoptimized Prediction of Stocks
Optimum Prediction Thresholds
Optimum Prediction Based on Paper Trading
Optimum Prediction Based on Multiple Stocks
Input Selection
Portfolio Management
- Portfolio Management Overview
Portfolio Management Long and Short
Hedged Portfolio
Panel of Experts
- E-minis Combined Nets Hybrid
DayTrading – Models for Specific Times.
Appendix A
- How a Genetic Algorithm Works
Appendix B
How a Neural Network Works
- How Does a Neural Network Learn?
Network Structure
About the Authors
References
Index
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PDF Ebook Successful Trading Using Artificial Intelligence
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